[
    {
        "func_name": "extract_questions",
        "idx": "0",
        "repo_name": "sal-uva___radical-serp-searcher",
        "func_path": "chan_questions.py",
        "orig_func": "def extract_questions(string: str) -> list:\n    \"\"\"\n\tSplit a string intro sentences, return those ending with a question mark.\n\t\"\"\"\n    sentences = re.split('(?<!\\\\w\\\\.\\\\w.)(?<![A-Z][a-z]\\\\.)(?<=[.?!\\\\n])\\\\s', string)\n    questions = []\n    for sentence in sentences:\n        sentence = sentence.strip()\n        if sentence.endswith('?'):\n            questions.append(sentence)\n    questions = list(set(questions))\n    return questions",
        "orig_context": "```python\n## chan_questions.py\nimport re\n\ndef extract_questions(string: str) -> list:\n\t\"\"\"\n\tSplit a string intro sentences, return those ending with a question mark.\n\t\"\"\"\n\n\tsentences = re.split(r'(?<!\\w\\.\\w.)(?<![A-Z][a-z]\\.)(?<=[.?!\\n])\\s', string)\n\tquestions = []\n\n\tfor sentence in sentences:\n\n\t\t# Strip, but keep capital letters so LLMs can infer meaning from them.\n\t\tsentence = sentence.strip()\n\n\t\tif sentence.endswith(\"?\"):\n\t\t\tquestions.append(sentence)\n\n\t# Only unique questions\n\tquestions = list(set(questions))\n\n\treturn questions\n\n```\n\n\n",
        "eval_script": "## chan_questions.py\nimport re\n\ndef extract_questions(string: str) -> list:\n    \"\"\"\n    Split a string into sentences, return those ending with a question mark.\n    \"\"\"\n    sentences = re.split(r'(?<!\\w\\.\\w.)(?<![A-Z][a-z]\\.)(?<=[.?!\\n])\\s', string)\n    questions = []\n\n    for sentence in sentences:\n        # Strip, but keep capital letters so LLMs can infer meaning from them.\n        sentence = sentence.strip()\n\n        if sentence.endswith(\"?\"):\n            questions.append(sentence)\n\n    # Only unique questions\n    questions = list(set(questions))\n\n    return questions\n\n\ndef test_extract_questions():\n    # Test case 1: String with multiple questions\n    string1 = \"What is your name? How old are you? This is not a question.\"\n    assert extract_questions(string1) == extract_questions_new_implementation(string1)\n\n    # Test case 2: String with no questions\n    string2 = \"This is a statement. This is another statement.\"\n    assert extract_questions(string2) == extract_questions_new_implementation(string2)\n\n    # Test case 3: String with mixed content\n    string3 = \"Is this a question? Yes, it is. But this is not.\"\n    assert extract_questions(string3) == extract_questions_new_implementation(string3)\n\n    # Test case 4: Empty string\n    string4 = \"\"\n    assert extract_questions(string4) == extract_questions_new_implementation(string4)\n\n    # Test case 5: String with only questions\n    string5 = \"Where are you? What time is it? Who are you?\"\n    assert extract_questions(string5) == extract_questions_new_implementation(string5)\n\n    # Test case 6: String with punctuation\n    string6 = \"Hello! How are you? I'm fine.\"\n    assert extract_questions(string6) == extract_questions_new_implementation(string6)\n\n    # Test case 7: String with newlines\n    string7 = \"Is this a question?\\nYes, it is.\\nWhat about this one?\"\n    assert extract_questions(string7) == extract_questions_new_implementation(string7)\n\n    # Test case 8: String with duplicate questions\n    string8 = \"What is your name? What is your name? How old are you?\"\n    assert extract_questions(string8) == extract_questions_new_implementation(string8)\n\n    # Test case 9: String with abbreviations\n    string9 = \"Dr. Smith is here. Is he available? Yes, he is.\"\n    assert extract_questions(string9) == extract_questions_new_implementation(string9)\n\n    # Test case 10: String with mixed case\n    string10 = \"WHAT IS THIS? This is a test. is this working?\"\n    assert extract_questions(string10) == extract_questions_new_implementation(string10)\n\nif __name__ == \"__main__\":\n    test_extract_questions()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       9      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  9      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION and the ORIGINAL FUNCTION are essentially the same in terms of functionality. Both functions aim to extract questions from a given string by splitting the string into sentences and then checking if each sentence ends with a question mark. The regex pattern used for splitting sentences is identical in both functions, with the only difference being the use of a raw string (r'') in the REVISED FUNCTION, which does not affect the functionality. Both functions strip whitespace from sentences and ensure that only unique questions are returned by converting the list of questions to a set and back to a list. The comments and docstrings have been slightly altered for clarity, but these changes do not affect the functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `extract_questions` function returns a list of questions, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `extract_questions` and `extract_questions_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `extract_questions` and `extract_questions_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `extract_questions` returns a list.\n- CONDITION 5: The test cases cover a variety of scenarios, including strings with multiple questions, no questions, mixed content, empty strings, only questions, punctuation, newlines, duplicate questions, abbreviations, and mixed case. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "d3ca09e42b501292a67e6cccd746418c97d2f593"
    },
    {
        "func_name": "score_explicit_question",
        "idx": "2",
        "repo_name": "sal-uva___radical-serp-searcher",
        "func_path": "chan_questions.py",
        "orig_func": "def score_explicit_question(string: str) -> list:\n    \"\"\"\n\tUses LLMs to score a question based on whether it is considered explicit or implicit.\n\n\tUses OpenAI.\n\n\t\"\"\"\n    prompt = prompts.IS_EXPLICIT\n    answer = get_openai_answer(prompt.replace('[input]', string))\n    results = json.loads(answer)['results']\n    return results",
        "orig_context": "```python\n## helpers.py\nimport openai\n\nimport config\n\ndef get_openai_answer(prompt: str, response_format=\"json_object\", model=None):\n\t# initiate\n\tclient = openai.OpenAI(api_key=config.OPENAI_KEY)\n\n\tif not model:\n\t\tmodel = config.MODEL\n\n\t# Get response\n\tresponse = client.chat.completions.create(\n\t\tmodel=model,\n\t\ttemperature=config.TEMPERATURE,\n\t\tmax_tokens=config.MAX_OUTPUT_TOKENS,\n\t\tresponse_format={\"type\": response_format},\n\t\tmessages=[{\n\t\t\t\"role\": \"user\",\n\t\t\t\"content\": prompt\n\t\t}]\n\t)\n\n\treturn response.choices[0].message.content\n\n```\n\n\n```python\n## chan_questions.py\nimport json\n\nimport prompts\n\nfrom helpers import get_openai_answer, chunker, clean_and_hash, clean_html, query_to_search_url\n\ndef score_explicit_question(string: str) -> list:\n\t\"\"\"\n\tUses LLMs to score a question based on whether it is considered explicit or implicit.\n\n\tUses OpenAI.\n\n\t\"\"\"\n\n\tprompt = prompts.IS_EXPLICIT\n\n\tanswer = get_openai_answer(prompt.replace(\"[input]\", string))\n\n\tresults = json.loads(answer)[\"results\"]\n\treturn results\n\n```\n\n\n",
        "eval_script": "import json\n\n# Mock configuration\nclass config:\n    OPENAI_KEY = \"mock_openai_key\"\n    MODEL = \"mock_model\"\n    TEMPERATURE = 0.5\n    MAX_OUTPUT_TOKENS = 100\n\n# Mock prompts\nclass prompts:\n    IS_EXPLICIT = \"Is the following question explicit or implicit? [input]\"\n\n# Mock OpenAI API response\ndef get_openai_answer(prompt: str, response_format=\"json_object\", model=None):\n    # Mock response simulating the OpenAI API\n    mock_response = {\n        \"results\": [\n            {\"question\": prompt, \"score\": \"explicit\" if \"explicit\" in prompt else \"implicit\"}\n        ]\n    }\n    return json.dumps(mock_response)\n\n# The original function from chan_questions.py\ndef score_explicit_question(string: str) -> list:\n    \"\"\"\n    Uses LLMs to score a question based on whether it is considered explicit or implicit.\n\n    Uses OpenAI.\n\n    \"\"\"\n    prompt = prompts.IS_EXPLICIT\n\n    answer = get_openai_answer(prompt.replace(\"[input]\", string))\n\n    results = json.loads(answer)[\"results\"]\n    return results\n\n\ndef test_score_explicit_question():\n    # Test case 1: Explicit question\n    input_str1 = \"Is this an explicit question?\"\n    assert score_explicit_question(input_str1) == score_explicit_question_new_implementation(input_str1)\n\n    # Test case 2: Implicit question\n    input_str2 = \"What is the meaning of life?\"\n    assert score_explicit_question(input_str2) == score_explicit_question_new_implementation(input_str2)\n\n    # Test case 3: Neutral question (contains neither explicit nor implicit)\n    input_str3 = \"How are you?\"\n    assert score_explicit_question(input_str3) == score_explicit_question_new_implementation(input_str3)\n\n    # Test case 4: Very short question\n    input_str4 = \"?\"\n    assert score_explicit_question(input_str4) == score_explicit_question_new_implementation(input_str4)\n\n    # Test case 5: Very long question\n    input_str5 = \"Is this an explicit question?\" * 100\n    assert score_explicit_question(input_str5) == score_explicit_question_new_implementation(input_str5)\n\n    # Test case 6: Case sensitivity\n    input_str6 = \"is this an explicit question?\"\n    assert score_explicit_question(input_str6) == score_explicit_question_new_implementation(input_str6)\n\n    # Test case 7: Different punctuation\n    input_str7 = \"Is this an explicit question!\"\n    assert score_explicit_question(input_str7) == score_explicit_question_new_implementation(input_str7)\n\n    # Test case 8: Special characters\n    input_str8 = \"Is this an explicit question? #$%\"\n    assert score_explicit_question(input_str8) == score_explicit_question_new_implementation(input_str8)\n\n    # Test case 9: Numbers in question\n    input_str9 = \"Is 42 an explicit number?\"\n    assert score_explicit_question(input_str9) == score_explicit_question_new_implementation(input_str9)\n\n    # Test case 10: Empty string\n    input_str10 = \"\"\n    assert score_explicit_question(input_str10) == score_explicit_question_new_implementation(input_str10)\n\n    # Test case 11: Leading and trailing whitespace\n    input_str11 = \"   Is this an explicit question?   \"\n    assert score_explicit_question(input_str11) == score_explicit_question_new_implementation(input_str11)\n\n    # Test case 12: Excessive internal whitespace\n    input_str12 = \"Is    this    an    explicit    question?\"\n    assert score_explicit_question(input_str12) == score_explicit_question_new_implementation(input_str12)\n\n# Main function\nif __name__ == \"__main__\":\n    test_score_explicit_question()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions use the same prompt from the `prompts` class, call the `get_openai_answer` function with the same parameters, and parse the JSON response in the same way to return the results. The mock setup in the revised code does not alter the functionality of the `score_explicit_question` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `score_explicit_question` returns a list, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `score_explicit_question` and `score_explicit_question_new_implementation`, which means they check return values or variable states, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `score_explicit_question` and `score_explicit_question_new_implementation` directly. If the new implementation has the same functionality, it will pass all tests; otherwise, it will not. This satisfies the condition.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `score_explicit_question` returns a list.\n- CONDITION 5: The test cases cover a variety of inputs, including explicit, implicit, neutral, short, long, case sensitivity, punctuation, special characters, numbers, empty strings, and whitespace variations. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "d3ca09e42b501292a67e6cccd746418c97d2f593"
    },
    {
        "func_name": "parse_ops_from_catalog",
        "idx": "3",
        "repo_name": "sal-uva___radical-serp-searcher",
        "func_path": "chan_questions.py",
        "orig_func": "def parse_ops_from_catalog(in_catalog: list) -> list:\n    \"\"\"\n\tExtracts only the relevant OP data from a catalog file.\n\t\"\"\"\n    ops = []\n    for page in in_catalog:\n        for thread in page['threads']:\n            op = {'id': thread['no'], 'timestamp_utc': thread['time'], 'title': clean_html(thread.get('sub', '')), 'body': clean_html(thread.get('com', '')), 'replies': thread['replies'], 'board': thread.get('board', '')}\n            ops.append(op)\n    return ops",
        "orig_context": "```python\n## helpers.py\nimport html2text\n\ndef clean_html(html_string: str) -> str:\n\t\"\"\"\n\tClean up a HTML string.\n\t\"\"\"\n\th = html2text.HTML2Text()\n\n\t# Don't wrap lines!\n\th.body_width = 0\n\n\tcleaned = h.handle(html_string)\n\n\tif not cleaned:\n\t\tcleaned = \"\"\n\n\treturn cleaned\n\n```\n\n\n```python\n## chan_questions.py\nfrom helpers import get_openai_answer, chunker, clean_and_hash, clean_html, query_to_search_url\n\ndef parse_ops_from_catalog(in_catalog: list) -> list:\n\t\"\"\"\n\tExtracts only the relevant OP data from a catalog file.\n\t\"\"\"\n\n\tops = []\n\n\tfor page in in_catalog:\n\t\tfor thread in page[\"threads\"]:\n\t\t\top = {\n\t\t\t\t\"id\": thread[\"no\"],\n\t\t\t\t\"timestamp_utc\": thread[\"time\"],\n\t\t\t\t\"title\": clean_html(thread.get(\"sub\", \"\")),\n\t\t\t\t\"body\": clean_html(thread.get(\"com\", \"\")),\n\t\t\t\t\"replies\": thread[\"replies\"],\n\t\t\t\t\"board\": thread.get(\"board\", \"\")\n\t\t\t}\n\n\t\t\tops.append(op)\n\n\treturn ops\n\n```\n\n\n",
        "eval_script": "# Import necessary library\nimport html2text\n\ndef clean_html(html_string: str) -> str:\n    \"\"\"\n    Clean up a HTML string.\n    \"\"\"\n    h = html2text.HTML2Text()\n\n    # Don't wrap lines!\n    h.body_width = 0\n\n    cleaned = h.handle(html_string)\n\n    if not cleaned:\n        cleaned = \"\"\n\n    return cleaned\n\ndef parse_ops_from_catalog(in_catalog: list) -> list:\n    \"\"\"\n    Extracts only the relevant OP data from a catalog file.\n    \"\"\"\n\n    ops = []\n\n    for page in in_catalog:\n        for thread in page[\"threads\"]:\n            op = {\n                \"id\": thread[\"no\"],\n                \"timestamp_utc\": thread[\"time\"],\n                \"title\": clean_html(thread.get(\"sub\", \"\")),\n                \"body\": clean_html(thread.get(\"com\", \"\")),\n                \"replies\": thread[\"replies\"],\n                \"board\": thread.get(\"board\", \"\")\n            }\n\n            ops.append(op)\n\n    return ops\n\n\ndef test_parse_ops_from_catalog():\n    # Test case 1: Basic test with all fields present\n    catalog1 = [\n        {\n            \"threads\": [\n                {\n                    \"no\": 1,\n                    \"time\": 1234567890,\n                    \"sub\": \"<b>Title</b>\",\n                    \"com\": \"<i>Body</i>\",\n                    \"replies\": 5,\n                    \"board\": \"test\"\n                }\n            ]\n        }\n    ]\n    assert parse_ops_from_catalog(catalog1) == parse_ops_from_catalog_new_implementation(catalog1)\n\n    # Test case 2: Test with missing optional fields \"sub\" and \"com\"\n    catalog2 = [\n        {\n            \"threads\": [\n                {\n                    \"no\": 2,\n                    \"time\": 1234567891,\n                    \"replies\": 3\n                }\n            ]\n        }\n    ]\n    assert parse_ops_from_catalog(catalog2) == parse_ops_from_catalog_new_implementation(catalog2)\n\n    # Test case 3: Test with multiple threads and different boards\n    catalog3 = [\n        {\n            \"threads\": [\n                {\n                    \"no\": 3,\n                    \"time\": 1234567892,\n                    \"sub\": \"Another <b>Title</b>\",\n                    \"com\": \"Another <i>Body</i>\",\n                    \"replies\": 10,\n                    \"board\": \"general\"\n                },\n                {\n                    \"no\": 4,\n                    \"time\": 1234567893,\n                    \"sub\": \"Third <b>Title</b>\",\n                    \"com\": \"Third <i>Body</i>\",\n                    \"replies\": 0,\n                    \"board\": \"random\"\n                }\n            ]\n        }\n    ]\n    assert parse_ops_from_catalog(catalog3) == parse_ops_from_catalog_new_implementation(catalog3)\n\nif __name__ == \"__main__\":\n    test_parse_ops_from_catalog()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `parse_ops_from_catalog` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions iterate over the input catalog, extract relevant data from each thread, clean the HTML from the 'sub' and 'com' fields using the `clean_html` function, and append the processed data to the `ops` list. The structure and logic of the function remain unchanged, and the `clean_html` function is defined in the revised code to ensure it works in an isolated environment. The test cases provided in the revised code further confirm that the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `parse_ops_from_catalog` returns a list of dictionaries, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `parse_ops_from_catalog` and `parse_ops_from_catalog_new_implementation` for the same input, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two functions, which is reasonable given that `parse_ops_from_catalog` returns a value.\n- CONDITION 5: The test cases cover different scenarios, including basic tests, tests with missing optional fields, and tests with multiple threads and boards, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "d3ca09e42b501292a67e6cccd746418c97d2f593"
    },
    {
        "func_name": "memorySortC",
        "idx": "12",
        "repo_name": "DrakeSeteraO___Memory-Sort",
        "func_path": "MemorySort.py",
        "orig_func": "def memorySortC(lst: list) -> list:\n    \"\"\"Sorts the inputted list of character elements in ascending order utilizing memory to speed up computation time\n\n    Args:\n        lst (list): List of character elements to get sorted\n\n    Returns:\n        list: sorted list of character elements\n    \"\"\"\n    high: int = ord(lst[0])\n    low: int = high\n    for l in lst:\n        temp: int = ord(l)\n        if temp > high:\n            high: int = temp\n        elif temp < low:\n            low: int = temp\n    ran: int = high - low + 1\n    amount: list = [0] * ran\n    for l in lst:\n        amount[ord(l) - low] += 1\n    output: list = [''] * len(lst)\n    p: int = 0\n    for o in range(len(amount)):\n        for _ in range(amount[o]):\n            output[p] = chr(o + low)\n            p: int = p + 1\n    return output",
        "orig_context": "```python\n## MemorySort.py\ndef memorySortC(lst: list) -> list:\n    \"\"\"Sorts the inputted list of character elements in ascending order utilizing memory to speed up computation time\n\n    Args:\n        lst (list): List of character elements to get sorted\n\n    Returns:\n        list: sorted list of character elements\n    \"\"\"\n\n    \n    high: int = ord(lst[0])\n    low: int = high\n    for l in lst:\n        temp: int = ord(l)\n        if temp > high:\n            high: int = temp\n        elif temp < low:\n            low: int = temp\n    ran: int = high - low + 1\n    amount: list = [0] * ran\n\n    \n    for l in lst:\n        amount[ord(l) - low] += 1\n\n    \n    output: list = [''] * len(lst)\n    p: int = 0\n    for o in range(len(amount)):\n        for _ in range(amount[o]):\n            output[p] = chr(o + low)\n            p: int = p + 1\n\n          \n    return output\n\n```\n\n\n",
        "eval_script": "## MemorySort.py\ndef memorySortC(lst: list) -> list:\n    \"\"\"Sorts the inputted list of character elements in ascending order utilizing memory to speed up computation time\n\n    Args:\n        lst (list): List of character elements to get sorted\n\n    Returns:\n        list: sorted list of character elements\n    \"\"\"\n\n    high: int = ord(lst[0])\n    low: int = high\n    for l in lst:\n        temp: int = ord(l)\n        if temp > high:\n            high: int = temp\n        elif temp < low:\n            low: int = temp\n    ran: int = high - low + 1\n    amount: list = [0] * ran\n\n    for l in lst:\n        amount[ord(l) - low] += 1\n\n    output: list = [''] * len(lst)\n    p: int = 0\n    for o in range(len(amount)):\n        for _ in range(amount[o]):\n            output[p] = chr(o + low)\n            p: int = p + 1\n\n    return output\n\n\ndef test_memorySortC():\n    # Test case 1: Normal case with mixed characters\n    assert memorySortC(['d', 'a', 'c', 'b']) == memorySortC_new_implementation(['d', 'a', 'c', 'b'])\n    \n    # Test case 2: List with duplicate characters\n    assert memorySortC(['a', 'b', 'a', 'c', 'b']) == memorySortC_new_implementation(['a', 'b', 'a', 'c', 'b'])\n    \n    # Test case 3: Already sorted list\n    assert memorySortC(['a', 'b', 'c', 'd']) == memorySortC_new_implementation(['a', 'b', 'c', 'd'])\n\nif __name__ == \"__main__\":\n    test_memorySortC()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      20      0     12      0   100%\n--------------------------------------------------------------------\nTOTAL                                 20      0     12      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining the ORIGINAL FUNCTION and the REVISED FUNCTION, it is clear that both functions are identical in terms of their logic and implementation. The code in both functions follows the same steps: determining the range of character ASCII values, counting occurrences of each character, and constructing the sorted output list. The REVISED FUNCTION includes additional test cases to verify its functionality, but the core logic of the function remains unchanged. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] is satisfied because `memorySortC` returns a sorted list, which is a return value.\n- [CONDITION 2] is satisfied because the test cases use assertions to check the return values of `memorySortC` and `memorySortC_new_implementation`, not printed or logged content.\n- [CONDITION 3] is satisfied because the test cases compare the outputs of `memorySortC` and `memorySortC_new_implementation` for the same inputs, ensuring that both functions must have the same functionality to pass the tests.\n- [CONDITION 4] is satisfied because the test cases use assertions to compare the return values of the two functions, which is appropriate given that `memorySortC` returns a list.\n- [CONDITION 5] is satisfied because the test cases cover different scenarios: a normal case with mixed characters, a case with duplicate characters, and an already sorted list. These are non-trivial cases that effectively test the sorting functionality.",
            "answer": "yes"
        },
        "commit_id": "853cc6c0fcfdc2bda7d5a01e3590e8ef36cdfa7e"
    },
    {
        "func_name": "find_target_sum_withinList",
        "idx": "15",
        "repo_name": "AgEpsilon24___commonAlgos",
        "func_path": "findTargetBySumTwoNumsInList.py",
        "orig_func": "def find_target_sum_withinList(numList, target):\n    \"\"\"\n    This function takes in a list of numbers and a target sum and returns a list of lists of numbers that add up to the target sum.\n\n    numlist: list of numbers\n    target: target sum to find in the list\n    results: list of grouped number that add up to the target sum i.e. [[1, 5], [2, 4]] for target sum of 6\n\n    \"\"\"\n    results = []\n    for i in range(0, len(numList)):\n        for j in range(i + 1, len(numList)):\n            if numList[i] + numList[j] == target:\n                results.append([numList[i], numList[j]])\n    return results",
        "orig_context": "```python\n## findTargetBySumTwoNumsInList.py\ndef find_target_sum_withinList(numList, target):\n    \"\"\"\n    This function takes in a list of numbers and a target sum and returns a list of lists of numbers that add up to the target sum.\n\n    numlist: list of numbers\n    target: target sum to find in the list\n    results: list of grouped number that add up to the target sum i.e. [[1, 5], [2, 4]] for target sum of 6\n\n    \"\"\"\n    results = []\n\n    #For every number in the list, the function will check if the sum of that number and any other number in the list is equal to the target sum.\n    for i in range(0, len(numList)):\n        for j in range(i+1, len(numList)):\n            #    If the sum of the two numbers is equal to the target sum, the two numbers will be added to a list and the list will be added to the results list.\n            if numList[i] + numList[j] == target:\n                results.append([numList[i], numList[j]])\n    return results\n\n```\n\n\n",
        "eval_script": "## findTargetBySumTwoNumsInList.py\ndef find_target_sum_withinList(numList, target):\n    \"\"\"\n    This function takes in a list of numbers and a target sum and returns a list of lists of numbers that add up to the target sum.\n\n    numlist: list of numbers\n    target: target sum to find in the list\n    results: list of grouped number that add up to the target sum i.e. [[1, 5], [2, 4]] for target sum of 6\n\n    \"\"\"\n    results = []\n\n    #For every number in the list, the function will check if the sum of that number and any other number in the list is equal to the target sum.\n    for i in range(0, len(numList)):\n        for j in range(i+1, len(numList)):\n            #    If the sum of the two numbers is equal to the target sum, the two numbers will be added to a list and the list will be added to the results list.\n            if numList[i] + numList[j] == target:\n                results.append([numList[i], numList[j]])\n    return results\n\n\ndef test_find_target_sum_withinList():\n    # Test case 1: Multiple pairs that sum to the target\n    numList1 = [1, 2, 3, 4, 5]\n    target1 = 6\n    expected1 = [[1, 5], [2, 4]]\n    assert find_target_sum_withinList(numList1, target1) == expected1\n    assert find_target_sum_withinList_new_implementation(numList1, target1) == expected1\n\n    # Test case 2: No pairs that sum to the target\n    numList2 = [1, 2, 3]\n    target2 = 7\n    expected2 = []\n    assert find_target_sum_withinList(numList2, target2) == expected2\n    assert find_target_sum_withinList_new_implementation(numList2, target2) == expected2\n\n    # Test case 3: Exactly one pair that sums to the target\n    numList3 = [1, 2, 3, 9]\n    target3 = 5\n    expected3 = [[2, 3]]\n    assert find_target_sum_withinList(numList3, target3) == expected3\n    assert find_target_sum_withinList_new_implementation(numList3, target3) == expected3\n\nif __name__ == \"__main__\":\n    test_find_target_sum_withinList()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions iterate over the list of numbers, checking pairs of numbers to see if they sum to the target, and append such pairs to the results list. The added comments in the REVISED FUNCTION do not alter its functionality. The test cases provided in the CODE are not relevant to the comparison of the two functions, as they are not executed or referenced in the REVISED FUNCTION itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `find_target_sum_withinList` returns a list of lists, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents.\n- CONDITION 3: The test cases compare the results of `find_target_sum_withinList` and `find_target_sum_withinList_new_implementation` to ensure they have the same functionality.\n- CONDITION 4: The test cases use assertions to compare the expected results with the actual return values, which is reasonable given that the function returns values.\n- CONDITION 5: The test cases cover multiple scenarios: multiple pairs, no pairs, and exactly one pair, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "969b2baf0cd1d0157a16f9045e71585c0f86fb68"
    },
    {
        "func_name": "makeRowZero",
        "idx": "16",
        "repo_name": "AgEpsilon24___commonAlgos",
        "func_path": "matrixZeroRowColum.py",
        "orig_func": "def makeRowZero(aList):\n    \"\"\"\n        makeRowZero function takes in a list and returns a list of zeros of the same length as the input list\n\n        aList: list of numbers\n\n        Below are the steps to create a list of zeros of the same length as the input list:\n    \"\"\"\n    row = [0 for i in range(len(aList))]\n    return row",
        "orig_context": "```python\n## matrixZeroRowColum.py\ndef makeRowZero(aList):\n    \"\"\"\n        makeRowZero function takes in a list and returns a list of zeros of the same length as the input list\n\n        aList: list of numbers\n\n        Below are the steps to create a list of zeros of the same length as the input list:\n    \"\"\"\n    # return [0] * len(aList) # create a list of zeros of the same length as the input list\n\n    row = [0 for i in range(len(aList))] # create a list of zeros of the same length as the input list\n    return row\n\n```\n\n\n",
        "eval_script": "## matrixZeroRowColum.py\ndef makeRowZero(aList):\n    \"\"\"\n        makeRowZero function takes in a list and returns a list of zeros of the same length as the input list\n\n        aList: list of numbers\n\n        Below are the steps to create a list of zeros of the same length as the input list:\n    \"\"\"\n    # return [0] * len(aList) # create a list of zeros of the same length as the input list\n\n    row = [0 for i in range(len(aList))] # create a list of zeros of the same length as the input list\n    return row\n\n\ndef test_makeRowZero():\n    # Test with an empty list\n    assert makeRowZero([]) == makeRowZero_new_implementation([]), \"Test with empty list failed\"\n\n    # Test with a single element list\n    assert makeRowZero([5]) == makeRowZero_new_implementation([5]), \"Test with single element list failed\"\n\n    # Test with a multiple elements list\n    assert makeRowZero([1, 2, 3]) == makeRowZero_new_implementation([1, 2, 3]), \"Test with multiple elements list failed\"\n\n    # Test with a list containing negative numbers\n    assert makeRowZero([-1, -2, -3]) == makeRowZero_new_implementation([-1, -2, -3]), \"Test with negative numbers failed\"\n\n    # Test with a list containing zeros\n    assert makeRowZero([0, 0, 0]) == makeRowZero_new_implementation([0, 0, 0]), \"Test with zeros failed\"\n\n    # Test with a list containing mixed data types\n    assert makeRowZero([1, 'a', 3.5]) == makeRowZero_new_implementation([1, 'a', 3.5]), \"Test with mixed data types failed\"\n\n    # Test with a large list\n    large_list = list(range(1000))\n    assert makeRowZero(large_list) == makeRowZero_new_implementation(large_list), \"Test with large list failed\"\n\n    # Test with a list containing repeated elements\n    assert makeRowZero([1, 1, 1, 1]) == makeRowZero_new_implementation([1, 1, 1, 1]), \"Test with repeated elements failed\"\n\nif __name__ == \"__main__\":\n    test_makeRowZero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions, `makeRowZero`, take a list as input and return a new list of zeros with the same length as the input list. The implementation uses a list comprehension to create the list of zeros, which is the same in both versions. The commented-out line in the REVISED FUNCTION offers an alternative way to achieve the same result but is not active in the code. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `makeRowZero` returns a list of zeros with the same length as the input list. Therefore, it satisfies this condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of `makeRowZero` and `makeRowZero_new_implementation`, not printed or logged contents. Thus, this condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `makeRowZero` and `makeRowZero_new_implementation` for various inputs. If `makeRowZero_new_implementation` passes all these tests, it must have the same functionality as `makeRowZero`. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of both implementations, which is reasonable given that `makeRowZero` returns a list. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including empty lists, lists with a single element, lists with multiple elements, negative numbers, zeros, mixed data types, large lists, and repeated elements. These tests are non-trivial and comprehensive. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "969b2baf0cd1d0157a16f9045e71585c0f86fb68"
    },
    {
        "func_name": "get_group_consecutive_common_chars",
        "idx": "19",
        "repo_name": "AgEpsilon24___commonAlgos",
        "func_path": "findCommonalityInWords.py",
        "orig_func": "def get_group_consecutive_common_chars(word1, word2):\n    \"\"\"\n        This function take a list and returns the characters of the words that are common in all the words in the list.\n\n        wordList: list of words\n    \"\"\"\n    result = []\n    for i in range(0, len(word1)):\n        if word1[i] == word2[i]:\n            result.append(word1[i])\n        else:\n            return result\n    return result",
        "orig_context": "```python\n## findCommonalityInWords.py\ndef get_group_consecutive_common_chars(word1, word2):\n    \"\"\"\n        This function take a list and returns the characters of the words that are common in all the words in the list.\n\n        wordList: list of words\n    \"\"\"\n\n    # Set the result to an empty list to store the common characters\n    result = []\n\n    # Get the common characters in the initial word and the next word by looping through the words characters one by one\n    for i in range(0, len(word1)):\n        if word1[i] == word2[i]:\n            result.append(word1[i])\n        else:\n            return result\n    \n    return result\n\n```\n\n\n",
        "eval_script": "## findCommonalityInWords.py\ndef get_group_consecutive_common_chars(word1, word2):\n    \"\"\"\n        This function takes two words and returns the consecutive characters \n        that are common in both words from the start.\n\n        word1: first word\n        word2: second word\n    \"\"\"\n\n    # Set the result to an empty list to store the common characters\n    result = []\n\n    # Get the common characters in the initial word and the next word by looping through the words characters one by one\n    for i in range(0, len(word1)):\n        if word1[i] == word2[i]:\n            result.append(word1[i])\n        else:\n            return result\n    \n    return result\n\n\ndef test_get_group_consecutive_common_chars():\n    # Test case 1: Common consecutive characters at the start\n    assert get_group_consecutive_common_chars(\"apple\", \"apricot\") == get_group_consecutive_common_chars_new_implementation(\"apple\", \"apricot\")\n\n    # Test case 2: No common consecutive characters\n    assert get_group_consecutive_common_chars(\"banana\", \"cherry\") == get_group_consecutive_common_chars_new_implementation(\"banana\", \"cherry\")\n\n    # Test case 3: Identical words\n    assert get_group_consecutive_common_chars(\"grape\", \"grape\") == get_group_consecutive_common_chars_new_implementation(\"grape\", \"grape\")\n\n    # Test case 4: One word is a prefix of the other\n    assert get_group_consecutive_common_chars(\"blue\", \"blueberry\") == get_group_consecutive_common_chars_new_implementation(\"blue\", \"blueberry\")\n\n    # Test case 5: Empty strings\n    assert get_group_consecutive_common_chars(\"\", \"\") == get_group_consecutive_common_chars_new_implementation(\"\", \"\")\n\n    # Test case 6: Case sensitivity\n    assert get_group_consecutive_common_chars(\"Apple\", \"apple\") == get_group_consecutive_common_chars_new_implementation(\"Apple\", \"apple\")\n\n    # Test case 7: Single character words\n    assert get_group_consecutive_common_chars(\"a\", \"a\") == get_group_consecutive_common_chars_new_implementation(\"a\", \"a\")\n    assert get_group_consecutive_common_chars(\"a\", \"b\") == get_group_consecutive_common_chars_new_implementation(\"a\", \"b\")\n\n    # Test case 8: Different lengths with no common prefix\n    assert get_group_consecutive_common_chars(\"short\", \"longer\") == get_group_consecutive_common_chars_new_implementation(\"short\", \"longer\")\n\n    # Test case 9: Special characters\n    assert get_group_consecutive_common_chars(\"hello!\", \"hello@\") == get_group_consecutive_common_chars_new_implementation(\"hello!\", \"hello@\")\n\n    # Test case 10: Long words\n    assert get_group_consecutive_common_chars(\"a\" * 1000, \"a\" * 1000) == get_group_consecutive_common_chars_new_implementation(\"a\" * 1000, \"a\" * 1000)\n\nif __name__ == \"__main__\":\n    test_get_group_consecutive_common_chars()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `get_group_consecutive_common_chars` and the revised function have the same logic and functionality. Both functions take two words as input and return a list of consecutive characters that are common at the start of both words. They iterate through the characters of the first word, comparing each character with the corresponding character in the second word. If the characters match, they are added to the result list; if they don't match, the function returns the result list immediately. The revised function includes additional comments and a more detailed docstring, but the core logic remains unchanged. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `get_group_consecutive_common_chars` returns a list of common consecutive characters, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare return values of the two implementations, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `get_group_consecutive_common_chars` and `get_group_consecutive_common_chars_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare return values, which is appropriate since the function returns a list.\n- CONDITION 5: The test cases cover a variety of scenarios, including common prefixes, no common characters, identical words, prefixes, empty strings, case sensitivity, single characters, different lengths, special characters, and long words, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "969b2baf0cd1d0157a16f9045e71585c0f86fb68"
    },
    {
        "func_name": "find_all_vowels_inWord",
        "idx": "20",
        "repo_name": "AgEpsilon24___commonAlgos",
        "func_path": "findCommonalityInWords.py",
        "orig_func": "def find_all_vowels_inWord(word: str):\n    \"\"\"\n        Identify if word contains a vowel and return the vowels in the word\n\n        word: word to check for vowels\n    \"\"\"\n    vowels = 'aeiou'\n    result = []\n    for i in word:\n        if i in vowels:\n            result.append(i)\n    return result",
        "orig_context": "```python\n## findCommonalityInWords.py\ndef find_all_vowels_inWord(word:str):\n    \"\"\"\n        Identify if word contains a vowel and return the vowels in the word\n\n        word: word to check for vowels\n    \"\"\"\n\n    vowels = \"aeiou\"\n    result = []\n\n    for i in word:\n        if i in vowels:\n            result.append(i)\n    \n    return result\n\n```\n\n\n",
        "eval_script": "## findCommonalityInWords.py\ndef find_all_vowels_inWord(word:str):\n    \"\"\"\n        Identify if word contains a vowel and return the vowels in the word\n\n        word: word to check for vowels\n    \"\"\"\n\n    vowels = \"aeiou\"\n    result = []\n\n    for i in word:\n        if i in vowels:\n            result.append(i)\n    \n    return result\n\n\ndef test_find_all_vowels_inWord():\n    # Test case 1: Word with multiple different vowels\n    word1 = \"education\"\n    assert find_all_vowels_inWord(word1) == find_all_vowels_inWord_new_implementation(word1), \"Test case 1 failed\"\n\n    # Test case 2: Word with no vowels\n    word2 = \"rhythm\"\n    assert find_all_vowels_inWord(word2) == find_all_vowels_inWord_new_implementation(word2), \"Test case 2 failed\"\n\n    # Test case 3: Word with repeated vowels\n    word3 = \"queue\"\n    assert find_all_vowels_inWord(word3) == find_all_vowels_inWord_new_implementation(word3), \"Test case 3 failed\"\n\n    # Test case 4: Empty string\n    word4 = \"\"\n    assert find_all_vowels_inWord(word4) == find_all_vowels_inWord_new_implementation(word4), \"Test case 4 failed\"\n\n    # Test case 5: Word with uppercase vowels\n    word5 = \"EDUCATION\"\n    assert find_all_vowels_inWord(word5) == find_all_vowels_inWord_new_implementation(word5), \"Test case 5 failed\"\n\n    # Test case 6: Word with mixed case vowels\n    word6 = \"EdUcAtIoN\"\n    assert find_all_vowels_inWord(word6) == find_all_vowels_inWord_new_implementation(word6), \"Test case 6 failed\"\n\n    # Test case 7: Word with non-alphabetic characters\n    word7 = \"e-d-u-c-a-t-i-o-n!\"\n    assert find_all_vowels_inWord(word7) == find_all_vowels_inWord_new_implementation(word7), \"Test case 7 failed\"\n\n    # Test case 8: Word with all vowels\n    word8 = \"aeiou\"\n    assert find_all_vowels_inWord(word8) == find_all_vowels_inWord_new_implementation(word8), \"Test case 8 failed\"\n\n    # Test case 9: Single vowel character\n    word9 = \"a\"\n    assert find_all_vowels_inWord(word9) == find_all_vowels_inWord_new_implementation(word9), \"Test case 9 failed\"\n\n    # Test case 10: Single consonant character\n    word10 = \"b\"\n    assert find_all_vowels_inWord(word10) == find_all_vowels_inWord_new_implementation(word10), \"Test case 10 failed\"\n\n    # Test case 11: Long string with repeated patterns\n    word11 = \"a\" * 1000 + \"e\" * 1000 + \"i\" * 1000 + \"o\" * 1000 + \"u\" * 1000\n    assert find_all_vowels_inWord(word11) == find_all_vowels_inWord_new_implementation(word11), \"Test case 11 failed\"\n\nif __name__ == \"__main__\":\n    test_find_all_vowels_inWord()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `find_all_vowels_inWord` is identical to the ORIGINAL FUNCTION in terms of implementation. Both functions iterate over the input string `word`, check if each character is a vowel (from the string \"aeiou\"), and append it to the `result` list if it is a vowel. The REVISED FUNCTION also includes a test suite to verify its correctness, but this does not affect the functionality of the function itself. Since the implementation of the function is exactly the same, the functionality is also the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The original `find_all_vowels_inWord` function returns a list of vowels found in the input word, satisfying the condition that it has return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `find_all_vowels_inWord` and `find_all_vowels_inWord_new_implementation`, not relying on printed or logged outputs.\n- CONDITION 3: The test cases ensure that both implementations must produce the same results for all inputs, meaning the new implementation must have the exact same functionality as the original.\n- CONDITION 4: The test cases use assertions to compare return values, which is appropriate given that the function returns a list of vowels. This is reasonable and aligns with the function's behavior.\n- CONDITION 5: The test cases cover a variety of scenarios, including words with different vowel compositions, no vowels, repeated vowels, mixed case, non-alphabetic characters, and long strings. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "969b2baf0cd1d0157a16f9045e71585c0f86fb68"
    },
    {
        "func_name": "find_longest_word_in_list",
        "idx": "21",
        "repo_name": "AgEpsilon24___commonAlgos",
        "func_path": "findCommonalityInWords.py",
        "orig_func": "def find_longest_word_in_list(wordsList: list):\n    \"\"\"\n        Find the longest word in the list\n\n        wordsList: list of words\n    \"\"\"\n    longestWord = wordsList[0]\n    for word in wordsList[1:]:\n        if len(word) > len(longestWord):\n            longestWord = word\n    return longestWord",
        "orig_context": "```python\n## findCommonalityInWords.py\ndef find_longest_word_in_list(wordsList:list):\n    \"\"\"\n        Find the longest word in the list\n\n        wordsList: list of words\n    \"\"\"\n\n    longestWord = wordsList[0]\n\n    for word in wordsList[1:]:\n        if len(word) > len(longestWord):\n            longestWord = word\n\n    return longestWord\n\n```\n\n\n",
        "eval_script": "## findCommonalityInWords.py\ndef find_longest_word_in_list(wordsList:list):\n    \"\"\"\n        Find the longest word in the list\n\n        wordsList: list of words\n    \"\"\"\n\n    longestWord = wordsList[0]\n\n    for word in wordsList[1:]:\n        if len(word) > len(longestWord):\n            longestWord = word\n\n    return longestWord\n\n\ndef test_find_longest_word_in_list():\n    # Test case 1: List with words of varying lengths\n    words1 = [\"apple\", \"banana\", \"cherry\", \"date\"]\n    assert find_longest_word_in_list(words1) == find_longest_word_in_list_new_implementation(words1)\n\n    # Test case 2: List with words of the same length\n    words2 = [\"dog\", \"cat\", \"bat\"]\n    assert find_longest_word_in_list(words2) == find_longest_word_in_list_new_implementation(words2)\n\n    # Test case 3: List with a single word\n    words3 = [\"elephant\"]\n    assert find_longest_word_in_list(words3) == find_longest_word_in_list_new_implementation(words3)\n\n    # Test case 4: Empty list\n    words4 = []\n    try:\n        assert find_longest_word_in_list(words4) == find_longest_word_in_list_new_implementation(words4)\n    except IndexError:\n        pass  # Expected behavior since the list is empty\n\n    # Test case 5: List with multiple longest words\n    words5 = [\"ant\", \"bear\", \"cat\", \"dog\"]\n    assert find_longest_word_in_list(words5) == find_longest_word_in_list_new_implementation(words5)\n\n    # Test case 6: List with non-string elements\n    words6 = [\"apple\", 123, \"banana\"]\n    try:\n        assert find_longest_word_in_list(words6) == find_longest_word_in_list_new_implementation(words6)\n    except TypeError:\n        pass  # Expected behavior since the list contains non-string elements\n\n    # Test case 7: List with special characters\n    words7 = [\"hello!\", \"world@\", \"#amazing\"]\n    assert find_longest_word_in_list(words7) == find_longest_word_in_list_new_implementation(words7)\n\n    # Test case 8: List with mixed case sensitivity\n    words8 = [\"Apple\", \"banana\", \"Cherry\"]\n    assert find_longest_word_in_list(words8) == find_longest_word_in_list_new_implementation(words8)\n\n    # Test case 9: List with very long words\n    words9 = [\"a\" * 1000, \"b\" * 999, \"c\" * 1000]\n    assert find_longest_word_in_list(words9) == find_longest_word_in_list_new_implementation(words9)\n\nif __name__ == \"__main__\":\n    test_find_longest_word_in_list()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `find_longest_word_in_list` is identical to the ORIGINAL FUNCTION. Both functions iterate over the list starting from the second element and update the `longestWord` variable if a longer word is found. The functionality is unchanged, as they both return the longest word from the list. The additional code in the revised version is for testing purposes and does not alter the functionality of the `find_longest_word_in_list` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `find_longest_word_in_list` function returns the longest word from the list, satisfying this condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `find_longest_word_in_list` and `find_longest_word_in_list_new_implementation`, ensuring that the new implementation must have the exact same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare return values, which is appropriate given that the function returns a value. The test cases also handle exceptions for empty lists and non-string elements, which is reasonable.\n- CONDITION 5: The test cases cover a variety of scenarios, including varying word lengths, single-word lists, empty lists, non-string elements, special characters, mixed case sensitivity, and very long words, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "969b2baf0cd1d0157a16f9045e71585c0f86fb68"
    },
    {
        "func_name": "kill_items",
        "idx": "59",
        "repo_name": "ChenyangGao___p115client",
        "func_path": "modules/p115updatedb/p115updatedb/updatedb.py",
        "orig_func": "def kill_items(con: Connection | Cursor, ids: int | Iterable[int], /, commit: bool=False) -> Cursor:\n    \"\"\"\u4f7f\u7528 id \u53bb\u7b5b\u9009\u548c\u79fb\u9664\u4e00\u7ec4\u6570\u636e\n\n    :param con: \u6570\u636e\u5e93\u8fde\u63a5\u6216\u6e38\u6807\n    :param ids: \u4e00\u7ec4 id\uff0c\u4f1a\u88ab\u79fb\u9664\n    :param commit: \u662f\u5426\u63d0\u4ea4\n\n    :return: \u6e38\u6807\n    \"\"\"\n    if isinstance(ids, int):\n        cond = f'id = {ids:d}'\n    else:\n        cond = 'id IN (%s)' % (','.join(map(str, ids)) or 'NULL')\n    sql = 'UPDATE data SET is_alive=0 WHERE ' + cond\n    return execute(con, sql, commit=commit)",
        "orig_context": "```python\n## modules/p115updatedb/p115updatedb/updatedb.py\nfrom collections.abc import Iterator, Iterable, Mapping\n\nfrom sqlite3 import connect, Connection, Cursor\n\nfrom sqlitetools import execute, find, transact, upsert_items, AutoCloseConnection\n\ndef kill_items(\n    con: Connection | Cursor, \n    ids: int | Iterable[int], \n    /, \n    commit: bool = False, \n) -> Cursor:\n    \"\"\"\u4f7f\u7528 id \u53bb\u7b5b\u9009\u548c\u79fb\u9664\u4e00\u7ec4\u6570\u636e\n\n    :param con: \u6570\u636e\u5e93\u8fde\u63a5\u6216\u6e38\u6807\n    :param ids: \u4e00\u7ec4 id\uff0c\u4f1a\u88ab\u79fb\u9664\n    :param commit: \u662f\u5426\u63d0\u4ea4\n\n    :return: \u6e38\u6807\n    \"\"\"\n    if isinstance(ids, int):\n        cond = f\"id = {ids:d}\"\n    else:\n        cond = \"id IN (%s)\" % (\",\".join(map(str, ids)) or \"NULL\")\n    sql = \"UPDATE data SET is_alive=0 WHERE \" + cond\n    return execute(con, sql, commit=commit)\n\n```\n\n\n",
        "eval_script": "from collections.abc import Iterable\nfrom sqlite3 import connect, Connection, Cursor\n\ndef execute(con: Connection | Cursor, sql: str, commit: bool = False) -> Cursor:\n    \"\"\"Execute a SQL command.\"\"\"\n    cur = con.cursor()\n    cur.execute(sql)\n    if commit:\n        con.commit()\n    return cur\n\ndef kill_items(\n    con: Connection | Cursor, \n    ids: int | Iterable[int], \n    /, \n    commit: bool = False, \n) -> Cursor:\n    \"\"\"\u4f7f\u7528 id \u53bb\u7b5b\u9009\u548c\u79fb\u9664\u4e00\u7ec4\u6570\u636e\n\n    :param con: \u6570\u636e\u5e93\u8fde\u63a5\u6216\u6e38\u6807\n    :param ids: \u4e00\u7ec4 id\uff0c\u4f1a\u88ab\u79fb\u9664\n    :param commit: \u662f\u5426\u63d0\u4ea4\n\n    :return: \u6e38\u6807\n    \"\"\"\n    if isinstance(ids, int):\n        cond = f\"id = {ids:d}\"\n    else:\n        cond = \"id IN (%s)\" % (\",\".join(map(str, ids)) or \"NULL\")\n    sql = \"UPDATE data SET is_alive=0 WHERE \" + cond\n    return execute(con, sql, commit=commit)\n\n\n# Mock setup for testing\ndef setup_database():\n    con = connect(':memory:')  # Create an in-memory SQLite database\n    cur = con.cursor()\n    cur.execute('CREATE TABLE data (id INTEGER PRIMARY KEY, is_alive INTEGER)')\n    cur.executemany('INSERT INTO data (id, is_alive) VALUES (?, ?)', [(1, 1), (2, 1), (3, 1)])\n    con.commit()\n    return con\n\ndef test_kill_items():\n    # Test case 1: Single ID\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, 1, commit=True)\n    kill_items_new_implementation(con2, 1, commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 1 failed\"\n\n    # Test case 2: Multiple IDs\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, [1, 2], commit=True)\n    kill_items_new_implementation(con2, [1, 2], commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 2 failed\"\n\n    # Test case 3: Empty list\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, [], commit=True)\n    kill_items_new_implementation(con2, [], commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 3 failed\"\n\n    # Test case 4: Non-existent ID\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, 99, commit=True)\n    kill_items_new_implementation(con2, 99, commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 4 failed\"\n\n    # Test case 5: Mixed valid and invalid IDs\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, [1, 99], commit=True)\n    kill_items_new_implementation(con2, [1, 99], commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 5 failed\"\n\n    # Test case 6: No commit\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, 1, commit=False)\n    kill_items_new_implementation(con2, 1, commit=False)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 6 failed\"\n\n    # Test case 7: Large number of IDs\n    con1 = setup_database()\n    con2 = setup_database()\n    ids = list(range(1, 1000))\n    kill_items(con1, ids, commit=True)\n    kill_items_new_implementation(con2, ids, commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 7 failed\"\n\n    # Test case 8: Boundary values\n    con1 = setup_database()\n    con2 = setup_database()\n    kill_items(con1, [0, 2147483647], commit=True)\n    kill_items_new_implementation(con2, [0, 2147483647], commit=True)\n    cur1 = con1.cursor()\n    cur2 = con2.cursor()\n    cur1.execute('SELECT * FROM data')\n    cur2.execute('SELECT * FROM data')\n    assert cur1.fetchall() == cur2.fetchall(), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test_kill_items()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `kill_items` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions take a database connection or cursor, a single ID or an iterable of IDs, and an optional commit flag. They construct a SQL query to update the `is_alive` field to 0 for the specified IDs and execute this query using the `execute` function. The SQL condition string is constructed in the same way in both functions, and the `execute` function is called with the same parameters. The test cases provided in the code also confirm that the behavior of the REVISED FUNCTION matches the expected behavior of the ORIGINAL FUNCTION across various scenarios. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `kill_items` function modifies the database state by executing an SQL update command. It returns a `Cursor` object, which is a return value.\n- CONDITION 2: The test cases check the state of the database by comparing the results of SQL queries (`cur1.fetchall()` and `cur2.fetchall()`) after executing `kill_items` and `kill_items_new_implementation`. They do not rely on printed or logged content.\n- CONDITION 3: The test cases compare the database states after executing both implementations, ensuring that `kill_items_new_implementation` must have the same functionality as `kill_items` to pass.\n- CONDITION 4: The test cases use assertions to compare the results of SQL queries, which is reasonable given that `kill_items` modifies the database state.\n- CONDITION 5: The test cases cover various scenarios, including single and multiple IDs, empty lists, non-existent IDs, mixed valid and invalid IDs, no commit, a large number of IDs, and boundary values. These are non-trivial and comprehensive.",
            "answer": "yes"
        },
        "commit_id": "e54918dd4e8e810f6b6f5665deefd3265ce53cbb"
    },
    {
        "func_name": "sort",
        "idx": "64",
        "repo_name": "ChenyangGao___p115client",
        "func_path": "modules/p115updatedb/p115updatedb/updatedb.py",
        "orig_func": "def sort(data: list[dict], /, reverse: bool=False) -> list[dict]:\n    \"\"\"\u5bf9\u6587\u4ef6\u4fe1\u606f\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u4f7f\u5f97\u5982\u679c\u67d0\u4e2a\u5143\u7d20\u662f\u53e6\u4e00\u4e2a\u5143\u7d20\u7684\u7236\u8282\u70b9\uff0c\u5219\u540e\u8005\u5728\u524d\n\n    :param data: \u5f85\u6392\u5e8f\u7684\u6587\u4ef6\u4fe1\u606f\u5217\u8868\n    :param reverse: \u662f\u5426\u4f60\u9700\u6392\u5217\n\n    :return: \u539f\u5730\u6392\u5e8f\uff0c\u8fd4\u56de\u4f20\u5165\u7684\u5217\u8868\u672c\u8eab\n    \"\"\"\n    d: dict[int, int] = {a['id']: a['parent_id'] for a in data}\n    depth_d: dict[int, int] = {}\n\n    def depth(id: int, /) -> int:\n        try:\n            return depth_d[id]\n        except KeyError:\n            if id in d:\n                return 1 + depth(d[id])\n            return 0\n    data.sort(key=lambda a: depth(a['id']), reverse=reverse)\n    return data",
        "orig_context": "```python\n## modules/p115updatedb/p115updatedb/updatedb.py\ndef sort(\n    data: list[dict], \n    /, \n    reverse: bool = False, \n) -> list[dict]:\n    \"\"\"\u5bf9\u6587\u4ef6\u4fe1\u606f\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u4f7f\u5f97\u5982\u679c\u67d0\u4e2a\u5143\u7d20\u662f\u53e6\u4e00\u4e2a\u5143\u7d20\u7684\u7236\u8282\u70b9\uff0c\u5219\u540e\u8005\u5728\u524d\n\n    :param data: \u5f85\u6392\u5e8f\u7684\u6587\u4ef6\u4fe1\u606f\u5217\u8868\n    :param reverse: \u662f\u5426\u4f60\u9700\u6392\u5217\n\n    :return: \u539f\u5730\u6392\u5e8f\uff0c\u8fd4\u56de\u4f20\u5165\u7684\u5217\u8868\u672c\u8eab\n    \"\"\"\n    d: dict[int, int] = {a[\"id\"]: a[\"parent_id\"] for a in data}\n    depth_d: dict[int, int] = {}\n    def depth(id: int, /) -> int:\n        try:\n            return depth_d[id]\n        except KeyError:\n            if id in d:\n                return 1 + depth(d[id])\n            return 0\n    data.sort(key=lambda a: depth(a[\"id\"]), reverse=reverse)\n    return data\n\n```\n\n\n",
        "eval_script": "## modules/p115updatedb/p115updatedb/updatedb.py\ndef sort(\n    data: list[dict], \n    /, \n    reverse: bool = False, \n) -> list[dict]:\n    \"\"\"\u5bf9\u6587\u4ef6\u4fe1\u606f\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u4f7f\u5f97\u5982\u679c\u67d0\u4e2a\u5143\u7d20\u662f\u53e6\u4e00\u4e2a\u5143\u7d20\u7684\u7236\u8282\u70b9\uff0c\u5219\u540e\u8005\u5728\u524d\n\n    :param data: \u5f85\u6392\u5e8f\u7684\u6587\u4ef6\u4fe1\u606f\u5217\u8868\n    :param reverse: \u662f\u5426\u4f60\u9700\u6392\u5217\n\n    :return: \u539f\u5730\u6392\u5e8f\uff0c\u8fd4\u56de\u4f20\u5165\u7684\u5217\u8868\u672c\u8eab\n    \"\"\"\n    d: dict[int, int] = {a[\"id\"]: a[\"parent_id\"] for a in data}\n    depth_d: dict[int, int] = {}\n    def depth(id: int, /) -> int:\n        try:\n            return depth_d[id]\n        except KeyError:\n            if id in d:\n                return 1 + depth(d[id])\n            return 0\n    data.sort(key=lambda a: depth(a[\"id\"]), reverse=reverse)\n    return data\n\n\ndef test_sort():\n    # Test case 1: Default order\n    data1 = [\n        {\"id\": 1, \"parent_id\": 0},\n        {\"id\": 2, \"parent_id\": 1},\n        {\"id\": 3, \"parent_id\": 1}\n    ]\n    assert sort(data1.copy()) == sort_new_implementation(data1.copy())\n\n    # Test case 2: Reverse order\n    data2 = [\n        {\"id\": 1, \"parent_id\": 0},\n        {\"id\": 2, \"parent_id\": 1},\n        {\"id\": 3, \"parent_id\": 1}\n    ]\n    assert sort(data2.copy(), reverse=True) == sort_new_implementation(data2.copy(), reverse=True)\n\n    # Test case 3: No parent\n    data3 = [\n        {\"id\": 1, \"parent_id\": 0},\n        {\"id\": 2, \"parent_id\": 0},\n        {\"id\": 3, \"parent_id\": 0}\n    ]\n    assert sort(data3.copy()) == sort_new_implementation(data3.copy())\n\n    # Test case 4: Empty list\n    data4 = []\n    assert sort(data4.copy()) == sort_new_implementation(data4.copy())\n\n    # Test case 5: Single element\n    data5 = [{\"id\": 1, \"parent_id\": 0}]\n    assert sort(data5.copy()) == sort_new_implementation(data5.copy())\n\n    # Test case 6: Deep hierarchy\n    data6 = [\n        {\"id\": 1, \"parent_id\": 0},\n        {\"id\": 2, \"parent_id\": 1},\n        {\"id\": 3, \"parent_id\": 2},\n        {\"id\": 4, \"parent_id\": 3}\n    ]\n    assert sort(data6.copy()) == sort_new_implementation(data6.copy())\n\n    # Test case 7: Multiple roots\n    data7 = [\n        {\"id\": 1, \"parent_id\": 0},\n        {\"id\": 2, \"parent_id\": 0},\n        {\"id\": 3, \"parent_id\": 1},\n        {\"id\": 4, \"parent_id\": 2}\n    ]\n    assert sort(data7.copy()) == sort_new_implementation(data7.copy())\n\n    # Test case 8: Circular reference\n    data8 = [\n        {\"id\": 1, \"parent_id\": 2},\n        {\"id\": 2, \"parent_id\": 1}\n    ]\n    try:\n        sort(data8.copy())\n    except RecursionError:\n        pass\n    try:\n        sort_new_implementation(data8.copy())\n    except RecursionError:\n        pass\n\n    # Test case 9: Non-sequential IDs\n    data9 = [\n        {\"id\": 10, \"parent_id\": 0},\n        {\"id\": 20, \"parent_id\": 10},\n        {\"id\": 30, \"parent_id\": 10}\n    ]\n    assert sort(data9.copy()) == sort_new_implementation(data9.copy())\n\nif __name__ == \"__main__\":\n    test_sort()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      12      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                 12      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions take a list of dictionaries, each containing 'id' and 'parent_id', and sort them based on the depth of each element in a hierarchy. The depth is calculated recursively, and the sorted order ensures that if an element is a parent of another, the latter appears first in the list. The only differences between the two functions are minor formatting changes, such as the use of double quotes instead of single quotes for dictionary keys in the REVISED FUNCTION. These changes do not affect the functionality of the code. The test cases provided in the CODE verify that the REVISED FUNCTION behaves as expected in various scenarios, including default and reverse order, no parent, empty list, single element, deep hierarchy, multiple roots, circular references, and non-sequential IDs. Since the functionality remains unchanged, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `sort` function returns a list, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `sort` and `sort_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `sort` and `sort_new_implementation` for various inputs, ensuring that `sort_new_implementation` must have the same functionality as `sort` to pass all tests.\n- CONDITION 4: The test cases use `assert` statements to compare the return values of the two implementations, which is reasonable given that `sort` returns a list.\n- CONDITION 5: The test cases cover a variety of scenarios, including default order, reverse order, no parent, empty list, single element, deep hierarchy, multiple roots, circular reference, and non-sequential IDs. These cases are non-trivial and test different aspects of the sorting functionality.",
            "answer": "yes"
        },
        "commit_id": "e54918dd4e8e810f6b6f5665deefd3265ce53cbb"
    },
    {
        "func_name": "P115ECDHCipher.decode_token",
        "idx": "67",
        "repo_name": "ChenyangGao___p115client",
        "func_path": "modules/p115cipher/p115cipher/normal.py",
        "orig_func": "@staticmethod\ndef decode_token(data: str | bytes) -> tuple[bytes, int]:\n    \"\"\"\u89e3\u5bc6 token \u6570\u636e\uff0c\u8fd4\u56de pub_key \u548c timestamp \u7684\u5143\u7ec4\"\"\"\n    data = b64decode(data)\n    r1 = data[15]\n    r2 = data[39]\n    return (bytes((c ^ r1 for c in data[:15])) + bytes((c ^ r2 for c in data[24:39])), from_bytes(bytes((i ^ r1 for i in data[20:24])), byteorder='little'))",
        "orig_context": "```python\n## modules/p115cipher/p115cipher/const.py\nfrom typing import Final\n\nCRC_SALT: Final = b\"^j>WD3Kr?J2gLFjD4W2y@\"\n\nECDH_REMOTE_PUBKEY: Final = bytes((\n    0x57, 0xA2, 0x92, 0x57, 0xCD, 0x23, 0x20, 0xE5, 0xD6, 0xD1, 0x43, 0x32, 0x2F, 0xA4, 0xBB, 0x8A, \n    0x3C, 0xF9, 0xD3, 0xCC, 0x62, 0x3E, 0xF5, 0xED, 0xAC, 0x62, 0xB7, 0x67, 0x8A, 0x89, 0xC9, 0x1A, \n    0x83, 0xBA, 0x80, 0x0D, 0x61, 0x29, 0xF5, 0x22, 0xD0, 0x34, 0xC8, 0x95, 0xDD, 0x24, 0x65, 0x24, \n    0x3A, 0xDD, 0xC2, 0x50, 0x95, 0x3B, 0xEE, 0xBA, \n))\n\n```\n\n\n```python\n## modules/p115cipher/p115cipher/common.py\nfrom functools import partial\n\nfrom .const import G_kts, ECDH_REMOTE_PUBKEY, RSA_PUBKEY_PAIR\n\nto_bytes = partial(int.to_bytes, byteorder=\"big\", signed=False)\n\nfrom_bytes = partial(int.from_bytes, byteorder=\"big\", signed=False)\n\ndef generate_ecdh_pair() -> tuple[bytes, bytes]:\n    from ecdsa import ECDH, NIST224p, SigningKey # type: ignore\n\n    sk = SigningKey.generate(NIST224p)\n    pk = sk.verifying_key\n    ecdh = ECDH(NIST224p)\n    ecdh.load_private_key(sk)\n    ecdh.load_received_public_key_bytes(ECDH_REMOTE_PUBKEY)\n    public = pk.pubkey.point.to_bytes()\n    x, y = public[:28], public[28:]\n    pub_key = bytes((28 + 1, 0x02 + (from_bytes(y) & 1))) + x\n    # NOTE: Roughly equivalent to\n    # n = int((ecdh.public_key.pubkey.point * from_bytes(sk.to_string())).x())\n    # secret = to_bytes(n, (n.bit_length() + 0b111) >> 3)\n    secret = ecdh.generate_sharedsecret_bytes()\n    return pub_key, secret\n\n```\n\n\n```python\n## modules/p115cipher/p115cipher/normal.py\nfrom base64 import b64decode, b64encode\n\nfrom binascii import crc32\n\nfrom random import randrange\n\nfrom .const import G_key_l, CRC_SALT, RSA_PUBKEY_PAIR\n\nfrom .common import Buffer, RSA_encrypt, gen_key, from_bytes, to_bytes, xor, generate_ecdh_pair\n\nclass P115ECDHCipher:\n\n    def __init__(self):\n        pub_key, secret = generate_ecdh_pair()\n        self.pub_key: bytes = pub_key\n        # NOTE: use AES-128\n        self.aes_key: bytes = secret[:16]\n        self.aes_iv: bytes  = secret[-16:]\n\n    def encode(self, text: bytes | bytearray | str, /) -> bytes:\n        \"\u52a0\u5bc6\u6570\u636e\"\n        from Crypto.Cipher import AES\n\n        if isinstance(text, str):\n            text = bytes(text, \"utf-8\")\n        pad_size = 16 - (len(text) & 15)\n        return AES.new(self.aes_key, AES.MODE_CBC, self.aes_iv).encrypt(\n            text + to_bytes(pad_size) * pad_size)\n\n    def decode(\n        self, \n        cipher_text: bytes | bytearray, \n        /, \n        decompress: bool = False, \n    ) -> bytes:\n        \"\u89e3\u5bc6\u6570\u636e\"\n        from Crypto.Cipher import AES\n\n        data = AES.new(self.aes_key, AES.MODE_CBC, self.aes_iv).decrypt(\n            cipher_text[:len(cipher_text) & -16])\n        if decompress:\n            from lz4.block import decompress as lz4_block_decompress # type: ignore\n            size = data[0] + (data[1] << 8)\n            data = lz4_block_decompress(data[2:size+2], 0x2000)\n        else:\n            padding = data[-1]\n            if all(c == padding for c in data[-padding:]):\n                data = data[:-padding]\n        return data\n\n    def encode_token(self, /, timestamp: int) -> bytes:\n        \"\u63a5\u53d7\u4e00\u4e2a\u65f6\u95f4\u6233\uff08\u5355\u4f4d\u662f\u79d2\uff09\uff0c\u8fd4\u56de\u4e00\u4e2a token\uff0c\u4f1a\u628a pub_key \u548c timestamp \u90fd\u7f16\u7801\u5728\u5185\"\n        r1, r2 = randrange(256), randrange(256)\n        token = bytearray()\n        ts = to_bytes(timestamp, (timestamp.bit_length() + 0b111) >> 3)\n        if isinstance(self, P115ECDHCipher):\n            pub_key = self.pub_key\n        else:\n            pub_key = self\n        token.extend(pub_key[i]^r1 for i in range(15))\n        token.append(r1)\n        token.append(0x73^r1)\n        token.extend((r1,)*3)\n        token.extend(r1^ts[3-i] for i in range(4))\n        token.extend(pub_key[i]^r2 for i in range(15, len(pub_key)))\n        token.append(r2)\n        token.append(0x01^r2)\n        token.extend((r2,)*3)\n        crc = crc32(CRC_SALT+token) & 0xffffffff\n        h_crc32 = to_bytes(crc, 4)\n        token.extend(h_crc32[3-i] for i in range(4))\n        return b64encode(token)\n\n    @staticmethod\n    def decode_token(data: str | bytes) -> tuple[bytes, int]:\n        \"\u89e3\u5bc6 token \u6570\u636e\uff0c\u8fd4\u56de pub_key \u548c timestamp \u7684\u5143\u7ec4\"\n        data = b64decode(data)\n        r1 = data[15]\n        r2 = data[39]\n        return (\n            bytes(c ^ r1 for c in data[:15]) + bytes(c ^ r2 for c in data[24:39]), \n            from_bytes(bytes(i ^ r1 for i in data[20:24]), byteorder=\"little\"), \n        )\n\n```\n\n\n",
        "eval_script": "from base64 import b64decode, b64encode\nfrom binascii import crc32\nfrom random import randrange\nfrom functools import partial\n\n# Mock constants and functions that are not provided\nG_key_l = None\nRSA_PUBKEY_PAIR = None\n\n# Constants from const.py\nCRC_SALT = b\"^j>WD3Kr?J2gLFjD4W2y@\"\nECDH_REMOTE_PUBKEY = bytes((\n    0x57, 0xA2, 0x92, 0x57, 0xCD, 0x23, 0x20, 0xE5, 0xD6, 0xD1, 0x43, 0x32, 0x2F, 0xA4, 0xBB, 0x8A, \n    0x3C, 0xF9, 0xD3, 0xCC, 0x62, 0x3E, 0xF5, 0xED, 0xAC, 0x62, 0xB7, 0x67, 0x8A, 0x89, 0xC9, 0x1A, \n    0x83, 0xBA, 0x80, 0x0D, 0x61, 0x29, 0xF5, 0x22, 0xD0, 0x34, 0xC8, 0x95, 0xDD, 0x24, 0x65, 0x24, \n    0x3A, 0xDD, 0xC2, 0x50, 0x95, 0x3B, 0xEE, 0xBA, \n))\n\n# Functions from common.py\nto_bytes = partial(int.to_bytes, byteorder=\"big\", signed=False)\nfrom_bytes = partial(int.from_bytes, byteorder=\"big\", signed=False)\n\ndef generate_ecdh_pair() -> tuple[bytes, bytes]:\n    from ecdsa import ECDH, NIST224p, SigningKey # type: ignore\n\n    sk = SigningKey.generate(NIST224p)\n    pk = sk.verifying_key\n    ecdh = ECDH(NIST224p)\n    ecdh.load_private_key(sk)\n    ecdh.load_received_public_key_bytes(ECDH_REMOTE_PUBKEY)\n    public = pk.pubkey.point.to_bytes()\n    x, y = public[:28], public[28:]\n    pub_key = bytes((28 + 1, 0x02 + (from_bytes(y) & 1))) + x\n    secret = ecdh.generate_sharedsecret_bytes()\n    return pub_key, secret\n\n# Class from normal.py\nclass P115ECDHCipher:\n\n    def __init__(self):\n        pub_key, secret = generate_ecdh_pair()\n        self.pub_key: bytes = pub_key\n        self.aes_key: bytes = secret[:16]\n        self.aes_iv: bytes  = secret[-16:]\n\n    def encode(self, text: bytes | bytearray | str, /) -> bytes:\n        from Crypto.Cipher import AES\n\n        if isinstance(text, str):\n            text = bytes(text, \"utf-8\")\n        pad_size = 16 - (len(text) & 15)\n        return AES.new(self.aes_key, AES.MODE_CBC, self.aes_iv).encrypt(\n            text + to_bytes(pad_size) * pad_size)\n\n    def decode(\n        self, \n        cipher_text: bytes | bytearray, \n        /, \n        decompress: bool = False, \n    ) -> bytes:\n        from Crypto.Cipher import AES\n\n        data = AES.new(self.aes_key, AES.MODE_CBC, self.aes_iv).decrypt(\n            cipher_text[:len(cipher_text) & -16])\n        if decompress:\n            from lz4.block import decompress as lz4_block_decompress # type: ignore\n            size = data[0] + (data[1] << 8)\n            data = lz4_block_decompress(data[2:size+2], 0x2000)\n        else:\n            padding = data[-1]\n            if all(c == padding for c in data[-padding:]):\n                data = data[:-padding]\n        return data\n\n    def encode_token(self, /, timestamp: int) -> bytes:\n        r1, r2 = randrange(256), randrange(256)\n        token = bytearray()\n        ts = to_bytes(timestamp, 4)  # Ensure ts is always 4 bytes long\n        if isinstance(self, P115ECDHCipher):\n            pub_key = self.pub_key\n        else:\n            pub_key = self\n        token.extend(pub_key[i]^r1 for i in range(15))\n        token.append(r1)\n        token.append(0x73^r1)\n        token.extend((r1,)*3)\n        token.extend(r1^ts[3-i] for i in range(4))\n        token.extend(pub_key[i]^r2 for i in range(15, len(pub_key)))\n        token.append(r2)\n        token.append(0x01^r2)\n        token.extend((r2,)*3)\n        crc = crc32(CRC_SALT+token) & 0xffffffff\n        h_crc32 = to_bytes(crc, 4)\n        token.extend(h_crc32[3-i] for i in range(4))\n        return b64encode(token)\n\n    @staticmethod\n    def decode_token(data: str | bytes) -> tuple[bytes, int]:\n        data = b64decode(data)\n        r1 = data[15]\n        r2 = data[39]\n        return (\n            bytes(c ^ r1 for c in data[:15]) + bytes(c ^ r2 for c in data[24:39]), \n            from_bytes(bytes(i ^ r1 for i in data[20:24]), byteorder=\"little\"), \n        )\n\n\ndef test_decode_token():\n    cipher = P115ECDHCipher()\n    \n    # Test case 1: Standard token\n    token = cipher.encode_token(1234567890)\n    assert P115ECDHCipher.decode_token(token) == P115ECDHCipher.decode_token_new_implementation(token)\n    \n    # Test case 2: Minimal timestamp\n    token = cipher.encode_token(0)\n    assert P115ECDHCipher.decode_token(token) == P115ECDHCipher.decode_token_new_implementation(token)\n    \n    # Test case 3: Maximal timestamp\n    token = cipher.encode_token(2**31 - 1)\n    assert P115ECDHCipher.decode_token(token) == P115ECDHCipher.decode_token_new_implementation(token)\n\nif __name__ == \"__main__\":\n    test_decode_token()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon comparing the ORIGINAL FUNCTION and the REVISED FUNCTION, we can observe that both functions perform the same operations. They both decode a base64-encoded input, extract two specific bytes (r1 and r2) from the decoded data, and use these bytes to XOR-decode specific segments of the data. The first segment (bytes 0 to 14) is XORed with r1, and the second segment (bytes 24 to 38) is XORed with r2. Both functions then return a tuple consisting of the decoded public key and the timestamp, which is extracted by XORing bytes 20 to 23 with r1 and converting them from bytes to an integer using little-endian byte order. The REVISED FUNCTION is essentially a direct copy of the ORIGINAL FUNCTION with no changes to the logic or functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `decode_token` function returns a tuple consisting of a `bytes` object and an `int`. This satisfies the condition as it has return values.\n\n2. **CONDITION 2**: The test cases in `test_decode_token` use assertions to compare the return values of `decode_token` and `decode_token_new_implementation`. They do not rely on printed or logged output, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `decode_token` and `decode_token_new_implementation` for the same input tokens. This ensures that `decode_token_new_implementation` must have the exact same functionality as `decode_token` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The assertions in the test cases are reasonable because they compare the return values of two functions that are expected to have the same functionality. There is no misuse of assertions, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover a range of scenarios: a standard timestamp, the minimal timestamp, and the maximal timestamp. This provides a non-trivial set of tests that check the function's behavior across different inputs, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "e54918dd4e8e810f6b6f5665deefd3265ce53cbb"
    },
    {
        "func_name": "notas",
        "idx": "82",
        "repo_name": "NikolasCaio___python",
        "func_path": "nabara desafios/desafio103.py",
        "orig_func": "def notas(*n, sit=False):\n    \"\"\"\n   ---> Funcao para analisar notas e situacoes de varios alunos\n    :param n: uma ou mais notas dos alunos(aceitavel)\n    :param sit: valor opcional, indicando se deve ou nao adicionar a situacao\n    :return: dicionario com varias informacoes sobre a situacao da turma.\n    \"\"\"\n    r = dict()\n    r['total'] = len(n)\n    r['maior'] = max(n)\n    r['menor'] = min(n)\n    r['media'] = sum(n) / len(n)\n    if sit:\n        if r['media'] >= 7:\n            r['situacao'] = 'BOA!'\n        elif r['media'] >= 5:\n            r['situacao'] = 'Razoavel'\n        else:\n            r['situacao'] = 'Ruim'\n    return r",
        "orig_context": "```python\n## nabara desafios/desafio103.py\ndef notas(*n, sit=False):\n    \"\"\"\n   ---> Funcao para analisar notas e situacoes de varios alunos\n    :param n: uma ou mais notas dos alunos(aceitavel)\n    :param sit: valor opcional, indicando se deve ou nao adicionar a situacao\n    :return: dicionario com varias informacoes sobre a situacao da turma.\n    \"\"\"\n    r = dict()\n    r['total'] = len(n)\n    r['maior'] = max(n)\n    r['menor'] = min(n)\n    r['media'] = sum(n)/len(n)\n    if sit:\n        if r['media'] >= 7:\n            r['situacao'] = 'BOA!'\n        elif r['media'] >= 5:\n            r['situacao'] = 'Razoavel'\n        else:\n            r['situacao'] = 'Ruim'\n    return r\n\n```\n\n\n",
        "eval_script": "def notas(*n, sit=False):\n    \"\"\"\n   ---> Funcao para analisar notas e situacoes de varios alunos\n    :param n: uma ou mais notas dos alunos(aceitavel)\n    :param sit: valor opcional, indicando se deve ou nao adicionar a situacao\n    :return: dicionario com varias informacoes sobre a situacao da turma.\n    \"\"\"\n    r = dict()\n    r['total'] = len(n)\n    r['maior'] = max(n)\n    r['menor'] = min(n)\n    r['media'] = sum(n)/len(n)\n    if sit:\n        if r['media'] >= 7:\n            r['situacao'] = 'BOA!'\n        elif r['media'] >= 5:\n            r['situacao'] = 'Razoavel'\n        else:\n            r['situacao'] = 'Ruim'\n    return r\n\n\ndef test_notas():\n    # Test case 1: Basic functionality without situation\n    assert notas(5.5, 7.0, 8.5) == notas_new_implementation(5.5, 7.0, 8.5)\n\n    # Test case 2: Basic functionality with situation\n    assert notas(5.5, 7.0, 8.5, sit=True) == notas_new_implementation(5.5, 7.0, 8.5, sit=True)\n\n    # Test case 3: Edge case with all grades being the same\n    assert notas(6.0, 6.0, 6.0, sit=True) == notas_new_implementation(6.0, 6.0, 6.0, sit=True)\n\n    # Test case 4: Situation 'Ruim' when average is below 5\n    assert notas(4.0, 3.0, 2.0, sit=True) == notas_new_implementation(4.0, 3.0, 2.0, sit=True)\n\nif __name__ == \"__main__\":\n    test_notas()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of code. Every line, including the logic for calculating total, maior, menor, media, and situacao, is exactly the same. The test cases provided in the CODE are meant to verify the functionality of the function, but they do not alter the function itself. Since the REVISED FUNCTION is a direct copy of the ORIGINAL FUNCTION, their functionality is exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `notas` function returns a dictionary containing various statistics about the grades, so it satisfies this condition.\n- CONDITION 2: The test cases use assertions to compare the return values of `notas` and `notas_new_implementation`, without checking printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `notas` and `notas_new_implementation` directly. If `notas_new_implementation` has the same functionality as `notas`, it will pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to check the equality of the outputs from both implementations, which is reasonable given that `notas` returns a dictionary. This condition is satisfied.\n- CONDITION 5: The test cases cover various scenarios, including basic functionality, inclusion of the situation, edge cases with identical grades, and specific situations based on average grades. These are non-trivial tests that cover different aspects of the function's behavior, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "44c9f6c1c6c3acd164ac4cbe2c05b80c99d692de"
    },
    {
        "func_name": "fatorial",
        "idx": "83",
        "repo_name": "NikolasCaio___python",
        "func_path": "nabara desafios/desafio100.py",
        "orig_func": "def fatorial(n, show=False):\n    \"\"\"\n    ->  Calcula o fatorial de um numero.\n    :param n: O numero a ser calculado\n    :param show: (opcional) mostrar ou nao a conta\n    :return: O valor do fatorial de um numero n.\n    \"\"\"\n    f = 1\n    for c in range(n, 0, -1):\n        if show:\n            print(c, end=' ')\n            if c > 1:\n                print('x', end=' ')\n            else:\n                print('=', end=' ')\n        f *= c\n    return f",
        "orig_context": "```python\n## nabara desafios/desafio100.py\ndef fatorial(n, show=False):\n    \"\"\"\n    ->  Calcula o fatorial de um numero.\n    :param n: O numero a ser calculado\n    :param show: (opcional) mostrar ou nao a conta\n    :return: O valor do fatorial de um numero n.\n    \"\"\"\n    f = 1\n    for c in range(n, 0, -1):\n        if show:\n            print(c, end=\" \")\n            if c > 1:\n                print('x', end=\" \")\n            else:\n                print('=', end=\" \")\n        f *= c\n    return f\n\n```\n\n\n",
        "eval_script": "def fatorial(n, show=False):\n    \"\"\"\n    ->  Calcula o fatorial de um numero.\n    :param n: O numero a ser calculado\n    :param show: (opcional) mostrar ou nao a conta\n    :return: O valor do fatorial de um numero n.\n    \"\"\"\n    f = 1\n    for c in range(n, 0, -1):\n        if show:\n            print(c, end=\" \")\n            if c > 1:\n                print('x', end=\" \")\n            else:\n                print('=', end=\" \")\n        f *= c\n    return f\n\n\ndef test_fatorial():\n    # Test with n = 0, factorial of 0 is 1\n    assert fatorial(0) == fatorial_new_implementation(0)\n    # Test with n = 5, factorial of 5 is 120\n    assert fatorial(5) == fatorial_new_implementation(5)\n    # Test with n = 10, factorial of 10 is 3628800\n    assert fatorial(10) == fatorial_new_implementation(10)\n    # Test with n = 5 and show=True\n    assert fatorial(5, show=True) == fatorial_new_implementation(5, show=True)\n    # Test with n = 1 and show=True\n    assert fatorial(1, show=True) == fatorial_new_implementation(1, show=True)\n\nif __name__ == \"__main__\":\n    test_fatorial()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      10      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 10      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of logic and implementation. The only difference is in the style of quotation marks used in the print statements (single quotes in the original and double quotes in the revised), which does not affect the functionality of the code. The test cases provided in the CODE are meant to verify the correctness of the function but do not alter the function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `fatorial` function returns the factorial of a number `n`, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the `fatorial` function, not printed or logged contents.\n- CONDITION 3: The test cases compare the return values of `fatorial` and `fatorial_new_implementation` for various inputs, ensuring that `fatorial_new_implementation` must have the same functionality to pass all tests.\n- CONDITION 4: The assertions are reasonable as they compare the return values of the two implementations for the same inputs, including cases with the `show` parameter set to `True`.\n- CONDITION 5: The test cases cover a range of inputs, including edge cases like `n = 0` and typical cases like `n = 5` and `n = 10`, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "44c9f6c1c6c3acd164ac4cbe2c05b80c99d692de"
    },
    {
        "func_name": "set_up_test_cp_dir",
        "idx": "96",
        "repo_name": "msebi___google-photos-takeout-manager",
        "func_path": "move_imgs/img_mover_functions.py",
        "orig_func": "def set_up_test_cp_dir(src_dir, dst_dir):\n    \"\"\"\n\n    :param src_dir:\n    :param dst_dir:\n    :return:\n\n    function adds media files in dir as list\n    \"\"\"\n    os.makedirs(dst_dir, exist_ok=True)\n    cp_recursive_overwrite(src_dir, dst_dir)\n    media_files_in_dir = get_media_files_in_dir(dst_dir)\n    return media_files_in_dir",
        "orig_context": "```python\n## move_imgs/img_mover_functions.py\nimport ntpath\n\nimport os\n\nimport re\n\nimport shutil\n\ndef get_file_extension(file_path):\n    head, tail = ntpath.split(file_path)\n    file_name = tail or ntpath.basename(tail)\n    return os.path.splitext(file_name)[1]\n\ndef cp_recursive_overwrite(src, dest, ignore=None):\n    if os.path.isdir(src):\n        if not os.path.isdir(dest):\n            os.makedirs(dest)\n        files = os.listdir(src)\n        if ignore is not None:\n            ignored = ignore(src, files)\n        else:\n            ignored = set()\n        for f in files:\n            if f not in ignored:\n                cp_recursive_overwrite(os.path.join(src, f),\n                                       os.path.join(dest, f),\n                                       ignore)\n    else:\n        shutil.copyfile(src, dest)\n\ndef is_video_or_image_file(file_path, extensions):\n    extension = get_file_extension(file_path)\n\n    # flatten extensions (1 level deep lists)\n    abc = extensions\n    flattened_extensions = [a for ab in abc for a in ab]\n\n    is_media_file = False\n    if extension in flattened_extensions:\n        is_media_file = True\n\n    return is_media_file\n\nimg_file_ext = ['.apng', '.png', '.avif', '.gif', '.jpg', '.jpeg', '.jfif', '.pjpeg',\n                '.pjp', '.svg', '.webp', '.bmp', '.ico', '.tiff']\n\nvid_file_ext = ['.mp3', '.mp4', '.avi']\n\npdf_file_ext = ['.pdf']\n\njson_file_ext = ['.json']\n\ndef get_files_in_dir(directory, extensions=None):\n    if extensions is None:\n        extensions = [img_file_ext,\n                      vid_file_ext,\n                      pdf_file_ext,\n                      json_file_ext]\n\n    media_files_in_dir = []\n    for root, dirs, files in os.walk(directory):\n        for file in files:\n\n            if not is_video_or_image_file(file, extensions):\n                continue\n\n            media_files_in_dir.append(file)\n\n    return media_files_in_dir\n\ndef get_media_files_in_dir(directory):\n    # skip print orders\n    print_order_regex = 'Print Order \\\\d{21}$'\n    m = re.search(print_order_regex, directory)\n    if m:\n        return []\n\n    media_files_in_dir = get_files_in_dir(directory)\n\n    return media_files_in_dir\n\ndef set_up_test_cp_dir(src_dir, dst_dir):\n    \"\"\"\n\n    :param src_dir:\n    :param dst_dir:\n    :return:\n\n    function adds media files in dir as list\n    \"\"\"\n    os.makedirs(dst_dir, exist_ok=True)\n    cp_recursive_overwrite(src_dir, dst_dir)\n    media_files_in_dir = get_media_files_in_dir(dst_dir)\n\n    return media_files_in_dir\n\n```\n\n\n",
        "eval_script": "## move_imgs/img_mover_functions.py\nimport ntpath\nimport os\nimport re\nimport shutil\n\ndef get_file_extension(file_path):\n    head, tail = ntpath.split(file_path)\n    file_name = tail or ntpath.basename(tail)\n    return os.path.splitext(file_name)[1]\n\ndef cp_recursive_overwrite(src, dest, ignore=None):\n    if os.path.isdir(src):\n        if not os.path.isdir(dest):\n            os.makedirs(dest)\n        files = os.listdir(src)\n        if ignore is not None:\n            ignored = ignore(src, files)\n        else:\n            ignored = set()\n        for f in files:\n            if f not in ignored:\n                cp_recursive_overwrite(os.path.join(src, f),\n                                       os.path.join(dest, f),\n                                       ignore)\n    else:\n        shutil.copyfile(src, dest)\n\ndef is_video_or_image_file(file_path, extensions):\n    extension = get_file_extension(file_path)\n\n    # flatten extensions (1 level deep lists)\n    abc = extensions\n    flattened_extensions = [a for ab in abc for a in ab]\n\n    is_media_file = False\n    if extension in flattened_extensions:\n        is_media_file = True\n\n    return is_media_file\n\nimg_file_ext = ['.apng', '.png', '.avif', '.gif', '.jpg', '.jpeg', '.jfif', '.pjpeg',\n                '.pjp', '.svg', '.webp', '.bmp', '.ico', '.tiff']\n\nvid_file_ext = ['.mp3', '.mp4', '.avi']\n\npdf_file_ext = ['.pdf']\n\njson_file_ext = ['.json']\n\ndef get_files_in_dir(directory, extensions=None):\n    if extensions is None:\n        extensions = [img_file_ext,\n                      vid_file_ext,\n                      pdf_file_ext,\n                      json_file_ext]\n\n    media_files_in_dir = []\n    for root, dirs, files in os.walk(directory):\n        for file in files:\n\n            if not is_video_or_image_file(file, extensions):\n                continue\n\n            media_files_in_dir.append(file)\n\n    return media_files_in_dir\n\ndef get_media_files_in_dir(directory):\n    # skip print orders\n    print_order_regex = 'Print Order \\\\d{21}$'\n    m = re.search(print_order_regex, directory)\n    if m:\n        return []\n\n    media_files_in_dir = get_files_in_dir(directory)\n\n    return media_files_in_dir\n\ndef set_up_test_cp_dir(src_dir, dst_dir):\n    \"\"\"\n\n    :param src_dir:\n    :param dst_dir:\n    :return:\n\n    function adds media files in dir as list\n    \"\"\"\n    os.makedirs(dst_dir, exist_ok=True)\n    cp_recursive_overwrite(src_dir, dst_dir)\n    media_files_in_dir = get_media_files_in_dir(dst_dir)\n\n    return media_files_in_dir\n\n\ndef test_set_up_test_cp_dir():\n    # Create temporary directories and files for testing\n    src_dir = '/home/user/tmp/src'\n    dst_dir = '/home/user/tmp/dst'\n    os.makedirs(src_dir, exist_ok=True)\n    \n    # Create test files\n    with open(os.path.join(src_dir, 'test1.jpg'), 'w') as f:\n        f.write('test image content')\n    with open(os.path.join(src_dir, 'test2.mp4'), 'w') as f:\n        f.write('test video content')\n    with open(os.path.join(src_dir, 'test3.txt'), 'w') as f:\n        f.write('test text content')\n    \n    # Test original implementation\n    result_original = set_up_test_cp_dir(src_dir, dst_dir)\n    \n    # Reset destination directory\n    shutil.rmtree(dst_dir)\n    os.makedirs(dst_dir, exist_ok=True)\n    \n    # Test new implementation\n    result_new = set_up_test_cp_dir_new_implementation(src_dir, dst_dir)\n    \n    # Assertions\n    assert set(result_original) == set(result_new), \"Mismatch in media files list\"\n    assert 'test1.jpg' in result_new, \"Image file missing in new implementation\"\n    assert 'test2.mp4' in result_new, \"Video file missing in new implementation\"\n    assert 'test3.txt' not in result_new, \"Non-media file incorrectly included in new implementation\"\n\nif __name__ == \"__main__\":\n    test_set_up_test_cp_dir()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `set_up_test_cp_dir` is identical to the ORIGINAL FUNCTION. Both functions create the destination directory if it doesn't exist, copy files from the source directory to the destination directory using `cp_recursive_overwrite`, and then retrieve a list of media files in the destination directory using `get_media_files_in_dir`. The functionality and the implementation of the REVISED FUNCTION are exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `set_up_test_cp_dir` function returns a list of media files in the destination directory, satisfying the condition of having a return value.\n  \n- CONDITION 2: The test function checks the return values (`result_original` and `result_new`) and does not rely on printed or logged content. It uses assertions to compare the lists of media files, which is appropriate.\n\n- CONDITION 3: The test cases compare the results of the original and new implementations by checking that the sets of media files are equal and that specific files are included or excluded. This ensures that the new implementation must have the same functionality as the original to pass the tests.\n\n- CONDITION 4: The assertions used in the test function are reasonable. They check for equality of the sets of media files and verify the presence or absence of specific files, which aligns with the expected behavior of the function.\n\n- CONDITION 5: The test cases are non-trivial as they involve creating temporary directories and files, copying them, and verifying the results. The test checks for both inclusion and exclusion of specific file types, making it comprehensive.",
            "answer": "yes"
        },
        "commit_id": "ea4e39a685311273b80b60b7ac4c4e128726dc5b"
    },
    {
        "func_name": "set_up_test_cp_file",
        "idx": "97",
        "repo_name": "msebi___google-photos-takeout-manager",
        "func_path": "move_imgs/img_mover_functions.py",
        "orig_func": "def set_up_test_cp_file(src_file, dst_dir):\n    \"\"\"\n    :param src_file:\n    :param dst_dir:\n    :return:\n\n    function adds media file as list. Normally this isn't used except for setting up tests.\n    \"\"\"\n    os.makedirs(dst_dir, exist_ok=True)\n    shutil.copy(src_file, dst_dir)\n    head, tail = ntpath.split(src_file)\n    file_name = tail or ntpath.basename(tail)\n    json_file_name = file_name + json_file_ext[0]\n    json_file_name_path = os.path.join(head, json_file_name)\n    if os.path.isfile(json_file_name_path):\n        shutil.copy(json_file_name_path, dst_dir)\n    return [file_name]",
        "orig_context": "```python\n## move_imgs/img_mover_functions.py\nimport ntpath\n\nimport os\n\nimport shutil\n\njson_file_ext = ['.json']\n\ndef set_up_test_cp_file(src_file, dst_dir):\n    \"\"\"\n    :param src_file:\n    :param dst_dir:\n    :return:\n\n    function adds media file as list. Normally this isn't used except for setting up tests.\n    \"\"\"\n    os.makedirs(dst_dir, exist_ok=True)\n    shutil.copy(src_file, dst_dir)\n    head, tail = ntpath.split(src_file)\n    file_name = tail or ntpath.basename(tail)\n\n    json_file_name = file_name + json_file_ext[0]\n    json_file_name_path = os.path.join(head, json_file_name)\n    if os.path.isfile(json_file_name_path):\n        shutil.copy(json_file_name_path, dst_dir)\n\n    return [file_name]\n\n```\n\n\n",
        "eval_script": "import os\nimport shutil\nimport ntpath\n\njson_file_ext = ['.json']\n\ndef set_up_test_cp_file(src_file, dst_dir):\n    \"\"\"\n    :param src_file:\n    :param dst_dir:\n    :return:\n\n    function adds media file as list. Normally this isn't used except for setting up tests.\n    \"\"\"\n    os.makedirs(dst_dir, exist_ok=True)\n    shutil.copy(src_file, dst_dir)\n    head, tail = ntpath.split(src_file)\n    file_name = tail or ntpath.basename(tail)\n\n    json_file_name = file_name + json_file_ext[0]\n    json_file_name_path = os.path.join(head, json_file_name)\n    if os.path.isfile(json_file_name_path):\n        shutil.copy(json_file_name_path, dst_dir)\n\n    return [file_name]\n\n\ndef test_set_up_test_cp_file():\n    tmp_dir = '/home/user/tmp'\n    src_file = os.path.join(tmp_dir, 'test_file.txt')\n    json_file = os.path.join(tmp_dir, 'test_file.txt.json')\n    dst_dir = os.path.join(tmp_dir, 'destination')\n\n    # Ensure the temporary directory exists\n    os.makedirs(tmp_dir, exist_ok=True)\n\n    # Create a mock source file\n    with open(src_file, 'w') as f:\n        f.write('This is a test file.')\n\n    # Create a corresponding JSON file\n    with open(json_file, 'w') as f:\n        f.write('{\"key\": \"value\"}')\n\n    # Test when both source file and JSON file exist\n    result_original = set_up_test_cp_file(src_file, dst_dir)\n    result_new = set_up_test_cp_file_new_implementation(src_file, dst_dir)\n    assert result_original == result_new, \"Test failed when both source and JSON file exist.\"\n\n    # Test when only the source file exists\n    os.remove(json_file)\n    result_original = set_up_test_cp_file(src_file, dst_dir)\n    result_new = set_up_test_cp_file_new_implementation(src_file, dst_dir)\n    assert result_original == result_new, \"Test failed when only the source file exists.\"\n\n    # Test with a different file extension\n    src_file_different_ext = os.path.join(tmp_dir, 'test_file_other.txt')\n    with open(src_file_different_ext, 'w') as f:\n        f.write('This is another test file.')\n\n    result_original = set_up_test_cp_file(src_file_different_ext, dst_dir)\n    result_new = set_up_test_cp_file_new_implementation(src_file_different_ext, dst_dir)\n    assert result_original == result_new, \"Test failed with a different file extension.\"\n\nif __name__ == \"__main__\":\n    test_set_up_test_cp_file()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      10      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                 10      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they create the destination directory if it doesn't exist, copy the source file to the destination directory, determine the JSON file name based on the source file name, check if the JSON file exists, and if it does, copy it to the destination directory. Finally, they return a list containing the file name. There are no differences in the logic or functionality between the two functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `set_up_test_cp_file` returns a list containing the file name, which satisfies the condition of having return values.\n- CONDITION 2: The test cases check the return values of the function, as seen in the assertions comparing `result_original` and `result_new`. There is no checking of printed or logged content.\n- CONDITION 3: The test cases compare the outputs of `set_up_test_cp_file` and `set_up_test_cp_file_new_implementation` under various conditions, ensuring that the new implementation must have the same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that the function returns a list.\n- CONDITION 5: The test cases cover different scenarios: when both the source file and JSON file exist, when only the source file exists, and when using a different file extension. These scenarios are non-trivial and test the function's behavior under different conditions.",
            "answer": "yes"
        },
        "commit_id": "ea4e39a685311273b80b60b7ac4c4e128726dc5b"
    },
    {
        "func_name": "handle_agent_messages",
        "idx": "110",
        "repo_name": "imanoop7___Autogen-With-Panel-UI-Example-UseCase-Planner",
        "func_path": "app.py",
        "orig_func": "def handle_agent_messages(recipient, messages, sender, config):\n    \"\"\"\n    Handle message passing between agents and update the chat interface.\n    \n    Args:\n        recipient: The agent receiving the message\n        messages: List of message objects\n        sender: The agent sending the message\n        config: Configuration dictionary\n    \n    Returns:\n        tuple: (False, None) to continue agent communication flow\n    \"\"\"\n    print(f'Messages from: {sender.name} sent to: {recipient.name} | num messages: {len(messages)} | message: {messages[-1]}')\n    if all((key in messages[-1] for key in ['name'])):\n        chat_interface.send(messages[-1]['content'], user=messages[-1]['name'], avatar=agent_avatars[messages[-1]['name']], respond=False)\n    else:\n        chat_interface.send(messages[-1]['content'], user='SecretGuy', avatar='\ud83e\udd77', respond=False)\n    return (False, None)",
        "orig_context": "```python\n## app.py\nimport autogen\n\nimport panel as pn\n\nllm_config_list = [\n    {\n        \"model\": \"tinyllama\",\n        \"base_url\": \"http://localhost:11434/v1\",\n        \"api_key\": \"ollama\",\n    }\n]\n\nllm_global_config = {\n    \"config_list\": llm_config_list, \n    \"temperature\": 0,  # Zero temperature for deterministic outputs\n    \"seed\": 53        # Fixed seed for reproducibility\n}\n\nadmin_agent = autogen.UserProxyAgent(\n    name=\"Admin\",\n    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"exit\"),\n    system_message=\"\"\"A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin. \n    Only say APPROVED in most cases, and say EXIT when nothing to be done further. Do not say others.\"\"\",\n    code_execution_config=False,\n    default_auto_reply=\"Approved\", \n    human_input_mode=\"NEVER\",\n    llm_config=llm_global_config,\n)\n\nengineer_agent = autogen.AssistantAgent(\n    name=\"Engineer\",\n    llm_config=llm_global_config,\n    system_message='''Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor.\nDon't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor.\nIf the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.\n''',\n)\n\nscientist_agent = autogen.AssistantAgent(\n    name=\"Scientist\",\n    llm_config=llm_global_config,\n    system_message=\"\"\"Scientist. You follow an approved plan. You are able to categorize papers after seeing their abstracts printed. You don't write code.\"\"\"\n)\n\nplanner_agent = autogen.AssistantAgent(\n    name=\"Planner\",\n    system_message='''Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval.\nThe plan may involve an engineer who can write code and a scientist who doesn't write code.\nExplain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist.\n''',\n    llm_config=llm_global_config,\n)\n\nexecutor_agent = autogen.UserProxyAgent(\n    name=\"Executor\",\n    system_message=\"Executor. Execute the code written by the engineer and report the result.\",\n    human_input_mode=\"NEVER\",\n    code_execution_config={\n        \"last_n_messages\": 3,\n        \"work_dir\": \"paper\",\n        \"use_docker\": False  # Disable docker for local execution\n    },\n)\n\ncritic_agent = autogen.AssistantAgent(\n    name=\"Critic\",\n    system_message=\"Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.\",\n    llm_config=llm_global_config,\n)\n\nagent_group_chat = autogen.GroupChat(\n    agents=[admin_agent, engineer_agent, scientist_agent, planner_agent, executor_agent, critic_agent], \n    messages=[], \n    max_round=50\n)\n\nchat_manager = autogen.GroupChatManager(groupchat=agent_group_chat, llm_config=llm_global_config)\n\nagent_avatars = {\n    admin_agent.name: \"\ud83d\udc68\u200d\ud83d\udcbc\",\n    engineer_agent.name: \"\ud83d\udc69\u200d\ud83d\udcbb\",\n    scientist_agent.name: \"\ud83d\udc69\u200d\ud83d\udd2c\",\n    planner_agent.name: \"\ud83d\uddd3\",\n    executor_agent.name: \"\ud83d\udee0\",\n    critic_agent.name: '\ud83d\udcdd'\n}\n\ndef callback(contents: str, user: str, instance: pn.chat.ChatInterface):\n    admin_agent.initiate_chat(chat_manager, message=contents)\n\nchat_interface = pn.chat.ChatInterface(callback=callback)\n\ndef handle_agent_messages(recipient, messages, sender, config):\n    \"\"\"\n    Handle message passing between agents and update the chat interface.\n    \n    Args:\n        recipient: The agent receiving the message\n        messages: List of message objects\n        sender: The agent sending the message\n        config: Configuration dictionary\n    \n    Returns:\n        tuple: (False, None) to continue agent communication flow\n    \"\"\"\n    print(f\"Messages from: {sender.name} sent to: {recipient.name} | num messages: {len(messages)} | message: {messages[-1]}\")\n    \n    # Check if message has required attributes and send to appropriate user\n    if all(key in messages[-1] for key in ['name']):\n        chat_interface.send(\n            messages[-1]['content'],\n            user=messages[-1]['name'],\n            avatar=agent_avatars[messages[-1]['name']],\n            respond=False\n        )\n    else:\n        chat_interface.send(\n            messages[-1]['content'],\n            user='SecretGuy',\n            avatar='\ud83e\udd77',\n            respond=False\n        )\n\n    return False, None\n\n```\n\n\n",
        "eval_script": "## app.py\nimport panel as pn\n\n# Mock implementations for the autogen module\nclass UserProxyAgent:\n    def __init__(self, name, is_termination_msg, system_message, code_execution_config, default_auto_reply, human_input_mode, llm_config):\n        self.name = name\n        self.is_termination_msg = is_termination_msg\n        self.system_message = system_message\n        self.code_execution_config = code_execution_config\n        self.default_auto_reply = default_auto_reply\n        self.human_input_mode = human_input_mode\n        self.llm_config = llm_config\n\n    def initiate_chat(self, chat_manager, message):\n        print(f\"Initiating chat with message: {message}\")\n\nclass AssistantAgent:\n    def __init__(self, name, llm_config, system_message):\n        self.name = name\n\nclass GroupChat:\n    def __init__(self, agents, messages, max_round):\n        self.agents = agents\n\nclass GroupChatManager:\n    def __init__(self, groupchat, llm_config):\n        self.groupchat = groupchat\n\n# Mock autogen module\nautogen = type('autogen', (object,), {\n    'UserProxyAgent': UserProxyAgent,\n    'AssistantAgent': AssistantAgent,\n    'GroupChat': GroupChat,\n    'GroupChatManager': GroupChatManager\n})\n\nllm_config_list = [\n    {\n        \"model\": \"tinyllama\",\n        \"base_url\": \"http://localhost:11434/v1\",\n        \"api_key\": \"ollama\",\n    }\n]\n\nllm_global_config = {\n    \"config_list\": llm_config_list, \n    \"temperature\": 0,  # Zero temperature for deterministic outputs\n    \"seed\": 53        # Fixed seed for reproducibility\n}\n\nadmin_agent = autogen.UserProxyAgent(\n    name=\"Admin\",\n    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"exit\"),\n    system_message=\"\"\"A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin. \n    Only say APPROVED in most cases, and say EXIT when nothing to be done further. Do not say others.\"\"\",\n    code_execution_config=False,\n    default_auto_reply=\"Approved\", \n    human_input_mode=\"NEVER\",\n    llm_config=llm_global_config,\n)\n\nengineer_agent = autogen.AssistantAgent(\n    name=\"Engineer\",\n    llm_config=llm_global_config,\n    system_message='''Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor.\nDon't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor.\nIf the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.\n''',\n)\n\nscientist_agent = autogen.AssistantAgent(\n    name=\"Scientist\",\n    llm_config=llm_global_config,\n    system_message=\"\"\"Scientist. You follow an approved plan. You are able to categorize papers after seeing their abstracts printed. You don't write code.\"\"\"\n)\n\nplanner_agent = autogen.AssistantAgent(\n    name=\"Planner\",\n    system_message='''Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval.\nThe plan may involve an engineer who can write code and a scientist who doesn't write code.\nExplain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist.\n''',\n    llm_config=llm_global_config,\n)\n\nexecutor_agent = autogen.UserProxyAgent(\n    name=\"Executor\",\n    is_termination_msg=None,\n    system_message=\"Executor. Execute the code written by the engineer and report the result.\",\n    code_execution_config={\n        \"last_n_messages\": 3,\n        \"work_dir\": \"paper\",\n        \"use_docker\": False  # Disable docker for local execution\n    },\n    default_auto_reply=None,\n    human_input_mode=\"NEVER\",\n    llm_config=llm_global_config,\n)\n\ncritic_agent = autogen.AssistantAgent(\n    name=\"Critic\",\n    system_message=\"Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.\",\n    llm_config=llm_global_config,\n)\n\nagent_group_chat = autogen.GroupChat(\n    agents=[admin_agent, engineer_agent, scientist_agent, planner_agent, executor_agent, critic_agent], \n    messages=[], \n    max_round=50\n)\n\nchat_manager = autogen.GroupChatManager(groupchat=agent_group_chat, llm_config=llm_global_config)\n\nagent_avatars = {\n    admin_agent.name: \"\ud83d\udc68\u200d\ud83d\udcbc\",\n    engineer_agent.name: \"\ud83d\udc69\u200d\ud83d\udcbb\",\n    scientist_agent.name: \"\ud83d\udc69\u200d\ud83d\udd2c\",\n    planner_agent.name: \"\ud83d\uddd3\",\n    executor_agent.name: \"\ud83d\udee0\",\n    critic_agent.name: '\ud83d\udcdd'\n}\n\ndef callback(contents: str, user: str, instance: pn.chat.ChatInterface):\n    admin_agent.initiate_chat(chat_manager, message=contents)\n\nchat_interface = pn.chat.ChatInterface(callback=callback)\n\ndef handle_agent_messages(recipient, messages, sender, config):\n    \"\"\"\n    Handle message passing between agents and update the chat interface.\n    \n    Args:\n        recipient: The agent receiving the message\n        messages: List of message objects\n        sender: The agent sending the message\n        config: Configuration dictionary\n    \n    Returns:\n        tuple: (False, None) to continue agent communication flow\n    \"\"\"\n    print(f\"Messages from: {sender.name} sent to: {recipient.name} | num messages: {len(messages)} | message: {messages[-1]}\")\n    \n    # Check if message has required attributes and send to appropriate user\n    if all(key in messages[-1] for key in ['name']):\n        chat_interface.send(\n            messages[-1]['content'],\n            user=messages[-1]['name'],\n            avatar=agent_avatars[messages[-1]['name']],\n            respond=False\n        )\n    else:\n        chat_interface.send(\n            messages[-1]['content'],\n            user='SecretGuy',\n            avatar='\ud83e\udd77',\n            respond=False\n        )\n\n    return False, None\n\n\ndef test_handle_agent_messages():\n    # Test case 1: Message with 'name' attribute\n    recipient = engineer_agent\n    messages = [{'name': 'Admin', 'content': 'Please execute the plan.'}]\n    sender = admin_agent\n    config = {}\n    assert handle_agent_messages(recipient, messages, sender, config) == handle_agent_messages_new_implementation(recipient, messages, sender, config)\n\n    # Test case 2: Message without 'name' attribute\n    messages = [{'content': 'Please execute the plan.'}]\n    assert handle_agent_messages(recipient, messages, sender, config) == handle_agent_messages_new_implementation(recipient, messages, sender, config)\n\n    # Test case 3: Different sender\n    sender = planner_agent\n    messages = [{'name': 'Planner', 'content': 'Here is the plan.'}]\n    assert handle_agent_messages(recipient, messages, sender, config) == handle_agent_messages_new_implementation(recipient, messages, sender, config)\n\nif __name__ == \"__main__\":\n    test_handle_agent_messages()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions perform the same operations: they print a message indicating the sender, recipient, number of messages, and the last message content. They then check if the last message in the list contains a 'name' key. If it does, they send the message content to the chat interface with the corresponding user and avatar. If it does not, they send the message content with a default user 'SecretGuy' and avatar '\ud83e\udd77'. Finally, both functions return the tuple (False, None). The implementation details, logic, and flow are consistent between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `handle_agent_messages` function returns a tuple `(False, None)`, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `handle_agent_messages` and `handle_agent_messages_new_implementation`, which means they are checking return values, not printed or logged contents.\n- CONDITION 3: The test cases use assertions to ensure that `handle_agent_messages` and `handle_agent_messages_new_implementation` produce the same output for given inputs, which implies that `handle_agent_messages_new_implementation` will pass all test cases only if it has the same functionality as `handle_agent_messages`.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `handle_agent_messages` has return values.\n- CONDITION 5: The test cases cover different scenarios: a message with a 'name' attribute, a message without a 'name' attribute, and a different sender. These are non-trivial test cases that check different aspects of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "54fcc1ce6158f8730922857eb2ee5e202cddd55f"
    },
    {
        "func_name": "HeytechFlowHandler.async_get_options_flow",
        "idx": "117",
        "repo_name": "ansgarschulte___heytech-homeassistant-integration",
        "func_path": "custom_components/heytech/config_flow.py",
        "orig_func": "@staticmethod\ndef async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlow:\n    \"\"\"Get the options flow for this handler.\"\"\"\n    return HeytechOptionsFlowHandler(config_entry)",
        "orig_context": "```python\n## custom_components/heytech/const.py\nfrom logging import Logger, getLogger\n\nLOGGER: Logger = getLogger(__package__)\n\nDOMAIN = \"heytech\"\n\nCONF_PIN = \"pin\"\n\nCONF_SHUTTERS = \"shutters\"\n\nCONF_MAX_AUTO_SHUTTERS = \"max_auto_shutters\"\n\n```\n\n\n```python\n## custom_components/heytech/api.py\nclass IntegrationHeytechApiClientError(Exception):\n    \"\"\"Exception to indicate a general API error.\"\"\"\n\nclass IntegrationHeytechApiClientCommunicationError(IntegrationHeytechApiClientError):\n    \"\"\"Exception to indicate a communication error.\"\"\"\n\n    def __str__(self) -> str:\n        \"\"\"Return a string representation of the error.\"\"\"\n        if self.__cause__:\n            return f\"Error communicating with Heytech device: {self.__cause__}\"\n        return \"Error communicating with Heytech device\"\n\n```\n\n\n```python\n## custom_components/heytech/config_flow.py\nfrom typing import TYPE_CHECKING, Any\n\nimport voluptuous as vol\n\nfrom homeassistant.config_entries import ConfigEntry, ConfigFlow, OptionsFlow\n\nfrom homeassistant.const import CONF_HOST, CONF_NAME, CONF_PORT\n\nfrom homeassistant.helpers import selector\n\nfrom .api import (\n    IntegrationHeytechApiClientCommunicationError,\n    IntegrationHeytechApiClientError,\n)\n\nfrom .const import CONF_MAX_AUTO_SHUTTERS, CONF_PIN, CONF_SHUTTERS, DOMAIN, LOGGER\n\nfrom homeassistant import data_entry_flow\n\nclass HeytechOptionsFlowHandler(OptionsFlow):\n    \"\"\"Handle Heytech options.\"\"\"\n\n    def __init__(self, config_entry: ConfigEntry) -> None:\n        \"\"\"Initialize Heytech options flow.\"\"\"\n        self.config_entry = config_entry\n        self._shutters: dict[str, str] = dict(\n            self.config_entry.options.get(\n                CONF_SHUTTERS,\n                self.config_entry.data.get(CONF_SHUTTERS, {}),\n            )\n        )\n        self._shutter_name: str | None = None\n        self._shutter_channels: str | None = None\n\n    async def async_step_init(\n        self, _user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Manage the options.\"\"\"\n        return await self.async_step_shutter_menu()\n\n    async def async_step_shutter_menu(\n        self, user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Menu for managing shutters.\"\"\"\n        if user_input is not None:\n            menu_option = user_input[\"menu_option\"]\n            if menu_option == \"add_shutter\":\n                return await self.async_step_add_shutter()\n            if menu_option == \"remove_shutter\":\n                return await self.async_step_remove_shutter()\n            if menu_option == \"finish\":\n                # Check if shutters have changed\n                original_shutters = self.config_entry.options.get(\n                    CONF_SHUTTERS,\n                    self.config_entry.data.get(CONF_SHUTTERS, {}),\n                )\n                if self._shutters != original_shutters:\n                    return self.async_create_entry(\n                        title=\"\",\n                        data={CONF_SHUTTERS: self._shutters},\n                    )\n                return self.async_abort(reason=\"no_changes\")\n        options = [\n            (\"add_shutter\", \"Add Shutter\"),\n            (\"remove_shutter\", \"Remove Shutter\"),\n            (\"finish\", \"Finish\"),\n        ]\n        data_schema = vol.Schema(\n            {\n                vol.Required(\"menu_option\"): selector.SelectSelector(\n                    selector.SelectSelectorConfig(\n                        options=[\n                            {\"value\": val, \"label\": label} for val, label in options\n                        ],\n                        mode=selector.SelectSelectorMode.DROPDOWN,\n                    )\n                )\n            }\n        )\n        return self.async_show_form(\n            step_id=\"shutter_menu\",\n            data_schema=data_schema,\n        )\n\n    async def async_step_add_shutter(\n        self, user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Add a shutter.\"\"\"\n        errors: dict[str, str] = {}\n        if user_input is not None:\n            self._shutter_name = user_input[CONF_NAME]\n            self._shutter_channels = user_input[\"channels\"]\n            # Validate channels input\n            try:\n                [int(ch.strip()) for ch in self._shutter_channels.split(\",\")]\n            except ValueError:\n                errors[\"channels\"] = \"invalid_channels\"\n                return await self._show_add_shutter_form(user_input, errors)\n            # Add shutter to shutters dict\n            self._shutters[self._shutter_name] = self._shutter_channels\n            # Ask if the user wants to add another shutter\n            if user_input.get(\"add_another\"):\n                return await self.async_step_add_shutter()\n            return await self.async_step_shutter_menu()\n        return await self._show_add_shutter_form(user_input, errors)\n\n    async def _show_add_shutter_form(\n        self, user_input: dict[str, Any] | None, errors: dict[str, str]\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Show the form to add a shutter.\"\"\"\n        return self.async_show_form(\n            step_id=\"add_shutter\",\n            data_schema=vol.Schema(\n                {\n                    vol.Required(\n                        CONF_NAME,\n                        default=(user_input or {}).get(CONF_NAME, \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.TEXT,\n                        ),\n                    ),\n                    vol.Required(\n                        \"channels\",\n                        default=(user_input or {}).get(\"channels\", \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.TEXT,\n                            multiline=False,\n                        ),\n                    ),\n                    vol.Optional(\n                        \"add_another\",\n                        default=True,\n                    ): selector.BooleanSelector(),\n                }\n            ),\n            errors=errors,\n        )\n\n    async def async_step_remove_shutter(\n        self, user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Remove a shutter.\"\"\"\n        errors: dict[str, str] = {}\n        if not self._shutters:\n            return self.async_abort(reason=\"no_shutters_to_remove\")\n        if user_input is not None:\n            shutter_to_remove = user_input[\"shutter\"]\n            if shutter_to_remove in self._shutters:\n                del self._shutters[shutter_to_remove]\n                return await self.async_step_shutter_menu()\n            errors[\"shutter\"] = \"shutter_not_found\"\n        data_schema = vol.Schema(\n            {\n                vol.Required(\"shutter\"): selector.SelectSelector(\n                    selector.SelectSelectorConfig(\n                        options=[\n                            {\"value\": name, \"label\": name} for name in self._shutters\n                        ],\n                        mode=selector.SelectSelectorMode.DROPDOWN,\n                    )\n                )\n            }\n        )\n        return self.async_show_form(\n            step_id=\"remove_shutter\",\n            data_schema=data_schema,\n            errors=errors,\n        )\n\nclass HeytechFlowHandler(ConfigFlow, domain=DOMAIN):\n    \"\"\"Config flow for Heytech.\"\"\"\n\n    VERSION = 1\n\n    def __init__(self) -> None:\n        \"\"\"Initialize the Heytech flow handler.\"\"\"\n        self._host: str | None = None\n        self._port: int | None = None\n        self._pin: str | None = None\n        self._max_auto_shutters: int | None = None\n        self._add_custom_shutters: bool = False\n        self._shutters: dict[str, str] = {}\n        self._shutter_name: str | None = None\n        self._shutter_channels: str | None = None\n\n    @staticmethod\n    def async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlow:\n        \"\"\"Get the options flow for this handler.\"\"\"\n        return HeytechOptionsFlowHandler(config_entry)\n\n    async def async_step_user(\n        self,\n        user_input: dict[str, Any] | None = None,\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Handle a flow initialized by the user.\"\"\"\n        _errors: dict[str, str] = {}\n        if user_input is not None:\n            self._host = user_input[CONF_HOST]\n            self._port = int(user_input.get(CONF_PORT, \"1002\"))\n            self._pin = user_input.get(CONF_PIN, \"\")\n            self._max_auto_shutters = user_input.get(CONF_MAX_AUTO_SHUTTERS, 10)\n            self._add_custom_shutters = user_input.get(\"add_custom_shutters\", False)\n\n            # Validate connection\n            try:\n                await self._test_credentials(self._host, self._port, self._pin)\n            except IntegrationHeytechApiClientCommunicationError as exception:\n                LOGGER.error(\"Communication error: %s\", exception)\n                _errors[\"base\"] = \"connection\"\n            except IntegrationHeytechApiClientError as exception:\n                LOGGER.exception(\"Unknown error: %s\", exception)\n                _errors[\"base\"] = \"unknown\"\n            else:\n                # Proceed to shutters configuration step\n                # if the user opts to add custom shutters\n                if self._add_custom_shutters:\n                    return await self.async_step_shutter()\n\n                # Skip custom shutters and create the entry directly\n                return self.async_create_entry(\n                    title=self._host or \"Heytech\",\n                    data={\n                        CONF_HOST: self._host,\n                        CONF_PORT: self._port,\n                        CONF_PIN: self._pin,\n                        CONF_MAX_AUTO_SHUTTERS: self._max_auto_shutters,\n                        CONF_SHUTTERS: {},  # No custom shutters\n                    },\n                )\n\n        return self.async_show_form(\n            step_id=\"user\",\n            data_schema=vol.Schema(\n                {\n                    vol.Required(\n                        CONF_HOST,\n                        default=(user_input or {}).get(CONF_HOST, \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.TEXT,\n                        ),\n                    ),\n                    vol.Required(\n                        CONF_PORT,\n                        default=(user_input or {}).get(CONF_PORT, 1002),\n                    ): selector.NumberSelector(\n                        selector.NumberSelectorConfig(\n                            min=1,\n                            max=65535,\n                            mode=selector.NumberSelectorMode.BOX,\n                        ),\n                    ),\n                    vol.Optional(\n                        CONF_PIN,\n                        default=(user_input or {}).get(CONF_PIN, \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.PASSWORD,\n                        ),\n                    ),\n                    vol.Optional(\n                        CONF_MAX_AUTO_SHUTTERS,\n                        default=(user_input or {}).get(CONF_MAX_AUTO_SHUTTERS, 10),\n                    ): selector.NumberSelector(\n                        selector.NumberSelectorConfig(\n                            min=1,\n                            max=50,\n                            mode=selector.NumberSelectorMode.BOX,\n                        ),\n                    ),\n                    vol.Optional(\n                        \"add_custom_shutters\", default=False\n                    ): selector.BooleanSelector(),\n                },\n            ),\n            errors=_errors,\n        )\n\n    async def async_step_shutter(\n        self,\n        user_input: dict[str, Any] | None = None,\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Step to add shutters.\"\"\"\n        _errors: dict[str, str] = {}\n        if user_input is not None:\n            self._shutter_name = user_input[CONF_NAME]\n            self._shutter_channels = user_input[\"channels\"]\n\n            # Validate channels input\n            try:\n                [int(ch.strip()) for ch in self._shutter_channels.split(\",\")]\n            except ValueError:\n                _errors[\"channels\"] = \"invalid_channels\"\n                return await self._show_shutter_form(user_input, _errors)\n\n            # Store the shutter configuration\n            self._shutters[self._shutter_name] = self._shutter_channels\n\n            # Ask the user if they want to add another shutter\n            if user_input.get(\"add_another\"):\n                return await self.async_step_shutter()\n\n            # All shutters added, create the entry\n            return self.async_create_entry(\n                title=self._host or \"Heytech\",\n                data={\n                    CONF_HOST: self._host,\n                    CONF_PORT: int(self._port),\n                    CONF_PIN: self._pin,\n                    CONF_MAX_AUTO_SHUTTERS: self._max_auto_shutters,\n                    CONF_SHUTTERS: self._shutters,\n                },\n            )\n\n        return await self._show_shutter_form(user_input, _errors)\n\n    async def _show_shutter_form(\n        self, user_input: dict[str, Any] | None, errors: dict[str, str]\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Show the form to input a shutter.\"\"\"\n        return self.async_show_form(\n            step_id=\"shutter\",\n            data_schema=vol.Schema(\n                {\n                    vol.Required(\n                        CONF_NAME, default=(user_input or {}).get(CONF_NAME, \"\")\n                    ): selector.TextSelector(),\n                    vol.Required(\n                        \"channels\", default=(user_input or {}).get(\"channels\", \"\")\n                    ): selector.TextSelector(),\n                    vol.Optional(\n                        \"add_another\", default=True\n                    ): selector.BooleanSelector(),\n                }\n            ),\n            errors=errors,\n        )\n\n    async def _test_credentials(self, host: str, port: int, pin: str) -> None:\n        \"\"\"Validate credentials.\"\"\"\n\n```\n\n\n",
        "eval_script": "# Mocking necessary components and imports\nfrom typing import Any, Dict, Optional, Union\n\n# Mocking Home Assistant components\nclass ConfigEntry:\n    def __init__(self, data: Dict[str, Any], options: Dict[str, Any] = None):\n        self.data = data\n        self.options = options or {}\n\nclass OptionsFlow:\n    async def async_show_form(self, step_id: str, data_schema: Any, errors: Optional[Dict[str, str]] = None):\n        return {\"step_id\": step_id, \"data_schema\": data_schema, \"errors\": errors}\n\n    async def async_create_entry(self, title: str, data: Dict[str, Any]):\n        return {\"title\": title, \"data\": data}\n\n    async def async_abort(self, reason: str):\n        return {\"reason\": reason}\n\nclass ConfigFlow:\n    async def async_show_form(self, step_id: str, data_schema: Any, errors: Optional[Dict[str, str]] = None):\n        return {\"step_id\": step_id, \"data_schema\": data_schema, \"errors\": errors}\n\n    async def async_create_entry(self, title: str, data: Dict[str, Any]):\n        return {\"title\": title, \"data\": data}\n\n# Mocking Home Assistant's data_entry_flow\nclass data_entry_flow:\n    FlowResult = Dict[str, Any]\n\n# Mocking Home Assistant's selector\nclass selector:\n    class SelectSelector:\n        def __init__(self, config: Any):\n            pass\n\n    class SelectSelectorConfig:\n        def __init__(self, options: Any, mode: Any):\n            pass\n\n    class SelectSelectorMode:\n        DROPDOWN = \"dropdown\"\n\n    class TextSelector:\n        def __init__(self, config: Any):\n            pass\n\n    class TextSelectorConfig:\n        def __init__(self, type: Any, multiline: bool = False):\n            pass\n\n    class TextSelectorType:\n        TEXT = \"text\"\n        PASSWORD = \"password\"\n\n    class NumberSelector:\n        def __init__(self, config: Any):\n            pass\n\n    class NumberSelectorConfig:\n        def __init__(self, min: int, max: int, mode: Any):\n            pass\n\n    class NumberSelectorMode:\n        BOX = \"box\"\n\n    class BooleanSelector:\n        pass\n\n# Mocking voluptuous\nclass vol:\n    @staticmethod\n    def Schema(schema: Any):\n        return schema\n\n    @staticmethod\n    def Required(key: str, default: Any = None):\n        return key\n\n    @staticmethod\n    def Optional(key: str, default: Any = None):\n        return key\n\n# Importing constants and exceptions from the context\nfrom logging import Logger, getLogger\n\nLOGGER: Logger = getLogger(__package__)\n\nDOMAIN = \"heytech\"\n\nCONF_PIN = \"pin\"\n\nCONF_SHUTTERS = \"shutters\"\n\nCONF_MAX_AUTO_SHUTTERS = \"max_auto_shutters\"\n\nCONF_HOST = \"host\"\n\nCONF_NAME = \"name\"\n\nCONF_PORT = \"port\"\n\nclass IntegrationHeytechApiClientError(Exception):\n    \"\"\"Exception to indicate a general API error.\"\"\"\n\nclass IntegrationHeytechApiClientCommunicationError(IntegrationHeytechApiClientError):\n    \"\"\"Exception to indicate a communication error.\"\"\"\n\n    def __str__(self) -> str:\n        \"\"\"Return a string representation of the error.\"\"\"\n        if self.__cause__:\n            return f\"Error communicating with Heytech device: {self.__cause__}\"\n        return \"Error communicating with Heytech device\"\n\n# Main code\nclass HeytechOptionsFlowHandler(OptionsFlow):\n    \"\"\"Handle Heytech options.\"\"\"\n\n    def __init__(self, config_entry: ConfigEntry) -> None:\n        \"\"\"Initialize Heytech options flow.\"\"\"\n        self.config_entry = config_entry\n        self._shutters: dict[str, str] = dict(\n            self.config_entry.options.get(\n                CONF_SHUTTERS,\n                self.config_entry.data.get(CONF_SHUTTERS, {}),\n            )\n        )\n        self._shutter_name: str | None = None\n        self._shutter_channels: str | None = None\n\n    async def async_step_init(\n        self, _user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Manage the options.\"\"\"\n        return await self.async_step_shutter_menu()\n\n    async def async_step_shutter_menu(\n        self, user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Menu for managing shutters.\"\"\"\n        if user_input is not None:\n            menu_option = user_input[\"menu_option\"]\n            if menu_option == \"add_shutter\":\n                return await self.async_step_add_shutter()\n            if menu_option == \"remove_shutter\":\n                return await self.async_step_remove_shutter()\n            if menu_option == \"finish\":\n                # Check if shutters have changed\n                original_shutters = self.config_entry.options.get(\n                    CONF_SHUTTERS,\n                    self.config_entry.data.get(CONF_SHUTTERS, {}),\n                )\n                if self._shutters != original_shutters:\n                    return self.async_create_entry(\n                        title=\"\",\n                        data={CONF_SHUTTERS: self._shutters},\n                    )\n                return self.async_abort(reason=\"no_changes\")\n        options = [\n            (\"add_shutter\", \"Add Shutter\"),\n            (\"remove_shutter\", \"Remove Shutter\"),\n            (\"finish\", \"Finish\"),\n        ]\n        data_schema = vol.Schema(\n            {\n                vol.Required(\"menu_option\"): selector.SelectSelector(\n                    selector.SelectSelectorConfig(\n                        options=[\n                            {\"value\": val, \"label\": label} for val, label in options\n                        ],\n                        mode=selector.SelectSelectorMode.DROPDOWN,\n                    )\n                )\n            }\n        )\n        return self.async_show_form(\n            step_id=\"shutter_menu\",\n            data_schema=data_schema,\n        )\n\n    async def async_step_add_shutter(\n        self, user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Add a shutter.\"\"\"\n        errors: dict[str, str] = {}\n        if user_input is not None:\n            self._shutter_name = user_input[CONF_NAME]\n            self._shutter_channels = user_input[\"channels\"]\n            # Validate channels input\n            try:\n                [int(ch.strip()) for ch in self._shutter_channels.split(\",\")]\n            except ValueError:\n                errors[\"channels\"] = \"invalid_channels\"\n                return await self._show_add_shutter_form(user_input, errors)\n            # Add shutter to shutters dict\n            self._shutters[self._shutter_name] = self._shutter_channels\n            # Ask if the user wants to add another shutter\n            if user_input.get(\"add_another\"):\n                return await self.async_step_add_shutter()\n            return await self.async_step_shutter_menu()\n        return await self._show_add_shutter_form(user_input, errors)\n\n    async def _show_add_shutter_form(\n        self, user_input: dict[str, Any] | None, errors: dict[str, str]\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Show the form to add a shutter.\"\"\"\n        return self.async_show_form(\n            step_id=\"add_shutter\",\n            data_schema=vol.Schema(\n                {\n                    vol.Required(\n                        CONF_NAME,\n                        default=(user_input or {}).get(CONF_NAME, \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.TEXT,\n                        ),\n                    ),\n                    vol.Required(\n                        \"channels\",\n                        default=(user_input or {}).get(\"channels\", \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.TEXT,\n                            multiline=False,\n                        ),\n                    ),\n                    vol.Optional(\n                        \"add_another\",\n                        default=True,\n                    ): selector.BooleanSelector(),\n                }\n            ),\n            errors=errors,\n        )\n\n    async def async_step_remove_shutter(\n        self, user_input: dict[str, Any] | None = None\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Remove a shutter.\"\"\"\n        errors: dict[str, str] = {}\n        if not self._shutters:\n            return self.async_abort(reason=\"no_shutters_to_remove\")\n        if user_input is not None:\n            shutter_to_remove = user_input[\"shutter\"]\n            if shutter_to_remove in self._shutters:\n                del self._shutters[shutter_to_remove]\n                return await self.async_step_shutter_menu()\n            errors[\"shutter\"] = \"shutter_not_found\"\n        data_schema = vol.Schema(\n            {\n                vol.Required(\"shutter\"): selector.SelectSelector(\n                    selector.SelectSelectorConfig(\n                        options=[\n                            {\"value\": name, \"label\": name} for name in self._shutters\n                        ],\n                        mode=selector.SelectSelectorMode.DROPDOWN,\n                    )\n                )\n            }\n        )\n        return self.async_show_form(\n            step_id=\"remove_shutter\",\n            data_schema=data_schema,\n            errors=errors,\n        )\n\nclass HeytechFlowHandler(ConfigFlow):\n    \"\"\"Config flow for Heytech.\"\"\"\n\n    VERSION = 1\n\n    def __init__(self) -> None:\n        \"\"\"Initialize the Heytech flow handler.\"\"\"\n        self._host: str | None = None\n        self._port: int | None = None\n        self._pin: str | None = None\n        self._max_auto_shutters: int | None = None\n        self._add_custom_shutters: bool = False\n        self._shutters: dict[str, str] = {}\n        self._shutter_name: str | None = None\n        self._shutter_channels: str | None = None\n\n    @staticmethod\n    def async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlow:\n        \"\"\"Get the options flow for this handler.\"\"\"\n        return HeytechOptionsFlowHandler(config_entry)\n\n\n    async def async_step_user(\n        self,\n        user_input: dict[str, Any] | None = None,\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Handle a flow initialized by the user.\"\"\"\n        _errors: dict[str, str] = {}\n        if user_input is not None:\n            self._host = user_input[CONF_HOST]\n            self._port = int(user_input.get(CONF_PORT, \"1002\"))\n            self._pin = user_input.get(CONF_PIN, \"\")\n            self._max_auto_shutters = user_input.get(CONF_MAX_AUTO_SHUTTERS, 10)\n            self._add_custom_shutters = user_input.get(\"add_custom_shutters\", False)\n\n            # Validate connection\n            try:\n                await self._test_credentials(self._host, self._port, self._pin)\n            except IntegrationHeytechApiClientCommunicationError as exception:\n                LOGGER.error(\"Communication error: %s\", exception)\n                _errors[\"base\"] = \"connection\"\n            except IntegrationHeytechApiClientError as exception:\n                LOGGER.exception(\"Unknown error: %s\", exception)\n                _errors[\"base\"] = \"unknown\"\n            else:\n                # Proceed to shutters configuration step\n                # if the user opts to add custom shutters\n                if self._add_custom_shutters:\n                    return await self.async_step_shutter()\n\n                # Skip custom shutters and create the entry directly\n                return self.async_create_entry(\n                    title=self._host or \"Heytech\",\n                    data={\n                        CONF_HOST: self._host,\n                        CONF_PORT: self._port,\n                        CONF_PIN: self._pin,\n                        CONF_MAX_AUTO_SHUTTERS: self._max_auto_shutters,\n                        CONF_SHUTTERS: {},  # No custom shutters\n                    },\n                )\n\n        return self.async_show_form(\n            step_id=\"user\",\n            data_schema=vol.Schema(\n                {\n                    vol.Required(\n                        CONF_HOST,\n                        default=(user_input or {}).get(CONF_HOST, \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.TEXT,\n                        ),\n                    ),\n                    vol.Required(\n                        CONF_PORT,\n                        default=(user_input or {}).get(CONF_PORT, 1002),\n                    ): selector.NumberSelector(\n                        selector.NumberSelectorConfig(\n                            min=1,\n                            max=65535,\n                            mode=selector.NumberSelectorMode.BOX,\n                        ),\n                    ),\n                    vol.Optional(\n                        CONF_PIN,\n                        default=(user_input or {}).get(CONF_PIN, \"\"),\n                    ): selector.TextSelector(\n                        selector.TextSelectorConfig(\n                            type=selector.TextSelectorType.PASSWORD,\n                        ),\n                    ),\n                    vol.Optional(\n                        CONF_MAX_AUTO_SHUTTERS,\n                        default=(user_input or {}).get(CONF_MAX_AUTO_SHUTTERS, 10),\n                    ): selector.NumberSelector(\n                        selector.NumberSelectorConfig(\n                            min=1,\n                            max=50,\n                            mode=selector.NumberSelectorMode.BOX,\n                        ),\n                    ),\n                    vol.Optional(\n                        \"add_custom_shutters\", default=False\n                    ): selector.BooleanSelector(),\n                },\n            ),\n            errors=_errors,\n        )\n\n    async def async_step_shutter(\n        self,\n        user_input: dict[str, Any] | None = None,\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Step to add shutters.\"\"\"\n        _errors: dict[str, str] = {}\n        if user_input is not None:\n            self._shutter_name = user_input[CONF_NAME]\n            self._shutter_channels = user_input[\"channels\"]\n\n            # Validate channels input\n            try:\n                [int(ch.strip()) for ch in self._shutter_channels.split(\",\")]\n            except ValueError:\n                _errors[\"channels\"] = \"invalid_channels\"\n                return await self._show_shutter_form(user_input, _errors)\n\n            # Store the shutter configuration\n            self._shutters[self._shutter_name] = self._shutter_channels\n\n            # Ask the user if they want to add another shutter\n            if user_input.get(\"add_another\"):\n                return await self.async_step_shutter()\n\n            # All shutters added, create the entry\n            return self.async_create_entry(\n                title=self._host or \"Heytech\",\n                data={\n                    CONF_HOST: self._host,\n                    CONF_PORT: int(self._port),\n                    CONF_PIN: self._pin,\n                    CONF_MAX_AUTO_SHUTTERS: self._max_auto_shutters,\n                    CONF_SHUTTERS: self._shutters,\n                },\n            )\n\n        return await self._show_shutter_form(user_input, _errors)\n\n    async def _show_shutter_form(\n        self, user_input: dict[str, Any] | None, errors: dict[str, str]\n    ) -> data_entry_flow.FlowResult:\n        \"\"\"Show the form to input a shutter.\"\"\"\n        return self.async_show_form(\n            step_id=\"shutter\",\n            data_schema=vol.Schema(\n                {\n                    vol.Required(\n                        CONF_NAME, default=(user_input or {}).get(CONF_NAME, \"\")\n                    ): selector.TextSelector(),\n                    vol.Required(\n                        \"channels\", default=(user_input or {}).get(\"channels\", \"\")\n                    ): selector.TextSelector(),\n                    vol.Optional(\n                        \"add_another\", default=True\n                    ): selector.BooleanSelector(),\n                }\n            ),\n            errors=errors,\n        )\n\n    async def _test_credentials(self, host: str, port: int, pin: str) -> None:\n        \"\"\"Validate credentials.\"\"\"\n        # Mock implementation of credential testing\n        if not host or not port or not pin:\n            raise IntegrationHeytechApiClientCommunicationError(\"Invalid credentials\")\n\ndef test_async_get_options_flow():\n    \"\"\"Test to ensure both implementations of async_get_options_flow are equivalent.\"\"\"\n    # Test case 1: Default configuration\n    config_entry_1 = ConfigEntry(data={CONF_SHUTTERS: {\"shutter1\": \"1,2,3\"}})\n    result_old_1 = HeytechFlowHandler.async_get_options_flow(config_entry_1)\n    result_new_1 = HeytechFlowHandler.async_get_options_flow_new_implementation(config_entry_1)\n    assert isinstance(result_old_1, HeytechOptionsFlowHandler)\n    assert isinstance(result_new_1, HeytechOptionsFlowHandler)\n    assert result_old_1.config_entry.data == result_new_1.config_entry.data\n\n    # Test case 2: No shutters in configuration\n    config_entry_2 = ConfigEntry(data={})\n    result_old_2 = HeytechFlowHandler.async_get_options_flow(config_entry_2)\n    result_new_2 = HeytechFlowHandler.async_get_options_flow_new_implementation(config_entry_2)\n    assert isinstance(result_old_2, HeytechOptionsFlowHandler)\n    assert isinstance(result_new_2, HeytechOptionsFlowHandler)\n    assert result_old_2.config_entry.data == result_new_2.config_entry.data\n\n    # Test case 3: Options override data\n    config_entry_3 = ConfigEntry(data={CONF_SHUTTERS: {\"shutter1\": \"1,2,3\"}}, options={CONF_SHUTTERS: {\"shutter2\": \"4,5,6\"}})\n    result_old_3 = HeytechFlowHandler.async_get_options_flow(config_entry_3)\n    result_new_3 = HeytechFlowHandler.async_get_options_flow_new_implementation(config_entry_3)\n    assert isinstance(result_old_3, HeytechOptionsFlowHandler)\n    assert isinstance(result_new_3, HeytechOptionsFlowHandler)\n    assert result_old_3.config_entry.options == result_new_3.config_entry.options\n\nif __name__ == \"__main__\":\n    test_async_get_options_flow()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `async_get_options_flow` is a static method that takes a `ConfigEntry` object as an argument and returns an instance of `HeytechOptionsFlowHandler` initialized with the given `config_entry`. The revised function in the provided code is identical in functionality. It is also a static method that takes a `ConfigEntry` object and returns an instance of `HeytechOptionsFlowHandler` initialized with the same `config_entry`. There are no changes in the logic or behavior of the function between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `async_get_options_flow` function returns an instance of `HeytechOptionsFlowHandler`, which is a class that encapsulates the configuration entry. This satisfies the condition as it returns a value.\n\n2. **CONDITION 2**: The test cases use assertions to check the type and data of the returned objects from `async_get_options_flow` and `async_get_options_flow_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the instances returned by both implementations to ensure they are of the same type and have equivalent data or options. This ensures that both implementations have the same functionality, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the data and options of the returned instances, which is appropriate given that `async_get_options_flow` returns an object. The test cases do not use inappropriate assertions, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover different scenarios: default configuration, no shutters, and options overriding data. These scenarios are non-trivial as they test different aspects of the function's behavior, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "1decb7b760019ad308763a8ced87f800a5448f4f"
    },
    {
        "func_name": "get_client",
        "idx": "118",
        "repo_name": "vikahl___issue-downloader",
        "func_path": "src/issue_downloader/github_utils.py",
        "orig_func": "def get_client(token: str, base_url: str) -> httpx.Client:\n    \"\"\"Create a client ready for the Github API.\n\n    Using a client makes the requests slightly faster and its a convenient way\n    to set headers.\n    \"\"\"\n    c = httpx.Client(base_url=base_url)\n    c.headers.update({'Authorization': f'Bearer {token}', 'Accept': 'application/json'})\n    return c",
        "orig_context": "```python\n## src/issue_downloader/github_utils.py\nimport httpx\n\ndef get_client(token: str, base_url: str) -> httpx.Client:\n    \"\"\"Create a client ready for the Github API.\n\n    Using a client makes the requests slightly faster and its a convenient way\n    to set headers.\n    \"\"\"\n    c = httpx.Client(base_url=base_url)\n    c.headers.update(\n        {\n            \"Authorization\": f\"Bearer {token}\",\n            \"Accept\": \"application/json\",\n        }\n    )\n\n    return c\n\n```\n\n\n",
        "eval_script": "## src/issue_downloader/github_utils.py\nimport httpx\n\ndef get_client(token: str, base_url: str) -> httpx.Client:\n    \"\"\"Create a client ready for the Github API.\n\n    Using a client makes the requests slightly faster and its a convenient way\n    to set headers.\n    \"\"\"\n    c = httpx.Client(base_url=base_url)\n    c.headers.update(\n        {\n            \"Authorization\": f\"Bearer {token}\",\n            \"Accept\": \"application/json\",\n        }\n    )\n\n    return c\n\n\ndef test_get_client():\n    # Test case 1: Basic functionality\n    token = \"test_token\"\n    base_url = \"https://api.testgithub.com\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match\"\n    assert client_old.base_url == client_new.base_url, \"Base URLs do not match\"\n\n    # Test case 2: Different token\n    token = \"another_token\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with different token\"\n\n    # Test case 3: Different base URL\n    base_url = \"https://api.anothergithub.com\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with different base URL\"\n\n    # Test case 4: Empty token\n    token = \"\"\n    base_url = \"https://api.testgithub.com\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with empty token\"\n\n    # Test case 5: Empty base URL\n    token = \"test_token\"\n    base_url = \"\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with empty base URL\"\n\n    # Test case 6: Special characters in token\n    token = \"special!@#$%^&*()_+\"\n    base_url = \"https://api.testgithub.com\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with special characters in token\"\n\n    # Test case 7: Special characters in base URL\n    token = \"test_token\"\n    base_url = \"https://api.testgithub.com/!@#$%^&*()_+\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with special characters in base URL\"\n\n    # Test case 8: Very long token\n    token = \"a\" * 1000\n    base_url = \"https://api.testgithub.com\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with very long token\"\n\n    # Test case 9: Very long base URL\n    token = \"test_token\"\n    base_url = \"https://\" + \"a\" * 1000 + \".com\"\n    client_old = get_client(token, base_url)\n    client_new = get_client_new_implementation(token, base_url)\n    assert client_old.headers == client_new.headers, \"Headers do not match with very long base URL\"\n\nif __name__ == \"__main__\":\n    test_get_client()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions create an `httpx.Client` with the same base URL and update the headers with the same authorization and accept values. The test cases provided in the code are designed to verify that the behavior of the function remains consistent across different inputs, but they do not alter the functionality of the `get_client` function itself. Since the REVISED FUNCTION does not change the logic or output of the ORIGINAL FUNCTION, they are the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `get_client` function returns an `httpx.Client` object, satisfying the condition that it has return values.\n- CONDITION 2: The test cases use assertions to compare the headers and base URLs of the returned `httpx.Client` objects, checking return values and not printed or logged content.\n- CONDITION 3: The test cases compare the headers and base URLs of the clients returned by `get_client` and `get_client_new_implementation`. If the new implementation has the same functionality, it will pass all tests, satisfying this condition.\n- CONDITION 4: The test cases and assertions are reasonable, as they compare the expected attributes of the `httpx.Client` objects. There are no inappropriate assertions.\n- CONDITION 5: The test cases cover various scenarios, including different tokens, base URLs, empty values, special characters, and long strings, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "df06ad87783910f066c175e6fc56b6d967782458"
    },
    {
        "func_name": "get_settings_key",
        "idx": "121",
        "repo_name": "vikahl___issue-downloader",
        "func_path": "src/issue_downloader/settings.py",
        "orig_func": "def get_settings_key(issue_save_path: pathlib.Path, url: str, org: Optional[str]=None, repos: Optional[list[str]]=None, include_archived: bool=False, include_closed: bool=True) -> str:\n    \"\"\"Create a hash of arguments that can be used as key to save settings.\"\"\"\n    settings_key = f'{issue_save_path}:{url}:{org}:{repos}:{include_archived}:{include_closed}'\n    return hashlib.sha256(settings_key.encode('utf-8')).hexdigest()",
        "orig_context": "```python\n## src/issue_downloader/settings.py\nimport hashlib\n\nimport pathlib\n\nfrom typing import Optional\n\ndef get_settings_key(\n    issue_save_path: pathlib.Path,\n    url: str,\n    org: Optional[str] = None,\n    repos: Optional[list[str]] = None,\n    include_archived: bool = False,\n    include_closed: bool = True,\n) -> str:\n    \"\"\"Create a hash of arguments that can be used as key to save settings.\"\"\"\n\n    settings_key = (\n        f\"{issue_save_path}:{url}:{org}:{repos}:{include_archived}:{include_closed}\"\n    )\n    return hashlib.sha256(settings_key.encode(\"utf-8\")).hexdigest()\n\n```\n\n\n",
        "eval_script": "## src/issue_downloader/settings.py\nimport hashlib\n\nimport pathlib\n\nfrom typing import Optional\n\ndef get_settings_key(\n    issue_save_path: pathlib.Path,\n    url: str,\n    org: Optional[str] = None,\n    repos: Optional[list[str]] = None,\n    include_archived: bool = False,\n    include_closed: bool = True,\n) -> str:\n    \"\"\"Create a hash of arguments that can be used as key to save settings.\"\"\"\n\n    settings_key = (\n        f\"{issue_save_path}:{url}:{org}:{repos}:{include_archived}:{include_closed}\"\n    )\n    return hashlib.sha256(settings_key.encode(\"utf-8\")).hexdigest()\n\n\ndef test_get_settings_key():\n    # Test case 1: Basic test with all parameters\n    path = pathlib.Path(\"/home/user/tmp\")\n    url = \"https://example.com\"\n    org = \"example_org\"\n    repos = [\"repo1\", \"repo2\"]\n    include_archived = True\n    include_closed = False\n    assert get_settings_key(path, url, org, repos, include_archived, include_closed) == \\\n           get_settings_key_new_implementation(path, url, org, repos, include_archived, include_closed)\n\n    # Test case 2: Test with default optional parameters\n    assert get_settings_key(path, url) == \\\n           get_settings_key_new_implementation(path, url)\n\n    # Test case 3: Test with None values for org and repos\n    assert get_settings_key(path, url, None, None, include_archived, include_closed) == \\\n           get_settings_key_new_implementation(path, url, None, None, include_archived, include_closed)\n\nif __name__ == \"__main__\":\n    test_get_settings_key()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions take the same parameters, construct a string `settings_key` using those parameters, and then return the SHA-256 hash of that string. The only difference is in formatting and the inclusion of additional test cases in the revised code, which do not alter the functionality of the `get_settings_key` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `get_settings_key` function returns a string, which is a hash of the input arguments. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of `get_settings_key` and `get_settings_key_new_implementation`, not printed or logged content. This satisfies the condition.\n- CONDITION 3: The test cases compare the return values of `get_settings_key` and `get_settings_key_new_implementation` for various inputs. If `get_settings_key_new_implementation` has the same functionality, it will produce the same hash for the same inputs, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable given that `get_settings_key` returns a value. This satisfies the condition.\n- CONDITION 5: The test cases cover different scenarios: all parameters provided, default optional parameters, and `None` values for optional parameters. This provides a non-trivial set of test cases.",
            "answer": "yes"
        },
        "commit_id": "df06ad87783910f066c175e6fc56b6d967782458"
    },
    {
        "func_name": "Issue.reactions_grouped",
        "idx": "124",
        "repo_name": "vikahl___issue-downloader",
        "func_path": "src/issue_downloader/models.py",
        "orig_func": "def reactions_grouped(self) -> dict[str, list[str]]:\n    \"\"\"Group reaction with reaction as key and reactee as value.\"\"\"\n    out: dict[str, list[str]] = {}\n    if self.reactions:\n        for r in self.reactions:\n            out.setdefault(r.content, []).append(r.user)\n    return out",
        "orig_context": "```python\n## src/issue_downloader/models.py\nimport datetime\n\nfrom typing import Any, Literal, Optional\n\nimport pydantic\n\nclass Repository(pydantic.BaseModel):\n    id: str\n    name: str\n    owner: str\n    is_archived: bool\n    archived_at: datetime.datetime | None = None\n\n    @pydantic.field_validator(\"owner\", mode=\"before\")\n    def unpack_owner(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        return str(v[\"login\"])\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique objects through the Github id\"\"\"\n        return hash(self.id)\n\nREACTION_MAPPING = {\n    \"THUMBS_UP\": \"\ud83d\udc4d\",\n    \"THUMBS_DOWN\": \"\ud83d\udc4e\",\n    \"LAUGH\": \"\ud83d\ude00\",\n    \"HOORAY\": \"\ud83c\udf89\",\n    \"CONFUSED\": \"\ud83d\ude15\",\n    \"HEART\": \"\u2764\ufe0f\",\n    \"ROCKET\": \"\ud83d\ude80\",\n    \"EYES\": \"\ud83d\udc40\",\n}\n\nclass Reaction(pydantic.BaseModel):\n    content: str\n    user: str\n\n    @pydantic.field_validator(\"content\", mode=\"before\")\n    def emoji_content(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        return REACTION_MAPPING[v]\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique reactions\"\"\"\n        return hash((self.content, self.user))\n\nclass Label(pydantic.BaseModel):\n    name: str\n    description: str | None = None\n\n    def __str__(self) -> str:\n        if self.description:\n            return f\"{self.name} ({self.description})\"\n        else:\n            return self.name\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique labels\"\"\"\n        return hash((self.name, self.description))\n\nclass Comment(pydantic.BaseModel):\n    id: str\n    body: str\n    author: str\n\n    created_at: datetime.datetime\n    reactions: list[Reaction] | None = None\n\n    @pydantic.field_validator(\"author\", mode=\"before\")\n    def unpack_author(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        try:\n            return str(v[\"login\"])\n        except TypeError:\n            return \"\"\n\n    @pydantic.field_validator(\"reactions\", mode=\"before\")\n    def parse_react(cls, v: Any, _: pydantic.ValidationInfo) -> list[Reaction]:\n        return parse_reactions(v)\n\n    @pydantic.field_validator(\"body\", mode=\"before\")\n    def convert_line_endings(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        s: str = v.encode(\"utf-8\").replace(b\"\\r\\n\", b\"\\n\").decode(\"utf-8\")\n        return s\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique objects through the Github id\"\"\"\n        return hash(self.id)\n\ndef parse_reactions(data: dict[str, Any]) -> list[Reaction]:\n    \"\"\"Parse reactions from GraphQL response.\n\n    data is the reactions object from the api response, including \"edges\" key.\n    \"\"\"\n\n    return [\n        Reaction(user=r[\"node\"][\"user\"][\"login\"], content=r[\"node\"][\"content\"])\n        for r in data[\"edges\"]\n    ]\n\nclass Issue(pydantic.BaseModel):\n    author: str\n    body: str\n    created_at: datetime.datetime\n    id: str\n    number: int\n    repository: Repository\n    state: Literal[\"OPEN\", \"CLOSED\"]\n    title: str\n    updated_at: datetime.datetime\n    url: str\n\n    assignees: list[str] | None = None\n    closed_at: datetime.datetime | None = None\n    comments: list[Comment] | None = None\n    labels: list[Label] | None = None\n    reactions: list[Reaction] | None = None\n    state_reason: str | None = None\n\n    @pydantic.field_validator(\"author\", mode=\"before\")\n    def unpack_author(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        try:\n            return str(v[\"login\"])\n        except TypeError:\n            return \"\"\n\n    @pydantic.field_validator(\"body\", mode=\"before\")\n    def convert_line_endings(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        s: str = v.encode(\"utf-8\").replace(b\"\\r\\n\", b\"\\n\").decode(\"utf-8\")\n        return s\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique objects through the Github id\"\"\"\n        return hash(self.id)\n\n    def reactions_grouped(self) -> dict[str, list[str]]:\n        \"\"\"Group reaction with reaction as key and reactee as value.\"\"\"\n        out: dict[str, list[str]] = {}\n        if self.reactions:\n            for r in self.reactions:\n                out.setdefault(r.content, []).append(r.user)\n        return out\n\n    def as_markdown(self) -> str:\n        \"\"\"Return a Markdown string for the issue.\"\"\"\n        out = []\n\n        out.append(f\"# {self.title}\\n\")\n\n        out.append(\n            (\n                f\"[{self.repository.owner}/{self.repository.name}#{self.number}]\"\n                f\"({self.url})\\n\"\n            )\n        )\n\n        # Add a note about the repository\n        if self.repository.is_archived:\n            out.append(f\"Repository was archived at {self.repository.archived_at}\\n\")\n\n        # Issue state\n        if self.state == \"CLOSED\":\n            out.append(f\"Issue was closed at {self.closed_at} ({self.state_reason})\\n\")\n\n        # Updated_at will always have a value, but might be the same as the\n        # creation time. Only add it if it is different.\n        if self.updated_at == self.created_at:\n            out.append(f\"{self.author} created at {self.created_at}\\n\")\n        else:\n            out.append(\n                (\n                    f\"{self.author} created at {self.created_at}.\"\n                    f\" Updated at {self.updated_at}\\n\"\n                )\n            )\n\n        if self.assignees:\n            out.append(f\"Assigned to {', '.join(self.assignees)}\")\n\n        # Add labels:\n        if self.labels:\n            out.append(\"Labels:\\n\")\n            out.extend(f\"- {label}\" for label in self.labels)\n\n        # Add reactions\n        if self.reactions:\n            out.append(\"Reactions:\\n\")\n            out.extend(\n                f\"{r} ({', '.join(u)})\" for r, u in self.reactions_grouped().items()\n            )\n\n        # Add the body itself\n        out.append(\"\\n\\n---\\n\")\n        out.append(self.body)\n        out.append(\"\\n---\\n\\n\")\n\n        # Add comments\n        if self.comments:\n            out.append(\"## Comments\")\n            for c in self.comments:\n                out.append(f\"### {c.author} (on {c.created_at})\\n\")\n\n                # TODO: Fix \\r\\n in output\n                out.append(c.body)\n\n                out.append(\"\\n---\\n\")\n                if c.reactions:\n                    out.extend(\n                        f\"{r} ({', '.join(u)})\"\n                        for r, u in self.reactions_grouped().items()\n                    )\n\n        return \"\\n\".join(out)\n\n```\n\n\n",
        "eval_script": "import datetime\nfrom typing import Any, Literal, Optional\nimport pydantic\n\nclass Repository(pydantic.BaseModel):\n    id: str\n    name: str\n    owner: str\n    is_archived: bool\n    archived_at: datetime.datetime | None = None\n\n    @pydantic.field_validator(\"owner\", mode=\"before\")\n    def unpack_owner(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        return str(v[\"login\"])\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique objects through the Github id\"\"\"\n        return hash(self.id)\n\nREACTION_MAPPING = {\n    \"THUMBS_UP\": \"\ud83d\udc4d\",\n    \"THUMBS_DOWN\": \"\ud83d\udc4e\",\n    \"LAUGH\": \"\ud83d\ude00\",\n    \"HOORAY\": \"\ud83c\udf89\",\n    \"CONFUSED\": \"\ud83d\ude15\",\n    \"HEART\": \"\u2764\ufe0f\",\n    \"ROCKET\": \"\ud83d\ude80\",\n    \"EYES\": \"\ud83d\udc40\",\n}\n\nclass Reaction(pydantic.BaseModel):\n    content: str\n    user: str\n\n    @pydantic.field_validator(\"content\", mode=\"before\")\n    def emoji_content(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        return REACTION_MAPPING[v]\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique reactions\"\"\"\n        return hash((self.content, self.user))\n\nclass Label(pydantic.BaseModel):\n    name: str\n    description: str | None = None\n\n    def __str__(self) -> str:\n        if self.description:\n            return f\"{self.name} ({self.description})\"\n        else:\n            return self.name\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique labels\"\"\"\n        return hash((self.name, self.description))\n\nclass Comment(pydantic.BaseModel):\n    id: str\n    body: str\n    author: str\n\n    created_at: datetime.datetime\n    reactions: list[Reaction] | None = None\n\n    @pydantic.field_validator(\"author\", mode=\"before\")\n    def unpack_author(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        try:\n            return str(v[\"login\"])\n        except TypeError:\n            return \"\"\n\n    @pydantic.field_validator(\"reactions\", mode=\"before\")\n    def parse_react(cls, v: Any, _: pydantic.ValidationInfo) -> list[Reaction]:\n        return parse_reactions(v)\n\n    @pydantic.field_validator(\"body\", mode=\"before\")\n    def convert_line_endings(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        s: str = v.encode(\"utf-8\").replace(b\"\\r\\n\", b\"\\n\").decode(\"utf-8\")\n        return s\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique objects through the Github id\"\"\"\n        return hash(self.id)\n\ndef parse_reactions(data: dict[str, Any]) -> list[Reaction]:\n    \"\"\"Parse reactions from GraphQL response.\n\n    data is the reactions object from the api response, including \"edges\" key.\n    \"\"\"\n\n    return [\n        Reaction(user=r[\"node\"][\"user\"][\"login\"], content=r[\"node\"][\"content\"])\n        for r in data[\"edges\"]\n    ]\n\nclass Issue(pydantic.BaseModel):\n    author: str\n    body: str\n    created_at: datetime.datetime\n    id: str\n    number: int\n    repository: Repository\n    state: Literal[\"OPEN\", \"CLOSED\"]\n    title: str\n    updated_at: datetime.datetime\n    url: str\n\n    assignees: list[str] | None = None\n    closed_at: datetime.datetime | None = None\n    comments: list[Comment] | None = None\n    labels: list[Label] | None = None\n    reactions: list[Reaction] | None = None\n    state_reason: str | None = None\n\n    @pydantic.field_validator(\"author\", mode=\"before\")\n    def unpack_author(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        try:\n            return str(v[\"login\"])\n        except TypeError:\n            return \"\"\n\n    @pydantic.field_validator(\"body\", mode=\"before\")\n    def convert_line_endings(cls, v: Any, _: pydantic.ValidationInfo) -> str:\n        s: str = v.encode(\"utf-8\").replace(b\"\\r\\n\", b\"\\n\").decode(\"utf-8\")\n        return s\n\n    def __hash__(self) -> int:\n        \"\"\"Hashable function to identify unique objects through the Github id\"\"\"\n        return hash(self.id)\n\n    def reactions_grouped(self) -> dict[str, list[str]]:\n        \"\"\"Group reaction with reaction as key and reactee as value.\"\"\"\n        out: dict[str, list[str]] = {}\n        if self.reactions:\n            for r in self.reactions:\n                out.setdefault(r.content, []).append(r.user)\n        return out\n\n\n    def as_markdown(self) -> str:\n        \"\"\"Return a Markdown string for the issue.\"\"\"\n        out = []\n\n        out.append(f\"# {self.title}\\n\")\n\n        out.append(\n            (\n                f\"[{self.repository.owner}/{self.repository.name}#{self.number}]\"\n                f\"({self.url})\\n\"\n            )\n        )\n\n        # Add a note about the repository\n        if self.repository.is_archived:\n            out.append(f\"Repository was archived at {self.repository.archived_at}\\n\")\n\n        # Issue state\n        if self.state == \"CLOSED\":\n            out.append(f\"Issue was closed at {self.closed_at} ({self.state_reason})\\n\")\n\n        # Updated_at will always have a value, but might be the same as the\n        # creation time. Only add it if it is different.\n        if self.updated_at == self.created_at:\n            out.append(f\"{self.author} created at {self.created_at}\\n\")\n        else:\n            out.append(\n                (\n                    f\"{self.author} created at {self.created_at}.\"\n                    f\" Updated at {self.updated_at}\\n\"\n                )\n            )\n\n        if self.assignees:\n            out.append(f\"Assigned to {', '.join(self.assignees)}\")\n\n        # Add labels:\n        if self.labels:\n            out.append(\"Labels:\\n\")\n            out.extend(f\"- {label}\" for label in self.labels)\n\n        # Add reactions\n        if self.reactions:\n            out.append(\"Reactions:\\n\")\n            out.extend(\n                f\"{r} ({', '.join(u)})\" for r, u in self.reactions_grouped().items()\n            )\n\n        # Add the body itself\n        out.append(\"\\n\\n---\\n\")\n        out.append(self.body)\n        out.append(\"\\n---\\n\\n\")\n\n        # Add comments\n        if self.comments:\n            out.append(\"## Comments\")\n            for c in self.comments:\n                out.append(f\"### {c.author} (on {c.created_at})\\n\")\n\n                # TODO: Fix \\r\\n in output\n                out.append(c.body)\n\n                out.append(\"\\n---\\n\")\n                if c.reactions:\n                    out.extend(\n                        f\"{r} ({', '.join(u)})\"\n                        for r, u in self.reactions_grouped().items()\n                    )\n\n        return \"\\n\".join(out)\n\ndef test_reactions_grouped():\n    # Define a repository instance for testing\n    repository = Repository(\n        id=\"repo1\",\n        name=\"TestRepo\",\n        owner={\"login\": \"owner1\"},\n        is_archived=False\n    )\n\n    # Test case 1: No reactions\n    issue1 = Issue(\n        author={\"login\": \"author1\"},\n        body=\"This is a test issue.\",\n        created_at=datetime.datetime.now(),\n        id=\"123\",\n        number=1,\n        repository=repository,\n        state=\"OPEN\",\n        title=\"Test Issue\",\n        updated_at=datetime.datetime.now(),\n        url=\"http://example.com\",\n        reactions=[]\n    )\n    assert issue1.reactions_grouped() == issue1.reactions_grouped_new_implementation()\n\n    # Test case 2: Multiple reactions of the same type\n    issue2 = Issue(\n        author={\"login\": \"author1\"},\n        body=\"This is a test issue.\",\n        created_at=datetime.datetime.now(),\n        id=\"123\",\n        number=1,\n        repository=repository,\n        state=\"OPEN\",\n        title=\"Test Issue\",\n        updated_at=datetime.datetime.now(),\n        url=\"http://example.com\",\n        reactions=[\n            Reaction(content=\"THUMBS_UP\", user=\"user1\"),\n            Reaction(content=\"THUMBS_UP\", user=\"user2\"),\n        ]\n    )\n    assert issue2.reactions_grouped() == issue2.reactions_grouped_new_implementation()\n\n    # Test case 3: Reactions of different types\n    issue3 = Issue(\n        author={\"login\": \"author1\"},\n        body=\"This is a test issue.\",\n        created_at=datetime.datetime.now(),\n        id=\"123\",\n        number=1,\n        repository=repository,\n        state=\"OPEN\",\n        title=\"Test Issue\",\n        updated_at=datetime.datetime.now(),\n        url=\"http://example.com\",\n        reactions=[\n            Reaction(content=\"THUMBS_UP\", user=\"user1\"),\n            Reaction(content=\"HEART\", user=\"user3\"),\n        ]\n    )\n    assert issue3.reactions_grouped() == issue3.reactions_grouped_new_implementation()\n\nif __name__ == \"__main__\":\n    test_reactions_grouped()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `reactions_grouped` in the `Issue` class is identical to the ORIGINAL FUNCTION. Both functions iterate over `self.reactions`, use `setdefault` to group reactions by their content, and append the user to the corresponding list. The logic and implementation are exactly the same in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `reactions_grouped` function returns a dictionary, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `reactions_grouped` and `reactions_grouped_new_implementation`, and do not rely on printed or logged outputs.\n- CONDITION 3: The test cases compare the outputs of `reactions_grouped` and `reactions_grouped_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `reactions_grouped` returns a value.\n- CONDITION 5: The test cases cover scenarios with no reactions, multiple reactions of the same type, and reactions of different types, providing a non-trivial set of cases to validate the functionality.",
            "answer": "yes"
        },
        "commit_id": "df06ad87783910f066c175e6fc56b6d967782458"
    },
    {
        "func_name": "APIDefinitionMerger.merge",
        "idx": "127",
        "repo_name": "damianpereira86___api-automation-agent",
        "func_path": "src/processors/swagger/api_definition_merger.py",
        "orig_func": "def merge(self, api_definition_list: List[Dict]) -> List[Dict]:\n    \"\"\"Merges API definitions by their base resources.\"\"\"\n    merged_definitions = {}\n    for item in api_definition_list:\n        if item['type'] == 'path':\n            base_path = '/' + item['path'].split('/', 2)[1]\n            if base_path not in merged_definitions:\n                item['path'] = base_path\n                merged_definitions[base_path] = copy.deepcopy(item)\n            else:\n                item_yaml = yaml.safe_load(item['yaml'])\n                merged_yaml = yaml.safe_load(merged_definitions[base_path]['yaml'])\n                for path, path_data in item_yaml['paths'].items():\n                    if path not in merged_yaml['paths']:\n                        merged_yaml['paths'].update({path: path_data})\n                merged_definitions[base_path]['yaml'] = yaml.dump(merged_yaml, sort_keys=False)\n        elif item['type'] == 'verb':\n            merged_definitions[f\"{item['path']}-{item['verb']}\"] = copy.deepcopy(item)\n    self.logger.info(f'Merged {len(merged_definitions)} API definitions')\n    return list(merged_definitions.values())",
        "orig_context": "```python\n## src/configuration/models.py\nfrom enum import Enum\n\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n```\n\n\n```python\n## src/configuration/config.py\nfrom enum import Enum\n\nfrom dataclasses import dataclass\n\nfrom typing import Any\n\nfrom .models import Model\n\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n```\n\n\n```python\n## src/utils/logger.py\nimport logging\n\nimport os\n\nimport sys\n\nfrom typing import Optional, List\n\nfrom src.configuration.config import Config\n\nclass Logger:\n    @staticmethod\n    def configure_logger(config: Config):\n        log_level = logging.DEBUG if config.debug else logging.INFO\n\n        # Default formats\n        stdout_format = \"%(message)s\"\n        file_format = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n\n        # Handlers\n        stdout_handler = logging.StreamHandler(sys.stdout)\n        stdout_handler.setLevel(log_level)\n        stdout_handler.setFormatter(logging.Formatter(stdout_format))\n\n        log_folder = \"logs/\"\n        os.makedirs(os.path.dirname(log_folder), exist_ok=True)\n        file_handler = MultilineFileHandler(\n            log_folder + config.destination_folder.split(\"/\")[-1] + \".log\"\n        )\n        file_handler.setLevel(logging.DEBUG)\n        file_handler.setFormatter(logging.Formatter(file_format))\n\n        logging.basicConfig(\n            format=\"%(message)s\",\n            level=log_level,\n            handlers=[stdout_handler, file_handler],\n        )\n        logging.getLogger(\"httpx\").setLevel(logging.WARNING)\n\n    @staticmethod\n    def get_logger(name: str):\n        return logging.getLogger(name)\n\nclass MultilineFileHandler(logging.FileHandler):\n    def __init__(self, filename, mode=\"a\", encoding=\"utf-8\", delay=False):\n        super().__init__(filename, mode, encoding, delay)\n\n    def emit(self, record):\n        try:\n            if not isinstance(record.msg, str):\n                record.msg = str(record.msg)\n\n            messages: List[str] = [\n                message for message in record.msg.split(\"\\n\") if message.strip()\n            ]\n\n            if not messages:\n                return\n\n            for message in messages:\n                new_record = logging.makeLogRecord(record.__dict__)\n                new_record.msg = message\n                super().emit(new_record)\n        except Exception:\n            self.handleError(record)\n\n```\n\n\n```python\n## src/processors/swagger/api_definition_merger.py\nimport copy\n\nfrom typing import List, Dict\n\nimport yaml\n\nfrom src.utils.logger import Logger\n\nclass APIDefinitionMerger:\n    \"\"\"Merges API definition components based on base resources.\"\"\"\n\n    def __init__(self):\n        self.logger = Logger.get_logger(__name__)\n\n    def merge(self, api_definition_list: List[Dict]) -> List[Dict]:\n        \"\"\"Merges API definitions by their base resources.\"\"\"\n        merged_definitions = {}\n\n        for item in api_definition_list:\n            if item[\"type\"] == \"path\":\n                base_path = \"/\" + item[\"path\"].split(\"/\", 2)[1]\n                if base_path not in merged_definitions:\n                    item[\"path\"] = base_path\n                    merged_definitions[base_path] = copy.deepcopy(item)\n                else:\n                    item_yaml = yaml.safe_load(item[\"yaml\"])\n                    merged_yaml = yaml.safe_load(merged_definitions[base_path][\"yaml\"])\n                    for path, path_data in item_yaml[\"paths\"].items():\n                        if path not in merged_yaml[\"paths\"]:\n                            merged_yaml[\"paths\"].update({path: path_data})\n                    merged_definitions[base_path][\"yaml\"] = yaml.dump(\n                        merged_yaml, sort_keys=False\n                    )\n            elif item[\"type\"] == \"verb\":\n                merged_definitions[f\"{item['path']}-{item['verb']}\"] = copy.deepcopy(item)\n\n        self.logger.info(f\"Merged {len(merged_definitions)} API definitions\")\n        return list(merged_definitions.values())\n\n```\n\n\n",
        "eval_script": "import copy\nimport logging\nimport os\nimport sys\nfrom typing import List, Dict, Any, Optional\nfrom enum import Enum\n\nimport yaml\n\n# Mock configuration classes and enums\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n# Logger setup\nclass Logger:\n    @staticmethod\n    def configure_logger(config: Config):\n        log_level = logging.DEBUG if config.debug else logging.INFO\n\n        # Default formats\n        stdout_format = \"%(message)s\"\n        file_format = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n\n        # Handlers\n        stdout_handler = logging.StreamHandler(sys.stdout)\n        stdout_handler.setLevel(log_level)\n        stdout_handler.setFormatter(logging.Formatter(stdout_format))\n\n        log_folder = \"/home/user/tmp/logs/\"\n        os.makedirs(os.path.dirname(log_folder), exist_ok=True)\n        file_handler = MultilineFileHandler(\n            log_folder + \"api_definition_merger.log\"\n        )\n        file_handler.setLevel(logging.DEBUG)\n        file_handler.setFormatter(logging.Formatter(file_format))\n\n        logging.basicConfig(\n            format=\"%(message)s\",\n            level=log_level,\n            handlers=[stdout_handler, file_handler],\n        )\n        logging.getLogger(\"httpx\").setLevel(logging.WARNING)\n\n    @staticmethod\n    def get_logger(name: str):\n        return logging.getLogger(name)\n\nclass MultilineFileHandler(logging.FileHandler):\n    def __init__(self, filename, mode=\"a\", encoding=\"utf-8\", delay=False):\n        super().__init__(filename, mode, encoding, delay)\n\n    def emit(self, record):\n        try:\n            if not isinstance(record.msg, str):\n                record.msg = str(record.msg)\n\n            messages: List[str] = [\n                message for message in record.msg.split(\"\\n\") if message.strip()\n            ]\n\n            if not messages:\n                return\n\n            for message in messages:\n                new_record = logging.makeLogRecord(record.__dict__)\n                new_record.msg = message\n                super().emit(new_record)\n        except Exception:\n            self.handleError(record)\n\n# APIDefinitionMerger class\nclass APIDefinitionMerger:\n    \"\"\"Merges API definition components based on base resources.\"\"\"\n\n    def __init__(self):\n        self.logger = Logger.get_logger(__name__)\n\n    def merge(self, api_definition_list: List[Dict]) -> List[Dict]:\n        \"\"\"Merges API definitions by their base resources.\"\"\"\n        merged_definitions = {}\n\n        for item in api_definition_list:\n            if item[\"type\"] == \"path\":\n                base_path = \"/\" + item[\"path\"].split(\"/\", 2)[1]\n                if base_path not in merged_definitions:\n                    item[\"path\"] = base_path\n                    merged_definitions[base_path] = copy.deepcopy(item)\n                else:\n                    item_yaml = yaml.safe_load(item[\"yaml\"])\n                    merged_yaml = yaml.safe_load(merged_definitions[base_path][\"yaml\"])\n                    for path, path_data in item_yaml[\"paths\"].items():\n                        if path not in merged_yaml[\"paths\"]:\n                            merged_yaml[\"paths\"].update({path: path_data})\n                    merged_definitions[base_path][\"yaml\"] = yaml.dump(\n                        merged_yaml, sort_keys=False\n                    )\n            elif item[\"type\"] == \"verb\":\n                merged_definitions[f\"{item['path']}-{item['verb']}\"] = copy.deepcopy(item)\n\n        self.logger.info(f\"Merged {len(merged_definitions)} API definitions\")\n        return list(merged_definitions.values())\n\n\ndef test_merge():\n    merger = APIDefinitionMerger()\n\n    # Test case 1: Basic path merging\n    api_definition_list_1 = [\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource\n            \"\"\"\n        },\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource/item\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource/item:\n                get:\n                  summary: Get item\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_1) == merger.merge_new_implementation(api_definition_list_1)\n\n    # Test case 2: Path with existing base path\n    api_definition_list_2 = [\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource\n            \"\"\"\n        },\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                post:\n                  summary: Create resource\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_2) == merger.merge_new_implementation(api_definition_list_2)\n\n    # Test case 3: Verb type merging\n    api_definition_list_3 = [\n        {\n            \"type\": \"verb\",\n            \"path\": \"/api/v1/resource\",\n            \"verb\": \"get\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_3) == merger.merge_new_implementation(api_definition_list_3)\n\n    # Test case 4: Empty input\n    api_definition_list_4 = []\n    assert merger.merge(api_definition_list_4) == merger.merge_new_implementation(api_definition_list_4)\n\n    # Test case 5: Multiple paths with different base paths\n    api_definition_list_5 = [\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource1\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource1:\n                get:\n                  summary: Get resource1\n            \"\"\"\n        },\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v2/resource2\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v2/resource2:\n                get:\n                  summary: Get resource2\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_5) == merger.merge_new_implementation(api_definition_list_5)\n\n    # Test case 6: Conflicting paths\n    api_definition_list_6 = [\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource\n            \"\"\"\n        },\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource with different description\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_6) == merger.merge_new_implementation(api_definition_list_6)\n\n    # Test case 7: Complex YAML structures\n    api_definition_list_7 = [\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/complex\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/complex:\n                get:\n                  summary: Get complex\n                  parameters:\n                    - name: id\n                      in: query\n                      required: true\n                      schema:\n                        type: string\n            \"\"\"\n        },\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/complex\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/complex:\n                post:\n                  summary: Post complex\n                  requestBody:\n                    content:\n                      application/json:\n                        schema:\n                          type: object\n                          properties:\n                            name:\n                              type: string\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_7) == merger.merge_new_implementation(api_definition_list_7)\n\n    # Test case 8: Duplicate paths\n    api_definition_list_8 = [\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource\n            \"\"\"\n        },\n        {\n            \"type\": \"path\",\n            \"path\": \"/api/v1/resource\",\n            \"yaml\": \"\"\"\n            paths:\n              /api/v1/resource:\n                get:\n                  summary: Get resource\n            \"\"\"\n        }\n    ]\n    assert merger.merge(api_definition_list_8) == merger.merge_new_implementation(api_definition_list_8)\n\nif __name__ == \"__main__\":\n    test_merge()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing both the original and revised functions, they appear to be identical in terms of functionality. Both functions iterate over the `api_definition_list`, check the `type` of each item, and merge the API definitions based on their base resources. The logic for handling 'path' and 'verb' types is consistent between the two functions. Additionally, both functions use `yaml.safe_load` and `yaml.dump` to handle YAML content, and they both log the number of merged definitions. There are no differences in the logic or the operations performed by the two functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `merge` function returns a list of dictionaries, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `merge` and `merge_new_implementation`, and do not rely on printed or logged contents. This satisfies the condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `merge` and `merge_new_implementation` directly. This ensures that `merge_new_implementation` can only pass if it has exactly the same functionality as `merge`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of `merge` and `merge_new_implementation`, which is reasonable given that `merge` returns a value. There are no unreasonable assertions present.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including basic path merging, paths with existing base paths, verb type merging, empty input, multiple paths with different base paths, conflicting paths, complex YAML structures, and duplicate paths. These are non-trivial cases that test different aspects of the merging functionality.",
            "answer": "yes"
        },
        "commit_id": "abad3c320d60c92f40ee7af0f52ab904df5f4fe7"
    },
    {
        "func_name": "APIDefinitionSplitter._create_entry",
        "idx": "129",
        "repo_name": "damianpereira86___api-automation-agent",
        "func_path": "src/processors/swagger/api_definition_splitter.py",
        "orig_func": "@staticmethod\ndef _create_entry(entry_type: str, path: Optional[str], verb: Optional[str], yaml_content: Dict) -> Dict:\n    \"\"\"Creates a standardized entry for API components.\"\"\"\n    return {'type': entry_type, 'path': path, 'verb': verb, 'yaml': yaml.dump(yaml_content, sort_keys=False)}",
        "orig_context": "```python\n## src/configuration/models.py\nfrom enum import Enum\n\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n```\n\n\n```python\n## src/configuration/config.py\nfrom enum import Enum\n\nfrom dataclasses import dataclass\n\nfrom typing import Any\n\nfrom .models import Model\n\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n```\n\n\n```python\n## src/utils/logger.py\nimport logging\n\nimport os\n\nimport sys\n\nfrom typing import Optional, List\n\nfrom src.configuration.config import Config\n\nclass Logger:\n    @staticmethod\n    def configure_logger(config: Config):\n        log_level = logging.DEBUG if config.debug else logging.INFO\n\n        # Default formats\n        stdout_format = \"%(message)s\"\n        file_format = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n\n        # Handlers\n        stdout_handler = logging.StreamHandler(sys.stdout)\n        stdout_handler.setLevel(log_level)\n        stdout_handler.setFormatter(logging.Formatter(stdout_format))\n\n        log_folder = \"logs/\"\n        os.makedirs(os.path.dirname(log_folder), exist_ok=True)\n        file_handler = MultilineFileHandler(\n            log_folder + config.destination_folder.split(\"/\")[-1] + \".log\"\n        )\n        file_handler.setLevel(logging.DEBUG)\n        file_handler.setFormatter(logging.Formatter(file_format))\n\n        logging.basicConfig(\n            format=\"%(message)s\",\n            level=log_level,\n            handlers=[stdout_handler, file_handler],\n        )\n        logging.getLogger(\"httpx\").setLevel(logging.WARNING)\n\n    @staticmethod\n    def get_logger(name: str):\n        return logging.getLogger(name)\n\nclass MultilineFileHandler(logging.FileHandler):\n    def __init__(self, filename, mode=\"a\", encoding=\"utf-8\", delay=False):\n        super().__init__(filename, mode, encoding, delay)\n\n    def emit(self, record):\n        try:\n            if not isinstance(record.msg, str):\n                record.msg = str(record.msg)\n\n            messages: List[str] = [\n                message for message in record.msg.split(\"\\n\") if message.strip()\n            ]\n\n            if not messages:\n                return\n\n            for message in messages:\n                new_record = logging.makeLogRecord(record.__dict__)\n                new_record.msg = message\n                super().emit(new_record)\n        except Exception:\n            self.handleError(record)\n\n```\n\n\n```python\n## src/processors/swagger/api_definition_splitter.py\nimport copy\n\nfrom typing import Optional, Dict, List\n\nimport yaml\n\nfrom src.utils.logger import Logger\n\nclass APIDefinitionSplitter:\n    \"\"\"Splits API definitions into smaller components.\"\"\"\n\n    def __init__(self):\n        self.logger = Logger.get_logger(__name__)\n\n    def split(self, api_definition: Dict) -> List[Dict]:\n        \"\"\"Splits the API definition into smaller, manageable parts.\"\"\"\n        self.logger.info(\"Splitting API definition into components...\")\n        api_definition_list = []\n\n        base_copy = copy.deepcopy(api_definition)\n        del base_copy[\"paths\"]\n        api_definition_list.append(self._create_entry(\"base\", None, None, base_copy))\n\n        # Split paths and verbs\n        for path, path_data in api_definition.get(\"paths\", {}).items():\n            # Path entry\n            path_copy = copy.deepcopy(api_definition)\n            path_copy[\"paths\"] = {path: path_data}\n            api_definition_list.append(self._create_entry(\"path\", path, None, path_copy))\n\n            # Verb entries\n            for verb, verb_data in path_data.items():\n                verb_copy = copy.deepcopy(path_copy)\n                verb_copy[\"paths\"][path] = {verb: verb_data}\n                api_definition_list.append(\n                    self._create_entry(\"verb\", path, verb.upper(), verb_copy)\n                )\n\n        self.logger.info(\"Successfully split API definition.\")\n        return api_definition_list\n\n    @staticmethod\n    def _create_entry(entry_type: str, path: Optional[str], verb: Optional[str], yaml_content: Dict) -> Dict:\n        \"\"\"Creates a standardized entry for API components.\"\"\"\n        return {\n            \"type\": entry_type,\n            \"path\": path,\n            \"verb\": verb,\n            \"yaml\": yaml.dump(yaml_content, sort_keys=False),\n        }\n\n```\n\n\n",
        "eval_script": "import copy\nimport logging\nimport os\nimport sys\nfrom typing import Optional, Dict, List, Any  # Added Any to the import statement\nimport yaml\nfrom enum import Enum\nfrom dataclasses import dataclass\n\n# Mocking the Model Enum from models.py\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n# Mocking the Config class from config.py\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n# Logger class from logger.py\nclass Logger:\n    @staticmethod\n    def configure_logger(config: Config):\n        log_level = logging.DEBUG if config.debug else logging.INFO\n\n        # Default formats\n        stdout_format = \"%(message)s\"\n        file_format = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n\n        # Handlers\n        stdout_handler = logging.StreamHandler(sys.stdout)\n        stdout_handler.setLevel(log_level)\n        stdout_handler.setFormatter(logging.Formatter(stdout_format))\n\n        log_folder = \"logs/\"\n        os.makedirs(os.path.dirname(log_folder), exist_ok=True)\n        file_handler = MultilineFileHandler(\n            log_folder + config.destination_folder.split(\"/\")[-1] + \".log\"\n        )\n        file_handler.setLevel(logging.DEBUG)\n        file_handler.setFormatter(logging.Formatter(file_format))\n\n        logging.basicConfig(\n            format=\"%(message)s\",\n            level=log_level,\n            handlers=[stdout_handler, file_handler],\n        )\n        logging.getLogger(\"httpx\").setLevel(logging.WARNING)\n\n    @staticmethod\n    def get_logger(name: str):\n        return logging.getLogger(name)\n\nclass MultilineFileHandler(logging.FileHandler):\n    def __init__(self, filename, mode=\"a\", encoding=\"utf-8\", delay=False):\n        super().__init__(filename, mode, encoding, delay)\n\n    def emit(self, record):\n        try:\n            if not isinstance(record.msg, str):\n                record.msg = str(record.msg)\n\n            messages: List[str] = [\n                message for message in record.msg.split(\"\\n\") if message.strip()\n            ]\n\n            if not messages:\n                return\n\n            for message in messages:\n                new_record = logging.makeLogRecord(record.__dict__)\n                new_record.msg = message\n                super().emit(new_record)\n        except Exception:\n            self.handleError(record)\n\n# APIDefinitionSplitter class from api_definition_splitter.py\nclass APIDefinitionSplitter:\n    \"\"\"Splits API definitions into smaller components.\"\"\"\n\n    def __init__(self):\n        self.logger = Logger.get_logger(__name__)\n\n    def split(self, api_definition: Dict) -> List[Dict]:\n        \"\"\"Splits the API definition into smaller, manageable parts.\"\"\"\n        self.logger.info(\"Splitting API definition into components...\")\n        api_definition_list = []\n\n        base_copy = copy.deepcopy(api_definition)\n        del base_copy[\"paths\"]\n        api_definition_list.append(self._create_entry(\"base\", None, None, base_copy))\n\n        # Split paths and verbs\n        for path, path_data in api_definition.get(\"paths\", {}).items():\n            # Path entry\n            path_copy = copy.deepcopy(api_definition)\n            path_copy[\"paths\"] = {path: path_data}\n            api_definition_list.append(self._create_entry(\"path\", path, None, path_copy))\n\n            # Verb entries\n            for verb, verb_data in path_data.items():\n                verb_copy = copy.deepcopy(path_copy)\n                verb_copy[\"paths\"][path] = {verb: verb_data}\n                api_definition_list.append(\n                    self._create_entry(\"verb\", path, verb.upper(), verb_copy)\n                )\n\n        self.logger.info(\"Successfully split API definition.\")\n        return api_definition_list\n\n    @staticmethod\n    def _create_entry(entry_type: str, path: Optional[str], verb: Optional[str], yaml_content: Dict) -> Dict:\n        \"\"\"Creates a standardized entry for API components.\"\"\"\n        return {\n            \"type\": entry_type,\n            \"path\": path,\n            \"verb\": verb,\n            \"yaml\": yaml.dump(yaml_content, sort_keys=False),\n        }\n\n\ndef test__create_entry():\n    # Test case 1: Base entry\n    yaml_content_1 = {\"info\": {\"title\": \"API\", \"version\": \"1.0\"}}\n    assert APIDefinitionSplitter._create_entry(\"base\", None, None, yaml_content_1) == \\\n           APIDefinitionSplitter._create_entry_new_implementation(\"base\", None, None, yaml_content_1)\n\n    # Test case 2: Path entry\n    yaml_content_2 = {\"paths\": {\"/example\": {}}}\n    assert APIDefinitionSplitter._create_entry(\"path\", \"/example\", None, yaml_content_2) == \\\n           APIDefinitionSplitter._create_entry_new_implementation(\"path\", \"/example\", None, yaml_content_2)\n\n    # Test case 3: Verb entry\n    yaml_content_3 = {\"paths\": {\"/example\": {\"get\": {}}}}\n    assert APIDefinitionSplitter._create_entry(\"verb\", \"/example\", \"GET\", yaml_content_3) == \\\n           APIDefinitionSplitter._create_entry_new_implementation(\"verb\", \"/example\", \"GET\", yaml_content_3)\n\nif __name__ == \"__main__\":\n    test__create_entry()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_create_entry` in the `APIDefinitionSplitter` class is identical to the ORIGINAL FUNCTION. Both functions are static methods that take the same parameters (`entry_type`, `path`, `verb`, `yaml_content`) and return a dictionary with the same structure. The dictionary includes the `type`, `path`, `verb`, and `yaml` keys, where the `yaml` key's value is generated using `yaml.dump(yaml_content, sort_keys=False)`. There are no changes in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_create_entry` function returns a dictionary, which satisfies the condition of having return values.\n\n- CONDITION 2: The test function `test__create_entry` uses assert statements to compare the return values of `_create_entry` and `_create_entry_new_implementation`, which means it checks the return values and not printed or logged contents.\n\n- CONDITION 3: The test cases compare the outputs of `_create_entry` and `_create_entry_new_implementation` directly. If `_create_entry_new_implementation` passes all these tests, it must have the same functionality as `_create_entry`, satisfying this condition.\n\n- CONDITION 4: The test cases use assert statements to compare the return values of the two implementations, which is reasonable given that `_create_entry` returns a dictionary. The test cases do not use inappropriate assertions.\n\n- CONDITION 5: The test cases cover different scenarios: a base entry, a path entry, and a verb entry. These are non-trivial as they test different aspects of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "abad3c320d60c92f40ee7af0f52ab904df5f4fe7"
    },
    {
        "func_name": "CommandService.run_command_with_fix",
        "idx": "131",
        "repo_name": "damianpereira86___api-automation-agent",
        "func_path": "src/services/command_service.py",
        "orig_func": "def run_command_with_fix(self, command_func: Callable, fix_func: Optional[Callable]=None, files: Optional[List[Dict[str, str]]]=None, max_retries: int=3) -> Tuple[bool, str]:\n    \"\"\"\n        Execute a command with retries and an optional fix function on failure.\n\n        Args:\n            command_func (Callable): The function that runs the command\n            fix_func (Optional[Callable]): Function to invoke if the command fails\n            files (Optional[List[Dict[str, str]]]): Files to pass to the command function\n            max_retries (int): Max number of retries on failure\n\n        Returns:\n            Tuple[bool, str]: Success status and output or error message\n        \"\"\"\n    files = files or []\n    retry_count = 0\n    while retry_count < max_retries:\n        if retry_count > 0:\n            self._log_message(f'\\nAttempt {retry_count + 1}/{max_retries}.')\n        elif retry_count == 0:\n            self._log_message('')\n        success, message = command_func(files)\n        if success:\n            return (success, message)\n        if fix_func:\n            self._log_message(f'Applying fix: {message}')\n            fix_func(files, message)\n        retry_count += 1\n    success, message = command_func(files)\n    if success:\n        return (success, message)\n    self._log_message(f'Command failed after {max_retries} attempts.', is_error=True)\n    return (False, message)",
        "orig_context": "```python\n## src/configuration/models.py\nfrom enum import Enum\n\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n```\n\n\n```python\n## src/configuration/config.py\nfrom enum import Enum\n\nfrom dataclasses import dataclass\n\nfrom typing import Any\n\nfrom .models import Model\n\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n```\n\n\n```python\n## src/services/command_service.py\nimport os\n\nimport subprocess\n\nimport logging\n\nfrom typing import List, Dict, Tuple, Optional, Callable\n\nfrom ..configuration.config import Config\n\ndef build_typescript_compiler_command(files: List[Dict[str, str]]) -> str:\n    \"\"\"Build the TypeScript compiler command for specific files\"\"\"\n    file_paths = \" \".join(file[\"path\"] for file in files)\n    return (\n        f\"npx tsc {file_paths} \"\n        \"--lib es2021 \"\n        \"--module NodeNext \"\n        \"--target ESNext \"\n        \"--strict \"\n        \"--esModuleInterop \"\n        \"--skipLibCheck \"\n        \"--forceConsistentCasingInFileNames \"\n        \"--moduleResolution nodenext \"\n        \"--allowUnusedLabels false \"\n        \"--allowUnreachableCode false \"\n        \"--exactOptionalPropertyTypes \"\n        \"--noFallthroughCasesInSwitch \"\n        \"--noImplicitOverride \"\n        \"--noImplicitReturns \"\n        \"--noPropertyAccessFromIndexSignature \"\n        \"--noUncheckedIndexedAccess \"\n        \"--noUnusedLocals \"\n        \"--noUnusedParameters \"\n        \"--checkJs \"\n        \"--noEmit\"\n    )\n\nclass CommandService:\n    \"\"\"\n    Service for running shell commands with real-time output and error handling.\n    \"\"\"\n\n    def __init__(self, config: Config, logger: Optional[logging.Logger] = None):\n        \"\"\"\n        Initialize CommandService with an optional logger.\n\n        Args:\n            config (Config): Configuration instance\n            logger (Optional[logging.Logger]): Logger instance (defaults to logger from logging module)\n        \"\"\"\n        self.config = config\n        self.logger = logger or logging.getLogger(__name__)\n\n    def _log_message(self, message: str, is_error: bool = False):\n        \"\"\"\n        Log a message with optional error severity.\n\n        Args:\n            message (str): Message to log\n            is_error (bool): Whether the message is an error\n        \"\"\"\n        log_method = self.logger.error if is_error else self.logger.info\n        log_method(message)\n\n    def run_command(self, command: str, cwd: Optional[str] = None) -> Tuple[bool, str]:\n        \"\"\"\n        Run a shell command with real-time output and error handling.\n\n        Args:\n            command (str): Command to execute\n            cwd (Optional[str]): Working directory for command execution\n\n        Returns:\n            Tuple[bool, str]: Success status and command output\n        \"\"\"\n        try:\n            self.logger.info(f\"Running command: {command}\")\n            process = subprocess.Popen(\n                command,\n                cwd=cwd or self.config.destination_folder,\n                shell=True,\n                stdout=subprocess.PIPE,\n                stderr=subprocess.STDOUT,\n                bufsize=0,\n                universal_newlines=True,\n                encoding=\"utf-8\",\n                env={\n                    **os.environ,\n                    \"PYTHONUNBUFFERED\": \"1\",\n                    \"FORCE_COLOR\": \"true\",\n                    \"TERM\": \"xterm-256color\",\n                    \"LANG\": \"en_US.UTF-8\",\n                    \"LC_ALL\": \"en_US.UTF-8\",\n                },\n            )\n\n            output_lines = []\n            while True:\n                output = process.stdout.readline()\n                if output:\n                    output_lines.append(output.rstrip())\n                    self._log_message(output.rstrip())\n\n                if output == \"\" and process.poll() is not None:\n                    break\n\n            success = process.returncode == 0\n            self._log_message(\n                (\n                    f\"\\033[92mCommand succeeded.\\033[0m\"\n                    if success\n                    else f\"\\033[91mCommand failed.\\033[0m\"\n                ),\n                is_error=not success,\n            )\n            return success, \"\\n\".join(output_lines)\n\n        except subprocess.SubprocessError as e:\n            self._log_message(f\"Subprocess error: {e}\", is_error=True)\n            return False, str(e)\n        except Exception as e:\n            self._log_message(f\"Unexpected error: {e}\", is_error=True)\n            return False, str(e)\n\n    def run_command_with_fix(\n        self,\n        command_func: Callable,\n        fix_func: Optional[Callable] = None,\n        files: Optional[List[Dict[str, str]]] = None,\n        max_retries: int = 3,\n    ) -> Tuple[bool, str]:\n        \"\"\"\n        Execute a command with retries and an optional fix function on failure.\n\n        Args:\n            command_func (Callable): The function that runs the command\n            fix_func (Optional[Callable]): Function to invoke if the command fails\n            files (Optional[List[Dict[str, str]]]): Files to pass to the command function\n            max_retries (int): Max number of retries on failure\n\n        Returns:\n            Tuple[bool, str]: Success status and output or error message\n        \"\"\"\n        files = files or []\n        retry_count = 0\n        while retry_count < max_retries:\n            if retry_count > 0:\n                self._log_message(f\"\\nAttempt {retry_count + 1}/{max_retries}.\")\n            elif retry_count == 0:\n                self._log_message(\"\")\n\n            success, message = command_func(files)\n\n            if success:\n                return success, message\n\n            if fix_func:\n                self._log_message(f\"Applying fix: {message}\")\n                fix_func(files, message)\n\n            retry_count += 1\n\n        success, message = command_func(files)\n\n        if success:\n            return success, message\n\n        self._log_message(\n            f\"Command failed after {max_retries} attempts.\", is_error=True\n        )\n        return False, message\n\n    def install_dependencies(self) -> Tuple[bool, str]:\n        \"\"\"Install npm dependencies\"\"\"\n        self._log_message(\"\\nInstalling dependencies...\")\n        return self.run_command(\"npm install --loglevel=error\")\n\n    def format_files(self) -> Tuple[bool, str]:\n        \"\"\"Format the generated files\"\"\"\n        self._log_message(\"\\nFormatting files...\")\n        return self.run_command(\"npm run prettify\")\n\n    def run_linter(self) -> Tuple[bool, str]:\n        \"\"\"Run the linter with auto-fix\"\"\"\n        self._log_message(\"\\nRunning linter...\")\n        return self.run_command(\"npm run lint:fix\")\n\n    def run_typescript_compiler(self) -> Tuple[bool, str]:\n        \"\"\"Run the TypeScript compiler\"\"\"\n        self._log_message(\"\\nRunning TypeScript compiler...\")\n        return self.run_command(\"npx tsc --noEmit\")\n\n    def run_typescript_compiler_for_files(\n        self,\n        files: List[Dict[str, str]],\n    ) -> Tuple[bool, str]:\n        \"\"\"Run TypeScript compiler for specific files\"\"\"\n        self._log_message(\n            f\"Running TypeScript compiler for files: {[file['path'] for file in files]}\"\n        )\n        compiler_command = build_typescript_compiler_command(files)\n        return self.run_command(compiler_command)\n\n    def run_tests(self) -> Tuple[bool, str]:\n        \"\"\"Run all tests\"\"\"\n        self._log_message(\"\\nRunning tests...\")\n        return self.run_command(\"npm run test\")\n\n    def run_specific_test(self, files: List[Dict[str, str]]) -> Tuple[bool, str]:\n        \"\"\"Run specific tests\"\"\"\n        file_paths = \" \".join(file[\"path\"] for file in files)\n        self._log_message(f\"\\nRunning tests: {file_paths}\")\n        return self.run_command(f\"mocha {file_paths} --timeout 10000\")\n\n```\n\n\n",
        "eval_script": "import os\nimport subprocess\nimport logging\nfrom typing import List, Dict, Tuple, Optional, Callable\nfrom enum import Enum\nfrom dataclasses import dataclass\nfrom typing import Any\n\n# Mocking the Model Enum from models.py\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n# Mocking the Envs and GenerationOptions Enums from config.py\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\n# Mocking the Config class from config.py\n@dataclass\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"/home/user/tmp\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n# Original function from command_service.py\ndef build_typescript_compiler_command(files: List[Dict[str, str]]) -> str:\n    \"\"\"Build the TypeScript compiler command for specific files\"\"\"\n    file_paths = \" \".join(file[\"path\"] for file in files)\n    return (\n        f\"npx tsc {file_paths} \"\n        \"--lib es2021 \"\n        \"--module NodeNext \"\n        \"--target ESNext \"\n        \"--strict \"\n        \"--esModuleInterop \"\n        \"--skipLibCheck \"\n        \"--forceConsistentCasingInFileNames \"\n        \"--moduleResolution nodenext \"\n        \"--allowUnusedLabels false \"\n        \"--allowUnreachableCode false \"\n        \"--exactOptionalPropertyTypes \"\n        \"--noFallthroughCasesInSwitch \"\n        \"--noImplicitOverride \"\n        \"--noImplicitReturns \"\n        \"--noPropertyAccessFromIndexSignature \"\n        \"--noUncheckedIndexedAccess \"\n        \"--noUnusedLocals \"\n        \"--noUnusedParameters \"\n        \"--checkJs \"\n        \"--noEmit\"\n    )\n\nclass CommandService:\n    \"\"\"\n    Service for running shell commands with real-time output and error handling.\n    \"\"\"\n\n    def __init__(self, config: Config, logger: Optional[logging.Logger] = None):\n        \"\"\"\n        Initialize CommandService with an optional logger.\n\n        Args:\n            config (Config): Configuration instance\n            logger (Optional[logging.Logger]): Logger instance (defaults to logger from logging module)\n        \"\"\"\n        self.config = config\n        self.logger = logger or logging.getLogger(__name__)\n\n    def _log_message(self, message: str, is_error: bool = False):\n        \"\"\"\n        Log a message with optional error severity.\n\n        Args:\n            message (str): Message to log\n            is_error (bool): Whether the message is an error\n        \"\"\"\n        log_method = self.logger.error if is_error else self.logger.info\n        log_method(message)\n\n    def run_command(self, command: str, cwd: Optional[str] = None) -> Tuple[bool, str]:\n        \"\"\"\n        Run a shell command with real-time output and error handling.\n\n        Args:\n            command (str): Command to execute\n            cwd (Optional[str]): Working directory for command execution\n\n        Returns:\n            Tuple[bool, str]: Success status and command output\n        \"\"\"\n        try:\n            self.logger.info(f\"Running command: {command}\")\n            process = subprocess.Popen(\n                command,\n                cwd=cwd or self.config.destination_folder,\n                shell=True,\n                stdout=subprocess.PIPE,\n                stderr=subprocess.STDOUT,\n                bufsize=0,\n                universal_newlines=True,\n                encoding=\"utf-8\",\n                env={\n                    **os.environ,\n                    \"PYTHONUNBUFFERED\": \"1\",\n                    \"FORCE_COLOR\": \"true\",\n                    \"TERM\": \"xterm-256color\",\n                    \"LANG\": \"en_US.UTF-8\",\n                    \"LC_ALL\": \"en_US.UTF-8\",\n                },\n            )\n\n            output_lines = []\n            while True:\n                output = process.stdout.readline()\n                if output:\n                    output_lines.append(output.rstrip())\n                    self._log_message(output.rstrip())\n\n                if output == \"\" and process.poll() is not None:\n                    break\n\n            success = process.returncode == 0\n            self._log_message(\n                (\n                    f\"\\033[92mCommand succeeded.\\033[0m\"\n                    if success\n                    else f\"\\033[91mCommand failed.\\033[0m\"\n                ),\n                is_error=not success,\n            )\n            return success, \"\\n\".join(output_lines)\n\n        except subprocess.SubprocessError as e:\n            self._log_message(f\"Subprocess error: {e}\", is_error=True)\n            return False, str(e)\n        except Exception as e:\n            self._log_message(f\"Unexpected error: {e}\", is_error=True)\n            return False, str(e)\n\n    def run_command_with_fix(\n        self,\n        command_func: Callable,\n        fix_func: Optional[Callable] = None,\n        files: Optional[List[Dict[str, str]]] = None,\n        max_retries: int = 3,\n    ) -> Tuple[bool, str]:\n        \"\"\"\n        Execute a command with retries and an optional fix function on failure.\n\n        Args:\n            command_func (Callable): The function that runs the command\n            fix_func (Optional[Callable]): Function to invoke if the command fails\n            files (Optional[List[Dict[str, str]]]): Files to pass to the command function\n            max_retries (int): Max number of retries on failure\n\n        Returns:\n            Tuple[bool, str]: Success status and output or error message\n        \"\"\"\n        files = files or []\n        retry_count = 0\n        while retry_count < max_retries:\n            if retry_count > 0:\n                self._log_message(f\"\\nAttempt {retry_count + 1}/{max_retries}.\")\n            elif retry_count == 0:\n                self._log_message(\"\")\n\n            success, message = command_func(files)\n\n            if success:\n                return success, message\n\n            if fix_func:\n                self._log_message(f\"Applying fix: {message}\")\n                fix_func(files, message)\n\n            retry_count += 1\n\n        success, message = command_func(files)\n\n        if success:\n            return success, message\n\n        self._log_message(\n            f\"Command failed after {max_retries} attempts.\", is_error=True\n        )\n        return False, message\n\n    def install_dependencies(self) -> Tuple[bool, str]:\n        \"\"\"Install npm dependencies\"\"\"\n        self._log_message(\"\\nInstalling dependencies...\")\n        return self.run_command(\"npm install --loglevel=error\")\n\n    def format_files(self) -> Tuple[bool, str]:\n        \"\"\"Format the generated files\"\"\"\n        self._log_message(\"\\nFormatting files...\")\n        return self.run_command(\"npm run prettify\")\n\n    def run_linter(self) -> Tuple[bool, str]:\n        \"\"\"Run the linter with auto-fix\"\"\"\n        self._log_message(\"\\nRunning linter...\")\n        return self.run_command(\"npm run lint:fix\")\n\n    def run_typescript_compiler(self) -> Tuple[bool, str]:\n        \"\"\"Run the TypeScript compiler\"\"\"\n        self._log_message(\"\\nRunning TypeScript compiler...\")\n        return self.run_command(\"npx tsc --noEmit\")\n\n    def run_typescript_compiler_for_files(\n        self,\n        files: List[Dict[str, str]],\n    ) -> Tuple[bool, str]:\n        \"\"\"Run TypeScript compiler for specific files\"\"\"\n        self._log_message(\n            f\"Running TypeScript compiler for files: {[file['path'] for file in files]}\"\n        )\n        compiler_command = build_typescript_compiler_command(files)\n        return self.run_command(compiler_command)\n\n    def run_tests(self) -> Tuple[bool, str]:\n        \"\"\"Run all tests\"\"\"\n        self._log_message(\"\\nRunning tests...\")\n        return self.run_command(\"npm run test\")\n\n    def run_specific_test(self, files: List[Dict[str, str]]) -> Tuple[bool, str]:\n        \"\"\"Run specific tests\"\"\"\n        file_paths = \" \".join(file[\"path\"] for file in files)\n        self._log_message(f\"\\nRunning tests: {file_paths}\")\n        return self.run_command(f\"mocha {file_paths} --timeout 10000\")\n\n\ndef test_run_command_with_fix():\n    config = Config()\n    service = CommandService(config)\n\n    # Mock command functions\n    def command_success(files):\n        return True, \"Success\"\n\n    def command_fail(files):\n        return False, \"Failure\"\n\n    def command_fail_then_succeed(files):\n        if not hasattr(command_fail_then_succeed, \"called\"):\n            command_fail_then_succeed.called = True\n            return False, \"Failure\"\n        return True, \"Success\"\n\n    def command_always_fail(files):\n        return False, \"Always fails\"\n\n    # Mock fix function\n    def fix_func(files, message):\n        pass\n\n    def fix_func_then_succeed(files, message):\n        if not hasattr(fix_func_then_succeed, \"called\"):\n            fix_func_then_succeed.called = True\n            return\n        files.append({\"path\": \"fixed\"})\n\n    # Test case 1: Command succeeds on first try\n    assert service.run_command_with_fix(command_success) == service.run_command_with_fix_new_implementation(command_success)\n\n    # Test case 2: Command fails, fix applied, then succeeds\n    assert service.run_command_with_fix(command_fail_then_succeed, fix_func) == service.run_command_with_fix_new_implementation(command_fail_then_succeed, fix_func)\n\n    # Test case 3: Command fails without fix\n    assert service.run_command_with_fix(command_fail) == service.run_command_with_fix_new_implementation(command_fail)\n\n    # Test case 4: Command with no files\n    assert service.run_command_with_fix(command_success, files=[]) == service.run_command_with_fix_new_implementation(command_success, files=[])\n\n    # Test case 5: Command with files\n    files = [{\"path\": \"file1.ts\"}, {\"path\": \"file2.ts\"}]\n    assert service.run_command_with_fix(command_success, files=files) == service.run_command_with_fix_new_implementation(command_success, files=files)\n\n    # Test case 6: Command fails with maximum retries\n    assert service.run_command_with_fix(command_always_fail, max_retries=2) == service.run_command_with_fix_new_implementation(command_always_fail, max_retries=2)\n\n    # Test case 7: Command fails, fix applied but always fails\n    assert service.run_command_with_fix(command_always_fail, fix_func) == service.run_command_with_fix_new_implementation(command_always_fail, fix_func)\n\n    # Test case 8: Command fails, fix applied, succeeds on second attempt\n    assert service.run_command_with_fix(command_fail, fix_func_then_succeed) == service.run_command_with_fix_new_implementation(command_fail, fix_func_then_succeed)\n\n    # Test case 9: Command with different configurations\n    config_prod = Config(env=Envs.PROD)\n    service_prod = CommandService(config_prod)\n    assert service_prod.run_command_with_fix(command_success) == service_prod.run_command_with_fix_new_implementation(command_success)\n\nif __name__ == \"__main__\":\n    test_run_command_with_fix()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing the original and revised functions, both implementations of `run_command_with_fix` are identical in terms of logic and structure. They both initialize `files` to an empty list if not provided, set `retry_count` to 0, and attempt to execute `command_func` up to `max_retries` times. If `command_func` fails and a `fix_func` is provided, it is executed with the current `files` and `message`. After exhausting retries, they both log a failure message and return the result of the final `command_func` execution. The test cases provided also confirm that the behavior of both functions is consistent across various scenarios. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `run_command_with_fix` function returns a tuple `(bool, str)`, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `run_command_with_fix` and `run_command_with_fix_new_implementation`, focusing on return values rather than printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `run_command_with_fix` and `run_command_with_fix_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass.\n- CONDITION 4: The test cases use reasonable assertions by comparing the return values of the two implementations, which is appropriate given that `run_command_with_fix` has return values.\n- CONDITION 5: The test cases cover various scenarios, including success on the first try, failure with and without a fix function, retries, and different configurations, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "abad3c320d60c92f40ee7af0f52ab904df5f4fe7"
    },
    {
        "func_name": "CommandService.run_typescript_compiler_for_files",
        "idx": "132",
        "repo_name": "damianpereira86___api-automation-agent",
        "func_path": "src/services/command_service.py",
        "orig_func": "def run_typescript_compiler_for_files(self, files: List[Dict[str, str]]) -> Tuple[bool, str]:\n    \"\"\"Run TypeScript compiler for specific files\"\"\"\n    self._log_message(f\"Running TypeScript compiler for files: {[file['path'] for file in files]}\")\n    compiler_command = build_typescript_compiler_command(files)\n    return self.run_command(compiler_command)",
        "orig_context": "```python\n## src/configuration/models.py\nfrom enum import Enum\n\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n```\n\n\n```python\n## src/configuration/config.py\nfrom enum import Enum\n\nfrom dataclasses import dataclass\n\nfrom typing import Any\n\nfrom .models import Model\n\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n```\n\n\n```python\n## src/services/command_service.py\nimport os\n\nimport subprocess\n\nimport logging\n\nfrom typing import List, Dict, Tuple, Optional, Callable\n\nfrom ..configuration.config import Config\n\ndef build_typescript_compiler_command(files: List[Dict[str, str]]) -> str:\n    \"\"\"Build the TypeScript compiler command for specific files\"\"\"\n    file_paths = \" \".join(file[\"path\"] for file in files)\n    return (\n        f\"npx tsc {file_paths} \"\n        \"--lib es2021 \"\n        \"--module NodeNext \"\n        \"--target ESNext \"\n        \"--strict \"\n        \"--esModuleInterop \"\n        \"--skipLibCheck \"\n        \"--forceConsistentCasingInFileNames \"\n        \"--moduleResolution nodenext \"\n        \"--allowUnusedLabels false \"\n        \"--allowUnreachableCode false \"\n        \"--exactOptionalPropertyTypes \"\n        \"--noFallthroughCasesInSwitch \"\n        \"--noImplicitOverride \"\n        \"--noImplicitReturns \"\n        \"--noPropertyAccessFromIndexSignature \"\n        \"--noUncheckedIndexedAccess \"\n        \"--noUnusedLocals \"\n        \"--noUnusedParameters \"\n        \"--checkJs \"\n        \"--noEmit\"\n    )\n\nclass CommandService:\n    \"\"\"\n    Service for running shell commands with real-time output and error handling.\n    \"\"\"\n\n    def __init__(self, config: Config, logger: Optional[logging.Logger] = None):\n        \"\"\"\n        Initialize CommandService with an optional logger.\n\n        Args:\n            config (Config): Configuration instance\n            logger (Optional[logging.Logger]): Logger instance (defaults to logger from logging module)\n        \"\"\"\n        self.config = config\n        self.logger = logger or logging.getLogger(__name__)\n\n    def _log_message(self, message: str, is_error: bool = False):\n        \"\"\"\n        Log a message with optional error severity.\n\n        Args:\n            message (str): Message to log\n            is_error (bool): Whether the message is an error\n        \"\"\"\n        log_method = self.logger.error if is_error else self.logger.info\n        log_method(message)\n\n    def run_command(self, command: str, cwd: Optional[str] = None) -> Tuple[bool, str]:\n        \"\"\"\n        Run a shell command with real-time output and error handling.\n\n        Args:\n            command (str): Command to execute\n            cwd (Optional[str]): Working directory for command execution\n\n        Returns:\n            Tuple[bool, str]: Success status and command output\n        \"\"\"\n        try:\n            self.logger.info(f\"Running command: {command}\")\n            process = subprocess.Popen(\n                command,\n                cwd=cwd or self.config.destination_folder,\n                shell=True,\n                stdout=subprocess.PIPE,\n                stderr=subprocess.STDOUT,\n                bufsize=0,\n                universal_newlines=True,\n                encoding=\"utf-8\",\n                env={\n                    **os.environ,\n                    \"PYTHONUNBUFFERED\": \"1\",\n                    \"FORCE_COLOR\": \"true\",\n                    \"TERM\": \"xterm-256color\",\n                    \"LANG\": \"en_US.UTF-8\",\n                    \"LC_ALL\": \"en_US.UTF-8\",\n                },\n            )\n\n            output_lines = []\n            while True:\n                output = process.stdout.readline()\n                if output:\n                    output_lines.append(output.rstrip())\n                    self._log_message(output.rstrip())\n\n                if output == \"\" and process.poll() is not None:\n                    break\n\n            success = process.returncode == 0\n            self._log_message(\n                (\n                    f\"\\033[92mCommand succeeded.\\033[0m\"\n                    if success\n                    else f\"\\033[91mCommand failed.\\033[0m\"\n                ),\n                is_error=not success,\n            )\n            return success, \"\\n\".join(output_lines)\n\n        except subprocess.SubprocessError as e:\n            self._log_message(f\"Subprocess error: {e}\", is_error=True)\n            return False, str(e)\n        except Exception as e:\n            self._log_message(f\"Unexpected error: {e}\", is_error=True)\n            return False, str(e)\n\n    def run_command_with_fix(\n        self,\n        command_func: Callable,\n        fix_func: Optional[Callable] = None,\n        files: Optional[List[Dict[str, str]]] = None,\n        max_retries: int = 3,\n    ) -> Tuple[bool, str]:\n        \"\"\"\n        Execute a command with retries and an optional fix function on failure.\n\n        Args:\n            command_func (Callable): The function that runs the command\n            fix_func (Optional[Callable]): Function to invoke if the command fails\n            files (Optional[List[Dict[str, str]]]): Files to pass to the command function\n            max_retries (int): Max number of retries on failure\n\n        Returns:\n            Tuple[bool, str]: Success status and output or error message\n        \"\"\"\n        files = files or []\n        retry_count = 0\n        while retry_count < max_retries:\n            if retry_count > 0:\n                self._log_message(f\"\\nAttempt {retry_count + 1}/{max_retries}.\")\n            elif retry_count == 0:\n                self._log_message(\"\")\n\n            success, message = command_func(files)\n\n            if success:\n                return success, message\n\n            if fix_func:\n                self._log_message(f\"Applying fix: {message}\")\n                fix_func(files, message)\n\n            retry_count += 1\n\n        success, message = command_func(files)\n\n        if success:\n            return success, message\n\n        self._log_message(\n            f\"Command failed after {max_retries} attempts.\", is_error=True\n        )\n        return False, message\n\n    def install_dependencies(self) -> Tuple[bool, str]:\n        \"\"\"Install npm dependencies\"\"\"\n        self._log_message(\"\\nInstalling dependencies...\")\n        return self.run_command(\"npm install --loglevel=error\")\n\n    def format_files(self) -> Tuple[bool, str]:\n        \"\"\"Format the generated files\"\"\"\n        self._log_message(\"\\nFormatting files...\")\n        return self.run_command(\"npm run prettify\")\n\n    def run_linter(self) -> Tuple[bool, str]:\n        \"\"\"Run the linter with auto-fix\"\"\"\n        self._log_message(\"\\nRunning linter...\")\n        return self.run_command(\"npm run lint:fix\")\n\n    def run_typescript_compiler(self) -> Tuple[bool, str]:\n        \"\"\"Run the TypeScript compiler\"\"\"\n        self._log_message(\"\\nRunning TypeScript compiler...\")\n        return self.run_command(\"npx tsc --noEmit\")\n\n    def run_typescript_compiler_for_files(\n        self,\n        files: List[Dict[str, str]],\n    ) -> Tuple[bool, str]:\n        \"\"\"Run TypeScript compiler for specific files\"\"\"\n        self._log_message(\n            f\"Running TypeScript compiler for files: {[file['path'] for file in files]}\"\n        )\n        compiler_command = build_typescript_compiler_command(files)\n        return self.run_command(compiler_command)\n\n    def run_tests(self) -> Tuple[bool, str]:\n        \"\"\"Run all tests\"\"\"\n        self._log_message(\"\\nRunning tests...\")\n        return self.run_command(\"npm run test\")\n\n    def run_specific_test(self, files: List[Dict[str, str]]) -> Tuple[bool, str]:\n        \"\"\"Run specific tests\"\"\"\n        file_paths = \" \".join(file[\"path\"] for file in files)\n        self._log_message(f\"\\nRunning tests: {file_paths}\")\n        return self.run_command(f\"mocha {file_paths} --timeout 10000\")\n\n```\n\n\n",
        "eval_script": "import os\nimport subprocess\nimport logging\nfrom typing import List, Dict, Tuple, Optional, Callable\nfrom enum import Enum\nfrom dataclasses import dataclass\nfrom typing import Any\n\n# Mock implementation of Model Enum from src/configuration/models.py\nclass Model(Enum):\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    CLAUDE_SONNET = \"claude-3-5-sonnet-20241022\"\n\n    def is_anthropic(self):\n        return self in [Model.CLAUDE_SONNET]\n\n# Mock implementation of Envs Enum from src/configuration/config.py\nclass Envs(Enum):\n    PROD = \"PROD\"\n    DEV = \"DEV\"\n\n# Mock implementation of GenerationOptions Enum from src/configuration/config.py\nclass GenerationOptions(Enum):\n    MODELS = \"models\"\n    MODELS_AND_FIRST_TEST = \"models_and_first_test\"\n    MODELS_AND_TESTS = \"models_and_tests\"\n\n# Mock implementation of Config class from src/configuration/config.py\n@dataclass\nclass Config:\n    env: Envs = Envs.DEV\n    debug: bool = False\n    model: Model = Model.CLAUDE_SONNET\n    generate: GenerationOptions = GenerationOptions.MODELS_AND_TESTS\n    anthropic_api_key: str = \"\"\n    openai_api_key: str = \"\"\n    api_file_path: str = \"\"\n    destination_folder: str = \"/home/user/tmp\"\n    endpoint: str = \"\"\n    use_existing_framework: bool = False\n\n    def update(self, updates: dict[str, Any]):\n        for key, value in updates.items():\n            setattr(self, key, value)\n\n# Function to build TypeScript compiler command\ndef build_typescript_compiler_command(files: List[Dict[str, str]]) -> str:\n    \"\"\"Build the TypeScript compiler command for specific files\"\"\"\n    file_paths = \" \".join(file[\"path\"] for file in files)\n    return (\n        f\"npx tsc {file_paths} \"\n        \"--lib es2021 \"\n        \"--module NodeNext \"\n        \"--target ESNext \"\n        \"--strict \"\n        \"--esModuleInterop \"\n        \"--skipLibCheck \"\n        \"--forceConsistentCasingInFileNames \"\n        \"--moduleResolution nodenext \"\n        \"--allowUnusedLabels false \"\n        \"--allowUnreachableCode false \"\n        \"--exactOptionalPropertyTypes \"\n        \"--noFallthroughCasesInSwitch \"\n        \"--noImplicitOverride \"\n        \"--noImplicitReturns \"\n        \"--noPropertyAccessFromIndexSignature \"\n        \"--noUncheckedIndexedAccess \"\n        \"--noUnusedLocals \"\n        \"--noUnusedParameters \"\n        \"--checkJs \"\n        \"--noEmit\"\n    )\n\n# CommandService class\nclass CommandService:\n    \"\"\"\n    Service for running shell commands with real-time output and error handling.\n    \"\"\"\n\n    def __init__(self, config: Config, logger: Optional[logging.Logger] = None):\n        \"\"\"\n        Initialize CommandService with an optional logger.\n\n        Args:\n            config (Config): Configuration instance\n            logger (Optional[logging.Logger]): Logger instance (defaults to logger from logging module)\n        \"\"\"\n        self.config = config\n        self.logger = logger or logging.getLogger(__name__)\n\n    def _log_message(self, message: str, is_error: bool = False):\n        \"\"\"\n        Log a message with optional error severity.\n\n        Args:\n            message (str): Message to log\n            is_error (bool): Whether the message is an error\n        \"\"\"\n        log_method = self.logger.error if is_error else self.logger.info\n        log_method(message)\n\n    def run_command(self, command: str, cwd: Optional[str] = None) -> Tuple[bool, str]:\n        \"\"\"\n        Run a shell command with real-time output and error handling.\n\n        Args:\n            command (str): Command to execute\n            cwd (Optional[str]): Working directory for command execution\n\n        Returns:\n            Tuple[bool, str]: Success status and command output\n        \"\"\"\n        try:\n            self.logger.info(f\"Running command: {command}\")\n            process = subprocess.Popen(\n                command,\n                cwd=cwd or self.config.destination_folder,\n                shell=True,\n                stdout=subprocess.PIPE,\n                stderr=subprocess.STDOUT,\n                bufsize=0,\n                universal_newlines=True,\n                encoding=\"utf-8\",\n                env={\n                    **os.environ,\n                    \"PYTHONUNBUFFERED\": \"1\",\n                    \"FORCE_COLOR\": \"true\",\n                    \"TERM\": \"xterm-256color\",\n                    \"LANG\": \"en_US.UTF-8\",\n                    \"LC_ALL\": \"en_US.UTF-8\",\n                },\n            )\n\n            output_lines = []\n            while True:\n                output = process.stdout.readline()\n                if output:\n                    output_lines.append(output.rstrip())\n                    self._log_message(output.rstrip())\n\n                if output == \"\" and process.poll() is not None:\n                    break\n\n            success = process.returncode == 0\n            self._log_message(\n                (\n                    f\"\\033[92mCommand succeeded.\\033[0m\"\n                    if success\n                    else f\"\\033[91mCommand failed.\\033[0m\"\n                ),\n                is_error=not success,\n            )\n            return success, \"\\n\".join(output_lines)\n\n        except subprocess.SubprocessError as e:\n            self._log_message(f\"Subprocess error: {e}\", is_error=True)\n            return False, str(e)\n        except Exception as e:\n            self._log_message(f\"Unexpected error: {e}\", is_error=True)\n            return False, str(e)\n\n    def run_command_with_fix(\n        self,\n        command_func: Callable,\n        fix_func: Optional[Callable] = None,\n        files: Optional[List[Dict[str, str]]] = None,\n        max_retries: int = 3,\n    ) -> Tuple[bool, str]:\n        \"\"\"\n        Execute a command with retries and an optional fix function on failure.\n\n        Args:\n            command_func (Callable): The function that runs the command\n            fix_func (Optional[Callable]): Function to invoke if the command fails\n            files (Optional[List[Dict[str, str]]]): Files to pass to the command function\n            max_retries (int): Max number of retries on failure\n\n        Returns:\n            Tuple[bool, str]: Success status and output or error message\n        \"\"\"\n        files = files or []\n        retry_count = 0\n        while retry_count < max_retries:\n            if retry_count > 0:\n                self._log_message(f\"\\nAttempt {retry_count + 1}/{max_retries}.\")\n            elif retry_count == 0:\n                self._log_message(\"\")\n\n            success, message = command_func(files)\n\n            if success:\n                return success, message\n\n            if fix_func:\n                self._log_message(f\"Applying fix: {message}\")\n                fix_func(files, message)\n\n            retry_count += 1\n\n        success, message = command_func(files)\n\n        if success:\n            return success, message\n\n        self._log_message(\n            f\"Command failed after {max_retries} attempts.\", is_error=True\n        )\n        return False, message\n\n    def install_dependencies(self) -> Tuple[bool, str]:\n        \"\"\"Install npm dependencies\"\"\"\n        self._log_message(\"\\nInstalling dependencies...\")\n        return self.run_command(\"npm install --loglevel=error\")\n\n    def format_files(self) -> Tuple[bool, str]:\n        \"\"\"Format the generated files\"\"\"\n        self._log_message(\"\\nFormatting files...\")\n        return self.run_command(\"npm run prettify\")\n\n    def run_linter(self) -> Tuple[bool, str]:\n        \"\"\"Run the linter with auto-fix\"\"\"\n        self._log_message(\"\\nRunning linter...\")\n        return self.run_command(\"npm run lint:fix\")\n\n    def run_typescript_compiler(self) -> Tuple[bool, str]:\n        \"\"\"Run the TypeScript compiler\"\"\"\n        self._log_message(\"\\nRunning TypeScript compiler...\")\n        return self.run_command(\"npx tsc --noEmit\")\n\n    def run_typescript_compiler_for_files(\n        self,\n        files: List[Dict[str, str]],\n    ) -> Tuple[bool, str]:\n        \"\"\"Run TypeScript compiler for specific files\"\"\"\n        self._log_message(\n            f\"Running TypeScript compiler for files: {[file['path'] for file in files]}\"\n        )\n        compiler_command = build_typescript_compiler_command(files)\n        return self.run_command(compiler_command)\n\n\n    def run_tests(self) -> Tuple[bool, str]:\n        \"\"\"Run all tests\"\"\"\n        self._log_message(\"\\nRunning tests...\")\n        return self.run_command(\"npm run test\")\n\n    def run_specific_test(self, files: List[Dict[str, str]]) -> Tuple[bool, str]:\n        \"\"\"Run specific tests\"\"\"\n        file_paths = \" \".join(file[\"path\"] for file in files)\n        self._log_message(f\"\\nRunning tests: {file_paths}\")\n        return self.run_command(f\"mocha {file_paths} --timeout 10000\")\n\ndef test_run_typescript_compiler_for_files():\n    config = Config()\n    logger = logging.getLogger(\"test_logger\")\n    command_service = CommandService(config, logger)\n\n    # Test case 1: Single file\n    files = [{\"path\": \"/home/user/tmp/test1.ts\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for single file\"\n    assert output_old == output_new, \"Output mismatch for single file\"\n\n    # Test case 2: Multiple files\n    files = [{\"path\": \"/home/user/tmp/test1.ts\"}, {\"path\": \"/home/user/tmp/test2.ts\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for multiple files\"\n    assert output_old == output_new, \"Output mismatch for multiple files\"\n\n    # Test case 3: No files\n    files = []\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for no files\"\n    assert output_old == output_new, \"Output mismatch for no files\"\n\n    # Test case 4: Invalid file path\n    files = [{\"path\": \"/invalid/path/test.ts\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for invalid file path\"\n    assert output_old == output_new, \"Output mismatch for invalid file path\"\n\n    # Test case 5: Non-TypeScript file\n    files = [{\"path\": \"/home/user/tmp/test.txt\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for non-TypeScript file\"\n    assert output_old == output_new, \"Output mismatch for non-TypeScript file\"\n\n    # Test case 6: Large number of files\n    files = [{\"path\": f\"/home/user/tmp/test{i}.ts\"} for i in range(100)]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for large number of files\"\n    assert output_old == output_new, \"Output mismatch for large number of files\"\n\n    # Test case 7: Files with compilation errors\n    files = [{\"path\": \"/home/user/tmp/error.ts\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for files with compilation errors\"\n    assert output_old == output_new, \"Output mismatch for files with compilation errors\"\n\n    # Test case 8: Files with warnings\n    files = [{\"path\": \"/home/user/tmp/warning.ts\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for files with warnings\"\n    assert output_old == output_new, \"Output mismatch for files with warnings\"\n\n    # Test case 9: Special characters in file paths\n    files = [{\"path\": \"/home/user/tmp/special char.ts\"}]\n    success_old, output_old = command_service.run_typescript_compiler_for_files(files)\n    success_new, output_new = command_service.run_typescript_compiler_for_files_new_implementation(files)\n    assert success_old == success_new, \"Success status mismatch for special characters in file paths\"\n    assert output_old == output_new, \"Output mismatch for special characters in file paths\"\n\nif __name__ == \"__main__\":\n    test_run_typescript_compiler_for_files()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The revised function `run_typescript_compiler_for_files` in the provided code is identical to the original function. Both functions log a message indicating the files for which the TypeScript compiler is being run, build the TypeScript compiler command using the `build_typescript_compiler_command` function, and then execute the command using the `run_command` method. There are no changes in the logic or functionality between the original and revised functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `run_typescript_compiler_for_files` returns a tuple `(bool, str)`, which satisfies the condition as it has return values.\n\n2. **CONDITION 2**: The test cases check the return values of `run_typescript_compiler_for_files` and `run_typescript_compiler_for_files_new_implementation` by comparing `success_old` with `success_new` and `output_old` with `output_new`. There is no reliance on printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `run_typescript_compiler_for_files` and `run_typescript_compiler_for_files_new_implementation` for various scenarios. If the new implementation has the same functionality, it will pass all tests. This condition is satisfied.\n\n4. **CONDITION 4**: The assertions in the test cases compare the success status and output of both implementations. Since `run_typescript_compiler_for_files` returns values, the assertions are reasonable and appropriate. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including single and multiple files, no files, invalid paths, non-TypeScript files, a large number of files, files with errors and warnings, and special characters in file paths. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "abad3c320d60c92f40ee7af0f52ab904df5f4fe7"
    },
    {
        "func_name": "get_image_size",
        "idx": "139",
        "repo_name": "aaru2075___WorksPerfectmanga",
        "func_path": "img2pdf/img_size.py",
        "orig_func": "def get_image_size(file_path):\n    \"\"\"\n    Return (width, height) for a given img file content - no external\n    dependencies except the os and struct builtin modules\n    \"\"\"\n    img = get_image_metadata(file_path)\n    return (img.width, img.height)",
        "orig_context": "```python\n## img2pdf/img_size.py\nimport collections\n\nimport json\n\nimport os\n\nimport io\n\nimport struct\n\nFILE_UNKNOWN = \"Sorry, don't know how to get size for this file.\"\n\nclass UnknownImageFormat(Exception):\n    pass\n\ntypes = collections.OrderedDict()\n\nBMP = types['BMP'] = 'BMP'\n\nGIF = types['GIF'] = 'GIF'\n\nICO = types['ICO'] = 'ICO'\n\nJPEG = types['JPEG'] = 'JPEG'\n\nPNG = types['PNG'] = 'PNG'\n\nTIFF = types['TIFF'] = 'TIFF'\n\nWEBP = types['WEBP'] = 'WEBP'\n\nimage_fields = ['path', 'type', 'file_size', 'width', 'height']\n\nclass Image(collections.namedtuple('Image', image_fields)):\n\n    def to_str_row(self):\n        return (\"%d\\t%d\\t%d\\t%s\\t%s\" % (\n            self.width,\n            self.height,\n            self.file_size,\n            self.type,\n            self.path.replace('\\t', '\\\\t'),\n        ))\n\n    def to_str_row_verbose(self):\n        return (\"%d\\t%d\\t%d\\t%s\\t%s\\t##%s\" % (\n            self.width,\n            self.height,\n            self.file_size,\n            self.type,\n            self.path.replace('\\t', '\\\\t'),\n            self))\n\n    def to_str_json(self, indent=None):\n        return json.dumps(self._asdict(), indent=indent)\n\ndef get_image_metadata(file_path):\n    \"\"\"\n    Return an `Image` object for a given img file content - no external\n    dependencies except the os and struct builtin modules\n\n    Args:\n        file_path (str): path to an image file\n\n    Returns:\n        Image: (path, type, file_size, width, height)\n    \"\"\"\n    size = os.path.getsize(file_path)\n\n    # be explicit with open arguments - we need binary mode\n    with io.open(file_path, \"rb\") as input:\n        return get_image_metadata_from_bytesio(input, size, file_path)\n\ndef get_image_metadata_from_bytesio(input, size, file_path=None):\n    \"\"\"\n    Return an `Image` object for a given img file content - no external\n    dependencies except the os and struct builtin modules\n\n    Args:\n        input (io.IOBase): io object support read & seek\n        size (int): size of buffer in byte\n        file_path (str): path to an image file\n\n    Returns:\n        Image: (path, type, file_size, width, height)\n    \"\"\"\n    height = -1\n    width = -1\n    data = input.read(30)\n    msg = \" raised while trying to decode as JPEG.\"\n\n    if (size >= 10) and data[:6] in (b'GIF87a', b'GIF89a'):\n        # GIFs\n        imgtype = GIF\n        w, h = struct.unpack(\"<HH\", data[6:10])\n        width = int(w)\n        height = int(h)\n    elif ((size >= 24) and data.startswith(b'\\211PNG\\r\\n\\032\\n')\n            and (data[12:16] == b'IHDR')):\n        # PNGs\n        imgtype = PNG\n        w, h = struct.unpack(\">LL\", data[16:24])\n        width = int(w)\n        height = int(h)\n    elif (size >= 16) and data.startswith(b'\\211PNG\\r\\n\\032\\n'):\n        # older PNGs\n        imgtype = PNG\n        w, h = struct.unpack(\">LL\", data[8:16])\n        width = int(w)\n        height = int(h)\n    elif (size >= 2) and data.startswith(b'\\377\\330'):\n        # JPEG\n        imgtype = JPEG\n        input.seek(0)\n        input.read(2)\n        b = input.read(1)\n        try:\n            while (b and ord(b) != 0xDA):\n                while (ord(b) != 0xFF):\n                    b = input.read(1)\n                while (ord(b) == 0xFF):\n                    b = input.read(1)\n                if (ord(b) >= 0xC0 and ord(b) <= 0xC3):\n                    input.read(3)\n                    h, w = struct.unpack(\">HH\", input.read(4))\n                    break\n                else:\n                    input.read(\n                        int(struct.unpack(\">H\", input.read(2))[0]) - 2)\n                b = input.read(1)\n            width = int(w)\n            height = int(h)\n        except struct.error:\n            raise UnknownImageFormat(\"StructError\" + msg)\n        except ValueError:\n            raise UnknownImageFormat(\"ValueError\" + msg)\n        except Exception as e:\n            raise UnknownImageFormat(e.__class__.__name__ + msg)\n    elif (size >= 30) and data.startswith(b'RIFF') and data[8:15] == b'WEBPVP8':\n        # WEBP\n        imgtype = WEBP\n        width, height = int(data[26]) | int(data[27]) << 8, int(data[28]) | int(data[29]) << 8\n    elif (size >= 26) and data.startswith(b'BM'):\n        # BMP\n        imgtype = 'BMP'\n        headersize = struct.unpack(\"<I\", data[14:18])[0]\n        if headersize == 12:\n            w, h = struct.unpack(\"<HH\", data[18:22])\n            width = int(w)\n            height = int(h)\n        elif headersize >= 40:\n            w, h = struct.unpack(\"<ii\", data[18:26])\n            width = int(w)\n            # as h is negative when stored upside down\n            height = abs(int(h))\n        else:\n            raise UnknownImageFormat(\n                \"Unkown DIB header size:\" +\n                str(headersize))\n    elif (size >= 8) and data[:4] in (b\"II\\052\\000\", b\"MM\\000\\052\"):\n        # Standard TIFF, big- or little-endian\n        # BigTIFF and other different but TIFF-like formats are not\n        # supported currently\n        imgtype = TIFF\n        byteOrder = data[:2]\n        boChar = \">\" if byteOrder == \"MM\" else \"<\"\n        # maps TIFF type id to size (in bytes)\n        # and python format char for struct\n        tiffTypes = {\n            1: (1, boChar + \"B\"),  # BYTE\n            2: (1, boChar + \"c\"),  # ASCII\n            3: (2, boChar + \"H\"),  # SHORT\n            4: (4, boChar + \"L\"),  # LONG\n            5: (8, boChar + \"LL\"),  # RATIONAL\n            6: (1, boChar + \"b\"),  # SBYTE\n            7: (1, boChar + \"c\"),  # UNDEFINED\n            8: (2, boChar + \"h\"),  # SSHORT\n            9: (4, boChar + \"l\"),  # SLONG\n            10: (8, boChar + \"ll\"),  # SRATIONAL\n            11: (4, boChar + \"f\"),  # FLOAT\n            12: (8, boChar + \"d\")   # DOUBLE\n        }\n        ifdOffset = struct.unpack(boChar + \"L\", data[4:8])[0]\n        try:\n            countSize = 2\n            input.seek(ifdOffset)\n            ec = input.read(countSize)\n            ifdEntryCount = struct.unpack(boChar + \"H\", ec)[0]\n            # 2 bytes: TagId + 2 bytes: type + 4 bytes: count of values + 4\n            # bytes: value offset\n            ifdEntrySize = 12\n            for i in range(ifdEntryCount):\n                entryOffset = ifdOffset + countSize + i * ifdEntrySize\n                input.seek(entryOffset)\n                tag = input.read(2)\n                tag = struct.unpack(boChar + \"H\", tag)[0]\n                if(tag == 256 or tag == 257):\n                    # if type indicates that value fits into 4 bytes, value\n                    # offset is not an offset but value itself\n                    type = input.read(2)\n                    type = struct.unpack(boChar + \"H\", type)[0]\n                    if type not in tiffTypes:\n                        raise UnknownImageFormat(\n                            \"Unkown TIFF field type:\" +\n                            str(type))\n                    typeSize = tiffTypes[type][0]\n                    typeChar = tiffTypes[type][1]\n                    input.seek(entryOffset + 8)\n                    value = input.read(typeSize)\n                    value = int(struct.unpack(typeChar, value)[0])\n                    if tag == 256:\n                        width = value\n                    else:\n                        height = value\n                if width > -1 and height > -1:\n                    break\n        except Exception as e:\n            raise UnknownImageFormat(str(e))\n    elif size >= 2:\n            # see http://en.wikipedia.org/wiki/ICO_(file_format)\n        imgtype = 'ICO'\n        input.seek(0)\n        reserved = input.read(2)\n        if 0 != struct.unpack(\"<H\", reserved)[0]:\n            raise UnknownImageFormat(FILE_UNKNOWN)\n        format = input.read(2)\n        assert 1 == struct.unpack(\"<H\", format)[0]\n        num = input.read(2)\n        num = struct.unpack(\"<H\", num)[0]\n        if num > 1:\n            import warnings\n            warnings.warn(\"ICO File contains more than one image\")\n        # http://msdn.microsoft.com/en-us/library/ms997538.aspx\n        w = input.read(1)\n        h = input.read(1)\n        width = ord(w)\n        height = ord(h)\n    else:\n        raise UnknownImageFormat(FILE_UNKNOWN)\n\n    return Image(path=file_path,\n                 type=imgtype,\n                 file_size=size,\n                 width=width,\n                 height=height)\n\ndef get_image_size(file_path):\n    \"\"\"\n    Return (width, height) for a given img file content - no external\n    dependencies except the os and struct builtin modules\n    \"\"\"\n    img = get_image_metadata(file_path)\n    return (img.width, img.height)\n\n```\n\n\n",
        "eval_script": "## img2pdf/img_size.py\nimport collections\nimport json\nimport os\nimport io\nimport struct\nfrom PIL import Image as PILImage\n\nFILE_UNKNOWN = \"Sorry, don't know how to get size for this file.\"\n\nclass UnknownImageFormat(Exception):\n    pass\n\ntypes = collections.OrderedDict()\n\nBMP = types['BMP'] = 'BMP'\nGIF = types['GIF'] = 'GIF'\nICO = types['ICO'] = 'ICO'\nJPEG = types['JPEG'] = 'JPEG'\nPNG = types['PNG'] = 'PNG'\nTIFF = types['TIFF'] = 'TIFF'\nWEBP = types['WEBP'] = 'WEBP'\n\nimage_fields = ['path', 'type', 'file_size', 'width', 'height']\n\nclass Image(collections.namedtuple('Image', image_fields)):\n\n    def to_str_row(self):\n        return (\"%d\\t%d\\t%d\\t%s\\t%s\" % (\n            self.width,\n            self.height,\n            self.file_size,\n            self.type,\n            self.path.replace('\\t', '\\\\t'),\n        ))\n\n    def to_str_row_verbose(self):\n        return (\"%d\\t%d\\t%d\\t%s\\t%s\\t##%s\" % (\n            self.width,\n            self.height,\n            self.file_size,\n            self.type,\n            self.path.replace('\\t', '\\\\t'),\n            self))\n\n    def to_str_json(self, indent=None):\n        return json.dumps(self._asdict(), indent=indent)\n\ndef get_image_metadata(file_path):\n    size = os.path.getsize(file_path)\n    with io.open(file_path, \"rb\") as input:\n        return get_image_metadata_from_bytesio(input, size, file_path)\n\ndef get_image_metadata_from_bytesio(input, size, file_path=None):\n    height = -1\n    width = -1\n    data = input.read(30)\n    msg = \" raised while trying to decode as JPEG.\"\n\n    if (size >= 10) and data[:6] in (b'GIF87a', b'GIF89a'):\n        imgtype = GIF\n        w, h = struct.unpack(\"<HH\", data[6:10])\n        width = int(w)\n        height = int(h)\n    elif ((size >= 24) and data.startswith(b'\\211PNG\\r\\n\\032\\n')\n            and (data[12:16] == b'IHDR')):\n        imgtype = PNG\n        w, h = struct.unpack(\">LL\", data[16:24])\n        width = int(w)\n        height = int(h)\n    elif (size >= 16) and data.startswith(b'\\211PNG\\r\\n\\032\\n'):\n        imgtype = PNG\n        w, h = struct.unpack(\">LL\", data[8:16])\n        width = int(w)\n        height = int(h)\n    elif (size >= 2) and data.startswith(b'\\377\\330'):\n        imgtype = JPEG\n        input.seek(0)\n        input.read(2)\n        b = input.read(1)\n        try:\n            while (b and ord(b) != 0xDA):\n                while (ord(b) != 0xFF):\n                    b = input.read(1)\n                while (ord(b) == 0xFF):\n                    b = input.read(1)\n                if (ord(b) >= 0xC0 and ord(b) <= 0xC3):\n                    input.read(3)\n                    h, w = struct.unpack(\">HH\", input.read(4))\n                    break\n                else:\n                    input.read(\n                        int(struct.unpack(\">H\", input.read(2))[0]) - 2)\n                b = input.read(1)\n            width = int(w)\n            height = int(h)\n        except struct.error:\n            raise UnknownImageFormat(\"StructError\" + msg)\n        except ValueError:\n            raise UnknownImageFormat(\"ValueError\" + msg)\n        except Exception as e:\n            raise UnknownImageFormat(e.__class__.__name__ + msg)\n    elif (size >= 30) and data.startswith(b'RIFF') and data[8:15] == b'WEBPVP8':\n        imgtype = WEBP\n        width, height = int(data[26]) | int(data[27]) << 8, int(data[28]) | int(data[29]) << 8\n    elif (size >= 26) and data.startswith(b'BM'):\n        imgtype = 'BMP'\n        headersize = struct.unpack(\"<I\", data[14:18])[0]\n        if headersize == 12:\n            w, h = struct.unpack(\"<HH\", data[18:22])\n            width = int(w)\n            height = int(h)\n        elif headersize >= 40:\n            w, h = struct.unpack(\"<ii\", data[18:26])\n            width = int(w)\n            height = abs(int(h))\n        else:\n            raise UnknownImageFormat(\n                \"Unkown DIB header size:\" +\n                str(headersize))\n    elif (size >= 8) and data[:4] in (b\"II\\052\\000\", b\"MM\\000\\052\"):\n        imgtype = TIFF\n        byteOrder = data[:2]\n        boChar = \">\" if byteOrder == \"MM\" else \"<\"\n        tiffTypes = {\n            1: (1, boChar + \"B\"),\n            2: (1, boChar + \"c\"),\n            3: (2, boChar + \"H\"),\n            4: (4, boChar + \"L\"),\n            5: (8, boChar + \"LL\"),\n            6: (1, boChar + \"b\"),\n            7: (1, boChar + \"c\"),\n            8: (2, boChar + \"h\"),\n            9: (4, boChar + \"l\"),\n            10: (8, boChar + \"ll\"),\n            11: (4, boChar + \"f\"),\n            12: (8, boChar + \"d\")\n        }\n        ifdOffset = struct.unpack(boChar + \"L\", data[4:8])[0]\n        try:\n            countSize = 2\n            input.seek(ifdOffset)\n            ec = input.read(countSize)\n            ifdEntryCount = struct.unpack(boChar + \"H\", ec)[0]\n            ifdEntrySize = 12\n            for i in range(ifdEntryCount):\n                entryOffset = ifdOffset + countSize + i * ifdEntrySize\n                input.seek(entryOffset)\n                tag = input.read(2)\n                tag = struct.unpack(boChar + \"H\", tag)[0]\n                if(tag == 256 or tag == 257):\n                    type = input.read(2)\n                    type = struct.unpack(boChar + \"H\", type)[0]\n                    if type not in tiffTypes:\n                        raise UnknownImageFormat(\n                            \"Unkown TIFF field type:\" +\n                            str(type))\n                    typeSize = tiffTypes[type][0]\n                    typeChar = tiffTypes[type][1]\n                    input.seek(entryOffset + 8)\n                    value = input.read(typeSize)\n                    value = int(struct.unpack(typeChar, value)[0])\n                    if tag == 256:\n                        width = value\n                    else:\n                        height = value\n                if width > -1 and height > -1:\n                    break\n        except Exception as e:\n            raise UnknownImageFormat(str(e))\n    elif size >= 2:\n        imgtype = 'ICO'\n        input.seek(0)\n        reserved = input.read(2)\n        if 0 != struct.unpack(\"<H\", reserved)[0]:\n            raise UnknownImageFormat(FILE_UNKNOWN)\n        format = input.read(2)\n        assert 1 == struct.unpack(\"<H\", format)[0]\n        num = input.read(2)\n        num = struct.unpack(\"<H\", num)[0]\n        if num > 1:\n            import warnings\n            warnings.warn(\"ICO File contains more than one image\")\n        w = input.read(1)\n        h = input.read(1)\n        width = ord(w)\n        height = ord(h)\n    else:\n        raise UnknownImageFormat(FILE_UNKNOWN)\n\n    return Image(path=file_path,\n                 type=imgtype,\n                 file_size=size,\n                 width=width,\n                 height=height)\n\ndef get_image_size(file_path):\n    img = get_image_metadata(file_path)\n    return (img.width, img.height)\n\n\ndef create_test_images():\n    # Create a simple PNG image\n    png_path = '/home/user/tmp/test_image.png'\n    png_image = PILImage.new('RGB', (100, 100), color = 'red')\n    png_image.save(png_path)\n\n    # Create a simple JPEG image\n    jpeg_path = '/home/user/tmp/test_image.jpeg'\n    jpeg_image = PILImage.new('RGB', (100, 100), color = 'blue')\n    jpeg_image.save(jpeg_path)\n\n    # Create a simple GIF image\n    gif_path = '/home/user/tmp/test_image.gif'\n    gif_image = PILImage.new('RGB', (100, 100), color = 'green')\n    gif_image.save(gif_path)\n\ndef test_get_image_size():\n    create_test_images()\n\n    # Test case 1: PNG image\n    png_path = '/home/user/tmp/test_image.png'\n    assert get_image_size(png_path) == get_image_size_new_implementation(png_path)\n\n    # Test case 2: JPEG image\n    jpeg_path = '/home/user/tmp/test_image.jpeg'\n    assert get_image_size(jpeg_path) == get_image_size_new_implementation(jpeg_path)\n\n    # Test case 3: GIF image\n    gif_path = '/home/user/tmp/test_image.gif'\n    assert get_image_size(gif_path) == get_image_size_new_implementation(gif_path)\n\nif __name__ == \"__main__\":\n    test_get_image_size()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `get_image_size` calls another function `get_image_metadata` to retrieve image metadata and then returns the width and height of the image. The revised function `get_image_size` does exactly the same thing: it calls `get_image_metadata` and returns the width and height from the resulting `Image` object. The functionality of both functions is identical as they both rely on the `get_image_metadata` function to obtain the necessary image dimensions. The additional code in the revised version, such as the `create_test_images` and `test_get_image_size` functions, is for testing purposes and does not alter the functionality of the `get_image_size` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `get_image_size` returns a tuple containing the width and height of the image, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `get_image_size` and `get_image_size_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `get_image_size` and `get_image_size_new_implementation` for the same input files. This ensures that `get_image_size_new_implementation` must have the same functionality as `get_image_size` to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values of the two functions, which is reasonable given that `get_image_size` returns values.\n- CONDITION 5: The test cases cover three different image formats (PNG, JPEG, GIF), which are common and non-trivial, providing a reasonable breadth of testing.",
            "answer": "yes"
        },
        "commit_id": "2a0f9e93f224f3c3df619774ee88648663f347c6"
    },
    {
        "func_name": "calculate_score",
        "idx": "163",
        "repo_name": "Raghavk25___Python-Projects",
        "func_path": "Blackjack game/main.py",
        "orig_func": "def calculate_score(cards):\n    \"\"\"Takes a list of cards and returns the score calculated from the cards.\"\"\"\n    if sum(cards) == 21 and len(cards) == 2:\n        return 0\n    if sum(cards) > 21 and 11 in cards:\n        cards.remove(11)\n        cards.append(1)\n    return sum(cards)",
        "orig_context": "```python\n## Blackjack game/main.py\ndef calculate_score(cards):\n    \"\"\"Takes a list of cards and returns the score calculated from the cards.\"\"\"\n    if sum(cards) == 21 and len(cards) == 2:\n        return 0\n    if sum(cards) > 21 and 11 in cards:\n        cards.remove(11)\n        cards.append(1)\n    return sum(cards)\n\n```\n\n\n",
        "eval_script": "## Blackjack game/main.py\ndef calculate_score(cards):\n    \"\"\"Takes a list of cards and returns the score calculated from the cards.\"\"\"\n    if sum(cards) == 21 and len(cards) == 2:\n        return 0\n    if sum(cards) > 21 and 11 in cards:\n        cards.remove(11)\n        cards.append(1)\n    return sum(cards)\n\n\ndef test_calculate_score():\n    # Test case where the sum is exactly 21 with two cards (Blackjack)\n    assert calculate_score([10, 11]) == calculate_score_new_implementation([10, 11])\n\n    # Test case where the sum is greater than 21 and there is an 11 (Ace)\n    assert calculate_score([10, 11, 2]) == calculate_score_new_implementation([10, 11, 2])\n\n    # General case where the sum is returned\n    assert calculate_score([5, 6, 7]) == calculate_score_new_implementation([5, 6, 7])\n\n    # Test case with multiple Aces\n    assert calculate_score([11, 11, 9]) == calculate_score_new_implementation([11, 11, 9])\n\n    # Test case where the sum is greater than 21 without any Aces\n    assert calculate_score([10, 10, 5]) == calculate_score_new_implementation([10, 10, 5])\n\n    # Test case with an empty list\n    assert calculate_score([]) == calculate_score_new_implementation([])\n\n    # Test case with a single card\n    assert calculate_score([7]) == calculate_score_new_implementation([7])\n\n    # Test case with all Aces\n    assert calculate_score([11, 11, 11]) == calculate_score_new_implementation([11, 11, 11])\n\nif __name__ == \"__main__\":\n    test_calculate_score()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `calculate_score` is identical to the ORIGINAL FUNCTION in terms of code and logic. Both functions take a list of cards, check for a blackjack condition (sum of 21 with two cards), adjust for Aces if the sum exceeds 21, and return the sum of the cards. The additional code in the revised version is a set of test cases to verify the function's behavior, but it does not alter the function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n1. **CONDITION 1**: The `calculate_score` function returns a value, which is the score calculated from the list of cards. Therefore, it satisfies this condition.\n2. **CONDITION 2**: The test cases use assertions to check the return values of the `calculate_score` function against the `calculate_score_new_implementation` function. There is no checking of printed or logged contents, so this condition is satisfied.\n3. **CONDITION 3**: The test cases cover various scenarios, including edge cases like Blackjack, bust with Aces, general cases, and empty or single card lists. These tests ensure that any implementation that passes them must have the same functionality as the original `calculate_score`. Therefore, this condition is satisfied.\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `calculate_score` returns a value. There are no unreasonable assertions, so this condition is satisfied.\n5. **CONDITION 5**: The test cases are non-trivial as they cover a wide range of scenarios, including edge cases and typical cases in a Blackjack game. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "c2db76206f6c30acfec660e9704a953551cd01dd"
    },
    {
        "func_name": "CoffeeMaker.is_resource_sufficient",
        "idx": "165",
        "repo_name": "Raghavk25___Python-Projects",
        "func_path": "Coffee maker/coffee_maker.py",
        "orig_func": "def is_resource_sufficient(self, drink):\n    \"\"\"Returns True when order can be made, False if ingredients are insufficient.\"\"\"\n    can_make = True\n    for item in drink.ingredients:\n        if drink.ingredients[item] > self.resources[item]:\n            print(f'\\nSorry, there is not enough {item}.')\n            can_make = False\n    return can_make",
        "orig_context": "```python\n## Coffee maker/coffee_maker.py\nfrom replit import clear\n\nimport time\n\nclass CoffeeMaker:\n    \"\"\"Models the machine that makes the coffee\"\"\"\n    def __init__(self):\n        self.resources = {\n            \"water\": 3000,\n            \"milk\": 2000,\n            \"coffee\": 400,\n        }\n\n    def report(self):\n        \"\"\"Prints a report of all resources.\"\"\"\n        if input(\"\\nEnter password: \") == \"Kaffeeland\":\n            clear()\n            print(\"REMAINING INGREDIENTS\\n\")\n            print(f\"Water  : {self.resources['water']}ml\")\n            print(f\"Coffee : {self.resources['coffee']}g\")\n            print(f\"Milk   : {self.resources['milk']}ml\")\n        else:\n            clear()\n            print(\"Incorrect password.\")\n        time.sleep(7)\n        clear()\n        \n\n    def is_resource_sufficient(self, drink):\n        \"\"\"Returns True when order can be made, False if ingredients are insufficient.\"\"\"\n        can_make = True\n        for item in drink.ingredients:\n            if drink.ingredients[item] > self.resources[item]:\n                print(f\"\\nSorry, there is not enough {item}.\")\n                can_make = False\n        return can_make\n\n    def make_coffee(self, order):\n        \"\"\"Deducts the required ingredients from the resources.\"\"\"\n        for item in order.ingredients:\n            self.resources[item] -= order.ingredients[item]\n        print(f\"Here is your {order.name}\\nEnjoy!\")\n        time.sleep(7)\n        clear()\n\n```\n\n\n",
        "eval_script": "## Coffee maker/coffee_maker.py\nfrom replit import clear\n\nimport time\n\nclass CoffeeMaker:\n    \"\"\"Models the machine that makes the coffee\"\"\"\n    def __init__(self):\n        self.resources = {\n            \"water\": 3000,\n            \"milk\": 2000,\n            \"coffee\": 400,\n        }\n\n    def report(self):\n        \"\"\"Prints a report of all resources.\"\"\"\n        if input(\"\\nEnter password: \") == \"Kaffeeland\":\n            clear()\n            print(\"REMAINING INGREDIENTS\\n\")\n            print(f\"Water  : {self.resources['water']}ml\")\n            print(f\"Coffee : {self.resources['coffee']}g\")\n            print(f\"Milk   : {self.resources['milk']}ml\")\n        else:\n            clear()\n            print(\"Incorrect password.\")\n        time.sleep(7)\n        clear()\n        \n\n    def is_resource_sufficient(self, drink):\n        \"\"\"Returns True when order can be made, False if ingredients are insufficient.\"\"\"\n        can_make = True\n        for item in drink.ingredients:\n            if drink.ingredients[item] > self.resources[item]:\n                print(f\"\\nSorry, there is not enough {item}.\")\n                can_make = False\n        return can_make\n\n\n    def make_coffee(self, order):\n        \"\"\"Deducts the required ingredients from the resources.\"\"\"\n        for item in order.ingredients:\n            self.resources[item] -= order.ingredients[item]\n        print(f\"Here is your {order.name}\\nEnjoy!\")\n        time.sleep(7)\n        clear()\n\n# Mock drink class for testing\nclass Drink:\n    def __init__(self, name, ingredients):\n        self.name = name\n        self.ingredients = ingredients\n\ndef test_is_resource_sufficient():\n    coffee_maker = CoffeeMaker()\n\n    # Test case 1: All resources are sufficient\n    drink1 = Drink(\"Latte\", {\"water\": 200, \"milk\": 150, \"coffee\": 24})\n    assert coffee_maker.is_resource_sufficient(drink1) == coffee_maker.is_resource_sufficient_new_implementation(drink1)\n\n    # Test case 2: Insufficient water\n    drink2 = Drink(\"Espresso\", {\"water\": 3100, \"milk\": 0, \"coffee\": 18})\n    assert coffee_maker.is_resource_sufficient(drink2) == coffee_maker.is_resource_sufficient_new_implementation(drink2)\n\n    # Test case 3: Insufficient coffee\n    drink3 = Drink(\"Cappuccino\", {\"water\": 250, \"milk\": 100, \"coffee\": 500})\n    assert coffee_maker.is_resource_sufficient(drink3) == coffee_maker.is_resource_sufficient_new_implementation(drink3)\n\n    # Test case 4: Exact resource match\n    drink4 = Drink(\"Exact Match\", {\"water\": 3000, \"milk\": 2000, \"coffee\": 400})\n    assert coffee_maker.is_resource_sufficient(drink4) == coffee_maker.is_resource_sufficient_new_implementation(drink4)\n\n    # Test case 5: Multiple insufficient resources\n    drink5 = Drink(\"Double Trouble\", {\"water\": 3100, \"milk\": 2100, \"coffee\": 18})\n    assert coffee_maker.is_resource_sufficient(drink5) == coffee_maker.is_resource_sufficient_new_implementation(drink5)\n\n    # Test case 6: Zero resource requirement\n    drink6 = Drink(\"Zero Coffee\", {\"water\": 200, \"milk\": 150, \"coffee\": 0})\n    assert coffee_maker.is_resource_sufficient(drink6) == coffee_maker.is_resource_sufficient_new_implementation(drink6)\n\n    # Test case 7: No resources needed\n    drink7 = Drink(\"Air\", {\"water\": 0, \"milk\": 0, \"coffee\": 0})\n    assert coffee_maker.is_resource_sufficient(drink7) == coffee_maker.is_resource_sufficient_new_implementation(drink7)\n\n    # Test case 8: Boundary condition\n    drink8 = Drink(\"Boundary\", {\"water\": 3001, \"milk\": 2000, \"coffee\": 400})\n    assert coffee_maker.is_resource_sufficient(drink8) == coffee_maker.is_resource_sufficient_new_implementation(drink8)\n\nif __name__ == \"__main__\":\n    test_is_resource_sufficient()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `is_resource_sufficient` in the provided code is identical to the ORIGINAL FUNCTION. Both functions iterate over the ingredients of the drink, compare them to the available resources, print a message if any resource is insufficient, and return a boolean indicating whether the drink can be made. There are no changes in logic or functionality between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `is_resource_sufficient` returns a boolean value indicating whether the resources are sufficient or not. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of `is_resource_sufficient` and `is_resource_sufficient_new_implementation`, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the return values of `is_resource_sufficient` and `is_resource_sufficient_new_implementation` for various scenarios, ensuring that the new implementation must have the exact same functionality to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `is_resource_sufficient` returns a boolean. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including sufficient resources, insufficient resources, exact matches, and boundary conditions, making them non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "c2db76206f6c30acfec660e9704a953551cd01dd"
    },
    {
        "func_name": "Menu.get_items",
        "idx": "167",
        "repo_name": "Raghavk25___Python-Projects",
        "func_path": "Coffee maker/menu.py",
        "orig_func": "def get_items(self):\n    \"\"\"Returns all the names of the available menu items\"\"\"\n    options = ''\n    for item in self.menu:\n        options += f'Press {item.index} for {item.name} : ${item.cost}\\n'\n    options += f'Press 4 to view machine earnings (for office use).\\n'\n    options += f'Press 5 to view remaining ingredients (for office use).\\n'\n    options += f'Press 6 to shut down the machine (for office use).\\n'\n    return options",
        "orig_context": "```python\n## Coffee maker/menu.py\nfrom replit import clear\n\nimport time\n\nclass MenuItem:\n    \"\"\"Models each Menu Item.\"\"\"\n    def __init__(self, name, water, milk, coffee, cost, index):\n        self.name = name\n        self.cost = cost\n        self.index = index\n        self.ingredients = {\n            \"water\": water,\n            \"milk\": milk,\n            \"coffee\": coffee\n        }\n\nclass Menu:\n    \"\"\"Models the Menu with drinks.\"\"\"\n    def __init__(self):\n        self.menu = [\n            MenuItem(name = \"Espresso  \", water = 50, milk = 0, coffee = 18, cost = 1.5, index = 1),\n            MenuItem(name = \"Latte     \", water = 200, milk = 150, coffee = 24, cost = 2.5, index = 2),\n            MenuItem(name = \"Cappuccino\", water = 250, milk = 50, coffee = 24, cost = 3, index = 3),\n        ]\n\n    def get_items(self):\n        \"\"\"Returns all the names of the available menu items\"\"\"\n        options = '' \n        for item in self.menu:\n            options += f\"Press {item.index} for {item.name} : ${item.cost}\\n\"\n        options += f\"Press 4 to view machine earnings (for office use).\\n\"\n        options += f\"Press 5 to view remaining ingredients (for office use).\\n\"\n        options += f\"Press 6 to shut down the machine (for office use).\\n\"\n        return options\n\n    def find_drink(self, order_name):\n        \"\"\"Searches the menu for a particular drink by name. Returns that item if it exists, otherwise returns None\"\"\"\n        for item in self.menu:\n            if item.index == order_name:\n                return item\n        print(\"Sorry that item is not available.\")\n    \n    def password(self):\n        if input(\"\\nEnter password: \") == \"Kaffeeland\":\n            clear()\n            return True\n        else:\n            clear()\n            return False\n        \n    def wait_and_clear(self):\n        time.sleep(7)\n        clear()\n\n```\n\n\n",
        "eval_script": "## Coffee maker/menu.py\n\nimport time\n\nclass MenuItem:\n    \"\"\"Models each Menu Item.\"\"\"\n    def __init__(self, name, water, milk, coffee, cost, index):\n        self.name = name\n        self.cost = cost\n        self.index = index\n        self.ingredients = {\n            \"water\": water,\n            \"milk\": milk,\n            \"coffee\": coffee\n        }\n\nclass Menu:\n    \"\"\"Models the Menu with drinks.\"\"\"\n    def __init__(self):\n        self.menu = [\n            MenuItem(name = \"Espresso\", water = 50, milk = 0, coffee = 18, cost = 1.5, index = 1),\n            MenuItem(name = \"Latte\", water = 200, milk = 150, coffee = 24, cost = 2.5, index = 2),\n            MenuItem(name = \"Cappuccino\", water = 250, milk = 50, coffee = 24, cost = 3, index = 3),\n        ]\n\n    def get_items(self):\n        \"\"\"Returns all the names of the available menu items\"\"\"\n        options = '' \n        for item in self.menu:\n            options += f\"Press {item.index} for {item.name} : ${item.cost}\\n\"\n        options += f\"Press 4 to view machine earnings (for office use).\\n\"\n        options += f\"Press 5 to view remaining ingredients (for office use).\\n\"\n        options += f\"Press 6 to shut down the machine (for office use).\\n\"\n        return options\n\n\n    def find_drink(self, order_name):\n        \"\"\"Searches the menu for a particular drink by name. Returns that item if it exists, otherwise returns None\"\"\"\n        for item in self.menu:\n            if item.index == order_name:\n                return item\n        print(\"Sorry that item is not available.\")\n    \n    def password(self):\n        if input(\"\\nEnter password: \") == \"Kaffeeland\":\n            return True\n        else:\n            return False\n        \n    def wait_and_clear(self):\n        time.sleep(7)\n\ndef test_get_items():\n    menu = Menu()\n    original_output = menu.get_items()\n    new_output = menu.get_items_new_implementation()\n    \n    # Assert that both implementations return the same output\n    assert original_output == new_output, \"Outputs do not match for get_items implementations\"\n    \n    # Check specific parts of the output\n    assert \"Press 1 for Espresso : $1.5\" in original_output, \"Espresso option missing in original implementation\"\n    assert \"Press 1 for Espresso : $1.5\" in new_output, \"Espresso option missing in new implementation\"\n    \n    # Check the additional options\n    assert \"Press 4 to view machine earnings (for office use).\" in original_output, \"Office use options missing in original implementation\"\n    assert \"Press 4 to view machine earnings (for office use).\" in new_output, \"Office use options missing in new implementation\"\n\nif __name__ == \"__main__\":\n    test_get_items()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions iterate over a list of menu items and construct a string that lists each item with its index, name, and cost. They also append additional options for office use at the end of the string. The logic and output format are the same in both versions. The test function provided in the code is not relevant to the comparison of the two `get_items` functions, as it refers to a non-existent `get_items_new_implementation` method and is not part of the functionality comparison.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `get_items` function returns a string containing the menu options, satisfying the condition that it has return values.\n  \n- **CONDITION 2**: The test function `test_get_items` checks the return values of `get_items` and `get_items_new_implementation` by comparing their outputs and using assertions to check specific parts of the output. It does not rely on printed or logged content, satisfying this condition.\n\n- **CONDITION 3**: The test cases compare the entire output of both implementations and check specific expected strings within the output. This ensures that `get_items_new_implementation` must have the exact same functionality as `get_items` to pass the tests, satisfying this condition.\n\n- **CONDITION 4**: The test cases use assertions to compare the outputs of the two implementations and check for specific expected strings. These assertions are reasonable given that `get_items` returns a string. Therefore, this condition is satisfied.\n\n- **CONDITION 5**: The test cases are non-trivial as they not only compare the entire output but also check for specific expected strings within the output, ensuring that the menu options and additional options are correctly formatted and present. This satisfies the condition of non-triviality.",
            "answer": "yes"
        },
        "commit_id": "c2db76206f6c30acfec660e9704a953551cd01dd"
    },
    {
        "func_name": "voigt_indices",
        "idx": "179",
        "repo_name": "DorianDepriester___Elasticipy",
        "func_path": "src/Elasticipy/FourthOrderTensor.py",
        "orig_func": "def voigt_indices(i, j):\n    \"\"\"\n    Translate the two-index notation to one-index notation\n\n    Parameters\n    ----------\n    i : int or np.ndarray\n        First index\n    j : int or np.ndarray\n        Second index\n\n    Returns\n    -------\n    Index in the vector of length 6\n    \"\"\"\n    voigt_mat = np.array([[0, 5, 4], [5, 1, 3], [4, 3, 2]])\n    return voigt_mat[i, j]",
        "orig_context": "```python\n## src/Elasticipy/FourthOrderTensor.py\nimport numpy as np\n\ndef voigt_indices(i, j):\n    \"\"\"\n    Translate the two-index notation to one-index notation\n\n    Parameters\n    ----------\n    i : int or np.ndarray\n        First index\n    j : int or np.ndarray\n        Second index\n\n    Returns\n    -------\n    Index in the vector of length 6\n    \"\"\"\n    voigt_mat = np.array([[0, 5, 4],\n                          [5, 1, 3],\n                          [4, 3, 2]])\n    return voigt_mat[i, j]\n\n```\n\n\n",
        "eval_script": "## src/Elasticipy/FourthOrderTensor.py\nimport numpy as np\n\ndef voigt_indices(i, j):\n    \"\"\"\n    Translate the two-index notation to one-index notation\n\n    Parameters\n    ----------\n    i : int or np.ndarray\n        First index\n    j : int or np.ndarray\n        Second index\n\n    Returns\n    -------\n    Index in the vector of length 6\n    \"\"\"\n    voigt_mat = np.array([[0, 5, 4],\n                          [5, 1, 3],\n                          [4, 3, 2]])\n    return voigt_mat[i, j]\n\n\ndef test_voigt_indices():\n    # Test diagonal elements\n    assert voigt_indices(0, 0) == voigt_indices_new_implementation(0, 0)\n    assert voigt_indices(1, 1) == voigt_indices_new_implementation(1, 1)\n    assert voigt_indices(2, 2) == voigt_indices_new_implementation(2, 2)\n\n    # Test off-diagonal elements\n    assert voigt_indices(0, 1) == voigt_indices_new_implementation(0, 1)\n    assert voigt_indices(1, 2) == voigt_indices_new_implementation(1, 2)\n    assert voigt_indices(2, 0) == voigt_indices_new_implementation(2, 0)\n\n    # Test another set of off-diagonal elements\n    assert voigt_indices(0, 2) == voigt_indices_new_implementation(0, 2)\n    assert voigt_indices(1, 0) == voigt_indices_new_implementation(1, 0)\n    assert voigt_indices(2, 1) == voigt_indices_new_implementation(2, 1)\n\n    # Test with numpy arrays\n    i_array = np.array([0, 1, 2])\n    j_array = np.array([2, 0, 1])\n    assert np.array_equal(voigt_indices(i_array, j_array), voigt_indices_new_implementation(i_array, j_array))\n\n    # Test with out-of-bounds indices\n    try:\n        voigt_indices(3, 0)\n    except IndexError:\n        pass\n    else:\n        assert False, \"Expected IndexError for out-of-bounds index\"\n\n    try:\n        voigt_indices_new_implementation(3, 0)\n    except IndexError:\n        pass\n    else:\n        assert False, \"Expected IndexError for out-of-bounds index\"\n\n    # Test with negative indices\n    assert voigt_indices(-1, -1) == voigt_indices_new_implementation(-1, -1)\n    assert voigt_indices(-3, -3) == voigt_indices_new_implementation(-3, -3)\n\nif __name__ == \"__main__\":\n    test_voigt_indices()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions define a 3x3 numpy array `voigt_mat` and return the element at position `[i, j]`. The test function `test_voigt_indices()` is not part of the original function and is used to verify the functionality of the `voigt_indices` function against a hypothetical `voigt_indices_new_implementation`. However, this does not affect the comparison of the `voigt_indices` function itself. Since the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `voigt_indices` function returns a value, specifically an index from the `voigt_mat` array, satisfying this condition.\n- CONDITION 2: The test cases use assertions to check return values and do not rely on printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `voigt_indices` and `voigt_indices_new_implementation` for various inputs, ensuring that both functions must have the same functionality to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions appropriately to compare the return values of the two implementations, and they handle exceptions correctly, satisfying this condition.\n- CONDITION 5: The test cases cover a range of scenarios, including diagonal and off-diagonal elements, array inputs, out-of-bounds indices, and negative indices, making them non-trivial and satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "b0d25e34f09fe66c00cd6bf1dddf165157443060"
    },
    {
        "func_name": "unvoigt_index",
        "idx": "180",
        "repo_name": "DorianDepriester___Elasticipy",
        "func_path": "src/Elasticipy/FourthOrderTensor.py",
        "orig_func": "def unvoigt_index(i):\n    \"\"\"\n    Translate the one-index notation to two-index notation\n\n    Parameters\n    ----------\n    i : int or np.ndarray\n        Index to translate\n    \"\"\"\n    inverse_voigt_mat = np.array([[0, 0], [1, 1], [2, 2], [1, 2], [0, 2], [0, 1]])\n    return inverse_voigt_mat[i]",
        "orig_context": "```python\n## src/Elasticipy/FourthOrderTensor.py\nimport numpy as np\n\ndef unvoigt_index(i):\n    \"\"\"\n    Translate the one-index notation to two-index notation\n\n    Parameters\n    ----------\n    i : int or np.ndarray\n        Index to translate\n    \"\"\"\n    inverse_voigt_mat = np.array([[0, 0],\n                                  [1, 1],\n                                  [2, 2],\n                                  [1, 2],\n                                  [0, 2],\n                                  [0, 1]])\n    return inverse_voigt_mat[i]\n\n```\n\n\n",
        "eval_script": "## src/Elasticipy/FourthOrderTensor.py\nimport numpy as np\n\ndef unvoigt_index(i):\n    \"\"\"\n    Translate the one-index notation to two-index notation\n\n    Parameters\n    ----------\n    i : int or np.ndarray\n        Index to translate\n    \"\"\"\n    inverse_voigt_mat = np.array([[0, 0],\n                                  [1, 1],\n                                  [2, 2],\n                                  [1, 2],\n                                  [0, 2],\n                                  [0, 1]])\n    return inverse_voigt_mat[i]\n\n\ndef test_unvoigt_index():\n    # Test case 1: Single integer input\n    assert np.array_equal(unvoigt_index(0), unvoigt_index_new_implementation(0)), \"Test case 1 failed\"\n    \n    # Test case 2: Array input\n    assert np.array_equal(unvoigt_index(np.array([0, 1, 2])), unvoigt_index_new_implementation(np.array([0, 1, 2]))), \"Test case 2 failed\"\n    \n    # Test case 3: Another single integer input\n    assert np.array_equal(unvoigt_index(5), unvoigt_index_new_implementation(5)), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_unvoigt_index()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions define the same `inverse_voigt_mat` array and return the same result for any given input `i`. The additional code in the revised version includes a test function `test_unvoigt_index()` and a main execution block, but these do not alter the functionality of the `unvoigt_index` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `unvoigt_index` returns a value, specifically an element from the `inverse_voigt_mat` array, based on the input index `i`. Therefore, it satisfies CONDITION 1.\n- CONDITION 2: The test cases use `np.array_equal` to compare the return values of `unvoigt_index` and `unvoigt_index_new_implementation`, which means they are checking return values, not printed or logged contents. Thus, CONDITION 2 is satisfied.\n- CONDITION 3: The test cases compare the outputs of `unvoigt_index` and `unvoigt_index_new_implementation` for specific inputs. If `unvoigt_index_new_implementation` passes all these tests, it must have the same functionality as `unvoigt_index`, satisfying CONDITION 3.\n- CONDITION 4: The test cases use `np.array_equal` to assert the equality of the outputs, which is appropriate given that `unvoigt_index` returns numpy arrays. The assert statements are reasonable, satisfying CONDITION 4.\n- CONDITION 5: The test cases cover different scenarios: a single integer input, an array input, and another single integer input. These are non-trivial as they test the function's behavior with both scalar and array inputs, satisfying CONDITION 5.",
            "answer": "yes"
        },
        "commit_id": "b0d25e34f09fe66c00cd6bf1dddf165157443060"
    },
    {
        "func_name": "SecondOrderTensor.load_from_txt",
        "idx": "187",
        "repo_name": "DorianDepriester___Elasticipy",
        "func_path": "src/Elasticipy/SecondOrderTensor.py",
        "orig_func": "@classmethod\ndef load_from_txt(cls, file, name_prefix='', **kwargs):\n    \"\"\"\n        Load a tensor array from text file.\n\n        Parameters\n        ----------\n        file : str or file\n            Textfile to read the components from.\n        name_prefix : str, optional\n            Prefix to add to each column when parsing the file. For instance, with name_prefix='E', the function will\n            look for columns names E11, E12, E13 etc.\n\n        Returns\n        -------\n        SecondOrderTensor\n            Flat (1D) tensor constructed from the values given in the text file\n        \"\"\"\n    df = pd.read_csv(file, **kwargs)\n    matrix = np.zeros((len(df), 3, 3))\n    for i in range(3):\n        if cls is SkewSymmetricSecondOrderTensor:\n            r = range(i + 1, 3)\n        elif cls is SymmetricSecondOrderTensor:\n            r = range(i, 3)\n        else:\n            r = range(3)\n        for j in r:\n            key = name_prefix + '{}{}'.format(i + 1, j + 1)\n            matrix[:, i, j] = df[key]\n    return cls(matrix)",
        "orig_context": "```python\n# src/Elasticipy/SecondOrderTensor.py\n\nclass SecondOrderTensor:\n    \"\"\"\n    Template class for manipulation of second order tensors or arrays of second order tensors\n\n    Attributes\n    ----------\n    matrix : np.ndarray\n        (...,3,3) matrix storing all the components of the tensor\n\n    \"\"\"\n    name = 'Second-order tensor'\n    'Name to use when printing the tensor'\n\n    def __init__(self, matrix):\n        \"\"\"\n        Create an array of second-order tensors.\n\n        The input argument can be:\n            - an array of shape (3,3) defining all the components of the tensor;\n            - a stack of matrices, that is an array of shape (...,3,3).\n\n        Parameters\n        ----------\n        matrix : list or np.ndarray\n            (3,3) matrix, stack of (3,3) matrices\n        \"\"\"\n        matrix = np.array(matrix)\n        shape = matrix.shape\n        if len(shape) > 1 and shape[-2:] == (3, 3):\n            self.matrix = matrix\n        else:\n            raise ValueError('The input matrix must be of shape (3,3) or (...,3,3)')\n\n    def __repr__(self):\n        s = self.name + '\\n'\n        if self.shape:\n            s += 'Shape={}'.format(self.shape)\n        else:\n            s += self.matrix.__str__()\n        return s\n\n    def __getitem__(self, index):\n        return self.__class__(self.matrix[index])\n\n    def __setitem__(self, index, value):\n        if isinstance(value, (float, np.ndarray)):\n            self.matrix[index] = value\n        elif type(value) == self.__class__:\n            self.matrix[index] = value.matrix\n        else:\n            raise NotImplementedError('The r.h.s must be either float, a ndarray or an object of class {}'.format(self.__class__))\n\n    def __add__(self, other):\n        if type(self) == type(other):\n            return self.__class__(self.matrix + other.matrix)\n        elif isinstance(other, (int, float, np.ndarray)):\n            mat = self.matrix + other\n            if isinstance(self, SkewSymmetricSecondOrderTensor):\n                return SecondOrderTensor(mat)\n            else:\n                return self.__class__(mat)\n        elif isinstance(other, SecondOrderTensor):\n            return SecondOrderTensor(self.matrix + other.matrix)\n        else:\n            raise NotImplementedError('The element to add must be a number, a numpy.ndarray or a tensor.')\n\n    def __radd__(self, other):\n        return self + other\n\n    def __sub__(self, other):\n        if type(self) == type(other):\n            return self.__class__(self.matrix - other.matrix)\n        elif isinstance(other, (int, float, np.ndarray)):\n            return self.__class__(self.matrix - other)\n        else:\n            raise NotImplementedError('The element to subtract must be a number, a numpy ndarray or a tensor.')\n\n    def __neg__(self):\n        return self.__class__(-self.matrix)\n\n    def __rsub__(self, other):\n        return -self + other\n\n    @property\n    def shape(self):\n        \"\"\"\n        Return the shape of the tensor array\n\n        Returns\n        -------\n        tuple\n            Shape of array\n\n        See Also\n        --------\n        ndim : number of dimensions\n        \"\"\"\n        *shape, _, _ = self.matrix.shape\n        return tuple(shape)\n\n    @property\n    def ndim(self):\n        \"\"\"\n        Return the number of dimensions of the tensor array\n\n        Returns\n        -------\n        int\n            number of dimensions\n\n        See Also\n        --------\n        shape : shape of tensor array\n        \"\"\"\n        return len(self.shape)\n\n    @property\n    def C(self):\n        \"\"\"\n        Return tensor components\n\n        For instance T.C[i,j] returns all the (i,j)-th components of each tensor in the array.\n\n        Returns\n        -------\n        np.ndarray\n            Tensor components\n        \"\"\"\n        return _MatrixProxy(self.matrix)\n\n    def eig(self):\n        \"\"\"\n        Compute the eigenvalues and eigenvectors of the tensor\n\n        Returns\n        -------\n        lambda : np.ndarray\n            Eigenvalues of each tensor.\n        v : np.ndarray\n            Eigenvectors of teach tensor.\n\n        See Also\n        --------\n        eigvals : return only the eigenvalues (without directions)\n        principal_directions : return only the principal directions (without eigenvalues)\n        \"\"\"\n        return np.linalg.eig(self.matrix)\n\n    def eigvals(self):\n        \"\"\"\n        Compute the eigenvalues of the tensor, without computing the associated eigenvectors\n\n        Returns\n        -------\n        numpy.ndarray\n            Eigenvalues\n\n        See Also\n        --------\n        eig : compute the eigenvalues and the eigenvector\n        \"\"\"\n        return np.linalg.eigvals(self.matrix)\n\n    def principal_directions(self):\n        \"\"\"\n        Principal directions of the tensors\n\n        Returns\n        -------\n        np.ndarray\n            Principal directions of each tensor of the tensor array\n\n        See Also\n        --------\n        eig : Return both eigenvalues and corresponding principal directions\n        \"\"\"\n        return self.eig()[1]\n\n    @property\n    def I1(self):\n        \"\"\"\n        First invariant of the tensor (trace)\n\n        Returns\n        -------\n        np.ndarray or float\n            First invariant(s) of the tensor(s)\n\n        See Also\n        --------\n        I2 : Second invariant of the tensors\n        I3 : Third invariant of the tensors (det)\n        \"\"\"\n        return self.matrix.trace(axis1=-1, axis2=-2)\n\n    @property\n    def I2(self):\n        \"\"\"\n        Second invariant of the tensor\n\n        For a matrix M, it is defined as::\n\n            I_2 = 0.5 * ( np.trace(M)**2 + np.trace(np.matmul(M, M.T)) )\n\n        Returns\n        -------\n        np.array or float\n            Second invariant(s) of the tensor(s)\n\n        See Also\n        --------\n        I1 : First invariant of the tensors (trace)\n        I3 : Third invariant of the tensors (det)\n        \"\"\"\n        a = self.I1 ** 2\n        b = np.matmul(self.matrix, self._transposeTensor()).trace(axis1=-1, axis2=-2)\n        return 0.5 * (a - b)\n\n    @property\n    def I3(self):\n        \"\"\"\n        Third invariant of the tensor (determinant)\n\n        Returns\n        -------\n        np.array or float\n            Third invariant(s) of the tensor(s)\n\n        See Also\n        --------\n        I1 : First invariant of the tensors (trace)\n        I2 : Second invariant of the tensors\n        \"\"\"\n        return np.linalg.det(self.matrix)\n\n    @property\n    def J1(self):\n        \"\"\"\n        First invariant of the deviatoric part of the stress tensor. It is always zeros, as the deviatoric part as null\n        trace.\n\n        Returns\n        -------\n        float or np.ndarray\n            zero(s)\n        \"\"\"\n        if self.shape:\n            return np.zeros(self.shape)\n        else:\n            return 0.0\n\n    @property\n    def J2(self):\n        \"\"\"\n        Second invariant of the deviatoric part of the tensor.\n\n        Returns\n        -------\n        float or np.ndarray\n            J2 invariant\n        \"\"\"\n        return -self.deviatoric_part().I2\n\n    @property\n    def J3(self):\n        \"\"\"\n        Third invariant of the deviatoric part of the tensor.\n\n        Returns\n        -------\n        float or np.ndarray\n            J3 invariant\n        \"\"\"\n        return self.deviatoric_part().I3\n\n    def trace(self):\n        \"\"\"\n        Return the traces of the tensor array\n\n        Returns\n        -------\n        np.ndarray or float\n            traces of each tensor of the tensor array\n\n        See Also\n        --------\n        I1 : First invariant of the tensors (trace)\n        I2 : Second invariant of the tensors\n        I3 : Third invariant of the tensors (det)\n        \"\"\"\n        return self.I1\n\n    def __mul__(self, B):\n        \"\"\"\n        Element-wise matrix multiplication of arrays of tensors. Each tensor of the resulting tensor array is computed\n        as the matrix product of the tensor components.\n\n        Parameters\n        ----------\n        B : SecondOrderTensor or np.ndarray or Rotation or float\n            If B is a numpy array, we must have::\n\n                B.shape == (..., 3, 3)\n\n        Returns\n        -------\n            Array of tensors populated with element-wise matrix multiplication.\n\n        See Also\n        --------\n        matmul : matrix-like multiplication of tensor arrays\n        \"\"\"\n        if isinstance(B, SecondOrderTensor):\n            new_mat = np.matmul(self.matrix, B.matrix)\n            return SecondOrderTensor(new_mat)\n        elif isinstance(B, Rotation) or is_orix_rotation(B):\n            rotation_matrices, transpose_matrices = rotation_to_matrix(B, return_transpose=True)\n            new_matrix = np.matmul(np.matmul(transpose_matrices, self.matrix), rotation_matrices)\n            return self.__class__(new_matrix)\n        elif isinstance(B, (float, int)):\n            return self.__class__(self.matrix * B)\n        elif isinstance(B, np.ndarray):\n            if B.shape == self.shape:\n                new_matrix = np.einsum('...ij,...->...ij', self.matrix, B)\n                return self.__class__(new_matrix)\n            elif B.shape == self.matrix.shape:\n                return self.__class__(np.matmul(self.matrix, B))\n            else:\n                err_msg = 'For a tensor of shape {}, the input argument must be an array of shape {} or {}'.format(self.shape, self.shape, self.shape + (3, 3))\n                raise ValueError(err_msg)\n        else:\n            raise ValueError('The input argument must be a tensor, an ndarray, a rotation or a scalar value.')\n\n    def __rmul__(self, other):\n        if isinstance(other, (float, int)):\n            return self.__mul__(other)\n        else:\n            raise NotImplementedError('Left multiplication is only implemented for scalar values.')\n\n    def __truediv__(self, other):\n        new_mat = np.zeros(self.matrix.shape)\n        non_zero = np.any(self.matrix, axis=(-1, -2))\n        if isinstance(other, (float, int)):\n            new_mat[non_zero] = self.matrix[non_zero] / other\n        elif isinstance(other, np.ndarray) and self.shape == other.shape:\n            new_mat[non_zero] = np.einsum('pij,p->pij', self.matrix[non_zero], 1 / other[non_zero])\n            return self.__class__(new_mat)\n        else:\n            raise NotImplementedError('Tensors can only be divided by scalar values or by arrays of the same shape.')\n        return self.__class__(new_mat)\n\n    def __eq__(self, other) -> np.ndarray:\n        \"\"\"\n        Check whether the tensors in the tensor array are equal\n\n        Parameters\n        ----------\n        other : SecondOrderTensor or np.ndarray\n            Tensor to compare with\n\n        Returns\n        -------\n        np.array of bool\n            True element is True if the corresponding tensors are equal.\n        \"\"\"\n        if isinstance(other, SecondOrderTensor):\n            return self == other.matrix\n        elif isinstance(other, np.ndarray):\n            if other.shape == (3, 3) or other.shape == self.shape + (3, 3):\n                return np.all(self.matrix == other, axis=(-2, -1))\n            else:\n                raise ValueError('The value to compare must be an array of shape {} or {}'.format(self.shape, self.shape + (3, 3)))\n\n    def matmul(self, other):\n        \"\"\"\n        Perform matrix-like product between tensor arrays. Each \"product\" is a matrix product between\n        the tensor components.\n\n        If A.shape=(a1, a2, ..., an) and B.shape=(b1, b2, ..., bn), with C=A.matmul(B), we have::\n\n            C.shape = (a1, a2, ..., an, b1, b2, ..., bn)\n\n        and::\n\n            C[i,j,k,...,p,q,r...] = np.matmul(A[i,j,k,...], B[p,q,r,...])\n\n        Parameters\n        ----------\n        other : SecondOrderTensor or np.ndarray or Rotation\n            Tensor array or rotation to right-multiply by. If Rotation is provided, the rotations are applied on each\n            tensor.\n\n        Returns\n        -------\n        SecondOrderTensor\n            Tensor array\n\n        See Also\n        --------\n        __mul__ : Element-wise matrix product\n        \"\"\"\n        if isinstance(other, SecondOrderTensor):\n            other_matrix = other.matrix\n        elif isinstance(other, Rotation) or is_orix_rotation(Rotation):\n            other_matrix = rotation_to_matrix(other)\n        else:\n            other_matrix = other\n        matrix = self.matrix\n        shape_matrix = matrix.shape[:-2]\n        shape_other = other_matrix.shape[:-2]\n        extra_dim_matrix = len(shape_other)\n        extra_dim_other = len(shape_matrix)\n        matrix_expanded = matrix.reshape(shape_matrix + (1,) * extra_dim_other + (3, 3))\n        other_expanded = other_matrix.reshape((1,) * extra_dim_matrix + shape_other + (3, 3))\n        if isinstance(other, Rotation):\n            other_expanded_t = _transpose_matrix(other_expanded)\n            new_mat = np.matmul(np.matmul(other_expanded_t, matrix_expanded), other_expanded)\n            return self.__class__(np.squeeze(new_mat))\n        else:\n            new_mat = np.matmul(matrix_expanded, other_expanded)\n            return SecondOrderTensor(np.squeeze(new_mat))\n\n    def transposeArray(self):\n        \"\"\"\n        Transpose the array of tensors\n\n        If A is a tensor array of shape [s1, s2, ..., sn], A.T is of shape [sn, ..., s2, s1].\n\n        Returns\n        -------\n        SecondOrderTensor\n            Transposed array\n\n        See Also\n        --------\n        T : transpose the tensor array (not the components)\n        \"\"\"\n        if self.ndim < 2:\n            return self\n        else:\n            matrix = self.matrix\n            ndim = matrix.ndim\n            new_axes = np.hstack((ndim - 3 - np.arange(ndim - 2), -2, -1))\n            transposed_arr = np.transpose(matrix, new_axes)\n            return self.__class__(transposed_arr)\n\n    @property\n    def T(self):\n        \"\"\"\n        Transpose the array of tensors.\n\n        It is actually an alias for transposeArray()\n\n        Returns\n        -------\n        SecondOrderTensor\n            Transposed array\n        \"\"\"\n        return self.transposeArray()\n\n    def _transposeTensor(self):\n        return _transpose_matrix(self.matrix)\n\n    def transposeTensor(self):\n        \"\"\"\n        Transpose of tensors of the tensor array\n\n        Returns\n        -------\n        SecondOrderTensor\n            Array of transposed tensors of the tensor array\n\n        See Also\n        --------\n        Transpose : transpose the array (not the components)\n        \"\"\"\n        return self.__class__(self._transposeTensor())\n\n    def ddot(self, other):\n        \"\"\"\n        Double dot product (contraction of tensor product, usually denoted \":\") of two tensors.\n\n        For two tensors whose matrices are M1 and M2::\n\n            M1.ddot(M2) == np.trace(np.matmul(M1, M2))\n\n        Parameters\n        ----------\n        other : SecondOrderTensor or np.ndarray\n            Tensor or tensor array to multiply by before contraction.\n\n        Returns\n        -------\n        float or np.ndarray\n            Result of double dot product\n\n        See Also\n        --------\n        matmul : matrix-like product between two tensor arrays.\n\n        \"\"\"\n        tensor_prod = self.transposeTensor() * other\n        return tensor_prod.trace()\n\n    def _flatten(self):\n        if self.shape:\n            new_len = np.prod(self.shape)\n            return np.reshape(self.matrix, (new_len, 3, 3))\n        else:\n            return self.matrix\n\n    def _stats(self, fun, axis=None):\n        if axis is None:\n            flat_mat = self._flatten()\n            new_matrix = fun(flat_mat, axis=0)\n        else:\n            if axis < 0:\n                axis += -2\n            if axis > self.ndim - 1 or axis < -self.ndim - 2:\n                raise ValueError('The axis index is out of bounds for tensor array of shape {}'.format(self.shape))\n            new_matrix = fun(self.matrix, axis=axis)\n        return self.__class__(new_matrix)\n\n    def flatten(self):\n        \"\"\"\n        Flatten the array of tensors.\n\n        If T is of shape [s1, s2, ..., sn], the shape for T.flatten() is [s1*s2*...*sn].\n\n        Returns\n        -------\n        SecondOrderTensor\n            Flattened array (vector) of tensor\n\n        See Also\n        --------\n        ndim : number of dimensions of the tensor array\n        shape : shape of the tensor array\n        reshape : reshape a tensor array\n        \"\"\"\n        return self.__class__(self._flatten())\n\n    def reshape(self, shape, **kwargs):\n        \"\"\"\n        Reshape the array of tensors\n\n        Parameters\n        ----------\n        shape : tuple\n            New shape of the array\n        kwargs : dict\n            Keyword arguments passed to numpy.reshape()\n\n        Returns\n        -------\n        SecondOrderTensor\n            Reshaped array\n\n        See Also\n        --------\n        flatten : flatten an array to 1D\n        \"\"\"\n        new_matrix = self.matrix.reshape(shape + (3, 3), **kwargs)\n        return self.__class__(new_matrix)\n\n    def mean(self, axis=None):\n        \"\"\"\n        Arithmetic mean value\n\n        Parameters\n        ----------\n        axis : int or None, default None\n            Axis to compute the mean along with.\n            If None, returns the overall mean (mean of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n            Mean tensor\n\n        See Also\n        --------\n        std : Standard deviation\n        min : Minimum value\n        max : Maximum value\n        \"\"\"\n        if self.ndim:\n            return self._stats(np.mean, axis=axis)\n        else:\n            return self\n\n    def std(self, axis=None):\n        \"\"\"\n        Standard deviation\n\n        Parameters\n        ----------\n        axis : int or None, default None\n            Axis to compute standard deviation along with.\n            If None, returns the overall standard deviation (std of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n            Tensor of standard deviation\n\n        See Also\n        --------\n        mean : Mean value\n        min : Minimum value\n        max : Maximum value\n          \"\"\"\n        if self.ndim:\n            return self._stats(np.std, axis=axis)\n        else:\n            return self.__class__(np.zeros((3, 3)))\n\n    def min(self, axis=None):\n        \"\"\"\n        Minimum value\n\n        Parameters\n        ----------\n        axis : int or None, default None\n           Axis to compute minimum along with.\n           If None, returns the overall minimum (min of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n           Minimum value of tensors\n\n        See Also\n        --------\n        max : Maximum value\n        mean : Mean value\n        std : Standard deviation\n        \"\"\"\n        if self.ndim:\n            return self._stats(np.min, axis=axis)\n        else:\n            return self\n\n    def max(self, axis=None):\n        \"\"\"\n        Maximum value\n\n        Parameters\n        ----------\n        axis : int or None, default None\n            Axis to compute maximum along with.\n            If None, returns the overall maximum (max of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n            Maximum value of tensors\n\n        See Also\n        --------\n        min : Minimum value\n        mean : Mean value\n        std : Standard deviation\n        \"\"\"\n        if self.ndim:\n            return self._stats(np.max, axis=axis)\n        else:\n            return self\n\n    def _symmetric_part(self):\n        return 0.5 * (self.matrix + self._transposeTensor())\n\n    def symmetric_part(self):\n        \"\"\"\n        Symmetric part of the tensor\n\n        Returns\n        -------\n        SymmetricSecondOrderTensor\n            Symmetric tensor\n\n        See Also\n        --------\n        skewPart : Skew-symmetric part of the tensor\n        \"\"\"\n        return SymmetricSecondOrderTensor(self._symmetric_part())\n\n    def skew_part(self):\n        \"\"\"\n        Skew-symmetric part of the tensor\n\n        Returns\n        -------\n        SkewSymmetricSecondOrderTensor\n            Skew-symmetric tensor\n        \"\"\"\n        new_mat = 0.5 * (self.matrix - self._transposeTensor())\n        return SkewSymmetricSecondOrderTensor(new_mat)\n\n    def spherical_part(self):\n        \"\"\"\n        Spherical (hydrostatic) part of the tensor\n\n        Returns\n        -------\n        self\n            Spherical part\n\n        See Also\n        --------\n        I1 : compute the first invariant of the tensor\n        deviatoricPart : deviatoric the part of the tensor\n        \"\"\"\n        s = self.I1 / 3\n        return self.eye(self.shape) * s\n\n    def deviatoric_part(self):\n        \"\"\"\n        Deviatoric part of the tensor\n\n        Returns\n        -------\n        self\n\n        See Also\n        --------\n        sphericalPart : spherical part of the tensor\n        \"\"\"\n        return self - self.spherical_part()\n\n    @classmethod\n    def eye(cls, shape=()):\n        \"\"\"\n        Create an array of tensors populated with identity matrices\n\n        Parameters\n        ----------\n        shape : tuple or int, default ()\n            If not provided, it just creates a single identity tensor. Otherwise, the tensor array will be of the\n            specified shape.\n\n        Returns\n        -------\n        cls\n            Array of identity tensors\n\n        See Also\n        --------\n        ones : creates an array of tensors full of ones\n        zeros : creates an array full of zero tensors\n        \"\"\"\n        if isinstance(shape, int):\n            matrix_shape = (shape, 3, 3)\n        else:\n            matrix_shape = shape + (3, 3)\n        eye = np.zeros(matrix_shape)\n        eye[..., np.arange(3), np.arange(3)] = 1\n        return cls(eye)\n\n    @classmethod\n    def ones(cls, shape=()):\n        \"\"\"\n        Create an array of tensors populated with matrices of full of ones.\n\n        Parameters\n        ----------\n        shape : tuple or int, default ()\n            If not provided, it just creates a single tensor of ones. Otherwise, the tensor array will be of the\n            specified shape.\n\n        Returns\n        -------\n        cls\n            Array of ones tensors\n\n        See Also\n        --------\n        eye : creates an array of identity tensors\n        zeros : creates an array full of zero tensors\n        \"\"\"\n        if isinstance(shape, int):\n            matrix_shape = (shape, 3, 3)\n        else:\n            matrix_shape = shape + (3, 3)\n        ones = np.ones(matrix_shape)\n        return cls(ones)\n\n    @classmethod\n    def zeros(cls, shape=()):\n        \"\"\"\n        Create an array of tensors populated with matrices full of zeros.\n\n        Parameters\n        ----------\n        shape : tuple or int, default ()\n            If not provided, it just creates a single tensor of ones. Otherwise, the tensor array will be of the\n            specified shape.\n\n        Returns\n        -------\n        cls\n            Array of ones tensors\n\n        See Also\n        --------\n        eye : creates an array of identity tensors\n        ones : creates an array of tensors full of ones\n        \"\"\"\n        if isinstance(shape, int):\n            matrix_shape = (shape, 3, 3)\n        else:\n            matrix_shape = shape + (3, 3)\n        zeros = np.zeros(matrix_shape)\n        return cls(zeros)\n\n    @classmethod\n    def tensile(cls, u, magnitude):\n        \"\"\"\n        Create an array of tensors corresponding to tensile state along a given direction.\n\n        Parameters\n        ----------\n        u : np.ndarray or list\n            Tensile direction. Must be a 3D vector.\n        magnitude : float or np.ndarray or list\n            Magnitude of the tensile state to consider. If a list or an array is provided, the shape of the tensor array\n            will be of the same shape as magnitude.\n        Returns\n        -------\n        cls\n            tensor or tensor array\n        \"\"\"\n        mat = _tensor_from_direction_magnitude(u, u, magnitude)\n        return cls(mat)\n\n    @classmethod\n    def rand(cls, shape=None, seed=None):\n        \"\"\"\n        Generate a tensor array, populated with random uniform values in [0,1).\n\n        Parameters\n        ----------\n        shape : tuple, optional\n            Shape of the tensor array. If not provided, a single tensor is returned\n        seed : int, optional\n            Sets the seed for random generation. Useful to ensure reproducibility\n\n        Returns\n        -------\n        cls\n            Tensor or tensor array of uniform random value\n\n        See Also\n        --------\n        randn : Generate a random sample of tensors whose components follows a normal distribution\n\n        Examples\n        --------\n        Generate a single random tensor:\n\n        >>> from Elasticipy.SecondOrderTensor import SecondOrderTensor as tensor\n        >>> tensor.rand(seed=123)\n        Second-order tensor\n        [[0.68235186 0.05382102 0.22035987]\n         [0.18437181 0.1759059  0.81209451]\n         [0.923345   0.2765744  0.81975456]]\n\n        Now try with tensor array:\n        >>> t = tensor.rand(shape=(100,50))\n        >>> t.shape\n        (100,50)\n        \"\"\"\n        if shape is None:\n            shape = (3, 3)\n        else:\n            shape = shape + (3, 3)\n        rng = np.random.default_rng(seed)\n        a = rng.random(shape)\n        if issubclass(cls, SymmetricSecondOrderTensor):\n            a = _symmetric_part(a)\n        return cls(a)\n\n    def inv(self):\n        \"\"\"Compute the reciprocal (inverse) tensor\"\"\"\n        return SecondOrderTensor(np.linalg.inv(self.matrix))\n\n    @classmethod\n    def randn(cls, mean=np.zeros((3, 3)), std=np.ones((3, 3)), shape=None, seed=None):\n        \"\"\"\n        Generate a tensor array, populated with components follow a normal distribution.\n\n        Parameters\n        ----------\n        mean : list of numpy.ndarray, optional\n            (3,3) matrix providing the mean values of the components.\n        std : list of numpy.ndarray, optional\n            (3,3) matrix providing the standard deviations of the components.\n        shape : tuple, optional\n            Shape of the tensor array\n        seed : int, optional\n            Sets the seed for random generation. Useful to ensure reproducibility\n\n        Returns\n        -------\n        cls\n            Tensor or tensor array of normal random value\n        \"\"\"\n        if shape is None:\n            new_shape = (3, 3)\n        else:\n            new_shape = shape + (3, 3)\n        rng = np.random.default_rng(seed)\n        mat = np.zeros(new_shape)\n        mean = np.asarray(mean)\n        std = np.asarray(std)\n        for i in range(0, 3):\n            for j in range(0, 3):\n                mat[..., i, j] = rng.normal(mean[i, j], std[i, j], shape)\n        if issubclass(cls, SymmetricSecondOrderTensor):\n            mat = _symmetric_part(mat)\n        return cls(mat)\n\n    @classmethod\n    def shear(cls, u, v, magnitude):\n        \"\"\"\n        Create an array of tensors corresponding to shear state along two orthogonal directions.\n\n        Parameters\n        ----------\n        u : np.ndarray or list\n            First direction. Must be a 3D vector.\n        v : np.ndarray or list\n            Second direction. Must be a 3D vector.\n        magnitude : float or np.ndarray or list\n            Magnitude of the shear state to consider. If a list or an array is provided, the shape of the tensor array\n            will be of the same shape as magnitude.\n        Returns\n        -------\n        cls\n            tensor or tensor array\n        \"\"\"\n        if np.abs(np.dot(u, v)) > 1e-05:\n            raise ValueError('u and v must be orthogonal')\n        mat = _tensor_from_direction_magnitude(u, v, magnitude)\n        return cls(mat)\n\n    def div(self, axes=None, spacing=1.0):\n        \"\"\"\n        Compute the divergence vector of the tensor array, along given axes.\n\n        If the tensor has n dimensions, the divergence vector will be computed along its m first axes, with\n        m = min(n, 3), except if specified in the ``axes`` parameter (see below).\n\n        Parameters\n        ----------\n        axes : list of int, tuple of int, int or None, default None\n            Indices of axes along which to compute the divergence vector. If None (default), the m first axes of the\n            array will be used to compute the derivatives.\n        spacing : float or np.ndarray or list, default 1.\n            Spacing between samples the in each direction. If a scalar value is provided, the spacing is assumed equal\n            in each direction. If an array or a list is provided, spacing[i] must return the spacing along the i-th\n            axis (spacing[i] can be float or np.ndarray).\n\n        Returns\n        -------\n        np.ndarray\n            Divergence vector of the tensor array. If the tensor array is of shape (m,n,...,q), the divergence vector\n            will be of shape (m,n,...,q,3).\n\n        Notes\n        -----\n        The divergence of a tensor field :math:`\\\\mathbf{t}(\\\\mathbf{x})` is defined as:\n\n        .. math::\n\n            [\\\\nabla\\\\cdot\\\\mathbf{t}]_i = \\\\frac{\\\\partial t_{ij}}{\\\\partial x_j}\n\n        The main application of this operator is for balance of linear momentum of stress tensor:\n\n        .. math::\n\n            \\\\rho \\\\mathbf{\\\\gamma} = \\\\nabla\\\\cdot\\\\mathbf{\\\\sigma} + \\\\rho\\\\mathbf{b}\n\n        where :math:`\\\\mathbf{\\\\sigma}` is the stress tensor, :math:`\\\\mathbf{\\\\gamma}` is the acceleration,\n        :math:`\\\\mathbf{b}` is the body force density and :math:`\\\\rho` is the mass density.\n\n        In this function, the derivatives are computed with ``numpy.grad`` function.\n        \"\"\"\n        ndim = min(self.ndim, 3)\n        if isinstance(spacing, (float, int)):\n            spacing = [spacing, spacing, spacing]\n        if axes is None:\n            axes = range(ndim)\n        elif isinstance(axes, int):\n            axes = (axes,)\n        elif not isinstance(axes, (tuple, list)):\n            raise TypeError('axes must be int, tuple of int, or list of int.')\n        if len(axes) > ndim:\n            error_msg = 'The number of axes must be less or equal to the number of dimensions ({}), and cannot exceed 3'.format(self.ndim)\n            raise ValueError(error_msg)\n        else:\n            ndim = len(axes)\n        if max(axes) >= ndim:\n            raise IndexError('axes index must be in range of dimensions ({})'.format(self.ndim))\n        div = np.zeros(self.shape + (3,))\n        for dim in range(0, ndim):\n            div += np.gradient(self.C[:, dim], spacing[dim], axis=axes[dim])\n        return div\n\n    def save(self, file, **kwargs):\n        \"\"\"\n        Save the tensor array as binary file (.npy format).\n\n        This function uses numpy.save function.\n\n        Parameters\n        ----------\n        file : file, str or pathlib.Path\n            File or filename to which the tensor is saved.\n        kwargs : dict\n            Keyword arguments passed to numpy.save()\n\n        See Also\n        --------\n        load_from_npy : load a tensor array from a numpy file\n        \"\"\"\n        np.save(file, self.matrix, **kwargs)\n\n    @classmethod\n    def load_from_npy(cls, file, **kwargs):\n        \"\"\"\n        Load a tensor array for .npy file.\n\n        This function uses numpy.load()\n\n        Parameters\n        ----------\n        file : file, str or pathlib.Path\n            File to read to create the array\n        kwargs : dict\n            Keyword arguments passed to numpy.load()\n\n        Returns\n        -------\n        SecondOrderTensor\n            Tensor array\n\n        See Also\n        --------\n        save : save the tensor array as a numpy file\n        \"\"\"\n        matrix = np.load(file, **kwargs)\n        if matrix.shape[-2:] != (3, 3):\n            raise ValueError('The shape of the array to load must be (...,3,3).')\n        else:\n            return cls(matrix)\n\n    def save_as_txt(self, file, name_prefix='', **kwargs):\n        \"\"\"\n        Save the tensor array to human-readable text file.\n\n        The array must be 1D. The i-th row of the file will provide the components of the i-th tensor in of the array.\n        This function uses pandas.DataFrame.to_csv().\n\n        Parameters\n        ----------\n        file : file or str\n            File to dump tensor components to.\n        name_prefix : str, optional\n            Prefix to add for naming the columns. For instance, name_prefix='E' will result in columns named E11, E12,\n            E13 etc.\n        kwargs : dict\n            Keyword arguments passed to pandas.DataFrame.to_csv()\n        \"\"\"\n        if self.ndim > 1:\n            raise ValueError('The array must be flatten before getting dumped to text file.')\n        else:\n            d = dict()\n            for i in range(3):\n                if isinstance(self, SkewSymmetricSecondOrderTensor):\n                    r = range(i + 1, 3)\n                elif isinstance(self, SymmetricSecondOrderTensor):\n                    r = range(i, 3)\n                else:\n                    r = range(3)\n                for j in r:\n                    key = name_prefix + '{}{}'.format(i + 1, j + 1)\n                    d[key] = self.C[i, j]\n            df = pd.DataFrame(d)\n            df.to_csv(file, index=False, **kwargs)\n\n    def to_pymatgen(self):\n        \"\"\"\n        Convert the second order object into an object compatible with pymatgen.\n\n        The object to use must be either a single tensor, or a flat tensor array. In the latter case, the output will be\n        a list of pymatgen's tensors.\n\n        Returns\n        -------\n        pymatgen.analysis.elasticity.Strain, pymatgen.analysis.elasticity.Stress, pymatgen.core.tensors.Tensor or list\n            The type of output depends on the type of object to use:\n                - if the object is of class StrainTensor, the output will be of class pymatgen.analysis.elasticity.Strain\n                - if the object is of class StressTensor, the output will be of class pymatgen.analysis.elasticity.Stress\n                - otherwise, the output will be of class pymatgen.core.tensors.Tensor\n\n        See Also\n        --------\n        flatten : Converts a tensor array to 1D tensor array\n        \"\"\"\n        try:\n            from Elasticipy.StressStrainTensors import StrainTensor, StressTensor\n            if isinstance(self, StrainTensor):\n                from pymatgen.analysis.elasticity import Strain as Constructor\n            elif isinstance(self, StressTensor):\n                from pymatgen.analysis.elasticity import Stress as Constructor\n            else:\n                from pymatgen.core.tensors import Tensor as Constructor\n        except ImportError:\n            raise ModuleNotFoundError('Module pymatgen is required for this function.')\n        if self.ndim > 1:\n            raise ValueError('The array must be flattened (1D tensor array) before converting to pytmatgen.')\n        if self.shape:\n            return [Constructor(self[i].matrix) for i in range(self.shape[0])]\n        else:\n            return Constructor(self.matrix)\n\n    @classmethod\n    def load_from_txt(cls, file, name_prefix='', **kwargs):\n        \"\"\"\n        Load a tensor array from text file.\n\n        Parameters\n        ----------\n        file : str or file\n            Textfile to read the components from.\n        name_prefix : str, optional\n            Prefix to add to each column when parsing the file. For instance, with name_prefix='E', the function will\n            look for columns names E11, E12, E13 etc.\n\n        Returns\n        -------\n        SecondOrderTensor\n            Flat (1D) tensor constructed from the values given in the text file\n        \"\"\"\n        df = pd.read_csv(file, **kwargs)\n        matrix = np.zeros((len(df), 3, 3))\n        for i in range(3):\n            if cls is SkewSymmetricSecondOrderTensor:\n                r = range(i + 1, 3)\n            elif cls is SymmetricSecondOrderTensor:\n                r = range(i, 3)\n            else:\n                r = range(3)\n            for j in r:\n                key = name_prefix + '{}{}'.format(i + 1, j + 1)\n                matrix[:, i, j] = df[key]\n        return cls(matrix)\n```\n",
        "eval_script": "# Import necessary libraries\nimport numpy as np\nimport pandas as pd\n\n# src/Elasticipy/SecondOrderTensor.py\n\nclass SecondOrderTensor:\n    \"\"\"\n    Template class for manipulation of second order tensors or arrays of second order tensors\n\n    Attributes\n    ----------\n    matrix : np.ndarray\n        (...,3,3) matrix storing all the components of the tensor\n\n    \"\"\"\n    name = 'Second-order tensor'\n    'Name to use when printing the tensor'\n\n    def __init__(self, matrix):\n        \"\"\"\n        Create an array of second-order tensors.\n\n        The input argument can be:\n            - an array of shape (3,3) defining all the components of the tensor;\n            - a stack of matrices, that is an array of shape (...,3,3).\n\n        Parameters\n        ----------\n        matrix : list or np.ndarray\n            (3,3) matrix, stack of (3,3) matrices\n        \"\"\"\n        matrix = np.array(matrix)\n        shape = matrix.shape\n        if len(shape) > 1 and shape[-2:] == (3, 3):\n            self.matrix = matrix\n        else:\n            raise ValueError('The input matrix must be of shape (3,3) or (...,3,3)')\n\n    def __repr__(self):\n        s = self.name + '\\n'\n        if self.shape:\n            s += 'Shape={}'.format(self.shape)\n        else:\n            s += self.matrix.__str__()\n        return s\n\n    def __getitem__(self, index):\n        return self.__class__(self.matrix[index])\n\n    def __setitem__(self, index, value):\n        if isinstance(value, (float, np.ndarray)):\n            self.matrix[index] = value\n        elif type(value) == self.__class__:\n            self.matrix[index] = value.matrix\n        else:\n            raise NotImplementedError('The r.h.s must be either float, a ndarray or an object of class {}'.format(self.__class__))\n\n    def __add__(self, other):\n        if type(self) == type(other):\n            return self.__class__(self.matrix + other.matrix)\n        elif isinstance(other, (int, float, np.ndarray)):\n            mat = self.matrix + other\n            if isinstance(self, SkewSymmetricSecondOrderTensor):\n                return SecondOrderTensor(mat)\n            else:\n                return self.__class__(mat)\n        elif isinstance(other, SecondOrderTensor):\n            return SecondOrderTensor(self.matrix + other.matrix)\n        else:\n            raise NotImplementedError('The element to add must be a number, a numpy.ndarray or a tensor.')\n\n    def __radd__(self, other):\n        return self + other\n\n    def __sub__(self, other):\n        if type(self) == type(other):\n            return self.__class__(self.matrix - other.matrix)\n        elif isinstance(other, (int, float, np.ndarray)):\n            return self.__class__(self.matrix - other)\n        else:\n            raise NotImplementedError('The element to subtract must be a number, a numpy ndarray or a tensor.')\n\n    def __neg__(self):\n        return self.__class__(-self.matrix)\n\n    def __rsub__(self, other):\n        return -self + other\n\n    @property\n    def shape(self):\n        \"\"\"\n        Return the shape of the tensor array\n\n        Returns\n        -------\n        tuple\n            Shape of array\n\n        See Also\n        --------\n        ndim : number of dimensions\n        \"\"\"\n        *shape, _, _ = self.matrix.shape\n        return tuple(shape)\n\n    @property\n    def ndim(self):\n        \"\"\"\n        Return the number of dimensions of the tensor array\n\n        Returns\n        -------\n        int\n            number of dimensions\n\n        See Also\n        --------\n        shape : shape of tensor array\n        \"\"\"\n        return len(self.shape)\n\n    @property\n    def C(self):\n        \"\"\"\n        Return tensor components\n\n        For instance T.C[i,j] returns all the (i,j)-th components of each tensor in the array.\n\n        Returns\n        -------\n        np.ndarray\n            Tensor components\n        \"\"\"\n        return _MatrixProxy(self.matrix)\n\n    def eig(self):\n        \"\"\"\n        Compute the eigenvalues and eigenvectors of the tensor\n\n        Returns\n        -------\n        lambda : np.ndarray\n            Eigenvalues of each tensor.\n        v : np.ndarray\n            Eigenvectors of teach tensor.\n\n        See Also\n        --------\n        eigvals : return only the eigenvalues (without directions)\n        principal_directions : return only the principal directions (without eigenvalues)\n        \"\"\"\n        return np.linalg.eig(self.matrix)\n\n    def eigvals(self):\n        \"\"\"\n        Compute the eigenvalues of the tensor, without computing the associated eigenvectors\n\n        Returns\n        -------\n        numpy.ndarray\n            Eigenvalues\n\n        See Also\n        --------\n        eig : compute the eigenvalues and the eigenvector\n        \"\"\"\n        return np.linalg.eigvals(self.matrix)\n\n    def principal_directions(self):\n        \"\"\"\n        Principal directions of the tensors\n\n        Returns\n        -------\n        np.ndarray\n            Principal directions of each tensor of the tensor array\n\n        See Also\n        --------\n        eig : Return both eigenvalues and corresponding principal directions\n        \"\"\"\n        return self.eig()[1]\n\n    @property\n    def I1(self):\n        \"\"\"\n        First invariant of the tensor (trace)\n\n        Returns\n        -------\n        np.ndarray or float\n            First invariant(s) of the tensor(s)\n\n        See Also\n        --------\n        I2 : Second invariant of the tensors\n        I3 : Third invariant of the tensors (det)\n        \"\"\"\n        return self.matrix.trace(axis1=-1, axis2=-2)\n\n    @property\n    def I2(self):\n        \"\"\"\n        Second invariant of the tensor\n\n        For a matrix M, it is defined as::\n\n            I_2 = 0.5 * ( np.trace(M)**2 + np.trace(np.matmul(M, M.T)) )\n\n        Returns\n        -------\n        np.array or float\n            Second invariant(s) of the tensor(s)\n\n        See Also\n        --------\n        I1 : First invariant of the tensors (trace)\n        I3 : Third invariant of the tensors (det)\n        \"\"\"\n        a = self.I1 ** 2\n        b = np.matmul(self.matrix, self._transposeTensor()).trace(axis1=-1, axis2=-2)\n        return 0.5 * (a - b)\n\n    @property\n    def I3(self):\n        \"\"\"\n        Third invariant of the tensor (determinant)\n\n        Returns\n        -------\n        np.array or float\n            Third invariant(s) of the tensor(s)\n\n        See Also\n        --------\n        I1 : First invariant of the tensors (trace)\n        I2 : Second invariant of the tensors\n        \"\"\"\n        return np.linalg.det(self.matrix)\n\n    @property\n    def J1(self):\n        \"\"\"\n        First invariant of the deviatoric part of the stress tensor. It is always zeros, as the deviatoric part as null\n        trace.\n\n        Returns\n        -------\n        float or np.ndarray\n            zero(s)\n        \"\"\"\n        if self.shape:\n            return np.zeros(self.shape)\n        else:\n            return 0.0\n\n    @property\n    def J2(self):\n        \"\"\"\n        Second invariant of the deviatoric part of the tensor.\n\n        Returns\n        -------\n        float or np.ndarray\n            J2 invariant\n        \"\"\"\n        return -self.deviatoric_part().I2\n\n    @property\n    def J3(self):\n        \"\"\"\n        Third invariant of the deviatoric part of the tensor.\n\n        Returns\n        -------\n        float or np.ndarray\n            J3 invariant\n        \"\"\"\n        return self.deviatoric_part().I3\n\n    def trace(self):\n        \"\"\"\n        Return the traces of the tensor array\n\n        Returns\n        -------\n        np.ndarray or float\n            traces of each tensor of the tensor array\n\n        See Also\n        --------\n        I1 : First invariant of the tensors (trace)\n        I2 : Second invariant of the tensors\n        I3 : Third invariant of the tensors (det)\n        \"\"\"\n        return self.I1\n\n    def __mul__(self, B):\n        \"\"\"\n        Element-wise matrix multiplication of arrays of tensors. Each tensor of the resulting tensor array is computed\n        as the matrix product of the tensor components.\n\n        Parameters\n        ----------\n        B : SecondOrderTensor or np.ndarray or Rotation or float\n            If B is a numpy array, we must have::\n\n                B.shape == (..., 3, 3)\n\n        Returns\n        -------\n            Array of tensors populated with element-wise matrix multiplication.\n\n        See Also\n        --------\n        matmul : matrix-like multiplication of tensor arrays\n        \"\"\"\n        if isinstance(B, SecondOrderTensor):\n            new_mat = np.matmul(self.matrix, B.matrix)\n            return SecondOrderTensor(new_mat)\n        elif isinstance(B, Rotation) or is_orix_rotation(B):\n            rotation_matrices, transpose_matrices = rotation_to_matrix(B, return_transpose=True)\n            new_matrix = np.matmul(np.matmul(transpose_matrices, self.matrix), rotation_matrices)\n            return self.__class__(new_matrix)\n        elif isinstance(B, (float, int)):\n            return self.__class__(self.matrix * B)\n        elif isinstance(B, np.ndarray):\n            if B.shape == self.shape:\n                new_matrix = np.einsum('...ij,...->...ij', self.matrix, B)\n                return self.__class__(new_matrix)\n            elif B.shape == self.matrix.shape:\n                return self.__class__(np.matmul(self.matrix, B))\n            else:\n                err_msg = 'For a tensor of shape {}, the input argument must be an array of shape {} or {}'.format(self.shape, self.shape, self.shape + (3, 3))\n                raise ValueError(err_msg)\n        else:\n            raise ValueError('The input argument must be a tensor, an ndarray, a rotation or a scalar value.')\n\n    def __rmul__(self, other):\n        if isinstance(other, (float, int)):\n            return self.__mul__(other)\n        else:\n            raise NotImplementedError('Left multiplication is only implemented for scalar values.')\n\n    def __truediv__(self, other):\n        new_mat = np.zeros(self.matrix.shape)\n        non_zero = np.any(self.matrix, axis=(-1, -2))\n        if isinstance(other, (float, int)):\n            new_mat[non_zero] = self.matrix[non_zero] / other\n        elif isinstance(other, np.ndarray) and self.shape == other.shape:\n            new_mat[non_zero] = np.einsum('pij,p->pij', self.matrix[non_zero], 1 / other[non_zero])\n            return self.__class__(new_mat)\n        else:\n            raise NotImplementedError('Tensors can only be divided by scalar values or by arrays of the same shape.')\n        return self.__class__(new_mat)\n\n    def __eq__(self, other) -> np.ndarray:\n        \"\"\"\n        Check whether the tensors in the tensor array are equal\n\n        Parameters\n        ----------\n        other : SecondOrderTensor or np.ndarray\n            Tensor to compare with\n\n        Returns\n        -------\n        np.array of bool\n            True element is True if the corresponding tensors are equal.\n        \"\"\"\n        if isinstance(other, SecondOrderTensor):\n            return self == other.matrix\n        elif isinstance(other, np.ndarray):\n            if other.shape == (3, 3) or other.shape == self.shape + (3, 3):\n                return np.all(self.matrix == other, axis=(-2, -1))\n            else:\n                raise ValueError('The value to compare must be an array of shape {} or {}'.format(self.shape, self.shape + (3, 3)))\n\n    def matmul(self, other):\n        \"\"\"\n        Perform matrix-like product between tensor arrays. Each \"product\" is a matrix product between\n        the tensor components.\n\n        If A.shape=(a1, a2, ..., an) and B.shape=(b1, b2, ..., bn), with C=A.matmul(B), we have::\n\n            C.shape = (a1, a2, ..., an, b1, b2, ..., bn)\n\n        and::\n\n            C[i,j,k,...,p,q,r...] = np.matmul(A[i,j,k,...], B[p,q,r,...])\n\n        Parameters\n        ----------\n        other : SecondOrderTensor or np.ndarray or Rotation\n            Tensor array or rotation to right-multiply by. If Rotation is provided, the rotations are applied on each\n            tensor.\n\n        Returns\n        -------\n        SecondOrderTensor\n            Tensor array\n\n        See Also\n        --------\n        __mul__ : Element-wise matrix product\n        \"\"\"\n        if isinstance(other, SecondOrderTensor):\n            other_matrix = other.matrix\n        elif isinstance(other, Rotation) or is_orix_rotation(Rotation):\n            other_matrix = rotation_to_matrix(other)\n        else:\n            other_matrix = other\n        matrix = self.matrix\n        shape_matrix = matrix.shape[:-2]\n        shape_other = other_matrix.shape[:-2]\n        extra_dim_matrix = len(shape_other)\n        extra_dim_other = len(shape_matrix)\n        matrix_expanded = matrix.reshape(shape_matrix + (1,) * extra_dim_other + (3, 3))\n        other_expanded = other_matrix.reshape((1,) * extra_dim_matrix + shape_other + (3, 3))\n        if isinstance(other, Rotation):\n            other_expanded_t = _transpose_matrix(other_expanded)\n            new_mat = np.matmul(np.matmul(other_expanded_t, matrix_expanded), other_expanded)\n            return self.__class__(np.squeeze(new_mat))\n        else:\n            new_mat = np.matmul(matrix_expanded, other_expanded)\n            return SecondOrderTensor(np.squeeze(new_mat))\n\n    def transposeArray(self):\n        \"\"\"\n        Transpose the array of tensors\n\n        If A is a tensor array of shape [s1, s2, ..., sn], A.T is of shape [sn, ..., s2, s1].\n\n        Returns\n        -------\n        SecondOrderTensor\n            Transposed array\n\n        See Also\n        --------\n        T : transpose the tensor array (not the components)\n        \"\"\"\n        if self.ndim < 2:\n            return self\n        else:\n            matrix = self.matrix\n            ndim = matrix.ndim\n            new_axes = np.hstack((ndim - 3 - np.arange(ndim - 2), -2, -1))\n            transposed_arr = np.transpose(matrix, new_axes)\n            return self.__class__(transposed_arr)\n\n    @property\n    def T(self):\n        \"\"\"\n        Transpose the array of tensors.\n\n        It is actually an alias for transposeArray()\n\n        Returns\n        -------\n        SecondOrderTensor\n            Transposed array\n        \"\"\"\n        return self.transposeArray()\n\n    def _transposeTensor(self):\n        return _transpose_matrix(self.matrix)\n\n    def transposeTensor(self):\n        \"\"\"\n        Transpose of tensors of the tensor array\n\n        Returns\n        -------\n        SecondOrderTensor\n            Array of transposed tensors of the tensor array\n\n        See Also\n        --------\n        Transpose : transpose the array (not the components)\n        \"\"\"\n        return self.__class__(self._transposeTensor())\n\n    def ddot(self, other):\n        \"\"\"\n        Double dot product (contraction of tensor product, usually denoted \":\") of two tensors.\n\n        For two tensors whose matrices are M1 and M2::\n\n            M1.ddot(M2) == np.trace(np.matmul(M1, M2))\n\n        Parameters\n        ----------\n        other : SecondOrderTensor or np.ndarray\n            Tensor or tensor array to multiply by before contraction.\n\n        Returns\n        -------\n        float or np.ndarray\n            Result of double dot product\n\n        See Also\n        --------\n        matmul : matrix-like product between two tensor arrays.\n\n        \"\"\"\n        tensor_prod = self.transposeTensor() * other\n        return tensor_prod.trace()\n\n    def _flatten(self):\n        if self.shape:\n            new_len = np.prod(self.shape)\n            return np.reshape(self.matrix, (new_len, 3, 3))\n        else:\n            return self.matrix\n\n    def _stats(self, fun, axis=None):\n        if axis is None:\n            flat_mat = self._flatten()\n            new_matrix = fun(flat_mat, axis=0)\n        else:\n            if axis < 0:\n                axis += -2\n            if axis > self.ndim - 1 or axis < -self.ndim - 2:\n                raise ValueError('The axis index is out of bounds for tensor array of shape {}'.format(self.shape))\n            new_matrix = fun(self.matrix, axis=axis)\n        return self.__class__(new_matrix)\n\n    def flatten(self):\n        \"\"\"\n        Flatten the array of tensors.\n\n        If T is of shape [s1, s2, ..., sn], the shape for T.flatten() is [s1*s2*...*sn].\n\n        Returns\n        -------\n        SecondOrderTensor\n            Flattened array (vector) of tensor\n\n        See Also\n        --------\n        ndim : number of dimensions of the tensor array\n        shape : shape of the tensor array\n        reshape : reshape a tensor array\n        \"\"\"\n        return self.__class__(self._flatten())\n\n    def reshape(self, shape, **kwargs):\n        \"\"\"\n        Reshape the array of tensors\n\n        Parameters\n        ----------\n        shape : tuple\n            New shape of the array\n        kwargs : dict\n            Keyword arguments passed to numpy.reshape()\n\n        Returns\n        -------\n        SecondOrderTensor\n            Reshaped array\n\n        See Also\n        --------\n        flatten : flatten an array to 1D\n        \"\"\"\n        new_matrix = self.matrix.reshape(shape + (3, 3), **kwargs)\n        return self.__class__(new_matrix)\n\n    def mean(self, axis=None):\n        \"\"\"\n        Arithmetic mean value\n\n        Parameters\n        ----------\n        axis : int or None, default None\n            Axis to compute the mean along with.\n            If None, returns the overall mean (mean of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n            Mean tensor\n\n        See Also\n        --------\n        std : Standard deviation\n        min : Minimum value\n        max : Maximum value\n        \"\"\"\n        if self.ndim:\n            return self._stats(np.mean, axis=axis)\n        else:\n            return self\n\n    def std(self, axis=None):\n        \"\"\"\n        Standard deviation\n\n        Parameters\n        ----------\n        axis : int or None, default None\n            Axis to compute standard deviation along with.\n            If None, returns the overall standard deviation (std of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n            Tensor of standard deviation\n\n        See Also\n        --------\n        mean : Mean value\n        min : Minimum value\n        max : Maximum value\n          \"\"\"\n        if self.ndim:\n            return self._stats(np.std, axis=axis)\n        else:\n            return self.__class__(np.zeros((3, 3)))\n\n    def min(self, axis=None):\n        \"\"\"\n        Minimum value\n\n        Parameters\n        ----------\n        axis : int or None, default None\n           Axis to compute minimum along with.\n           If None, returns the overall minimum (min of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n           Minimum value of tensors\n\n        See Also\n        --------\n        max : Maximum value\n        mean : Mean value\n        std : Standard deviation\n        \"\"\"\n        if self.ndim:\n            return self._stats(np.min, axis=axis)\n        else:\n            return self\n\n    def max(self, axis=None):\n        \"\"\"\n        Maximum value\n\n        Parameters\n        ----------\n        axis : int or None, default None\n            Axis to compute maximum along with.\n            If None, returns the overall maximum (max of flattened array)\n\n        Returns\n        -------\n        SecondOrderTensor\n            Maximum value of tensors\n\n        See Also\n        --------\n        min : Minimum value\n        mean : Mean value\n        std : Standard deviation\n        \"\"\"\n        if self.ndim:\n            return self._stats(np.max, axis=axis)\n        else:\n            return self\n\n    def _symmetric_part(self):\n        return 0.5 * (self.matrix + self._transposeTensor())\n\n    def symmetric_part(self):\n        \"\"\"\n        Symmetric part of the tensor\n\n        Returns\n        -------\n        SymmetricSecondOrderTensor\n            Symmetric tensor\n\n        See Also\n        --------\n        skewPart : Skew-symmetric part of the tensor\n        \"\"\"\n        return SymmetricSecondOrderTensor(self._symmetric_part())\n\n    def skew_part(self):\n        \"\"\"\n        Skew-symmetric part of the tensor\n\n        Returns\n        -------\n        SkewSymmetricSecondOrderTensor\n            Skew-symmetric tensor\n        \"\"\"\n        new_mat = 0.5 * (self.matrix - self._transposeTensor())\n        return SkewSymmetricSecondOrderTensor(new_mat)\n\n    def spherical_part(self):\n        \"\"\"\n        Spherical (hydrostatic) part of the tensor\n\n        Returns\n        -------\n        self\n            Spherical part\n\n        See Also\n        --------\n        I1 : compute the first invariant of the tensor\n        deviatoricPart : deviatoric the part of the tensor\n        \"\"\"\n        s = self.I1 / 3\n        return self.eye(self.shape) * s\n\n    def deviatoric_part(self):\n        \"\"\"\n        Deviatoric part of the tensor\n\n        Returns\n        -------\n        self\n\n        See Also\n        --------\n        sphericalPart : spherical part of the tensor\n        \"\"\"\n        return self - self.spherical_part()\n\n    @classmethod\n    def eye(cls, shape=()):\n        \"\"\"\n        Create an array of tensors populated with identity matrices\n\n        Parameters\n        ----------\n        shape : tuple or int, default ()\n            If not provided, it just creates a single identity tensor. Otherwise, the tensor array will be of the\n            specified shape.\n\n        Returns\n        -------\n        cls\n            Array of identity tensors\n\n        See Also\n        --------\n        ones : creates an array of tensors full of ones\n        zeros : creates an array full of zero tensors\n        \"\"\"\n        if isinstance(shape, int):\n            matrix_shape = (shape, 3, 3)\n        else:\n            matrix_shape = shape + (3, 3)\n        eye = np.zeros(matrix_shape)\n        eye[..., np.arange(3), np.arange(3)] = 1\n        return cls(eye)\n\n    @classmethod\n    def ones(cls, shape=()):\n        \"\"\"\n        Create an array of tensors populated with matrices of full of ones.\n\n        Parameters\n        ----------\n        shape : tuple or int, default ()\n            If not provided, it just creates a single tensor of ones. Otherwise, the tensor array will be of the\n            specified shape.\n\n        Returns\n        -------\n        cls\n            Array of ones tensors\n\n        See Also\n        --------\n        eye : creates an array of identity tensors\n        zeros : creates an array full of zero tensors\n        \"\"\"\n        if isinstance(shape, int):\n            matrix_shape = (shape, 3, 3)\n        else:\n            matrix_shape = shape + (3, 3)\n        ones = np.ones(matrix_shape)\n        return cls(ones)\n\n    @classmethod\n    def zeros(cls, shape=()):\n        \"\"\"\n        Create an array of tensors populated with matrices full of zeros.\n\n        Parameters\n        ----------\n        shape : tuple or int, default ()\n            If not provided, it just creates a single tensor of ones. Otherwise, the tensor array will be of the\n            specified shape.\n\n        Returns\n        -------\n        cls\n            Array of ones tensors\n\n        See Also\n        --------\n        eye : creates an array of identity tensors\n        ones : creates an array of tensors full of ones\n        \"\"\"\n        if isinstance(shape, int):\n            matrix_shape = (shape, 3, 3)\n        else:\n            matrix_shape = shape + (3, 3)\n        zeros = np.zeros(matrix_shape)\n        return cls(zeros)\n\n    @classmethod\n    def tensile(cls, u, magnitude):\n        \"\"\"\n        Create an array of tensors corresponding to tensile state along a given direction.\n\n        Parameters\n        ----------\n        u : np.ndarray or list\n            Tensile direction. Must be a 3D vector.\n        magnitude : float or np.ndarray or list\n            Magnitude of the tensile state to consider. If a list or an array is provided, the shape of the tensor array\n            will be of the same shape as magnitude.\n        Returns\n        -------\n        cls\n            tensor or tensor array\n        \"\"\"\n        mat = _tensor_from_direction_magnitude(u, u, magnitude)\n        return cls(mat)\n\n    @classmethod\n    def rand(cls, shape=None, seed=None):\n        \"\"\"\n        Generate a tensor array, populated with random uniform values in [0,1).\n\n        Parameters\n        ----------\n        shape : tuple, optional\n            Shape of the tensor array. If not provided, a single tensor is returned\n        seed : int, optional\n            Sets the seed for random generation. Useful to ensure reproducibility\n\n        Returns\n        -------\n        cls\n            Tensor or tensor array of uniform random value\n\n        See Also\n        --------\n        randn : Generate a random sample of tensors whose components follows a normal distribution\n\n        Examples\n        --------\n        Generate a single random tensor:\n\n        >>> from Elasticipy.SecondOrderTensor import SecondOrderTensor as tensor\n        >>> tensor.rand(seed=123)\n        Second-order tensor\n        [[0.68235186 0.05382102 0.22035987]\n         [0.18437181 0.1759059  0.81209451]\n         [0.923345   0.2765744  0.81975456]]\n\n        Now try with tensor array:\n        >>> t = tensor.rand(shape=(100,50))\n        >>> t.shape\n        (100,50)\n        \"\"\"\n        if shape is None:\n            shape = (3, 3)\n        else:\n            shape = shape + (3, 3)\n        rng = np.random.default_rng(seed)\n        a = rng.random(shape)\n        if issubclass(cls, SymmetricSecondOrderTensor):\n            a = _symmetric_part(a)\n        return cls(a)\n\n    def inv(self):\n        \"\"\"Compute the reciprocal (inverse) tensor\"\"\"\n        return SecondOrderTensor(np.linalg.inv(self.matrix))\n\n    @classmethod\n    def randn(cls, mean=np.zeros((3, 3)), std=np.ones((3, 3)), shape=None, seed=None):\n        \"\"\"\n        Generate a tensor array, populated with components follow a normal distribution.\n\n        Parameters\n        ----------\n        mean : list of numpy.ndarray, optional\n            (3,3) matrix providing the mean values of the components.\n        std : list of numpy.ndarray, optional\n            (3,3) matrix providing the standard deviations of the components.\n        shape : tuple, optional\n            Shape of the tensor array\n        seed : int, optional\n            Sets the seed for random generation. Useful to ensure reproducibility\n\n        Returns\n        -------\n        cls\n            Tensor or tensor array of normal random value\n        \"\"\"\n        if shape is None:\n            new_shape = (3, 3)\n        else:\n            new_shape = shape + (3, 3)\n        rng = np.random.default_rng(seed)\n        mat = np.zeros(new_shape)\n        mean = np.asarray(mean)\n        std = np.asarray(std)\n        for i in range(0, 3):\n            for j in range(0, 3):\n                mat[..., i, j] = rng.normal(mean[i, j], std[i, j], shape)\n        if issubclass(cls, SymmetricSecondOrderTensor):\n            mat = _symmetric_part(mat)\n        return cls(mat)\n\n    @classmethod\n    def shear(cls, u, v, magnitude):\n        \"\"\"\n        Create an array of tensors corresponding to shear state along two orthogonal directions.\n\n        Parameters\n        ----------\n        u : np.ndarray or list\n            First direction. Must be a 3D vector.\n        v : np.ndarray or list\n            Second direction. Must be a 3D vector.\n        magnitude : float or np.ndarray or list\n            Magnitude of the shear state to consider. If a list or an array is provided, the shape of the tensor array\n            will be of the same shape as magnitude.\n        Returns\n        -------\n        cls\n            tensor or tensor array\n        \"\"\"\n        if np.abs(np.dot(u, v)) > 1e-05:\n            raise ValueError('u and v must be orthogonal')\n        mat = _tensor_from_direction_magnitude(u, v, magnitude)\n        return cls(mat)\n\n    def div(self, axes=None, spacing=1.0):\n        \"\"\"\n        Compute the divergence vector of the tensor array, along given axes.\n\n        If the tensor has n dimensions, the divergence vector will be computed along its m first axes, with\n        m = min(n, 3), except if specified in the ``axes`` parameter (see below).\n\n        Parameters\n        ----------\n        axes : list of int, tuple of int, int or None, default None\n            Indices of axes along which to compute the divergence vector. If None (default), the m first axes of the\n            array will be used to compute the derivatives.\n        spacing : float or np.ndarray or list, default 1.\n            Spacing between samples the in each direction. If a scalar value is provided, the spacing is assumed equal\n            in each direction. If an array or a list is provided, spacing[i] must return the spacing along the i-th\n            axis (spacing[i] can be float or np.ndarray).\n\n        Returns\n        -------\n        np.ndarray\n            Divergence vector of the tensor array. If the tensor array is of shape (m,n,...,q), the divergence vector\n            will be of shape (m,n,...,q,3).\n\n        Notes\n        -----\n        The divergence of a tensor field :math:`\\\\mathbf{t}(\\\\mathbf{x})` is defined as:\n\n        .. math::\n\n            [\\\\nabla\\\\cdot\\\\mathbf{t}]_i = \\\\frac{\\\\partial t_{ij}}{\\\\partial x_j}\n\n        The main application of this operator is for balance of linear momentum of stress tensor:\n\n        .. math::\n\n            \\\\rho \\\\mathbf{\\\\gamma} = \\\\nabla\\\\cdot\\\\mathbf{\\\\sigma} + \\\\rho\\\\mathbf{b}\n\n        where :math:`\\\\mathbf{\\\\sigma}` is the stress tensor, :math:`\\\\mathbf{\\\\gamma}` is the acceleration,\n        :math:`\\\\mathbf{b}` is the body force density and :math:`\\\\rho` is the mass density.\n\n        In this function, the derivatives are computed with ``numpy.grad`` function.\n        \"\"\"\n        ndim = min(self.ndim, 3)\n        if isinstance(spacing, (float, int)):\n            spacing = [spacing, spacing, spacing]\n        if axes is None:\n            axes = range(ndim)\n        elif isinstance(axes, int):\n            axes = (axes,)\n        elif not isinstance(axes, (tuple, list)):\n            raise TypeError('axes must be int, tuple of int, or list of int.')\n        if len(axes) > ndim:\n            error_msg = 'The number of axes must be less or equal to the number of dimensions ({}), and cannot exceed 3'.format(self.ndim)\n            raise ValueError(error_msg)\n        else:\n            ndim = len(axes)\n        if max(axes) >= ndim:\n            raise IndexError('axes index must be in range of dimensions ({})'.format(self.ndim))\n        div = np.zeros(self.shape + (3,))\n        for dim in range(0, ndim):\n            div += np.gradient(self.C[:, dim], spacing[dim], axis=axes[dim])\n        return div\n\n    def save(self, file, **kwargs):\n        \"\"\"\n        Save the tensor array as binary file (.npy format).\n\n        This function uses numpy.save function.\n\n        Parameters\n        ----------\n        file : file, str or pathlib.Path\n            File or filename to which the tensor is saved.\n        kwargs : dict\n            Keyword arguments passed to numpy.save()\n\n        See Also\n        --------\n        load_from_npy : load a tensor array from a numpy file\n        \"\"\"\n        np.save(file, self.matrix, **kwargs)\n\n    @classmethod\n    def load_from_npy(cls, file, **kwargs):\n        \"\"\"\n        Load a tensor array for .npy file.\n\n        This function uses numpy.load()\n\n        Parameters\n        ----------\n        file : file, str or pathlib.Path\n            File to read to create the array\n        kwargs : dict\n            Keyword arguments passed to numpy.load()\n\n        Returns\n        -------\n        SecondOrderTensor\n            Tensor array\n\n        See Also\n        --------\n        save : save the tensor array as a numpy file\n        \"\"\"\n        matrix = np.load(file, **kwargs)\n        if matrix.shape[-2:] != (3, 3):\n            raise ValueError('The shape of the array to load must be (...,3,3).')\n        else:\n            return cls(matrix)\n\n    def save_as_txt(self, file, name_prefix='', **kwargs):\n        \"\"\"\n        Save the tensor array to human-readable text file.\n\n        The array must be 1D. The i-th row of the file will provide the components of the i-th tensor in of the array.\n        This function uses pandas.DataFrame.to_csv().\n\n        Parameters\n        ----------\n        file : file or str\n            File to dump tensor components to.\n        name_prefix : str, optional\n            Prefix to add for naming the columns. For instance, name_prefix='E' will result in columns named E11, E12,\n            E13 etc.\n        kwargs : dict\n            Keyword arguments passed to pandas.DataFrame.to_csv()\n        \"\"\"\n        if self.ndim > 1:\n            raise ValueError('The array must be flatten before getting dumped to text file.')\n        else:\n            d = dict()\n            for i in range(3):\n                if isinstance(self, SkewSymmetricSecondOrderTensor):\n                    r = range(i + 1, 3)\n                elif isinstance(self, SymmetricSecondOrderTensor):\n                    r = range(i, 3)\n                else:\n                    r = range(3)\n                for j in r:\n                    key = name_prefix + '{}{}'.format(i + 1, j + 1)\n                    d[key] = self.C[i, j]\n            df = pd.DataFrame(d)\n            df.to_csv(file, index=False, **kwargs)\n\n    def to_pymatgen(self):\n        \"\"\"\n        Convert the second order object into an object compatible with pymatgen.\n\n        The object to use must be either a single tensor, or a flat tensor array. In the latter case, the output will be\n        a list of pymatgen's tensors.\n\n        Returns\n        -------\n        pymatgen.analysis.elasticity.Strain, pymatgen.analysis.elasticity.Stress, pymatgen.core.tensors.Tensor or list\n            The type of output depends on the type of object to use:\n                - if the object is of class StrainTensor, the output will be of class pymatgen.analysis.elasticity.Strain\n                - if the object is of class StressTensor, the output will be of class pymatgen.analysis.elasticity.Stress\n                - otherwise, the output will be of class pymatgen.core.tensors.Tensor\n\n        See Also\n        --------\n        flatten : Converts a tensor array to 1D tensor array\n        \"\"\"\n        try:\n            from Elasticipy.StressStrainTensors import StrainTensor, StressTensor\n            if isinstance(self, StrainTensor):\n                from pymatgen.analysis.elasticity import Strain as Constructor\n            elif isinstance(self, StressTensor):\n                from pymatgen.analysis.elasticity import Stress as Constructor\n            else:\n                from pymatgen.core.tensors import Tensor as Constructor\n        except ImportError:\n            raise ModuleNotFoundError('Module pymatgen is required for this function.')\n        if self.ndim > 1:\n            raise ValueError('The array must be flattened (1D tensor array) before converting to pytmatgen.')\n        if self.shape:\n            return [Constructor(self[i].matrix) for i in range(self.shape[0])]\n        else:\n            return Constructor(self.matrix)\n\n    @classmethod\n    def load_from_txt(cls, file, name_prefix='', **kwargs):\n        \"\"\"\n        Load a tensor array from text file.\n\n        Parameters\n        ----------\n        file : str or file\n            Textfile to read the components from.\n        name_prefix : str, optional\n            Prefix to add to each column when parsing the file. For instance, with name_prefix='E', the function will\n            look for columns names E11, E12, E13 etc.\n\n        Returns\n        -------\n        SecondOrderTensor\n            Flat (1D) tensor constructed from the values given in the text file\n        \"\"\"\n        df = pd.read_csv(file, **kwargs)\n        matrix = np.zeros((len(df), 3, 3))\n        for i in range(3):\n            if cls is SkewSymmetricSecondOrderTensor:\n                r = range(i + 1, 3)\n            elif cls is SymmetricSecondOrderTensor:\n                r = range(i, 3)\n            else:\n                r = range(3)\n            for j in r:\n                key = name_prefix + '{}{}'.format(i + 1, j + 1)\n                matrix[:, i, j] = df[key]\n        return cls(matrix)\n\n\n# Mock classes to avoid undefined references\nclass SkewSymmetricSecondOrderTensor(SecondOrderTensor):\n    pass\n\nclass SymmetricSecondOrderTensor(SecondOrderTensor):\n    pass\n\n\ndef test_load_from_txt():\n    # Test case 1: General tensor\n    file_path = '/home/user/tmp/tensor.txt'\n    data = {\n        '11': [1.0, 2.0],\n        '12': [0.0, 0.0],\n        '13': [0.0, 0.0],\n        '21': [0.0, 0.0],\n        '22': [1.0, 2.0],\n        '23': [0.0, 0.0],\n        '31': [0.0, 0.0],\n        '32': [0.0, 0.0],\n        '33': [1.0, 2.0]\n    }\n    df = pd.DataFrame(data)\n    df.to_csv(file_path, index=False)\n\n    tensor_old = SecondOrderTensor.load_from_txt(file_path)\n    tensor_new = SecondOrderTensor.load_from_txt_new_implementation(file_path)\n    assert np.array_equal(tensor_old.matrix, tensor_new.matrix)\n\n    # Test case 2: Symmetric tensor\n    data_symmetric = {\n        '11': [1.0],\n        '12': [0.5],\n        '13': [0.5],\n        '22': [1.0],\n        '23': [0.5],\n        '33': [1.0]\n    }\n    df_symmetric = pd.DataFrame(data_symmetric)\n    df_symmetric.to_csv(file_path, index=False)\n\n    tensor_old_sym = SymmetricSecondOrderTensor.load_from_txt(file_path)\n    tensor_new_sym = SymmetricSecondOrderTensor.load_from_txt_new_implementation(file_path)\n    assert np.array_equal(tensor_old_sym.matrix, tensor_new_sym.matrix)\n\n    # Test case 3: Skew-symmetric tensor\n    data_skew = {\n        '12': [0.5],\n        '13': [0.5],\n        '23': [0.5]\n    }\n    df_skew = pd.DataFrame(data_skew)\n    df_skew.to_csv(file_path, index=False)\n\n    tensor_old_skew = SkewSymmetricSecondOrderTensor.load_from_txt(file_path)\n    tensor_new_skew = SkewSymmetricSecondOrderTensor.load_from_txt_new_implementation(file_path)\n    assert np.array_equal(tensor_old_skew.matrix, tensor_new_skew.matrix)\n\nif __name__ == \"__main__\":\n    test_load_from_txt()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of their implementation. Both functions read a text file using `pandas.read_csv`, initialize a zero matrix of shape `(len(df), 3, 3)`, and populate this matrix based on the class type (`SkewSymmetricSecondOrderTensor`, `SymmetricSecondOrderTensor`, or other). The logic for determining the range of indices to populate in the matrix is also the same. The function then returns an instance of the class with the constructed matrix. There are no differences in the logic or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `load_from_txt` function returns an instance of `SecondOrderTensor` (or its subclasses), which contains a `matrix` attribute. This satisfies the condition as it returns a value.\n\n2. **CONDITION 2**: The test cases use `assert np.array_equal(tensor_old.matrix, tensor_new.matrix)` to check the equality of the matrices from the old and new implementations. This checks the state of the `matrix` attribute and not any printed or logged content.\n\n3. **CONDITION 3**: The test cases compare the `matrix` attribute of the tensors returned by `load_from_txt` and `load_from_txt_new_implementation`. This ensures that the new implementation must have the exact same functionality as the old one to pass the tests.\n\n4. **CONDITION 4**: The test cases do not use any unreasonable assertions. They correctly compare the `matrix` attributes of the tensors returned by both implementations.\n\n5. **CONDITION 5**: The test cases cover different scenarios: a general tensor, a symmetric tensor, and a skew-symmetric tensor. This provides a non-trivial set of tests that check different aspects of the functionality.",
            "answer": "yes"
        },
        "commit_id": "b0d25e34f09fe66c00cd6bf1dddf165157443060"
    },
    {
        "func_name": "HyperSphericalFunction.eval_spherical",
        "idx": "194",
        "repo_name": "DorianDepriester___Elasticipy",
        "func_path": "src/Elasticipy/SphericalFunction.py",
        "orig_func": "def eval_spherical(self, *args, degrees=False):\n    \"\"\"\n        Evaluate value along a given (set of) direction(s) defined by its (their) spherical coordinates.\n\n        Parameters\n        ----------\n        args : list or np.ndarray\n            [phi, theta, psi] where phi denotes the azimuth angle from X axis to the first direction (u), theta is\n            the latitude angle from Z axis (theta==0 -> u = Z axis), and psi is the angle defining the orientation of\n            the second direction (v) in the plane orthogonal to u, as illustrated below:\n\n            .. image:: ../../docs/_static/images/HyperSphericalCoordinates.png\n\n\n        degrees : bool, default False\n            If True, the angles are given in degrees instead of radians.\n\n        Returns\n        -------\n        float or np.ndarray\n            If only one direction is given as a tuple of floats [nx, ny, nz], the result is a float;\n        otherwise, the result is a nd.array.\n\n        See Also\n        --------\n        eval : evaluate the function along two orthogonal directions (u,v))\n        \"\"\"\n    angles = np.atleast_2d(args)\n    if degrees:\n        angles = np.radians(angles)\n    phi, theta, psi = angles.T\n    u, v = sph2cart(phi, theta, psi)\n    values = self.eval(u, v)\n    if np.array(args).shape == (3,) and (not isinstance(args, np.ndarray)):\n        return values[0]\n    else:\n        return values",
        "orig_context": "```python\n# src/Elasticipy/SphericalFunction.py\n\nfrom numpy import cos, sin\nimport numpy as np\n\ndef sph2cart(*args):\n    \"\"\"\n    Converts spherical/hyperspherical coordinates to cartesian coordinates.\n\n    Parameters\n    ----------\n    args : tuple\n        (phi, theta) angles for spherical coordinates of direction u, where phi denotes the azimuth from X and theta is\n        the colatitude angle from Z.\n        If a third argument is passed, it defines the third angle in hyperspherical coordinate system (psi), that is\n        the orientation of the second vector v, orthogonal to u.\n\n    Returns\n    -------\n    numpy.ndarray\n        directions u expressed in cartesian coordinates system.\n    tuple of numpy.ndarray, numpy.ndarray\n        If a third angle is passed, returns a tuple:\n        - The first element is `u`, the directions expressed in the cartesian coordinate system.\n        - The second element is `v`, the direction of the second vector orthogonal to `u`, also expressed in the\n        cartesian coordinate system.\n    \"\"\"\n    phi, theta, *psi = args\n    phi_vec = np.array(phi).flatten()\n    theta_vec = np.array(theta).flatten()\n    u = np.array([cos(phi_vec) * sin(theta_vec), sin(phi_vec) * sin(theta_vec), cos(theta_vec)]).T\n    if not psi:\n        return u\n    else:\n        psi_vec = np.array(psi).flatten()\n        e_phi = np.array([-sin(phi_vec), cos(phi_vec), np.zeros(phi_vec.shape)])\n        e_theta = np.array([cos(theta_vec) * cos(phi_vec), cos(theta_vec) * sin(phi_vec), -sin(theta_vec)])\n        v = cos(psi_vec) * e_phi + sin(psi_vec) * e_theta\n        return (u, v.T)\nfrom scipy import optimize\nfrom matplotlib import pyplot as plt, cm\nfrom matplotlib.colors import Normalize\n\ndef _plot3D(fig, u, r, **kwargs):\n    norm = Normalize(vmin=r.min(), vmax=r.max())\n    colors = cm.viridis(norm(r))\n    ax = fig.add_subplot(1, 1, 1, projection='3d')\n    xyz = (u.T * r.T).T\n    ax.plot_surface(xyz[:, :, 0], xyz[:, :, 1], xyz[:, :, 2], facecolors=colors, rstride=1, cstride=1, antialiased=False, **kwargs)\n    mappable = cm.ScalarMappable(cmap='viridis', norm=norm)\n    mappable.set_array([])\n    fig.colorbar(mappable, ax=ax)\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n    return ax\nfrom scipy import integrate as integrate\n\ndef _integrate_over_unit_sphere(phi, theta, values=None, psi=None):\n    sine = np.sin(theta)\n    if values is None:\n        values = np.ones(phi.shape)\n    if psi is None:\n        return integrate.trapezoid(integrate.trapezoid(values * sine, axis=0, x=phi[:, 0]), axis=0, x=theta[0, :])\n    else:\n        return integrate.trapezoid(integrate.trapezoid(integrate.trapezoid(values * sine, axis=0, x=phi[:, 0, 0]), axis=0, x=theta[0, :, 0]), x=psi[0, 0, :])\nfrom Elasticipy.PoleFigure import add_polefigure\n\ndef uniform_spherical_distribution(n_evals, seed=None, return_orthogonal=False):\n    \"\"\"\n    Create a set of vectors whose projections over the unit sphere are uniformly distributed.\n\n    Parameters\n    ----------\n    n_evals : int\n        Number of vectors to generate\n    seed : int, default None\n        Sets the seed for the random values. Useful if one wants to ensure reproducibility.\n    return_orthogonal : bool, default False\n        If true, also return a second set of vectors which are orthogonal to the first one.\n\n    Returns\n    -------\n    u : np.ndarray\n        Random set of vectors whose projections over the unit sphere are uniform.\n    v : np.ndarray\n        Set of vectors with the same properties as u, but orthogonal to u.\n        Returned only if return_orthogonal is True\n\n    Notes\n    -----\n    The returned vector(s) are not unit. If needed, one can use:\n        u = (u.T / np.linalg.norm(u, axis=1)).T\n    \"\"\"\n    if seed is None:\n        rng = np.random\n    else:\n        rng = np.random.default_rng(seed)\n    u = rng.normal(size=(n_evals, 3))\n    u /= np.linalg.norm(u, axis=1, keepdims=True)\n    if return_orthogonal:\n        if seed is not None:\n            rng = np.random.default_rng(seed + 1)\n        u2 = rng.normal(size=(n_evals, 3))\n        return (u, np.cross(u, u2))\n    else:\n        return u\n\ndef _create_xyz_section(ax, section_name, polar_angle):\n    ax.title.set_text('{}-{} plane'.format(*section_name))\n    if section_name == 'XY':\n        phi = polar_angle\n        theta = np.pi / 2 * np.ones(len(polar_angle))\n    elif section_name == 'XZ':\n        phi = np.zeros(len(polar_angle))\n        theta = np.pi / 2 - polar_angle\n    else:\n        phi = np.pi / 2 * np.ones(len(polar_angle))\n        theta = np.pi / 2 - polar_angle\n    ax.set_xticks(np.linspace(0, 3 * np.pi / 2, 4))\n    h_direction, v_direction = section_name\n    ax.set_xticklabels((h_direction, v_direction, '-' + h_direction, '-' + v_direction))\n    return (phi, theta, ax)\n\nclass SphericalFunction:\n    \"\"\"\n    Class for spherical functions, that is, functions that depend on directions in 3D space.\n\n    Attribute\n    ---------\n    fun : function to use\n    \"\"\"\n    domain = np.array([[0, 2 * np.pi], [0, np.pi / 2]])\n    name = 'Spherical function'\n    'Bounds to consider in spherical coordinates'\n\n    def __init__(self, fun, symmetry=True):\n        \"\"\"\n        Create a spherical function, that is, a function that depends on one direction only.\n\n        Parameters\n        ----------\n        fun : callable\n            Function to return\n        symmetry : bool, optional\n            Set to true if fun(u)==fun(-u)\n        \"\"\"\n        self.fun = fun\n        self.symmetry = symmetry\n\n    def __repr__(self):\n        val_min, _ = self.min()\n        val_max, _ = self.max()\n        s = '{}\\n'.format(self.name)\n        s += 'Min={}, Max={}'.format(val_min, val_max)\n        return s\n\n    def __add__(self, other):\n        if type(other) is self.__class__:\n\n            def fun(*x):\n                return self.fun(*x) + other.fun(*x)\n            return self.__class__(fun)\n        elif isinstance(other, (float, int, np.number)):\n\n            def fun(*x):\n                return self.fun(*x) + other\n            return self.__class__(fun)\n        else:\n            msg_error = 'A {} can only be added to another {} or a scalar value.'.format(self.name, self.name)\n            raise NotImplementedError(msg_error)\n\n    def __sub__(self, other):\n        if isinstance(other, self.__class__):\n\n            def fun(*x):\n                return self.fun(*x) - other.fun(*x)\n            return self.__class__(fun)\n        else:\n            return self.__add__(-other)\n\n    def __mul__(self, other):\n        if type(other) is self.__class__:\n\n            def fun(*x):\n                return self.fun(*x) * other.fun(*x)\n        elif isinstance(other, (float, int, np.number)):\n\n            def fun(*x):\n                return self.fun(*x) * other\n        else:\n            msg_error = 'A {} can only be multiplied by another {} or a scalar value.'.format(self.name, self.name)\n            raise NotImplementedError(msg_error)\n        return self.__class__(fun)\n\n    def __rmul__(self, other):\n        return self.__mul__(other)\n\n    def __truediv__(self, other):\n        if type(other) is self.__class__:\n\n            def fun(*x):\n                return self.fun(*x) / other.fun(*x)\n        elif isinstance(other, (float, int, np.number)):\n\n            def fun(*x):\n                return self.fun(*x) / other\n        else:\n            raise NotImplementedError('A SphericalFunction can only be divided by a scalar value of another SphericalFunction.')\n        return self.__class__(fun)\n\n    def eval(self, u):\n        \"\"\"\n        Evaluate value along a given (set of) direction(s).\n\n        Parameters\n        ----------\n        u : np.ndarray or list\n            Direction(s) to estimate the value along with. It can be of a unique direction [nx, ny, nz],\n            or a set of directions (e.g. [[n1x, n1y n1z],[n2x, n2y, n2z],...]).\n\n        Returns\n        -------\n        float or np.ndarray\n            If only one direction is given as a tuple of floats [nx, ny, nz], the result is a float; otherwise, the\n            result is a nd.array.\n\n        See Also\n        --------\n        eval_spherical : evaluate the function along a given direction given using the spherical coordinates\n        \"\"\"\n        u_vec = np.atleast_2d(u)\n        norm = np.linalg.norm(u_vec, axis=1)\n        if np.any(norm < 1e-09):\n            raise ValueError('The input vector cannot be zeros')\n        u_vec = (u_vec.T / norm).T\n        values = self.fun(u_vec)\n        if isinstance(u, list) and np.array(u).shape == (3,):\n            return values[0]\n        else:\n            return values\n\n# ...\n\nclass HyperSphericalFunction(SphericalFunction):\n    \"\"\"\n    Class for defining functions that depend on two orthogonal directions u and v.\n    \"\"\"\n    domain = np.array([[0, 2 * np.pi], [0, np.pi / 2], [0, np.pi]])\n    name = 'Hyperspherical function'\n\n    def __init__(self, fun):\n        \"\"\"\n        Create a hyperspherical function, that is, a function that depends on two orthogonal directions only.\n        \"\"\"\n        super().__init__(fun)\n\n    def eval(self, u, *args):\n        \"\"\"\n        Evaluate the Hyperspherical function with respect to two orthogonal directions.\n\n        Parameters\n        ----------\n        u : list or np.ndarray\n            First axis\n        args : list or np.ndarray\n            Second axis\n\n        Returns\n        -------\n        float or np.ndarray\n            Function value\n\n        See Also\n        --------\n        eval_spherical : evaluate the function along a direction defined by its spherical coordinates.\n        \"\"\"\n        m_vec = np.atleast_2d(u)\n        n_vec = np.atleast_2d(*args)\n        norm_1 = np.linalg.norm(m_vec, axis=1)\n        norm_2 = np.linalg.norm(n_vec, axis=1)\n        if np.any(norm_1 < 1e-09) or np.any(norm_2 < 1e-09):\n            raise ValueError('The input vector cannot be zeros')\n        m_vec = (m_vec.T / norm_1).T\n        n_vec = (n_vec.T / norm_2).T\n        dot = np.abs(np.einsum('ij,ij->i', m_vec, n_vec))\n        if np.any(dot > 1e-09):\n            raise ValueError('The two directions must be orthogonal.')\n        values = self.fun(m_vec, n_vec)\n        if np.array(u).shape == (3,) and (not isinstance(u, np.ndarray)):\n            return values[0]\n        else:\n            return values\n\n    def mean(self, method='trapezoid', n_evals=int(1000000.0), seed=None):\n        if method == 'exact':\n\n            def fun(psi, theta, phi):\n                return self.eval_spherical(phi, theta, psi) * sin(theta)\n            domain = self.domain.flatten()\n            q = integrate.tplquad(fun, *domain)\n            return q[0] / (2 * np.pi ** 2)\n        elif method == 'trapezoid':\n            (phi, theta, psi), evals = self.evaluate_on_spherical_grid(n_evals)\n            dom_size = _integrate_over_unit_sphere(phi, theta, psi=psi)\n            integral = _integrate_over_unit_sphere(phi, theta, psi=psi, values=evals)\n            return integral / dom_size\n        else:\n            u, v = uniform_spherical_distribution(n_evals, seed=seed, return_orthogonal=True)\n            return np.mean(self.eval(u, v))\n\n    def var(self, method='trapezoid', n_evals=int(1000000.0), mean=None, seed=None):\n        if method == 'exact':\n            if mean is None:\n                mean = self.mean(method='exact')\n\n            def fun(psi, theta, phi):\n                return (mean - self.eval_spherical(phi, theta, psi)) ** 2 * sin(theta)\n            domain = self.domain.flatten()\n            q = integrate.tplquad(fun, *domain)\n            return q[0] / (2 * np.pi ** 2)\n        if method == 'trapezoid':\n            (phi, theta, psi), evals = self.evaluate_on_spherical_grid(n_evals)\n            dom_size = _integrate_over_unit_sphere(phi, theta, psi=psi)\n            if mean is None:\n                mean = self.mean(method='trapezoid', n_evals=n_evals)\n            return _integrate_over_unit_sphere(phi, theta, psi=psi, values=(evals - mean) ** 2) / dom_size\n        else:\n            u, v = uniform_spherical_distribution(n_evals, seed=seed, return_orthogonal=True)\n            return np.var(self.eval(u, v))\n\n    def evaluate_on_spherical_grid(self, n, return_in_spherical=True, use_symmetry=True):\n        \"\"\"\n        Create a set of vectors corresponding to a spherical grid (phi,theta), then flatten it.\n\n        Parameters\n        ----------\n        n : int or tuple of int\n            If int, gives the overall number of evaluations over the unit hypersphere. If a tuple is passed, they gieve\n            the number of angles to consider for (hyper)spherical coordinates (n_phi, n_theta, n_psi).\n        return_in_spherical : bool, optional\n            If true, the first output argument will be the spherical coordinates (phi, theta). Otherwise, the cartersian\n            coordinates are returned\n        use_symmetry : bool, optional\n            Whether to use take advantage ot symmetry\n\n        Returns\n        -------\n        tuple\n            Coordinates of evaluation, either in spherical of cartesian coordinates\n        numpy.ndarray\n            Grid of evaluated values\n        \"\"\"\n        symmetry = self.symmetry and use_symmetry\n        if isinstance(n, int):\n            if symmetry:\n                n_phi = int(2 * n ** (1 / 3)) + 1\n                n_theta = int(n_phi / 4) + 1\n                n_psi = int(n_phi / 2) + 1\n            else:\n                n_phi = int(4 * n ** (1 / 3)) + 1\n                n_theta = int(n_phi / 2) + 1\n                n_psi = int(n_phi / 2) + 1\n        else:\n            n_phi, n_theta, n_psi = n\n        if symmetry:\n            theta_max = np.pi / 2\n        else:\n            theta_max = np.pi\n        phi = np.linspace(0, 2 * np.pi, n_phi)\n        theta = np.linspace(0, theta_max, n_theta)\n        psi = np.linspace(0, np.pi, n_psi)\n        phi_grid, theta_grid, psi_grid = np.meshgrid(phi, theta, psi, indexing='ij')\n        u, v = sph2cart(phi_grid.flatten(), theta_grid.flatten(), psi_grid)\n        evals = self.eval(u, v)\n        evals_grid = evals.reshape((n_phi, n_theta, n_psi))\n        if return_in_spherical:\n            return ((phi_grid, theta_grid, psi_grid), evals_grid)\n        else:\n            u_r = u.reshape((n_phi, n_theta, n_psi, 3))\n            v_r = v.reshape((n_phi, n_theta, n_psi, 3))\n            return ((u_r, v_r), evals_grid)\n\n    def plot3D(self, n_phi=50, n_theta=50, n_psi=50, which='mean', fig=None, **kwargs):\n        \"\"\"\n        Generate a 3D plot representing the evaluation of spherical harmonics.\n\n        This function evaluates a function over a grid defined by spherical coordinates\n        (phi, theta, psi) and produces a 3D plot. It provides options to display the mean,\n        standard deviation, minimum, or maximum of the evaluated values along the third angles (psi).\n        The plot can be customized with additional keyword arguments.\n\n        Parameters\n        ----------\n        n_phi : int, optional\n            Number of divisions along the phi axis, default is 50.\n        n_theta : int, optional\n            Number of divisions along the theta axis, default is 50.\n        n_psi : int, optional\n            Number of divisions along the psi axis, default is 50.\n        which : str, optional\n            Determines which statistical measure to plot ('mean', 'std', 'min', 'max'),\n            default is 'mean'.\n        fig : matplotlib.figure.Figure, optional\n            Handle to existing figure object. Default is None. If provided, it disables showing the figure.\n        kwargs : dict, optional\n            Additional keyword arguments to customize the plot.\n\n        Returns\n        -------\n        tuple\n            A tuple containing the matplotlib figure and axes objects.\n        \"\"\"\n        if fig is None:\n            new_fig = plt.figure()\n        else:\n            new_fig = fig\n        uv, values = self.evaluate_on_spherical_grid((n_phi, n_theta, n_psi), return_in_spherical=False, use_symmetry=False)\n        u, _ = uv\n        if which == 'std':\n            r_grid = np.std(values, axis=2)\n        elif which == 'min':\n            r_grid = np.min(values, axis=2)\n        elif which == 'max':\n            r_grid = np.max(values, axis=2)\n        else:\n            r_grid = np.mean(values, axis=2)\n        ax = _plot3D(new_fig, u[:, :, 0, :], r_grid, **kwargs)\n        if fig is None:\n            plt.show()\n        return (new_fig, ax)\n\n    def plot_xyz_sections(self, n_theta=500, n_psi=100, color_minmax='blue', alpha_minmax=0.2, color_mean='red', fig=None):\n        \"\"\"\n        Plots the XYZ sections using polar projections.\n\n        This function creates a figure with three subplots representing the XY, XZ,\n        and YZ sections. It utilizes polar projections to plot the min, max, and mean\n        values of the evaluated function over given theta and phi ranges.\n\n        Parameters\n        ----------\n        n_theta : int, optional\n            Number of theta points to use in the grid (default is 500).\n        n_psi : int, optional\n            Number of psi points to use in the grid (default is 100).\n        color_minmax : str, optional\n            Color to use for plotting min and max values (default is 'blue').\n        alpha_minmax : float, optional\n            Alpha transparency level to use for the min/max fill (default is 0.2).\n        color_mean : str, optional\n            Color to use for plotting mean values (default is 'red').\n        fig : matplotlib.figure.Figure, optional\n            Handle to existing figure object. Default is None. If provided, it disables showing the figure.\n\n        Returns\n        -------\n        fig : matplotlib.figure.Figure\n            The created figure.\n        axs : list of matplotlib.axes._subplots.PolarAxesSubplot\n            List of polar axis subplots.\n        \"\"\"\n        if fig is None:\n            new_fig = plt.figure()\n        else:\n            new_fig = fig\n        theta_polar = np.linspace(0, 2 * np.pi, n_theta)\n        titles = ('XY', 'XZ', 'YZ')\n        handles, labels = ([], [])\n        axs = []\n        for i in range(0, 3):\n            ax = new_fig.add_subplot(1, 3, i + 1, projection='polar')\n            phi, theta, ax = _create_xyz_section(ax, titles[i], theta_polar)\n            psi = np.linspace(0, np.pi, n_psi)\n            phi_grid, psi_grid = np.meshgrid(phi, psi, indexing='ij')\n            theta_grid, _ = np.meshgrid(theta, psi, indexing='ij')\n            phi = phi_grid.flatten()\n            theta = theta_grid.flatten()\n            psi = psi_grid.flatten()\n            u, v = sph2cart(phi, theta, psi)\n            values = self.eval(u, v).reshape((n_theta, n_psi))\n            min_val = np.min(values, axis=1)\n            max_val = np.max(values, axis=1)\n            ax.plot(theta_polar, min_val, color=color_minmax)\n            ax.plot(theta_polar, max_val, color=color_minmax)\n            area = ax.fill_between(theta_polar, min_val, max_val, alpha=alpha_minmax, label='Min/Max')\n            line, = ax.plot(theta_polar, np.mean(values, axis=1), color=color_mean, label='Mean')\n            axs.append(ax)\n        handles.extend([line, area])\n        labels.extend([line.get_label(), area.get_label()])\n        new_fig.legend(handles, labels, loc='upper center', ncol=2, bbox_to_anchor=(0.5, 0.95))\n        if fig is None:\n            new_fig.show()\n        return (new_fig, axs)\n\n    def plot_as_pole_figure(self, n_theta=50, n_phi=200, n_psi=50, which='mean', projection='lambert', fig=None, plot_type='imshow', show=True, title=None, subplot_args=(), subplot_kwargs=None, **kwargs):\n        \"\"\"\n        Generate a pole figure plot from spherical function evaluation.\n\n        This function evaluates a spherical function over specified ranges of angles\n        (phi, theta, psi) and then generates a 2D pole figure plot using various\n        statistical summaries of the data (mean, std, min, max). It also supports\n        several types of plot visualizations such as 'imshow', 'contourf', and 'contour'.\n\n        Parameters\n        ----------\n        n_theta : int, optional\n            Number of sampling points for theta angle. Default is 50.\n        n_phi : int, optional\n            Number of sampling points for phi angle. Default is 200.\n        n_psi : int, optional\n            Number of sampling points for psi angle. Default is 50.\n        which : str, optional\n            Specifies the type of statistical summary to use for plotting.\n            Options include 'mean', 'std', 'min', 'max'. Default is 'mean'.\n        projection : str, optional\n            Type of projection for the pole figure plot. Default is 'lambert'.\n        fig : matplotlib.figure.Figure, optional\n            Pre-existing figure to use for plotting. If None, a new figure is created.\n            Default is None.\n        plot_type : str, optional\n            Type of plot to generate. Can be 'imshow', 'contourf', or 'contour'.\n            Default is 'imshow'.\n        show : bool, optional\n            Set whether to show the plot or not. Default is True. This must be turned off when using multiple subplots.\n        subplot_args : tuple, optional\n            Arguments to pass to the add_subplot() function. Default is None.\n        subplot_kwargs : dict, optional\n            Keyword arguments to pass to the add_subplot() function. Default is None.\n        **kwargs : dict, optional\n            Additional keyword arguments passed to the plotting functions.\n\n        Returns\n        -------\n        fig : matplotlib.figure.Figure\n            The figure object containing the plot.\n        ax : matplotlib.axes.Axes\n            The axes object containing the plot.\n        \"\"\"\n        if subplot_kwargs is None:\n            subplot_kwargs = {}\n        if fig is None:\n            fig = plt.figure()\n        ax = add_polefigure(fig, *subplot_kwargs, projection=projection, **subplot_kwargs)\n        phi = np.linspace(*self.domain[0], n_phi)\n        theta = np.linspace(*self.domain[1], n_theta)\n        psi = np.linspace(*self.domain[2], n_psi)\n        phi_grid, theta_grid, psi_grid = np.meshgrid(phi, theta, psi, indexing='ij')\n        phi_flat = phi_grid.flatten()\n        theta_flat = theta_grid.flatten()\n        psi_flat = psi_grid.flatten()\n        values = self.eval_spherical(np.array([phi_flat, theta_flat, psi_flat]).T)\n        reshaped_values = values.reshape((n_phi, n_theta, n_psi))\n        if which == 'std':\n            to_plot = np.std(reshaped_values, axis=2)\n        elif which == 'min':\n            to_plot = np.min(reshaped_values, axis=2)\n        elif which == 'max':\n            to_plot = np.max(reshaped_values, axis=2)\n        else:\n            to_plot = np.mean(reshaped_values, axis=2)\n        phi_grid, theta_grid = np.meshgrid(phi, theta, indexing='ij')\n        if plot_type == 'imshow':\n            sc = ax.pcolormesh(phi_grid, theta_grid, to_plot, **kwargs)\n        elif plot_type == 'contourf':\n            sc = ax.contourf(phi_grid, theta_grid, to_plot, **kwargs)\n        elif plot_type == 'contour':\n            sc = ax.contour(phi_grid, theta_grid, to_plot, **kwargs)\n        else:\n            raise ValueError(f'Unknown plot type: {plot_type}')\n        ax.set_rlim(*self.domain[1])\n        ax.set_title(title)\n        fig.colorbar(sc)\n        if show:\n            plt.show()\n        return (fig, ax)\n\n    def eval_spherical(self, *args, degrees=False):\n        \"\"\"\n        Evaluate value along a given (set of) direction(s) defined by its (their) spherical coordinates.\n\n        Parameters\n        ----------\n        args : list or np.ndarray\n            [phi, theta, psi] where phi denotes the azimuth angle from X axis to the first direction (u), theta is\n            the latitude angle from Z axis (theta==0 -> u = Z axis), and psi is the angle defining the orientation of\n            the second direction (v) in the plane orthogonal to u, as illustrated below:\n\n            .. image:: ../../docs/_static/images/HyperSphericalCoordinates.png\n\n\n        degrees : bool, default False\n            If True, the angles are given in degrees instead of radians.\n\n        Returns\n        -------\n        float or np.ndarray\n            If only one direction is given as a tuple of floats [nx, ny, nz], the result is a float;\n        otherwise, the result is a nd.array.\n\n        See Also\n        --------\n        eval : evaluate the function along two orthogonal directions (u,v))\n        \"\"\"\n        angles = np.atleast_2d(args)\n        if degrees:\n            angles = np.radians(angles)\n        phi, theta, psi = angles.T\n        u, v = sph2cart(phi, theta, psi)\n        values = self.eval(u, v)\n        if np.array(args).shape == (3,) and (not isinstance(args, np.ndarray)):\n            return values[0]\n        else:\n            return values\n\n```\n",
        "eval_script": "# src/Elasticipy/SphericalFunction.py\n\nfrom numpy import cos, sin\nimport numpy as np\n\ndef sph2cart(*args):\n    \"\"\"\n    Converts spherical/hyperspherical coordinates to cartesian coordinates.\n\n    Parameters\n    ----------\n    args : tuple\n        (phi, theta) angles for spherical coordinates of direction u, where phi denotes the azimuth from X and theta is\n        the colatitude angle from Z.\n        If a third argument is passed, it defines the third angle in hyperspherical coordinate system (psi), that is\n        the orientation of the second vector v, orthogonal to u.\n\n    Returns\n    -------\n    numpy.ndarray\n        directions u expressed in cartesian coordinates system.\n    tuple of numpy.ndarray, numpy.ndarray\n        If a third angle is passed, returns a tuple:\n        - The first element is `u`, the directions expressed in the cartesian coordinate system.\n        - The second element is `v`, the direction of the second vector orthogonal to `u`, also expressed in the\n        cartesian coordinate system.\n    \"\"\"\n    phi, theta, *psi = args\n    phi_vec = np.array(phi).flatten()\n    theta_vec = np.array(theta).flatten()\n    u = np.array([cos(phi_vec) * sin(theta_vec), sin(phi_vec) * sin(theta_vec), cos(theta_vec)]).T\n    if not psi:\n        return u\n    else:\n        psi_vec = np.array(psi).flatten()\n        e_phi = np.array([-sin(phi_vec), cos(phi_vec), np.zeros(phi_vec.shape)])\n        e_theta = np.array([cos(theta_vec) * cos(phi_vec), cos(theta_vec) * sin(phi_vec), -sin(theta_vec)])\n        v = cos(psi_vec) * e_phi + sin(psi_vec) * e_theta\n        return (u, v.T)\nfrom scipy import optimize\nfrom matplotlib import pyplot as plt, cm\nfrom matplotlib.colors import Normalize\n\ndef _plot3D(fig, u, r, **kwargs):\n    norm = Normalize(vmin=r.min(), vmax=r.max())\n    colors = cm.viridis(norm(r))\n    ax = fig.add_subplot(1, 1, 1, projection='3d')\n    xyz = (u.T * r.T).T\n    ax.plot_surface(xyz[:, :, 0], xyz[:, :, 1], xyz[:, :, 2], facecolors=colors, rstride=1, cstride=1, antialiased=False, **kwargs)\n    mappable = cm.ScalarMappable(cmap='viridis', norm=norm)\n    mappable.set_array([])\n    fig.colorbar(mappable, ax=ax)\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n    return ax\nfrom scipy import integrate as integrate\n\ndef _integrate_over_unit_sphere(phi, theta, values=None, psi=None):\n    sine = np.sin(theta)\n    if values is None:\n        values = np.ones(phi.shape)\n    if psi is None:\n        return integrate.trapezoid(integrate.trapezoid(values * sine, axis=0, x=phi[:, 0]), axis=0, x=theta[0, :])\n    else:\n        return integrate.trapezoid(integrate.trapezoid(integrate.trapezoid(values * sine, axis=0, x=phi[:, 0, 0]), axis=0, x=theta[0, :, 0]), x=psi[0, 0, :])\nfrom Elasticipy.PoleFigure import add_polefigure\n\ndef uniform_spherical_distribution(n_evals, seed=None, return_orthogonal=False):\n    \"\"\"\n    Create a set of vectors whose projections over the unit sphere are uniformly distributed.\n\n    Parameters\n    ----------\n    n_evals : int\n        Number of vectors to generate\n    seed : int, default None\n        Sets the seed for the random values. Useful if one wants to ensure reproducibility.\n    return_orthogonal : bool, default False\n        If true, also return a second set of vectors which are orthogonal to the first one.\n\n    Returns\n    -------\n    u : np.ndarray\n        Random set of vectors whose projections over the unit sphere are uniform.\n    v : np.ndarray\n        Set of vectors with the same properties as u, but orthogonal to u.\n        Returned only if return_orthogonal is True\n\n    Notes\n    -----\n    The returned vector(s) are not unit. If needed, one can use:\n        u = (u.T / np.linalg.norm(u, axis=1)).T\n    \"\"\"\n    if seed is None:\n        rng = np.random\n    else:\n        rng = np.random.default_rng(seed)\n    u = rng.normal(size=(n_evals, 3))\n    u /= np.linalg.norm(u, axis=1, keepdims=True)\n    if return_orthogonal:\n        if seed is not None:\n            rng = np.random.default_rng(seed + 1)\n        u2 = rng.normal(size=(n_evals, 3))\n        return (u, np.cross(u, u2))\n    else:\n        return u\n\ndef _create_xyz_section(ax, section_name, polar_angle):\n    ax.title.set_text('{}-{} plane'.format(*section_name))\n    if section_name == 'XY':\n        phi = polar_angle\n        theta = np.pi / 2 * np.ones(len(polar_angle))\n    elif section_name == 'XZ':\n        phi = np.zeros(len(polar_angle))\n        theta = np.pi / 2 - polar_angle\n    else:\n        phi = np.pi / 2 * np.ones(len(polar_angle))\n        theta = np.pi / 2 - polar_angle\n    ax.set_xticks(np.linspace(0, 3 * np.pi / 2, 4))\n    h_direction, v_direction = section_name\n    ax.set_xticklabels((h_direction, v_direction, '-' + h_direction, '-' + v_direction))\n    return (phi, theta, ax)\n\nclass SphericalFunction:\n    \"\"\"\n    Class for spherical functions, that is, functions that depend on directions in 3D space.\n\n    Attribute\n    ---------\n    fun : function to use\n    \"\"\"\n    domain = np.array([[0, 2 * np.pi], [0, np.pi / 2]])\n    name = 'Spherical function'\n    'Bounds to consider in spherical coordinates'\n\n    def __init__(self, fun, symmetry=True):\n        \"\"\"\n        Create a spherical function, that is, a function that depends on one direction only.\n\n        Parameters\n        ----------\n        fun : callable\n            Function to return\n        symmetry : bool, optional\n            Set to true if fun(u)==fun(-u)\n        \"\"\"\n        self.fun = fun\n        self.symmetry = symmetry\n\n    def __repr__(self):\n        val_min, _ = self.min()\n        val_max, _ = self.max()\n        s = '{}\\n'.format(self.name)\n        s += 'Min={}, Max={}'.format(val_min, val_max)\n        return s\n\n    def __add__(self, other):\n        if type(other) is self.__class__:\n\n            def fun(*x):\n                return self.fun(*x) + other.fun(*x)\n            return self.__class__(fun)\n        elif isinstance(other, (float, int, np.number)):\n\n            def fun(*x):\n                return self.fun(*x) + other\n            return self.__class__(fun)\n        else:\n            msg_error = 'A {} can only be added to another {} or a scalar value.'.format(self.name, self.name)\n            raise NotImplementedError(msg_error)\n\n    def __sub__(self, other):\n        if isinstance(other, self.__class__):\n\n            def fun(*x):\n                return self.fun(*x) - other.fun(*x)\n            return self.__class__(fun)\n        else:\n            return self.__add__(-other)\n\n    def __mul__(self, other):\n        if type(other) is self.__class__:\n\n            def fun(*x):\n                return self.fun(*x) * other.fun(*x)\n        elif isinstance(other, (float, int, np.number)):\n\n            def fun(*x):\n                return self.fun(*x) * other\n        else:\n            msg_error = 'A {} can only be multiplied by another {} or a scalar value.'.format(self.name, self.name)\n            raise NotImplementedError(msg_error)\n        return self.__class__(fun)\n\n    def __rmul__(self, other):\n        return self.__mul__(other)\n\n    def __truediv__(self, other):\n        if type(other) is self.__class__:\n\n            def fun(*x):\n                return self.fun(*x) / other.fun(*x)\n        elif isinstance(other, (float, int, np.number)):\n\n            def fun(*x):\n                return self.fun(*x) / other\n        else:\n            raise NotImplementedError('A SphericalFunction can only be divided by a scalar value of another SphericalFunction.')\n        return self.__class__(fun)\n\n    def eval(self, u):\n        \"\"\"\n        Evaluate value along a given (set of) direction(s).\n\n        Parameters\n        ----------\n        u : np.ndarray or list\n            Direction(s) to estimate the value along with. It can be of a unique direction [nx, ny, nz],\n            or a set of directions (e.g. [[n1x, n1y n1z],[n2x, n2y, n2z],...]).\n\n        Returns\n        -------\n        float or np.ndarray\n            If only one direction is given as a tuple of floats [nx, ny, nz], the result is a float; otherwise, the\n            result is a nd.array.\n\n        See Also\n        --------\n        eval_spherical : evaluate the function along a given direction given using the spherical coordinates\n        \"\"\"\n        u_vec = np.atleast_2d(u)\n        norm = np.linalg.norm(u_vec, axis=1)\n        if np.any(norm < 1e-09):\n            raise ValueError('The input vector cannot be zeros')\n        u_vec = (u_vec.T / norm).T\n        values = self.fun(u_vec)\n        if isinstance(u, list) and np.array(u).shape == (3,):\n            return values[0]\n        else:\n            return values\n\n# ...\n\nclass HyperSphericalFunction(SphericalFunction):\n    \"\"\"\n    Class for defining functions that depend on two orthogonal directions u and v.\n    \"\"\"\n    domain = np.array([[0, 2 * np.pi], [0, np.pi / 2], [0, np.pi]])\n    name = 'Hyperspherical function'\n\n    def __init__(self, fun):\n        \"\"\"\n        Create a hyperspherical function, that is, a function that depends on two orthogonal directions only.\n        \"\"\"\n        super().__init__(fun)\n\n    def eval(self, u, *args):\n        \"\"\"\n        Evaluate the Hyperspherical function with respect to two orthogonal directions.\n\n        Parameters\n        ----------\n        u : list or np.ndarray\n            First axis\n        args : list or np.ndarray\n            Second axis\n\n        Returns\n        -------\n        float or np.ndarray\n            Function value\n\n        See Also\n        --------\n        eval_spherical : evaluate the function along a direction defined by its spherical coordinates.\n        \"\"\"\n        m_vec = np.atleast_2d(u)\n        n_vec = np.atleast_2d(*args)\n        norm_1 = np.linalg.norm(m_vec, axis=1)\n        norm_2 = np.linalg.norm(n_vec, axis=1)\n        if np.any(norm_1 < 1e-09) or np.any(norm_2 < 1e-09):\n            raise ValueError('The input vector cannot be zeros')\n        m_vec = (m_vec.T / norm_1).T\n        n_vec = (n_vec.T / norm_2).T\n        dot = np.abs(np.einsum('ij,ij->i', m_vec, n_vec))\n        if np.any(dot > 1e-09):\n            raise ValueError('The two directions must be orthogonal.')\n        values = self.fun(m_vec, n_vec)\n        if np.array(u).shape == (3,) and (not isinstance(u, np.ndarray)):\n            return values[0]\n        else:\n            return values\n\n    def mean(self, method='trapezoid', n_evals=int(1000000.0), seed=None):\n        if method == 'exact':\n\n            def fun(psi, theta, phi):\n                return self.eval_spherical(phi, theta, psi) * sin(theta)\n            domain = self.domain.flatten()\n            q = integrate.tplquad(fun, *domain)\n            return q[0] / (2 * np.pi ** 2)\n        elif method == 'trapezoid':\n            (phi, theta, psi), evals = self.evaluate_on_spherical_grid(n_evals)\n            dom_size = _integrate_over_unit_sphere(phi, theta, psi=psi)\n            integral = _integrate_over_unit_sphere(phi, theta, psi=psi, values=evals)\n            return integral / dom_size\n        else:\n            u, v = uniform_spherical_distribution(n_evals, seed=seed, return_orthogonal=True)\n            return np.mean(self.eval(u, v))\n\n    def var(self, method='trapezoid', n_evals=int(1000000.0), mean=None, seed=None):\n        if method == 'exact':\n            if mean is None:\n                mean = self.mean(method='exact')\n\n            def fun(psi, theta, phi):\n                return (mean - self.eval_spherical(phi, theta, psi)) ** 2 * sin(theta)\n            domain = self.domain.flatten()\n            q = integrate.tplquad(fun, *domain)\n            return q[0] / (2 * np.pi ** 2)\n        if method == 'trapezoid':\n            (phi, theta, psi), evals = self.evaluate_on_spherical_grid(n_evals)\n            dom_size = _integrate_over_unit_sphere(phi, theta, psi=psi)\n            if mean is None:\n                mean = self.mean(method='trapezoid', n_evals=n_evals)\n            return _integrate_over_unit_sphere(phi, theta, psi=psi, values=(evals - mean) ** 2) / dom_size\n        else:\n            u, v = uniform_spherical_distribution(n_evals, seed=seed, return_orthogonal=True)\n            return np.var(self.eval(u, v))\n\n    def evaluate_on_spherical_grid(self, n, return_in_spherical=True, use_symmetry=True):\n        \"\"\"\n        Create a set of vectors corresponding to a spherical grid (phi,theta), then flatten it.\n\n        Parameters\n        ----------\n        n : int or tuple of int\n            If int, gives the overall number of evaluations over the unit hypersphere. If a tuple is passed, they gieve\n            the number of angles to consider for (hyper)spherical coordinates (n_phi, n_theta, n_psi).\n        return_in_spherical : bool, optional\n            If true, the first output argument will be the spherical coordinates (phi, theta). Otherwise, the cartersian\n            coordinates are returned\n        use_symmetry : bool, optional\n            Whether to use take advantage ot symmetry\n\n        Returns\n        -------\n        tuple\n            Coordinates of evaluation, either in spherical of cartesian coordinates\n        numpy.ndarray\n            Grid of evaluated values\n        \"\"\"\n        symmetry = self.symmetry and use_symmetry\n        if isinstance(n, int):\n            if symmetry:\n                n_phi = int(2 * n ** (1 / 3)) + 1\n                n_theta = int(n_phi / 4) + 1\n                n_psi = int(n_phi / 2) + 1\n            else:\n                n_phi = int(4 * n ** (1 / 3)) + 1\n                n_theta = int(n_phi / 2) + 1\n                n_psi = int(n_phi / 2) + 1\n        else:\n            n_phi, n_theta, n_psi = n\n        if symmetry:\n            theta_max = np.pi / 2\n        else:\n            theta_max = np.pi\n        phi = np.linspace(0, 2 * np.pi, n_phi)\n        theta = np.linspace(0, theta_max, n_theta)\n        psi = np.linspace(0, np.pi, n_psi)\n        phi_grid, theta_grid, psi_grid = np.meshgrid(phi, theta, psi, indexing='ij')\n        u, v = sph2cart(phi_grid.flatten(), theta_grid.flatten(), psi_grid)\n        evals = self.eval(u, v)\n        evals_grid = evals.reshape((n_phi, n_theta, n_psi))\n        if return_in_spherical:\n            return ((phi_grid, theta_grid, psi_grid), evals_grid)\n        else:\n            u_r = u.reshape((n_phi, n_theta, n_psi, 3))\n            v_r = v.reshape((n_phi, n_theta, n_psi, 3))\n            return ((u_r, v_r), evals_grid)\n\n    def plot3D(self, n_phi=50, n_theta=50, n_psi=50, which='mean', fig=None, **kwargs):\n        \"\"\"\n        Generate a 3D plot representing the evaluation of spherical harmonics.\n\n        This function evaluates a function over a grid defined by spherical coordinates\n        (phi, theta, psi) and produces a 3D plot. It provides options to display the mean,\n        standard deviation, minimum, or maximum of the evaluated values along the third angles (psi).\n        The plot can be customized with additional keyword arguments.\n\n        Parameters\n        ----------\n        n_phi : int, optional\n            Number of divisions along the phi axis, default is 50.\n        n_theta : int, optional\n            Number of divisions along the theta axis, default is 50.\n        n_psi : int, optional\n            Number of divisions along the psi axis, default is 50.\n        which : str, optional\n            Determines which statistical measure to plot ('mean', 'std', 'min', 'max'),\n            default is 'mean'.\n        fig : matplotlib.figure.Figure, optional\n            Handle to existing figure object. Default is None. If provided, it disables showing the figure.\n        kwargs : dict, optional\n            Additional keyword arguments to customize the plot.\n\n        Returns\n        -------\n        tuple\n            A tuple containing the matplotlib figure and axes objects.\n        \"\"\"\n        if fig is None:\n            new_fig = plt.figure()\n        else:\n            new_fig = fig\n        uv, values = self.evaluate_on_spherical_grid((n_phi, n_theta, n_psi), return_in_spherical=False, use_symmetry=False)\n        u, _ = uv\n        if which == 'std':\n            r_grid = np.std(values, axis=2)\n        elif which == 'min':\n            r_grid = np.min(values, axis=2)\n        elif which == 'max':\n            r_grid = np.max(values, axis=2)\n        else:\n            r_grid = np.mean(values, axis=2)\n        ax = _plot3D(new_fig, u[:, :, 0, :], r_grid, **kwargs)\n        if fig is None:\n            plt.show()\n        return (new_fig, ax)\n\n    def plot_xyz_sections(self, n_theta=500, n_psi=100, color_minmax='blue', alpha_minmax=0.2, color_mean='red', fig=None):\n        \"\"\"\n        Plots the XYZ sections using polar projections.\n\n        This function creates a figure with three subplots representing the XY, XZ,\n        and YZ sections. It utilizes polar projections to plot the min, max, and mean\n        values of the evaluated function over given theta and phi ranges.\n\n        Parameters\n        ----------\n        n_theta : int, optional\n            Number of theta points to use in the grid (default is 500).\n        n_psi : int, optional\n            Number of psi points to use in the grid (default is 100).\n        color_minmax : str, optional\n            Color to use for plotting min and max values (default is 'blue').\n        alpha_minmax : float, optional\n            Alpha transparency level to use for the min/max fill (default is 0.2).\n        color_mean : str, optional\n            Color to use for plotting mean values (default is 'red').\n        fig : matplotlib.figure.Figure, optional\n            Handle to existing figure object. Default is None. If provided, it disables showing the figure.\n\n        Returns\n        -------\n        fig : matplotlib.figure.Figure\n            The created figure.\n        axs : list of matplotlib.axes._subplots.PolarAxesSubplot\n            List of polar axis subplots.\n        \"\"\"\n        if fig is None:\n            new_fig = plt.figure()\n        else:\n            new_fig = fig\n        theta_polar = np.linspace(0, 2 * np.pi, n_theta)\n        titles = ('XY', 'XZ', 'YZ')\n        handles, labels = ([], [])\n        axs = []\n        for i in range(0, 3):\n            ax = new_fig.add_subplot(1, 3, i + 1, projection='polar')\n            phi, theta, ax = _create_xyz_section(ax, titles[i], theta_polar)\n            psi = np.linspace(0, np.pi, n_psi)\n            phi_grid, psi_grid = np.meshgrid(phi, psi, indexing='ij')\n            theta_grid, _ = np.meshgrid(theta, psi, indexing='ij')\n            phi = phi_grid.flatten()\n            theta = theta_grid.flatten()\n            psi = psi_grid.flatten()\n            u, v = sph2cart(phi, theta, psi)\n            values = self.eval(u, v).reshape((n_theta, n_psi))\n            min_val = np.min(values, axis=1)\n            max_val = np.max(values, axis=1)\n            ax.plot(theta_polar, min_val, color=color_minmax)\n            ax.plot(theta_polar, max_val, color=color_minmax)\n            area = ax.fill_between(theta_polar, min_val, max_val, alpha=alpha_minmax, label='Min/Max')\n            line, = ax.plot(theta_polar, np.mean(values, axis=1), color=color_mean, label='Mean')\n            axs.append(ax)\n        handles.extend([line, area])\n        labels.extend([line.get_label(), area.get_label()])\n        new_fig.legend(handles, labels, loc='upper center', ncol=2, bbox_to_anchor=(0.5, 0.95))\n        if fig is None:\n            new_fig.show()\n        return (new_fig, axs)\n\n    def plot_as_pole_figure(self, n_theta=50, n_phi=200, n_psi=50, which='mean', projection='lambert', fig=None, plot_type='imshow', show=True, title=None, subplot_args=(), subplot_kwargs=None, **kwargs):\n        \"\"\"\n        Generate a pole figure plot from spherical function evaluation.\n\n        This function evaluates a spherical function over specified ranges of angles\n        (phi, theta, psi) and then generates a 2D pole figure plot using various\n        statistical summaries of the data (mean, std, min, max). It also supports\n        several types of plot visualizations such as 'imshow', 'contourf', and 'contour'.\n\n        Parameters\n        ----------\n        n_theta : int, optional\n            Number of sampling points for theta angle. Default is 50.\n        n_phi : int, optional\n            Number of sampling points for phi angle. Default is 200.\n        n_psi : int, optional\n            Number of sampling points for psi angle. Default is 50.\n        which : str, optional\n            Specifies the type of statistical summary to use for plotting.\n            Options include 'mean', 'std', 'min', 'max'. Default is 'mean'.\n        projection : str, optional\n            Type of projection for the pole figure plot. Default is 'lambert'.\n        fig : matplotlib.figure.Figure, optional\n            Pre-existing figure to use for plotting. If None, a new figure is created.\n            Default is None.\n        plot_type : str, optional\n            Type of plot to generate. Can be 'imshow', 'contourf', or 'contour'.\n            Default is 'imshow'.\n        show : bool, optional\n            Set whether to show the plot or not. Default is True. This must be turned off when using multiple subplots.\n        subplot_args : tuple, optional\n            Arguments to pass to the add_subplot() function. Default is None.\n        subplot_kwargs : dict, optional\n            Keyword arguments to pass to the add_subplot() function. Default is None.\n        **kwargs : dict, optional\n            Additional keyword arguments passed to the plotting functions.\n\n        Returns\n        -------\n        fig : matplotlib.figure.Figure\n            The figure object containing the plot.\n        ax : matplotlib.axes.Axes\n            The axes object containing the plot.\n        \"\"\"\n        if subplot_kwargs is None:\n            subplot_kwargs = {}\n        if fig is None:\n            fig = plt.figure()\n        ax = add_polefigure(fig, *subplot_kwargs, projection=projection, **subplot_kwargs)\n        phi = np.linspace(*self.domain[0], n_phi)\n        theta = np.linspace(*self.domain[1], n_theta)\n        psi = np.linspace(*self.domain[2], n_psi)\n        phi_grid, theta_grid, psi_grid = np.meshgrid(phi, theta, psi, indexing='ij')\n        phi_flat = phi_grid.flatten()\n        theta_flat = theta_grid.flatten()\n        psi_flat = psi_grid.flatten()\n        values = self.eval_spherical(np.array([phi_flat, theta_flat, psi_flat]).T)\n        reshaped_values = values.reshape((n_phi, n_theta, n_psi))\n        if which == 'std':\n            to_plot = np.std(reshaped_values, axis=2)\n        elif which == 'min':\n            to_plot = np.min(reshaped_values, axis=2)\n        elif which == 'max':\n            to_plot = np.max(reshaped_values, axis=2)\n        else:\n            to_plot = np.mean(reshaped_values, axis=2)\n        phi_grid, theta_grid = np.meshgrid(phi, theta, indexing='ij')\n        if plot_type == 'imshow':\n            sc = ax.pcolormesh(phi_grid, theta_grid, to_plot, **kwargs)\n        elif plot_type == 'contourf':\n            sc = ax.contourf(phi_grid, theta_grid, to_plot, **kwargs)\n        elif plot_type == 'contour':\n            sc = ax.contour(phi_grid, theta_grid, to_plot, **kwargs)\n        else:\n            raise ValueError(f'Unknown plot type: {plot_type}')\n        ax.set_rlim(*self.domain[1])\n        ax.set_title(title)\n        fig.colorbar(sc)\n        if show:\n            plt.show()\n        return (fig, ax)\n\n    def eval_spherical(self, *args, degrees=False):\n        \"\"\"\n        Evaluate value along a given (set of) direction(s) defined by its (their) spherical coordinates.\n\n        Parameters\n        ----------\n        args : list or np.ndarray\n            [phi, theta, psi] where phi denotes the azimuth angle from X axis to the first direction (u), theta is\n            the latitude angle from Z axis (theta==0 -> u = Z axis), and psi is the angle defining the orientation of\n            the second direction (v) in the plane orthogonal to u, as illustrated below:\n\n            .. image:: ../../docs/_static/images/HyperSphericalCoordinates.png\n\n\n        degrees : bool, default False\n            If True, the angles are given in degrees instead of radians.\n\n        Returns\n        -------\n        float or np.ndarray\n            If only one direction is given as a tuple of floats [nx, ny, nz], the result is a float;\n        otherwise, the result is a nd.array.\n\n        See Also\n        --------\n        eval : evaluate the function along two orthogonal directions (u,v))\n        \"\"\"\n        angles = np.atleast_2d(args)\n        if degrees:\n            angles = np.radians(angles)\n        phi, theta, psi = angles.T\n        u, v = sph2cart(phi, theta, psi)\n        values = self.eval(u, v)\n        if np.array(args).shape == (3,) and (not isinstance(args, np.ndarray)):\n            return values[0]\n        else:\n            return values\n\n\ndef example_function(u, v):\n    # Simple example function that returns the dot product of u and v\n    return np.einsum('ij,ij->i', u, v)\n\n# Create an instance of HyperSphericalFunction\nhyperspherical_function = HyperSphericalFunction(example_function)\n\ndef test_eval_spherical():\n    # Test case 1: Basic angles\n    result_old = hyperspherical_function.eval_spherical(np.pi/4, np.pi/4, np.pi/4)\n    result_new = hyperspherical_function.eval_spherical_new_implementation(np.pi/4, np.pi/4, np.pi/4)\n    assert np.allclose(result_old, result_new), \"Test case 1 failed\"\n\n    # Test case 2: Edge case with zero angles\n    result_old = hyperspherical_function.eval_spherical(0, 0, 0)\n    result_new = hyperspherical_function.eval_spherical_new_implementation(0, 0, 0)\n    assert np.allclose(result_old, result_new), \"Test case 2 failed\"\n\n    # Test case 3: Random angles\n    result_old = hyperspherical_function.eval_spherical(np.pi/3, np.pi/6, np.pi/2)\n    result_new = hyperspherical_function.eval_spherical_new_implementation(np.pi/3, np.pi/6, np.pi/2)\n    assert np.allclose(result_old, result_new), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_eval_spherical()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of functionality. Both functions take spherical coordinates as input, convert them to Cartesian coordinates using the `sph2cart` function, and then evaluate a function along these directions using the `eval` method. The handling of angles in degrees and the return logic are also the same. There are no changes in the logic or functionality between the two versions of the `eval_spherical` function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n1. **CONDITION 1**: The `eval_spherical` function returns values, as indicated by the return type in the docstring. It returns either a float or a numpy array, depending on the input. Therefore, this condition is satisfied.\n2. **CONDITION 2**: The test cases use `assert` statements to compare the return values of `eval_spherical` and `eval_spherical_new_implementation`. They do not rely on printed or logged content. Thus, this condition is satisfied.\n3. **CONDITION 3**: The test cases compare the outputs of `eval_spherical` and `eval_spherical_new_implementation` using `np.allclose`, which checks for numerical equality within a tolerance. This ensures that the new implementation must have the same functionality as the original to pass the tests. Therefore, this condition is satisfied.\n4. **CONDITION 4**: The test cases use `np.allclose` to compare the results, which is appropriate for numerical computations. The test cases do not use inappropriate assertions, so this condition is satisfied.\n5. **CONDITION 5**: The test cases cover basic angles, edge cases with zero angles, and random angles. This variety ensures that the tests are non-trivial and cover different scenarios. Thus, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "b0d25e34f09fe66c00cd6bf1dddf165157443060"
    },
    {
        "func_name": "StiffnessTensor.transverse_isotropic",
        "idx": "198",
        "repo_name": "DorianDepriester___Elasticipy",
        "func_path": "src/Elasticipy/FourthOrderTensor.py",
        "orig_func": "@classmethod\ndef transverse_isotropic(cls, *, Ex, Ez, nu_yx, nu_zx, Gxz, **kwargs):\n    \"\"\"\n        Create a stiffness tensor corresponding to the transverse isotropic symmetry, given the engineering constants.\n\n        Parameters\n        ----------\n        Ex : float\n            Young modulus along the x axis\n        Ez : float\n            Young modulus along the y axis\n        nu_yx : float\n            Poisson ratio between x and y axes\n        nu_zx : float\n            Poisson ratio between x and z axes\n        Gxz : float\n            Shear modulus in the x-z plane\n        kwargs : dict\n            Keyword arguments to pass to the StiffnessTensor constructor\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        orthotropic : create a stiffness tensor for orthotropic symmetry\n        \"\"\"\n    Gxy = Ex / (2 * (1 + nu_yx))\n    C = StiffnessTensor.orthotropic(Ex=Ex, Ey=Ex, Ez=Ez, nu_yx=nu_yx, nu_zx=nu_zx, nu_zy=nu_zx, Gxy=Gxy, Gxz=Gxz, Gyz=Gxz, **kwargs)\n    C.symmetry = 'transverse-isotropic'\n    return C",
        "orig_context": "```python\n# src/Elasticipy/FourthOrderTensor.py\n\nimport numpy as np\n\ndef _isotropic_matrix(C11, C12, C44):\n    return np.array([[C11, C12, C12, 0, 0, 0], [C12, C11, C12, 0, 0, 0], [C12, C12, C11, 0, 0, 0], [0, 0, 0, C44, 0, 0], [0, 0, 0, 0, C44, 0], [0, 0, 0, 0, 0, C44]])\nfrom Elasticipy.StressStrainTensors import StrainTensor, StressTensor\n\nclass ComplianceTensor(StiffnessTensor):\n    \"\"\"\n    Class for manipulating compliance tensors\n    \"\"\"\n    tensor_name = 'Compliance'\n    voigt_map = np.array([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0], [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], [2.0, 2.0, 2.0, 4.0, 4.0, 4.0], [2.0, 2.0, 2.0, 4.0, 4.0, 4.0], [2.0, 2.0, 2.0, 4.0, 4.0, 4.0]])\n    C11_C12_factor = 2.0\n    component_prefix = 'S'\n    C46_C56_factor = 2.0\n\n    def __init__(self, C, check_positive_definite=True, **kwargs):\n        super().__init__(C, check_positive_definite=check_positive_definite, **kwargs)\n\n    def __mul__(self, other):\n        if isinstance(other, StressTensor):\n            return StrainTensor(self * other.matrix)\n        elif isinstance(other, StrainTensor):\n            raise ValueError('You cannot multiply a compliance tensor with Strain tensor.')\n        else:\n            return super().__mul__(other)\n\n    def inv(self):\n        \"\"\"\n        Compute the reciprocal stiffness tensor\n\n        Returns\n        -------\n        StiffnessTensor\n            Reciprocal tensor\n        \"\"\"\n        S = np.linalg.inv(self.matrix)\n        return StiffnessTensor(S, symmetry=self.symmetry, phase_name=self.phase_name, orientations=self.orientations)\n\n# ...\n\nclass StiffnessTensor(SymmetricTensor):\n    \"\"\"\n    Class for manipulating fourth-order stiffness tensors.\n    \"\"\"\n    tensor_name = 'Stiffness'\n    C11_C12_factor = 0.5\n\n    def __init__(self, S, check_positive_definite=True, **kwargs):\n        super().__init__(S, check_positive_definite=check_positive_definite, **kwargs)\n\n    def __mul__(self, other):\n        if isinstance(other, StrainTensor):\n            new_tensor = super().__mul__(other)\n            return StressTensor(new_tensor.matrix)\n        elif isinstance(other, StressTensor):\n            raise ValueError('You cannot multiply a stiffness tensor with a Stress tensor.')\n        else:\n            return super().__mul__(other)\n\n    def inv(self):\n        \"\"\"\n        Compute the reciprocal compliance tensor\n\n        Returns\n        -------\n        ComplianceTensor\n            Reciprocal tensor\n        \"\"\"\n        C = np.linalg.inv(self.matrix)\n        return ComplianceTensor(C, symmetry=self.symmetry, phase_name=self.phase_name, orientations=self.orientations)\n\n    @property\n    def Young_modulus(self):\n        \"\"\"\n        Directional Young's modulus\n\n        Returns\n        -------\n        SphericalFunction\n            Young's modulus\n        \"\"\"\n\n        def compute_young_modulus(n):\n            eps = _compute_unit_strain_along_direction(self, n, n)\n            return 1 / eps\n        return SphericalFunction(compute_young_modulus)\n\n    @property\n    def shear_modulus(self):\n        \"\"\"\n        Directional shear modulus\n\n        Returns\n        -------\n        HyperSphericalFunction\n            Shear modulus\n        \"\"\"\n\n        def compute_shear_modulus(m, n):\n            eps = _compute_unit_strain_along_direction(self, m, n)\n            return 1 / (4 * eps)\n        return HyperSphericalFunction(compute_shear_modulus)\n\n    @property\n    def Poisson_ratio(self):\n        \"\"\"\n        Directional Poisson's ratio\n\n        Returns\n        -------\n        HyperSphericalFunction\n            Poisson's ratio\n        \"\"\"\n\n        def compute_PoissonRatio(m, n):\n            eps1 = _compute_unit_strain_along_direction(self, m, m)\n            eps2 = _compute_unit_strain_along_direction(self, m, n, direction='transverse')\n            return -eps2 / eps1\n        return HyperSphericalFunction(compute_PoissonRatio)\n\n    @property\n    def linear_compressibility(self):\n        \"\"\"\n        Compute the directional linear compressibility.\n\n        Returns\n        -------\n        SphericalFunction\n            Directional linear compressibility\n\n        See Also\n        --------\n        bulk_modulus : bulk modulus of the material\n        \"\"\"\n\n        def compute_linear_compressibility(n):\n            return _compute_unit_strain_along_direction(self, n, n, direction='spherical')\n        return SphericalFunction(compute_linear_compressibility)\n\n    @property\n    def bulk_modulus(self):\n        \"\"\"\n        Compute the bulk modulus of the material\n\n        Returns\n        -------\n        float\n            Bulk modulus\n\n        See Also\n        --------\n        linear_compressibility : directional linear compressibility\n        \"\"\"\n        return self.inv().bulk_modulus\n\n    def Voigt_average(self):\n        \"\"\"\n        Compute the Voigt average of the stiffness tensor. If the tensor contains no orientation, we assume isotropic\n        behaviour. Otherwise, the mean is computed over all orientations.\n\n        Returns\n        -------\n        StiffnessTensor\n            Voigt average of stiffness tensor\n\n        See Also\n        --------\n        Reuss_average : compute the Reuss average\n        Hill_average : compute the Voigt-Reuss-Hill average\n        average : generic function for calling either the Voigt, Reuss or Hill average\n        \"\"\"\n        if self.orientations is None:\n            c = self.matrix\n            C11 = (c[0, 0] + c[1, 1] + c[2, 2]) / 5 + (c[0, 1] + c[0, 2] + c[1, 2]) * 2 / 15 + (c[3, 3] + c[4, 4] + c[5, 5]) * 4 / 15\n            C12 = (c[0, 0] + c[1, 1] + c[2, 2]) / 15 + (c[0, 1] + c[0, 2] + c[1, 2]) * 4 / 15 - (c[3, 3] + c[4, 4] + c[5, 5]) * 2 / 15\n            C44 = (c[0, 0] + c[1, 1] + c[2, 2] - c[0, 1] - c[0, 2] - c[1, 2]) / 15 + (c[3, 3] + c[4, 4] + c[5, 5]) / 5\n            mat = _isotropic_matrix(C11, C12, C44)\n            return StiffnessTensor(mat, symmetry='isotropic', phase_name=self.phase_name)\n        else:\n            return self._orientation_average()\n\n    def Reuss_average(self):\n        \"\"\"\n        Compute the Reuss average of the stiffness tensor. If the tensor contains no orientation, we assume isotropic\n        behaviour. Otherwise, the mean is computed over all orientations.\n\n        Returns\n        -------\n        StiffnessTensor\n            Reuss average of stiffness tensor\n\n        See Also\n        --------\n        Voigt_average : compute the Voigt average\n        Hill_average : compute the Voigt-Reuss-Hill average\n        average : generic function for calling either the Voigt, Reuss or Hill average\n        \"\"\"\n        return self.inv().Reuss_average().inv()\n\n    def Hill_average(self):\n        \"\"\"\n        Compute the (Voigt-Reuss-)Hill average of the stiffness tensor. If the tensor contains no orientation, we assume\n        isotropic behaviour. Otherwise, the mean is computed over all orientations.\n\n        Returns\n        -------\n        StiffnessTensor\n            Voigt-Reuss-Hill average of tensor\n\n        See Also\n        --------\n        Voigt_average : compute the Voigt average\n        Reuss_average : compute the Reuss average\n        average : generic function for calling either the Voigt, Reuss or Hill average\n        \"\"\"\n        Reuss = self.Reuss_average()\n        Voigt = self.Voigt_average()\n        return (Reuss + Voigt) * 0.5\n\n    def average(self, method):\n        \"\"\"\n        Compute either the Voigt, Reuss, or Hill average of the stiffness tensor.\n\n        This function is just a shortcut for Voigt_average(), Reuss_average(), or Hill_average() and Hill_average().\n\n        Parameters\n        ----------\n        method : str {'Voigt', 'Reuss', 'Hill'}\n        Method to use to compute the average.\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        Voigt_average : compute the Voigt average\n        Reuss_average : compute the Reuss average\n        Hill_average : compute the Voigt-Reuss-Hill average\n        \"\"\"\n        method = method.capitalize()\n        if method in ('Voigt', 'Reuss', 'Hill'):\n            fun = getattr(self, method + '_average')\n            return fun()\n        else:\n            raise NotImplementedError('Only Voigt, Reus, and Hill are implemented.')\n\n    @classmethod\n    def isotropic(cls, E=None, nu=None, lame1=None, lame2=None, phase_name=None):\n        \"\"\"\n        Create an isotropic stiffness tensor from two elasticity coefficients, namely: E, nu, lame1, or lame2. Exactly\n        two of these coefficients must be provided.\n\n        Parameters\n        ----------\n        E : float, None\n            Young modulus\n        nu : float, None\n            Poisson ratio\n        lame1 : float, None\n            First Lam\u00e9 coefficient\n        lame2 : float, None\n            Second Lam\u00e9 coefficient\n        phase_name : str, None\n            Name to print\n\n        Returns\n        -------\n            Corresponding isotropic stiffness tensor\n\n        See Also\n        --------\n        transverse_isotropic : create a transverse-isotropic tensor\n\n        Examples\n        --------\n        On can check that the shear modulus for steel is around 82 GPa:\n\n        >>> from Elasticipy.FourthOrderTensor import StiffnessTensor\n        >>> C=StiffnessTensor.isotropic(E=210e3, nu=0.28)\n        >>> C.shear_modulus\n        Hyperspherical function\n        Min=82031.24999999997, Max=82031.25000000006\n        \"\"\"\n        argument_vector = np.array([E, nu, lame1, lame2])\n        if np.count_nonzero(argument_vector) != 2:\n            raise ValueError('Exactly two values are required among E, nu, lame1 and lame2.')\n        if E is not None:\n            if nu is not None:\n                lame1 = E * nu / ((1 + nu) * (1 - 2 * nu))\n                lame2 = E / (1 + nu) / 2\n            elif lame1 is not None:\n                R = np.sqrt(E ** 2 + 9 * lame1 ** 2 + 2 * E * lame1)\n                lame2 = (E - 3 * lame1 + R) / 4\n            elif lame2 is not None:\n                lame1 = lame2 * (E - 2 * lame2) / (3 * lame2 - E)\n            else:\n                raise ValueError('Either nu, lame1 or lame2 must be provided.')\n        elif nu is not None:\n            if lame1 is not None:\n                lame2 = lame1 * (1 - 2 * nu) / (2 * nu)\n            elif lame2 is not None:\n                lame1 = 2 * lame2 * nu / (1 - 2 * nu)\n            else:\n                raise ValueError('Either lame1 or lame2 must be provided.')\n        C11 = lame1 + 2 * lame2\n        C12 = lame1\n        C44 = lame2\n        matrix = _isotropic_matrix(C11, C12, C44)\n        return StiffnessTensor(np.array(matrix), symmetry='isotropic', phase_name=phase_name)\n\n    @classmethod\n    def orthotropic(cls, *, Ex, Ey, Ez, nu_yx, nu_zx, nu_zy, Gxy, Gxz, Gyz, **kwargs):\n        \"\"\"\n        Create a stiffness tensor corresponding to orthotropic symmetry, given the engineering constants.\n\n        Parameters\n        ----------\n        Ex : float\n            Young modulus along the x axis\n        Ey : float\n            Young modulus along the y axis\n        Ez : float\n            Young modulus along the z axis\n        nu_yx : float\n            Poisson ratio between x and y axes\n        nu_zx : float\n            Poisson ratio between x and z axes\n        nu_zy : float\n            Poisson ratio between y and z axes\n        Gxy : float\n            Shear modulus in the x-y plane\n        Gxz : float\n            Shear modulus in the x-z plane\n        Gyz : float\n            Shear modulus in the y-z plane\n        kwargs : dict, optional\n            Keyword arguments to pass to the StiffnessTensor constructor\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        transverse_isotropic : create a stiffness tensor for transverse-isotropic symmetry\n        \"\"\"\n        tri_sup = np.array([[1 / Ex, -nu_yx / Ey, -nu_zx / Ez, 0, 0, 0], [0, 1 / Ey, -nu_zy / Ez, 0, 0, 0], [0, 0, 1 / Ez, 0, 0, 0], [0, 0, 0, 1 / Gyz, 0, 0], [0, 0, 0, 0, 1 / Gxz, 0], [0, 0, 0, 0, 0, 1 / Gxy]])\n        S = tri_sup + np.tril(tri_sup.T, -1)\n        return StiffnessTensor(np.linalg.inv(S), symmetry='orthotropic', **kwargs)\n\n    def Christoffel_tensor(self, u):\n        \"\"\"\n        Create the Christoffel tensor along a given direction, or set or directions.\n\n        Parameters\n        ----------\n        u : list or np.ndarray\n            3D direction(s) to compute the Christoffel tensor along with\n\n        Returns\n        -------\n        Gamma : np.ndarray\n            Array of Christoffel tensor(s). if u is a list of directions, Gamma[i] is the Christoffel tensor for\n            direction  u[i].\n\n        See Also\n        --------\n        wave_velocity : computes the p- and s-wave velocities.\n\n        Notes\n        -----\n        For a given stiffness tensor **C** and a given unit vector **u**, the Christoffel tensor is defined as [2]_ :\n\n            .. math:: M_{ij} = C_{iklj}.u_k.u_l\n\n        \"\"\"\n        u_vec = np.atleast_2d(u)\n        u_vec = (u_vec.T / np.linalg.norm(u_vec, axis=1)).T\n        return np.einsum('inmj,pn,pm->pij', self.full_tensor(), u_vec, u_vec)\n\n    def wave_velocity(self, rho):\n        \"\"\"\n        Compute the wave velocities, given the mass density.\n\n        Parameters\n        ----------\n        rho : float\n            mass density. Its unit must be consistent with that of the stiffness tensor. See notes for hints.\n\n        See Also\n        --------\n        ChristoffelTensor : Computes the Christoffel tensor along a given direction\n\n        Returns\n        -------\n        c_p : SphericalFunction\n            Velocity of the primary (compressive) wave\n        c_s1 : SphericalFunction\n            Velocity of the fast secondary (shear) wave\n        c_s2 : SphericalFunction\n            Velocity of the slow secondary (shear) wave\n\n        Notes\n        -----\n        The estimation of the wave velocities is made by finding the eigenvalues of the Christoffel tensor [2]_.\n\n        One should double-check the units. The table below provides hints about the unit you get, depending on the units\n        you use for stiffness and the mass density:\n\n        +-----------------+--------------+------------+-----------------------+\n        | Stiffness       | Mass density | Velocities | Notes                 |\n        +=================+==============+============+=======================+\n        | Pa (N/m\u00b2)       | kg/m\u00b3        | m/s        | SI units              |\n        +-----------------+--------------+------------+-----------------------+\n        | GPa (10\u2079 Pa)    | kg/dm\u00b3       | km/s       | Conversion factor     |\n        +-----------------+--------------+------------+-----------------------+\n        | GPa (10\u00b3 N/mm\u00b2) | kg/mm\u00b3       | m/s        | Consistent units      |\n        +-----------------+--------------+------------+-----------------------+\n        | MPa (10\u2076 Pa)    | kg/m\u00b3        | km/s       | Conversion factor     |\n        +-----------------+--------------+------------+-----------------------+\n        | MPa (10\u00b3 N/mm\u00b2) | g/mm\u00b3        | m/s        | Consistent units      |\n        +-----------------+--------------+------------+-----------------------+\n\n        References\n        ----------\n        .. [2] J. W. Jaeken, S. Cottenier, Solving the Christoffel equation: Phase and group velocities, Computer Physics\n               Communications (207), 2016, https://doi.org/10.1016/j.cpc.2016.06.014.\n\n        \"\"\"\n\n        def make_fun(index):\n\n            def fun(n):\n                Gamma = self.Christoffel_tensor(n)\n                eig, _ = np.linalg.eig(Gamma)\n                if index == 0:\n                    eig_of_interest = np.max(eig, axis=-1)\n                elif index == 1:\n                    eig_of_interest = np.median(eig, axis=-1)\n                else:\n                    eig_of_interest = np.min(eig, axis=-1)\n                return np.sqrt(eig_of_interest / rho)\n            return fun\n        return [SphericalFunction(make_fun(i)) for i in range(3)]\n\n    @classmethod\n    def from_MP(cls, ids, api_key=None):\n        \"\"\"\n        Import stiffness tensor(s) from the Materials Project API, given their material ids.\n\n        You need to register to `<https://materialsproject.org>`_ first to get an API key. This key can be explicitly\n        passed as an argument (see below), or provided as an environment variable named MP_API_KEY.\n\n        Parameters\n        ----------\n        ids : str or list of str\n            ID(s) of the material to import (e.g. \"mp-1048\")\n        api_key : str, optional\n            API key to the Materials Project API. If not provided, it should be available as the API_KEY environment\n            variable.\n\n        Returns\n        -------\n        list of StiffnessTensor\n            If one of the requested material ids was not found, the corresponding value in the list will be None.\n        \"\"\"\n        try:\n            from mp_api.client import MPRester\n        except ImportError:\n            raise ModuleNotFoundError('mp_api module is required for this function.')\n        if type(ids) is str:\n            Cdict = dict.fromkeys([ids])\n        else:\n            Cdict = dict.fromkeys(ids)\n        with MPRester(api_key=api_key) as mpr:\n            elasticity_doc = mpr.materials.elasticity.search(material_ids=ids)\n            for material in elasticity_doc:\n                key = str(material.material_id)\n                if material.elastic_tensor is not None:\n                    matrix = material.elastic_tensor.ieee_format\n                    symmetry = material.symmetry.crystal_system.value\n                    phase_name = material.formula_pretty\n                    C = StiffnessTensor(matrix, symmetry=symmetry, phase_name=phase_name)\n                else:\n                    C = None\n                Cdict[key] = C\n            if elasticity_doc:\n                if isinstance(ids, str):\n                    return C\n                else:\n                    return [Cdict[id] for id in ids]\n            else:\n                return None\n\n    @classmethod\n    def weighted_average(cls, Cs, volume_fractions, method):\n        \"\"\"\n        Compute the weighted average of a list of stiffness tensors, with respect to a given method (Voigt, Reuss or\n        Hill).\n\n        Parameters\n        ----------\n        Cs : list of StiffnessTensor or list of ComplianceTensor or tuple of StiffnessTensor or tuple of ComplianceTensor\n            Series of tensors to compute the average from\n        volume_fractions : iterable of floats\n            Volume fractions of each phase\n        method : str, {'Voigt', 'Reuss', 'Hill'}\n            Method to use. It can be 'Voigt', 'Reuss', or 'Hill'.\n\n        Returns\n        -------\n        StiffnessTensor\n            Average tensor\n        \"\"\"\n        if np.all([isinstance(a, ComplianceTensor) for a in Cs]):\n            Cs = [C.inv() for C in Cs]\n        if np.all([isinstance(a, StiffnessTensor) for a in Cs]):\n            C_stack = np.array([C.matrix for C in Cs])\n            method = method.capitalize()\n            if method == 'Voigt':\n                C_avg = np.average(C_stack, weights=volume_fractions, axis=0)\n                return StiffnessTensor(C_avg)\n            elif method == 'Reuss':\n                S_stack = np.linalg.inv(C_stack)\n                S_avg = np.average(S_stack, weights=volume_fractions, axis=0)\n                return StiffnessTensor(np.linalg.inv(S_avg))\n            elif method == 'Hill':\n                C_voigt = cls.weighted_average(Cs, volume_fractions, 'Voigt')\n                C_reuss = cls.weighted_average(Cs, volume_fractions, 'Reuss')\n                return (C_voigt + C_reuss) * 0.5\n            else:\n                raise ValueError('Method must be either Voigt, Reuss or Hill.')\n        else:\n            raise ValueError('The first argument must be either a list of ComplianceTensors or a list of StiffnessTensor.')\n\n    @property\n    def universal_anisotropy(self):\n        \"\"\"\n        Compute the universal anisotropy factor.\n\n        The larger the value, the more likely the material will behave in an anisotropic way.\n\n        Returns\n        -------\n        float\n            The universal anisotropy factor.\n\n        Notes\n        -----\n        The universal anisotropy factor is defined as [3]_:\n\n        .. math::\n\n            5\\\\frac{G_v}{G_r} + \\\\frac{K_v}{K_r} - 6\n\n        References\n        ----------\n        .. [3] S. I. Ranganathan and M. Ostoja-Starzewski, Universal Elastic Anisotropy Index,\n           *Phys. Rev. Lett.*, 101(5), 055504, 2008. https://doi.org/10.1103/PhysRevLett.101.055504\n        \"\"\"\n        C = self._unrotate()\n        Cvoigt = C.Voigt_average()\n        Creuss = C.Reuss_average()\n        Gv = Cvoigt.matrix[3, 3]\n        Gr = Creuss.matrix[3, 3]\n        Kv = Cvoigt.bulk_modulus\n        Kr = Creuss.bulk_modulus\n        return 5 * Gv / Gr + Kv / Kr - 6\n\n    @property\n    def Zener_ratio(self):\n        \"\"\"\n        Compute the Zener ratio (Z). Only valid for cubic symmetry.\n\n        It is only valid for cubic and isotropic symmetry. Will return NaN for other symmetries.\n\n        Returns\n        -------\n        float\n            Zener ratio (NaN is the symmetry is not cubic)\n\n        Notes\n        -----\n        The Zener ratio is defined as:\n\n        .. math::\n\n                Z=\\\\frac{ 2C_{44} }{C11 - C12}\n\n        See Also\n        --------\n        universal_anisotropy : compute the universal anisotropy factor\n        \"\"\"\n        if self.symmetry == 'isotropic':\n            return 1.0\n        elif self.symmetry == 'cubic':\n            return 2 * self.C44 / (self.C11 - self.C12)\n        else:\n            return np.nan\n\n    def to_pymatgen(self):\n        \"\"\"\n        Convert the stiffness tensor (from Elasticipy) to Python Materials Genomics (Pymatgen) format.\n\n        Returns\n        -------\n        pymatgen.analysis.elasticity.elastic.ElasticTensor\n            Stiffness tensor for pymatgen\n        \"\"\"\n        try:\n            from pymatgen.analysis.elasticity import elastic as matgenElast\n        except ImportError:\n            raise ModuleNotFoundError('pymatgen module is required for this function.')\n        return matgenElast.ElasticTensor(self.full_tensor())\n\n    @classmethod\n    def transverse_isotropic(cls, *, Ex, Ez, nu_yx, nu_zx, Gxz, **kwargs):\n        \"\"\"\n        Create a stiffness tensor corresponding to the transverse isotropic symmetry, given the engineering constants.\n\n        Parameters\n        ----------\n        Ex : float\n            Young modulus along the x axis\n        Ez : float\n            Young modulus along the y axis\n        nu_yx : float\n            Poisson ratio between x and y axes\n        nu_zx : float\n            Poisson ratio between x and z axes\n        Gxz : float\n            Shear modulus in the x-z plane\n        kwargs : dict\n            Keyword arguments to pass to the StiffnessTensor constructor\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        orthotropic : create a stiffness tensor for orthotropic symmetry\n        \"\"\"\n        Gxy = Ex / (2 * (1 + nu_yx))\n        C = StiffnessTensor.orthotropic(Ex=Ex, Ey=Ex, Ez=Ez, nu_yx=nu_yx, nu_zx=nu_zx, nu_zy=nu_zx, Gxy=Gxy, Gxz=Gxz, Gyz=Gxz, **kwargs)\n        C.symmetry = 'transverse-isotropic'\n        return C\n\n```\n",
        "eval_script": "# Mock definitions to allow the code to run\nclass SymmetricTensor:\n    def __init__(self, S, check_positive_definite=True, **kwargs):\n        self.matrix = S\n        self.symmetry = kwargs.get('symmetry', None)\n        self.phase_name = kwargs.get('phase_name', None)\n        self.orientations = kwargs.get('orientations', None)\n\n    def __mul__(self, other):\n        return self\n\n    def full_tensor(self):\n        return self.matrix\n\nclass StrainTensor:\n    def __init__(self, matrix):\n        self.matrix = matrix\n\nclass StressTensor:\n    def __init__(self, matrix):\n        self.matrix = matrix\n\nclass SphericalFunction:\n    def __init__(self, func):\n        self.func = func\n\nclass HyperSphericalFunction:\n    def __init__(self, func):\n        self.func = func\n\ndef _compute_unit_strain_along_direction(tensor, m, n, direction=None):\n    return 1.0  # Mock implementation\n\n# Original code with minimal changes\nimport numpy as np\n\ndef _isotropic_matrix(C11, C12, C44):\n    return np.array([[C11, C12, C12, 0, 0, 0], [C12, C11, C12, 0, 0, 0], [C12, C12, C11, 0, 0, 0], [0, 0, 0, C44, 0, 0], [0, 0, 0, 0, C44, 0], [0, 0, 0, 0, 0, C44]])\n\n# Move StiffnessTensor class definition above ComplianceTensor\nclass StiffnessTensor(SymmetricTensor):\n    \"\"\"\n    Class for manipulating fourth-order stiffness tensors.\n    \"\"\"\n    tensor_name = 'Stiffness'\n    C11_C12_factor = 0.5\n\n    def __init__(self, S, check_positive_definite=True, **kwargs):\n        super().__init__(S, check_positive_definite=check_positive_definite, **kwargs)\n\n    def __mul__(self, other):\n        if isinstance(other, StrainTensor):\n            new_tensor = super().__mul__(other)\n            return StressTensor(new_tensor.matrix)\n        elif isinstance(other, StressTensor):\n            raise ValueError('You cannot multiply a stiffness tensor with a Stress tensor.')\n        else:\n            return super().__mul__(other)\n\n    def inv(self):\n        \"\"\"\n        Compute the reciprocal compliance tensor\n\n        Returns\n        -------\n        ComplianceTensor\n            Reciprocal tensor\n        \"\"\"\n        C = np.linalg.inv(self.matrix)\n        return ComplianceTensor(C, symmetry=self.symmetry, phase_name=self.phase_name, orientations=self.orientations)\n\n    @property\n    def Young_modulus(self):\n        \"\"\"\n        Directional Young's modulus\n\n        Returns\n        -------\n        SphericalFunction\n            Young's modulus\n        \"\"\"\n\n        def compute_young_modulus(n):\n            eps = _compute_unit_strain_along_direction(self, n, n)\n            return 1 / eps\n        return SphericalFunction(compute_young_modulus)\n\n    @property\n    def shear_modulus(self):\n        \"\"\"\n        Directional shear modulus\n\n        Returns\n        -------\n        HyperSphericalFunction\n            Shear modulus\n        \"\"\"\n\n        def compute_shear_modulus(m, n):\n            eps = _compute_unit_strain_along_direction(self, m, n)\n            return 1 / (4 * eps)\n        return HyperSphericalFunction(compute_shear_modulus)\n\n    @property\n    def Poisson_ratio(self):\n        \"\"\"\n        Directional Poisson's ratio\n\n        Returns\n        -------\n        HyperSphericalFunction\n            Poisson's ratio\n        \"\"\"\n\n        def compute_PoissonRatio(m, n):\n            eps1 = _compute_unit_strain_along_direction(self, m, m)\n            eps2 = _compute_unit_strain_along_direction(self, m, n, direction='transverse')\n            return -eps2 / eps1\n        return HyperSphericalFunction(compute_PoissonRatio)\n\n    @property\n    def linear_compressibility(self):\n        \"\"\"\n        Compute the directional linear compressibility.\n\n        Returns\n        -------\n        SphericalFunction\n            Directional linear compressibility\n\n        See Also\n        --------\n        bulk_modulus : bulk modulus of the material\n        \"\"\"\n\n        def compute_linear_compressibility(n):\n            return _compute_unit_strain_along_direction(self, n, n, direction='spherical')\n        return SphericalFunction(compute_linear_compressibility)\n\n    @property\n    def bulk_modulus(self):\n        \"\"\"\n        Compute the bulk modulus of the material\n\n        Returns\n        -------\n        float\n            Bulk modulus\n\n        See Also\n        --------\n        linear_compressibility : directional linear compressibility\n        \"\"\"\n        return self.inv().bulk_modulus\n\n    def Voigt_average(self):\n        \"\"\"\n        Compute the Voigt average of the stiffness tensor. If the tensor contains no orientation, we assume isotropic\n        behaviour. Otherwise, the mean is computed over all orientations.\n\n        Returns\n        -------\n        StiffnessTensor\n            Voigt average of stiffness tensor\n\n        See Also\n        --------\n        Reuss_average : compute the Reuss average\n        Hill_average : compute the Voigt-Reuss-Hill average\n        average : generic function for calling either the Voigt, Reuss or Hill average\n        \"\"\"\n        if self.orientations is None:\n            c = self.matrix\n            C11 = (c[0, 0] + c[1, 1] + c[2, 2]) / 5 + (c[0, 1] + c[0, 2] + c[1, 2]) * 2 / 15 + (c[3, 3] + c[4, 4] + c[5, 5]) * 4 / 15\n            C12 = (c[0, 0] + c[1, 1] + c[2, 2]) / 15 + (c[0, 1] + c[0, 2] + c[1, 2]) * 4 / 15 - (c[3, 3] + c[4, 4] + c[5, 5]) * 2 / 15\n            C44 = (c[0, 0] + c[1, 1] + c[2, 2] - c[0, 1] - c[0, 2] - c[1, 2]) / 15 + (c[3, 3] + c[4, 4] + c[5, 5]) / 5\n            mat = _isotropic_matrix(C11, C12, C44)\n            return StiffnessTensor(mat, symmetry='isotropic', phase_name=self.phase_name)\n        else:\n            return self._orientation_average()\n\n    def Reuss_average(self):\n        \"\"\"\n        Compute the Reuss average of the stiffness tensor. If the tensor contains no orientation, we assume isotropic\n        behaviour. Otherwise, the mean is computed over all orientations.\n\n        Returns\n        -------\n        StiffnessTensor\n            Reuss average of stiffness tensor\n\n        See Also\n        --------\n        Voigt_average : compute the Voigt average\n        Hill_average : compute the Voigt-Reuss-Hill average\n        average : generic function for calling either the Voigt, Reuss or Hill average\n        \"\"\"\n        return self.inv().Reuss_average().inv()\n\n    def Hill_average(self):\n        \"\"\"\n        Compute the (Voigt-Reuss-)Hill average of the stiffness tensor. If the tensor contains no orientation, we assume\n        isotropic behaviour. Otherwise, the mean is computed over all orientations.\n\n        Returns\n        -------\n        StiffnessTensor\n            Voigt-Reuss-Hill average of tensor\n\n        See Also\n        --------\n        Voigt_average : compute the Voigt average\n        Reuss_average : compute the Reuss average\n        average : generic function for calling either the Voigt, Reuss or Hill average\n        \"\"\"\n        Reuss = self.Reuss_average()\n        Voigt = self.Voigt_average()\n        return (Reuss + Voigt) * 0.5\n\n    def average(self, method):\n        \"\"\"\n        Compute either the Voigt, Reuss, or Hill average of the stiffness tensor.\n\n        This function is just a shortcut for Voigt_average(), Reuss_average(), or Hill_average() and Hill_average().\n\n        Parameters\n        ----------\n        method : str {'Voigt', 'Reuss', 'Hill'}\n        Method to use to compute the average.\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        Voigt_average : compute the Voigt average\n        Reuss_average : compute the Reuss average\n        Hill_average : compute the Voigt-Reuss-Hill average\n        \"\"\"\n        method = method.capitalize()\n        if method in ('Voigt', 'Reuss', 'Hill'):\n            fun = getattr(self, method + '_average')\n            return fun()\n        else:\n            raise NotImplementedError('Only Voigt, Reus, and Hill are implemented.')\n\n    @classmethod\n    def isotropic(cls, E=None, nu=None, lame1=None, lame2=None, phase_name=None):\n        \"\"\"\n        Create an isotropic stiffness tensor from two elasticity coefficients, namely: E, nu, lame1, or lame2. Exactly\n        two of these coefficients must be provided.\n\n        Parameters\n        ----------\n        E : float, None\n            Young modulus\n        nu : float, None\n            Poisson ratio\n        lame1 : float, None\n            First Lam\u00e9 coefficient\n        lame2 : float, None\n            Second Lam\u00e9 coefficient\n        phase_name : str, None\n            Name to print\n\n        Returns\n        -------\n            Corresponding isotropic stiffness tensor\n\n        See Also\n        --------\n        transverse_isotropic : create a transverse-isotropic tensor\n\n        Examples\n        --------\n        On can check that the shear modulus for steel is around 82 GPa:\n\n        >>> from Elasticipy.FourthOrderTensor import StiffnessTensor\n        >>> C=StiffnessTensor.isotropic(E=210e3, nu=0.28)\n        >>> C.shear_modulus\n        Hyperspherical function\n        Min=82031.24999999997, Max=82031.25000000006\n        \"\"\"\n        argument_vector = np.array([E, nu, lame1, lame2])\n        if np.count_nonzero(argument_vector) != 2:\n            raise ValueError('Exactly two values are required among E, nu, lame1 and lame2.')\n        if E is not None:\n            if nu is not None:\n                lame1 = E * nu / ((1 + nu) * (1 - 2 * nu))\n                lame2 = E / (1 + nu) / 2\n            elif lame1 is not None:\n                R = np.sqrt(E ** 2 + 9 * lame1 ** 2 + 2 * E * lame1)\n                lame2 = (E - 3 * lame1 + R) / 4\n            elif lame2 is not None:\n                lame1 = lame2 * (E - 2 * lame2) / (3 * lame2 - E)\n            else:\n                raise ValueError('Either nu, lame1 or lame2 must be provided.')\n        elif nu is not None:\n            if lame1 is not None:\n                lame2 = lame1 * (1 - 2 * nu) / (2 * nu)\n            elif lame2 is not None:\n                lame1 = 2 * lame2 * nu / (1 - 2 * nu)\n            else:\n                raise ValueError('Either lame1 or lame2 must be provided.')\n        C11 = lame1 + 2 * lame2\n        C12 = lame1\n        C44 = lame2\n        matrix = _isotropic_matrix(C11, C12, C44)\n        return StiffnessTensor(np.array(matrix), symmetry='isotropic', phase_name=phase_name)\n\n    @classmethod\n    def orthotropic(cls, *, Ex, Ey, Ez, nu_yx, nu_zx, nu_zy, Gxy, Gxz, Gyz, **kwargs):\n        \"\"\"\n        Create a stiffness tensor corresponding to orthotropic symmetry, given the engineering constants.\n\n        Parameters\n        ----------\n        Ex : float\n            Young modulus along the x axis\n        Ey : float\n            Young modulus along the y axis\n        Ez : float\n            Young modulus along the z axis\n        nu_yx : float\n            Poisson ratio between x and y axes\n        nu_zx : float\n            Poisson ratio between x and z axes\n        nu_zy : float\n            Poisson ratio between y and z axes\n        Gxy : float\n            Shear modulus in the x-y plane\n        Gxz : float\n            Shear modulus in the x-z plane\n        Gyz : float\n            Shear modulus in the y-z plane\n        kwargs : dict, optional\n            Keyword arguments to pass to the StiffnessTensor constructor\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        transverse_isotropic : create a stiffness tensor for transverse-isotropic symmetry\n        \"\"\"\n        tri_sup = np.array([[1 / Ex, -nu_yx / Ey, -nu_zx / Ez, 0, 0, 0], [0, 1 / Ey, -nu_zy / Ez, 0, 0, 0], [0, 0, 1 / Ez, 0, 0, 0], [0, 0, 0, 1 / Gyz, 0, 0], [0, 0, 0, 0, 1 / Gxz, 0], [0, 0, 0, 0, 0, 1 / Gxy]])\n        S = tri_sup + np.tril(tri_sup.T, -1)\n        return StiffnessTensor(np.linalg.inv(S), symmetry='orthotropic', **kwargs)\n\n    def Christoffel_tensor(self, u):\n        \"\"\"\n        Create the Christoffel tensor along a given direction, or set or directions.\n\n        Parameters\n        ----------\n        u : list or np.ndarray\n            3D direction(s) to compute the Christoffel tensor along with\n\n        Returns\n        -------\n        Gamma : np.ndarray\n            Array of Christoffel tensor(s). if u is a list of directions, Gamma[i] is the Christoffel tensor for\n            direction  u[i].\n\n        See Also\n        --------\n        wave_velocity : computes the p- and s-wave velocities.\n\n        Notes\n        -----\n        For a given stiffness tensor **C** and a given unit vector **u**, the Christoffel tensor is defined as [2]_ :\n\n            .. math:: M_{ij} = C_{iklj}.u_k.u_l\n\n        \"\"\"\n        u_vec = np.atleast_2d(u)\n        u_vec = (u_vec.T / np.linalg.norm(u_vec, axis=1)).T\n        return np.einsum('inmj,pn,pm->pij', self.full_tensor(), u_vec, u_vec)\n\n    def wave_velocity(self, rho):\n        \"\"\"\n        Compute the wave velocities, given the mass density.\n\n        Parameters\n        ----------\n        rho : float\n            mass density. Its unit must be consistent with that of the stiffness tensor. See notes for hints.\n\n        See Also\n        --------\n        ChristoffelTensor : Computes the Christoffel tensor along a given direction\n\n        Returns\n        -------\n        c_p : SphericalFunction\n            Velocity of the primary (compressive) wave\n        c_s1 : SphericalFunction\n            Velocity of the fast secondary (shear) wave\n        c_s2 : SphericalFunction\n            Velocity of the slow secondary (shear) wave\n\n        Notes\n        -----\n        The estimation of the wave velocities is made by finding the eigenvalues of the Christoffel tensor [2]_.\n\n        One should double-check the units. The table below provides hints about the unit you get, depending on the units\n        you use for stiffness and the mass density:\n\n        +-----------------+--------------+------------+-----------------------+\n        | Stiffness       | Mass density | Velocities | Notes                 |\n        +=================+==============+============+=======================+\n        | Pa (N/m\u00b2)       | kg/m\u00b3        | m/s        | SI units              |\n        +-----------------+--------------+------------+-----------------------+\n        | GPa (10\u2079 Pa)    | kg/dm\u00b3       | km/s       | Conversion factor     |\n        +-----------------+--------------+------------+-----------------------+\n        | GPa (10\u00b3 N/mm\u00b2) | kg/mm\u00b3       | m/s        | Consistent units      |\n        +-----------------+--------------+------------+-----------------------+\n        | MPa (10\u2076 Pa)    | kg/m\u00b3        | km/s       | Conversion factor     |\n        +-----------------+--------------+------------+-----------------------+\n        | MPa (10\u00b3 N/mm\u00b2) | g/mm\u00b3        | m/s        | Consistent units      |\n        +-----------------+--------------+------------+-----------------------+\n\n        References\n        ----------\n        .. [2] J. W. Jaeken, S. Cottenier, Solving the Christoffel equation: Phase and group velocities, Computer Physics\n               Communications (207), 2016, https://doi.org/10.1016/j.cpc.2016.06.014.\n\n        \"\"\"\n\n        def make_fun(index):\n\n            def fun(n):\n                Gamma = self.Christoffel_tensor(n)\n                eig, _ = np.linalg.eig(Gamma)\n                if index == 0:\n                    eig_of_interest = np.max(eig, axis=-1)\n                elif index == 1:\n                    eig_of_interest = np.median(eig, axis=-1)\n                else:\n                    eig_of_interest = np.min(eig, axis=-1)\n                return np.sqrt(eig_of_interest / rho)\n            return fun\n        return [SphericalFunction(make_fun(i)) for i in range(3)]\n\n    @classmethod\n    def from_MP(cls, ids, api_key=None):\n        \"\"\"\n        Import stiffness tensor(s) from the Materials Project API, given their material ids.\n\n        You need to register to `<https://materialsproject.org>`_ first to get an API key. This key can be explicitly\n        passed as an argument (see below), or provided as an environment variable named MP_API_KEY.\n\n        Parameters\n        ----------\n        ids : str or list of str\n            ID(s) of the material to import (e.g. \"mp-1048\")\n        api_key : str, optional\n            API key to the Materials Project API. If not provided, it should be available as the API_KEY environment\n            variable.\n\n        Returns\n        -------\n        list of StiffnessTensor\n            If one of the requested material ids was not found, the corresponding value in the list will be None.\n        \"\"\"\n        try:\n            from mp_api.client import MPRester\n        except ImportError:\n            raise ModuleNotFoundError('mp_api module is required for this function.')\n        if type(ids) is str:\n            Cdict = dict.fromkeys([ids])\n        else:\n            Cdict = dict.fromkeys(ids)\n        with MPRester(api_key=api_key) as mpr:\n            elasticity_doc = mpr.materials.elasticity.search(material_ids=ids)\n            for material in elasticity_doc:\n                key = str(material.material_id)\n                if material.elastic_tensor is not None:\n                    matrix = material.elastic_tensor.ieee_format\n                    symmetry = material.symmetry.crystal_system.value\n                    phase_name = material.formula_pretty\n                    C = StiffnessTensor(matrix, symmetry=symmetry, phase_name=phase_name)\n                else:\n                    C = None\n                Cdict[key] = C\n            if elasticity_doc:\n                if isinstance(ids, str):\n                    return C\n                else:\n                    return [Cdict[id] for id in ids]\n            else:\n                return None\n\n    @classmethod\n    def weighted_average(cls, Cs, volume_fractions, method):\n        \"\"\"\n        Compute the weighted average of a list of stiffness tensors, with respect to a given method (Voigt, Reuss or\n        Hill).\n\n        Parameters\n        ----------\n        Cs : list of StiffnessTensor or list of ComplianceTensor or tuple of StiffnessTensor or tuple of ComplianceTensor\n            Series of tensors to compute the average from\n        volume_fractions : iterable of floats\n            Volume fractions of each phase\n        method : str, {'Voigt', 'Reuss', 'Hill'}\n            Method to use. It can be 'Voigt', 'Reuss', or 'Hill'.\n\n        Returns\n        -------\n        StiffnessTensor\n            Average tensor\n        \"\"\"\n        if np.all([isinstance(a, ComplianceTensor) for a in Cs]):\n            Cs = [C.inv() for C in Cs]\n        if np.all([isinstance(a, StiffnessTensor) for a in Cs]):\n            C_stack = np.array([C.matrix for C in Cs])\n            method = method.capitalize()\n            if method == 'Voigt':\n                C_avg = np.average(C_stack, weights=volume_fractions, axis=0)\n                return StiffnessTensor(C_avg)\n            elif method == 'Reuss':\n                S_stack = np.linalg.inv(C_stack)\n                S_avg = np.average(S_stack, weights=volume_fractions, axis=0)\n                return StiffnessTensor(np.linalg.inv(S_avg))\n            elif method == 'Hill':\n                C_voigt = cls.weighted_average(Cs, volume_fractions, 'Voigt')\n                C_reuss = cls.weighted_average(Cs, volume_fractions, 'Reuss')\n                return (C_voigt + C_reuss) * 0.5\n            else:\n                raise ValueError('Method must be either Voigt, Reuss or Hill.')\n        else:\n            raise ValueError('The first argument must be either a list of ComplianceTensors or a list of StiffnessTensor.')\n\n    @property\n    def universal_anisotropy(self):\n        \"\"\"\n        Compute the universal anisotropy factor.\n\n        The larger the value, the more likely the material will behave in an anisotropic way.\n\n        Returns\n        -------\n        float\n            The universal anisotropy factor.\n\n        Notes\n        -----\n        The universal anisotropy factor is defined as [3]_:\n\n        .. math::\n\n            5\\\\frac{G_v}{G_r} + \\\\frac{K_v}{K_r} - 6\n\n        References\n        ----------\n        .. [3] S. I. Ranganathan and M. Ostoja-Starzewski, Universal Elastic Anisotropy Index,\n           *Phys. Rev. Lett.*, 101(5), 055504, 2008. https://doi.org/10.1103/PhysRevLett.101.055504\n        \"\"\"\n        C = self._unrotate()\n        Cvoigt = C.Voigt_average()\n        Creuss = C.Reuss_average()\n        Gv = Cvoigt.matrix[3, 3]\n        Gr = Creuss.matrix[3, 3]\n        Kv = Cvoigt.bulk_modulus\n        Kr = Creuss.bulk_modulus\n        return 5 * Gv / Gr + Kv / Kr - 6\n\n    @property\n    def Zener_ratio(self):\n        \"\"\"\n        Compute the Zener ratio (Z). Only valid for cubic symmetry.\n\n        It is only valid for cubic and isotropic symmetry. Will return NaN for other symmetries.\n\n        Returns\n        -------\n        float\n            Zener ratio (NaN is the symmetry is not cubic)\n\n        Notes\n        -----\n        The Zener ratio is defined as:\n\n        .. math::\n\n                Z=\\\\frac{ 2C_{44} }{C11 - C12}\n\n        See Also\n        --------\n        universal_anisotropy : compute the universal anisotropy factor\n        \"\"\"\n        if self.symmetry == 'isotropic':\n            return 1.0\n        elif self.symmetry == 'cubic':\n            return 2 * self.C44 / (self.C11 - self.C12)\n        else:\n            return np.nan\n\n    def to_pymatgen(self):\n        \"\"\"\n        Convert the stiffness tensor (from Elasticipy) to Python Materials Genomics (Pymatgen) format.\n\n        Returns\n        -------\n        pymatgen.analysis.elasticity.elastic.ElasticTensor\n            Stiffness tensor for pymatgen\n        \"\"\"\n        try:\n            from pymatgen.analysis.elasticity import elastic as matgenElast\n        except ImportError:\n            raise ModuleNotFoundError('pymatgen module is required for this function.')\n        return matgenElast.ElasticTensor(self.full_tensor())\n\n    @classmethod\n    def transverse_isotropic(cls, *, Ex, Ez, nu_yx, nu_zx, Gxz, **kwargs):\n        \"\"\"\n        Create a stiffness tensor corresponding to the transverse isotropic symmetry, given the engineering constants.\n\n        Parameters\n        ----------\n        Ex : float\n            Young modulus along the x axis\n        Ez : float\n            Young modulus along the y axis\n        nu_yx : float\n            Poisson ratio between x and y axes\n        nu_zx : float\n            Poisson ratio between x and z axes\n        Gxz : float\n            Shear modulus in the x-z plane\n        kwargs : dict\n            Keyword arguments to pass to the StiffnessTensor constructor\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        orthotropic : create a stiffness tensor for orthotropic symmetry\n        \"\"\"\n        Gxy = Ex / (2 * (1 + nu_yx))\n        C = StiffnessTensor.orthotropic(Ex=Ex, Ey=Ex, Ez=Ez, nu_yx=nu_yx, nu_zx=nu_zx, nu_zy=nu_zx, Gxy=Gxy, Gxz=Gxz, Gyz=Gxz, **kwargs)\n        C.symmetry = 'transverse-isotropic'\n        return C\n\n\nclass ComplianceTensor(StiffnessTensor):\n    \"\"\"\n    Class for manipulating compliance tensors\n    \"\"\"\n    tensor_name = 'Compliance'\n    voigt_map = np.array([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0], [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], [2.0, 2.0, 2.0, 4.0, 4.0, 4.0], [2.0, 2.0, 2.0, 4.0, 4.0, 4.0], [2.0, 2.0, 2.0, 4.0, 4.0, 4.0]])\n    C11_C12_factor = 2.0\n    component_prefix = 'S'\n    C46_C56_factor = 2.0\n\n    def __init__(self, C, check_positive_definite=True, **kwargs):\n        super().__init__(C, check_positive_definite=check_positive_definite, **kwargs)\n\n    def __mul__(self, other):\n        if isinstance(other, StressTensor):\n            return StrainTensor(self * other.matrix)\n        elif isinstance(other, StrainTensor):\n            raise ValueError('You cannot multiply a compliance tensor with Strain tensor.')\n        else:\n            return super().__mul__(other)\n\n    def inv(self):\n        \"\"\"\n        Compute the reciprocal stiffness tensor\n\n        Returns\n        -------\n        StiffnessTensor\n            Reciprocal tensor\n        \"\"\"\n        S = np.linalg.inv(self.matrix)\n        return StiffnessTensor(S, symmetry=self.symmetry, phase_name=self.phase_name, orientations=self.orientations)\n\n# ...\n\ndef test_transverse_isotropic():\n    # Test case 1\n    tensor1 = StiffnessTensor.transverse_isotropic(Ex=100, Ez=150, nu_yx=0.3, nu_zx=0.25, Gxz=50)\n    tensor2 = StiffnessTensor.transverse_isotropic_new_implementation(Ex=100, Ez=150, nu_yx=0.3, nu_zx=0.25, Gxz=50)\n    assert np.allclose(tensor1.matrix, tensor2.matrix), \"Test case 1 failed\"\n\n    # Test case 2\n    tensor1 = StiffnessTensor.transverse_isotropic(Ex=200, Ez=250, nu_yx=0.35, nu_zx=0.28, Gxz=70)\n    tensor2 = StiffnessTensor.transverse_isotropic_new_implementation(Ex=200, Ez=250, nu_yx=0.35, nu_zx=0.28, Gxz=70)\n    assert np.allclose(tensor1.matrix, tensor2.matrix), \"Test case 2 failed\"\n\n    # Test case 3\n    tensor1 = StiffnessTensor.transverse_isotropic(Ex=300, Ez=350, nu_yx=0.25, nu_zx=0.22, Gxz=80)\n    tensor2 = StiffnessTensor.transverse_isotropic_new_implementation(Ex=300, Ez=350, nu_yx=0.25, nu_zx=0.22, Gxz=80)\n    assert np.allclose(tensor1.matrix, tensor2.matrix), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_transverse_isotropic()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are class methods of the `StiffnessTensor` class and are named `transverse_isotropic`. They take the same parameters: `Ex`, `Ez`, `nu_yx`, `nu_zx`, `Gxz`, and `kwargs`. The logic within the function is the same: it calculates `Gxy` using the formula `Ex / (2 * (1 + nu_yx))`, calls the `orthotropic` method with the appropriate parameters, and sets the `symmetry` attribute of the resulting tensor to `'transverse-isotropic'`. The function then returns this tensor. Since the implementation and functionality are unchanged, the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `transverse_isotropic` function returns a `StiffnessTensor` object, which contains a matrix attribute. This satisfies the condition as the function has a return value that can be checked.\n\n2. **CONDITION 2**: The test cases use `assert np.allclose(tensor1.matrix, tensor2.matrix)` to check the equality of the matrices of the tensors returned by the two implementations. This checks the return values and does not involve any printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the matrices of the tensors returned by both implementations using `np.allclose`, which ensures that the new implementation must have the exact same functionality as the original to pass the tests. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use `assert np.allclose(tensor1.matrix, tensor2.matrix)`, which is a reasonable way to compare the outputs of the two implementations since both return a `StiffnessTensor` object with a matrix attribute. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover different sets of parameters for the `transverse_isotropic` function, ensuring that the tests are non-trivial and cover a range of possible inputs. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "b0d25e34f09fe66c00cd6bf1dddf165157443060"
    },
    {
        "func_name": "SymmetricTensor.orthorhombic",
        "idx": "199",
        "repo_name": "DorianDepriester___Elasticipy",
        "func_path": "src/Elasticipy/FourthOrderTensor.py",
        "orig_func": "@classmethod\ndef orthorhombic(cls, *, C11=0.0, C12=0.0, C13=0.0, C22=0.0, C23=0.0, C33=0.0, C44=0.0, C55=0.0, C66=0.0, phase_name=None):\n    \"\"\"\n        Create a fourth-order tensor from orthorhombic symmetry.\n\n        Parameters\n        ----------\n        C11, C12, C13, C22, C23, C33, C44, C55, C66 : float\n            Components of the tensor, using the Voigt notation\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        monoclinic : create a tensor from monoclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n    return cls.fromCrystalSymmetry(symmetry='orthorhombic', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, phase_name=phase_name, prefix='C')",
        "orig_context": "```python\n# src/Elasticipy/FourthOrderTensor.py\n\nclass SymmetricTensor:\n    \"\"\"\n    Template class for manipulating symmetric fourth-order tensors.\n\n    Attributes\n    ----------\n    matrix : np.ndarray\n        (6,6) matrix gathering all the components of the tensor, using the Voigt notation.\n    symmetry : str\n        Symmetry of the tensor\n\n    \"\"\"\n    tensor_name = 'Symmetric'\n    voigt_map = np.ones((6, 6))\n    C11_C12_factor = 0.5\n    C46_C56_factor = 1.0\n    component_prefix = 'C'\n\n    def __init__(self, M, phase_name=None, symmetry='Triclinic', orientations=None, check_symmetry=True, check_positive_definite=False):\n        \"\"\"\n        Construct of stiffness tensor from a (6,6) matrix.\n\n        The input matrix must be symmetric, otherwise an error is thrown (except if check_symmetry==False, see below)\n\n        Parameters\n        ----------\n        M : np.ndarray\n            (6,6) matrix corresponding to the stiffness tensor, written using the Voigt notation, or array of shape\n            (3,3,3,3).\n        phase_name : str, default None\n            Name to display\n        symmetry : str, default Triclinic\n            Name of the crystal's symmetry\n        check_symmetry : bool, optional\n            Whether to check or not that the input matrix is symmetric.\n        check_positive_definite : bool, optional\n            Whether to check or not that the input matrix is definite positive\n        \"\"\"\n        M = np.asarray(M)\n        if M.shape == (6, 6):\n            matrix = M\n        elif M.shape == (3, 3, 3, 3):\n            matrix = self._full_to_matrix(M)\n        else:\n            raise ValueError('The input matrix must of shape (6,6)')\n        if check_symmetry and (not np.all(np.isclose(matrix, matrix.T))):\n            raise ValueError('The input matrix must be symmetric')\n        if check_positive_definite:\n            _check_definite_positive(matrix)\n        self.matrix = matrix\n        self.phase_name = phase_name\n        self.symmetry = symmetry\n        self.orientations = orientations\n        for i in range(0, 6):\n            for j in range(0, 6):\n\n                def getter(obj, I=i, J=j):\n                    return obj.matrix[I, J]\n                getter.__doc__ = f'Returns the ({i + 1},{j + 1}) component of the {self.tensor_name} matrix.'\n                component_name = 'C{}{}'.format(i + 1, j + 1)\n                setattr(self.__class__, component_name, property(getter))\n\n    def __repr__(self):\n        if self.phase_name is None:\n            heading = '{} tensor (in Voigt notation):\\n'.format(self.tensor_name)\n        else:\n            heading = '{} tensor (in Voigt notation) for {}:\\n'.format(self.tensor_name, self.phase_name)\n        print_symmetry = '\\nSymmetry: {}'.format(self.symmetry)\n        msg = heading + self.matrix.__str__() + print_symmetry\n        if self.orientations is not None:\n            msg = msg + '\\n{} orientations'.format(len(self))\n        return msg\n\n    def __len__(self):\n        if self.orientations is None:\n            return 1\n        else:\n            return len(self.orientations)\n\n    def full_tensor(self):\n        \"\"\"\n        Returns the full (unvoigted) tensor, as a [3, 3, 3, 3] array\n\n        Returns\n        -------\n        np.ndarray\n            Full tensor (4-index notation)\n        \"\"\"\n        i, j, k, ell = np.indices((3, 3, 3, 3))\n        ij = voigt_indices(i, j)\n        kl = voigt_indices(k, ell)\n        m = self.matrix[ij, kl] / self.voigt_map[ij, kl]\n        if self.orientations is None:\n            return m\n        else:\n            return _rotate_tensor(m, self.orientations)\n\n    @classmethod\n    def _full_to_matrix(cls, full_tensor):\n        ij, kl = np.indices((6, 6))\n        i, j = unvoigt_index(ij).T\n        k, ell = unvoigt_index(kl).T\n        return full_tensor[i, j, k, ell] * cls.voigt_map[ij, kl]\n\n    def rotate(self, rotation):\n        \"\"\"\n        Apply a single rotation to a tensor, and return its component into the rotated frame.\n\n        Parameters\n        ----------\n        rotation : Rotation or orix.quaternion.rotation.Rotation\n            Rotation to apply\n\n        Returns\n        -------\n        SymmetricTensor\n            Rotated tensor\n        \"\"\"\n        if _is_single_rotation(rotation):\n            rotated_tensor = _rotate_tensor(self.full_tensor(), rotation)\n            rotated_matrix = self._full_to_matrix(rotated_tensor)\n            return self.__class__(rotated_matrix)\n        else:\n            raise ValueError('The rotation to apply must be single')\n\n    def _unrotate(self):\n        unrotated_tensor = deepcopy(self)\n        unrotated_tensor.orientations = None\n        return unrotated_tensor\n\n    def __add__(self, other):\n        if isinstance(other, np.ndarray):\n            if other.shape == (6, 6):\n                mat = self.matrix + other\n            elif other.shape == (3, 3, 3, 3):\n                mat = self._full_to_matrix(self.full_tensor() + other)\n            else:\n                raise ValueError('The input argument must be either a 6x6 matrix or a (3,3,3,3) array.')\n        elif isinstance(other, SymmetricTensor):\n            if type(other) == type(self):\n                mat = self.matrix + other.matrix\n            else:\n                raise ValueError('The two tensors to add must be of the same class.')\n        else:\n            raise ValueError('I dont know how to add {} with {}.'.format(type(self), type(other)))\n        return self.__class__(mat)\n\n    def __sub__(self, other):\n        if isinstance(other, SymmetricTensor):\n            return self.__add__(-other.matrix)\n        else:\n            return self.__add__(-other)\n\n    def __mul__(self, other):\n        if isinstance(other, SymmetricSecondOrderTensor):\n            return SymmetricSecondOrderTensor(self * other.matrix)\n        elif isinstance(other, np.ndarray):\n            if other.shape[-2:] == (3, 3):\n                if self.orientations is None:\n                    return np.einsum('ijkl,...kl->...ij', self.full_tensor(), other)\n                else:\n                    return np.einsum('qijkl,...kl->q...ij', self.full_tensor(), other)\n        elif isinstance(other, Rotation) or is_orix_rotation(other):\n            if _is_single_rotation(other):\n                return self.rotate(other)\n            else:\n                return self.__class__(self.matrix, symmetry=self.symmetry, orientations=other, phase_name=self.phase_name)\n        else:\n            return self.__class__(self.matrix * other, symmetry=self.symmetry)\n\n    def __rmul__(self, other):\n        if isinstance(other, (Rotation, float, int, np.number)) or is_orix_rotation(other):\n            return self * other\n        else:\n            raise NotImplementedError('A fourth order tensor can be left-multiplied by rotations or scalar only.')\n\n    def __truediv__(self, other):\n        if isinstance(other, (float, int, np.number)):\n            return self.__class__(self.matrix / other, symmetry=self.symmetry)\n        else:\n            raise NotImplementedError\n\n    def __eq__(self, other):\n        if isinstance(other, SymmetricTensor):\n            return np.all(self.matrix == other.matrix) and np.all(self.orientations == other.orientations)\n        elif isinstance(other, np.ndarray) and other.shape == (6, 6):\n            return np.all(self.matrix == other)\n        else:\n            raise NotImplementedError('The element to compare with must be a fourth-order tensor or an array of shape (6,6).')\n\n    def _orientation_average(self):\n        mean_full_tensor = np.mean(self.full_tensor(), axis=0)\n        mean_matrix = self._full_to_matrix(mean_full_tensor)\n        return self.__class__(mean_matrix)\n\n    @classmethod\n    def _matrixFromCrystalSymmetry(cls, symmetry='Triclinic', point_group=None, diad='y', prefix=None, **kwargs):\n        if prefix is None:\n            prefix = cls.component_prefix\n        values = _parse_tensor_components(prefix, **kwargs)\n        C = np.zeros((6, 6))\n        symmetry = symmetry.capitalize()\n        if (symmetry == 'tetragonal' or symmetry == 'trigonal') and point_group is None:\n            raise ValueError('For tetragonal and trigonal symmetries, the point group is mandatory.')\n        tetra_1 = ['4', '-4', '4/m']\n        tetra_2 = ['4mm', '-42m', '422', '4/mmm']\n        trigo_1 = ['3', '-3']\n        trigo_2 = ['32', '-3m', '3m']\n        if point_group is not None:\n            if point_group in tetra_1 or point_group in tetra_2:\n                symmetry = 'Tetragonal'\n            elif point_group in trigo_1 or point_group in trigo_2:\n                symmetry = 'Trigonal'\n        symmetry_description = SYMMETRIES[symmetry]\n        if symmetry == 'Tetragonal':\n            if point_group in tetra_1:\n                symmetry_description = symmetry_description[', '.join(tetra_1)]\n            else:\n                symmetry_description = symmetry_description[', '.join(tetra_2)]\n        elif symmetry == 'Trigonal':\n            if point_group in trigo_1:\n                symmetry_description = symmetry_description[', '.join(trigo_1)]\n            else:\n                symmetry_description = symmetry_description[', '.join(trigo_2)]\n        elif symmetry == 'Monoclinic':\n            symmetry_description = symmetry_description['Diad || ' + diad]\n        for required_field in symmetry_description.required:\n            C[required_field] = values[_indices2str(required_field)]\n        for equality in symmetry_description.equal:\n            for index in equality[1]:\n                C[index] = C[equality[0]]\n        for opposite in symmetry_description.opposite:\n            for index in opposite[1]:\n                C[index] = -C[opposite[0]]\n        C11_C12 = symmetry_description.C11_C12\n        if C11_C12:\n            for index in C11_C12:\n                C[index] = (C[0, 0] - C[0, 1]) * cls.C11_C12_factor\n        if symmetry == 'Trigonal':\n            C[3, 5] = cls.C46_C56_factor * C[3, 5]\n            C[4, 5] = cls.C46_C56_factor * C[4, 5]\n        return C + np.tril(C.T, -1)\n\n    @classmethod\n    def fromCrystalSymmetry(cls, symmetry='Triclinic', point_group=None, diad='y', phase_name=None, prefix=None, **kwargs):\n        \"\"\"\n        Create a fourth-order tensor from limited number of components, taking advantage of crystallographic symmetries\n\n        Parameters\n        ----------\n        symmetry : str, default Triclinic\n            Name of the crystallographic symmetry\n        point_group : str\n            Point group of the considered crystal. Only used (and mandatory) for tetragonal and trigonal symmetries.\n        diad : str {'x', 'y'}, default 'x'\n            Alignment convention. Sets whether x||a or y||b. Only used for monoclinic symmetry.\n        phase_name : str, default None\n            Name to use when printing the tensor\n        prefix : str, default None\n            Define the prefix to use when providing the components. By default, it is 'C' for stiffness tensors, 'S' for\n            compliance.\n        kwargs\n            Keywords describing all the necessary components, depending on the crystal's symmetry and the type of tensor.\n            For Stiffness, they should be named as 'Cij' (e.g. C11=..., C12=...).\n            For Comliance, they should be named as 'Sij' (e.g. S11=..., S12=...).\n            See examples below. The behaviour can be overriten with the prefix option (see above)\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        StiffnessTensor.isotropic : creates an isotropic stiffness tensor from two paremeters (e.g. E and v).\n\n        Notes\n        -----\n        The relationships between the tensor's components depend on the crystallogrpahic symmetry [1]_.\n\n        References\n        ----------\n        .. [1] Nye, J. F. Physical Properties of Crystals. London: Oxford University Press, 1959.\n\n        Examples\n        --------\n        >>> from Elasticipy.FourthOrderTensor import StiffnessTensor\n\n        >>> StiffnessTensor.fromCrystalSymmetry(symmetry='monoclinic', diad='y', phase_name='TiNi',\n        ...                                     C11=231, C12=127, C13=104,\n        ...                                     C22=240, C23=131, C33=175,\n        ...                                     C44=81, C55=11, C66=85,\n        ...                                     C15=-18, C25=1, C35=-3, C46=3)\n        Stiffness tensor (in Voigt notation) for TiNi:\n        [[231. 127. 104.   0. -18.   0.]\n         [127. 240. 131.   0.   1.   0.]\n         [104. 131. 175.   0.  -3.   0.]\n         [  0.   0.   0.  81.   0.   3.]\n         [-18.   1.  -3.   0.  11.   0.]\n         [  0.   0.   0.   3.   0.  85.]]\n        Symmetry: monoclinic\n\n        >>> from Elasticipy.FourthOrderTensor import ComplianceTensor\n\n        >>> ComplianceTensor.fromCrystalSymmetry(symmetry='monoclinic', diad='y', phase_name='TiNi',\n        ...                                      S11=8, S12=-3, S13=-2,\n        ...                                      S22=8, S23=-5, S33=10,\n        ...                                      S44=12, S55=116, S66=12,\n        ...                                      S15=14, S25=-8, S35=0, S46=0)\n        Compliance tensor (in Voigt notation) for TiNi:\n        [[  8.  -3.  -2.   0.  14.   0.]\n         [ -3.   8.  -5.   0.  -8.   0.]\n         [ -2.  -5.  10.   0.   0.   0.]\n         [  0.   0.   0.  12.   0.   0.]\n         [ 14.  -8.   0.   0. 116.   0.]\n         [  0.   0.   0.   0.   0.  12.]]\n        Symmetry: monoclinic\n        \"\"\"\n        matrix = cls._matrixFromCrystalSymmetry(point_group=point_group, diad=diad, symmetry=symmetry, prefix=prefix, **kwargs)\n        return cls(matrix, symmetry=symmetry, phase_name=phase_name)\n\n    @classmethod\n    def hexagonal(cls, *, C11=0.0, C12=0.0, C13=0.0, C33=0.0, C44=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from hexagonal symmetry.\n\n        Parameters\n        ----------\n        C11, C12 , C13, C33, C44 : float\n            Components of the tensor, using the Voigt notation\n        phase_name : str, optional\n            Phase name to display\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        transverse_isotropic : creates a transverse-isotropic tensor from engineering parameters\n        cubic : create a tensor from cubic symmetry\n        tetragonal : create a tensor from tetragonal symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(symmetry='hexagonal', C11=C11, C12=C12, C13=C13, C33=C33, C44=C44, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def trigonal(cls, *, C11=0.0, C12=0.0, C13=0.0, C14=0.0, C33=0.0, C44=0.0, C15=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from trigonal symmetry.\n\n        Parameters\n        ----------\n        C11, C12, C13, C14, C33, C44 : float\n            Components of the tensor, using the Voigt notation\n        C15 : float, optional\n            C15 component of the tensor, only used for point groups 3 and -3.\n        phase_name : str, optional\n            Phase name to display\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        tetragonal : create a tensor from tetragonal symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(point_group='3', C11=C11, C12=C12, C13=C13, C14=C14, C15=C15, C33=C33, C44=C44, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def tetragonal(cls, *, C11=0.0, C12=0.0, C13=0.0, C33=0.0, C44=0.0, C16=0.0, C66=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from tetragonal symmetry.\n\n        Parameters\n        ----------\n        C11,  C12, C13, C33, C44, C66 : float\n            Components of the tensor, using the Voigt notation\n        C16 : float, optional\n            C16 component in Voigt notation (for point groups 4, -4 and 4/m only)\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        trigonal : create a tensor from trigonal symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(point_group='4', C11=C11, C12=C12, C13=C13, C16=C16, C33=C33, C44=C44, C66=C66, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def cubic(cls, *, C11=0.0, C12=0.0, C44=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from cubic symmetry.\n\n        Parameters\n        ----------\n        C11 , C12, C44 : float\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        hexagonal : create a tensor from hexagonal symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(symmetry='cubic', C11=C11, C12=C12, C44=C44, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def monoclinic(cls, *, C11=0.0, C12=0.0, C13=0.0, C22=0.0, C23=0.0, C33=0.0, C44=0.0, C55=0.0, C66=0.0, C15=None, C25=None, C35=None, C46=None, C16=None, C26=None, C36=None, C45=None, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from monoclinic symmetry. It automatically detects whether the components are given\n        according to the Y or Z diad, depending on the input arguments.\n\n        For Diad || y, C15, C25, C35 and C46 must be provided.\n        For Diad || z, C16, C26, C36 and C45 must be provided.\n\n        Parameters\n        ----------\n        C11, C12 , C13, C22, C23, C33, C44, C55, C66 : float\n            Components of the tensor, using the Voigt notation\n        C15 : float, optional\n            C15 component of the tensor (if Diad || y)\n        C25 : float, optional\n            C25 component of the tensor (if Diad || y)\n        C35 : float, optional\n            C35 component of the tensor (if Diad || y)\n        C46 : float, optional\n            C46 component of the tensor (if Diad || y)\n        C16 : float, optional\n            C16 component of the tensor (if Diad || z)\n        C26 : float, optional\n            C26 component of the tensor (if Diad || z)\n        C36 : float, optional\n            C36 component of the tensor (if Diad || z)\n        C45 : float, optional\n            C45 component of the tensor (if Diad || z)\n        phase_name : str, optional\n            Name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        triclinic : create a tensor from triclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        diad_y = not None in (C15, C25, C35, C46)\n        diad_z = not None in (C16, C26, C36, C45)\n        if diad_y and diad_z:\n            raise KeyError('Ambiguous diad. Provide either C15, C25, C35 and C46; or C16, C26, C36 and C45')\n        elif diad_y:\n            return cls.fromCrystalSymmetry(symmetry='monoclinic', diad='y', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, C15=C15, C25=C25, C35=C35, C46=C46, phase_name=phase_name, prefix='C')\n        elif diad_z:\n            return cls.fromCrystalSymmetry(symmetry='monoclinic', diad='z', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, C16=C16, C26=C26, C36=C36, C45=C45, phase_name=phase_name, prefix='C')\n        else:\n            raise KeyError('For monoclinic symmetry, one should provide either C15, C25, C35 and C46, or C16, C26, C36 and C45.')\n\n    @classmethod\n    def triclinic(cls, C11=0.0, C12=0.0, C13=0.0, C14=0.0, C15=0.0, C16=0.0, C22=0.0, C23=0.0, C24=0.0, C25=0.0, C26=0.0, C33=0.0, C34=0.0, C35=0.0, C36=0.0, C44=0.0, C45=0.0, C46=0.0, C55=0.0, C56=0.0, C66=0.0, phase_name=None):\n        \"\"\"\n\n        Parameters\n        ----------\n        C11 , C12 , C13 , C14 , C15 , C16 , C22 , C23 , C24 , C25 , C26 , C33 , C34 , C35 , C36 , C44 , C45 , C46 , C55 , C56 , C66 : float\n            Components of the tensor\n        phase_name : str, optional\n            Name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        monoclinic : create a tensor from monoclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        matrix = np.array([[C11, C12, C13, C14, C15, C16], [C12, C22, C23, C24, C25, C26], [C13, C23, C33, C34, C35, C36], [C14, C24, C34, C44, C45, C46], [C15, C25, C35, C45, C55, C56], [C16, C26, C36, C46, C56, C66]])\n        return cls(matrix, phase_name=phase_name)\n\n    def save_to_txt(self, filename, matrix_only=False):\n        \"\"\"\n        Save the tensor to a text file.\n\n        Parameters\n        ----------\n        filename : str\n            Filename to save the tensor to.\n        matrix_only : bool, False\n            If true, only the components of tje stiffness tensor is saved (no data about phase nor symmetry)\n\n        See Also\n        --------\n        from_txt_file : create a tensor from text file\n\n        \"\"\"\n        with open(filename, 'w') as f:\n            if not matrix_only:\n                if self.phase_name is not None:\n                    f.write(f'Phase Name: {self.phase_name}\\n')\n                f.write(f'Symmetry: {self.symmetry}\\n')\n            for row in self.matrix:\n                f.write('  ' + '  '.join((f'{value:8.2f}' for value in row)) + '\\n')\n\n    @classmethod\n    def from_txt_file(cls, filename):\n        \"\"\"\n        Load the tensor from a text file.\n\n        The two first lines can have data about phase name and symmetry, but this is not mandatory.\n\n        Parameters\n        ----------\n        filename : str\n            Filename to load the tensor from.\n\n        Returns\n        -------\n        SymmetricTensor\n            The reconstructed tensor read from the file.\n\n        See Also\n        --------\n        save_to_txt : create a tensor from text file\n\n        \"\"\"\n        with open(filename, 'r') as f:\n            lines = f.readlines()\n        phase_name = None\n        symmetry = 'Triclinic'\n        matrix_start_index = 0\n        if lines and lines[0].startswith('Phase Name:'):\n            phase_name = lines[0].split(': ', 1)[1].strip()\n            matrix_start_index += 1\n        if len(lines) > matrix_start_index and lines[matrix_start_index].startswith('Symmetry:'):\n            symmetry = lines[matrix_start_index].split(': ', 1)[1].strip()\n            matrix_start_index += 1\n        matrix = np.loadtxt(lines[matrix_start_index:])\n        return cls(matrix, phase_name=phase_name, symmetry=symmetry)\n\n    def __getitem__(self, item):\n        if self.orientations is None:\n            raise IndexError('The tensor has no orientation, therefore it cannot be indexed.')\n        else:\n            return self._unrotate() * self.orientations[item]\n\n    @classmethod\n    def orthorhombic(cls, *, C11=0.0, C12=0.0, C13=0.0, C22=0.0, C23=0.0, C33=0.0, C44=0.0, C55=0.0, C66=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from orthorhombic symmetry.\n\n        Parameters\n        ----------\n        C11, C12, C13, C22, C23, C33, C44, C55, C66 : float\n            Components of the tensor, using the Voigt notation\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        monoclinic : create a tensor from monoclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(symmetry='orthorhombic', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, phase_name=phase_name, prefix='C')\n```\n",
        "eval_script": "# Mock implementations and imports to make the code executable\n\nimport numpy as np\nfrom copy import deepcopy\n\n# Mock SYMMETRIES dictionary\nSYMMETRIES = {\n    'Orthorhombic': {\n        'required': [(0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 2), (3, 3), (4, 4), (5, 5)],\n        'equal': [],\n        'opposite': [],\n        'C11_C12': []\n    }\n}\n\n# Mock function for _parse_tensor_components\ndef _parse_tensor_components(prefix, **kwargs):\n    return kwargs\n\n# Mock function for _indices2str\ndef _indices2str(indices):\n    return 'C{}{}'.format(indices[0] + 1, indices[1] + 1)\n\n# Mock function for _check_definite_positive\ndef _check_definite_positive(matrix):\n    pass\n\n# Mock function for voigt_indices\ndef voigt_indices(i, j):\n    return i * 3 + j\n\n# Mock function for unvoigt_index\ndef unvoigt_index(ij):\n    return np.array([(ij // 3, ij % 3)])\n\n# Mock function for _rotate_tensor\ndef _rotate_tensor(tensor, orientations):\n    return tensor\n\n# Mock function for _is_single_rotation\ndef _is_single_rotation(rotation):\n    return True\n\n# Mock class for SymmetricSecondOrderTensor\nclass SymmetricSecondOrderTensor:\n    def __init__(self, matrix):\n        self.matrix = matrix\n\n# Mock class for Rotation\nclass Rotation:\n    pass\n\n# Mock function for is_orix_rotation\ndef is_orix_rotation(rotation):\n    return False\n\n# Now, the original class definition with the orthorhombic method can be used directly.\n\nclass SymmetricTensor:\n    \"\"\"\n    Template class for manipulating symmetric fourth-order tensors.\n\n    Attributes\n    ----------\n    matrix : np.ndarray\n        (6,6) matrix gathering all the components of the tensor, using the Voigt notation.\n    symmetry : str\n        Symmetry of the tensor\n\n    \"\"\"\n    tensor_name = 'Symmetric'\n    voigt_map = np.ones((6, 6))\n    C11_C12_factor = 0.5\n    C46_C56_factor = 1.0\n    component_prefix = 'C'\n\n    def __init__(self, M, phase_name=None, symmetry='Triclinic', orientations=None, check_symmetry=True, check_positive_definite=False):\n        \"\"\"\n        Construct of stiffness tensor from a (6,6) matrix.\n\n        The input matrix must be symmetric, otherwise an error is thrown (except if check_symmetry==False, see below)\n\n        Parameters\n        ----------\n        M : np.ndarray\n            (6,6) matrix corresponding to the stiffness tensor, written using the Voigt notation, or array of shape\n            (3,3,3,3).\n        phase_name : str, default None\n            Name to display\n        symmetry : str, default Triclinic\n            Name of the crystal's symmetry\n        check_symmetry : bool, optional\n            Whether to check or not that the input matrix is symmetric.\n        check_positive_definite : bool, optional\n            Whether to check or not that the input matrix is definite positive\n        \"\"\"\n        M = np.asarray(M)\n        if M.shape == (6, 6):\n            matrix = M\n        elif M.shape == (3, 3, 3, 3):\n            matrix = self._full_to_matrix(M)\n        else:\n            raise ValueError('The input matrix must of shape (6,6)')\n        if check_symmetry and (not np.all(np.isclose(matrix, matrix.T))):\n            raise ValueError('The input matrix must be symmetric')\n        if check_positive_definite:\n            _check_definite_positive(matrix)\n        self.matrix = matrix\n        self.phase_name = phase_name\n        self.symmetry = symmetry\n        self.orientations = orientations\n        for i in range(0, 6):\n            for j in range(0, 6):\n\n                def getter(obj, I=i, J=j):\n                    return obj.matrix[I, J]\n                getter.__doc__ = f'Returns the ({i + 1},{j + 1}) component of the {self.tensor_name} matrix.'\n                component_name = 'C{}{}'.format(i + 1, j + 1)\n                setattr(self.__class__, component_name, property(getter))\n\n    def __repr__(self):\n        if self.phase_name is None:\n            heading = '{} tensor (in Voigt notation):\\n'.format(self.tensor_name)\n        else:\n            heading = '{} tensor (in Voigt notation) for {}:\\n'.format(self.tensor_name, self.phase_name)\n        print_symmetry = '\\nSymmetry: {}'.format(self.symmetry)\n        msg = heading + self.matrix.__str__() + print_symmetry\n        if self.orientations is not None:\n            msg = msg + '\\n{} orientations'.format(len(self))\n        return msg\n\n    def __len__(self):\n        if self.orientations is None:\n            return 1\n        else:\n            return len(self.orientations)\n\n    def full_tensor(self):\n        \"\"\"\n        Returns the full (unvoigted) tensor, as a [3, 3, 3, 3] array\n\n        Returns\n        -------\n        np.ndarray\n            Full tensor (4-index notation)\n        \"\"\"\n        i, j, k, ell = np.indices((3, 3, 3, 3))\n        ij = voigt_indices(i, j)\n        kl = voigt_indices(k, ell)\n        m = self.matrix[ij, kl] / self.voigt_map[ij, kl]\n        if self.orientations is None:\n            return m\n        else:\n            return _rotate_tensor(m, self.orientations)\n\n    @classmethod\n    def _full_to_matrix(cls, full_tensor):\n        ij, kl = np.indices((6, 6))\n        i, j = unvoigt_index(ij).T\n        k, ell = unvoigt_index(kl).T\n        return full_tensor[i, j, k, ell] * cls.voigt_map[ij, kl]\n\n    def rotate(self, rotation):\n        \"\"\"\n        Apply a single rotation to a tensor, and return its component into the rotated frame.\n\n        Parameters\n        ----------\n        rotation : Rotation or orix.quaternion.rotation.Rotation\n            Rotation to apply\n\n        Returns\n        -------\n        SymmetricTensor\n            Rotated tensor\n        \"\"\"\n        if _is_single_rotation(rotation):\n            rotated_tensor = _rotate_tensor(self.full_tensor(), rotation)\n            rotated_matrix = self._full_to_matrix(rotated_tensor)\n            return self.__class__(rotated_matrix)\n        else:\n            raise ValueError('The rotation to apply must be single')\n\n    def _unrotate(self):\n        unrotated_tensor = deepcopy(self)\n        unrotated_tensor.orientations = None\n        return unrotated_tensor\n\n    def __add__(self, other):\n        if isinstance(other, np.ndarray):\n            if other.shape == (6, 6):\n                mat = self.matrix + other\n            elif other.shape == (3, 3, 3, 3):\n                mat = self._full_to_matrix(self.full_tensor() + other)\n            else:\n                raise ValueError('The input argument must be either a 6x6 matrix or a (3,3,3,3) array.')\n        elif isinstance(other, SymmetricTensor):\n            if type(other) == type(self):\n                mat = self.matrix + other.matrix\n            else:\n                raise ValueError('The two tensors to add must be of the same class.')\n        else:\n            raise ValueError('I dont know how to add {} with {}.'.format(type(self), type(other)))\n        return self.__class__(mat)\n\n    def __sub__(self, other):\n        if isinstance(other, SymmetricTensor):\n            return self.__add__(-other.matrix)\n        else:\n            return self.__add__(-other)\n\n    def __mul__(self, other):\n        if isinstance(other, SymmetricSecondOrderTensor):\n            return SymmetricSecondOrderTensor(self * other.matrix)\n        elif isinstance(other, np.ndarray):\n            if other.shape[-2:] == (3, 3):\n                if self.orientations is None:\n                    return np.einsum('ijkl,...kl->...ij', self.full_tensor(), other)\n                else:\n                    return np.einsum('qijkl,...kl->q...ij', self.full_tensor(), other)\n        elif isinstance(other, Rotation) or is_orix_rotation(other):\n            if _is_single_rotation(other):\n                return self.rotate(other)\n            else:\n                return self.__class__(self.matrix, symmetry=self.symmetry, orientations=other, phase_name=self.phase_name)\n        else:\n            return self.__class__(self.matrix * other, symmetry=self.symmetry)\n\n    def __rmul__(self, other):\n        if isinstance(other, (Rotation, float, int, np.number)) or is_orix_rotation(other):\n            return self * other\n        else:\n            raise NotImplementedError('A fourth order tensor can be left-multiplied by rotations or scalar only.')\n\n    def __truediv__(self, other):\n        if isinstance(other, (float, int, np.number)):\n            return self.__class__(self.matrix / other, symmetry=self.symmetry)\n        else:\n            raise NotImplementedError\n\n    def __eq__(self, other):\n        if isinstance(other, SymmetricTensor):\n            return np.all(self.matrix == other.matrix) and np.all(self.orientations == other.orientations)\n        elif isinstance(other, np.ndarray) and other.shape == (6, 6):\n            return np.all(self.matrix == other)\n        else:\n            raise NotImplementedError('The element to compare with must be a fourth-order tensor or an array of shape (6,6).')\n\n    def _orientation_average(self):\n        mean_full_tensor = np.mean(self.full_tensor(), axis=0)\n        mean_matrix = self._full_to_matrix(mean_full_tensor)\n        return self.__class__(mean_matrix)\n\n    @classmethod\n    def _matrixFromCrystalSymmetry(cls, symmetry='Triclinic', point_group=None, diad='y', prefix=None, **kwargs):\n        if prefix is None:\n            prefix = cls.component_prefix\n        values = _parse_tensor_components(prefix, **kwargs)\n        C = np.zeros((6, 6))\n        symmetry = symmetry.capitalize()\n        if (symmetry == 'tetragonal' or symmetry == 'trigonal') and point_group is None:\n            raise ValueError('For tetragonal and trigonal symmetries, the point group is mandatory.')\n        tetra_1 = ['4', '-4', '4/m']\n        tetra_2 = ['4mm', '-42m', '422', '4/mmm']\n        trigo_1 = ['3', '-3']\n        trigo_2 = ['32', '-3m', '3m']\n        if point_group is not None:\n            if point_group in tetra_1 or point_group in tetra_2:\n                symmetry = 'Tetragonal'\n            elif point_group in trigo_1 or point_group in trigo_2:\n                symmetry = 'Trigonal'\n        symmetry_description = SYMMETRIES[symmetry]\n        if symmetry == 'Tetragonal':\n            if point_group in tetra_1:\n                symmetry_description = symmetry_description[', '.join(tetra_1)]\n            else:\n                symmetry_description = symmetry_description[', '.join(tetra_2)]\n        elif symmetry == 'Trigonal':\n            if point_group in trigo_1:\n                symmetry_description = symmetry_description[', '.join(trigo_1)]\n            else:\n                symmetry_description = symmetry_description[', '.join(trigo_2)]\n        elif symmetry == 'Monoclinic':\n            symmetry_description = symmetry_description['Diad || ' + diad]\n        for required_field in symmetry_description['required']:\n            C[required_field] = values[_indices2str(required_field)]\n        for equality in symmetry_description['equal']:\n            for index in equality[1]:\n                C[index] = C[equality[0]]\n        for opposite in symmetry_description['opposite']:\n            for index in opposite[1]:\n                C[index] = -C[opposite[0]]\n        C11_C12 = symmetry_description['C11_C12']\n        if C11_C12:\n            for index in C11_C12:\n                C[index] = (C[0, 0] - C[0, 1]) * cls.C11_C12_factor\n        if symmetry == 'Trigonal':\n            C[3, 5] = cls.C46_C56_factor * C[3, 5]\n            C[4, 5] = cls.C46_C56_factor * C[4, 5]\n        return C + np.tril(C.T, -1)\n\n    @classmethod\n    def fromCrystalSymmetry(cls, symmetry='Triclinic', point_group=None, diad='y', phase_name=None, prefix=None, **kwargs):\n        \"\"\"\n        Create a fourth-order tensor from limited number of components, taking advantage of crystallographic symmetries\n\n        Parameters\n        ----------\n        symmetry : str, default Triclinic\n            Name of the crystallographic symmetry\n        point_group : str\n            Point group of the considered crystal. Only used (and mandatory) for tetragonal and trigonal symmetries.\n        diad : str {'x', 'y'}, default 'x'\n            Alignment convention. Sets whether x||a or y||b. Only used for monoclinic symmetry.\n        phase_name : str, default None\n            Name to use when printing the tensor\n        prefix : str, default None\n            Define the prefix to use when providing the components. By default, it is 'C' for stiffness tensors, 'S' for\n            compliance.\n        kwargs\n            Keywords describing all the necessary components, depending on the crystal's symmetry and the type of tensor.\n            For Stiffness, they should be named as 'Cij' (e.g. C11=..., C12=...).\n            For Comliance, they should be named as 'Sij' (e.g. S11=..., S12=...).\n            See examples below. The behaviour can be overriten with the prefix option (see above)\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        StiffnessTensor.isotropic : creates an isotropic stiffness tensor from two paremeters (e.g. E and v).\n\n        Notes\n        -----\n        The relationships between the tensor's components depend on the crystallogrpahic symmetry [1]_.\n\n        References\n        ----------\n        .. [1] Nye, J. F. Physical Properties of Crystals. London: Oxford University Press, 1959.\n\n        Examples\n        --------\n        >>> from Elasticipy.FourthOrderTensor import StiffnessTensor\n\n        >>> StiffnessTensor.fromCrystalSymmetry(symmetry='monoclinic', diad='y', phase_name='TiNi',\n        ...                                     C11=231, C12=127, C13=104,\n        ...                                     C22=240, C23=131, C33=175,\n        ...                                     C44=81, C55=11, C66=85,\n        ...                                     C15=-18, C25=1, C35=-3, C46=3)\n        Stiffness tensor (in Voigt notation) for TiNi:\n        [[231. 127. 104.   0. -18.   0.]\n         [127. 240. 131.   0.   1.   0.]\n         [104. 131. 175.   0.  -3.   0.]\n         [  0.   0.   0.  81.   0.   3.]\n         [-18.   1.  -3.   0.  11.   0.]\n         [  0.   0.   0.   3.   0.  85.]]\n        Symmetry: monoclinic\n\n        >>> from Elasticipy.FourthOrderTensor import ComplianceTensor\n\n        >>> ComplianceTensor.fromCrystalSymmetry(symmetry='monoclinic', diad='y', phase_name='TiNi',\n        ...                                      S11=8, S12=-3, S13=-2,\n        ...                                      S22=8, S23=-5, S33=10,\n        ...                                      S44=12, S55=116, S66=12,\n        ...                                      S15=14, S25=-8, S35=0, S46=0)\n        Compliance tensor (in Voigt notation) for TiNi:\n        [[  8.  -3.  -2.   0.  14.   0.]\n         [ -3.   8.  -5.   0.  -8.   0.]\n         [ -2.  -5.  10.   0.   0.   0.]\n         [  0.   0.   0.  12.   0.   0.]\n         [ 14.  -8.   0.   0. 116.   0.]\n         [  0.   0.   0.   0.   0.  12.]]\n        Symmetry: monoclinic\n        \"\"\"\n        matrix = cls._matrixFromCrystalSymmetry(point_group=point_group, diad=diad, symmetry=symmetry, prefix=prefix, **kwargs)\n        return cls(matrix, symmetry=symmetry, phase_name=phase_name)\n\n    @classmethod\n    def hexagonal(cls, *, C11=0.0, C12=0.0, C13=0.0, C33=0.0, C44=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from hexagonal symmetry.\n\n        Parameters\n        ----------\n        C11, C12 , C13, C33, C44 : float\n            Components of the tensor, using the Voigt notation\n        phase_name : str, optional\n            Phase name to display\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        transverse_isotropic : creates a transverse-isotropic tensor from engineering parameters\n        cubic : create a tensor from cubic symmetry\n        tetragonal : create a tensor from tetragonal symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(symmetry='hexagonal', C11=C11, C12=C12, C13=C13, C33=C33, C44=C44, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def trigonal(cls, *, C11=0.0, C12=0.0, C13=0.0, C14=0.0, C33=0.0, C44=0.0, C15=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from trigonal symmetry.\n\n        Parameters\n        ----------\n        C11, C12, C13, C14, C33, C44 : float\n            Components of the tensor, using the Voigt notation\n        C15 : float, optional\n            C15 component of the tensor, only used for point groups 3 and -3.\n        phase_name : str, optional\n            Phase name to display\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        tetragonal : create a tensor from tetragonal symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(point_group='3', C11=C11, C12=C12, C13=C13, C14=C14, C15=C15, C33=C33, C44=C44, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def tetragonal(cls, *, C11=0.0, C12=0.0, C13=0.0, C33=0.0, C44=0.0, C16=0.0, C66=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from tetragonal symmetry.\n\n        Parameters\n        ----------\n        C11,  C12, C13, C33, C44, C66 : float\n            Components of the tensor, using the Voigt notation\n        C16 : float, optional\n            C16 component in Voigt notation (for point groups 4, -4 and 4/m only)\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        trigonal : create a tensor from trigonal symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(point_group='4', C11=C11, C12=C12, C13=C13, C16=C16, C33=C33, C44=C44, C66=C66, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def cubic(cls, *, C11=0.0, C12=0.0, C44=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from cubic symmetry.\n\n        Parameters\n        ----------\n        C11 , C12, C44 : float\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        StiffnessTensor\n\n        See Also\n        --------\n        hexagonal : create a tensor from hexagonal symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(symmetry='cubic', C11=C11, C12=C12, C44=C44, phase_name=phase_name, prefix='C')\n\n    @classmethod\n    def monoclinic(cls, *, C11=0.0, C12=0.0, C13=0.0, C22=0.0, C23=0.0, C33=0.0, C44=0.0, C55=0.0, C66=0.0, C15=None, C25=None, C35=None, C46=None, C16=None, C26=None, C36=None, C45=None, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from monoclinic symmetry. It automatically detects whether the components are given\n        according to the Y or Z diad, depending on the input arguments.\n\n        For Diad || y, C15, C25, C35 and C46 must be provided.\n        For Diad || z, C16, C26, C36 and C45 must be provided.\n\n        Parameters\n        ----------\n        C11, C12 , C13, C22, C23, C33, C44, C55, C66 : float\n            Components of the tensor, using the Voigt notation\n        C15 : float, optional\n            C15 component of the tensor (if Diad || y)\n        C25 : float, optional\n            C25 component of the tensor (if Diad || y)\n        C35 : float, optional\n            C35 component of the tensor (if Diad || y)\n        C46 : float, optional\n            C46 component of the tensor (if Diad || y)\n        C16 : float, optional\n            C16 component of the tensor (if Diad || z)\n        C26 : float, optional\n            C26 component of the tensor (if Diad || z)\n        C36 : float, optional\n            C36 component of the tensor (if Diad || z)\n        C45 : float, optional\n            C45 component of the tensor (if Diad || z)\n        phase_name : str, optional\n            Name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        triclinic : create a tensor from triclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        diad_y = not None in (C15, C25, C35, C46)\n        diad_z = not None in (C16, C26, C36, C45)\n        if diad_y and diad_z:\n            raise KeyError('Ambiguous diad. Provide either C15, C25, C35 and C46; or C16, C26, C36 and C45')\n        elif diad_y:\n            return cls.fromCrystalSymmetry(symmetry='monoclinic', diad='y', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, C15=C15, C25=C25, C35=C35, C46=C46, phase_name=phase_name, prefix='C')\n        elif diad_z:\n            return cls.fromCrystalSymmetry(symmetry='monoclinic', diad='z', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, C16=C16, C26=C26, C36=C36, C45=C45, phase_name=phase_name, prefix='C')\n        else:\n            raise KeyError('For monoclinic symmetry, one should provide either C15, C25, C35 and C46, or C16, C26, C36 and C45.')\n\n    @classmethod\n    def triclinic(cls, C11=0.0, C12=0.0, C13=0.0, C14=0.0, C15=0.0, C16=0.0, C22=0.0, C23=0.0, C24=0.0, C25=0.0, C26=0.0, C33=0.0, C34=0.0, C35=0.0, C36=0.0, C44=0.0, C45=0.0, C46=0.0, C55=0.0, C56=0.0, C66=0.0, phase_name=None):\n        \"\"\"\n\n        Parameters\n        ----------\n        C11 , C12 , C13 , C14 , C15 , C16 , C22 , C23 , C24 , C25 , C26 , C33 , C34 , C35 , C36 , C44 , C45 , C46 , C55 , C56 , C66 : float\n            Components of the tensor\n        phase_name : str, optional\n            Name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        monoclinic : create a tensor from monoclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        matrix = np.array([[C11, C12, C13, C14, C15, C16], [C12, C22, C23, C24, C25, C26], [C13, C23, C33, C34, C35, C36], [C14, C24, C34, C44, C45, C46], [C15, C25, C35, C45, C55, C56], [C16, C26, C36, C46, C56, C66]])\n        return cls(matrix, phase_name=phase_name)\n\n    def save_to_txt(self, filename, matrix_only=False):\n        \"\"\"\n        Save the tensor to a text file.\n\n        Parameters\n        ----------\n        filename : str\n            Filename to save the tensor to.\n        matrix_only : bool, False\n            If true, only the components of tje stiffness tensor is saved (no data about phase nor symmetry)\n\n        See Also\n        --------\n        from_txt_file : create a tensor from text file\n\n        \"\"\"\n        with open(filename, 'w') as f:\n            if not matrix_only:\n                if self.phase_name is not None:\n                    f.write(f'Phase Name: {self.phase_name}\\n')\n                f.write(f'Symmetry: {self.symmetry}\\n')\n            for row in self.matrix:\n                f.write('  ' + '  '.join((f'{value:8.2f}' for value in row)) + '\\n')\n\n    @classmethod\n    def from_txt_file(cls, filename):\n        \"\"\"\n        Load the tensor from a text file.\n\n        The two first lines can have data about phase name and symmetry, but this is not mandatory.\n\n        Parameters\n        ----------\n        filename : str\n            Filename to load the tensor from.\n\n        Returns\n        -------\n        SymmetricTensor\n            The reconstructed tensor read from the file.\n\n        See Also\n        --------\n        save_to_txt : create a tensor from text file\n\n        \"\"\"\n        with open(filename, 'r') as f:\n            lines = f.readlines()\n        phase_name = None\n        symmetry = 'Triclinic'\n        matrix_start_index = 0\n        if lines and lines[0].startswith('Phase Name:'):\n            phase_name = lines[0].split(': ', 1)[1].strip()\n            matrix_start_index += 1\n        if len(lines) > matrix_start_index and lines[matrix_start_index].startswith('Symmetry:'):\n            symmetry = lines[matrix_start_index].split(': ', 1)[1].strip()\n            matrix_start_index += 1\n        matrix = np.loadtxt(lines[matrix_start_index:])\n        return cls(matrix, phase_name=phase_name, symmetry=symmetry)\n\n    def __getitem__(self, item):\n        if self.orientations is None:\n            raise IndexError('The tensor has no orientation, therefore it cannot be indexed.')\n        else:\n            return self._unrotate() * self.orientations[item]\n\n    @classmethod\n    def orthorhombic(cls, *, C11=0.0, C12=0.0, C13=0.0, C22=0.0, C23=0.0, C33=0.0, C44=0.0, C55=0.0, C66=0.0, phase_name=None):\n        \"\"\"\n        Create a fourth-order tensor from orthorhombic symmetry.\n\n        Parameters\n        ----------\n        C11, C12, C13, C22, C23, C33, C44, C55, C66 : float\n            Components of the tensor, using the Voigt notation\n        phase_name : str, optional\n            Phase name to display\n\n        Returns\n        -------\n        FourthOrderTensor\n\n        See Also\n        --------\n        monoclinic : create a tensor from monoclinic symmetry\n        orthorhombic : create a tensor from orthorhombic symmetry\n        \"\"\"\n        return cls.fromCrystalSymmetry(symmetry='orthorhombic', C11=C11, C12=C12, C13=C13, C22=C22, C23=C23, C33=C33, C44=C44, C55=C55, C66=C66, phase_name=phase_name, prefix='C')\n\n\ndef test_orthorhombic():\n    # Test case 1\n    tensor1 = SymmetricTensor.orthorhombic(C11=1.0, C12=2.0, C13=3.0, C22=4.0, C23=5.0, C33=6.0, C44=7.0, C55=8.0, C66=9.0)\n    tensor2 = SymmetricTensor.orthorhombic_new_implementation(C11=1.0, C12=2.0, C13=3.0, C22=4.0, C23=5.0, C33=6.0, C44=7.0, C55=8.0, C66=9.0)\n    assert np.allclose(tensor1.matrix, tensor2.matrix), \"Test case 1 failed\"\n\n    # Test case 2\n    tensor1 = SymmetricTensor.orthorhombic(C11=10.0, C12=20.0, C13=30.0, C22=40.0, C23=50.0, C33=60.0, C44=70.0, C55=80.0, C66=90.0)\n    tensor2 = SymmetricTensor.orthorhombic_new_implementation(C11=10.0, C12=20.0, C13=30.0, C22=40.0, C23=50.0, C33=60.0, C44=70.0, C55=80.0, C66=90.0)\n    assert np.allclose(tensor1.matrix, tensor2.matrix), \"Test case 2 failed\"\n\n    # Test case 3\n    tensor1 = SymmetricTensor.orthorhombic(C11=0.1, C12=0.2, C13=0.3, C22=0.4, C23=0.5, C33=0.6, C44=0.7, C55=0.8, C66=0.9)\n    tensor2 = SymmetricTensor.orthorhombic_new_implementation(C11=0.1, C12=0.2, C13=0.3, C22=0.4, C23=0.5, C33=0.6, C44=0.7, C55=0.8, C66=0.9)\n    assert np.allclose(tensor1.matrix, tensor2.matrix), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_orthorhombic()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function both create a fourth-order tensor from orthorhombic symmetry using the same parameters and return the result of calling `fromCrystalSymmetry` with the same arguments. The revised function is part of a class `SymmetricTensor`, which is consistent with the original function being a class method. The functionality of both functions is identical as they both rely on the `fromCrystalSymmetry` method to construct the tensor. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `orthorhombic` function returns an instance of `SymmetricTensor`, which is a class that contains a `matrix` attribute. This satisfies the condition as it returns a value (the tensor object).\n\n2. **CONDITION 2**: The test cases use `assert np.allclose(tensor1.matrix, tensor2.matrix)` to compare the matrices of the tensors returned by `orthorhombic` and `orthorhombic_new_implementation`. This checks the return values (the `matrix` attribute of the tensor objects) and does not involve checking printed or logged contents.\n\n3. **CONDITION 3**: The test cases compare the matrices of tensors created by `orthorhombic` and `orthorhombic_new_implementation` using `np.allclose`, which ensures that the new implementation must have the exact same functionality as the original to pass the tests.\n\n4. **CONDITION 4**: The test cases use a reasonable method (`np.allclose`) to compare the matrices of the tensors, which is appropriate given that `orthorhombic` returns a tensor object with a `matrix` attribute.\n\n5. **CONDITION 5**: The test cases cover a range of input values, including small, large, and fractional numbers, which makes them non-trivial and ensures that the function is tested under different scenarios.",
            "answer": "yes"
        },
        "commit_id": "b0d25e34f09fe66c00cd6bf1dddf165157443060"
    },
    {
        "func_name": "setup_logging",
        "idx": "214",
        "repo_name": "peiman___filecombinator",
        "func_path": "filecombinator/core/logging.py",
        "orig_func": "def setup_logging(log_file: Optional[str]=None, verbose: bool=False) -> logging.Logger:\n    \"\"\"Set up logging configuration.\n\n    Args:\n        log_file: Optional path to log file\n        verbose: Whether to enable verbose logging\n\n    Returns:\n        logging.Logger: Configured logger instance\n    \"\"\"\n    if log_file:\n        os.makedirs(os.path.dirname(log_file), exist_ok=True)\n    logger = logging.getLogger('FileCombinator')\n    logger.setLevel(logging.DEBUG if verbose else logging.INFO)\n    logger.handlers = []\n    detailed_formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n    simple_formatter = logging.Formatter('%(levelname)s: %(message)s')\n    console_handler = logging.StreamHandler()\n    console_handler.setFormatter(simple_formatter)\n    console_handler.setLevel(logging.DEBUG if verbose else logging.INFO)\n    logger.addHandler(console_handler)\n    if log_file:\n        file_handler = RotatingFileHandler(log_file, maxBytes=10 * 1024 * 1024, backupCount=5, encoding='utf-8')\n        file_handler.setFormatter(detailed_formatter)\n        file_handler.setLevel(logging.DEBUG)\n        logger.addHandler(file_handler)\n    return logger",
        "orig_context": "```python\n## filecombinator/core/logging.py\nimport logging\n\nimport os\n\nfrom logging.handlers import RotatingFileHandler\n\nfrom typing import Optional\n\ndef setup_logging(\n    log_file: Optional[str] = None, verbose: bool = False\n) -> logging.Logger:\n    \"\"\"Set up logging configuration.\n\n    Args:\n        log_file: Optional path to log file\n        verbose: Whether to enable verbose logging\n\n    Returns:\n        logging.Logger: Configured logger instance\n    \"\"\"\n    if log_file:\n        os.makedirs(os.path.dirname(log_file), exist_ok=True)\n\n    logger = logging.getLogger(\"FileCombinator\")\n    # Set level based on verbose flag\n    logger.setLevel(logging.DEBUG if verbose else logging.INFO)\n\n    # Clear any existing handlers\n    logger.handlers = []\n\n    # Create formatters\n    detailed_formatter = logging.Formatter(\n        \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n    )\n    simple_formatter = logging.Formatter(\"%(levelname)s: %(message)s\")\n\n    # Console handler\n    console_handler = logging.StreamHandler()\n    console_handler.setFormatter(simple_formatter)\n    # Set handler level based on verbose flag\n    console_handler.setLevel(logging.DEBUG if verbose else logging.INFO)\n    logger.addHandler(console_handler)\n\n    # File handler if log_file is specified\n    if log_file:\n        file_handler = RotatingFileHandler(\n            log_file,\n            maxBytes=10 * 1024 * 1024,  # 10MB\n            backupCount=5,\n            encoding=\"utf-8\",\n        )\n        file_handler.setFormatter(detailed_formatter)\n        # Always set file handler to DEBUG to capture all logs\n        file_handler.setLevel(logging.DEBUG)\n        logger.addHandler(file_handler)\n\n    return logger\n\n```\n\n\n",
        "eval_script": "## filecombinator/core/logging.py\nimport logging\n\nimport os\n\nfrom logging.handlers import RotatingFileHandler\n\nfrom typing import Optional\n\ndef setup_logging(\n    log_file: Optional[str] = None, verbose: bool = False\n) -> logging.Logger:\n    \"\"\"Set up logging configuration.\n\n    Args:\n        log_file: Optional path to log file\n        verbose: Whether to enable verbose logging\n\n    Returns:\n        logging.Logger: Configured logger instance\n    \"\"\"\n    if log_file:\n        os.makedirs(os.path.dirname(log_file), exist_ok=True)\n\n    logger = logging.getLogger(\"FileCombinator\")\n    # Set level based on verbose flag\n    logger.setLevel(logging.DEBUG if verbose else logging.INFO)\n\n    # Clear any existing handlers\n    logger.handlers = []\n\n    # Create formatters\n    detailed_formatter = logging.Formatter(\n        \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n    )\n    simple_formatter = logging.Formatter(\"%(levelname)s: %(message)s\")\n\n    # Console handler\n    console_handler = logging.StreamHandler()\n    console_handler.setFormatter(simple_formatter)\n    # Set handler level based on verbose flag\n    console_handler.setLevel(logging.DEBUG if verbose else logging.INFO)\n    logger.addHandler(console_handler)\n\n    # File handler if log_file is specified\n    if log_file:\n        file_handler = RotatingFileHandler(\n            log_file,\n            maxBytes=10 * 1024 * 1024,  # 10MB\n            backupCount=5,\n            encoding=\"utf-8\",\n        )\n        file_handler.setFormatter(detailed_formatter)\n        # Always set file handler to DEBUG to capture all logs\n        file_handler.setLevel(logging.DEBUG)\n        logger.addHandler(file_handler)\n\n    return logger\n\n\ndef test_setup_logging():\n    import os\n\n    # Test 1: No log file, not verbose\n    logger1 = setup_logging()\n    logger2 = setup_logging_new_implementation()\n    assert logger1.level == logger2.level\n    assert len(logger1.handlers) == len(logger2.handlers)\n    assert isinstance(logger1.handlers[0], logging.StreamHandler)\n\n    # Test 2: With log file, not verbose\n    log_file = \"/home/user/tmp/test.log\"\n    logger1 = setup_logging(log_file=log_file)\n    logger2 = setup_logging_new_implementation(log_file=log_file)\n    assert logger1.level == logger2.level\n    assert len(logger1.handlers) == len(logger2.handlers)\n    assert isinstance(logger1.handlers[1], RotatingFileHandler)\n    assert os.path.exists(log_file)\n\n    # Test 3: No log file, verbose\n    logger1 = setup_logging(verbose=True)\n    logger2 = setup_logging_new_implementation(verbose=True)\n    assert logger1.level == logger2.level\n    assert len(logger1.handlers) == len(logger2.handlers)\n    assert logger1.handlers[0].level == logger2.handlers[0].level\n\n    # Test 4: With log file, verbose\n    log_file = \"/home/user/tmp/test_verbose.log\"\n    logger1 = setup_logging(log_file=log_file, verbose=True)\n    logger2 = setup_logging_new_implementation(log_file=log_file, verbose=True)\n    assert logger1.level == logger2.level\n    assert len(logger1.handlers) == len(logger2.handlers)\n    assert isinstance(logger1.handlers[1], RotatingFileHandler)\n    assert logger1.handlers[0].level == logger2.handlers[0].level\n    assert os.path.exists(log_file)\n\n    # Test 5: Different log file path\n    log_file = \"/home/user/tmp/different_path/test.log\"\n    logger1 = setup_logging(log_file=log_file)\n    logger2 = setup_logging_new_implementation(log_file=log_file)\n    assert logger1.level == logger2.level\n    assert len(logger1.handlers) == len(logger2.handlers)\n    assert isinstance(logger1.handlers[1], RotatingFileHandler)\n    assert os.path.exists(log_file)\n\n    # Test 6: Handler order and levels\n    logger1 = setup_logging(log_file=log_file, verbose=True)\n    logger2 = setup_logging_new_implementation(log_file=log_file, verbose=True)\n    assert isinstance(logger1.handlers[0], logging.StreamHandler)\n    assert isinstance(logger1.handlers[1], RotatingFileHandler)\n    assert logger1.handlers[0].level == logging.DEBUG\n    assert logger1.handlers[1].level == logging.DEBUG\n\nif __name__ == \"__main__\":\n    test_setup_logging()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      18      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 18      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION and the ORIGINAL FUNCTION are functionally identical. Both functions set up a logger with the same configurations: they create a logger named \"FileCombinator\", set its level based on the `verbose` flag, clear existing handlers, and add a console handler with a simple formatter. If a `log_file` is provided, they add a file handler with a detailed formatter and set its level to DEBUG. The REVISED FUNCTION includes additional comments and slight formatting changes, but these do not affect the functionality. The test cases provided further confirm that the behavior of both functions is the same under various conditions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `setup_logging` function returns a `logging.Logger` instance, which satisfies this condition as it has a return value.\n- CONDITION 2: The test cases check the properties of the returned `Logger` object, such as its level and handlers, and do not rely on printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the `Logger` instances returned by `setup_logging` and `setup_logging_new_implementation` by checking their levels, handlers, and handler types. This ensures that `setup_logging_new_implementation` must have the same functionality as `setup_logging` to pass the tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the properties of the `Logger` instances, which is reasonable given that `setup_logging` returns a `Logger`. The test cases do not use inappropriate assertions like comparing the return values directly. This condition is satisfied.\n- CONDITION 5: The test cases cover various scenarios, including different combinations of `log_file` and `verbose` parameters, checking handler types, levels, and the existence of log files. These tests are non-trivial as they cover different configurations and ensure the logger is set up correctly. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "fa2c815e82f2615451a2e6a3157a46f9aaab023b"
    },
    {
        "func_name": "get_config",
        "idx": "217",
        "repo_name": "peiman___filecombinator",
        "func_path": "filecombinator/core/config.py",
        "orig_func": "def get_config(additional_excludes: Optional[Set[str]]=None) -> Config:\n    \"\"\"Get configuration with optional additional excludes.\n\n    Args:\n        additional_excludes: Additional patterns to exclude\n\n    Returns:\n        Config: Combined configuration\n    \"\"\"\n    config = load_config_file()\n    if additional_excludes:\n        config.exclude_patterns.update(additional_excludes)\n    return config",
        "orig_context": "```python\n## filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n\n    pass\n\nclass ConfigurationError(FileCombinatorError):\n    \"\"\"Raised when there's an error in configuration.\"\"\"\n\n    pass\n\n```\n\n\n```python\n## filecombinator/core/logging.py\nimport logging\n\n```\n\n\n```python\n## filecombinator/core/config.py\nimport logging\n\nimport os\n\nfrom dataclasses import dataclass, field\n\nfrom typing import Any, Dict, Optional, Set\n\nimport yaml\n\nfrom .exceptions import ConfigurationError\n\nlogger = logging.getLogger(__name__)\n\nclass Config:\n    \"\"\"Configuration container for FileCombinator.\"\"\"\n\n    exclude_patterns: Set[str] = field(default_factory=set)\n    log_file: str = \"logs/file_combinator.log\"\n    output_suffix: str = \"_file_combinator_output.txt\"\n\ndef load_config_file(config_path: Optional[str] = None) -> Config:\n    \"\"\"Load configuration from YAML file.\n\n    Args:\n        config_path: Optional path to config file\n\n    Returns:\n        Config: Loaded configuration\n\n    Raises:\n        ValueError: If config file is invalid\n        ConfigurationError: If config file cannot be loaded\n    \"\"\"\n    if config_path is None:\n        config_path = os.path.join(os.path.dirname(__file__), \"config.yaml\")\n\n    try:\n        with open(config_path, \"r\", encoding=\"utf-8\") as f:\n            try:\n                config_data = yaml.safe_load(f)\n            except yaml.YAMLError as e:\n                raise ValueError(f\"Invalid YAML format in config file: {e}\") from e\n\n            if not isinstance(config_data, dict):\n                raise ValueError(\"Config file must contain a YAML dictionary\")\n\n            config = Config()\n            config_dict: Dict[str, Any] = config_data\n\n            # Load exclude patterns\n            if \"exclude_patterns\" in config_dict:\n                patterns = config_dict[\"exclude_patterns\"]\n                if not isinstance(patterns, list):\n                    raise ValueError(\"exclude_patterns must be a list\")\n                config.exclude_patterns = set(patterns)\n\n            # Load logging configuration\n            if \"logging\" in config_dict:\n                logging_config = config_dict[\"logging\"]\n                if isinstance(logging_config, dict):\n                    config.log_file = logging_config.get(\n                        \"default_log_file\", config.log_file\n                    )\n\n            # Load output configuration\n            if \"output\" in config_dict:\n                output_config = config_dict[\"output\"]\n                if isinstance(output_config, dict):\n                    config.output_suffix = output_config.get(\n                        \"file_suffix\", config.output_suffix\n                    )\n\n            return config\n\n    except OSError as e:\n        logger.error(\"Failed to load config file: %s\", e)\n        raise ConfigurationError(f\"Could not load config file: {e}\") from e\n\ndef get_config(additional_excludes: Optional[Set[str]] = None) -> Config:\n    \"\"\"Get configuration with optional additional excludes.\n\n    Args:\n        additional_excludes: Additional patterns to exclude\n\n    Returns:\n        Config: Combined configuration\n    \"\"\"\n    config = load_config_file()\n\n    if additional_excludes:\n        config.exclude_patterns.update(additional_excludes)\n\n    return config\n\n```\n\n\n",
        "eval_script": "import logging\nimport os\nfrom dataclasses import dataclass, field\nfrom typing import Any, Dict, Optional, Set\nimport yaml\n\n# Mock ConfigurationError class from filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n    pass\n\nclass ConfigurationError(FileCombinatorError):\n    \"\"\"Raised when there's an error in configuration.\"\"\"\n    pass\n\nlogger = logging.getLogger(__name__)\n\n@dataclass\nclass Config:\n    \"\"\"Configuration container for FileCombinator.\"\"\"\n    exclude_patterns: Set[str] = field(default_factory=set)\n    log_file: str = \"logs/file_combinator.log\"\n    output_suffix: str = \"_file_combinator_output.txt\"\n\ndef load_config_file(config_path: Optional[str] = None) -> Config:\n    \"\"\"Load configuration from YAML file.\n\n    Args:\n        config_path: Optional path to config file\n\n    Returns:\n        Config: Loaded configuration\n\n    Raises:\n        ValueError: If config file is invalid\n        ConfigurationError: If config file cannot be loaded\n    \"\"\"\n    if config_path is None:\n        config_path = os.path.join(\"/home/user/tmp\", \"config.yaml\")\n\n    try:\n        with open(config_path, \"r\", encoding=\"utf-8\") as f:\n            try:\n                config_data = yaml.safe_load(f)\n            except yaml.YAMLError as e:\n                raise ValueError(f\"Invalid YAML format in config file: {e}\") from e\n\n            if not isinstance(config_data, dict):\n                raise ValueError(\"Config file must contain a YAML dictionary\")\n\n            config = Config()\n            config_dict: Dict[str, Any] = config_data\n\n            # Load exclude patterns\n            if \"exclude_patterns\" in config_dict:\n                patterns = config_dict[\"exclude_patterns\"]\n                if not isinstance(patterns, list):\n                    raise ValueError(\"exclude_patterns must be a list\")\n                config.exclude_patterns = set(patterns)\n\n            # Load logging configuration\n            if \"logging\" in config_dict:\n                logging_config = config_dict[\"logging\"]\n                if isinstance(logging_config, dict):\n                    config.log_file = logging_config.get(\n                        \"default_log_file\", config.log_file\n                    )\n\n            # Load output configuration\n            if \"output\" in config_dict:\n                output_config = config_dict[\"output\"]\n                if isinstance(output_config, dict):\n                    config.output_suffix = output_config.get(\n                        \"file_suffix\", config.output_suffix\n                    )\n\n            return config\n\n    except OSError as e:\n        logger.error(\"Failed to load config file: %s\", e)\n        raise ConfigurationError(f\"Could not load config file: {e}\") from e\n\ndef get_config(additional_excludes: Optional[Set[str]] = None) -> Config:\n    \"\"\"Get configuration with optional additional excludes.\n\n    Args:\n        additional_excludes: Additional patterns to exclude\n\n    Returns:\n        Config: Combined configuration\n    \"\"\"\n    config = load_config_file()\n\n    if additional_excludes:\n        config.exclude_patterns.update(additional_excludes)\n\n    return config\n\n# Create a mock config.yaml file in /home/user/tmp\nmock_config_content = \"\"\"\nexclude_patterns:\n  - \"*.tmp\"\n  - \"*.log\"\nlogging:\n  default_log_file: \"logs/mock_log.log\"\noutput:\n  file_suffix: \"_mock_output.txt\"\n\"\"\"\n\nos.makedirs(\"/home/user/tmp\", exist_ok=True)\nwith open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n    f.write(mock_config_content)\n\n\ndef test_get_config():\n    \"\"\"Test function to compare get_config and get_config_new_implementation.\"\"\"\n    # Test 1: Default configuration\n    config1 = get_config()\n    config2 = get_config_new_implementation()\n    assert config1 == config2, \"Default configuration mismatch\"\n\n    # Test 2: With additional excludes\n    additional_excludes = {\"*.bak\"}\n    config1 = get_config(additional_excludes)\n    config2 = get_config_new_implementation(additional_excludes)\n    assert config1 == config2, \"Configuration with additional excludes mismatch\"\n\n    # Test 3: Check specific configuration values\n    assert config1.log_file == \"logs/mock_log.log\", \"Log file mismatch\"\n    assert config1.output_suffix == \"_mock_output.txt\", \"Output suffix mismatch\"\n    assert \"*.bak\" in config1.exclude_patterns, \"Additional exclude pattern missing\"\n\n    # Test 4: Empty additional excludes\n    additional_excludes = set()\n    config1 = get_config(additional_excludes)\n    config2 = get_config_new_implementation(additional_excludes)\n    assert config1 == config2, \"Configuration with empty additional excludes mismatch\"\n\n    # Test 5: Non-existent config file\n    try:\n        os.remove(\"/home/user/tmp/config.yaml\")\n        get_config()\n    except ConfigurationError:\n        pass\n    else:\n        assert False, \"Expected ConfigurationError for non-existent config file\"\n\n    # Restore mock config file\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(mock_config_content)\n\n    # Test 6: Invalid YAML format\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(\"invalid_yaml: [unclosed_list\")\n    try:\n        get_config()\n    except ValueError as e:\n        assert \"Invalid YAML format\" in str(e), \"Expected ValueError for invalid YAML format\"\n    else:\n        assert False, \"Expected ValueError for invalid YAML format\"\n\n    # Restore mock config file\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(mock_config_content)\n\n    # Test 7: Invalid config structure\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(\"- not_a_dict\")\n    try:\n        get_config()\n    except ValueError as e:\n        assert \"Config file must contain a YAML dictionary\" in str(e), \"Expected ValueError for invalid config structure\"\n    else:\n        assert False, \"Expected ValueError for invalid config structure\"\n\n    # Restore mock config file\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(mock_config_content)\n\n    # Test 8: Invalid exclude_patterns type\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(\"exclude_patterns: not_a_list\")\n    try:\n        get_config()\n    except ValueError as e:\n        assert \"exclude_patterns must be a list\" in str(e), \"Expected ValueError for invalid exclude_patterns type\"\n    else:\n        assert False, \"Expected ValueError for invalid exclude_patterns type\"\n\n    # Restore mock config file\n    with open(\"/home/user/tmp/config.yaml\", \"w\", encoding=\"utf-8\") as f:\n        f.write(mock_config_content)\n\nif __name__ == \"__main__\":\n    test_get_config()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_config` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions load a configuration using `load_config_file()` and update the `exclude_patterns` set with any additional excludes provided. The rest of the code, including the `load_config_file` function and the test cases, does not alter the functionality of `get_config` itself. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_config` function returns a `Config` object, satisfying this condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare return values and check specific attributes of the `Config` object, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases cover various scenarios, including default configuration, additional excludes, and error handling for invalid configurations. These tests ensure that `get_config_new_implementation` must have the same functionality as `get_config` to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases and assertions are reasonable. They check for equality of configurations and handle exceptions appropriately. The assertions are based on the expected behavior of the `get_config` function, satisfying this condition.\n- CONDITION 5: The test cases are non-trivial as they cover different scenarios, including valid configurations, additional excludes, and various error conditions, ensuring comprehensive testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "fa2c815e82f2615451a2e6a3157a46f9aaab023b"
    },
    {
        "func_name": "FileTypeDetector._check_for_binary_content",
        "idx": "223",
        "repo_name": "peiman___filecombinator",
        "func_path": "filecombinator/core/file_utils.py",
        "orig_func": "def _check_for_binary_content(self, chunk: bytes) -> bool:\n    \"\"\"Check if content chunk appears to be binary.\n\n        Args:\n            chunk: Bytes to check\n\n        Returns:\n            bool: True if content appears binary, False otherwise\n        \"\"\"\n    if not chunk:\n        return False\n    if b'\\x00' in chunk:\n        logger.debug('Found null bytes in content')\n        return True\n    try:\n        chunk.decode('utf-8', errors='strict')\n        return False\n    except UnicodeDecodeError:\n        logger.debug('Content failed UTF-8 decoding')\n        return True",
        "orig_context": "```python\n## filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n\n    pass\n\nclass FileProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a file.\"\"\"\n\n    pass\n\n```\n\n\n```python\n## filecombinator/core/logging.py\nimport logging\n\n```\n\n\n```python\n## filecombinator/core/file_utils.py\nimport logging\n\nimport os\n\nfrom pathlib import Path\n\nfrom typing import Any, Optional, Set, Type\n\nfrom .exceptions import FileProcessingError\n\nlogger = logging.getLogger(__name__)\n\nimport magic\n\nMAGIC_AVAILABLE = False\n\nclass FileTypeDetector:\n    \"\"\"Handles file type detection and categorization.\"\"\"\n\n    # Image file extensions\n    IMAGE_EXTENSIONS: Set[str] = {\n        \".jpg\",\n        \".jpeg\",\n        \".png\",\n        \".gif\",\n        \".bmp\",\n        \".tiff\",\n        \".webp\",\n        \".svg\",\n        \".ico\",\n    }\n\n    # Known binary file extensions\n    BINARY_EXTENSIONS: Set[str] = {\n        \".pyc\",\n        \".pyo\",\n        \".pyd\",\n        \".so\",\n        \".dll\",\n        \".dylib\",\n        \".exe\",\n        \".bin\",\n        \".coverage\",\n        \".pkl\",\n        \".pdb\",\n        \".o\",\n        \".obj\",\n        \".db\",\n        \".sqlite\",\n        \".sqlite3\",\n        \".jar\",\n        \".war\",\n        \".class\",\n        \".pdf\",\n    }\n\n    # Known text file extensions\n    TEXT_EXTENSIONS: Set[str] = {\n        \".txt\",\n        \".json\",\n        \".xml\",\n        \".yaml\",\n        \".yml\",\n        \".md\",\n        \".py\",\n        \".js\",\n        \".html\",\n        \".css\",\n        \".csv\",\n        \".log\",\n        \".ini\",\n        \".conf\",\n        \".toml\",\n    }\n\n    # Known text MIME types\n    TEXT_MIME_TYPES: Set[str] = {\n        \"text/\",\n        \"application/json\",\n        \"application/x-ndjson\",  # Added for newline-delimited JSON\n        \"application/xml\",\n        \"application/x-empty\",\n        \"application/x-yaml\",\n        \"application/x-javascript\",\n        \"application/javascript\",\n        \"inode/x-empty\",\n    }\n\n    def __init__(self) -> None:\n        \"\"\"Initialize the FileTypeDetector.\"\"\"\n        self.mime: Optional[Any] = None\n        if MAGIC_AVAILABLE:\n            try:\n                self.mime = magic.Magic(mime=True)\n                logger.debug(\"Magic library initialized successfully\")\n            except Exception as e:  # pragma: no cover\n                logger.debug(\"Could not initialize magic library: %s\", e)\n                self.mime = None\n\n    def _check_for_binary_content(self, chunk: bytes) -> bool:\n        \"\"\"Check if content chunk appears to be binary.\n\n        Args:\n            chunk: Bytes to check\n\n        Returns:\n            bool: True if content appears binary, False otherwise\n        \"\"\"\n        # Empty content is considered text\n        if not chunk:\n            return False\n\n        # Check for null bytes\n        if b\"\\x00\" in chunk:\n            logger.debug(\"Found null bytes in content\")\n            return True\n\n        # Try to decode as text\n        try:\n            chunk.decode(\"utf-8\", errors=\"strict\")\n            return False\n        except UnicodeDecodeError:\n            logger.debug(\"Content failed UTF-8 decoding\")\n            return True\n\n    def _read_file_chunk(self, file_path: str) -> bytes:\n        \"\"\"Read a chunk of file content safely.\n\n        Args:\n            file_path: Path to the file to read\n\n        Returns:\n            bytes: The read chunk of data\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        try:\n            with open(file_path, \"rb\") as f:\n                return f.read(8192)\n        except IOError as e:\n            logger.error(\"Error reading file %s: %s\", file_path, e)\n            raise FileProcessingError(f\"Error reading file {file_path}: {e}\")\n\n    def is_image_file(self, file_path: str | Path) -> bool:\n        \"\"\"Check if a file is an image.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is an image, False otherwise\n        \"\"\"\n        file_path_str = str(file_path)\n        if not os.path.exists(file_path_str):\n            logger.debug(\"File %s does not exist\", file_path_str)\n            return False\n\n        # Check extension first\n        extension = Path(file_path_str).suffix.lower()\n        if extension in self.IMAGE_EXTENSIONS:\n            logger.debug(\"File %s identified as image by extension\", file_path_str)\n            return True\n\n        # Try MIME type detection\n        if self.mime:\n            try:\n                mime_type = self.mime.from_file(file_path_str)\n                if mime_type.startswith(\"image/\"):\n                    logger.debug(\n                        \"File %s identified as image by MIME type\", file_path_str\n                    )\n                    return True\n            except Exception as e:\n                logger.warning(\"Error checking MIME type for %s: %s\", file_path_str, e)\n\n        return False\n\n    def is_binary_file(self, file_path: str | Path) -> bool:\n        \"\"\"Detect if a file is binary.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is binary, False otherwise\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        file_path_str = str(file_path)\n        logger.debug(\"Checking if file is binary: %s\", file_path_str)\n\n        if not os.path.exists(file_path_str):\n            logger.error(\"File does not exist: %s\", file_path_str)\n            raise FileProcessingError(f\"File does not exist: {file_path_str}\")\n\n        # Empty files are treated as text files\n        size = os.path.getsize(file_path_str)\n        logger.debug(\"File size: %d bytes\", size)\n        if size == 0:\n            logger.debug(\"Empty file %s treated as text\", file_path_str)\n            return False\n\n        # For .txt files, always check content regardless of MIME type\n        extension = Path(file_path_str).suffix.lower()\n        logger.debug(\"File extension: %s\", extension)\n        if extension == \".txt\":\n            logger.debug(\"Text file found, will check content regardless of MIME type\")\n        else:\n            # For non-txt files, check known extensions first\n            if extension in self.TEXT_EXTENSIONS:\n                logger.debug(\"File %s identified as text by extension\", file_path_str)\n                return False\n            if extension in self.BINARY_EXTENSIONS:\n                logger.debug(\"File %s identified as binary by extension\", file_path_str)\n                return True\n\n            # Then try MIME type detection\n            if self.mime:\n                try:\n                    mime_type = self.mime.from_file(file_path_str)\n                    logger.debug(\"MIME type for %s: %s\", file_path_str, mime_type)\n\n                    # Check for text MIME types\n                    for text_mime in self.TEXT_MIME_TYPES:\n                        if mime_type.startswith(text_mime):\n                            logger.debug(\n                                \"File %s identified as text by MIME type %s\",\n                                file_path_str,\n                                mime_type,\n                            )\n                            return False\n                    logger.debug(\"No matching text MIME type found\")\n                except Exception as e:\n                    logger.warning(\n                        \"Error checking mime type for %s: %s\", file_path_str, e\n                    )\n\n        # For .txt files and files not identified by extension or MIME type,\n        # check content\n        logger.debug(\"Performing content analysis\")\n        try:\n            chunk = self._read_file_chunk(file_path_str)\n            is_binary = self._check_for_binary_content(chunk)\n            logger.debug(\n                \"Content analysis result for %s: %s\",\n                file_path_str,\n                \"binary\" if is_binary else \"text\",\n            )\n            return is_binary\n        except Exception as e:\n            logger.error(\"Error during binary detection: %s\", e, exc_info=True)\n            raise FileProcessingError(f\"Error reading file {file_path_str}: {e}\")\n\n```\n\n\n",
        "eval_script": "import logging\nimport os\nfrom pathlib import Path\nfrom typing import Any, Optional, Set\n\n# Define the exceptions\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n    pass\n\nclass FileProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a file.\"\"\"\n    pass\n\n# Set up logging\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\n# Mock the magic module since we cannot use external libraries\nclass MockMagic:\n    def from_file(self, file_path):\n        return \"application/octet-stream\"\n\nmagic = MockMagic()\nMAGIC_AVAILABLE = True\n\nclass FileTypeDetector:\n    \"\"\"Handles file type detection and categorization.\"\"\"\n\n    IMAGE_EXTENSIONS: Set[str] = {\n        \".jpg\",\n        \".jpeg\",\n        \".png\",\n        \".gif\",\n        \".bmp\",\n        \".tiff\",\n        \".webp\",\n        \".svg\",\n        \".ico\",\n    }\n\n    BINARY_EXTENSIONS: Set[str] = {\n        \".pyc\",\n        \".pyo\",\n        \".pyd\",\n        \".so\",\n        \".dll\",\n        \".dylib\",\n        \".exe\",\n        \".bin\",\n        \".coverage\",\n        \".pkl\",\n        \".pdb\",\n        \".o\",\n        \".obj\",\n        \".db\",\n        \".sqlite\",\n        \".sqlite3\",\n        \".jar\",\n        \".war\",\n        \".class\",\n        \".pdf\",\n    }\n\n    TEXT_EXTENSIONS: Set[str] = {\n        \".txt\",\n        \".json\",\n        \".xml\",\n        \".yaml\",\n        \".yml\",\n        \".md\",\n        \".py\",\n        \".js\",\n        \".html\",\n        \".css\",\n        \".csv\",\n        \".log\",\n        \".ini\",\n        \".conf\",\n        \".toml\",\n    }\n\n    TEXT_MIME_TYPES: Set[str] = {\n        \"text/\",\n        \"application/json\",\n        \"application/x-ndjson\",\n        \"application/xml\",\n        \"application/x-empty\",\n        \"application/x-yaml\",\n        \"application/x-javascript\",\n        \"application/javascript\",\n        \"inode/x-empty\",\n    }\n\n    def __init__(self) -> None:\n        \"\"\"Initialize the FileTypeDetector.\"\"\"\n        self.mime: Optional[Any] = None\n        if MAGIC_AVAILABLE:\n            try:\n                self.mime = magic\n                logger.debug(\"Magic library initialized successfully\")\n            except Exception as e:\n                logger.debug(\"Could not initialize magic library: %s\", e)\n                self.mime = None\n\n    def _check_for_binary_content(self, chunk: bytes) -> bool:\n        \"\"\"Check if content chunk appears to be binary.\n\n        Args:\n            chunk: Bytes to check\n\n        Returns:\n            bool: True if content appears binary, False otherwise\n        \"\"\"\n        if not chunk:\n            return False\n\n        if b\"\\x00\" in chunk:\n            logger.debug(\"Found null bytes in content\")\n            return True\n\n        try:\n            chunk.decode(\"utf-8\", errors=\"strict\")\n            return False\n        except UnicodeDecodeError:\n            logger.debug(\"Content failed UTF-8 decoding\")\n            return True\n\n\n    def _read_file_chunk(self, file_path: str) -> bytes:\n        \"\"\"Read a chunk of file content safely.\n\n        Args:\n            file_path: Path to the file to read\n\n        Returns:\n            bytes: The read chunk of data\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        try:\n            with open(file_path, \"rb\") as f:\n                return f.read(8192)\n        except IOError as e:\n            logger.error(\"Error reading file %s: %s\", file_path, e)\n            raise FileProcessingError(f\"Error reading file {file_path}: {e}\")\n\n    def is_image_file(self, file_path: str | Path) -> bool:\n        \"\"\"Check if a file is an image.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is an image, False otherwise\n        \"\"\"\n        file_path_str = str(file_path)\n        if not os.path.exists(file_path_str):\n            logger.debug(\"File %s does not exist\", file_path_str)\n            return False\n\n        extension = Path(file_path_str).suffix.lower()\n        if extension in self.IMAGE_EXTENSIONS:\n            logger.debug(\"File %s identified as image by extension\", file_path_str)\n            return True\n\n        if self.mime:\n            try:\n                mime_type = self.mime.from_file(file_path_str)\n                if mime_type.startswith(\"image/\"):\n                    logger.debug(\"File %s identified as image by MIME type\", file_path_str)\n                    return True\n            except Exception as e:\n                logger.warning(\"Error checking MIME type for %s: %s\", file_path_str, e)\n\n        return False\n\n    def is_binary_file(self, file_path: str | Path) -> bool:\n        \"\"\"Detect if a file is binary.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is binary, False otherwise\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        file_path_str = str(file_path)\n        logger.debug(\"Checking if file is binary: %s\", file_path_str)\n\n        if not os.path.exists(file_path_str):\n            logger.error(\"File does not exist: %s\", file_path_str)\n            raise FileProcessingError(f\"File does not exist: {file_path_str}\")\n\n        size = os.path.getsize(file_path_str)\n        logger.debug(\"File size: %d bytes\", size)\n        if size == 0:\n            logger.debug(\"Empty file %s treated as text\", file_path_str)\n            return False\n\n        extension = Path(file_path_str).suffix.lower()\n        logger.debug(\"File extension: %s\", extension)\n        if extension == \".txt\":\n            logger.debug(\"Text file found, will check content regardless of MIME type\")\n        else:\n            if extension in self.TEXT_EXTENSIONS:\n                logger.debug(\"File %s identified as text by extension\", file_path_str)\n                return False\n            if extension in self.BINARY_EXTENSIONS:\n                logger.debug(\"File %s identified as binary by extension\", file_path_str)\n                return True\n\n            if self.mime:\n                try:\n                    mime_type = self.mime.from_file(file_path_str)\n                    logger.debug(\"MIME type for %s: %s\", file_path_str, mime_type)\n\n                    for text_mime in self.TEXT_MIME_TYPES:\n                        if mime_type.startswith(text_mime):\n                            logger.debug(\"File %s identified as text by MIME type %s\", file_path_str, mime_type)\n                            return False\n                    logger.debug(\"No matching text MIME type found\")\n                except Exception as e:\n                    logger.warning(\"Error checking mime type for %s: %s\", file_path_str, e)\n\n        logger.debug(\"Performing content analysis\")\n        try:\n            chunk = self._read_file_chunk(file_path_str)\n            is_binary = self._check_for_binary_content(chunk)\n            logger.debug(\"Content analysis result for %s: %s\", file_path_str, \"binary\" if is_binary else \"text\")\n            return is_binary\n        except Exception as e:\n            logger.error(\"Error during binary detection: %s\", e, exc_info=True)\n            raise FileProcessingError(f\"Error reading file {file_path_str}: {e}\")\n\ndef test__check_for_binary_content():\n    detector = FileTypeDetector()\n\n    # Test with empty content\n    chunk = b\"\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\n    # Test with content containing null byte\n    chunk = b\"hello\\x00world\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\n    # Test with valid UTF-8 content\n    chunk = b\"hello world\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\n    # Test with non-UTF-8 encodable content\n    chunk = b\"\\x80\\x81\\x82\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\n    # Test with high ASCII characters\n    chunk = b\"hello \\xC2\\xA9 world\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\n    # Test with mixed encodings\n    chunk = b\"hello \\xC2\\xA9 \\x80\\x81\\x82 world\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\n    # Test with control characters\n    chunk = b\"hello\\x07world\"\n    assert detector._check_for_binary_content(chunk) == detector._check_for_binary_content_new_implementation(chunk)\n\nif __name__ == \"__main__\":\n    test__check_for_binary_content()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_check_for_binary_content` is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions check if a given byte chunk is binary by first checking for null bytes and then attempting to decode the chunk as UTF-8, logging appropriate debug messages in each case. The logic and flow of the function remain unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The function `_check_for_binary_content` returns a boolean value indicating whether the content appears to be binary. This satisfies the condition as it has a return value.\n  \n- **CONDITION 2**: The test cases in `test__check_for_binary_content` use assertions to compare the return values of `_check_for_binary_content` and `_check_for_binary_content_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n- **CONDITION 3**: The test cases compare the return values of `_check_for_binary_content` and `_check_for_binary_content_new_implementation` for various inputs. If the new implementation has the same functionality, it will pass all tests. This condition is satisfied.\n\n- **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `_check_for_binary_content` returns a boolean. This condition is satisfied.\n\n- **CONDITION 5**: The test cases cover a range of scenarios, including empty content, content with null bytes, valid UTF-8 content, non-UTF-8 encodable content, high ASCII characters, mixed encodings, and control characters. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "fa2c815e82f2615451a2e6a3157a46f9aaab023b"
    },
    {
        "func_name": "FileTypeDetector._read_file_chunk",
        "idx": "224",
        "repo_name": "peiman___filecombinator",
        "func_path": "filecombinator/core/file_utils.py",
        "orig_func": "def _read_file_chunk(self, file_path: str) -> bytes:\n    \"\"\"Read a chunk of file content safely.\n\n        Args:\n            file_path: Path to the file to read\n\n        Returns:\n            bytes: The read chunk of data\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n    try:\n        with open(file_path, 'rb') as f:\n            return f.read(8192)\n    except IOError as e:\n        logger.error('Error reading file %s: %s', file_path, e)\n        raise FileProcessingError(f'Error reading file {file_path}: {e}')",
        "orig_context": "```python\n## filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n\n    pass\n\nclass FileProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a file.\"\"\"\n\n    pass\n\n```\n\n\n```python\n## filecombinator/core/logging.py\nimport logging\n\n```\n\n\n```python\n## filecombinator/core/file_utils.py\nimport logging\n\nimport os\n\nfrom pathlib import Path\n\nfrom typing import Any, Optional, Set, Type\n\nfrom .exceptions import FileProcessingError\n\nlogger = logging.getLogger(__name__)\n\nimport magic\n\nMAGIC_AVAILABLE = False\n\nclass FileTypeDetector:\n    \"\"\"Handles file type detection and categorization.\"\"\"\n\n    # Image file extensions\n    IMAGE_EXTENSIONS: Set[str] = {\n        \".jpg\",\n        \".jpeg\",\n        \".png\",\n        \".gif\",\n        \".bmp\",\n        \".tiff\",\n        \".webp\",\n        \".svg\",\n        \".ico\",\n    }\n\n    # Known binary file extensions\n    BINARY_EXTENSIONS: Set[str] = {\n        \".pyc\",\n        \".pyo\",\n        \".pyd\",\n        \".so\",\n        \".dll\",\n        \".dylib\",\n        \".exe\",\n        \".bin\",\n        \".coverage\",\n        \".pkl\",\n        \".pdb\",\n        \".o\",\n        \".obj\",\n        \".db\",\n        \".sqlite\",\n        \".sqlite3\",\n        \".jar\",\n        \".war\",\n        \".class\",\n        \".pdf\",\n    }\n\n    # Known text file extensions\n    TEXT_EXTENSIONS: Set[str] = {\n        \".txt\",\n        \".json\",\n        \".xml\",\n        \".yaml\",\n        \".yml\",\n        \".md\",\n        \".py\",\n        \".js\",\n        \".html\",\n        \".css\",\n        \".csv\",\n        \".log\",\n        \".ini\",\n        \".conf\",\n        \".toml\",\n    }\n\n    # Known text MIME types\n    TEXT_MIME_TYPES: Set[str] = {\n        \"text/\",\n        \"application/json\",\n        \"application/x-ndjson\",  # Added for newline-delimited JSON\n        \"application/xml\",\n        \"application/x-empty\",\n        \"application/x-yaml\",\n        \"application/x-javascript\",\n        \"application/javascript\",\n        \"inode/x-empty\",\n    }\n\n    def __init__(self) -> None:\n        \"\"\"Initialize the FileTypeDetector.\"\"\"\n        self.mime: Optional[Any] = None\n        if MAGIC_AVAILABLE:\n            try:\n                self.mime = magic.Magic(mime=True)\n                logger.debug(\"Magic library initialized successfully\")\n            except Exception as e:  # pragma: no cover\n                logger.debug(\"Could not initialize magic library: %s\", e)\n                self.mime = None\n\n    def _check_for_binary_content(self, chunk: bytes) -> bool:\n        \"\"\"Check if content chunk appears to be binary.\n\n        Args:\n            chunk: Bytes to check\n\n        Returns:\n            bool: True if content appears binary, False otherwise\n        \"\"\"\n        # Empty content is considered text\n        if not chunk:\n            return False\n\n        # Check for null bytes\n        if b\"\\x00\" in chunk:\n            logger.debug(\"Found null bytes in content\")\n            return True\n\n        # Try to decode as text\n        try:\n            chunk.decode(\"utf-8\", errors=\"strict\")\n            return False\n        except UnicodeDecodeError:\n            logger.debug(\"Content failed UTF-8 decoding\")\n            return True\n\n    def _read_file_chunk(self, file_path: str) -> bytes:\n        \"\"\"Read a chunk of file content safely.\n\n        Args:\n            file_path: Path to the file to read\n\n        Returns:\n            bytes: The read chunk of data\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        try:\n            with open(file_path, \"rb\") as f:\n                return f.read(8192)\n        except IOError as e:\n            logger.error(\"Error reading file %s: %s\", file_path, e)\n            raise FileProcessingError(f\"Error reading file {file_path}: {e}\")\n\n    def is_image_file(self, file_path: str | Path) -> bool:\n        \"\"\"Check if a file is an image.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is an image, False otherwise\n        \"\"\"\n        file_path_str = str(file_path)\n        if not os.path.exists(file_path_str):\n            logger.debug(\"File %s does not exist\", file_path_str)\n            return False\n\n        # Check extension first\n        extension = Path(file_path_str).suffix.lower()\n        if extension in self.IMAGE_EXTENSIONS:\n            logger.debug(\"File %s identified as image by extension\", file_path_str)\n            return True\n\n        # Try MIME type detection\n        if self.mime:\n            try:\n                mime_type = self.mime.from_file(file_path_str)\n                if mime_type.startswith(\"image/\"):\n                    logger.debug(\n                        \"File %s identified as image by MIME type\", file_path_str\n                    )\n                    return True\n            except Exception as e:\n                logger.warning(\"Error checking MIME type for %s: %s\", file_path_str, e)\n\n        return False\n\n    def is_binary_file(self, file_path: str | Path) -> bool:\n        \"\"\"Detect if a file is binary.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is binary, False otherwise\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        file_path_str = str(file_path)\n        logger.debug(\"Checking if file is binary: %s\", file_path_str)\n\n        if not os.path.exists(file_path_str):\n            logger.error(\"File does not exist: %s\", file_path_str)\n            raise FileProcessingError(f\"File does not exist: {file_path_str}\")\n\n        # Empty files are treated as text files\n        size = os.path.getsize(file_path_str)\n        logger.debug(\"File size: %d bytes\", size)\n        if size == 0:\n            logger.debug(\"Empty file %s treated as text\", file_path_str)\n            return False\n\n        # For .txt files, always check content regardless of MIME type\n        extension = Path(file_path_str).suffix.lower()\n        logger.debug(\"File extension: %s\", extension)\n        if extension == \".txt\":\n            logger.debug(\"Text file found, will check content regardless of MIME type\")\n        else:\n            # For non-txt files, check known extensions first\n            if extension in self.TEXT_EXTENSIONS:\n                logger.debug(\"File %s identified as text by extension\", file_path_str)\n                return False\n            if extension in self.BINARY_EXTENSIONS:\n                logger.debug(\"File %s identified as binary by extension\", file_path_str)\n                return True\n\n            # Then try MIME type detection\n            if self.mime:\n                try:\n                    mime_type = self.mime.from_file(file_path_str)\n                    logger.debug(\"MIME type for %s: %s\", file_path_str, mime_type)\n\n                    # Check for text MIME types\n                    for text_mime in self.TEXT_MIME_TYPES:\n                        if mime_type.startswith(text_mime):\n                            logger.debug(\n                                \"File %s identified as text by MIME type %s\",\n                                file_path_str,\n                                mime_type,\n                            )\n                            return False\n                    logger.debug(\"No matching text MIME type found\")\n                except Exception as e:\n                    logger.warning(\n                        \"Error checking mime type for %s: %s\", file_path_str, e\n                    )\n\n        # For .txt files and files not identified by extension or MIME type,\n        # check content\n        logger.debug(\"Performing content analysis\")\n        try:\n            chunk = self._read_file_chunk(file_path_str)\n            is_binary = self._check_for_binary_content(chunk)\n            logger.debug(\n                \"Content analysis result for %s: %s\",\n                file_path_str,\n                \"binary\" if is_binary else \"text\",\n            )\n            return is_binary\n        except Exception as e:\n            logger.error(\"Error during binary detection: %s\", e, exc_info=True)\n            raise FileProcessingError(f\"Error reading file {file_path_str}: {e}\")\n\n```\n\n\n",
        "eval_script": "import logging\nimport os\nfrom pathlib import Path\nfrom typing import Any, Optional, Set\n\n# Exception classes from filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n    pass\n\nclass FileProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a file.\"\"\"\n    pass\n\n# Configure logging\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\n# Mocking the magic module since we cannot use external libraries\nclass MagicMock:\n    def from_file(self, file_path):\n        return \"application/octet-stream\"  # Default to binary for testing\n\nmagic = MagicMock()\nMAGIC_AVAILABLE = True\n\nclass FileTypeDetector:\n    \"\"\"Handles file type detection and categorization.\"\"\"\n\n    IMAGE_EXTENSIONS: Set[str] = {\n        \".jpg\",\n        \".jpeg\",\n        \".png\",\n        \".gif\",\n        \".bmp\",\n        \".tiff\",\n        \".webp\",\n        \".svg\",\n        \".ico\",\n    }\n\n    BINARY_EXTENSIONS: Set[str] = {\n        \".pyc\",\n        \".pyo\",\n        \".pyd\",\n        \".so\",\n        \".dll\",\n        \".dylib\",\n        \".exe\",\n        \".bin\",\n        \".coverage\",\n        \".pkl\",\n        \".pdb\",\n        \".o\",\n        \".obj\",\n        \".db\",\n        \".sqlite\",\n        \".sqlite3\",\n        \".jar\",\n        \".war\",\n        \".class\",\n        \".pdf\",\n    }\n\n    TEXT_EXTENSIONS: Set[str] = {\n        \".txt\",\n        \".json\",\n        \".xml\",\n        \".yaml\",\n        \".yml\",\n        \".md\",\n        \".py\",\n        \".js\",\n        \".html\",\n        \".css\",\n        \".csv\",\n        \".log\",\n        \".ini\",\n        \".conf\",\n        \".toml\",\n    }\n\n    TEXT_MIME_TYPES: Set[str] = {\n        \"text/\",\n        \"application/json\",\n        \"application/x-ndjson\",\n        \"application/xml\",\n        \"application/x-empty\",\n        \"application/x-yaml\",\n        \"application/x-javascript\",\n        \"application/javascript\",\n        \"inode/x-empty\",\n    }\n\n    def __init__(self) -> None:\n        \"\"\"Initialize the FileTypeDetector.\"\"\"\n        self.mime: Optional[Any] = None\n        if MAGIC_AVAILABLE:\n            try:\n                self.mime = magic\n                logger.debug(\"Magic library initialized successfully\")\n            except Exception as e:  # pragma: no cover\n                logger.debug(\"Could not initialize magic library: %s\", e)\n                self.mime = None\n\n    def _check_for_binary_content(self, chunk: bytes) -> bool:\n        \"\"\"Check if content chunk appears to be binary.\n\n        Args:\n            chunk: Bytes to check\n\n        Returns:\n            bool: True if content appears binary, False otherwise\n        \"\"\"\n        if not chunk:\n            return False\n\n        if b\"\\x00\" in chunk:\n            logger.debug(\"Found null bytes in content\")\n            return True\n\n        try:\n            chunk.decode(\"utf-8\", errors=\"strict\")\n            return False\n        except UnicodeDecodeError:\n            logger.debug(\"Content failed UTF-8 decoding\")\n            return True\n\n    def _read_file_chunk(self, file_path: str) -> bytes:\n        \"\"\"Read a chunk of file content safely.\n\n        Args:\n            file_path: Path to the file to read\n\n        Returns:\n            bytes: The read chunk of data\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        try:\n            with open(file_path, \"rb\") as f:\n                return f.read(8192)\n        except IOError as e:\n            logger.error(\"Error reading file %s: %s\", file_path, e)\n            raise FileProcessingError(f\"Error reading file {file_path}: {e}\")\n\n\n    def is_image_file(self, file_path: str | Path) -> bool:\n        \"\"\"Check if a file is an image.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is an image, False otherwise\n        \"\"\"\n        file_path_str = str(file_path)\n        if not os.path.exists(file_path_str):\n            logger.debug(\"File %s does not exist\", file_path_str)\n            return False\n\n        extension = Path(file_path_str).suffix.lower()\n        if extension in self.IMAGE_EXTENSIONS:\n            logger.debug(\"File %s identified as image by extension\", file_path_str)\n            return True\n\n        if self.mime:\n            try:\n                mime_type = self.mime.from_file(file_path_str)\n                if mime_type.startswith(\"image/\"):\n                    logger.debug(\n                        \"File %s identified as image by MIME type\", file_path_str\n                    )\n                    return True\n            except Exception as e:\n                logger.warning(\"Error checking MIME type for %s: %s\", file_path_str, e)\n\n        return False\n\n    def is_binary_file(self, file_path: str | Path) -> bool:\n        \"\"\"Detect if a file is binary.\n\n        Args:\n            file_path: Path to the file to check\n\n        Returns:\n            bool: True if the file is binary, False otherwise\n\n        Raises:\n            FileProcessingError: If there's an error reading the file\n        \"\"\"\n        file_path_str = str(file_path)\n        logger.debug(\"Checking if file is binary: %s\", file_path_str)\n\n        if not os.path.exists(file_path_str):\n            logger.error(\"File does not exist: %s\", file_path_str)\n            raise FileProcessingError(f\"File does not exist: {file_path_str}\")\n\n        size = os.path.getsize(file_path_str)\n        logger.debug(\"File size: %d bytes\", size)\n        if size == 0:\n            logger.debug(\"Empty file %s treated as text\", file_path_str)\n            return False\n\n        extension = Path(file_path_str).suffix.lower()\n        logger.debug(\"File extension: %s\", extension)\n        if extension == \".txt\":\n            logger.debug(\"Text file found, will check content regardless of MIME type\")\n        else:\n            if extension in self.TEXT_EXTENSIONS:\n                logger.debug(\"File %s identified as text by extension\", file_path_str)\n                return False\n            if extension in self.BINARY_EXTENSIONS:\n                logger.debug(\"File %s identified as binary by extension\", file_path_str)\n                return True\n\n            if self.mime:\n                try:\n                    mime_type = self.mime.from_file(file_path_str)\n                    logger.debug(\"MIME type for %s: %s\", file_path_str, mime_type)\n\n                    for text_mime in self.TEXT_MIME_TYPES:\n                        if mime_type.startswith(text_mime):\n                            logger.debug(\n                                \"File %s identified as text by MIME type %s\",\n                                file_path_str,\n                                mime_type,\n                            )\n                            return False\n                    logger.debug(\"No matching text MIME type found\")\n                except Exception as e:\n                    logger.warning(\n                        \"Error checking mime type for %s: %s\", file_path_str, e\n                    )\n\n        logger.debug(\"Performing content analysis\")\n        try:\n            chunk = self._read_file_chunk(file_path_str)\n            is_binary = self._check_for_binary_content(chunk)\n            logger.debug(\n                \"Content analysis result for %s: %s\",\n                file_path_str,\n                \"binary\" if is_binary else \"text\",\n            )\n            return is_binary\n        except Exception as e:\n            logger.error(\"Error during binary detection: %s\", e, exc_info=True)\n            raise FileProcessingError(f\"Error reading file {file_path_str}: {e}\")\n\ndef test__read_file_chunk():\n    detector = FileTypeDetector()\n    \n    # Test 1: Non-empty file\n    file_path_1 = '/home/user/tmp/test_file_1.txt'\n    with open(file_path_1, 'wb') as f:\n        f.write(b'Hello World!')\n    \n    assert detector._read_file_chunk(file_path_1) == detector._read_file_chunk_new_implementation(file_path_1)\n    \n    # Test 2: Empty file\n    file_path_2 = '/home/user/tmp/test_file_2.txt'\n    with open(file_path_2, 'wb') as f:\n        pass\n    \n    assert detector._read_file_chunk(file_path_2) == detector._read_file_chunk_new_implementation(file_path_2)\n    \n    # Test 3: File with exactly 8192 bytes\n    file_path_3 = '/home/user/tmp/test_file_3.txt'\n    with open(file_path_3, 'wb') as f:\n        f.write(b'a' * 8192)\n    \n    assert detector._read_file_chunk(file_path_3) == detector._read_file_chunk_new_implementation(file_path_3)\n\nif __name__ == \"__main__\":\n    test__read_file_chunk()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_read_file_chunk` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions attempt to open a file in binary mode and read a chunk of 8192 bytes. If an IOError occurs during this process, both functions log an error message and raise a `FileProcessingError` with a similar message. The logging and exception handling are consistent between the two implementations. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_read_file_chunk` function returns a value, specifically a chunk of bytes read from a file. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases use assertions to compare the return values of `_read_file_chunk` and `_read_file_chunk_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_read_file_chunk` and `_read_file_chunk_new_implementation` for the same input, ensuring that the new implementation must have the same functionality to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_read_file_chunk` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover non-empty files, empty files, and files with exactly 8192 bytes, which are non-trivial scenarios that test different aspects of file reading. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "fa2c815e82f2615451a2e6a3157a46f9aaab023b"
    },
    {
        "func_name": "DirectoryProcessor.is_excluded",
        "idx": "225",
        "repo_name": "peiman___filecombinator",
        "func_path": "filecombinator/processors/directory.py",
        "orig_func": "def is_excluded(self, path: Path) -> bool:\n    \"\"\"Check if a path should be excluded.\n\n        Args:\n            path: Path to check\n\n        Returns:\n            bool: True if path should be excluded, False otherwise\n        \"\"\"\n    path_abs = os.path.abspath(path)\n    output_abs = os.path.abspath(self.output_file) if self.output_file else None\n    if output_abs and path_abs == output_abs:\n        logger.debug('Skipping output file: %s', path)\n        return True\n    file_name = os.path.basename(path)\n    if file_name.endswith('_file_combinator_output.txt'):\n        logger.debug('Skipping file combinator output file: %s', path)\n        return True\n    excluded = any((exclude in path.parts for exclude in self.exclude_patterns))\n    if excluded:\n        logger.debug('Excluded path: %s', path)\n    return excluded",
        "orig_context": "```python\n## filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n\n    pass\n\nclass DirectoryProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a directory.\"\"\"\n\n    pass\n\n```\n\n\n```python\n## filecombinator/processors/directory.py\nimport logging\n\nimport os\n\nfrom pathlib import Path\n\nfrom typing import Protocol\n\nfrom ..core.exceptions import DirectoryProcessingError\n\nlogger = logging.getLogger(__name__)\n\nclass FileCallback(Protocol):\n    \"\"\"Protocol for file callback functions.\"\"\"\n\n    def __call__(self, file_path: str) -> None:\n        \"\"\"Call the callback function with the file path.\"\"\"\n        ...\n\nclass DirectoryProcessor:\n    \"\"\"Handles directory traversal and tree generation.\"\"\"\n\n    def __init__(\n        self, exclude_patterns: set[str], output_file: str | None = None\n    ) -> None:\n        \"\"\"Initialize DirectoryProcessor.\n\n        Args:\n            exclude_patterns: Set of patterns to exclude from processing\n            output_file: Optional path to output file to exclude from processing\n        \"\"\"\n        self.exclude_patterns = exclude_patterns\n        self.output_file = output_file\n\n    def is_excluded(self, path: Path) -> bool:\n        \"\"\"Check if a path should be excluded.\n\n        Args:\n            path: Path to check\n\n        Returns:\n            bool: True if path should be excluded, False otherwise\n        \"\"\"\n        path_abs = os.path.abspath(path)\n        output_abs = os.path.abspath(self.output_file) if self.output_file else None\n\n        if output_abs and path_abs == output_abs:\n            logger.debug(\"Skipping output file: %s\", path)\n            return True\n\n        file_name = os.path.basename(path)\n        if file_name.endswith(\"_file_combinator_output.txt\"):\n            logger.debug(\"Skipping file combinator output file: %s\", path)\n            return True\n\n        excluded = any(exclude in path.parts for exclude in self.exclude_patterns)\n        if excluded:\n            logger.debug(\"Excluded path: %s\", path)\n\n        return excluded\n\n    def generate_tree(self, start_path: str | Path) -> str:\n        \"\"\"Generate a string representation of the directory tree.\n\n        Args:\n            start_path: Root path to start tree generation from\n\n        Returns:\n            str: String representation of the directory tree\n\n        Raises:\n            DirectoryProcessingError: If there's an error generating the tree\n        \"\"\"\n        if not os.path.exists(str(start_path)):\n            raise DirectoryProcessingError(f\"Directory does not exist: {start_path}\")\n\n        try:\n            entries = [\n                e for e in os.scandir(start_path) if not self.is_excluded(Path(e.path))\n            ]\n\n            if not entries:\n                return \"\"  # Return empty string for empty directories\n\n            lines = []\n            root_name = os.path.basename(start_path) or str(start_path)\n            lines.append(root_name + \"/\")\n\n            def add_to_tree(dir_path: Path, prefix: str = \"\") -> None:\n                entries = sorted(os.scandir(dir_path), key=lambda e: e.name)\n                entries = [e for e in entries if not self.is_excluded(Path(e.path))]\n\n                for i, entry in enumerate(entries):\n                    is_last = i == len(entries) - 1\n                    connector = \"\u2514\u2500\u2500 \" if is_last else \"\u251c\u2500\u2500 \"\n                    lines.append(f\"{prefix}{connector}{entry.name}\")\n\n                    if entry.is_dir():\n                        next_prefix = prefix + (\"    \" if is_last else \"\u2502   \")\n                        add_to_tree(Path(entry.path), next_prefix)\n\n            add_to_tree(Path(start_path))\n            return \"\\n\".join(lines)\n\n        except Exception as e:\n            logger.error(\"Error generating directory tree: %s\", e)\n            raise DirectoryProcessingError(\n                f\"Failed to generate directory tree: {e}\"\n            ) from e\n\n    def process_directory(\n        self,\n        directory: str | Path,\n        callback: FileCallback,\n    ) -> None:\n        \"\"\"Process all files in a directory recursively.\n\n        Args:\n            directory: Directory to process\n            callback: Function to call for each file\n\n        Raises:\n            DirectoryProcessingError: If directory can't be processed\n        \"\"\"\n        if not os.path.exists(directory):\n            raise DirectoryProcessingError(f\"Directory does not exist: {directory}\")\n\n        try:\n            for root, dirs, files in os.walk(directory):\n                # Filter out excluded directories\n                dirs[:] = [\n                    d for d in dirs if not self.is_excluded(Path(os.path.join(root, d)))\n                ]\n\n                # Process files\n                for file in sorted(files):\n                    file_path = os.path.join(root, file)\n                    if not self.is_excluded(Path(file_path)):\n                        callback(file_path)\n\n        except OSError as e:\n            logger.error(\"Error processing directory %s: %s\", directory, e)\n            raise DirectoryProcessingError(\n                f\"Failed to process directory {directory}: {e}\"\n            ) from e\n\n```\n\n\n",
        "eval_script": "import logging\nimport os\nfrom pathlib import Path\nfrom typing import Protocol\n\n# Define the exceptions as provided in the context\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n    pass\n\nclass DirectoryProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a directory.\"\"\"\n    pass\n\n# Set up basic logging configuration\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\n# Define the FileCallback protocol\nclass FileCallback(Protocol):\n    \"\"\"Protocol for file callback functions.\"\"\"\n    def __call__(self, file_path: str) -> None:\n        \"\"\"Call the callback function with the file path.\"\"\"\n        ...\n\n# Define the DirectoryProcessor class\nclass DirectoryProcessor:\n    \"\"\"Handles directory traversal and tree generation.\"\"\"\n    def __init__(self, exclude_patterns: set[str], output_file: str | None = None) -> None:\n        \"\"\"Initialize DirectoryProcessor.\n\n        Args:\n            exclude_patterns: Set of patterns to exclude from processing\n            output_file: Optional path to output file to exclude from processing\n        \"\"\"\n        self.exclude_patterns = exclude_patterns\n        self.output_file = output_file\n\n    def is_excluded(self, path: Path) -> bool:\n        \"\"\"Check if a path should be excluded.\n\n        Args:\n            path: Path to check\n\n        Returns:\n            bool: True if path should be excluded, False otherwise\n        \"\"\"\n        path_abs = os.path.abspath(path)\n        output_abs = os.path.abspath(self.output_file) if self.output_file else None\n\n        if output_abs and path_abs == output_abs:\n            logger.debug(\"Skipping output file: %s\", path)\n            return True\n\n        file_name = os.path.basename(path)\n        if file_name.endswith(\"_file_combinator_output.txt\"):\n            logger.debug(\"Skipping file combinator output file: %s\", path)\n            return True\n\n        excluded = any(exclude in path.parts for exclude in self.exclude_patterns)\n        if excluded:\n            logger.debug(\"Excluded path: %s\", path)\n\n        return excluded\n\n\n    def generate_tree(self, start_path: str | Path) -> str:\n        \"\"\"Generate a string representation of the directory tree.\n\n        Args:\n            start_path: Root path to start tree generation from\n\n        Returns:\n            str: String representation of the directory tree\n\n        Raises:\n            DirectoryProcessingError: If there's an error generating the tree\n        \"\"\"\n        if not os.path.exists(str(start_path)):\n            raise DirectoryProcessingError(f\"Directory does not exist: {start_path}\")\n\n        try:\n            entries = [\n                e for e in os.scandir(start_path) if not self.is_excluded(Path(e.path))\n            ]\n\n            if not entries:\n                return \"\"  # Return empty string for empty directories\n\n            lines = []\n            root_name = os.path.basename(start_path) or str(start_path)\n            lines.append(root_name + \"/\")\n\n            def add_to_tree(dir_path: Path, prefix: str = \"\") -> None:\n                entries = sorted(os.scandir(dir_path), key=lambda e: e.name)\n                entries = [e for e in entries if not self.is_excluded(Path(e.path))]\n\n                for i, entry in enumerate(entries):\n                    is_last = i == len(entries) - 1\n                    connector = \"\u2514\u2500\u2500 \" if is_last else \"\u251c\u2500\u2500 \"\n                    lines.append(f\"{prefix}{connector}{entry.name}\")\n\n                    if entry.is_dir():\n                        next_prefix = prefix + (\"    \" if is_last else \"\u2502   \")\n                        add_to_tree(Path(entry.path), next_prefix)\n\n            add_to_tree(Path(start_path))\n            return \"\\n\".join(lines)\n\n        except Exception as e:\n            logger.error(\"Error generating directory tree: %s\", e)\n            raise DirectoryProcessingError(\n                f\"Failed to generate directory tree: {e}\"\n            ) from e\n\n    def process_directory(\n        self,\n        directory: str | Path,\n        callback: FileCallback,\n    ) -> None:\n        \"\"\"Process all files in a directory recursively.\n\n        Args:\n            directory: Directory to process\n            callback: Function to call for each file\n\n        Raises:\n            DirectoryProcessingError: If directory can't be processed\n        \"\"\"\n        if not os.path.exists(directory):\n            raise DirectoryProcessingError(f\"Directory does not exist: {directory}\")\n\n        try:\n            for root, dirs, files in os.walk(directory):\n                # Filter out excluded directories\n                dirs[:] = [\n                    d for d in dirs if not self.is_excluded(Path(os.path.join(root, d)))\n                ]\n\n                # Process files\n                for file in sorted(files):\n                    file_path = os.path.join(root, file)\n                    if not self.is_excluded(Path(file_path)):\n                        callback(file_path)\n\n        except OSError as e:\n            logger.error(\"Error processing directory %s: %s\", directory, e)\n            raise DirectoryProcessingError(\n                f\"Failed to process directory {directory}: {e}\"\n            ) from e\n\ndef test_is_excluded():\n    processor = DirectoryProcessor(exclude_patterns={\"exclude\"}, output_file=\"/home/user/tmp/output.txt\")\n\n    # Test case 1: Path is the output file\n    path1 = Path(\"/home/user/tmp/output.txt\")\n    assert processor.is_excluded(path1) == processor.is_excluded_new_implementation(path1)\n\n    # Test case 2: Path ends with \"_file_combinator_output.txt\"\n    path2 = Path(\"/home/user/tmp/some_file_combinator_output.txt\")\n    assert processor.is_excluded(path2) == processor.is_excluded_new_implementation(path2)\n\n    # Test case 3: Path contains an excluded pattern\n    path3 = Path(\"/home/user/tmp/exclude/somefile.txt\")\n    assert processor.is_excluded(path3) == processor.is_excluded_new_implementation(path3)\n\n    # Additional test case: Path is not excluded\n    path4 = Path(\"/home/user/tmp/include/somefile.txt\")\n    assert processor.is_excluded(path4) == processor.is_excluded_new_implementation(path4)\n\n    # Additional test case: Empty path\n    path5 = Path(\"\")\n    assert processor.is_excluded(path5) == processor.is_excluded_new_implementation(path5)\n\n    # Additional test case: Root directory\n    path6 = Path(\"/\")\n    assert processor.is_excluded(path6) == processor.is_excluded_new_implementation(path6)\n\n    # Additional test case: Nested exclusion\n    path7 = Path(\"/home/user/tmp/nested/exclude/somefile.txt\")\n    assert processor.is_excluded(path7) == processor.is_excluded_new_implementation(path7)\n\n    # Additional test case: No exclusion patterns\n    processor_no_exclude = DirectoryProcessor(exclude_patterns=set(), output_file=\"/home/user/tmp/output.txt\")\n    path8 = Path(\"/home/user/tmp/include/somefile.txt\")\n    assert processor_no_exclude.is_excluded(path8) == processor_no_exclude.is_excluded_new_implementation(path8)\n\n    # Additional test case: Different output file\n    processor_diff_output = DirectoryProcessor(exclude_patterns={\"exclude\"}, output_file=\"/home/user/tmp/different_output.txt\")\n    path9 = Path(\"/home/user/tmp/different_output.txt\")\n    assert processor_diff_output.is_excluded(path9) == processor_diff_output.is_excluded_new_implementation(path9)\n\n    # Additional test case: Case sensitivity\n    path10 = Path(\"/home/user/tmp/Exclude/somefile.txt\")\n    assert processor.is_excluded(path10) == processor.is_excluded_new_implementation(path10)\n\n    # Additional test case: Special characters\n    path11 = Path(\"/home/user/tmp/special_!@#$%^&*()_file.txt\")\n    assert processor.is_excluded(path11) == processor.is_excluded_new_implementation(path11)\n\nif __name__ == \"__main__\":\n    test_is_excluded()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions perform the same checks in the same order: they first check if the path is the same as the output file, then check if the file name ends with \"_file_combinator_output.txt\", and finally check if any part of the path matches the exclude patterns. The logic and sequence of operations are the same, and the logging messages are also identical. There are no differences in the logic or behavior of the two functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `is_excluded` function returns a boolean value indicating whether a path should be excluded. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases in `test_is_excluded` use assertions to compare the return values of `is_excluded` and `is_excluded_new_implementation`. There are no checks for printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the results of `is_excluded` and `is_excluded_new_implementation` for various paths. If `is_excluded_new_implementation` has the same functionality as `is_excluded`, it will pass all the test cases. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `is_excluded` returns a boolean. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including paths that match the output file, paths ending with a specific suffix, paths containing excluded patterns, paths that should not be excluded, empty paths, root directories, nested exclusions, no exclusion patterns, different output files, case sensitivity, and special characters. These test cases are non-trivial and cover a broad range of possible inputs, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "fa2c815e82f2615451a2e6a3157a46f9aaab023b"
    },
    {
        "func_name": "DirectoryProcessor.process_directory",
        "idx": "226",
        "repo_name": "peiman___filecombinator",
        "func_path": "filecombinator/processors/directory.py",
        "orig_func": "def process_directory(self, directory: str | Path, callback: FileCallback) -> None:\n    \"\"\"Process all files in a directory recursively.\n\n        Args:\n            directory: Directory to process\n            callback: Function to call for each file\n\n        Raises:\n            DirectoryProcessingError: If directory can't be processed\n        \"\"\"\n    if not os.path.exists(directory):\n        raise DirectoryProcessingError(f'Directory does not exist: {directory}')\n    try:\n        for root, dirs, files in os.walk(directory):\n            dirs[:] = [d for d in dirs if not self.is_excluded(Path(os.path.join(root, d)))]\n            for file in sorted(files):\n                file_path = os.path.join(root, file)\n                if not self.is_excluded(Path(file_path)):\n                    callback(file_path)\n    except OSError as e:\n        logger.error('Error processing directory %s: %s', directory, e)\n        raise DirectoryProcessingError(f'Failed to process directory {directory}: {e}') from e",
        "orig_context": "```python\n## filecombinator/core/exceptions.py\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n\n    pass\n\nclass DirectoryProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a directory.\"\"\"\n\n    pass\n\n```\n\n\n```python\n## filecombinator/processors/directory.py\nimport logging\n\nimport os\n\nfrom pathlib import Path\n\nfrom typing import Protocol\n\nfrom ..core.exceptions import DirectoryProcessingError\n\nlogger = logging.getLogger(__name__)\n\nclass FileCallback(Protocol):\n    \"\"\"Protocol for file callback functions.\"\"\"\n\n    def __call__(self, file_path: str) -> None:\n        \"\"\"Call the callback function with the file path.\"\"\"\n        ...\n\nclass DirectoryProcessor:\n    \"\"\"Handles directory traversal and tree generation.\"\"\"\n\n    def __init__(\n        self, exclude_patterns: set[str], output_file: str | None = None\n    ) -> None:\n        \"\"\"Initialize DirectoryProcessor.\n\n        Args:\n            exclude_patterns: Set of patterns to exclude from processing\n            output_file: Optional path to output file to exclude from processing\n        \"\"\"\n        self.exclude_patterns = exclude_patterns\n        self.output_file = output_file\n\n    def is_excluded(self, path: Path) -> bool:\n        \"\"\"Check if a path should be excluded.\n\n        Args:\n            path: Path to check\n\n        Returns:\n            bool: True if path should be excluded, False otherwise\n        \"\"\"\n        path_abs = os.path.abspath(path)\n        output_abs = os.path.abspath(self.output_file) if self.output_file else None\n\n        if output_abs and path_abs == output_abs:\n            logger.debug(\"Skipping output file: %s\", path)\n            return True\n\n        file_name = os.path.basename(path)\n        if file_name.endswith(\"_file_combinator_output.txt\"):\n            logger.debug(\"Skipping file combinator output file: %s\", path)\n            return True\n\n        excluded = any(exclude in path.parts for exclude in self.exclude_patterns)\n        if excluded:\n            logger.debug(\"Excluded path: %s\", path)\n\n        return excluded\n\n    def generate_tree(self, start_path: str | Path) -> str:\n        \"\"\"Generate a string representation of the directory tree.\n\n        Args:\n            start_path: Root path to start tree generation from\n\n        Returns:\n            str: String representation of the directory tree\n\n        Raises:\n            DirectoryProcessingError: If there's an error generating the tree\n        \"\"\"\n        if not os.path.exists(str(start_path)):\n            raise DirectoryProcessingError(f\"Directory does not exist: {start_path}\")\n\n        try:\n            entries = [\n                e for e in os.scandir(start_path) if not self.is_excluded(Path(e.path))\n            ]\n\n            if not entries:\n                return \"\"  # Return empty string for empty directories\n\n            lines = []\n            root_name = os.path.basename(start_path) or str(start_path)\n            lines.append(root_name + \"/\")\n\n            def add_to_tree(dir_path: Path, prefix: str = \"\") -> None:\n                entries = sorted(os.scandir(dir_path), key=lambda e: e.name)\n                entries = [e for e in entries if not self.is_excluded(Path(e.path))]\n\n                for i, entry in enumerate(entries):\n                    is_last = i == len(entries) - 1\n                    connector = \"\u2514\u2500\u2500 \" if is_last else \"\u251c\u2500\u2500 \"\n                    lines.append(f\"{prefix}{connector}{entry.name}\")\n\n                    if entry.is_dir():\n                        next_prefix = prefix + (\"    \" if is_last else \"\u2502   \")\n                        add_to_tree(Path(entry.path), next_prefix)\n\n            add_to_tree(Path(start_path))\n            return \"\\n\".join(lines)\n\n        except Exception as e:\n            logger.error(\"Error generating directory tree: %s\", e)\n            raise DirectoryProcessingError(\n                f\"Failed to generate directory tree: {e}\"\n            ) from e\n\n    def process_directory(\n        self,\n        directory: str | Path,\n        callback: FileCallback,\n    ) -> None:\n        \"\"\"Process all files in a directory recursively.\n\n        Args:\n            directory: Directory to process\n            callback: Function to call for each file\n\n        Raises:\n            DirectoryProcessingError: If directory can't be processed\n        \"\"\"\n        if not os.path.exists(directory):\n            raise DirectoryProcessingError(f\"Directory does not exist: {directory}\")\n\n        try:\n            for root, dirs, files in os.walk(directory):\n                # Filter out excluded directories\n                dirs[:] = [\n                    d for d in dirs if not self.is_excluded(Path(os.path.join(root, d)))\n                ]\n\n                # Process files\n                for file in sorted(files):\n                    file_path = os.path.join(root, file)\n                    if not self.is_excluded(Path(file_path)):\n                        callback(file_path)\n\n        except OSError as e:\n            logger.error(\"Error processing directory %s: %s\", directory, e)\n            raise DirectoryProcessingError(\n                f\"Failed to process directory {directory}: {e}\"\n            ) from e\n\n```\n\n\n",
        "eval_script": "import logging\nimport os\nfrom pathlib import Path\nfrom typing import Protocol\n\n# Exception classes from the context\nclass FileCombinatorError(Exception):\n    \"\"\"Base exception for FileCombinator errors.\"\"\"\n    pass\n\nclass DirectoryProcessingError(FileCombinatorError):\n    \"\"\"Raised when there's an error processing a directory.\"\"\"\n    pass\n\n# Set up basic logging configuration\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\nclass FileCallback(Protocol):\n    \"\"\"Protocol for file callback functions.\"\"\"\n    def __call__(self, file_path: str) -> None:\n        \"\"\"Call the callback function with the file path.\"\"\"\n        ...\n\nclass DirectoryProcessor:\n    \"\"\"Handles directory traversal and tree generation.\"\"\"\n    def __init__(self, exclude_patterns: set[str], output_file: str | None = None) -> None:\n        \"\"\"Initialize DirectoryProcessor.\n        Args:\n            exclude_patterns: Set of patterns to exclude from processing\n            output_file: Optional path to output file to exclude from processing\n        \"\"\"\n        self.exclude_patterns = exclude_patterns\n        self.output_file = output_file\n\n    def is_excluded(self, path: Path) -> bool:\n        \"\"\"Check if a path should be excluded.\n        Args:\n            path: Path to check\n        Returns:\n            bool: True if path should be excluded, False otherwise\n        \"\"\"\n        path_abs = os.path.abspath(path)\n        output_abs = os.path.abspath(self.output_file) if self.output_file else None\n\n        if output_abs and path_abs == output_abs:\n            logger.debug(\"Skipping output file: %s\", path)\n            return True\n\n        file_name = os.path.basename(path)\n        if file_name.endswith(\"_file_combinator_output.txt\"):\n            logger.debug(\"Skipping file combinator output file: %s\", path)\n            return True\n\n        excluded = any(exclude in path.parts for exclude in self.exclude_patterns)\n        if excluded:\n            logger.debug(\"Excluded path: %s\", path)\n\n        return excluded\n\n    def generate_tree(self, start_path: str | Path) -> str:\n        \"\"\"Generate a string representation of the directory tree.\n        Args:\n            start_path: Root path to start tree generation from\n        Returns:\n            str: String representation of the directory tree\n        Raises:\n            DirectoryProcessingError: If there's an error generating the tree\n        \"\"\"\n        if not os.path.exists(str(start_path)):\n            raise DirectoryProcessingError(f\"Directory does not exist: {start_path}\")\n\n        try:\n            entries = [\n                e for e in os.scandir(start_path) if not self.is_excluded(Path(e.path))\n            ]\n\n            if not entries:\n                return \"\"  # Return empty string for empty directories\n\n            lines = []\n            root_name = os.path.basename(start_path) or str(start_path)\n            lines.append(root_name + \"/\")\n\n            def add_to_tree(dir_path: Path, prefix: str = \"\") -> None:\n                entries = sorted(os.scandir(dir_path), key=lambda e: e.name)\n                entries = [e for e in entries if not self.is_excluded(Path(e.path))]\n\n                for i, entry in enumerate(entries):\n                    is_last = i == len(entries) - 1\n                    connector = \"\u2514\u2500\u2500 \" if is_last else \"\u251c\u2500\u2500 \"\n                    lines.append(f\"{prefix}{connector}{entry.name}\")\n\n                    if entry.is_dir():\n                        next_prefix = prefix + (\"    \" if is_last else \"\u2502   \")\n                        add_to_tree(Path(entry.path), next_prefix)\n\n            add_to_tree(Path(start_path))\n            return \"\\n\".join(lines)\n\n        except Exception as e:\n            logger.error(\"Error generating directory tree: %s\", e)\n            raise DirectoryProcessingError(\n                f\"Failed to generate directory tree: {e}\"\n            ) from e\n\n    def process_directory(self, directory: str | Path, callback: FileCallback) -> None:\n        \"\"\"Process all files in a directory recursively.\n        Args:\n            directory: Directory to process\n            callback: Function to call for each file\n        Raises:\n            DirectoryProcessingError: If directory can't be processed\n        \"\"\"\n        if not os.path.exists(directory):\n            raise DirectoryProcessingError(f\"Directory does not exist: {directory}\")\n\n        try:\n            for root, dirs, files in os.walk(directory):\n                # Filter out excluded directories\n                dirs[:] = [\n                    d for d in dirs if not self.is_excluded(Path(os.path.join(root, d)))\n                ]\n\n                # Process files\n                for file in sorted(files):\n                    file_path = os.path.join(root, file)\n                    if not self.is_excluded(Path(file_path)):\n                        callback(file_path)\n\n        except OSError as e:\n            logger.error(\"Error processing directory %s: %s\", directory, e)\n            raise DirectoryProcessingError(\n                f\"Failed to process directory {directory}: {e}\"\n            ) from e\n\n\ndef test_process_directory():\n    \"\"\"Test to compare the original and new implementation of process_directory.\"\"\"\n    # Setup test directory structure\n    test_dir = Path(\"/home/user/tmp/test_dir\")\n    test_dir.mkdir(parents=True, exist_ok=True)\n    (test_dir / \"file1.txt\").touch()\n    (test_dir / \"file2.txt\").touch()\n    nested_dir = test_dir / \"nested\"\n    nested_dir.mkdir(exist_ok=True)\n    (nested_dir / \"file3.txt\").touch()\n    (nested_dir / \"file4.txt\").touch()\n\n    # Initialize DirectoryProcessor\n    processor = DirectoryProcessor(exclude_patterns=set(), output_file=None)\n\n    # Mock callback to record processed files\n    original_processed_files = []\n    new_processed_files = []\n\n    def original_callback(file_path: str):\n        original_processed_files.append(file_path)\n\n    def new_callback(file_path: str):\n        new_processed_files.append(file_path)\n\n    # Process directory with original implementation\n    processor.process_directory(test_dir, original_callback)\n    # Process directory with new implementation\n    processor.process_directory_new_implementation(test_dir, new_callback)\n\n    # Assertions to ensure both implementations process the same files\n    assert len(original_processed_files) == len(new_processed_files), \"Mismatch in number of processed files\"\n    assert set(original_processed_files) == set(new_processed_files), \"Mismatch in processed files\"\n    assert original_processed_files == new_processed_files, \"Order of processed files differs\"\n\nif __name__ == \"__main__\":\n    test_process_directory()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions process all files in a directory recursively, applying a callback function to each file that is not excluded based on certain criteria. The logic for checking if a directory or file should be excluded is encapsulated in the `is_excluded` method, which is used in both functions. The error handling and logging are also consistent between the two implementations. The test function `test_process_directory` further confirms that both implementations produce the same results, as it asserts that the number and order of processed files are identical.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `process_directory` function does not return any value but modifies the `original_processed_files` and `new_processed_files` lists through the callbacks. This satisfies the condition as it modifies input arguments.\n  \n- CONDITION 2: The test cases check the contents of `original_processed_files` and `new_processed_files`, which are modified by the function, rather than checking printed or logged outputs. This condition is satisfied.\n\n- CONDITION 3: The test cases compare the number of processed files, the set of processed files, and the order of processed files between the original and new implementations. This ensures that the new implementation must have the exact same functionality as the original to pass all tests. Thus, this condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the lists of processed files, which are reasonable given that `process_directory` modifies these lists. This condition is satisfied.\n\n- CONDITION 5: The test cases are non-trivial as they involve setting up a directory structure, using callbacks to collect processed files, and performing multiple assertions to ensure both implementations behave identically. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "fa2c815e82f2615451a2e6a3157a46f9aaab023b"
    },
    {
        "func_name": "format_data",
        "idx": "227",
        "repo_name": "HopWater___100DaysOfPython",
        "func_path": "Day 14/Higher or Lower Project/solution.py",
        "orig_func": "def format_data(account):\n    \"\"\"Takes the account data and returns the printable format.\"\"\"\n    account_name = account['name_of_user']\n    account_descr = account['description']\n    account_country = account['country']\n    return f'{account_name}, a {account_descr}, from {account_country}'",
        "orig_context": "```python\n## Day 14/Higher or Lower Project/solution.py\ndef format_data(account):\n    \"\"\"Takes the account data and returns the printable format.\"\"\"\n    account_name = account[\"name_of_user\"]\n    account_descr = account[\"description\"]\n    account_country = account[\"country\"]\n    return f\"{account_name}, a {account_descr}, from {account_country}\"\n\n```\n\n\n",
        "eval_script": "## Day 14/Higher or Lower Project/solution.py\ndef format_data(account):\n    \"\"\"Takes the account data and returns the printable format.\"\"\"\n    account_name = account[\"name_of_user\"]\n    account_descr = account[\"description\"]\n    account_country = account[\"country\"]\n    return f\"{account_name}, a {account_descr}, from {account_country}\"\n\n\ndef test_format_data():\n    # Test case 1: Standard input\n    account1 = {\n        \"name_of_user\": \"John Doe\",\n        \"description\": \"software developer\",\n        \"country\": \"USA\"\n    }\n    assert format_data(account1) == format_data_new_implementation(account1)\n\n    # Test case 2: Different user and description\n    account2 = {\n        \"name_of_user\": \"Jane Smith\",\n        \"description\": \"graphic designer\",\n        \"country\": \"Canada\"\n    }\n    assert format_data(account2) == format_data_new_implementation(account2)\n\n    # Test case 3: Different country\n    account3 = {\n        \"name_of_user\": \"Alice Johnson\",\n        \"description\": \"data scientist\",\n        \"country\": \"UK\"\n    }\n    assert format_data(account3) == format_data_new_implementation(account3)\n\n    # Test case 4: Empty strings\n    account4 = {\n        \"name_of_user\": \"\",\n        \"description\": \"\",\n        \"country\": \"\"\n    }\n    assert format_data(account4) == format_data_new_implementation(account4)\n\n    # Test case 5: Special characters\n    account5 = {\n        \"name_of_user\": \"John @ Doe!\",\n        \"description\": \"developer #1\",\n        \"country\": \"U$A\"\n    }\n    assert format_data(account5) == format_data_new_implementation(account5)\n\n    # Test case 6: Numeric values\n    account6 = {\n        \"name_of_user\": \"12345\",\n        \"description\": \"67890\",\n        \"country\": \"98765\"\n    }\n    assert format_data(account6) == format_data_new_implementation(account6)\n\n    # Test case 7: Long strings\n    account7 = {\n        \"name_of_user\": \"John\" * 1000,\n        \"description\": \"developer\" * 1000,\n        \"country\": \"USA\" * 1000\n    }\n    assert format_data(account7) == format_data_new_implementation(account7)\n\n    # Test case 8: Missing fields\n    account8 = {\n        \"name_of_user\": \"John Doe\",\n        \"description\": \"software developer\"\n        # Missing country\n    }\n    try:\n        format_data(account8)\n        format_data_new_implementation(account8)\n    except KeyError:\n        pass  # Expected behavior\n\n    account9 = {\n        \"description\": \"software developer\",\n        \"country\": \"USA\"\n        # Missing name_of_user\n    }\n    try:\n        format_data(account9)\n        format_data_new_implementation(account9)\n    except KeyError:\n        pass  # Expected behavior\n\n    account10 = {\n        \"name_of_user\": \"John Doe\",\n        \"country\": \"USA\"\n        # Missing description\n    }\n    try:\n        format_data(account10)\n        format_data_new_implementation(account10)\n    except KeyError:\n        pass  # Expected behavior\n\nif __name__ == \"__main__\":\n    test_format_data()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions take an account dictionary as input and return a formatted string using the values associated with the keys 'name_of_user', 'description', and 'country'. The test cases provided in the revised code further confirm that the function behaves as expected under various scenarios, including standard inputs, missing fields, and special characters. The test cases ensure that the function raises a KeyError when expected, which is consistent with the behavior of the original function. Since the functionality and implementation of the REVISED FUNCTION are the same as the ORIGINAL FUNCTION, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `format_data` function returns a formatted string based on the input dictionary, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `format_data` and `format_data_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `format_data` and `format_data_new_implementation` for various inputs, ensuring that they must have exactly the same functionality to pass all tests.\n- CONDITION 4: The test cases use assertions appropriately to compare return values and handle exceptions for missing fields, which is reasonable given the function's expected behavior.\n- CONDITION 5: The test cases cover a variety of scenarios, including standard inputs, different users and descriptions, empty strings, special characters, numeric values, long strings, and missing fields, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "424a29f227ab6820f0c307e82076045fd6acade9"
    },
    {
        "func_name": "format_name",
        "idx": "228",
        "repo_name": "HopWater___100DaysOfPython",
        "func_path": "Day 10/Dcostrings/solution.py",
        "orig_func": "def format_name(f_name, l_name):\n    \"\"\"Take a first and last name_of_user and format it to return the\n    title case version of the name_of_user.\"\"\"\n    formated_f_name = f_name.title()\n    formated_l_name = l_name.title()\n    return f'{formated_f_name} {formated_l_name}'",
        "orig_context": "```python\n## Day 10/Dcostrings/solution.py\ndef format_name(f_name, l_name):\n    \"\"\"Take a first and last name_of_user and format it to return the\n    title case version of the name_of_user.\"\"\"\n    formated_f_name = f_name.title()\n    formated_l_name = l_name.title()\n    return f\"{formated_f_name} {formated_l_name}\"\n\n```\n\n\n",
        "eval_script": "## Day 10/Dcostrings/solution.py\ndef format_name(f_name, l_name):\n    \"\"\"Take a first and last name_of_user and format it to return the\n    title case version of the name_of_user.\"\"\"\n    formated_f_name = f_name.title()\n    formated_l_name = l_name.title()\n    return f\"{formated_f_name} {formated_l_name}\"\n\n\ndef test_format_name():\n    # Test case 1: Regular names\n    assert format_name(\"john\", \"doe\") == format_name_new_implementation(\"john\", \"doe\")\n    # Test case 2: Names with mixed case\n    assert format_name(\"jOhN\", \"dOe\") == format_name_new_implementation(\"jOhN\", \"dOe\")\n    # Test case 3: Names with special characters\n    assert format_name(\"john\", \"o'connor\") == format_name_new_implementation(\"john\", \"o'connor\")\n    # Test case 4: Empty strings\n    assert format_name(\"\", \"\") == format_name_new_implementation(\"\", \"\")\n    # Test case 5: Single character names\n    assert format_name(\"a\", \"b\") == format_name_new_implementation(\"a\", \"b\")\n    # Test case 6: Names with hyphens\n    assert format_name(\"anna-marie\", \"smith-jones\") == format_name_new_implementation(\"anna-marie\", \"smith-jones\")\n    # Test case 7: Names with spaces\n    assert format_name(\"mary jane\", \"watson parker\") == format_name_new_implementation(\"mary jane\", \"watson parker\")\n    # Test case 8: Non-ASCII characters\n    assert format_name(\"jos\u00e9\", \"ni\u00f1o\") == format_name_new_implementation(\"jos\u00e9\", \"ni\u00f1o\")\n    # Test case 9: Numeric characters\n    assert format_name(\"john2\", \"doe3\") == format_name_new_implementation(\"john2\", \"doe3\")\n\nif __name__ == \"__main__\":\n    test_format_name()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `format_name` is identical to the ORIGINAL FUNCTION. Both functions take two arguments, `f_name` and `l_name`, and return them in title case format. The only difference in the provided code is the addition of a test suite, which does not alter the functionality of the `format_name` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `format_name` function returns a formatted string, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `format_name` and `format_name_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the output of `format_name` with `format_name_new_implementation` for various inputs, ensuring that both functions must have the same functionality to pass all tests.\n- CONDITION 4: The assertions are reasonable as they compare the return values of both implementations, which is appropriate given that `format_name` returns a value.\n- CONDITION 5: The test cases cover a wide range of scenarios, including regular names, mixed case, special characters, empty strings, single characters, hyphens, spaces, non-ASCII characters, and numeric characters, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "424a29f227ab6820f0c307e82076045fd6acade9"
    },
    {
        "func_name": "stripext",
        "idx": "236",
        "repo_name": "thyeem___ouch",
        "func_path": "ouch/__init__.py",
        "orig_func": "def stripext(path, sep='.'):\n    \"\"\"Remove the file extension from a filepath, ``path``.\"\"\"\n    o = path.split(sep)\n    return unchars(o[:-1] if len(o) > 1 else o)",
        "orig_context": "```python\n## ouch/__init__.py\nfrom foc import *\n\ndef stripext(path, sep=\".\"):\n    \"\"\"Remove the file extension from a filepath, ``path``.\"\"\"\n    o = path.split(sep)\n    return unchars(o[:-1] if len(o) > 1 else o)\n\n```\n\n\n",
        "eval_script": "def unchars(parts):\n    \"\"\"Concatenate list of strings into a single string.\"\"\"\n    return ''.join(parts)\n\ndef stripext(path, sep=\".\"):\n    \"\"\"Remove the file extension from a filepath, ``path``.\"\"\"\n    o = path.split(sep)\n    return unchars(o[:-1] if len(o) > 1 else o)\n\n\ndef test_stripext():\n    # Test case 1: Path with multiple separators\n    assert stripext(\"example.file.txt\") == stripext_new_implementation(\"example.file.txt\")\n    # Test case 2: Path with a single separator\n    assert stripext(\"example.txt\") == stripext_new_implementation(\"example.txt\")\n    # Test case 3: Path with no separator\n    assert stripext(\"example\") == stripext_new_implementation(\"example\")\n    # Test case 4: Empty string\n    assert stripext(\"\") == stripext_new_implementation(\"\")\n    # Test case 5: Path with multiple extensions\n    assert stripext(\"archive.tar.gz\") == stripext_new_implementation(\"archive.tar.gz\")\n    # Test case 6: Custom separator\n    assert stripext(\"example-file-txt\", sep=\"-\") == stripext_new_implementation(\"example-file-txt\", sep=\"-\")\n    # Test case 7: Trailing separator\n    assert stripext(\"example.txt.\") == stripext_new_implementation(\"example.txt.\")\n    # Test case 8: Hidden file\n    assert stripext(\".gitignore\") == stripext_new_implementation(\".gitignore\")\n    # Test case 9: Long file name\n    assert stripext(\"a\" * 255 + \".txt\") == stripext_new_implementation(\"a\" * 255 + \".txt\")\n    # Test case 10: Path with dots in directories\n    assert stripext(\"dir.with.dots/file.txt\") == stripext_new_implementation(\"dir.with.dots/file.txt\")\n\nif __name__ == \"__main__\":\n    test_stripext()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions, `stripext`, take a file path and a separator as arguments, split the path using the separator, and then use the `unchars` function to concatenate all parts except the last one (if there are multiple parts). The `unchars` function is defined in the CODE and is used in the same way in both the ORIGINAL and REVISED FUNCTION. The functionality of both functions is the same, as they both aim to remove the file extension from a file path.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `stripext` function returns a value, which is the file path without the extension. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases use assertions to check the return values of the `stripext` function against the `stripext_new_implementation`. There is no checking of printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the output of `stripext` with `stripext_new_implementation` for various inputs. If `stripext_new_implementation` passes all these tests, it would have to have the same functionality as `stripext`, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `stripext` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including paths with multiple separators, no separators, custom separators, hidden files, and long file names. These are non-trivial cases that adequately test the functionality of the `stripext` function, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "230c58cb736fbddf66f1d67b7ba88e7dd2f489a0"
    },
    {
        "func_name": "getext",
        "idx": "237",
        "repo_name": "thyeem___ouch",
        "func_path": "ouch/__init__.py",
        "orig_func": "def getext(path, sep='.'):\n    \"\"\"Get the file extension from a filepath, ``path``.\"\"\"\n    o = path.split(sep)\n    return o[-1] if len(o) > 1 else None",
        "orig_context": "```python\n## ouch/__init__.py\ndef getext(path, sep=\".\"):\n    \"\"\"Get the file extension from a filepath, ``path``.\"\"\"\n    o = path.split(sep)\n    return o[-1] if len(o) > 1 else None\n\n```\n\n\n",
        "eval_script": "## ouch/__init__.py\ndef getext(path, sep=\".\"):\n    \"\"\"Get the file extension from a filepath, ``path``.\"\"\"\n    o = path.split(sep)\n    return o[-1] if len(o) > 1 else None\n\n\ndef test_getext():\n    # Test case 1: Typical file with extension\n    assert getext(\"file.txt\") == getext_new_implementation(\"file.txt\")\n    # Test case 2: File without extension\n    assert getext(\"file\") == getext_new_implementation(\"file\")\n    # Test case 3: File with multiple separators\n    assert getext(\"archive.tar.gz\") == getext_new_implementation(\"archive.tar.gz\")\n    # Test case 4: Hidden file with no extension\n    assert getext(\".gitignore\") == getext_new_implementation(\".gitignore\")\n    # Test case 5: File with no name, only extension\n    assert getext(\".bashrc\") == getext_new_implementation(\".bashrc\")\n    # Test case 6: File with multiple dots in the name\n    assert getext(\"file.name.with.dots.ext\") == getext_new_implementation(\"file.name.with.dots.ext\")\n    # Test case 7: Empty string as path\n    assert getext(\"\") == getext_new_implementation(\"\")\n    # Test case 8: Custom separator\n    assert getext(\"file-name-ext\", sep=\"-\") == getext_new_implementation(\"file-name-ext\", sep=\"-\")\n    # Test case 9: Trailing separator\n    assert getext(\"file.\") == getext_new_implementation(\"file.\")\n    assert getext(\"file..\") == getext_new_implementation(\"file..\")\n    # Test case 10: Directory path\n    assert getext(\"folder/\") == getext_new_implementation(\"folder/\")\n    # Test case 11: Complex path with directories\n    assert getext(\"dir/subdir/file.ext\") == getext_new_implementation(\"dir/subdir/file.ext\")\n\nif __name__ == \"__main__\":\n    test_getext()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `getext` is identical to the ORIGINAL FUNCTION. Both functions take a file path and a separator as arguments, split the path by the separator, and return the last element of the split result if there is more than one element; otherwise, they return None. The test cases provided in the revised code do not affect the functionality of the `getext` function itself, as they are separate from the function definition. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `getext` function returns a value, which is the file extension or `None`. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of `getext` and `getext_new_implementation`, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `getext` and `getext_new_implementation` for various inputs. If `getext_new_implementation` passes all these tests, it must have the same functionality as `getext`. This condition is satisfied.\n- CONDITION 4: The assertions are reasonable as they compare the return values of both implementations for the same inputs. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including typical files, files without extensions, hidden files, files with multiple separators, custom separators, and complex paths. This makes the test cases non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "230c58cb736fbddf66f1d67b7ba88e7dd2f489a0"
    },
    {
        "func_name": "reader",
        "idx": "240",
        "repo_name": "thyeem___ouch",
        "func_path": "ouch/__init__.py",
        "orig_func": "def reader(f=None, mode='r', zipf=False):\n    \"\"\"Get ready to read stream from a file or stdin, then returns the handle.\"\"\"\n    if f is not None:\n        guard(exists(f, 'f'), f'reader, not found such a file: {f}')\n    return sys.stdin if f is None else zipfile.ZipFile(normpath(f), mode) if zipf else open(normpath(f), mode)",
        "orig_context": "```python\n## ouch/__init__.py\nimport os\n\nimport sys\n\nimport zipfile\n\nfrom foc import *\n\ndef normpath(path, abs=False):\n    \"\"\"Normalize and expand a givien filepath, ``path``.\"\"\"\n    return cf_(\n        os.path.abspath if abs else id,\n        os.path.normpath,\n        os.path.expanduser,\n    )(path)\n\ndef exists(path, kind=None):\n    \"\"\"Check if a given filpath ``path`` exists.\"\"\"\n    path = normpath(path)\n    if kind == \"f\":\n        return os.path.isfile(path)\n    elif kind == \"d\":\n        return os.path.isdir(path)\n    else:\n        return os.path.exists(path)\n\ndef reader(f=None, mode=\"r\", zipf=False):\n    \"\"\"Get ready to read stream from a file or stdin, then returns the handle.\"\"\"\n    if f is not None:\n        guard(exists(f, \"f\"), f\"reader, not found such a file: {f}\")\n    return (\n        sys.stdin\n        if f is None\n        else zipfile.ZipFile(normpath(f), mode) if zipf else open(normpath(f), mode)\n    )\n\n```\n\n\n",
        "eval_script": "# Mock implementations for missing parts from foc module\n\ndef cf_(*funcs):\n    \"\"\"Chain functions together.\"\"\"\n    def chained_func(x):\n        for func in funcs:\n            x = func(x)\n        return x\n    return chained_func\n\ndef guard(condition, message):\n    \"\"\"Raise an exception if the condition is False.\"\"\"\n    if not condition:\n        raise FileNotFoundError(message)\n\n# Original code with mock implementations\nimport os\nimport sys\nimport zipfile\n\ndef normpath(path, abs=False):\n    \"\"\"Normalize and expand a given filepath, ``path``.\"\"\"\n    return cf_(\n        os.path.abspath if abs else lambda x: x,\n        os.path.normpath,\n        os.path.expanduser,\n    )(path)\n\ndef exists(path, kind=None):\n    \"\"\"Check if a given filepath ``path`` exists.\"\"\"\n    path = normpath(path)\n    if kind == \"f\":\n        return os.path.isfile(path)\n    elif kind == \"d\":\n        return os.path.isdir(path)\n    else:\n        return os.path.exists(path)\n\ndef reader(f=None, mode=\"r\", zipf=False):\n    \"\"\"Get ready to read stream from a file or stdin, then returns the handle.\"\"\"\n    if f is not None:\n        guard(exists(f, \"f\"), f\"reader, not found such a file: {f}\")\n    return (\n        sys.stdin\n        if f is None\n        else zipfile.ZipFile(normpath(f), mode) if zipf else open(normpath(f), mode)\n    )\n\n# Ensure the directory exists for file operations\nos.makedirs('/home/user/tmp', exist_ok=True)\n\n\ndef test_reader():\n    # Test case 1: Reading from a regular file\n    test_file_path = '/home/user/tmp/test_file.txt'\n    with open(test_file_path, 'w') as f:\n        f.write('Hello, World!')\n\n    with reader(test_file_path) as f1, reader_new_implementation(test_file_path) as f2:\n        assert f1.read() == f2.read(), \"Test case 1 failed: Regular file reading\"\n\n    # Test case 2: Reading from a zip file\n    test_zip_path = '/home/user/tmp/test_file.zip'\n    with zipfile.ZipFile(test_zip_path, 'w') as zf:\n        zf.writestr('test_file.txt', 'Hello, Zip World!')\n\n    with reader(test_zip_path, zipf=True) as z1, reader_new_implementation(test_zip_path, zipf=True) as z2:\n        with z1.open('test_file.txt') as f1, z2.open('test_file.txt') as f2:\n            assert f1.read() == f2.read(), \"Test case 2 failed: Zip file reading\"\n\n    # Test case 3: Reading from stdin (simulated)\n    assert reader() == reader_new_implementation() == sys.stdin, \"Test case 3 failed: Reading from stdin\"\n\n    # Test case 4: Non-existent file\n    non_existent_file = '/home/user/tmp/non_existent.txt'\n    try:\n        reader(non_existent_file)\n    except FileNotFoundError:\n        pass\n    else:\n        assert False, \"Test case 4 failed: Non-existent file should raise FileNotFoundError\"\n\n    try:\n        reader_new_implementation(non_existent_file)\n    except FileNotFoundError:\n        pass\n    else:\n        assert False, \"Test case 4 failed: Non-existent file should raise FileNotFoundError\"\n\n    # Test case 5: Directory instead of file\n    directory_path = '/home/user/tmp/'\n    try:\n        reader(directory_path)\n    except FileNotFoundError:\n        pass\n    else:\n        assert False, \"Test case 5 failed: Directory path should raise FileNotFoundError\"\n\n    try:\n        reader_new_implementation(directory_path)\n    except FileNotFoundError:\n        pass\n    else:\n        assert False, \"Test case 5 failed: Directory path should raise FileNotFoundError\"\n\n    # Test case 6: Binary file reading\n    binary_file_path = '/home/user/tmp/test_file.bin'\n    with open(binary_file_path, 'wb') as f:\n        f.write(b'\\x00\\x01\\x02\\x03')\n\n    with reader(binary_file_path, mode='rb') as f1, reader_new_implementation(binary_file_path, mode='rb') as f2:\n        assert f1.read() == f2.read(), \"Test case 6 failed: Binary file reading\"\n\n    # Test case 7: Empty file\n    empty_file_path = '/home/user/tmp/empty_file.txt'\n    open(empty_file_path, 'w').close()\n\n    with reader(empty_file_path) as f1, reader_new_implementation(empty_file_path) as f2:\n        assert f1.read() == f2.read() == '', \"Test case 7 failed: Empty file reading\"\n\n    # Test case 8: Invalid zip file\n    invalid_zip_path = '/home/user/tmp/invalid_zip.zip'\n    with open(invalid_zip_path, 'w') as f:\n        f.write('This is not a zip file')\n\n    try:\n        with reader(invalid_zip_path, zipf=True) as z:\n            pass\n    except zipfile.BadZipFile:\n        pass\n    else:\n        assert False, \"Test case 8 failed: Invalid zip file should raise BadZipFile\"\n\n    try:\n        with reader_new_implementation(invalid_zip_path, zipf=True) as z:\n            pass\n    except zipfile.BadZipFile:\n        pass\n    else:\n        assert False, \"Test case 8 failed: Invalid zip file should raise BadZipFile\"\n\nif __name__ == \"__main__\":\n    test_reader()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       4      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  4      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions perform the same operations: they check if a file exists using the `exists` function, raise a `FileNotFoundError` if it doesn't, and return a file handle or a zip file handle based on the parameters. The test cases provided in the code also confirm that the behavior of the REVISED FUNCTION matches the expected behavior of the ORIGINAL FUNCTION across various scenarios, including reading from regular files, zip files, stdin, handling non-existent files, directories, binary files, empty files, and invalid zip files. The mock implementations of `guard`, `normpath`, and `exists` do not alter the functionality of the `reader` function as they replicate the expected behavior of these components.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `reader` function returns a file handle or `sys.stdin`, which satisfies the condition of having return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `reader` and `reader_new_implementation`, and they check for exceptions, which means they do not rely on printed or logged contents.\n\n3. **CONDITION 3**: The test cases cover various scenarios, such as reading from regular files, zip files, stdin, handling non-existent files, directories, binary files, empty files, and invalid zip files. These tests ensure that `reader_new_implementation` must have the same functionality as `reader` to pass all tests.\n\n4. **CONDITION 4**: The assertions are reasonable. They compare the outputs of `reader` and `reader_new_implementation` directly, and they check for expected exceptions in error scenarios.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover a wide range of scenarios, including edge cases like empty files and invalid zip files, ensuring comprehensive testing of the `reader` function's behavior.",
            "answer": "yes"
        },
        "commit_id": "230c58cb736fbddf66f1d67b7ba88e7dd2f489a0"
    },
    {
        "func_name": "thread",
        "idx": "243",
        "repo_name": "thyeem___ouch",
        "func_path": "ouch/__init__.py",
        "orig_func": "def thread(daemon=False):\n    \"\"\"Decorator factory that turns functions into threading.Thread.\n\n    >>> mouse = thread()(mouse_listener)()  # doctest: +SKIP\n    >>> mouse.start()                       # doctest: +SKIP\n    >>> mouse.join()                        # doctest: +SKIP\n    \"\"\"\n\n    def t(f):\n\n        def go(*args, **kwargs):\n            return threading.Thread(target=f, args=args, kwargs=kwargs, daemon=daemon)\n        return go\n    return t",
        "orig_context": "```python\n## ouch/__init__.py\nimport threading\n\ndef thread(daemon=False):\n    \"\"\"Decorator factory that turns functions into threading.Thread.\n\n    >>> mouse = thread()(mouse_listener)()  # doctest: +SKIP\n    >>> mouse.start()                       # doctest: +SKIP\n    >>> mouse.join()                        # doctest: +SKIP\n    \"\"\"\n\n    def t(f):\n        def go(*args, **kwargs):\n            return threading.Thread(\n                target=f,\n                args=args,\n                kwargs=kwargs,\n                daemon=daemon,\n            )\n\n        return go\n\n    return t\n\n```\n\n\n",
        "eval_script": "## ouch/__init__.py\nimport threading\nimport time\n\ndef thread(daemon=False):\n    \"\"\"Decorator factory that turns functions into threading.Thread.\n\n    >>> mouse = thread()(mouse_listener)()  # doctest: +SKIP\n    >>> mouse.start()                       # doctest: +SKIP\n    >>> mouse.join()                        # doctest: +SKIP\n    \"\"\"\n\n    def t(f):\n        def go(*args, **kwargs):\n            return threading.Thread(\n                target=f,\n                args=args,\n                kwargs=kwargs,\n                daemon=daemon,\n            )\n\n        return go\n\n    return t\n\n# Sample function to demonstrate the thread decorator\ndef mouse_listener():\n    for i in range(5):\n        print(f\"Mouse listener active: {i}\")\n        time.sleep(1)\n\n\ndef test_thread():\n    # Test 1: Simple function with no arguments\n    def simple_function():\n        pass\n\n    original_thread = thread()(simple_function)()\n    new_thread = thread_new_implementation()(simple_function)()\n    assert isinstance(original_thread, threading.Thread)\n    assert isinstance(new_thread, threading.Thread)\n\n    # Test 2: Function with arguments\n    def function_with_args(x, y):\n        return x + y\n\n    original_thread = thread()(function_with_args)(1, 2)\n    new_thread = thread_new_implementation()(function_with_args)(1, 2)\n    assert original_thread._args == (1, 2)\n    assert new_thread._args == (1, 2)\n\n    # Test 3: Daemon flag\n    original_thread = thread(daemon=True)(simple_function)()\n    new_thread = thread_new_implementation(daemon=True)(simple_function)()\n    assert original_thread.daemon is True\n    assert new_thread.daemon is True\n\n    # Test 4: Function with keyword arguments\n    def function_with_kwargs(x, y=0):\n        return x + y\n\n    original_thread = thread()(function_with_kwargs)(1, y=2)\n    new_thread = thread_new_implementation()(function_with_kwargs)(1, y=2)\n    assert original_thread._kwargs == {'y': 2}\n    assert new_thread._kwargs == {'y': 2}\n\n    # Test 5: Function with variable arguments\n    def function_with_var_args(*args, **kwargs):\n        return sum(args) + sum(kwargs.values())\n\n    original_thread = thread()(function_with_var_args)(1, 2, 3, a=4, b=5)\n    new_thread = thread_new_implementation()(function_with_var_args)(1, 2, 3, a=4, b=5)\n    assert original_thread._args == (1, 2, 3)\n    assert new_thread._args == (1, 2, 3)\n    assert original_thread._kwargs == {'a': 4, 'b': 5}\n    assert new_thread._kwargs == {'a': 4, 'b': 5}\n\n    # Test 6: Thread execution\n    result = []\n\n    def append_to_result(x):\n        result.append(x)\n\n    original_thread = thread()(append_to_result)(10)\n    new_thread = thread_new_implementation()(append_to_result)(20)\n    original_thread.start()\n    new_thread.start()\n    original_thread.join()\n    new_thread.join()\n    assert result == [10, 20]\n\n    # Test 7: Multiple threads\n    def increment_counter(counter):\n        for _ in range(1000):\n            counter[0] += 1\n\n    counter = [0]\n    threads = [thread()(increment_counter)(counter) for _ in range(10)]\n    new_threads = [thread_new_implementation()(increment_counter)(counter) for _ in range(10)]\n    for t in threads + new_threads:\n        t.start()\n    for t in threads + new_threads:\n        t.join()\n    assert counter[0] == 20000\n\n    # Test 8: Thread naming\n    def named_function():\n        pass\n\n    original_thread = thread()(named_function)()\n    new_thread = thread_new_implementation()(named_function)()\n    original_thread.name = \"OriginalThread\"\n    new_thread.name = \"NewThread\"\n    assert original_thread.name == \"OriginalThread\"\n    assert new_thread.name == \"NewThread\"\n\nif __name__ == \"__main__\":\n    test_thread()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions define a decorator factory that creates a threading.Thread object with the same parameters: target, args, kwargs, and daemon. The additional code in the revised version, such as imports, sample functions, and tests, does not alter the functionality of the `thread` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `thread` function returns a `threading.Thread` object, which is a return value. This satisfies CONDITION 1.\n- CONDITION 2: The test cases use assertions to check the properties of the `threading.Thread` objects (e.g., `_args`, `_kwargs`, `daemon`, `name`) and the state of the `result` and `counter` variables. They do not rely on printed or logged content. This satisfies CONDITION 2.\n- CONDITION 3: The test cases cover various aspects of the `thread` function's behavior, such as handling arguments, daemon threads, and thread execution. If `thread_new_implementation` behaves differently, it would fail these tests. This satisfies CONDITION 3.\n- CONDITION 4: The assertions are reasonable and appropriate for testing the behavior of the `thread` function. They do not make unreasonable assumptions about the function's behavior. This satisfies CONDITION 4.\n- CONDITION 5: The test cases cover a range of scenarios, including argument handling, daemon threads, thread execution, and thread naming. These are non-trivial aspects of the `thread` function's behavior. This satisfies CONDITION 5.",
            "answer": "yes"
        },
        "commit_id": "230c58cb736fbddf66f1d67b7ba88e7dd2f489a0"
    },
    {
        "func_name": "base58e",
        "idx": "245",
        "repo_name": "thyeem___ouch",
        "func_path": "ouch/__init__.py",
        "orig_func": "def base58e(x):\n    \"\"\"Encode bytes to Base58.\n\n    >>> base58e(b\"sofia-maria-golden-girls\")\n    'BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe'\n    \"\"\"\n    num = bytes_to_int(x)\n    result = ''\n    while num > 0:\n        num, rem = divmod(num, 58)\n        result = _BASE58_CHARS[rem] + result\n    return result",
        "orig_context": "```python\n## ouch/__init__.py\ndef bytes_to_int(x, byteorder=\"big\"):\n    return int.from_bytes(x, byteorder=byteorder)\n\n_BASE58_CHARS = \"123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz\"\n\ndef base58e(x):\n    \"\"\"Encode bytes to Base58.\n\n    >>> base58e(b\"sofia-maria-golden-girls\")\n    'BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe'\n    \"\"\"\n    num = bytes_to_int(x)\n    result = \"\"\n    while num > 0:\n        num, rem = divmod(num, 58)\n        result = _BASE58_CHARS[rem] + result\n    return result\n\n```\n\n\n",
        "eval_script": "## ouch/__init__.py\ndef bytes_to_int(x, byteorder=\"big\"):\n    return int.from_bytes(x, byteorder=byteorder)\n\n_BASE58_CHARS = \"123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz\"\n\ndef base58e(x):\n    \"\"\"Encode bytes to Base58.\n\n    >>> base58e(b\"sofia-maria-golden-girls\")\n    'BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe'\n    \"\"\"\n    num = bytes_to_int(x)\n    result = \"\"\n    while num > 0:\n        num, rem = divmod(num, 58)\n        result = _BASE58_CHARS[rem] + result\n    return result\n\n\ndef test_base58e():\n    # Test with a simple byte string\n    assert base58e(b\"hello\") == base58e_new_implementation(b\"hello\")\n\n    # Test with a more complex byte string\n    assert base58e(b\"sofia-maria-golden-girls\") == base58e_new_implementation(b\"sofia-maria-golden-girls\")\n\n    # Test with an empty byte string\n    assert base58e(b\"\") == base58e_new_implementation(b\"\")\n\n    # Test with single-byte inputs\n    for i in range(256):\n        assert base58e(bytes([i])) == base58e_new_implementation(bytes([i]))\n\n    # Test with a large byte string\n    large_input = b\"a\" * 1000\n    assert base58e(large_input) == base58e_new_implementation(large_input)\n\n    # Test with leading zeros\n    assert base58e(b\"\\x00\\x00hello\") == base58e_new_implementation(b\"\\x00\\x00hello\")\n\n    # Test with non-ASCII bytes\n    non_ascii_input = b\"\\xff\\xfe\\xfd\\xfc\\xfb\"\n    assert base58e(non_ascii_input) == base58e_new_implementation(non_ascii_input)\n\n    # Test with repetitive patterns\n    repetitive_input = b\"abcabcabcabcabcabc\"\n    assert base58e(repetitive_input) == base58e_new_implementation(repetitive_input)\n\nif __name__ == \"__main__\":\n    test_base58e()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is provided in the CODE and is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions convert bytes to an integer using the `bytes_to_int` function, then encode the integer to a Base58 string using the `_BASE58_CHARS` string. The process of dividing the number by 58 and prepending the corresponding character from `_BASE58_CHARS` is the same in both functions. The test cases provided in the CODE further confirm that the functionality is preserved, as they compare the output of the original and revised implementations for various inputs. Since the logic and implementation are unchanged, the functionality is exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `base58e` function returns a value, which is a string representing the Base58 encoded version of the input bytes. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases use assertions to check the return values of the `base58e` function against the `base58e_new_implementation` function. There are no checks on printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `base58e` and `base58e_new_implementation` for various inputs. If `base58e_new_implementation` passes all these tests, it must have the same functionality as `base58e`, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `base58e` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of inputs, including simple and complex byte strings, empty strings, single-byte inputs, large inputs, inputs with leading zeros, non-ASCII bytes, and repetitive patterns. This variety ensures the test cases are non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "230c58cb736fbddf66f1d67b7ba88e7dd2f489a0"
    },
    {
        "func_name": "base58d",
        "idx": "246",
        "repo_name": "thyeem___ouch",
        "func_path": "ouch/__init__.py",
        "orig_func": "def base58d(x):\n    \"\"\"Decode the Base58-encoded back to bytes.\n\n    >>> base58d('BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe')\n    b'sofia-maria-golden-girls'\n    \"\"\"\n    num = 0\n    for c in x:\n        num = num * 58 + _BASE58_CHARS.index(c)\n    return int_to_bytes(num)",
        "orig_context": "```python\n## ouch/__init__.py\ndef int_to_bytes(x, size=None, byteorder=\"big\"):\n    if size is None:\n        size = (x.bit_length() + 7) // 8\n    return x.to_bytes(size, byteorder=byteorder)\n\n_BASE58_CHARS = \"123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz\"\n\ndef base58d(x):\n    \"\"\"Decode the Base58-encoded back to bytes.\n\n    >>> base58d('BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe')\n    b'sofia-maria-golden-girls'\n    \"\"\"\n    num = 0\n    for c in x:\n        num = num * 58 + _BASE58_CHARS.index(c)\n    return int_to_bytes(num)\n\n```\n\n\n",
        "eval_script": "## ouch/__init__.py\ndef int_to_bytes(x, size=None, byteorder=\"big\"):\n    if size is None:\n        size = (x.bit_length() + 7) // 8\n    return x.to_bytes(size, byteorder=byteorder)\n\n_BASE58_CHARS = \"123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz\"\n\ndef base58d(x):\n    \"\"\"Decode the Base58-encoded back to bytes.\n\n    >>> base58d('BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe')\n    b'sofia-maria-golden-girls'\n    \"\"\"\n    num = 0\n    for c in x:\n        num = num * 58 + _BASE58_CHARS.index(c)\n    return int_to_bytes(num)\n\n\ndef test_base58d():\n    # Test case 1: Typical input\n    assert base58d('BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe') == base58d_new_implementation('BXNAGjq4ty8AeedspDYRnHZwFTXtyQWNe')\n\n    # Test case 2: Input with leading '1's which should be handled as leading zero bytes\n    assert base58d('1112') == base58d_new_implementation('1112')\n\n    # Test case 3: Empty input\n    assert base58d('') == base58d_new_implementation('')\n\n    # Test case 4: Invalid characters (should raise an error)\n    try:\n        base58d('0OIl')\n        assert False, \"Expected an exception for invalid characters\"\n    except ValueError:\n        pass\n\n    try:\n        base58d_new_implementation('0OIl')\n        assert False, \"Expected an exception for invalid characters\"\n    except ValueError:\n        pass\n\n    # Test case 5: Single character input\n    assert base58d('2') == base58d_new_implementation('2')\n\n    # Test case 6: Long input\n    long_input = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz' * 10\n    assert base58d(long_input) == base58d_new_implementation(long_input)\n\n    # Test case 7: Boundary values\n    assert base58d('1') == base58d_new_implementation('1')\n    assert base58d('z') == base58d_new_implementation('z')\n    \nif __name__ == \"__main__\":\n    test_base58d()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `base58d` is identical to the ORIGINAL FUNCTION. Both functions decode a Base58-encoded string back to bytes by iterating over each character in the input string, converting it to a numerical value using the `_BASE58_CHARS` index, and accumulating this into a single integer `num`. This integer is then converted to bytes using the `int_to_bytes` function. The `int_to_bytes` function is defined in the revised code, but its functionality is standard and does not alter the behavior of `base58d`. The test cases provided in the revised code further confirm that the functionality remains consistent across various scenarios. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `base58d` returns a value, specifically a byte representation of the decoded Base58 string, satisfying this condition.\n- CONDITION 2: The test cases use assertions to check the return values of `base58d` and `base58d_new_implementation`, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `base58d` and `base58d_new_implementation` for various inputs, ensuring that `base58d_new_implementation` must have the same functionality as `base58d` to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare return values and handle exceptions appropriately, which is reasonable given the function's behavior. This satisfies the condition.\n- CONDITION 5: The test cases cover a range of scenarios, including typical inputs, edge cases, invalid inputs, and boundary values, making them non-trivial and satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "230c58cb736fbddf66f1d67b7ba88e7dd2f489a0"
    },
    {
        "func_name": "LNN.compute_dynamic_adjacency",
        "idx": "250",
        "repo_name": "RichardAragon___LagrangeanConceptGeometry",
        "func_path": "LNNPlusAIGeometry.py",
        "orig_func": "def compute_dynamic_adjacency(self, batch_data):\n    \"\"\"\n        Constructs a dynamic adjacency matrix using self-attention.\n        \"\"\"\n    batch_data = torch.relu(self.input_to_hidden(batch_data))\n    batch_data = batch_data.unsqueeze(1)\n    attn_output, attn_weights = self.attention(batch_data, batch_data, batch_data)\n    adjacency_matrix = torch.mean(attn_weights, dim=1).squeeze(1)\n    return adjacency_matrix",
        "orig_context": "```python\n## LNNPlusAIGeometry.py\nimport torch\n\nimport torch.nn as nn\n\nclass LNN(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(LNN, self).__init__()\n        self.input_to_hidden = nn.Linear(input_size, hidden_size)  # New projection layer\n        self.fc2 = nn.Linear(hidden_size, output_size)\n        self.dropout = nn.Dropout(0.5)\n        \n        # Self-attention mechanism for dynamic adjacency\n        self.attention = nn.MultiheadAttention(embed_dim=hidden_size, num_heads=4)\n        self.lambda_reg = 0.1  # Regularization parameter\n    \n    def forward(self, x):\n        # Project the input to the hidden size\n        x = torch.relu(self.input_to_hidden(x))\n        x = self.dropout(x)\n        return self.fc2(x)\n    \n    def compute_dynamic_adjacency(self, batch_data):\n        \"\"\"\n        Constructs a dynamic adjacency matrix using self-attention.\n        \"\"\"\n        # Project the input to the hidden size before using self-attention\n        batch_data = torch.relu(self.input_to_hidden(batch_data))\n        batch_data = batch_data.unsqueeze(1)  # Add sequence dimension for attention\n        attn_output, attn_weights = self.attention(batch_data, batch_data, batch_data)\n        adjacency_matrix = torch.mean(attn_weights, dim=1).squeeze(1)  # Average across heads\n        return adjacency_matrix\n    \n    def lagrangian_loss(self, output, target, batch_data):\n        \"\"\"\n        Combines Cross-Entropy Loss with Structural Regularization.\n        \"\"\"\n        # Cross-Entropy loss for classification\n        ce_loss = nn.CrossEntropyLoss()(output, target)\n        \n        # Compute dynamic adjacency matrix using attention\n        adjacency_matrix = self.compute_dynamic_adjacency(batch_data)\n        \n        # Apply transformations to outputs and enforce consistency with the adjacency matrix\n        transformed_output = torch.softmax(output, dim=1)\n        \n        # Structural regularization using dynamic adjacency matrix\n        structural_reg = torch.mean(torch.abs(adjacency_matrix @ transformed_output))\n        \n        # Combined loss\n        return ce_loss + self.lambda_reg * structural_reg\n\n```\n\n\n",
        "eval_script": "## LNNPlusAIGeometry.py\nimport torch\nimport torch.nn as nn\n\nclass LNN(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(LNN, self).__init__()\n        self.input_to_hidden = nn.Linear(input_size, hidden_size)  # New projection layer\n        self.fc2 = nn.Linear(hidden_size, output_size)\n        self.dropout = nn.Dropout(0.5)\n        \n        # Self-attention mechanism for dynamic adjacency\n        self.attention = nn.MultiheadAttention(embed_dim=hidden_size, num_heads=4)\n        self.lambda_reg = 0.1  # Regularization parameter\n    \n    def forward(self, x):\n        # Project the input to the hidden size\n        x = torch.relu(self.input_to_hidden(x))\n        x = self.dropout(x)\n        return self.fc2(x)\n    \n    def compute_dynamic_adjacency(self, batch_data):\n        \"\"\"\n        Constructs a dynamic adjacency matrix using self-attention.\n        \"\"\"\n        # Project the input to the hidden size before using self-attention\n        batch_data = torch.relu(self.input_to_hidden(batch_data))\n        batch_data = batch_data.unsqueeze(1)  # Add sequence dimension for attention\n        attn_output, attn_weights = self.attention(batch_data, batch_data, batch_data)\n        adjacency_matrix = torch.mean(attn_weights, dim=1).squeeze(1)  # Average across heads\n        return adjacency_matrix\n    \n\n\n    def lagrangian_loss(self, output, target, batch_data):\n        \"\"\"\n        Combines Cross-Entropy Loss with Structural Regularization.\n        \"\"\"\n        # Cross-Entropy loss for classification\n        ce_loss = nn.CrossEntropyLoss()(output, target)\n        \n        # Compute dynamic adjacency matrix using attention\n        adjacency_matrix = self.compute_dynamic_adjacency(batch_data)\n        \n        # Apply transformations to outputs and enforce consistency with the adjacency matrix\n        transformed_output = torch.softmax(output, dim=1)\n        \n        # Structural regularization using dynamic adjacency matrix\n        structural_reg = torch.mean(torch.abs(adjacency_matrix @ transformed_output))\n        \n        # Combined loss\n        return ce_loss + self.lambda_reg * structural_reg\n\ndef test_compute_dynamic_adjacency():\n    input_size = 10\n    hidden_size = 8\n    output_size = 5\n    lnn = LNN(input_size, hidden_size, output_size)\n\n    # Test case 1: Random input\n    batch_data_1 = torch.rand(3, input_size)\n    adj_original_1 = lnn.compute_dynamic_adjacency(batch_data_1)\n    adj_new_1 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_1)\n    assert torch.allclose(adj_original_1, adj_new_1), \"Test case 1 failed\"\n\n    # Test case 2: Zero input\n    batch_data_2 = torch.zeros(3, input_size)\n    adj_original_2 = lnn.compute_dynamic_adjacency(batch_data_2)\n    adj_new_2 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_2)\n    assert torch.allclose(adj_original_2, adj_new_2), \"Test case 2 failed\"\n\n    # Test case 3: Large input values\n    batch_data_3 = torch.full((3, input_size), 1000.0)\n    adj_original_3 = lnn.compute_dynamic_adjacency(batch_data_3)\n    adj_new_3 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_3)\n    assert torch.allclose(adj_original_3, adj_new_3), \"Test case 3 failed\"\n\n    # Test case 4: Negative input values\n    batch_data_4 = torch.full((3, input_size), -1000.0)\n    adj_original_4 = lnn.compute_dynamic_adjacency(batch_data_4)\n    adj_new_4 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_4)\n    assert torch.allclose(adj_original_4, adj_new_4), \"Test case 4 failed\"\n\n    # Test case 5: Single element batch\n    batch_data_5 = torch.rand(1, input_size)\n    adj_original_5 = lnn.compute_dynamic_adjacency(batch_data_5)\n    adj_new_5 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_5)\n    assert torch.allclose(adj_original_5, adj_new_5), \"Test case 5 failed\"\n\n    # Test case 6: Batch with varying values\n    batch_data_6 = torch.tensor([[1.0, -1.0, 0.0, 100.0, -100.0, 0.5, -0.5, 10.0, -10.0, 0.1]])\n    adj_original_6 = lnn.compute_dynamic_adjacency(batch_data_6)\n    adj_new_6 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_6)\n    assert torch.allclose(adj_original_6, adj_new_6), \"Test case 6 failed\"\n\n    # Test case 7: Very small input values\n    batch_data_7 = torch.full((3, input_size), 1e-10)\n    adj_original_7 = lnn.compute_dynamic_adjacency(batch_data_7)\n    adj_new_7 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_7)\n    assert torch.allclose(adj_original_7, adj_new_7), \"Test case 7 failed\"\n\n    # Test case 8: Batch with NaN values\n    batch_data_8 = torch.tensor([[float('nan')] * input_size])\n    try:\n        adj_original_8 = lnn.compute_dynamic_adjacency(batch_data_8)\n        adj_new_8 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_8)\n        assert torch.allclose(adj_original_8, adj_new_8), \"Test case 8 failed\"\n    except Exception as e:\n        print(f\"Test case 8 encountered an exception: {e}\")\n\n    # Test case 9: Batch with Inf values\n    batch_data_9 = torch.tensor([[float('inf')] * input_size])\n    try:\n        adj_original_9 = lnn.compute_dynamic_adjacency(batch_data_9)\n        adj_new_9 = lnn.compute_dynamic_adjacency_new_implementation(batch_data_9)\n        assert torch.allclose(adj_original_9, adj_new_9), \"Test case 9 failed\"\n    except Exception as e:\n        print(f\"Test case 9 encountered an exception: {e}\")\n\nif __name__ == \"__main__\":\n    test_compute_dynamic_adjacency()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the following steps:\n1. Apply a ReLU activation to the input after projecting it to a hidden size using a linear layer (`self.input_to_hidden`).\n2. Add a sequence dimension to the processed input using `unsqueeze(1)`.\n3. Use a self-attention mechanism (`self.attention`) to compute attention outputs and weights.\n4. Calculate the adjacency matrix by averaging the attention weights across the heads and removing the sequence dimension with `squeeze(1)`.\n\nThe functionality and sequence of operations in both functions are exactly the same. The additional code in the revised version, such as the class definition and other methods, does not affect the functionality of the `compute_dynamic_adjacency` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `compute_dynamic_adjacency` function returns an adjacency matrix, which is a return value. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases use assertions to check the return values of `compute_dynamic_adjacency` and `compute_dynamic_adjacency_new_implementation`. They do not rely on printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `compute_dynamic_adjacency` and `compute_dynamic_adjacency_new_implementation` using `torch.allclose`, which checks for equality within a tolerance. This ensures that `compute_dynamic_adjacency_new_implementation` must have the exact same functionality as `compute_dynamic_adjacency` to pass all tests, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations. Since `compute_dynamic_adjacency` returns a value, using assertions to compare these return values is reasonable. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a variety of input scenarios, including random inputs, zero inputs, large values, negative values, single element batches, varying values, very small values, NaN values, and Inf values. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "ad695ac66c2e866a78ba11207e7c09697b49d0c3"
    },
    {
        "func_name": "LNN.lagrangian_loss",
        "idx": "251",
        "repo_name": "RichardAragon___LagrangeanConceptGeometry",
        "func_path": "LNNPlusAIGeometry.py",
        "orig_func": "def lagrangian_loss(self, output, target, batch_data):\n    \"\"\"\n        Combines Cross-Entropy Loss with Structural Regularization.\n        \"\"\"\n    ce_loss = nn.CrossEntropyLoss()(output, target)\n    adjacency_matrix = self.compute_dynamic_adjacency(batch_data)\n    transformed_output = torch.softmax(output, dim=1)\n    structural_reg = torch.mean(torch.abs(adjacency_matrix @ transformed_output))\n    return ce_loss + self.lambda_reg * structural_reg",
        "orig_context": "```python\n## LNNPlusAIGeometry.py\nimport torch\n\nimport torch.nn as nn\n\nclass LNN(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(LNN, self).__init__()\n        self.input_to_hidden = nn.Linear(input_size, hidden_size)  # New projection layer\n        self.fc2 = nn.Linear(hidden_size, output_size)\n        self.dropout = nn.Dropout(0.5)\n        \n        # Self-attention mechanism for dynamic adjacency\n        self.attention = nn.MultiheadAttention(embed_dim=hidden_size, num_heads=4)\n        self.lambda_reg = 0.1  # Regularization parameter\n    \n    def forward(self, x):\n        # Project the input to the hidden size\n        x = torch.relu(self.input_to_hidden(x))\n        x = self.dropout(x)\n        return self.fc2(x)\n    \n    def compute_dynamic_adjacency(self, batch_data):\n        \"\"\"\n        Constructs a dynamic adjacency matrix using self-attention.\n        \"\"\"\n        # Project the input to the hidden size before using self-attention\n        batch_data = torch.relu(self.input_to_hidden(batch_data))\n        batch_data = batch_data.unsqueeze(1)  # Add sequence dimension for attention\n        attn_output, attn_weights = self.attention(batch_data, batch_data, batch_data)\n        adjacency_matrix = torch.mean(attn_weights, dim=1).squeeze(1)  # Average across heads\n        return adjacency_matrix\n    \n    def lagrangian_loss(self, output, target, batch_data):\n        \"\"\"\n        Combines Cross-Entropy Loss with Structural Regularization.\n        \"\"\"\n        # Cross-Entropy loss for classification\n        ce_loss = nn.CrossEntropyLoss()(output, target)\n        \n        # Compute dynamic adjacency matrix using attention\n        adjacency_matrix = self.compute_dynamic_adjacency(batch_data)\n        \n        # Apply transformations to outputs and enforce consistency with the adjacency matrix\n        transformed_output = torch.softmax(output, dim=1)\n        \n        # Structural regularization using dynamic adjacency matrix\n        structural_reg = torch.mean(torch.abs(adjacency_matrix @ transformed_output))\n        \n        # Combined loss\n        return ce_loss + self.lambda_reg * structural_reg\n\n```\n\n\n",
        "eval_script": "## LNNPlusAIGeometry.py\nimport torch\n\nimport torch.nn as nn\n\nclass LNN(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(LNN, self).__init__()\n        self.input_to_hidden = nn.Linear(input_size, hidden_size)  # New projection layer\n        self.fc2 = nn.Linear(hidden_size, output_size)\n        self.dropout = nn.Dropout(0.5)\n        \n        # Self-attention mechanism for dynamic adjacency\n        self.attention = nn.MultiheadAttention(embed_dim=hidden_size, num_heads=4)\n        self.lambda_reg = 0.1  # Regularization parameter\n    \n    def forward(self, x):\n        # Project the input to the hidden size\n        x = torch.relu(self.input_to_hidden(x))\n        x = self.dropout(x)\n        return self.fc2(x)\n    \n    def compute_dynamic_adjacency(self, batch_data):\n        \"\"\"\n        Constructs a dynamic adjacency matrix using self-attention.\n        \"\"\"\n        # Project the input to the hidden size before using self-attention\n        batch_data = torch.relu(self.input_to_hidden(batch_data))\n        batch_data = batch_data.unsqueeze(1)  # Add sequence dimension for attention\n        attn_output, attn_weights = self.attention(batch_data, batch_data, batch_data)\n        adjacency_matrix = torch.mean(attn_weights, dim=1).squeeze(1)  # Average across heads\n        return adjacency_matrix\n    \n    def lagrangian_loss(self, output, target, batch_data):\n        \"\"\"\n        Combines Cross-Entropy Loss with Structural Regularization.\n        \"\"\"\n        # Cross-Entropy loss for classification\n        ce_loss = nn.CrossEntropyLoss()(output, target)\n        \n        # Compute dynamic adjacency matrix using attention\n        adjacency_matrix = self.compute_dynamic_adjacency(batch_data)\n        \n        # Apply transformations to outputs and enforce consistency with the adjacency matrix\n        transformed_output = torch.softmax(output, dim=1)\n        \n        # Structural regularization using dynamic adjacency matrix\n        structural_reg = torch.mean(torch.abs(adjacency_matrix @ transformed_output))\n        \n        # Combined loss\n        return ce_loss + self.lambda_reg * structural_reg\n\n\ndef test_lagrangian_loss():\n    input_size = 10\n    hidden_size = 4  # Changed to a value divisible by num_heads (4)\n    output_size = 3\n    batch_size = 4\n\n    model = LNN(input_size, hidden_size, output_size)\n\n    # Generate random data for testing\n    output = torch.randn(batch_size, output_size, requires_grad=True)\n    target = torch.randint(0, output_size, (batch_size,))\n    batch_data = torch.randn(batch_size, input_size)\n\n    # Compute losses using both implementations\n    original_loss = model.lagrangian_loss(output, target, batch_data)\n    new_loss = model.lagrangian_loss_new_implementation(output, target, batch_data)\n\n    # Assert that both implementations produce the same result\n    assert torch.isclose(original_loss, new_loss), \"Losses do not match!\"\n\n    # Additional test cases to cover different scenarios\n    # Test with different batch size\n    batch_size = 2\n    output = torch.randn(batch_size, output_size, requires_grad=True)\n    target = torch.randint(0, output_size, (batch_size,))\n    batch_data = torch.randn(batch_size, input_size)\n    original_loss = model.lagrangian_loss(output, target, batch_data)\n    new_loss = model.lagrangian_loss_new_implementation(output, target, batch_data)\n    assert torch.isclose(original_loss, new_loss), \"Losses do not match for batch size 2!\"\n\n    # Test with different input size\n    input_size = 8\n    model = LNN(input_size, hidden_size, output_size)\n    batch_data = torch.randn(batch_size, input_size)\n    original_loss = model.lagrangian_loss(output, target, batch_data)\n    new_loss = model.lagrangian_loss_new_implementation(output, target, batch_data)\n    assert torch.isclose(original_loss, new_loss), \"Losses do not match for input size 8!\"\n\nif __name__ == \"__main__\":\n    test_lagrangian_loss()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions calculate the cross-entropy loss and a structural regularization term using a dynamic adjacency matrix, which is computed using the `compute_dynamic_adjacency` method. The structural regularization term is calculated by taking the mean of the absolute values of the product of the adjacency matrix and the softmax-transformed output. The final loss is the sum of the cross-entropy loss and the structural regularization term scaled by a regularization parameter (`self.lambda_reg`). The test cases provided in the code also suggest that the functionality of the revised function is intended to match the original function. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `lagrangian_loss` function returns a combined loss value, which is a tensor. This satisfies the condition as it has a return value.\n- [CONDITION 2] The test cases check the return values of the `lagrangian_loss` function by comparing them with the return values of `lagrangian_loss_new_implementation`. There is no checking of printed or logged contents.\n- [CONDITION 3] The test cases use `torch.isclose` to compare the losses from both implementations, which ensures that `lagrangian_loss_new_implementation` must have the same functionality as `lagrangian_loss` to pass the tests.\n- [CONDITION 4] The test cases use assertions to compare the return values of the two implementations, which is reasonable since `lagrangian_loss` returns a value.\n- [CONDITION 5] The test cases cover different scenarios, including different batch sizes and input sizes, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "ad695ac66c2e866a78ba11207e7c09697b49d0c3"
    },
    {
        "func_name": "Structure.adjacency_matrix",
        "idx": "252",
        "repo_name": "RichardAragon___LagrangeanConceptGeometry",
        "func_path": "demo.py",
        "orig_func": "def adjacency_matrix(self):\n    \"\"\"Creates an adjacency matrix for the structure.\"\"\"\n    size = len(self.nodes)\n    matrix = np.zeros((size, size))\n    for edge in self.edges:\n        source_idx = self.nodes.index(edge.source)\n        target_idx = self.nodes.index(edge.target)\n        matrix[source_idx, target_idx] = edge.weight\n    return matrix",
        "orig_context": "```python\n## demo.py\nimport numpy as np\n\nclass Structure:\n    def __init__(self, nodes, edges):\n        self.nodes = nodes\n        self.edges = edges\n    \n    def adjacency_matrix(self):\n        \"\"\"Creates an adjacency matrix for the structure.\"\"\"\n        size = len(self.nodes)\n        matrix = np.zeros((size, size))\n        for edge in self.edges:\n            source_idx = self.nodes.index(edge.source)\n            target_idx = self.nodes.index(edge.target)\n            matrix[source_idx, target_idx] = edge.weight\n        return matrix\n\n```\n\n\n",
        "eval_script": "## demo.py\nimport numpy as np\n\n# Define a simple Edge class to be used with the Structure class\nclass Edge:\n    def __init__(self, source, target, weight):\n        self.source = source\n        self.target = target\n        self.weight = weight\n\nclass Structure:\n    def __init__(self, nodes, edges):\n        self.nodes = nodes\n        self.edges = edges\n    \n    def adjacency_matrix(self):\n        \"\"\"Creates an adjacency matrix for the structure.\"\"\"\n        size = len(self.nodes)\n        matrix = np.zeros((size, size))\n        for edge in self.edges:\n            source_idx = self.nodes.index(edge.source)\n            target_idx = self.nodes.index(edge.target)\n            matrix[source_idx, target_idx] = edge.weight\n        return matrix\n\n\ndef test_adjacency_matrix():\n    # Test case 1: Simple graph\n    nodes1 = ['A', 'B', 'C']\n    edges1 = [Edge('A', 'B', 1), Edge('B', 'C', 2), Edge('C', 'A', 3)]\n    structure1 = Structure(nodes1, edges1)\n    assert np.array_equal(structure1.adjacency_matrix(), structure1.adjacency_matrix_new_implementation())\n\n    # Test case 2: Graph with no edges\n    nodes2 = ['A', 'B', 'C']\n    edges2 = []\n    structure2 = Structure(nodes2, edges2)\n    assert np.array_equal(structure2.adjacency_matrix(), structure2.adjacency_matrix_new_implementation())\n\n    # Test case 3: Graph with self-loop\n    nodes3 = ['A', 'B']\n    edges3 = [Edge('A', 'A', 5), Edge('A', 'B', 1)]\n    structure3 = Structure(nodes3, edges3)\n    assert np.array_equal(structure3.adjacency_matrix(), structure3.adjacency_matrix_new_implementation())\n\n    # Test case 4: Graph with multiple edges between the same nodes\n    nodes4 = ['A', 'B']\n    edges4 = [Edge('A', 'B', 2), Edge('A', 'B', 3)]\n    structure4 = Structure(nodes4, edges4)\n    assert np.array_equal(structure4.adjacency_matrix(), structure4.adjacency_matrix_new_implementation())\n\n    # Test case 5: Graph with disconnected nodes\n    nodes5 = ['A', 'B', 'C']\n    edges5 = [Edge('A', 'B', 1)]\n    structure5 = Structure(nodes5, edges5)\n    assert np.array_equal(structure5.adjacency_matrix(), structure5.adjacency_matrix_new_implementation())\n\n    # Test case 6: Graph with negative weights\n    nodes6 = ['A', 'B']\n    edges6 = [Edge('A', 'B', -1)]\n    structure6 = Structure(nodes6, edges6)\n    assert np.array_equal(structure6.adjacency_matrix(), structure6.adjacency_matrix_new_implementation())\n\n    # Test case 7: Graph with directed edges\n    nodes7 = ['A', 'B', 'C']\n    edges7 = [Edge('A', 'B', 1), Edge('B', 'A', 2), Edge('B', 'C', 3)]\n    structure7 = Structure(nodes7, edges7)\n    assert np.array_equal(structure7.adjacency_matrix(), structure7.adjacency_matrix_new_implementation())\n\n    # Test case 8: Graph with a single node\n    nodes8 = ['A']\n    edges8 = []\n    structure8 = Structure(nodes8, edges8)\n    assert np.array_equal(structure8.adjacency_matrix(), structure8.adjacency_matrix_new_implementation())\n\nif __name__ == \"__main__\":\n    test_adjacency_matrix()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are methods of a class (in the revised code, it's the `Structure` class), and they perform the same operations: they create an adjacency matrix for a graph structure by iterating over the edges, finding the indices of the source and target nodes, and setting the corresponding matrix entry to the edge's weight. The test cases provided in the revised code are meant to verify the functionality of the `adjacency_matrix` method, but they do not indicate any changes to the method itself. Since the implementation of the `adjacency_matrix` method is unchanged, the functionality remains the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `adjacency_matrix` function returns a value, specifically an adjacency matrix as a NumPy array. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases use `assert np.array_equal(...)` to compare the return values of `adjacency_matrix` and `adjacency_matrix_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `adjacency_matrix` and `adjacency_matrix_new_implementation` using `np.array_equal`, which checks for exact equality of the matrices. This ensures that `adjacency_matrix_new_implementation` can pass all the test cases if and only if it has the exact same functionality as `adjacency_matrix`.\n- CONDITION 4: The test cases use `assert` statements to compare the return values of the two implementations, which is reasonable given that `adjacency_matrix` returns a matrix. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including simple graphs, graphs with no edges, self-loops, multiple edges, disconnected nodes, negative weights, directed edges, and single-node graphs. This variety ensures that the test cases are non-trivial.",
            "answer": "yes"
        },
        "commit_id": "ad695ac66c2e866a78ba11207e7c09697b49d0c3"
    },
    {
        "func_name": "subsample_class_points",
        "idx": "253",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/plot/_data_wrangle.py",
        "orig_func": "def subsample_class_points(points, labels, n_per_class):\n    \"\"\"\n    Return a subsample of points for each class (whichever is\n    smaller of n_per_class or the total points in the class).\n\n    Parameters\n    ----------\n    points : torch.Tensor\n        Points to subsample. (n_points, n_dim)\n    labels : torch.Tensor\n        Labels or values of the points. (n_points,)\n    n_per_class : int\n        Number of points to subsample for each class.\n\n    Returns\n    -------\n    subsampled_points : torch.Tensor\n        Subsampled points. (n_classes * n_points_per_class, n_dim)\n    subsampled_labels : torch.Tensor\n        Labels or values of the subsampled points. (n_classes * n_points_per_class,)\n    \"\"\"\n    subsampled_points = []\n    subsampled_labels = []\n    for _i, label in enumerate(labels.unique()):\n        class_points = points[labels == label]\n        n_points_class = np.min([n_per_class, class_points.shape[0]])\n        subsampled_points.append(class_points[:n_points_class])\n        subsampled_labels.append(label.repeat(n_points_class))\n    subsampled_points = torch.cat(subsampled_points, dim=0)\n    subsampled_labels = torch.cat(subsampled_labels, dim=0)\n    return (subsampled_points, subsampled_labels)",
        "orig_context": "```python\n## src/sqfa/plot/_data_wrangle.py\nimport numpy as np\n\nimport torch\n\ndef subsample_class_points(points, labels, n_per_class):\n    \"\"\"\n    Return a subsample of points for each class (whichever is\n    smaller of n_per_class or the total points in the class).\n\n    Parameters\n    ----------\n    points : torch.Tensor\n        Points to subsample. (n_points, n_dim)\n    labels : torch.Tensor\n        Labels or values of the points. (n_points,)\n    n_per_class : int\n        Number of points to subsample for each class.\n\n    Returns\n    -------\n    subsampled_points : torch.Tensor\n        Subsampled points. (n_classes * n_points_per_class, n_dim)\n    subsampled_labels : torch.Tensor\n        Labels or values of the subsampled points. (n_classes * n_points_per_class,)\n    \"\"\"\n    subsampled_points = []\n    subsampled_labels = []\n\n    for _i, label in enumerate(labels.unique()):\n        class_points = points[labels == label]\n\n        n_points_class = np.min([n_per_class, class_points.shape[0]])\n        subsampled_points.append(class_points[:n_points_class])\n        subsampled_labels.append(label.repeat(n_points_class))\n\n    subsampled_points = torch.cat(subsampled_points, dim=0)\n    subsampled_labels = torch.cat(subsampled_labels, dim=0)\n    return subsampled_points, subsampled_labels\n\n```\n\n\n",
        "eval_script": "## src/sqfa/plot/_data_wrangle.py\nimport numpy as np\nimport torch\n\ndef subsample_class_points(points, labels, n_per_class):\n    \"\"\"\n    Return a subsample of points for each class (whichever is\n    smaller of n_per_class or the total points in the class).\n\n    Parameters\n    ----------\n    points : torch.Tensor\n        Points to subsample. (n_points, n_dim)\n    labels : torch.Tensor\n        Labels or values of the points. (n_points,)\n    n_per_class : int\n        Number of points to subsample for each class.\n\n    Returns\n    -------\n    subsampled_points : torch.Tensor\n        Subsampled points. (n_classes * n_points_per_class, n_dim)\n    subsampled_labels : torch.Tensor\n        Labels or values of the subsampled points. (n_classes * n_points_per_class,)\n    \"\"\"\n    subsampled_points = []\n    subsampled_labels = []\n\n    for _i, label in enumerate(labels.unique()):\n        class_points = points[labels == label]\n\n        n_points_class = np.min([n_per_class, class_points.shape[0]])\n        subsampled_points.append(class_points[:n_points_class])\n        subsampled_labels.append(label.repeat(n_points_class))\n\n    subsampled_points = torch.cat(subsampled_points, dim=0)\n    subsampled_labels = torch.cat(subsampled_labels, dim=0)\n    return subsampled_points, subsampled_labels\n\n\ndef test_subsample_class_points():\n    # Test case 1: n_per_class is less than the number of points in each class\n    points = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]])\n    labels = torch.tensor([0, 0, 1, 1])\n    n_per_class = 1\n    expected_points, expected_labels = subsample_class_points(points, labels, n_per_class)\n    new_points, new_labels = subsample_class_points_new_implementation(points, labels, n_per_class)\n    assert torch.equal(expected_points, new_points), \"Test case 1 failed for points\"\n    assert torch.equal(expected_labels, new_labels), \"Test case 1 failed for labels\"\n\n    # Test case 2: n_per_class is equal to the number of points in each class\n    n_per_class = 2\n    expected_points, expected_labels = subsample_class_points(points, labels, n_per_class)\n    new_points, new_labels = subsample_class_points_new_implementation(points, labels, n_per_class)\n    assert torch.equal(expected_points, new_points), \"Test case 2 failed for points\"\n    assert torch.equal(expected_labels, new_labels), \"Test case 2 failed for labels\"\n\n    # Test case 3: n_per_class is greater than the number of points in each class\n    n_per_class = 3\n    expected_points, expected_labels = subsample_class_points(points, labels, n_per_class)\n    new_points, new_labels = subsample_class_points_new_implementation(points, labels, n_per_class)\n    assert torch.equal(expected_points, new_points), \"Test case 3 failed for points\"\n    assert torch.equal(expected_labels, new_labels), \"Test case 3 failed for labels\"\n\nif __name__ == \"__main__\":\n    test_subsample_class_points()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of logic and implementation. The only difference is the addition of a test function `test_subsample_class_points()` in the revised code, which is used to verify the correctness of the function through test cases. The core functionality of `subsample_class_points` remains unchanged in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `subsample_class_points` function returns two values: `subsampled_points` and `subsampled_labels`. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases in `test_subsample_class_points` check the return values of the function using assertions. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `subsample_class_points` and `subsample_class_points_new_implementation` for various scenarios. This ensures that the new implementation can pass all test cases only if it has the exact same functionality as the original, satisfying this condition.\n\n- CONDITION 4: The test cases use `torch.equal` to compare the expected and actual outputs, which is appropriate for comparing tensors. The assertions are reasonable given that `subsample_class_points` returns values. This condition is satisfied.\n\n- CONDITION 5: The test cases cover different scenarios: when `n_per_class` is less than, equal to, and greater than the number of points in each class. These are non-trivial and cover edge cases, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "matrix_log_ref",
        "idx": "254",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "tests/test_linalg.py",
        "orig_func": "def matrix_log_ref(A):\n    \"\"\"\n    Compute the matrix logarithm of a tensor of SPD matrices using scipy.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        A tensor of SPD matrices of shape (n_matrices, n_dim, n_dim).\n\n    Returns\n    -------\n    log_A : torch.Tensor\n        A tensor of matrix logarithms of the input matrices of shape (n_matrices, n_dim, n_dim).\n    \"\"\"\n    if A.dim() < 3:\n        A = A.unsqueeze(0)\n    n_matrices = A.shape[0]\n    n_dim = A.shape[1]\n    log_A = torch.zeros((n_matrices, n_dim, n_dim))\n    for i in range(n_matrices):\n        log_A[i] = torch.as_tensor(scipy.linalg.logm(A[i]))\n    return log_A",
        "orig_context": "```python\n## tests/test_linalg.py\nimport scipy.linalg\n\nimport torch\n\ndef matrix_log_ref(A):\n    \"\"\"\n    Compute the matrix logarithm of a tensor of SPD matrices using scipy.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        A tensor of SPD matrices of shape (n_matrices, n_dim, n_dim).\n\n    Returns\n    -------\n    log_A : torch.Tensor\n        A tensor of matrix logarithms of the input matrices of shape (n_matrices, n_dim, n_dim).\n    \"\"\"\n    if A.dim() < 3:\n        A = A.unsqueeze(0)\n    n_matrices = A.shape[0]\n    n_dim = A.shape[1]\n    log_A = torch.zeros((n_matrices, n_dim, n_dim))\n    for i in range(n_matrices):\n        log_A[i] = torch.as_tensor(scipy.linalg.logm(A[i]))\n\n    return log_A\n\n```\n\n\n",
        "eval_script": "## tests/test_linalg.py\nimport scipy.linalg\nimport torch\n\ndef matrix_log_ref(A):\n    \"\"\"\n    Compute the matrix logarithm of a tensor of SPD matrices using scipy.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        A tensor of SPD matrices of shape (n_matrices, n_dim, n_dim).\n\n    Returns\n    -------\n    log_A : torch.Tensor\n        A tensor of matrix logarithms of the input matrices of shape (n_matrices, n_dim, n_dim).\n    \"\"\"\n    if A.dim() < 3:\n        A = A.unsqueeze(0)\n    n_matrices = A.shape[0]\n    n_dim = A.shape[1]\n    log_A = torch.zeros((n_matrices, n_dim, n_dim))\n    for i in range(n_matrices):\n        log_A[i] = torch.as_tensor(scipy.linalg.logm(A[i]))\n\n    return log_A\n\n\ndef test_matrix_log_ref():\n    # Test case 1: Single SPD matrix\n    A1 = torch.tensor([[4.0, 1.0], [1.0, 3.0]])\n    expected1 = matrix_log_ref(A1)\n    result1 = matrix_log_ref_new_implementation(A1)\n    assert torch.allclose(expected1, result1), \"Test case 1 failed\"\n\n    # Test case 2: Batch of SPD matrices\n    A2 = torch.stack([A1, A1 * 2])\n    expected2 = matrix_log_ref(A2)\n    result2 = matrix_log_ref_new_implementation(A2)\n    assert torch.allclose(expected2, result2), \"Test case 2 failed\"\n\n    # Test case 3: Already batched single SPD matrix\n    A3 = A1.unsqueeze(0)\n    expected3 = matrix_log_ref(A3)\n    result3 = matrix_log_ref_new_implementation(A3)\n    assert torch.allclose(expected3, result3), \"Test case 3 failed\"\n\n    # Test case 4: Higher dimensional SPD matrix\n    A4 = torch.tensor([[5.0, 2.0, 1.0], [2.0, 3.0, 1.0], [1.0, 1.0, 2.0]])\n    expected4 = matrix_log_ref(A4)\n    result4 = matrix_log_ref_new_implementation(A4)\n    assert torch.allclose(expected4, result4), \"Test case 4 failed\"\n\n    # Test case 5: Identity matrix\n    A5 = torch.eye(2)\n    expected5 = matrix_log_ref(A5)\n    result5 = matrix_log_ref_new_implementation(A5)\n    assert torch.allclose(expected5, result5), \"Test case 5 failed\"\n\n    # Test case 6: Random SPD matrix\n    A6 = torch.tensor([[6.0, 2.0], [2.0, 5.0]])\n    expected6 = matrix_log_ref(A6)\n    result6 = matrix_log_ref_new_implementation(A6)\n    assert torch.allclose(expected6, result6), \"Test case 6 failed\"\n\n    # Test case 7: Edge case of zero matrix (not SPD, but for robustness)\n    A7 = torch.zeros((2, 2))\n    try:\n        expected7 = matrix_log_ref(A7)\n        result7 = matrix_log_ref_new_implementation(A7)\n        assert torch.allclose(expected7, result7), \"Test case 7 failed\"\n    except Exception as e:\n        print(\"Test case 7 passed with exception:\", e)\n\nif __name__ == \"__main__\":\n    test_matrix_log_ref()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       9      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  9      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining the original and revised functions, they are identical in terms of implementation. Both functions take a tensor of SPD matrices, check if the input tensor has fewer than three dimensions, and if so, they add an extra dimension. They then compute the matrix logarithm for each matrix in the batch using `scipy.linalg.logm` and store the results in a new tensor. The revised function includes additional test cases to verify its correctness, but the core functionality of the `matrix_log_ref` function remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `matrix_log_ref` function returns a tensor `log_A`, which is the matrix logarithm of the input tensor `A`. Therefore, this condition is satisfied.\n  \n- CONDITION 2: The test cases use `assert torch.allclose(expected, result)` to compare the return values of `matrix_log_ref` and `matrix_log_ref_new_implementation`. There is no checking of printed or logged contents. This condition is satisfied.\n\n- CONDITION 3: The test cases compare the outputs of `matrix_log_ref` and `matrix_log_ref_new_implementation` using `torch.allclose`, which checks for element-wise equality within a tolerance. This ensures that `matrix_log_ref_new_implementation` must have the same functionality as `matrix_log_ref` to pass all tests. This condition is satisfied.\n\n- CONDITION 4: The test cases use the return values of `matrix_log_ref` and `matrix_log_ref_new_implementation` for assertions, which is appropriate since `matrix_log_ref` returns a value. The assert statements are reasonable. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including single SPD matrices, batches of matrices, identity matrices, and edge cases like zero matrices. These cases are non-trivial and test different aspects of the function's behavior. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "conjugate_matrix",
        "idx": "255",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/linalg.py",
        "orig_func": "def conjugate_matrix(A, B):\n    \"\"\"\n    Conjugate matrix A by B, i.e. compute B A B^T.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Matrix A. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Matrix B. Shape (n_batch_B, n_dim, n_out).\n\n    Returns\n    -------\n    C : torch.Tensor\n        The conjugated matrix. Shape (n_batch_A, n_batch_B, n_out, n_out).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError('B must have at least 2 dimensions.')\n    C = torch.einsum('...ij,njk,...kl->n...il', B, A, B.transpose(-2, -1))\n    squeeze_dim = 0 if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)",
        "orig_context": "```python\n## src/sqfa/linalg.py\nimport torch\n\ndef conjugate_matrix(A, B):\n    \"\"\"\n    Conjugate matrix A by B, i.e. compute B A B^T.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Matrix A. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Matrix B. Shape (n_batch_B, n_dim, n_out).\n\n    Returns\n    -------\n    C : torch.Tensor\n        The conjugated matrix. Shape (n_batch_A, n_batch_B, n_out, n_out).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError(\"B must have at least 2 dimensions.\")\n    # Use einsum\n    C = torch.einsum(\"...ij,njk,...kl->n...il\", B, A, B.transpose(-2, -1))\n    # Use matmul\n    # C = B[None, ...] @ A[:, None, ...] @ B.transpose(-2, -1)[None, ...]\n    squeeze_dim = (0) if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)\n\n```\n\n\n",
        "eval_script": "import torch\n\ndef conjugate_matrix(A, B):\n    \"\"\"\n    Conjugate matrix A by B, i.e. compute B A B^T.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Matrix A. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Matrix B. Shape (n_batch_B, n_dim, n_out).\n\n    Returns\n    -------\n    C : torch.Tensor\n        The conjugated matrix. Shape (n_batch_A, n_batch_B, n_out, n_out).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError(\"B must have at least 2 dimensions.\")\n    # Use einsum\n    C = torch.einsum(\"...ij,njk,...kl->n...il\", B, A, B.transpose(-2, -1))\n    # Use matmul\n    # C = B[None, ...] @ A[:, None, ...] @ B.transpose(-2, -1)[None, ...]\n    squeeze_dim = (0) if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)\n\n\ndef test_conjugate_matrix():\n    # Test case 1: Simple 2D case\n    A = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)\n    B = torch.tensor([[5, 6], [7, 8]], dtype=torch.float32)\n    result_original = conjugate_matrix(A, B)\n    result_new = conjugate_matrix_new_implementation(A, B)\n    assert torch.allclose(result_original, result_new), \"Test case 1 failed\"\n\n    # Test case 2: Batched A and B\n    A = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.float32)\n    B = torch.tensor([[[1, 0], [0, 1]], [[0, 1], [1, 0]]], dtype=torch.float32)\n    result_original = conjugate_matrix(A, B)\n    result_new = conjugate_matrix_new_implementation(A, B)\n    assert torch.allclose(result_original, result_new), \"Test case 2 failed\"\n\n    # Test case 3: Different batch sizes\n    A = torch.tensor([[[1, 2], [3, 4]]], dtype=torch.float32)\n    B = torch.tensor([[[1, 0], [0, 1]], [[0, 1], [1, 0]]], dtype=torch.float32)\n    result_original = conjugate_matrix(A, B)\n    result_new = conjugate_matrix_new_implementation(A, B)\n    assert torch.allclose(result_original, result_new), \"Test case 3 failed\"\n\n    # Test case 4: B with fewer than 2 dimensions\n    A = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)\n    B = torch.tensor([1, 0], dtype=torch.float32)  # 1D tensor\n    try:\n        conjugate_matrix_new_implementation(A, B)\n        assert False, \"Test case 4 failed: ValueError not raised\"\n    except ValueError as e:\n        assert str(e) == \"B must have at least 2 dimensions.\", \"Test case 4 failed: Incorrect error message\"\n\nif __name__ == \"__main__\":\n    test_conjugate_matrix()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       8      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  8      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is essentially the same as the ORIGINAL FUNCTION. The main logic of the function, which involves checking the dimensions of A and B, using `torch.einsum` to compute the conjugate matrix, and squeezing the result, remains unchanged. The only notable difference is a commented-out alternative implementation using `matmul`, which is not active and does not affect the function's behavior. Additionally, the test cases provided in the code ensure that the REVISED FUNCTION behaves as expected in various scenarios, including edge cases. Since the active code in the REVISED FUNCTION is identical to the ORIGINAL FUNCTION, their functionality is the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `conjugate_matrix` function returns a tensor `C`, which is the conjugated matrix. Therefore, it satisfies this condition as it has a return value.\n  \n- CONDITION 2: The test cases use `assert` statements to check the return values of the function, specifically using `torch.allclose` to compare tensors. There is no checking of printed or logged contents. Thus, this condition is satisfied.\n\n- CONDITION 3: The test cases compare the results of `conjugate_matrix` and `conjugate_matrix_new_implementation` using `torch.allclose`, which checks if the two tensors are element-wise equal within a tolerance. This ensures that the new implementation must have the exact same functionality to pass all test cases. Hence, this condition is satisfied.\n\n- CONDITION 4: The test cases use `assert` statements to compare the return values of the two implementations, which is reasonable since `conjugate_matrix` returns a value. Additionally, the test case for the ValueError checks the error message, which is appropriate. Therefore, this condition is satisfied.\n\n- CONDITION 5: The test cases cover various scenarios, including simple 2D matrices, batched matrices, different batch sizes, and an invalid input case for `B`. These are non-trivial and provide a reasonable coverage of potential edge cases. Thus, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "make_orthogonal_matrices",
        "idx": "258",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "tests/make_examples.py",
        "orig_func": "def make_orthogonal_matrices(n_matrices, n_dim):\n    \"\"\"Generate random orthogonal matrices.\"\"\"\n    low_tri = torch.randn(n_matrices, n_dim, n_dim)\n    low_tri = torch.tril(low_tri, diagonal=-1)\n    skew_sym = low_tri - low_tri.transpose(1, 2)\n    orthogonal = torch.matrix_exp(skew_sym)\n    return orthogonal",
        "orig_context": "```python\n## tests/make_examples.py\nimport torch\n\ndef make_orthogonal_matrices(n_matrices, n_dim):\n    \"\"\"Generate random orthogonal matrices.\"\"\"\n    low_tri = torch.randn(n_matrices, n_dim, n_dim)\n    low_tri = torch.tril(low_tri, diagonal=-1)\n    skew_sym = low_tri - low_tri.transpose(1, 2)\n    orthogonal = torch.matrix_exp(skew_sym)\n    return orthogonal\n\n```\n\n\n",
        "eval_script": "## tests/make_examples.py\nimport torch\n\ndef make_orthogonal_matrices(n_matrices, n_dim):\n    \"\"\"Generate random orthogonal matrices.\"\"\"\n    low_tri = torch.randn(n_matrices, n_dim, n_dim)\n    low_tri = torch.tril(low_tri, diagonal=-1)\n    skew_sym = low_tri - low_tri.transpose(1, 2)\n    orthogonal = torch.matrix_exp(skew_sym)\n    return orthogonal\n\n\ndef test_make_orthogonal_matrices():\n    # Set a fixed random seed for reproducibility\n    torch.manual_seed(0)\n\n    # Test case 1: Single matrix, 2x2\n    n_matrices, n_dim = 1, 2\n    result_original = make_orthogonal_matrices(n_matrices, n_dim)\n    torch.manual_seed(0)  # Reset seed before calling the new implementation\n    result_new = make_orthogonal_matrices_new_implementation(n_matrices, n_dim)\n    assert torch.allclose(result_original, result_new), \"Test case 1 failed\"\n\n    # Test case 2: Multiple matrices, 3x3\n    n_matrices, n_dim = 5, 3\n    torch.manual_seed(0)  # Reset seed before each test case\n    result_original = make_orthogonal_matrices(n_matrices, n_dim)\n    torch.manual_seed(0)  # Reset seed before calling the new implementation\n    result_new = make_orthogonal_matrices_new_implementation(n_matrices, n_dim)\n    assert torch.allclose(result_original, result_new), \"Test case 2 failed\"\n\n    # Test case 3: Single matrix, 4x4\n    n_matrices, n_dim = 1, 4\n    torch.manual_seed(0)  # Reset seed before each test case\n    result_original = make_orthogonal_matrices(n_matrices, n_dim)\n    torch.manual_seed(0)  # Reset seed before calling the new implementation\n    result_new = make_orthogonal_matrices_new_implementation(n_matrices, n_dim)\n    assert torch.allclose(result_original, result_new), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_make_orthogonal_matrices()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the code is identical to the ORIGINAL FUNCTION. Both functions are named `make_orthogonal_matrices` and have the same implementation, which involves generating random lower triangular matrices, creating skew-symmetric matrices, and then computing the matrix exponential to obtain orthogonal matrices. The test cases in the code refer to a `make_orthogonal_matrices_new_implementation` function, which is not provided, so we cannot compare it. However, since the REVISED FUNCTION itself is unchanged from the ORIGINAL FUNCTION, their functionality is the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `make_orthogonal_matrices` returns a value, specifically a tensor of orthogonal matrices, which satisfies this condition.\n- CONDITION 2: The test cases use `assert` statements to compare the return values of `make_orthogonal_matrices` and `make_orthogonal_matrices_new_implementation`, ensuring that they do not rely on printed or logged outputs. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of the original and new implementations using `torch.allclose`, which checks for equality within a tolerance. This ensures that the new implementation must have the exact same functionality to pass the tests, satisfying this condition.\n- CONDITION 4: The test cases use `assert` statements to compare the return values, which is appropriate given that the function returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover different scenarios: a single 2x2 matrix, multiple 3x3 matrices, and a single 4x4 matrix. These are non-trivial and provide a reasonable coverage of possible inputs, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "matrix_sqrt_ref",
        "idx": "262",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "tests/test_linalg.py",
        "orig_func": "def matrix_sqrt_ref(A):\n    \"\"\"\n    Compute the square root of a tensor of SPD matrices using scipy.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        A tensor of SPD matrices of shape (n_matrices, n_dim, n_dim).\n\n    Returns\n    -------\n    sqrt_A : torch.Tensor\n        A tensor of square roots of the input matrices of shape (n_matrices, n_dim, n_dim).\n    \"\"\"\n    if A.dim() < 3:\n        A = A.unsqueeze(0)\n    n_matrices = A.shape[0]\n    n_dim = A.shape[1]\n    sqrt_A = torch.zeros((n_matrices, n_dim, n_dim))\n    if n_dim > 1:\n        for i in range(n_matrices):\n            sqrt_A[i] = torch.as_tensor(scipy.linalg.sqrtm(A[i]))\n    else:\n        sqrt_A = torch.sqrt(A)\n    return sqrt_A",
        "orig_context": "```python\n## tests/test_linalg.py\nimport scipy.linalg\n\nimport torch\n\ndef matrix_sqrt_ref(A):\n    \"\"\"\n    Compute the square root of a tensor of SPD matrices using scipy.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        A tensor of SPD matrices of shape (n_matrices, n_dim, n_dim).\n\n    Returns\n    -------\n    sqrt_A : torch.Tensor\n        A tensor of square roots of the input matrices of shape (n_matrices, n_dim, n_dim).\n    \"\"\"\n    if A.dim() < 3:\n        A = A.unsqueeze(0)\n    n_matrices = A.shape[0]\n    n_dim = A.shape[1]\n    sqrt_A = torch.zeros((n_matrices, n_dim, n_dim))\n    if n_dim > 1:\n        for i in range(n_matrices):\n            sqrt_A[i] = torch.as_tensor(scipy.linalg.sqrtm(A[i]))\n    else:\n        sqrt_A = torch.sqrt(A)\n    return sqrt_A\n\n```\n\n\n",
        "eval_script": "## tests/test_linalg.py\nimport scipy.linalg\nimport torch\n\ndef matrix_sqrt_ref(A):\n    \"\"\"\n    Compute the square root of a tensor of SPD matrices using scipy.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        A tensor of SPD matrices of shape (n_matrices, n_dim, n_dim).\n\n    Returns\n    -------\n    sqrt_A : torch.Tensor\n        A tensor of square roots of the input matrices of shape (n_matrices, n_dim, n_dim).\n    \"\"\"\n    if A.dim() < 3:\n        A = A.unsqueeze(0)\n    n_matrices = A.shape[0]\n    n_dim = A.shape[1]\n    sqrt_A = torch.zeros((n_matrices, n_dim, n_dim))\n    if n_dim > 1:\n        for i in range(n_matrices):\n            sqrt_A[i] = torch.as_tensor(scipy.linalg.sqrtm(A[i]))\n    else:\n        sqrt_A = torch.sqrt(A)\n    return sqrt_A\n\n\ndef test_matrix_sqrt_ref():\n    # Test case 1: Single 2x2 matrix\n    A1 = torch.tensor([[[4.0, 1.0], [1.0, 3.0]]])\n    assert torch.allclose(matrix_sqrt_ref(A1), matrix_sqrt_ref_new_implementation(A1), atol=1e-5)\n\n    # Test case 2: Batch of 2x2 matrices\n    A2 = torch.tensor([[[4.0, 1.0], [1.0, 3.0]], [[9.0, 0.0], [0.0, 16.0]]])\n    assert torch.allclose(matrix_sqrt_ref(A2), matrix_sqrt_ref_new_implementation(A2), atol=1e-5)\n\n    # Test case 3: Single 1x1 matrix\n    A3 = torch.tensor([[4.0]])\n    assert torch.allclose(matrix_sqrt_ref(A3), matrix_sqrt_ref_new_implementation(A3), atol=1e-5)\n\n    # Test case 4: Single 3x3 matrix\n    A4 = torch.tensor([[[4.0, 1.0, 0.0], [1.0, 3.0, 1.0], [0.0, 1.0, 2.0]]])\n    assert torch.allclose(matrix_sqrt_ref(A4), matrix_sqrt_ref_new_implementation(A4), atol=1e-5)\n\n    # Test case 5: Batch of 3x3 matrices\n    A5 = torch.tensor([[[4.0, 1.0, 0.0], [1.0, 3.0, 1.0], [0.0, 1.0, 2.0]], \n                       [[9.0, 0.0, 0.0], [0.0, 16.0, 0.0], [0.0, 0.0, 25.0]]])\n    assert torch.allclose(matrix_sqrt_ref(A5), matrix_sqrt_ref_new_implementation(A5), atol=1e-5)\n\n    # Test case 6: Identity matrix\n    A6 = torch.eye(2).unsqueeze(0)\n    assert torch.allclose(matrix_sqrt_ref(A6), matrix_sqrt_ref_new_implementation(A6), atol=1e-5)\n\n    # Test case 7: Zero matrix\n    A7 = torch.zeros((1, 2, 2))\n    assert torch.allclose(matrix_sqrt_ref(A7), matrix_sqrt_ref_new_implementation(A7), atol=1e-5)\n\n    # Test case 8: Large values matrix\n    A8 = torch.tensor([[[1e10, 0.0], [0.0, 1e10]]])\n    assert torch.allclose(matrix_sqrt_ref(A8), matrix_sqrt_ref_new_implementation(A8), atol=1e-5)\n\n    # Test case 9: Small values matrix\n    A9 = torch.tensor([[[1e-10, 0.0], [0.0, 1e-10]]])\n    assert torch.allclose(matrix_sqrt_ref(A9), matrix_sqrt_ref_new_implementation(A9), atol=1e-5)\n\n    # Test case 10: Random symmetric positive definite matrix\n    A10 = torch.tensor([[[2.0, -1.0], [-1.0, 2.0]]])\n    assert torch.allclose(matrix_sqrt_ref(A10), matrix_sqrt_ref_new_implementation(A10), atol=1e-5)\n\nif __name__ == \"__main__\":\n    test_matrix_sqrt_ref()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions have the same logic and structure, including the use of `scipy.linalg.sqrtm` for computing the square root of matrices and handling the case where the input tensor has less than three dimensions by unsqueezing it. The test cases provided in the CODE are designed to validate the functionality of the `matrix_sqrt_ref` function, but they do not affect the function itself. Since the function definitions are exactly the same, the functionality is also the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `matrix_sqrt_ref` function returns a tensor, which satisfies the condition that it should have return values or modify input arguments.\n  \n- [CONDITION 2] The test cases use `torch.allclose` to compare the return values of `matrix_sqrt_ref` and `matrix_sqrt_ref_new_implementation`, ensuring that they check return values rather than printed or logged contents.\n\n- [CONDITION 3] The test cases compare the outputs of `matrix_sqrt_ref` and `matrix_sqrt_ref_new_implementation` for various inputs. If `matrix_sqrt_ref_new_implementation` passes all these tests, it must have the same functionality as `matrix_sqrt_ref`.\n\n- [CONDITION 4] The test cases use `assert` statements to compare the outputs of the two functions, which is reasonable since `matrix_sqrt_ref` returns a value.\n\n- [CONDITION 5] The test cases cover a variety of scenarios, including single matrices, batches of matrices, identity matrices, zero matrices, matrices with large and small values, and random symmetric positive definite matrices. This variety ensures that the test cases are non-trivial.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "spd_sqrt",
        "idx": "264",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/linalg.py",
        "orig_func": "def spd_sqrt(M):\n    \"\"\"\n    Compute the square root of a symmetric positive definite matrix.\n\n    Computes the symmetric positive definite matrix S such that SS = M.\n\n    Parameters\n    ----------\n    M : torch.Tensor\n        Symmetric positive definite matrices. Shape (..., n_dim, n_dim).\n\n    Returns\n    -------\n    M_sqrt : torch.Tensor\n        The square root of M. Shape (..., n_dim, n_dim).\n    \"\"\"\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    M_sqrt = torch.einsum('...ij,...j,...kj->...ik', eigvecs, torch.sqrt(eigvals), eigvecs)\n    return M_sqrt",
        "orig_context": "```python\n## src/sqfa/linalg.py\nimport torch\n\ndef spd_sqrt(M):\n    \"\"\"\n    Compute the square root of a symmetric positive definite matrix.\n\n    Computes the symmetric positive definite matrix S such that SS = M.\n\n    Parameters\n    ----------\n    M : torch.Tensor\n        Symmetric positive definite matrices. Shape (..., n_dim, n_dim).\n\n    Returns\n    -------\n    M_sqrt : torch.Tensor\n        The square root of M. Shape (..., n_dim, n_dim).\n    \"\"\"\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    M_sqrt = torch.einsum(\n        \"...ij,...j,...kj->...ik\", eigvecs, torch.sqrt(eigvals), eigvecs\n    )\n    return M_sqrt\n\n```\n\n\n",
        "eval_script": "## src/sqfa/linalg.py\nimport torch\n\ndef spd_sqrt(M):\n    \"\"\"\n    Compute the square root of a symmetric positive definite matrix.\n\n    Computes the symmetric positive definite matrix S such that SS = M.\n\n    Parameters\n    ----------\n    M : torch.Tensor\n        Symmetric positive definite matrices. Shape (..., n_dim, n_dim).\n\n    Returns\n    -------\n    M_sqrt : torch.Tensor\n        The square root of M. Shape (..., n_dim, n_dim).\n    \"\"\"\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    M_sqrt = torch.einsum(\n        \"...ij,...j,...kj->...ik\", eigvecs, torch.sqrt(eigvals), eigvecs\n    )\n    return M_sqrt\n\n\ndef test_spd_sqrt():\n    # Test case 1: Simple 2x2 matrix\n    M1 = torch.tensor([[4.0, 1.0], [1.0, 3.0]])\n    assert torch.allclose(spd_sqrt(M1), spd_sqrt_new_implementation(M1), atol=1e-6)\n\n    # Test case 2: Larger 3x3 matrix\n    M2 = torch.tensor([[6.0, 2.0, 1.0], [2.0, 5.0, 2.0], [1.0, 2.0, 4.0]])\n    assert torch.allclose(spd_sqrt(M2), spd_sqrt_new_implementation(M2), atol=1e-6)\n\n    # Test case 3: Batch of 2x2 matrices\n    M3 = torch.tensor([[[4.0, 1.0], [1.0, 3.0]], [[9.0, 3.0], [3.0, 7.0]]])\n    assert torch.allclose(spd_sqrt(M3), spd_sqrt_new_implementation(M3), atol=1e-6)\n\nif __name__ == \"__main__\":\n    test_spd_sqrt()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `spd_sqrt` is essentially the same as the ORIGINAL FUNCTION. Both functions compute the square root of a symmetric positive definite matrix using the eigenvalue decomposition method. The only difference is the formatting of the `torch.einsum` line, which does not affect the functionality. The REVISED FUNCTION also includes a test function `test_spd_sqrt` to verify the correctness of the implementation, but this does not alter the functionality of the `spd_sqrt` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `spd_sqrt` function returns a value, specifically a tensor representing the square root of the input matrix. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use `torch.allclose` to compare the return values of `spd_sqrt` and `spd_sqrt_new_implementation`, which means they are checking the return values and not printed or logged content. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `spd_sqrt` and `spd_sqrt_new_implementation` using `torch.allclose`, which checks for equality within a tolerance. This ensures that `spd_sqrt_new_implementation` must have the exact same functionality as `spd_sqrt` to pass all test cases. This condition is satisfied.\n- CONDITION 4: The test cases use `assert` statements to compare the outputs of the two implementations, which is reasonable given that `spd_sqrt` has a return value. This condition is satisfied.\n- CONDITION 5: The test cases include a simple 2x2 matrix, a larger 3x3 matrix, and a batch of 2x2 matrices, which are non-trivial and cover different scenarios. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "generalized_eigenvalues",
        "idx": "265",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/linalg.py",
        "orig_func": "def generalized_eigenvalues(A, B):\n    \"\"\"\n    Compute the generalized eigenvalues of the pair of symmetric positive\n    definite matrices (A, B).\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_B, n_dim, n_dim).\n\n    Returns\n    -------\n    eigenvalues : torch.Tensor\n        The generalized eigenvalues of the pair (A, B), sorted in descending\n        order. Shape (n_batch_A, n_batch_B, n_dim).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    B_inv_sqrt = spd_inv_sqrt(B)\n    A_conj = conjugate_matrix(A, B_inv_sqrt)\n    eigenvalues = torch.linalg.eigvalsh(A_conj)\n    return eigenvalues.flip(-1)",
        "orig_context": "```python\n## src/sqfa/linalg.py\nimport torch\n\ndef conjugate_matrix(A, B):\n    \"\"\"\n    Conjugate matrix A by B, i.e. compute B A B^T.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Matrix A. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Matrix B. Shape (n_batch_B, n_dim, n_out).\n\n    Returns\n    -------\n    C : torch.Tensor\n        The conjugated matrix. Shape (n_batch_A, n_batch_B, n_out, n_out).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError(\"B must have at least 2 dimensions.\")\n    # Use einsum\n    C = torch.einsum(\"...ij,njk,...kl->n...il\", B, A, B.transpose(-2, -1))\n    # Use matmul\n    # C = B[None, ...] @ A[:, None, ...] @ B.transpose(-2, -1)[None, ...]\n    squeeze_dim = (0) if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)\n\ndef spd_inv_sqrt(M):\n    \"\"\"\n    For symmetric positive definite matrix M, compute the inverse square\n    root of M.\n\n    Parameters\n    ----------\n    M : torch.Tensor\n        Symmetric positive definite matrices. Shape (n_batch, n_dim, n_dim).\n\n    Returns\n    -------\n    M_inv_sqrt : torch.Tensor\n        Inverse square root of M. Shape (n_batch, n_dim, n_dim).\n    \"\"\"\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    inv_sqrt_eigvals = torch.sqrt(1.0 / eigvals)\n    M_inv_sqrt = eigvecs * inv_sqrt_eigvals.unsqueeze(-2)\n    return M_inv_sqrt.transpose(-2, -1)\n\ndef generalized_eigenvalues(A, B):\n    \"\"\"\n    Compute the generalized eigenvalues of the pair of symmetric positive\n    definite matrices (A, B).\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_B, n_dim, n_dim).\n\n    Returns\n    -------\n    eigenvalues : torch.Tensor\n        The generalized eigenvalues of the pair (A, B), sorted in descending\n        order. Shape (n_batch_A, n_batch_B, n_dim).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    B_inv_sqrt = spd_inv_sqrt(B)\n    A_conj = conjugate_matrix(A, B_inv_sqrt)\n    eigenvalues = torch.linalg.eigvalsh(A_conj)\n    return eigenvalues.flip(-1)\n\n```\n\n\n",
        "eval_script": "## src/sqfa/linalg.py\nimport torch\n\ndef conjugate_matrix(A, B):\n    \"\"\"\n    Conjugate matrix A by B, i.e. compute B A B^T.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Matrix A. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Matrix B. Shape (n_batch_B, n_dim, n_out).\n\n    Returns\n    -------\n    C : torch.Tensor\n        The conjugated matrix. Shape (n_batch_A, n_batch_B, n_out, n_out).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError(\"B must have at least 2 dimensions.\")\n    # Use einsum\n    C = torch.einsum(\"...ij,njk,...kl->n...il\", B, A, B.transpose(-2, -1))\n    # Use matmul\n    # C = B[None, ...] @ A[:, None, ...] @ B.transpose(-2, -1)[None, ...]\n    squeeze_dim = (0) if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)\n\ndef spd_inv_sqrt(M):\n    \"\"\"\n    For symmetric positive definite matrix M, compute the inverse square\n    root of M.\n\n    Parameters\n    ----------\n    M : torch.Tensor\n        Symmetric positive definite matrices. Shape (n_batch, n_dim, n_dim).\n\n    Returns\n    -------\n    M_inv_sqrt : torch.Tensor\n        Inverse square root of M. Shape (n_batch, n_dim, n_dim).\n    \"\"\"\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    inv_sqrt_eigvals = torch.sqrt(1.0 / eigvals)\n    M_inv_sqrt = eigvecs * inv_sqrt_eigvals.unsqueeze(-2)\n    return M_inv_sqrt.transpose(-2, -1)\n\ndef generalized_eigenvalues(A, B):\n    \"\"\"\n    Compute the generalized eigenvalues of the pair of symmetric positive\n    definite matrices (A, B).\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_B, n_dim, n_dim).\n\n    Returns\n    -------\n    eigenvalues : torch.Tensor\n        The generalized eigenvalues of the pair (A, B), sorted in descending\n        order. Shape (n_batch_A, n_batch_B, n_dim).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    B_inv_sqrt = spd_inv_sqrt(B)\n    A_conj = conjugate_matrix(A, B_inv_sqrt)\n    eigenvalues = torch.linalg.eigvalsh(A_conj)\n    return eigenvalues.flip(-1)\n\n\ndef test_generalized_eigenvalues():\n    # Test case 1: Basic test with small matrices\n    A1 = torch.tensor([[2.0, 1.0], [1.0, 2.0]])\n    B1 = torch.tensor([[3.0, 0.0], [0.0, 3.0]])\n    assert torch.allclose(generalized_eigenvalues(A1, B1), generalized_eigenvalues_new_implementation(A1, B1))\n\n    # Test case 2: Batched matrices\n    A2 = torch.tensor([[[2.0, 1.0], [1.0, 2.0]], [[3.0, 0.0], [0.0, 3.0]]])\n    B2 = torch.tensor([[[3.0, 0.0], [0.0, 3.0]], [[4.0, 0.0], [0.0, 4.0]]])\n    assert torch.allclose(generalized_eigenvalues(A2, B2), generalized_eigenvalues_new_implementation(A2, B2))\n\n    # Test case 3: Larger matrices\n    A3 = torch.eye(4) * 5\n    B3 = torch.eye(4) * 6\n    assert torch.allclose(generalized_eigenvalues(A3, B3), generalized_eigenvalues_new_implementation(A3, B3))\n\n    # Test case 4: Identity matrices\n    A4 = torch.eye(3)\n    B4 = torch.eye(3)\n    assert torch.allclose(generalized_eigenvalues(A4, B4), generalized_eigenvalues_new_implementation(A4, B4))\n\n    # Test case 5: Random positive definite matrices\n    A5 = torch.tensor([[4.0, 1.0], [1.0, 3.0]])\n    B5 = torch.tensor([[2.0, 0.5], [0.5, 2.0]])\n    assert torch.allclose(generalized_eigenvalues(A5, B5), generalized_eigenvalues_new_implementation(A5, B5))\n\n    # Test case 6: High-dimensional matrices\n    A6 = torch.eye(10) * 7\n    B6 = torch.eye(10) * 8\n    assert torch.allclose(generalized_eigenvalues(A6, B6), generalized_eigenvalues_new_implementation(A6, B6))\n\n    # Test case 7: Edge case with small values\n    A7 = torch.tensor([[1e-10, 0], [0, 1e-10]])\n    B7 = torch.tensor([[1e-10, 0], [0, 1e-10]])\n    assert torch.allclose(generalized_eigenvalues(A7, B7), generalized_eigenvalues_new_implementation(A7, B7))\n\nif __name__ == \"__main__\":\n    test_generalized_eigenvalues()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they compute the inverse square root of matrix B using `spd_inv_sqrt`, conjugate matrix A with the result using `conjugate_matrix`, compute the eigenvalues of the conjugated matrix using `torch.linalg.eigvalsh`, and finally return the eigenvalues sorted in descending order by flipping them. The test cases provided in the revised code further confirm that the functionality remains consistent with the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `generalized_eigenvalues` returns a tensor of eigenvalues, so it satisfies this condition.\n- CONDITION 2: The test cases use `torch.allclose` to compare the return values of `generalized_eigenvalues` and `generalized_eigenvalues_new_implementation`, which means they are checking return values, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `generalized_eigenvalues` and `generalized_eigenvalues_new_implementation` using `torch.allclose`, which checks for approximate equality. This means that `generalized_eigenvalues_new_implementation` must have the same functionality as `generalized_eigenvalues` to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use `torch.allclose` to assert the equality of the outputs, which is reasonable given that `generalized_eigenvalues` returns a tensor. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic tests, batched matrices, larger matrices, identity matrices, random positive definite matrices, high-dimensional matrices, and edge cases with small values. These are non-trivial and comprehensive. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "make_dataset_points",
        "idx": "266",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "tests/make_examples.py",
        "orig_func": "def make_dataset_points(n_points, class_covariances):\n    \"\"\"Generate points from a dataset with n_points and n_classes.\"\"\"\n    n_dim = class_covariances.shape[-1]\n    n_classes = class_covariances.shape[0]\n    for i in range(n_classes):\n        cov = class_covariances[i]\n        mean = torch.zeros(n_dim)\n        class_points = torch.distributions.MultivariateNormal(mean, cov).sample((n_points,))\n        if i == 0:\n            points = class_points\n            labels = torch.ones(n_points) * i\n        else:\n            points = torch.cat((points, class_points), 0)\n            labels = torch.cat((labels, torch.ones(n_points) * i), 0)\n    return (points, labels)",
        "orig_context": "```python\n## tests/make_examples.py\nimport torch\n\ndef make_dataset_points(n_points, class_covariances):\n    \"\"\"Generate points from a dataset with n_points and n_classes.\"\"\"\n    n_dim = class_covariances.shape[-1]\n    n_classes = class_covariances.shape[0]\n    for i in range(n_classes):\n        cov = class_covariances[i]\n        mean = torch.zeros(n_dim)\n        class_points = torch.distributions.MultivariateNormal(mean, cov).sample(\n            (n_points,)\n        )\n        if i == 0:\n            points = class_points\n            labels = torch.ones(n_points) * i\n        else:\n            points = torch.cat((points, class_points), 0)\n            labels = torch.cat((labels, torch.ones(n_points) * i), 0)\n\n    return points, labels\n\n```\n\n\n",
        "eval_script": "## tests/make_examples.py\nimport torch\n\ndef make_dataset_points(n_points, class_covariances):\n    \"\"\"Generate points from a dataset with n_points and n_classes.\"\"\"\n    n_dim = class_covariances.shape[-1]\n    n_classes = class_covariances.shape[0]\n    for i in range(n_classes):\n        cov = class_covariances[i]\n        mean = torch.zeros(n_dim)\n        class_points = torch.distributions.MultivariateNormal(mean, cov).sample(\n            (n_points,)\n        )\n        if i == 0:\n            points = class_points\n            labels = torch.ones(n_points) * i\n        else:\n            points = torch.cat((points, class_points), 0)\n            labels = torch.cat((labels, torch.ones(n_points) * i), 0)\n\n    return points, labels\n\n\ndef test_make_dataset_points():\n    # Test case 1: Single class, single point\n    n_points = 1\n    class_covariances = torch.tensor([[[1.0]]])\n    torch.manual_seed(0)\n    points_old, labels_old = make_dataset_points(n_points, class_covariances)\n    torch.manual_seed(0)\n    points_new, labels_new = make_dataset_points_new_implementation(n_points, class_covariances)\n    assert torch.equal(points_old, points_new), \"Test case 1 failed: Points do not match\"\n    assert torch.equal(labels_old, labels_new), \"Test case 1 failed: Labels do not match\"\n\n    # Test case 2: Multiple classes, multiple points\n    n_points = 5\n    class_covariances = torch.tensor([[[1.0, 0.0], [0.0, 1.0]], [[2.0, 0.5], [0.5, 2.0]]])\n    torch.manual_seed(0)\n    points_old, labels_old = make_dataset_points(n_points, class_covariances)\n    torch.manual_seed(0)\n    points_new, labels_new = make_dataset_points_new_implementation(n_points, class_covariances)\n    assert torch.equal(points_old, points_new), \"Test case 2 failed: Points do not match\"\n    assert torch.equal(labels_old, labels_new), \"Test case 2 failed: Labels do not match\"\n\n    # Test case 3: Different dimensions\n    n_points = 3\n    class_covariances = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])\n    torch.manual_seed(0)\n    points_old, labels_old = make_dataset_points(n_points, class_covariances)\n    torch.manual_seed(0)\n    points_new, labels_new = make_dataset_points_new_implementation(n_points, class_covariances)\n    assert torch.equal(points_old, points_new), \"Test case 3 failed: Points do not match\"\n    assert torch.equal(labels_old, labels_new), \"Test case 3 failed: Labels do not match\"\n\nif __name__ == \"__main__\":\n    test_make_dataset_points()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are named `make_dataset_points` and have the same logic and structure. They both iterate over the number of classes, generate class points using a multivariate normal distribution, and concatenate these points and their corresponding labels. The only difference in the provided code is the addition of a test function `test_make_dataset_points`, which is used to verify the correctness of the function. However, the test function does not alter the functionality of the `make_dataset_points` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `make_dataset_points` function returns values (`points` and `labels`), satisfying this condition.\n- CONDITION 2: The test cases check the return values (`points` and `labels`) using `assert` statements, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `make_dataset_points` and `make_dataset_points_new_implementation` for the same inputs, ensuring that the new implementation must have the same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use `assert` statements to compare the outputs of the two implementations, which is reasonable given that the function returns values. This satisfies the condition.\n- CONDITION 5: The test cases cover various scenarios: a single class with a single point, multiple classes with multiple points, and different dimensions. These are non-trivial and cover a range of possible inputs, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "get_class_rgba",
        "idx": "267",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/plot/colors.py",
        "orig_func": "def get_class_rgba(color_map, values, color_limits=None):\n    \"\"\"\n    Get the RGBA color for each class based on the colormap and the values.\n\n    Parameters\n    ----------\n    color_map : str or matplotlib.colors.Colormap\n        The colormap to use.\n    values : list or numpy.ndarray or torch.Tensor\n        The value of each class to be used for the color scale\n    color_limits : list, optional\n        The minimum and maximum values for the color scale. If not provided,\n        the minimum and maximum values of `values` will be used.\n\n    Returns\n    -------\n    colors : numpy.ndarray (n_classes, 4)\n        The color for each class in RGBA format.\n    \"\"\"\n    color_map, color_normalizer = get_normalized_color_map(color_map, values, color_limits)\n    class_colors = color_map(color_normalizer(values))\n    return class_colors",
        "orig_context": "```python\n## src/sqfa/plot/colors.py\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nfrom matplotlib.colors import Normalize\n\ndef get_normalized_color_map(color_map, values, color_limits=None):\n    \"\"\"\n    Get the color map and normalizer for the given values.\n\n    Parameters\n    ----------\n    color_map : str or matplotlib.colors.Colormap\n        The colormap to use.\n    values : list or numpy.ndarray or torch.Tensor\n        The value of each class to be used for the color scale\n    color_limits : list, optional\n        The minimum and maximum values for the color scale. If not provided,\n        the minimum and maximum values of `values` will be used.\n\n    Returns\n    -------\n    color_map : matplotlib.colors.Colormap\n        The colormap to use.\n    color_normalizer : matplotlib.colors.Normalize\n        The normalizer for the colormap.\n    \"\"\"\n    values = np.array(values)\n    if isinstance(color_map, str):\n        color_map = plt.get_cmap(color_map)\n    if color_limits is None:\n        color_limits = [np.min(values), np.max(values)]\n    color_normalizer = Normalize(vmin=color_limits[0], vmax=color_limits[1])\n    return color_map, color_normalizer\n\ndef get_class_rgba(color_map, values, color_limits=None):\n    \"\"\"\n    Get the RGBA color for each class based on the colormap and the values.\n\n    Parameters\n    ----------\n    color_map : str or matplotlib.colors.Colormap\n        The colormap to use.\n    values : list or numpy.ndarray or torch.Tensor\n        The value of each class to be used for the color scale\n    color_limits : list, optional\n        The minimum and maximum values for the color scale. If not provided,\n        the minimum and maximum values of `values` will be used.\n\n    Returns\n    -------\n    colors : numpy.ndarray (n_classes, 4)\n        The color for each class in RGBA format.\n    \"\"\"\n    color_map, color_normalizer = get_normalized_color_map(\n        color_map, values, color_limits\n    )\n    class_colors = color_map(color_normalizer(values))\n    return class_colors\n\n```\n\n\n",
        "eval_script": "## src/sqfa/plot/colors.py\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nfrom matplotlib.colors import Normalize\n\ndef get_normalized_color_map(color_map, values, color_limits=None):\n    \"\"\"\n    Get the color map and normalizer for the given values.\n\n    Parameters\n    ----------\n    color_map : str or matplotlib.colors.Colormap\n        The colormap to use.\n    values : list or numpy.ndarray or torch.Tensor\n        The value of each class to be used for the color scale\n    color_limits : list, optional\n        The minimum and maximum values for the color scale. If not provided,\n        the minimum and maximum values of `values` will be used.\n\n    Returns\n    -------\n    color_map : matplotlib.colors.Colormap\n        The colormap to use.\n    color_normalizer : matplotlib.colors.Normalize\n        The normalizer for the colormap.\n    \"\"\"\n    values = np.array(values)\n    if isinstance(color_map, str):\n        color_map = plt.get_cmap(color_map)\n    if color_limits is None:\n        color_limits = [np.min(values), np.max(values)]\n    color_normalizer = Normalize(vmin=color_limits[0], vmax=color_limits[1])\n    return color_map, color_normalizer\n\ndef get_class_rgba(color_map, values, color_limits=None):\n    \"\"\"\n    Get the RGBA color for each class based on the colormap and the values.\n\n    Parameters\n    ----------\n    color_map : str or matplotlib.colors.Colormap\n        The colormap to use.\n    values : list or numpy.ndarray or torch.Tensor\n        The value of each class to be used for the color scale\n    color_limits : list, optional\n        The minimum and maximum values for the color scale. If not provided,\n        the minimum and maximum values of `values` will be used.\n\n    Returns\n    -------\n    colors : numpy.ndarray (n_classes, 4)\n        The color for each class in RGBA format.\n    \"\"\"\n    color_map, color_normalizer = get_normalized_color_map(\n        color_map, values, color_limits\n    )\n    class_colors = color_map(color_normalizer(values))\n    return class_colors\n\n\ndef test_get_class_rgba():\n    # Test case 1: Default color limits\n    color_map = 'viridis'\n    values = [0, 0.5, 1]\n    assert np.allclose(get_class_rgba(color_map, values), get_class_rgba_new_implementation(color_map, values))\n\n    # Test case 2: Specified color limits\n    color_limits = [0, 1]\n    assert np.allclose(get_class_rgba(color_map, values, color_limits), get_class_rgba_new_implementation(color_map, values, color_limits))\n\n    # Test case 3: Different colormap\n    color_map = 'plasma'\n    assert np.allclose(get_class_rgba(color_map, values), get_class_rgba_new_implementation(color_map, values))\n\nif __name__ == \"__main__\":\n    test_get_class_rgba()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_class_rgba` is identical to the ORIGINAL FUNCTION in terms of its implementation. Both functions call `get_normalized_color_map` to obtain a colormap and a normalizer, and then apply these to the input values to get the RGBA colors. The REVISED FUNCTION includes additional code for testing, but this does not affect the functionality of the `get_class_rgba` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `get_class_rgba` returns a numpy array of RGBA colors, satisfying the condition that it has return values.\n- CONDITION 2: The test cases use `np.allclose` to compare the return values of `get_class_rgba` and `get_class_rgba_new_implementation`, which means they are checking the return values, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `get_class_rgba` and `get_class_rgba_new_implementation` using `np.allclose`, ensuring that `get_class_rgba_new_implementation` must have exactly the same functionality to pass all tests.\n- CONDITION 4: The test cases use `np.allclose` to assert the equality of the outputs, which is reasonable given that `get_class_rgba` returns a numpy array.\n- CONDITION 5: The test cases cover different scenarios: default color limits, specified color limits, and different colormaps, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "FisherRao.get_class_distances",
        "idx": "270",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/model.py",
        "orig_func": "def get_class_distances(self, data_statistics, regularized=False):\n    \"\"\"\n        Compute the pairwise lower bounds to the Fisher-Rao distances.\n\n        Parameters\n        ----------\n        data_statistics : torch.Tensor or dict\n            - If a torch.Tensor, should have shape (n_classes, n_dim, n_dim) and contain\n              the scatter matrices (second moments) of the data for each class.\n            - If a dict, it should contain fields 'means' and 'covariances'.\n        regularized : bool\n            If True, regularize the distances by adding a small value to the\n            diagonal of the transformed scatter matrices. Default is False.\n\n        Returns\n        -------\n        torch.Tensor shape (n_classes, n_classes)\n            Pairwise distances between the transformed feature scatter matrices.\n        \"\"\"\n    if not isinstance(data_statistics, dict):\n        raise ValueError('Fisher-Rao distance requires class statistics dictionary as input')\n    feature_means = self.transform(data_statistics['means'])\n    feature_covariances = self.transform_scatters(data_statistics['covariances'])\n    if regularized:\n        feature_covariances = feature_covariances + self.diagonal_noise[None, :, :]\n    distances = self.distance_fun(feature_means, feature_covariances)\n    return distances",
        "orig_context": "```python\n# src/sqfa/_optim.py\n\nimport torch\n\ndef check_distances_valid(distances):\n    \"\"\"\n    Check if off-diagonal distances are valid. Raise an error if they are not.\n\n    Parameters\n    ----------\n    distances : torch.Tensor\n        Tensor of pairwise distances between covariance matrices.\n    \"\"\"\n    n_classes = distances.shape[0]\n    tril_ind = torch.tril_indices(n_classes, n_classes, offset=-1)\n    if torch.isnan(distances[tril_ind]).any():\n        raise ValueError('Some distances between classes are NaN.')\n    if torch.isinf(distances[tril_ind]).any():\n        raise ValueError('Some distances between classes are inf.')\nimport time\nfrom tqdm import tqdm\nfrom torch import optim\n\n# ...\n```\n```python\n# src/sqfa/linalg.py\n\nimport torch\n\ndef conjugate_matrix(A, B):\n    \"\"\"\n    Conjugate matrix A by B, i.e. compute B A B^T.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Matrix A. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Matrix B. Shape (n_batch_B, n_dim, n_out).\n\n    Returns\n    -------\n    C : torch.Tensor\n        The conjugated matrix. Shape (n_batch_A, n_batch_B, n_out, n_out).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError(\"B must have at least 2 dimensions.\")\n    # Use einsum\n    C = torch.einsum(\"...ij,njk,...kl->n...il\", B, A, B.transpose(-2, -1))\n    # Use matmul\n    # C = B[None, ...] @ A[:, None, ...] @ B.transpose(-2, -1)[None, ...]\n    squeeze_dim = (0) if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)\n\ndef spd_inv_sqrt(M):\n    \"\"\"\n    For symmetric positive definite matrix M, compute the inverse square\n    root of M.\n\n    Parameters\n    ----------\n    M : torch.Tensor\n        Symmetric positive definite matrices. Shape (n_batch, n_dim, n_dim).\n\n    Returns\n    -------\n    M_inv_sqrt : torch.Tensor\n        Inverse square root of M. Shape (n_batch, n_dim, n_dim).\n    \"\"\"\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    inv_sqrt_eigvals = torch.sqrt(1.0 / eigvals)\n    M_inv_sqrt = eigvecs * inv_sqrt_eigvals.unsqueeze(-2)\n    return M_inv_sqrt.transpose(-2, -1)\n\ndef generalized_eigenvalues(A, B):\n    \"\"\"\n    Compute the generalized eigenvalues of the pair of symmetric positive\n    definite matrices (A, B).\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_A, n_dim, n_dim).\n    B : torch.Tensor\n        Symmetric positive definite matrix. Shape (n_batch_B, n_dim, n_dim).\n\n    Returns\n    -------\n    eigenvalues : torch.Tensor\n        The generalized eigenvalues of the pair (A, B), sorted in descending\n        order. Shape (n_batch_A, n_batch_B, n_dim).\n        If a batch dimension is 1, it is squeezed out.\n    \"\"\"\n    B_inv_sqrt = spd_inv_sqrt(B)\n    A_conj = conjugate_matrix(A, B_inv_sqrt)\n    eigenvalues = torch.linalg.eigvalsh(A_conj)\n    return eigenvalues.flip(-1)\n\n\n```\n```python\n# src/sqfa/distances.py\n\nEPSILON = 1e-6\n\nfrom .linalg import (\n    generalized_eigenvalues,\n    spd_log,\n)\n\nimport torch\n\ndef affine_invariant_sq(A, B):\n    \"\"\"\n    Compute the squared affine invariant distance between SPD matrices.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Shape (n_batch_A, n_dim, n_dim), the first SPD matrix.\n    B : torch.Tensor\n        Shape (n_batch_B, n_dim, n_dim), the second SPD matrix.\n\n    Returns\n    -------\n    distance_squared : torch.Tensor\n        Shape (n_batch_A, n_batch_B), the squared affine invariant distance.\n    \"\"\"\n    # Compute the generalized eigenvalues\n    gen_eigvals = generalized_eigenvalues(A, B)\n    # Compute the distance\n    distance_squared = torch.sum(torch.log(gen_eigvals) ** 2, axis=-1)\n    return distance_squared\n\ndef affine_invariant(A, B):\n    \"\"\"\n    Compute the affine invariant distance between SPD matrices.\n    A small epsilon is added inside the square root to avoid gradient\n    instabilities.\n\n    Parameters\n    ----------\n    A : torch.Tensor\n        Shape (n_batch_A, n_dim, n_dim), the first SPD matrix.\n    B : torch.Tensor\n        Shape (n_batch_B, n_dim, n_dim), the second SPD matrix.\n\n    Returns\n    -------\n    distance : torch.Tensor\n        Shape (n_batch_A, n_batch_B), the affine invariant distance.\n    \"\"\"\n    return torch.sqrt(affine_invariant_sq(A, B) + EPSILON)\n\ndef _embed_gaussian(means, covariances):\n    \"\"\"\n    Embed the parameters of the Gaussian distribution in SPD,\n    by stacking the means and the covariances in the format\n    [covariances, means;\n    means.T, 1].\n\n    Parameters\n    ----------\n    means : torch.Tensor\n        Shape (n_classes, n_filters), the means.\n    covariances : torch.Tensor\n        Shape (n_classes, n_filters, n_filters), the covariance matrices.\n\n    Returns\n    -------\n    embedding : torch.Tensor\n        Shape (n_classes, n_filters+1, n_filters+1), the embedded SPD matrices.\n    \"\"\"\n    n_classes, n_filters = means.shape\n\n    mean_outer_prod = torch.einsum(\"ni,nj->nij\", means, means)\n    second_moments = covariances + mean_outer_prod\n\n    embedding = torch.cat([second_moments, means.unsqueeze(1)], dim=1)\n    one = torch.ones(n_classes, dtype=means.dtype, device=means.device)\n    means_long = torch.cat([means, one.unsqueeze(1)], dim=1)\n    embedding = torch.cat([embedding, means_long.unsqueeze(2)], dim=2)\n    return embedding\n\ndef fisher_rao_lower_bound_sq(means, covariances):\n    \"\"\"\n    Compute the Calvo & Oller lower bound of the Fisher-Rao squared\n    distance between Gaussians.\n\n    Parameters\n    ----------\n    means : torch.Tensor\n        Shape (n_classes, n_filters), the means.\n    covariances : torch.Tensor\n        Shape (n_classes, n_filters, n_filters), the covariance matrices.\n\n    Returns\n    -------\n    distance_squared : torch.Tensor\n        Shape (n_classes, n_classes), the lower bound of the Fisher-Rao squared\n        distance.\n    \"\"\"\n    embedding = _embed_gaussian(means, covariances)\n    distance_squared = affine_invariant_sq(embedding, embedding)\n    return distance_squared\n\ndef fisher_rao_lower_bound(means, covariances):\n    \"\"\"\n    Compute the Calvo & Oller lower bound of the Fisher-Rao squared\n    distance between Gaussians.\n\n    Parameters\n    ----------\n    means : torch.Tensor\n        Shape (n_classes, n_filters), the means.\n    covariances : torch.Tensor\n        Shape (n_classes, n_filters, n_filters), the covariance matrices.\n\n    Returns\n    -------\n    distance : torch.Tensor\n        Shape (n_classes, n_classes), the lower bound of the Fisher-Rao distance.\n    \"\"\"\n    return torch.sqrt(fisher_rao_lower_bound_sq(means, covariances) + EPSILON)\n\n\n```\n```python\n# src/sqfa/model.py\n\nfrom .statistics import class_statistics, pca, pca_from_scatter\nfrom .constraints import FixedFilters, Identity, Sphere\nfrom torch.nn.utils.parametrizations import orthogonal\nimport torch.nn as nn\nfrom ._optim import fitting_loop\nfrom torch.nn.utils.parametrize import register_parametrization, remove_parametrizations\nfrom .linalg import conjugate_matrix\n\ndef _check_statistics(data_statistics):\n    \"\"\"\n    Check that data_statistics is either:\n      1) a torch.Tensor of shape (n_classes, n_dim, n_dim), or\n      2) a dictionary containing at least the 'means' and 'covariances' keys.\n\n    Parameters\n    ----------\n    data_statistics : torch.Tensor or dict\n        Data statistics, either as a tensor with second moments or a dictionary\n        containing means and covariances.\n\n    Raises\n    ------\n    ValueError\n        If `data_statistics` is a dictionary but does not contain the required keys.\n    TypeError\n        If `data_statistics` is neither a dictionary nor a tensor-like object.\n    \"\"\"\n    if isinstance(data_statistics, dict):\n        required_keys = {'means', 'covariances'}\n        missing_keys = required_keys - set(data_statistics.keys())\n        if missing_keys:\n            raise ValueError(f'`data_statistics` dictionary must contain the keys {required_keys}. Missing keys: {missing_keys}')\n    elif not hasattr(data_statistics, 'shape'):\n        raise TypeError(\"`data_statistics` must be either a dict with 'means' and 'covariances' or a torch.Tensor of shape (n_classes, n_dim, n_dim).\")\nimport torch\n\ndef _stats_to_scatter(statistics):\n    \"\"\"\n    Convert data_statistics input to scatter matrices. This function\n    is used to allow the input to the model to be either\n    a dictionary with means and covariances or a tensor with the\n    second moments.\n\n    Parameters\n    ----------\n    statistics : torch.Tensor or dict\n        - If a torch.Tensor, should have shape (n_classes, n_dim, n_dim) and contain\n          the scatter matrices (second moments) of the data for each class.\n        - If a dict, it should contain fields 'means' and 'covariances'.\n\n    Returns\n    -------\n    torch.Tensor\n        Scatter matrices of shape (n_classes, n_dim, n_dim).\n    \"\"\"\n    if isinstance(statistics, dict):\n        _check_statistics(statistics)\n        mean_outer_prod = torch.einsum('ni,nj->nij', statistics['means'], statistics['means'])\n        scatter = statistics['covariances'] + mean_outer_prod\n    else:\n        scatter = statistics\n    return scatter\nfrom .distances import affine_invariant, fisher_rao_lower_bound\n\nclass SQFA(nn.Module):\n    \"\"\"Supervised Quadratic Feature Analysis (SQFA) model.\"\"\"\n\n    def __init__(self, n_dim, feature_noise=0, n_filters=2, filters=None, distance_fun=None, constraint='sphere'):\n        \"\"\"\n        Initialize SQFA.\n\n        Parameters\n        ----------\n        n_dim : int\n            Dimension of the input data space.\n        feature_noise : float\n            Noise added to the features outputs, i.e. a diagonal term added\n            to the covariance matrix of the features. Default is 0.\n        n_filters : int\n            Number of filters to use. Default is 2. If filters is provided,\n            n_filters is ignored.\n        filters : torch.Tensor\n            Filters to use. If n_filters is provided, filters are randomly\n            initialized. Default is None. Of shape (n_filters, n_dim).\n        distance_fun : callable\n            Function to compute the distance between the transformed feature\n            scatter matrices. Should take as input two tensors of shape\n            (n_classes, n_filters, n_filters) and return a matrix\n            of shape (n_classes, n_classes) with the pairwise distances\n            (or squared distances or similarities).\n            If None, then the Affine Invariant squared distance is used.\n        constraint : str\n            Constraint to apply to the filters. Can be 'none', 'sphere' or\n            'orthogonal'. Default is 'sphere'.\n        \"\"\"\n        super().__init__()\n        if filters is None:\n            filters = torch.randn(n_filters, n_dim)\n        else:\n            filters = torch.as_tensor(filters, dtype=torch.float32)\n        self.filters = nn.Parameter(filters)\n        feature_noise_mat = torch.as_tensor(feature_noise, dtype=torch.float32) * torch.eye(n_filters)\n        self.register_buffer('diagonal_noise', feature_noise_mat)\n        if distance_fun is None:\n            self.distance_fun = affine_invariant\n        else:\n            self.distance_fun = distance_fun\n        self.constraint = constraint\n        self._add_constraint(constraint=self.constraint)\n\n    def transform_scatters(self, data_scatters):\n        \"\"\"\n        Transform data scatter matrices to feature space scatter matrices.\n\n        Parameters\n        ----------\n        data_scatters : torch.Tensor\n            Tensor of shape (n_classes, n_dim, n_dim), with second\n            moment or covariance matrices.\n\n        Returns\n        -------\n        torch.Tensor shape (n_classes, n_filters, n_filters)\n            Covariances of the transformed features.\n        \"\"\"\n        feature_scatters = conjugate_matrix(data_scatters, self.filters)\n        return feature_scatters\n\n    def get_class_distances(self, data_statistics, regularized=False):\n        \"\"\"\n        Compute the pairwise distances between the feature scatter matrices of the\n        different classes.\n\n        Parameters\n        ----------\n        data_statistics : torch.Tensor or dict\n            - If a torch.Tensor, should have shape (n_classes, n_dim, n_dim) and contain\n              the scatter matrices (second moments) of the data for each class.\n            - If a dict, it should contain fields 'means' and 'covariances'.\n        regularized : bool\n            If True, regularize the distances by adding a small value to the\n            diagonal of the transformed scatter matrices. Default is False.\n\n        Returns\n        -------\n        torch.Tensor shape (n_classes, n_classes)\n            Pairwise distances between the transformed feature scatter matrices.\n        \"\"\"\n        data_scatters = _stats_to_scatter(data_statistics)\n        feature_scatters = self.transform_scatters(data_scatters)\n        if regularized:\n            feature_scatters = feature_scatters + self.diagonal_noise[None, :, :]\n        distances = self.distance_fun(feature_scatters, feature_scatters)\n        return distances\n\n    def transform(self, data_points):\n        \"\"\"\n        Transform data to feature space.\n\n        Parameters\n        ----------\n        data_points : torch.Tensor\n            Input data of shape (n_samples, n_dim).\n\n        Returns\n        -------\n        torch.Tensor shape (n_samples, n_filters)\n            Data transformed to feature space.\n        \"\"\"\n        transformed_points = torch.einsum('ij,nj->ni', self.filters, data_points)\n        return transformed_points\n\n    def fit_pca(self, X=None, data_statistics=None):\n        \"\"\"\n        Fit the SQFA filters to the data using PCA. This can be used to\n        initialize the filters before training.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input data of shape (n_samples, n_dim).\n        data_statistics : torch.Tensor\n            Tensor of shape (n_classes, n_dim, n_dim) with the second moments\n            of the data for each class. If None, then X and y must be provided.\n            Default is None.\n        \"\"\"\n        if X is None and data_statistics is None:\n            raise ValueError('Either X or data_statistics must be provided.')\n        if self.filters.shape[0] > self.filters.shape[1]:\n            raise ValueError('Number of filters must be less than or equal to the data dimension.')\n        n_components = self.filters.shape[0]\n        if data_statistics is None:\n            pca_filters = pca(X, n_components)\n        else:\n            data_scatters = _stats_to_scatter(data_statistics)\n            pca_filters = pca_from_scatter(data_scatters, n_components)\n        remove_parametrizations(self, 'filters')\n        self.filters = nn.Parameter(pca_filters)\n        self._add_constraint(constraint=self.constraint)\n\n    def fit(self, X=None, y=None, data_statistics=None, max_epochs=300, lr=0.1, estimator='empirical', pairwise=False, show_progress=True, return_loss=False, **kwargs):\n        \"\"\"\n        Fit the SQFA model to data using the LBFGS optimizer.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input data of shape (n_samples, n_dim). If data_statistics is None,\n            then X and y must be provided.\n        y : torch.Tensor\n            Labels of shape (n_samples,). If data_statistics is None, then X\n            and y must be provided. Labels must be integers starting from 0.\n        data_statistics : torch.Tensor or dict\n            - If a torch.Tensor, should have shape (n_classes, n_dim, n_dim) and contain\n              the scatter matrices (second moments) of the data for each class.\n            - If a dict, it should contain fields 'means' and 'covariances'\n        max_epochs : int, optional\n            Number of max training epochs. By default 50.\n        lr : float\n            Learning rate for the optimizer. Default is 0.1.\n        estimator:\n            Covariance estimator to use. Options are \"empirical\",\n            and \"oas\". Default is \"oas\".\n        pairwise : bool\n            If True, then filters are optimized pairwise (the first 2 filters\n            are optimized together, then held fixed and the next 2 filters are\n            optimized together, etc.). If False, all filters are optimized\n            together. Default is False.\n        show_progress : bool\n            If True, show a progress bar during training. Default is True.\n        return_loss : bool\n            If True, return the loss after training. Default is False.\n        **kwargs\n            Additional keyword arguments passed to the NAdam optimizer.\n        \"\"\"\n        if data_statistics is None:\n            if X is None or y is None:\n                raise ValueError('Either data_statistics or X and y must be provided.')\n            data_statistics = class_statistics(X, y, estimator=estimator)\n        if not pairwise:\n            loss, training_time = fitting_loop(model=self, data_statistics=data_statistics, max_epochs=max_epochs, lr=lr, show_progress=show_progress, return_loss=True, **kwargs)\n        else:\n            n_pairs = self.filters.shape[0] // 2\n            filters_original = self.filters.detach().clone()\n            noise_original = self.diagonal_noise.detach().clone()[0, 0]\n            if self.filters.shape[0] % 2 != 0:\n                raise ValueError('Number of filters must be even for pairwise training.')\n            loss = torch.tensor([])\n            training_time = torch.tensor([])\n            filters_last_trained = torch.zeros(0)\n            for i in range(n_pairs):\n                filters_last_trained = self.filters.detach().clone()\n                if i == 0:\n                    filters_new_init = filters_original[:2]\n                else:\n                    filters_new_init = torch.cat((filters_last_trained, filters_original[2 * i:2 * (i + 1)]))\n                remove_parametrizations(self, 'filters')\n                self.filters = nn.Parameter(filters_new_init)\n                self._add_constraint(constraint=self.constraint)\n                feature_noise_mat = noise_original * torch.eye(2 * (i + 1))\n                self.register_buffer('diagonal_noise', feature_noise_mat)\n                if i > 0:\n                    register_parametrization(self, 'filters', FixedFilters(n_row_fixed=i * 2))\n                loss_pair, training_time_pair = fitting_loop(model=self, data_statistics=data_statistics, max_epochs=max_epochs, lr=lr, show_progress=show_progress, return_loss=True, **kwargs)\n                remove_parametrizations(self, 'filters')\n                self._add_constraint(constraint=self.constraint)\n                loss = torch.cat((loss, loss_pair))\n                if training_time.numel() > 0:\n                    training_time_pair = training_time_pair + training_time[-1]\n                training_time = torch.cat((training_time, training_time_pair))\n        if return_loss:\n            return (loss, training_time)\n        else:\n            return None\n\n    def _add_constraint(self, constraint='none'):\n        \"\"\"\n        Add constraint to the filters.\n\n        Parameters\n        ----------\n        constraint : str\n            Constraint to apply to the filters. Can be 'none', 'sphere' or\n            'orthogonal'. Default is 'none'.\n        \"\"\"\n        if constraint == 'none':\n            register_parametrization(self, 'filters', Identity())\n        elif constraint == 'sphere':\n            register_parametrization(self, 'filters', Sphere())\n        elif constraint == 'orthogonal':\n            orthogonal(self, 'filters')\n\nclass FisherRao(SQFA):\n    \"\"\"\n    Supervised Quadratic Feature Analysis (SQFA) model, using\n    the lower bound on the Fisher-Rao distance as the loss function.\n    \"\"\"\n\n    def __init__(self, n_dim, feature_noise=0, n_filters=2, filters=None, constraint='sphere'):\n        \"\"\"\n        Initialize SQFA.\n\n        Parameters\n        ----------\n        n_dim : int\n            Dimension of the input data space.\n        feature_noise : float\n            Noise added to the features outputs, i.e. a diagonal term added\n            to the covariance matrix of the features. Default is 0.\n        n_filters : int\n            Number of filters to use. Default is 2. If filters is provided,\n            n_filters is ignored.\n        filters : torch.Tensor\n            Filters to use. If n_filters is provided, filters are randomly\n            initialized. Default is None. Of shape (n_filters, n_dim).\n        constraint : str\n            Constraint to apply to the filters. Can be 'none', 'sphere' or\n            'orthogonal'. Default is 'sphere'.\n        \"\"\"\n        super().__init__(n_dim=n_dim, feature_noise=feature_noise, n_filters=n_filters, filters=filters, distance_fun=fisher_rao_lower_bound, constraint=constraint)\n\n    def fit(self, X=None, y=None, data_statistics=None, max_epochs=300, lr=0.1, estimator='oas', pairwise=False, show_progress=True, return_loss=False, **kwargs):\n        \"\"\"\n        Fit the SQFA model to data using the LBFGS optimizer.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input data of shape (n_samples, n_dim). If data_statistics is None,\n            then X and y must be provided.\n        y : torch.Tensor\n            Labels of shape (n_samples,). If data_statistics is None, then X\n            and y must be provided. Labels must be integers starting from 0.\n        data_statistics : dict\n            Dictionary containing the fields 'means' and 'covariances'\n        max_epochs : int, optional\n            Number of max training epochs. By default 50.\n        lr : float\n            Learning rate for the optimizer. Default is 0.1.\n        estimator:\n            Covariance estimator to use. Options are \"empirical\",\n            and \"oas\". Default is \"oas\".\n        pairwise : bool\n            If True, then filters are optimized pairwise (the first 2 filters\n            are optimized together, then held fixed and the next 2 filters are\n            optimized together, etc.). If False, all filters are optimized\n            together. Default is False.\n        show_progress : bool\n            If True, show a progress bar during training. Default is True.\n        return_loss : bool\n            If True, return the loss after training. Default is False.\n        **kwargs\n            Additional keyword arguments passed to the NAdam optimizer.\n        \"\"\"\n        if data_statistics is None:\n            if X is None or y is None:\n                raise ValueError('Either data_statistics or X and y must be provided.')\n            data_statistics = class_statistics(X, y, estimator=estimator)\n        else:\n            if not isinstance(data_statistics, dict):\n                raise ValueError('Fisher-Rao distance requires class statistics dictionary or X and y as input')\n            _check_statistics(data_statistics)\n        loss, training_time = super().fit(X=None, y=None, data_statistics=data_statistics, max_epochs=max_epochs, lr=lr, estimator=estimator, pairwise=pairwise, show_progress=show_progress, return_loss=True, **kwargs)\n        if return_loss:\n            return (loss, training_time)\n        else:\n            return None\n\n    def get_class_distances(self, data_statistics, regularized=False):\n        \"\"\"\n        Compute the pairwise lower bounds to the Fisher-Rao distances.\n\n        Parameters\n        ----------\n        data_statistics : torch.Tensor or dict\n            - If a torch.Tensor, should have shape (n_classes, n_dim, n_dim) and contain\n              the scatter matrices (second moments) of the data for each class.\n            - If a dict, it should contain fields 'means' and 'covariances'.\n        regularized : bool\n            If True, regularize the distances by adding a small value to the\n            diagonal of the transformed scatter matrices. Default is False.\n\n        Returns\n        -------\n        torch.Tensor shape (n_classes, n_classes)\n            Pairwise distances between the transformed feature scatter matrices.\n        \"\"\"\n        if not isinstance(data_statistics, dict):\n            raise ValueError('Fisher-Rao distance requires class statistics dictionary as input')\n        feature_means = self.transform(data_statistics['means'])\n        feature_covariances = self.transform_scatters(data_statistics['covariances'])\n        if regularized:\n            feature_covariances = feature_covariances + self.diagonal_noise[None, :, :]\n        distances = self.distance_fun(feature_means, feature_covariances)\n        return distances\n```\n",
        "eval_script": "import torch\nimport torch.nn as nn\nfrom torch.nn.utils.parametrize import register_parametrization, remove_parametrizations\n\n# Constants\nEPSILON = 1e-6\n\n# Mock implementations for missing components\ndef class_statistics(X, y, estimator='empirical'):\n    # Mock implementation\n    return {'means': torch.mean(X, dim=0), 'covariances': torch.cov(X.T)}\n\ndef pca(X, n_components):\n    # Mock implementation\n    return torch.randn(n_components, X.shape[1])\n\ndef pca_from_scatter(data_scatters, n_components):\n    # Mock implementation\n    return torch.randn(n_components, data_scatters.shape[1])\n\ndef fitting_loop(model, data_statistics, max_epochs, lr, show_progress, return_loss, **kwargs):\n    # Mock implementation\n    return torch.tensor([]), torch.tensor([])\n\nclass FixedFilters(nn.Module):\n    def __init__(self, n_row_fixed):\n        super().__init__()\n\nclass Identity(nn.Module):\n    def forward(self, X):\n        return X\n\nclass Sphere(nn.Module):\n    def forward(self, X):\n        return X / torch.norm(X, dim=-1, keepdim=True)\n\n# Linalg functions\ndef conjugate_matrix(A, B):\n    if A.dim() == 2:\n        A = A.unsqueeze(0)\n    if B.dim() < 2:\n        raise ValueError(\"B must have at least 2 dimensions.\")\n    C = torch.einsum(\"...ij,njk,...kl->n...il\", B, A, B.transpose(-2, -1))\n    squeeze_dim = (0) if B.dim() == 2 else (0, 1)\n    return torch.squeeze(C, dim=squeeze_dim)\n\ndef spd_inv_sqrt(M):\n    eigvals, eigvecs = torch.linalg.eigh(M)\n    inv_sqrt_eigvals = torch.sqrt(1.0 / eigvals)\n    M_inv_sqrt = eigvecs * inv_sqrt_eigvals.unsqueeze(-2)\n    return M_inv_sqrt.transpose(-2, -1)\n\ndef generalized_eigenvalues(A, B):\n    B_inv_sqrt = spd_inv_sqrt(B)\n    A_conj = conjugate_matrix(A, B_inv_sqrt)\n    eigenvalues = torch.linalg.eigvalsh(A_conj)\n    return eigenvalues.flip(-1)\n\n# Distances functions\ndef affine_invariant_sq(A, B):\n    gen_eigvals = generalized_eigenvalues(A, B)\n    distance_squared = torch.sum(torch.log(gen_eigvals) ** 2, axis=-1)\n    return distance_squared\n\ndef affine_invariant(A, B):\n    return torch.sqrt(affine_invariant_sq(A, B) + EPSILON)\n\ndef _embed_gaussian(means, covariances):\n    n_classes, n_filters = means.shape\n    mean_outer_prod = torch.einsum(\"ni,nj->nij\", means, means)\n    second_moments = covariances + mean_outer_prod\n    embedding = torch.cat([second_moments, means.unsqueeze(1)], dim=1)\n    one = torch.ones(n_classes, dtype=means.dtype, device=means.device)\n    means_long = torch.cat([means, one.unsqueeze(1)], dim=1)\n    embedding = torch.cat([embedding, means_long.unsqueeze(2)], dim=2)\n    return embedding\n\ndef fisher_rao_lower_bound_sq(means, covariances):\n    embedding = _embed_gaussian(means, covariances)\n    distance_squared = affine_invariant_sq(embedding, embedding)\n    return distance_squared\n\ndef fisher_rao_lower_bound(means, covariances):\n    return torch.sqrt(fisher_rao_lower_bound_sq(means, covariances) + EPSILON)\n\n# SQFA class\nclass SQFA(nn.Module):\n    def __init__(self, n_dim, feature_noise=0, n_filters=2, filters=None, distance_fun=None, constraint='sphere'):\n        super().__init__()\n        if filters is None:\n            filters = torch.randn(n_filters, n_dim)\n        else:\n            filters = torch.as_tensor(filters, dtype=torch.float32)\n        self.filters = nn.Parameter(filters)\n        feature_noise_mat = torch.as_tensor(feature_noise, dtype=torch.float32) * torch.eye(n_filters)\n        self.register_buffer('diagonal_noise', feature_noise_mat)\n        if distance_fun is None:\n            self.distance_fun = affine_invariant\n        else:\n            self.distance_fun = distance_fun\n        self.constraint = constraint\n        self._add_constraint(constraint=self.constraint)\n\n    def transform_scatters(self, data_scatters):\n        feature_scatters = conjugate_matrix(data_scatters, self.filters)\n        return feature_scatters\n\n    def get_class_distances(self, data_statistics, regularized=False):\n        data_scatters = _stats_to_scatter(data_statistics)\n        feature_scatters = self.transform_scatters(data_scatters)\n        if regularized:\n            feature_scatters = feature_scatters + self.diagonal_noise[None, :, :]\n        distances = self.distance_fun(feature_scatters, feature_scatters)\n        return distances\n\n    def transform(self, data_points):\n        transformed_points = torch.einsum('ij,nj->ni', self.filters, data_points)\n        return transformed_points\n\n    def fit_pca(self, X=None, data_statistics=None):\n        if X is None and data_statistics is None:\n            raise ValueError('Either X or data_statistics must be provided.')\n        if self.filters.shape[0] > self.filters.shape[1]:\n            raise ValueError('Number of filters must be less than or equal to the data dimension.')\n        n_components = self.filters.shape[0]\n        if data_statistics is None:\n            pca_filters = pca(X, n_components)\n        else:\n            data_scatters = _stats_to_scatter(data_statistics)\n            pca_filters = pca_from_scatter(data_scatters, n_components)\n        remove_parametrizations(self, 'filters')\n        self.filters = nn.Parameter(pca_filters)\n        self._add_constraint(constraint=self.constraint)\n\n    def fit(self, X=None, y=None, data_statistics=None, max_epochs=300, lr=0.1, estimator='empirical', pairwise=False, show_progress=True, return_loss=False, **kwargs):\n        if data_statistics is None:\n            if X is None or y is None:\n                raise ValueError('Either data_statistics or X and y must be provided.')\n            data_statistics = class_statistics(X, y, estimator=estimator)\n        if not pairwise:\n            loss, training_time = fitting_loop(model=self, data_statistics=data_statistics, max_epochs=max_epochs, lr=lr, show_progress=show_progress, return_loss=True, **kwargs)\n        else:\n            n_pairs = self.filters.shape[0] // 2\n            filters_original = self.filters.detach().clone()\n            noise_original = self.diagonal_noise.detach().clone()[0, 0]\n            if self.filters.shape[0] % 2 != 0:\n                raise ValueError('Number of filters must be even for pairwise training.')\n            loss = torch.tensor([])\n            training_time = torch.tensor([])\n            filters_last_trained = torch.zeros(0)\n            for i in range(n_pairs):\n                filters_last_trained = self.filters.detach().clone()\n                if i == 0:\n                    filters_new_init = filters_original[:2]\n                else:\n                    filters_new_init = torch.cat((filters_last_trained, filters_original[2 * i:2 * (i + 1)]))\n                remove_parametrizations(self, 'filters')\n                self.filters = nn.Parameter(filters_new_init)\n                self._add_constraint(constraint=self.constraint)\n                feature_noise_mat = noise_original * torch.eye(2 * (i + 1))\n                self.register_buffer('diagonal_noise', feature_noise_mat)\n                if i > 0:\n                    register_parametrization(self, 'filters', FixedFilters(n_row_fixed=i * 2))\n                loss_pair, training_time_pair = fitting_loop(model=self, data_statistics=data_statistics, max_epochs=max_epochs, lr=lr, show_progress=show_progress, return_loss=True, **kwargs)\n                remove_parametrizations(self, 'filters')\n                self._add_constraint(constraint=self.constraint)\n                loss = torch.cat((loss, loss_pair))\n                if training_time.numel() > 0:\n                    training_time_pair = training_time_pair + training_time[-1]\n                training_time = torch.cat((training_time, training_time_pair))\n        if return_loss:\n            return (loss, training_time)\n        else:\n            return None\n\n    def _add_constraint(self, constraint='none'):\n        if constraint == 'none':\n            register_parametrization(self, 'filters', Identity())\n        elif constraint == 'sphere':\n            register_parametrization(self, 'filters', Sphere())\n        elif constraint == 'orthogonal':\n            orthogonal(self, 'filters')\n\n# FisherRao class\nclass FisherRao(SQFA):\n    def __init__(self, n_dim, feature_noise=0, n_filters=2, filters=None, constraint='sphere'):\n        super().__init__(n_dim=n_dim, feature_noise=feature_noise, n_filters=n_filters, filters=filters, distance_fun=fisher_rao_lower_bound, constraint=constraint)\n\n    def fit(self, X=None, y=None, data_statistics=None, max_epochs=300, lr=0.1, estimator='oas', pairwise=False, show_progress=True, return_loss=False, **kwargs):\n        if data_statistics is None:\n            if X is None or y is None:\n                raise ValueError('Either data_statistics or X and y must be provided.')\n            data_statistics = class_statistics(X, y, estimator=estimator)\n        else:\n            if not isinstance(data_statistics, dict):\n                raise ValueError('Fisher-Rao distance requires class statistics dictionary or X and y as input')\n            _check_statistics(data_statistics)\n        loss, training_time = super().fit(X=None, y=None, data_statistics=data_statistics, max_epochs=max_epochs, lr=lr, estimator=estimator, pairwise=pairwise, show_progress=show_progress, return_loss=True, **kwargs)\n        if return_loss:\n            return (loss, training_time)\n        else:\n            return None\n\n    def get_class_distances(self, data_statistics, regularized=False):\n        if not isinstance(data_statistics, dict):\n            raise ValueError('Fisher-Rao distance requires class statistics dictionary as input')\n        feature_means = self.transform(data_statistics['means'])\n        feature_covariances = self.transform_scatters(data_statistics['covariances'])\n        if regularized:\n            feature_covariances = feature_covariances + self.diagonal_noise[None, :, :]\n        distances = self.distance_fun(feature_means, feature_covariances)\n        return distances\n\n\ndef _check_statistics(data_statistics):\n    if isinstance(data_statistics, dict):\n        required_keys = {'means', 'covariances'}\n        missing_keys = required_keys - set(data_statistics.keys())\n        if missing_keys:\n            raise ValueError(f'`data_statistics` dictionary must contain the keys {required_keys}. Missing keys: {missing_keys}')\n    elif not hasattr(data_statistics, 'shape'):\n        raise TypeError(\"`data_statistics` must be either a dict with 'means' and 'covariances' or a torch.Tensor of shape (n_classes, n_dim, n_dim).\")\n\ndef _stats_to_scatter(statistics):\n    if isinstance(statistics, dict):\n        _check_statistics(statistics)\n        mean_outer_prod = torch.einsum('ni,nj->nij', statistics['means'], statistics['means'])\n        scatter = statistics['covariances'] + mean_outer_prod\n    else:\n        scatter = statistics\n    return scatter\n\ndef test_get_class_distances():\n    # Create a FisherRao instance\n    fisher_rao = FisherRao(n_dim=3, n_filters=2)\n\n    # Mock data statistics\n    data_statistics = {\n        'means': torch.randn(5, 3),\n        'covariances': torch.eye(3).repeat(5, 1, 1)\n    }\n\n    # Test non-regularized case\n    distances_original = fisher_rao.get_class_distances(data_statistics, regularized=False)\n    distances_new = fisher_rao.get_class_distances_new_implementation(data_statistics, regularized=False)\n    assert torch.allclose(distances_original, distances_new), \"Non-regularized distances do not match\"\n\n    # Test regularized case\n    distances_original_reg = fisher_rao.get_class_distances(data_statistics, regularized=True)\n    distances_new_reg = fisher_rao.get_class_distances_new_implementation(data_statistics, regularized=True)\n    assert torch.allclose(distances_original_reg, distances_new_reg), \"Regularized distances do not match\"\n\n    # Test with different data statistics\n    data_statistics_diff = {\n        'means': torch.randn(4, 3),\n        'covariances': torch.eye(3).repeat(4, 1, 1)\n    }\n    distances_original_diff = fisher_rao.get_class_distances(data_statistics_diff, regularized=False)\n    distances_new_diff = fisher_rao.get_class_distances_new_implementation(data_statistics_diff, regularized=False)\n    assert torch.allclose(distances_original_diff, distances_new_diff), \"Distances with different data statistics do not match\"\n\nif __name__ == \"__main__\":\n    test_get_class_distances()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the FisherRao class is identical to the ORIGINAL FUNCTION. Both implementations check if `data_statistics` is a dictionary, transform the means and covariances using the `transform` and `transform_scatters` methods, optionally add regularization, and compute distances using `self.distance_fun`. There are no changes in logic or functionality between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `get_class_distances` function returns a value, which is the `distances`. This satisfies the condition that the function should either have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases in `test_get_class_distances` use assertions to compare the return values of `get_class_distances` and `get_class_distances_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `get_class_distances` and `get_class_distances_new_implementation` using `torch.allclose`, which checks for equality within a tolerance. This ensures that the new implementation can only pass if it has the same functionality as the original, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assert statements to compare the return values of the two implementations, which is reasonable since `get_class_distances` returns a value. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover different scenarios: non-regularized and regularized cases, as well as different data statistics. This provides a non-trivial set of test cases, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "Sphere.forward",
        "idx": "271",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/constraints.py",
        "orig_func": "def forward(self, X):\n    \"\"\"\n        Normalize the input tensor so that it lies on the sphere.\n\n        The norm pooled across channels is computed and used to normalize the tensor.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor in Euclidean space with shape (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n    X_normalized = X / X.norm(dim=-1, keepdim=True)\n    return X_normalized",
        "orig_context": "```python\n## src/sqfa/constraints.py\nimport torch.nn as nn\n\nclass Sphere(nn.Module):\n    \"\"\"Constrains the input tensor to lie on the sphere.\"\"\"\n\n    def forward(self, X):\n        \"\"\"\n        Normalize the input tensor so that it lies on the sphere.\n\n        The norm pooled across channels is computed and used to normalize the tensor.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor in Euclidean space with shape (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n        X_normalized = X / X.norm(dim=-1, keepdim=True)\n        return X_normalized\n\n    def right_inverse(self, S):\n        \"\"\"\n        Identity function to assign to parametrization.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor. Should be different from zero.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor `S`.\n        \"\"\"\n        return S\n\n```\n\n\n",
        "eval_script": "import torch\nimport torch.nn as nn\n\nclass Sphere(nn.Module):\n    \"\"\"Constrains the input tensor to lie on the sphere.\"\"\"\n\n    def forward(self, X):\n        \"\"\"\n        Normalize the input tensor so that it lies on the sphere.\n\n        The norm pooled across channels is computed and used to normalize the tensor.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor in Euclidean space with shape (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n        X_normalized = X / X.norm(dim=-1, keepdim=True)\n        return X_normalized\n\n\n    def right_inverse(self, S):\n        \"\"\"\n        Identity function to assign to parametrization.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor. Should be different from zero.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor `S`.\n        \"\"\"\n        return S\n\ndef test_forward():\n    sphere = Sphere()\n    \n    # Test case 1: Simple 2D tensor\n    X1 = torch.tensor([[3.0, 4.0], [1.0, 2.0]])\n    assert torch.allclose(sphere.forward(X1), sphere.forward_new_implementation(X1)), \"Test case 1 failed\"\n\n    # Test case 2: Larger 2D tensor\n    X2 = torch.tensor([[1.0, 2.0, 2.0], [3.0, 4.0, 5.0]])\n    assert torch.allclose(sphere.forward(X2), sphere.forward_new_implementation(X2)), \"Test case 2 failed\"\n\n    # Test case 3: Tensor with negative values\n    X3 = torch.tensor([[-1.0, -2.0], [-3.0, -4.0]])\n    assert torch.allclose(sphere.forward(X3), sphere.forward_new_implementation(X3)), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_forward()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `forward` method within the `Sphere` class. This method is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions normalize the input tensor `X` by dividing it by its norm along the last dimension. The additional code in the revised version, such as the `right_inverse` method and the `test_forward` function, does not affect the functionality of the `forward` method. The `test_forward` function is meant to test the `forward` method, but it incorrectly references a non-existent method `forward_new_implementation`, which is irrelevant to the comparison of the `forward` method itself. Therefore, the functionality of the `forward` method remains unchanged between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `forward` function returns a normalized tensor, satisfying the condition of having return values.\n- [CONDITION 2] The test cases use `torch.allclose` to compare the return values of `forward` and `forward_new_implementation`, which checks the return values, not printed or logged contents.\n- [CONDITION 3] The test cases compare the outputs of `forward` and `forward_new_implementation` using `torch.allclose`. This ensures that `forward_new_implementation` must have the same functionality as `forward` to pass all tests.\n- [CONDITION 4] The test cases use `torch.allclose` to compare the outputs, which is reasonable for checking the equality of floating-point tensors.\n- [CONDITION 5] The test cases include a variety of inputs: a simple 2D tensor, a larger 2D tensor, and a tensor with negative values, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "Sphere.right_inverse",
        "idx": "272",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/constraints.py",
        "orig_func": "def right_inverse(self, S):\n    \"\"\"\n        Identity function to assign to parametrization.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor. Should be different from zero.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor `S`.\n        \"\"\"\n    return S",
        "orig_context": "```python\n## src/sqfa/constraints.py\nimport torch.nn as nn\n\nclass Sphere(nn.Module):\n    \"\"\"Constrains the input tensor to lie on the sphere.\"\"\"\n\n    def forward(self, X):\n        \"\"\"\n        Normalize the input tensor so that it lies on the sphere.\n\n        The norm pooled across channels is computed and used to normalize the tensor.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor in Euclidean space with shape (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n        X_normalized = X / X.norm(dim=-1, keepdim=True)\n        return X_normalized\n\n    def right_inverse(self, S):\n        \"\"\"\n        Identity function to assign to parametrization.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor. Should be different from zero.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor `S`.\n        \"\"\"\n        return S\n\n```\n\n\n",
        "eval_script": "## src/sqfa/constraints.py\nimport torch.nn as nn\nimport torch\n\nclass Sphere(nn.Module):\n    \"\"\"Constrains the input tensor to lie on the sphere.\"\"\"\n\n    def forward(self, X):\n        \"\"\"\n        Normalize the input tensor so that it lies on the sphere.\n\n        The norm pooled across channels is computed and used to normalize the tensor.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor in Euclidean space with shape (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n        X_normalized = X / X.norm(dim=-1, keepdim=True)\n        return X_normalized\n\n    def right_inverse(self, S):\n        \"\"\"\n        Identity function to assign to parametrization.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor. Should be different from zero.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor `S`.\n        \"\"\"\n        return S\n\n\ndef test_right_inverse():\n    sphere = Sphere()\n\n    # Test with a simple tensor\n    tensor1 = torch.tensor([1.0, 2.0, 3.0])\n    assert torch.equal(sphere.right_inverse(tensor1), sphere.right_inverse_new_implementation(tensor1)), \"Test case 1 failed\"\n\n    # Test with a zero tensor\n    tensor2 = torch.tensor([0.0, 0.0, 0.0])\n    assert torch.equal(sphere.right_inverse(tensor2), sphere.right_inverse_new_implementation(tensor2)), \"Test case 2 failed\"\n\n    # Test with a random tensor\n    tensor3 = torch.randn(5, 5)\n    assert torch.equal(sphere.right_inverse(tensor3), sphere.right_inverse_new_implementation(tensor3)), \"Test case 3 failed\"\n\n    # Test with a high-dimensional tensor\n    tensor4 = torch.randn(3, 3, 3, 3)\n    assert torch.equal(sphere.right_inverse(tensor4), sphere.right_inverse_new_implementation(tensor4)), \"Test case 4 failed\"\n\n    # Test with a tensor containing negative values\n    tensor5 = torch.tensor([-1.0, -2.0, -3.0])\n    assert torch.equal(sphere.right_inverse(tensor5), sphere.right_inverse_new_implementation(tensor5)), \"Test case 5 failed\"\n\n    # Test with a tensor containing large values\n    tensor6 = torch.tensor([1e10, 2e10, 3e10])\n    assert torch.equal(sphere.right_inverse(tensor6), sphere.right_inverse_new_implementation(tensor6)), \"Test case 6 failed\"\n\n    # Test with a non-contiguous tensor\n    tensor7 = torch.randn(10, 10)[::2]\n    assert torch.equal(sphere.right_inverse(tensor7), sphere.right_inverse_new_implementation(tensor7)), \"Test case 7 failed\"\n\n    # Test with a tensor of different data type (float64)\n    tensor8 = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float64)\n    assert torch.equal(sphere.right_inverse(tensor8), sphere.right_inverse_new_implementation(tensor8)), \"Test case 8 failed\"\n\n    # Test with a single element tensor\n    tensor9 = torch.tensor([42.0])\n    assert torch.equal(sphere.right_inverse(tensor9), sphere.right_inverse_new_implementation(tensor9)), \"Test case 9 failed\"\n\n    # Test with an empty tensor\n    tensor10 = torch.tensor([])\n    assert torch.equal(sphere.right_inverse(tensor10), sphere.right_inverse_new_implementation(tensor10)), \"Test case 10 failed\"\n\nif __name__ == \"__main__\":\n    test_right_inverse()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `right_inverse` is a simple identity function that returns the input tensor `S` as it is. The revised function in the `Sphere` class also implements the `right_inverse` method in the same way, returning the input tensor `S` without any modification. The test cases provided in the revised code are designed to compare the output of `right_inverse` with a non-existent method `right_inverse_new_implementation`, which is not defined in the code. However, this does not affect the fact that the `right_inverse` method in the revised code is functionally identical to the original function, as both simply return the input tensor.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `right_inverse` function returns the input tensor `S`, satisfying the condition of having return values.\n- CONDITION 2: The test cases use `torch.equal` to compare the return values of `right_inverse` and `right_inverse_new_implementation`, which checks the return values, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `right_inverse` and `right_inverse_new_implementation` for various inputs. If `right_inverse_new_implementation` behaves differently, it will fail the tests, ensuring it must have the same functionality as `right_inverse`.\n- CONDITION 4: The test cases use `assert` statements to compare the outputs of the two implementations, which is reasonable since `right_inverse` returns a value.\n- CONDITION 5: The test cases cover a variety of scenarios, including simple tensors, zero tensors, random tensors, high-dimensional tensors, tensors with negative and large values, non-contiguous tensors, different data types, single-element tensors, and empty tensors. This variety ensures the tests are non-trivial.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "Identity.right_inverse",
        "idx": "274",
        "repo_name": "dherrera1911___sqfa",
        "func_path": "src/sqfa/constraints.py",
        "orig_func": "def right_inverse(self, S):\n    \"\"\"\n        Identity function.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor.\n        \"\"\"\n    return S",
        "orig_context": "```python\n## src/sqfa/constraints.py\nimport torch.nn as nn\n\nclass Identity(nn.Module):\n    \"\"\"Leaves the input tensor unconstrained. Used for consistency.\"\"\"\n\n    def forward(self, X):\n        \"\"\"\n        Return the input tensor as is.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n        return X\n\n    def right_inverse(self, S):\n        \"\"\"\n        Identity function.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor.\n        \"\"\"\n        return S\n\n```\n\n\n",
        "eval_script": "## src/sqfa/constraints.py\nimport torch.nn as nn\nimport torch\n\nclass Identity(nn.Module):\n    \"\"\"Leaves the input tensor unconstrained. Used for consistency.\"\"\"\n\n    def forward(self, X):\n        \"\"\"\n        Return the input tensor as is.\n\n        Parameters\n        ----------\n        X : torch.Tensor\n            Input tensor (n_filters, n_dim).\n\n        Returns\n        -------\n        torch.Tensor\n            Normalized tensor lying on the sphere with shape\n            (n_filters, n_dim).\n        \"\"\"\n        return X\n\n    def right_inverse(self, S):\n        \"\"\"\n        Identity function.\n\n        Parameters\n        ----------\n        S : torch.Tensor\n            Input tensor.\n\n        Returns\n        -------\n        torch.Tensor\n            Returns the input tensor.\n        \"\"\"\n        return S\n\n\ndef test_right_inverse():\n    identity = Identity()\n    \n    # Test case 1: Simple tensor\n    tensor1 = torch.tensor([1, 2, 3])\n    assert torch.equal(identity.right_inverse(tensor1), identity.right_inverse_new_implementation(tensor1)), \"Test case 1 failed\"\n    \n    # Test case 2: 2D tensor\n    tensor2 = torch.tensor([[1, 2], [3, 4]])\n    assert torch.equal(identity.right_inverse(tensor2), identity.right_inverse_new_implementation(tensor2)), \"Test case 2 failed\"\n    \n    # Test case 3: Tensor with negative values\n    tensor3 = torch.tensor([-1, -2, -3])\n    assert torch.equal(identity.right_inverse(tensor3), identity.right_inverse_new_implementation(tensor3)), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_right_inverse()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `right_inverse` is a simple identity function that takes a tensor `S` as input and returns it unchanged. The revised function is part of a class `Identity` which inherits from `torch.nn.Module`. The `right_inverse` method in this class is also an identity function, taking a tensor `S` as input and returning it unchanged. The functionality of the `right_inverse` method in both the original and revised versions is identical, as they both return the input tensor without modification. The additional context of the revised function being part of a class does not alter the functionality of the `right_inverse` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `right_inverse` function returns the input tensor `S`, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use `torch.equal` to compare the return values of `right_inverse` and `right_inverse_new_implementation`, which checks the return values, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `right_inverse` and `right_inverse_new_implementation` for equality using `torch.equal`, which ensures that `right_inverse_new_implementation` must have the exact same functionality as `right_inverse` to pass all tests.\n- CONDITION 4: The test cases use `torch.equal` to assert the equality of the outputs, which is reasonable given that `right_inverse` returns a tensor.\n- CONDITION 5: The test cases cover different scenarios: a simple tensor, a 2D tensor, and a tensor with negative values, which are non-trivial and provide a reasonable coverage of possible input variations.",
            "answer": "yes"
        },
        "commit_id": "49f69f37d79f5584c829b584c6ffe7d5d4b07a8c"
    },
    {
        "func_name": "extract_all_zip_codes",
        "idx": "301",
        "repo_name": "rk4bir___USCodeKit",
        "func_path": "uscodekit/zip_code.py",
        "orig_func": "def extract_all_zip_codes(text: str) -> List[str]:\n    \"\"\"\n    Extracts all US ZIP codes from the given text.\n\n    This function uses a regular expression to find all US ZIP codes in the\n    provided text. It supports both the 5-digit format and the ZIP+4 format.\n\n    Args:\n        text (str): The text from which to extract the ZIP codes.\n\n    Returns:\n        List[str]: A list of extracted ZIP codes (empty if none found).\n    \"\"\"\n    zip_code_pattern = '\\\\b\\\\d{5}(?:-\\\\d{4})?\\\\b'\n    return re.findall(zip_code_pattern, text)",
        "orig_context": "```python\n## uscodekit/zip_code.py\nimport re\n\nfrom typing import Optional, List, Dict, Any\n\ndef extract_all_zip_codes(text: str) -> List[str]:\n    \"\"\"\n    Extracts all US ZIP codes from the given text.\n\n    This function uses a regular expression to find all US ZIP codes in the\n    provided text. It supports both the 5-digit format and the ZIP+4 format.\n\n    Args:\n        text (str): The text from which to extract the ZIP codes.\n\n    Returns:\n        List[str]: A list of extracted ZIP codes (empty if none found).\n    \"\"\"\n    # Regular expression for US ZIP codes: 5-digit or ZIP+4 format\n    zip_code_pattern = r\"\\b\\d{5}(?:-\\d{4})?\\b\"\n\n    return re.findall(zip_code_pattern, text)\n\n```\n\n\n",
        "eval_script": "## uscodekit/zip_code.py\nimport re\n\nfrom typing import Optional, List, Dict, Any\n\ndef extract_all_zip_codes(text: str) -> List[str]:\n    \"\"\"\n    Extracts all US ZIP codes from the given text.\n\n    This function uses a regular expression to find all US ZIP codes in the\n    provided text. It supports both the 5-digit format and the ZIP+4 format.\n\n    Args:\n        text (str): The text from which to extract the ZIP codes.\n\n    Returns:\n        List[str]: A list of extracted ZIP codes (empty if none found).\n    \"\"\"\n    # Regular expression for US ZIP codes: 5-digit or ZIP+4 format\n    zip_code_pattern = r\"\\b\\d{5}(?:-\\d{4})?\\b\"\n\n    return re.findall(zip_code_pattern, text)\n\n\ndef test_extract_all_zip_codes():\n    # Test case 1: No ZIP codes\n    text1 = \"This text contains no zip codes.\"\n    assert extract_all_zip_codes(text1) == extract_all_zip_codes_new_implementation(text1)\n\n    # Test case 2: Single 5-digit ZIP code\n    text2 = \"The ZIP code is 12345.\"\n    assert extract_all_zip_codes(text2) == extract_all_zip_codes_new_implementation(text2)\n\n    # Test case 3: Multiple ZIP codes including ZIP+4\n    text3 = \"Here are some ZIP codes: 12345, 67890-1234, and 54321.\"\n    assert extract_all_zip_codes(text3) == extract_all_zip_codes_new_implementation(text3)\n\n    # Test case 4: Empty string\n    text4 = \"\"\n    assert extract_all_zip_codes(text4) == extract_all_zip_codes_new_implementation(text4)\n\n    # Test case 5: ZIP code at the start\n    text5 = \"12345 is the starting ZIP code.\"\n    assert extract_all_zip_codes(text5) == extract_all_zip_codes_new_implementation(text5)\n\n    # Test case 6: ZIP code at the end\n    text6 = \"The ending ZIP code is 54321.\"\n    assert extract_all_zip_codes(text6) == extract_all_zip_codes_new_implementation(text6)\n\n    # Test case 7: ZIP code with leading and trailing spaces\n    text7 = \" ZIP code 12345 is here. \"\n    assert extract_all_zip_codes(text7) == extract_all_zip_codes_new_implementation(text7)\n\n    # Test case 8: Non-numeric characters adjacent to ZIP codes\n    text8 = \"ZIP codes like 12345abc should not include letters.\"\n    assert extract_all_zip_codes(text8) == extract_all_zip_codes_new_implementation(text8)\n\n    # Test case 9: ZIP code with hyphen but no extension\n    text9 = \"ZIP code 67890- is incomplete.\"\n    assert extract_all_zip_codes(text9) == extract_all_zip_codes_new_implementation(text9)\n\n    # Test case 10: Incorrect format (less than 5 digits)\n    text10 = \"ZIP code 1234 is not valid.\"\n    assert extract_all_zip_codes(text10) == extract_all_zip_codes_new_implementation(text10)\n\n    # Test case 11: ZIP code with special characters\n    text11 = \"Special characters around ZIP: *12345*.\"\n    assert extract_all_zip_codes(text11) == extract_all_zip_codes_new_implementation(text11)\n\n    # Test case 12: Multiple ZIP codes with different formats\n    text12 = \"ZIP codes: 12345, 67890-1234, 54321, and 98765-4321.\"\n    assert extract_all_zip_codes(text12) == extract_all_zip_codes_new_implementation(text12)\n\nif __name__ == \"__main__\":\n    test_extract_all_zip_codes()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original and revised functions both aim to extract US ZIP codes from a given text using a regular expression. The regular expression used in both functions is identical, `\\b\\d{5}(?:-\\d{4})?\\b`, which matches both 5-digit ZIP codes and ZIP+4 codes. The revised function includes additional imports and a test suite, but these do not affect the functionality of the `extract_all_zip_codes` function itself. The core logic of extracting ZIP codes remains unchanged between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `extract_all_zip_codes` function returns a list of strings, which satisfies this condition as it has return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `extract_all_zip_codes` and `extract_all_zip_codes_new_implementation`, which means they are checking return values, not printed or logged contents. This satisfies the condition.\n- CONDITION 3: The test cases compare the outputs of `extract_all_zip_codes` and `extract_all_zip_codes_new_implementation` for various inputs. This ensures that `extract_all_zip_codes_new_implementation` must have the same functionality as `extract_all_zip_codes` to pass all tests, satisfying this condition.\n- CONDITION 4: The assertions in the test cases are reasonable because they compare the outputs of the two functions directly. There is no use of inappropriate assertions like comparing function calls directly without considering their return values.\n- CONDITION 5: The test cases cover a variety of scenarios, including no ZIP codes, single and multiple ZIP codes, ZIP+4 format, incorrect formats, and special characters. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "7774501863d37a9647adfee66c90702b0fe2ef57"
    },
    {
        "func_name": "NAICS2022Service.industry_hierarchy",
        "idx": "305",
        "repo_name": "rk4bir___USCodeKit",
        "func_path": "uscodekit/services/naics.py",
        "orig_func": "def industry_hierarchy(self, code: str) -> Optional[Dict]:\n    \"\"\"\n        Generates a hierarchical representation of NAICS code data based on the given code,\n        handling different lengths (2, 3, 4, 5, or 6 digits).\n\n        Parameters:\n            code (str): The NAICS code for which to generate the hierarchy.\n\n        Returns:\n            dict: A hierarchical dictionary with details up to the specified code length.\n        \"\"\"\n    rec = self.get(code)\n    if rec is None:\n        return None\n    hierarchy = {'sector': None, 'subsector': None, 'industry_group': None, 'naics_industry': None, 'national_industry': None}\n    if len(code) >= 2:\n        hierarchy['sector'] = self.get(code[:2])\n    if len(code) >= 3:\n        hierarchy['subsector'] = self.get(code[:3])\n    if len(code) >= 4:\n        hierarchy['industry_group'] = self.get(code[:4])\n    if len(code) >= 5:\n        hierarchy['naics_industry'] = self.get(code[:5])\n    if len(code) == 6:\n        hierarchy['national_industry'] = self.get(code)\n    record = {'code': code, 'title': rec.get('title'), 'sector': hierarchy['sector'], 'subsector': hierarchy['subsector'], 'industry_group': hierarchy['industry_group'], 'naics_industry': hierarchy['naics_industry'], 'national_industry': hierarchy['national_industry']}\n    return {k: v for k, v in record.items() if v is not None}",
        "orig_context": "```python\n## uscodekit/services/naics.py\nimport os\n\nfrom functools import lru_cache\n\nfrom typing import List, Optional, Dict\n\nfrom uscodekit.configs import NAICS2022Config, Config\n\nfrom uscodekit.shared.jsonfile import decrypt\n\nclass NAICS2022Service:\n\n    def __init__(self):\n        if os.path.isfile(Config.encryption_key_fp) and os.path.isfile(\n            NAICS2022Config.encrypted_database_fp\n        ):\n            self.data = self.search_database\n        else:\n            print(Config.file_missing_message)\n            self.data = []\n\n    @property\n    @lru_cache(maxsize=1)\n    def search_database(self) -> List[Dict]:\n        \"\"\"\n        Property that returns a cached list of dictionaries representing the search database.\n\n        This method reads data from the file path specified in the NAICS2022Config.search_data_fp\n        and caches the result to improve performance on subsequent accesses.\n\n        Returns:\n            list[dict]: A list of dictionaries containing the search database data.\n        \"\"\"\n\n        if os.path.isfile(Config.encryption_key_fp) and os.path.isfile(\n            NAICS2022Config.encrypted_database_fp\n        ):\n            return decrypt(NAICS2022Config.encrypted_database_fp)\n        else:\n            print(Config.file_missing_message)\n            return []\n\n    def get(self, code: str) -> Optional[Dict]:\n        \"\"\"\n        Retrieve a dictionary from the search database that matches the given code.\n\n        Args:\n            code (str): The code to search for in the database.\n\n        Returns:\n            dict | None: The dictionary that matches the given code if found, otherwise None.\n        \"\"\"\n        res = list(filter(lambda x: x[\"code\"] == code, self.data))\n        return res[0] if res else None\n\n    def industry_name(self, code: str) -> Optional[str]:\n        \"\"\"\n        Retrieve the industry name from the search database that matches the given code.\n\n        Args:\n            code (str): The code to search for in the database.\n\n        Returns:\n            dict | None: The dictionary that matches the given code if found, otherwise None.\n        \"\"\"\n        try:\n            return self.get(code)[\"title\"]\n        except Exception as e:\n            return None\n\n    def search(self, query: str, top_n: int = 10) -> List[Dict]:\n        \"\"\"\n        Searches the database for matches on both 'code' and 'title' fields,\n        prioritizing exact matches, prefix matches, and then partial matches.\n\n        Parameters:\n            query (str): The search query to match against both 'code' and 'title'.\n\n        Returns:\n            list: A sorted list of dictionaries matching the query, ranked by relevance.\n\n        Example:\n        >>> search('Public Finance') =>\n        [\n            {'code': '92113', 'title': 'Public Finance Activities'},\n            {'code': '921130', 'title': 'Public Finance Activities'}\n        ]\n        \"\"\"\n        # Normalize the query\n        query = query.strip().lower()\n\n        # Define scoring criteria\n        results = []\n\n        for entry in self.data:\n            code = entry[\"code\"]\n            title = entry[\"title\"].lower()\n\n            # Initialize relevance score\n            relevance_score = 0\n\n            # Check for exact matches in code or title (highest relevance)\n            if query == code or query == title:\n                relevance_score = 3\n            # Check for prefix match in code (high relevance)\n            elif code.startswith(query):\n                relevance_score = 2\n            # Check for partial match in title (medium relevance)\n            elif query in title:\n                relevance_score = 1\n\n            # If there's a match, add to results with relevance score\n            if relevance_score > 0:\n                results.append((relevance_score, entry))\n\n        # Sort results by relevance score in descending order\n        results.sort(key=lambda x: x[0], reverse=True)\n\n        # Return only the matched entries, without scores\n        return [entry for _, entry in results[:top_n]]\n\n    def industry_hierarchy(self, code: str) -> Optional[Dict]:\n        \"\"\"\n        Generates a hierarchical representation of NAICS code data based on the given code,\n        handling different lengths (2, 3, 4, 5, or 6 digits).\n\n        Parameters:\n            code (str): The NAICS code for which to generate the hierarchy.\n\n        Returns:\n            dict: A hierarchical dictionary with details up to the specified code length.\n        \"\"\"\n        rec = self.get(code)\n        if rec is None:\n            return None\n\n        # Initialize the hierarchy dictionary\n        hierarchy = {\n            \"sector\": None,\n            \"subsector\": None,\n            \"industry_group\": None,\n            \"naics_industry\": None,\n            \"national_industry\": None,\n        }\n\n        # Determine hierarchy based on the length of the code\n        if len(code) >= 2:\n            hierarchy[\"sector\"] = self.get(code[:2])\n        if len(code) >= 3:\n            hierarchy[\"subsector\"] = self.get(code[:3])\n        if len(code) >= 4:\n            hierarchy[\"industry_group\"] = self.get(code[:4])\n        if len(code) >= 5:\n            hierarchy[\"naics_industry\"] = self.get(code[:5])\n        if len(code) == 6:\n            hierarchy[\"national_industry\"] = self.get(code)\n\n        # Build the final record with available levels\n        record = {\n            \"code\": code,\n            \"title\": rec.get(\"title\"),\n            \"sector\": hierarchy[\"sector\"],\n            \"subsector\": hierarchy[\"subsector\"],\n            \"industry_group\": hierarchy[\"industry_group\"],\n            \"naics_industry\": hierarchy[\"naics_industry\"],\n            \"national_industry\": hierarchy[\"national_industry\"],\n        }\n\n        # Remove None entries for levels not included based on code length\n        return {k: v for k, v in record.items() if v is not None}\n\n```\n\n\n",
        "eval_script": "## uscodekit/services/naics.py\nimport os\nfrom functools import lru_cache\nfrom typing import List, Optional, Dict\n\n# Mock configurations\nclass Config:\n    encryption_key_fp = \"/home/user/tmp/encryption_key\"\n    file_missing_message = \"Required file is missing.\"\n\nclass NAICS2022Config:\n    encrypted_database_fp = \"/home/user/tmp/encrypted_database\"\n\n# Mock decrypt function\ndef decrypt(file_path: str) -> List[Dict]:\n    # Return a sample dataset\n    return [\n        {\"code\": \"11\", \"title\": \"Agriculture, Forestry, Fishing and Hunting\"},\n        {\"code\": \"111\", \"title\": \"Crop Production\"},\n        {\"code\": \"1111\", \"title\": \"Oilseed and Grain Farming\"},\n        {\"code\": \"11111\", \"title\": \"Soybean Farming\"},\n        {\"code\": \"111110\", \"title\": \"Soybean Farming\"},\n    ]\n\n# Ensure dummy files exist\nos.makedirs(\"/home/user/tmp\", exist_ok=True)\nwith open(Config.encryption_key_fp, 'w') as f:\n    f.write(\"dummy_key\")\nwith open(NAICS2022Config.encrypted_database_fp, 'w') as f:\n    f.write(\"dummy_data\")\n\nclass NAICS2022Service:\n\n    def __init__(self):\n        if os.path.isfile(Config.encryption_key_fp) and os.path.isfile(\n            NAICS2022Config.encrypted_database_fp\n        ):\n            self.data = self.search_database\n        else:\n            print(Config.file_missing_message)\n            self.data = []\n\n    @property\n    @lru_cache(maxsize=1)\n    def search_database(self) -> List[Dict]:\n        if os.path.isfile(Config.encryption_key_fp) and os.path.isfile(\n            NAICS2022Config.encrypted_database_fp\n        ):\n            return decrypt(NAICS2022Config.encrypted_database_fp)\n        else:\n            print(Config.file_missing_message)\n            return []\n\n    def get(self, code: str) -> Optional[Dict]:\n        res = list(filter(lambda x: x[\"code\"] == code, self.data))\n        return res[0] if res else None\n\n    def industry_name(self, code: str) -> Optional[str]:\n        try:\n            return self.get(code)[\"title\"]\n        except Exception as e:\n            return None\n\n    def search(self, query: str, top_n: int = 10) -> List[Dict]:\n        query = query.strip().lower()\n        results = []\n\n        for entry in self.data:\n            code = entry[\"code\"]\n            title = entry[\"title\"].lower()\n            relevance_score = 0\n\n            if query == code or query == title:\n                relevance_score = 3\n            elif code.startswith(query):\n                relevance_score = 2\n            elif query in title:\n                relevance_score = 1\n\n            if relevance_score > 0:\n                results.append((relevance_score, entry))\n\n        results.sort(key=lambda x: x[0], reverse=True)\n        return [entry for _, entry in results[:top_n]]\n\n    def industry_hierarchy(self, code: str) -> Optional[Dict]:\n        rec = self.get(code)\n        if rec is None:\n            return None\n\n        hierarchy = {\n            \"sector\": None,\n            \"subsector\": None,\n            \"industry_group\": None,\n            \"naics_industry\": None,\n            \"national_industry\": None,\n        }\n\n        if len(code) >= 2:\n            hierarchy[\"sector\"] = self.get(code[:2])\n        if len(code) >= 3:\n            hierarchy[\"subsector\"] = self.get(code[:3])\n        if len(code) >= 4:\n            hierarchy[\"industry_group\"] = self.get(code[:4])\n        if len(code) >= 5:\n            hierarchy[\"naics_industry\"] = self.get(code[:5])\n        if len(code) == 6:\n            hierarchy[\"national_industry\"] = self.get(code)\n\n        record = {\n            \"code\": code,\n            \"title\": rec.get(\"title\"),\n            \"sector\": hierarchy[\"sector\"],\n            \"subsector\": hierarchy[\"subsector\"],\n            \"industry_group\": hierarchy[\"industry_group\"],\n            \"naics_industry\": hierarchy[\"naics_industry\"],\n            \"national_industry\": hierarchy[\"national_industry\"],\n        }\n\n        return {k: v for k, v in record.items() if v is not None}\n\n\ndef test_industry_hierarchy():\n    service = NAICS2022Service()\n\n    # Test case 1: Full hierarchy\n    code = \"111110\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 2: Partial hierarchy\n    code = \"111\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 3: No hierarchy\n    code = \"99\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 4: Single character code (edge case)\n    code = \"1\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 5: Empty code (edge case)\n    code = \"\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 6: Non-numeric code (edge case)\n    code = \"abc\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 7: Valid code with no corresponding data\n    code = \"222222\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\n    # Test case 8: Code with exact length of 6 but not in data\n    code = \"123456\"\n    expected = service.industry_hierarchy(code)\n    result = service.industry_hierarchy_new_implementation(code)\n    assert result == expected, f\"Test failed for code {code}. Expected {expected}, got {result}\"\n\nif __name__ == \"__main__\":\n    test_industry_hierarchy()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing both the original and revised functions, they perform the same operations. Both functions retrieve a record using the provided code, then build a hierarchy dictionary based on the length of the code. They populate the hierarchy with sector, subsector, industry group, NAICS industry, and national industry information, if available. Finally, they return a dictionary containing the code, title, and any non-null hierarchy levels. The revised function is embedded within a larger class structure, but the core logic and functionality of the `industry_hierarchy` method remain unchanged from the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `industry_hierarchy` function returns a dictionary or `None`, which satisfies the condition of having return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `industry_hierarchy` and `industry_hierarchy_new_implementation`. There are no checks for printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `industry_hierarchy` and `industry_hierarchy_new_implementation` directly for various inputs. This ensures that `industry_hierarchy_new_implementation` must have the exact same functionality to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `industry_hierarchy` returns a dictionary or `None`. The assertions are reasonable and correctly structured.\n\n5. **CONDITION 5**: The test cases cover a range of scenarios, including full hierarchy, partial hierarchy, no hierarchy, edge cases with single character, empty string, non-numeric codes, and valid codes with no data. This variety makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "7774501863d37a9647adfee66c90702b0fe2ef57"
    },
    {
        "func_name": "ItemManager._handle_special_item_effects",
        "idx": "311",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "item_manager.py",
        "orig_func": "def _handle_special_item_effects(self, item: str, location: str, game_state: Dict) -> None:\n    \"\"\"Handle special effects when using certain items in specific locations.\"\"\"\n    try:\n        special_effects = {('badge', 'smith_tower'): lambda: game_state.update({'has_badge_shown': True}), ('binoculars', 'observation_deck'): lambda: game_state.update({'observed_suspicious_activity': True}), ('old_key', 'suspicious_warehouse'): lambda: game_state.update({'warehouse_unlocked': True}), ('radio_manual', 'warehouse_office'): lambda: game_state.update({'understood_radio': True})}\n        effect = special_effects.get((item, location))\n        if effect:\n            effect()\n    except Exception as e:\n        logging.error(f'Error handling special effect for {item} at {location}: {e}')",
        "orig_context": "```python\n## item_manager.py\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport logging\n\nimport config\n\nclass ItemManager:\n    def __init__(self):\n        self.inventory: List[str] = []\n        self.newspaper_pieces: int = config.INITIAL_GAME_STATE.get(\"newspaper_pieces\", 0)\n        self.discovered_combinations: Set[str] = set()\n        self.removed_items: Set[str] = set()\n           \n             \n    def take_item(self, item: str, location_items: List[str], game_state: Dict) -> bool:\n        \"\"\"Pick up an item from the current location.\"\"\"\n        try:\n            if item not in location_items:\n                print(f\"There is no {item} here.\")\n                return False\n        \n            self.inventory.append(item)\n            self.removed_items.add(item)  # Track that this item has been removed\n        \n            # Handle special items first\n            if item == \"badge\":\n                game_state[\"has_badge\"] = True\n                print(\"You clip the badge to your belt. Its familiar weight is reassuring.\")\n                logging.info(\"Badge taken and has_badge state set to True\")\n                return True\n            \n            # Then handle newspaper pieces\n            if item.startswith(\"newspaper_piece_\"):\n                self.newspaper_pieces += 1\n                print(f\"You've found piece {self.newspaper_pieces} of 8 of the newspaper story.\")\n                if self.newspaper_pieces == 8:\n                    game_state[\"found_all_newspaper_pieces\"] = True\n                    print(\"\\nYou've collected all newspaper pieces!\")\n                    self.show_newspaper_story()\n                return True\n            \n            # Generic message for all other items\n            print(f\"You take the {item}.\")\n            return True\n        \n        except Exception as e:\n            logging.error(f\"Error taking item {item}: {e}\")\n            print(\"There was a problem picking up the item.\")\n            return False\n       \n    def examine_item(self, item: str, location_items: List[str], game_state: Dict) -> None:\n        \"\"\"Examine an item in inventory or in the current location.\"\"\"\n        try:\n            if item in self.inventory:\n                if item in config.ITEM_DESCRIPTIONS:\n                    print(\"\\n\" + config.ITEM_DESCRIPTIONS[item][\"detailed\"])\n                    if item == \"wallet\" and not game_state.get(\"discovered_clue\", False):\n                        game_state[\"discovered_clue\"] = True\n                        print(\"\\nThe business card seems suspicious. This could be a valuable lead.\")\n                    elif item == \"coded_message\" and not game_state.get(\"examined_code\", False):\n                        game_state[\"examined_code\"] = True\n                        print(\"\\nThe code looks like it might be decipherable with the right tools...\")\n                else:\n                    print(f\"You examine the {item} closely but find nothing unusual.\")\n            elif item in location_items:\n                print(f\"You'll need to take the {item} first to examine it closely.\")\n            else:\n                print(f\"You don't see any {item} here.\")\n            \n        except Exception as e:\n            logging.error(f\"Error examining item {item}: {e}\")\n            print(\"There was a problem examining the item.\")\n\n    def use_item(self, item: str, current_location: str, game_state: Dict) -> None:\n        \"\"\"Use an item from the inventory.\"\"\"\n        try:\n            if item not in self.inventory:\n                print(\"You don't have that item.\")\n                return\n           \n            item_data = config.ITEM_DESCRIPTIONS.get(item, {})\n            use_effects = item_data.get(\"use_effects\", {})\n            valid_locations = item_data.get(\"use_locations\", [])\n       \n            if current_location in use_effects or (\"all\" in use_effects and current_location in valid_locations):\n                effect = use_effects.get(current_location, use_effects.get(\"all\"))\n                print(\"\\n\" + effect)\n           \n                if item_data.get(\"consumable\", False):\n                    self.inventory.remove(item)\n                    print(f\"You no longer have the {item}.\")\n           \n                self._handle_special_item_effects(item, current_location, game_state)\n            else:\n                print(f\"You can't use the {item} here effectively.\")\n           \n        except Exception as e:\n            logging.error(f\"Error using item {item}: {e}\")\n            print(\"There was a problem using the item.\")\n\n    def _handle_special_item_effects(self, item: str, location: str, game_state: Dict) -> None:\n        \"\"\"Handle special effects when using certain items in specific locations.\"\"\"\n        try:\n            special_effects = {\n                (\"badge\", \"smith_tower\"): lambda: game_state.update({\"has_badge_shown\": True}),\n                (\"binoculars\", \"observation_deck\"): lambda: game_state.update({\"observed_suspicious_activity\": True}),\n                (\"old_key\", \"suspicious_warehouse\"): lambda: game_state.update({\"warehouse_unlocked\": True}),\n                (\"radio_manual\", \"warehouse_office\"): lambda: game_state.update({\"understood_radio\": True})\n        }\n       \n            effect = special_effects.get((item, location))\n            if effect:\n                effect()\n           \n        except Exception as e:\n            logging.error(f\"Error handling special effect for {item} at {location}: {e}\")\n\n    def combine_items(self, item1: str, item2: str, game_state: Dict) -> bool:\n        \"\"\"Attempt to combine two items from the inventory.\"\"\"\n        try:\n            if item1 not in self.inventory or item2 not in self.inventory:\n                print(\"You need both items in your inventory to combine them.\")\n                return False\n           \n            combo = frozenset([item1, item2])\n            if combo in config.ITEM_COMBINATIONS and combo not in self.discovered_combinations:\n                result = config.ITEM_COMBINATIONS[combo]\n                print(\"\\n\" + result['description'])\n           \n                if result['removes_items']:\n                    for item in result['removes_items']:\n                        if item in self.inventory:\n                            self.inventory.remove(item)\n           \n                game_state[result['result']] = True\n                self.discovered_combinations.add(combo)\n                return True\n            elif combo in self.discovered_combinations:\n                print(\"You've already discovered what these items reveal together.\")\n                return False\n            else:\n                print(\"These items can't be combined in any meaningful way.\")\n                return False\n           \n        except Exception as e:\n            logging.error(f\"Error combining items {item1} and {item2}: {e}\")\n            print(\"There was a problem combining the items.\")\n            return False\n\n    def show_inventory(self) -> None:\n        \"\"\"Display the current inventory contents with basic descriptions.\"\"\"\n        try:\n            if not self.inventory:\n                print(\"Your inventory is empty.\")\n                return\n           \n            print(\"\\nInventory:\")\n            for item in self.inventory:\n                basic_desc = config.ITEM_DESCRIPTIONS.get(item, {}).get(\"basic\", \"No description available.\")\n                print(f\"- {item}: {basic_desc}\")\n            print(\"\\nTip: Use 'examine <item>' for a closer look.\")\n       \n        except Exception as e:\n            logging.error(f\"Error showing inventory: {e}\")\n            print(\"There was a problem displaying the inventory.\")\n\n    def show_newspaper_story(self) -> None:\n        \"\"\"Display the complete newspaper story once all pieces are collected.\"\"\"\n        story = \"\"\"\n        SEATTLE POST-INTELLIGENCER, 1947\n        \n        MYSTERIOUS DISAPPEARANCES PLAGUE WATERFRONT\n        \n        A series of unexplained cargo disappearances has left Seattle Port Authority \n        officials baffled. The incidents, beginning shortly after the war's end, \n        have primarily involved medical supplies and machinery parts.\n        \n        Local stevedores report unusual night-time activities, but harbor patrol \n        investigations have yielded no concrete evidence. The recent conversion \n        of wartime shipping operations to civilian use has created opportunities \n        for those seeking to exploit the confusion.\n        \n        Port security chief Thomas McKinnon stated, \"We're working closely with \n        the police department to resolve these incidents.\" Sources suggest possible \n        connections to similar cases in San Francisco and Vancouver.\n        \n        [Further investigation reveals possible connections to black market medical \n        supply operations taking advantage of post-war shortages and reconstruction \n        efforts across the Pacific coast.]\n        \"\"\"\n        print(\"\\nAs you piece together the newspaper clippings, a bigger picture emerges...\")\n        print(story)\n        print(\"\\nThis could be the breakthrough you needed in the case.\")\n\n    def get_inventory(self) -> List[str]:\n        \"\"\"Get the current inventory contents.\"\"\"\n        return self.inventory.copy()\n\n    def has_item(self, item: str) -> bool:\n        \"\"\"Check if an item is in the inventory.\"\"\"\n        return item in self.inventory\n    \n    def get_inventory_state(self) -> Dict[str, any]:\n        \"\"\"Get the complete inventory state.\"\"\"\n        return {\n            \"inventory\": self.inventory.copy(),\n            \"newspaper_pieces\": self.newspaper_pieces,\n            \"discovered_combinations\": list(self.discovered_combinations),\n            \"removed_items\": list(self.removed_items)\n        }\n    \n    def restore_inventory_state(self, state: Dict[str, any]) -> None:\n        \"\"\"Restore inventory from saved state.\"\"\"\n        self.inventory = state.get(\"inventory\", []).copy()\n        self.newspaper_pieces = state.get(\"newspaper_pieces\", 0)\n        self.discovered_combinations = set(state.get(\"discovered_combinations\", []))\n        self.removed_items = set(state.get(\"removed_items\", []))\n\n```\n\n\n",
        "eval_script": "# item_manager.py\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport logging\n\n# Mocking the config module with necessary attributes for testing\nclass config:\n    INITIAL_GAME_STATE = {\"newspaper_pieces\": 0}\n    ITEM_DESCRIPTIONS = {\n        \"badge\": {\"detailed\": \"A shiny badge.\", \"use_effects\": {}, \"use_locations\": []},\n        \"binoculars\": {\"detailed\": \"A pair of binoculars.\", \"use_effects\": {}, \"use_locations\": []},\n        \"old_key\": {\"detailed\": \"An old rusty key.\", \"use_effects\": {}, \"use_locations\": []},\n        \"radio_manual\": {\"detailed\": \"A manual for a radio.\", \"use_effects\": {}, \"use_locations\": []},\n    }\n    ITEM_COMBINATIONS = {}\n\nclass ItemManager:\n    def __init__(self):\n        self.inventory: List[str] = []\n        self.newspaper_pieces: int = config.INITIAL_GAME_STATE.get(\"newspaper_pieces\", 0)\n        self.discovered_combinations: Set[str] = set()\n        self.removed_items: Set[str] = set()\n           \n             \n    def take_item(self, item: str, location_items: List[str], game_state: Dict) -> bool:\n        \"\"\"Pick up an item from the current location.\"\"\"\n        try:\n            if item not in location_items:\n                print(f\"There is no {item} here.\")\n                return False\n        \n            self.inventory.append(item)\n            self.removed_items.add(item)  # Track that this item has been removed\n        \n            # Handle special items first\n            if item == \"badge\":\n                game_state[\"has_badge\"] = True\n                print(\"You clip the badge to your belt. Its familiar weight is reassuring.\")\n                logging.info(\"Badge taken and has_badge state set to True\")\n                return True\n            \n            # Then handle newspaper pieces\n            if item.startswith(\"newspaper_piece_\"):\n                self.newspaper_pieces += 1\n                print(f\"You've found piece {self.newspaper_pieces} of 8 of the newspaper story.\")\n                if self.newspaper_pieces == 8:\n                    game_state[\"found_all_newspaper_pieces\"] = True\n                    print(\"\\nYou've collected all newspaper pieces!\")\n                    self.show_newspaper_story()\n                return True\n            \n            # Generic message for all other items\n            print(f\"You take the {item}.\")\n            return True\n        \n        except Exception as e:\n            logging.error(f\"Error taking item {item}: {e}\")\n            print(\"There was a problem picking up the item.\")\n            return False\n       \n    def examine_item(self, item: str, location_items: List[str], game_state: Dict) -> None:\n        \"\"\"Examine an item in inventory or in the current location.\"\"\"\n        try:\n            if item in self.inventory:\n                if item in config.ITEM_DESCRIPTIONS:\n                    print(\"\\n\" + config.ITEM_DESCRIPTIONS[item][\"detailed\"])\n                    if item == \"wallet\" and not game_state.get(\"discovered_clue\", False):\n                        game_state[\"discovered_clue\"] = True\n                        print(\"\\nThe business card seems suspicious. This could be a valuable lead.\")\n                    elif item == \"coded_message\" and not game_state.get(\"examined_code\", False):\n                        game_state[\"examined_code\"] = True\n                        print(\"\\nThe code looks like it might be decipherable with the right tools...\")\n                else:\n                    print(f\"You examine the {item} closely but find nothing unusual.\")\n            elif item in location_items:\n                print(f\"You'll need to take the {item} first to examine it closely.\")\n            else:\n                print(f\"You don't see any {item} here.\")\n            \n        except Exception as e:\n            logging.error(f\"Error examining item {item}: {e}\")\n            print(\"There was a problem examining the item.\")\n\n    def use_item(self, item: str, current_location: str, game_state: Dict) -> None:\n        \"\"\"Use an item from the inventory.\"\"\"\n        try:\n            if item not in self.inventory:\n                print(\"You don't have that item.\")\n                return\n           \n            item_data = config.ITEM_DESCRIPTIONS.get(item, {})\n            use_effects = item_data.get(\"use_effects\", {})\n            valid_locations = item_data.get(\"use_locations\", [])\n       \n            if current_location in use_effects or (\"all\" in use_effects and current_location in valid_locations):\n                effect = use_effects.get(current_location, use_effects.get(\"all\"))\n                print(\"\\n\" + effect)\n           \n                if item_data.get(\"consumable\", False):\n                    self.inventory.remove(item)\n                    print(f\"You no longer have the {item}.\")\n           \n                self._handle_special_item_effects(item, current_location, game_state)\n            else:\n                print(f\"You can't use the {item} here effectively.\")\n           \n        except Exception as e:\n            logging.error(f\"Error using item {item}: {e}\")\n            print(\"There was a problem using the item.\")\n\n    def _handle_special_item_effects(self, item: str, location: str, game_state: Dict) -> None:\n        \"\"\"Handle special effects when using certain items in specific locations.\"\"\"\n        try:\n            special_effects = {\n                (\"badge\", \"smith_tower\"): lambda: game_state.update({\"has_badge_shown\": True}),\n                (\"binoculars\", \"observation_deck\"): lambda: game_state.update({\"observed_suspicious_activity\": True}),\n                (\"old_key\", \"suspicious_warehouse\"): lambda: game_state.update({\"warehouse_unlocked\": True}),\n                (\"radio_manual\", \"warehouse_office\"): lambda: game_state.update({\"understood_radio\": True})\n        }\n       \n            effect = special_effects.get((item, location))\n            if effect:\n                effect()\n           \n        except Exception as e:\n            logging.error(f\"Error handling special effect for {item} at {location}: {e}\")\n\n\n    def combine_items(self, item1: str, item2: str, game_state: Dict) -> bool:\n        \"\"\"Attempt to combine two items from the inventory.\"\"\"\n        try:\n            if item1 not in self.inventory or item2 not in self.inventory:\n                print(\"You need both items in your inventory to combine them.\")\n                return False\n           \n            combo = frozenset([item1, item2])\n            if combo in config.ITEM_COMBINATIONS and combo not in self.discovered_combinations:\n                result = config.ITEM_COMBINATIONS[combo]\n                print(\"\\n\" + result['description'])\n           \n                if result['removes_items']:\n                    for item in result['removes_items']:\n                        if item in self.inventory:\n                            self.inventory.remove(item)\n           \n                game_state[result['result']] = True\n                self.discovered_combinations.add(combo)\n                return True\n            elif combo in self.discovered_combinations:\n                print(\"You've already discovered what these items reveal together.\")\n                return False\n            else:\n                print(\"These items can't be combined in any meaningful way.\")\n                return False\n           \n        except Exception as e:\n            logging.error(f\"Error combining items {item1} and {item2}: {e}\")\n            print(\"There was a problem combining the items.\")\n            return False\n\n    def show_inventory(self) -> None:\n        \"\"\"Display the current inventory contents with basic descriptions.\"\"\"\n        try:\n            if not self.inventory:\n                print(\"Your inventory is empty.\")\n                return\n           \n            print(\"\\nInventory:\")\n            for item in self.inventory:\n                basic_desc = config.ITEM_DESCRIPTIONS.get(item, {}).get(\"basic\", \"No description available.\")\n                print(f\"- {item}: {basic_desc}\")\n            print(\"\\nTip: Use 'examine <item>' for a closer look.\")\n       \n        except Exception as e:\n            logging.error(f\"Error showing inventory: {e}\")\n            print(\"There was a problem displaying the inventory.\")\n\n    def show_newspaper_story(self) -> None:\n        \"\"\"Display the complete newspaper story once all pieces are collected.\"\"\"\n        story = \"\"\"\n        SEATTLE POST-INTELLIGENCER, 1947\n        \n        MYSTERIOUS DISAPPEARANCES PLAGUE WATERFRONT\n        \n        A series of unexplained cargo disappearances has left Seattle Port Authority \n        officials baffled. The incidents, beginning shortly after the war's end, \n        have primarily involved medical supplies and machinery parts.\n        \n        Local stevedores report unusual night-time activities, but harbor patrol \n        investigations have yielded no concrete evidence. The recent conversion \n        of wartime shipping operations to civilian use has created opportunities \n        for those seeking to exploit the confusion.\n        \n        Port security chief Thomas McKinnon stated, \"We're working closely with \n        the police department to resolve these incidents.\" Sources suggest possible \n        connections to similar cases in San Francisco and Vancouver.\n        \n        [Further investigation reveals possible connections to black market medical \n        supply operations taking advantage of post-war shortages and reconstruction \n        efforts across the Pacific coast.]\n        \"\"\"\n        print(\"\\nAs you piece together the newspaper clippings, a bigger picture emerges...\")\n        print(story)\n        print(\"\\nThis could be the breakthrough you needed in the case.\")\n\n    def get_inventory(self) -> List[str]:\n        \"\"\"Get the current inventory contents.\"\"\"\n        return self.inventory.copy()\n\n    def has_item(self, item: str) -> bool:\n        \"\"\"Check if an item is in the inventory.\"\"\"\n        return item in self.inventory\n    \n    def get_inventory_state(self) -> Dict[str, any]:\n        \"\"\"Get the complete inventory state.\"\"\"\n        return {\n            \"inventory\": self.inventory.copy(),\n            \"newspaper_pieces\": self.newspaper_pieces,\n            \"discovered_combinations\": list(self.discovered_combinations),\n            \"removed_items\": list(self.removed_items)\n        }\n    \n    def restore_inventory_state(self, state: Dict[str, any]) -> None:\n        \"\"\"Restore inventory from saved state.\"\"\"\n        self.inventory = state.get(\"inventory\", []).copy()\n        self.newspaper_pieces = state.get(\"newspaper_pieces\", 0)\n        self.discovered_combinations = set(state.get(\"discovered_combinations\", []))\n        self.removed_items = set(state.get(\"removed_items\", []))\n\n    # New public method to call the private _handle_special_item_effects\n    def handle_special_item_effects(self, item: str, location: str, game_state: Dict) -> None:\n        \"\"\"Public method to handle special item effects.\"\"\"\n        self._handle_special_item_effects(item, location, game_state)\n\n\ndef test__handle_special_item_effects():\n    manager = ItemManager()\n    \n    # Test case 1: Using badge at smith_tower\n    game_state_1 = {}\n    manager._handle_special_item_effects(\"badge\", \"smith_tower\", game_state_1)\n    expected_state_1 = game_state_1.copy()\n    game_state_1_new = {}\n    manager._handle_special_item_effects_new_implementation(\"badge\", \"smith_tower\", game_state_1_new)\n    assert game_state_1 == game_state_1_new == expected_state_1, \"Test case 1 failed\"\n    \n    # Test case 2: Using binoculars at observation_deck\n    game_state_2 = {}\n    manager._handle_special_item_effects(\"binoculars\", \"observation_deck\", game_state_2)\n    expected_state_2 = game_state_2.copy()\n    game_state_2_new = {}\n    manager._handle_special_item_effects_new_implementation(\"binoculars\", \"observation_deck\", game_state_2_new)\n    assert game_state_2 == game_state_2_new == expected_state_2, \"Test case 2 failed\"\n    \n    # Test case 3: Using old_key at suspicious_warehouse\n    game_state_3 = {}\n    manager._handle_special_item_effects(\"old_key\", \"suspicious_warehouse\", game_state_3)\n    expected_state_3 = game_state_3.copy()\n    game_state_3_new = {}\n    manager._handle_special_item_effects_new_implementation(\"old_key\", \"suspicious_warehouse\", game_state_3_new)\n    assert game_state_3 == game_state_3_new == expected_state_3, \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test__handle_special_item_effects()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_handle_special_item_effects` in the `ItemManager` class is identical to the ORIGINAL FUNCTION. Both functions define a dictionary `special_effects` mapping tuples of `(item, location)` to lambda functions that update the `game_state` dictionary with specific keys and values. They both retrieve the effect using `special_effects.get((item, location))` and execute it if it exists. The exception handling with logging is also the same in both versions. There are no differences in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `_handle_special_item_effects` modifies the `game_state` dictionary, which is an input argument. This satisfies the condition that the function modifies input arguments or global variables.\n\n- CONDITION 2: The test cases only check the state of the `game_state` dictionary after calling the function, which means they are checking variable states and not printed or logged contents.\n\n- CONDITION 3: The test cases compare the results of `_handle_special_item_effects` and `_handle_special_item_effects_new_implementation` by asserting that the `game_state` dictionaries are equal after calling both implementations. This ensures that the new implementation must have the exact same functionality to pass the tests.\n\n- CONDITION 4: The test cases use assertions to compare the states of `game_state` after calling both implementations, which is reasonable given that the function modifies the `game_state` dictionary. There are no assertions comparing return values, which is appropriate since the function does not return any value.\n\n- CONDITION 5: The test cases cover different scenarios by using different items and locations, which are non-trivial as they test different branches of the function's logic.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "InputValidator.validate_item_name",
        "idx": "312",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "input_validator.py",
        "orig_func": "@staticmethod\ndef validate_item_name(item: str, max_length: int=50) -> bool:\n    \"\"\"\n        Validate item name.\n        \n        Args:\n            item: Item name to validate\n            max_length: Maximum allowed length\n            \n        Returns:\n            bool: True if item name is valid\n        \"\"\"\n    if not item or not isinstance(item, str):\n        return False\n    if len(item) > max_length:\n        return False\n    return item.replace('_', '').isalnum()",
        "orig_context": "```python\n## input_validator.py\nfrom typing import Set, Dict, Optional, Any\n\nimport logging\n\nclass InputValidator:\n    \"\"\"Centralized input validation functionality.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n    \n    @staticmethod\n    def validate_puzzle_input(input_str: str, \n                            valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\",\n                            min_length: int = 1,\n                            max_length: int = 50) -> bool:\n        \"\"\"\n        Validate input string for puzzles.\n        \n        Args:\n            input_str: Input to validate\n            valid_chars: String of allowed characters\n            min_length: Minimum input length\n            max_length: Maximum input length\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not input_str:\n            return False\n            \n        if not min_length <= len(input_str) <= max_length:\n            return False\n            \n        return all(char in valid_chars for char in input_str.upper())\n    \n    @staticmethod\n    def validate_direction(direction: str, valid_directions: Set[str]) -> bool:\n        \"\"\"\n        Validate movement direction.\n        \n        Args:\n            direction: Direction to validate\n            valid_directions: Set of valid directions\n            \n        Returns:\n            bool: True if direction is valid\n        \"\"\"\n        return direction.lower().strip() in {d.lower() for d in valid_directions}\n    \n    @staticmethod\n    def validate_command(command: str, valid_commands: Set[str]) -> bool:\n        \"\"\"\n        Validate game command.\n        \n        Args:\n            command: Command to validate\n            valid_commands: Set of valid commands\n            \n        Returns:\n            bool: True if command is valid\n        \"\"\"\n        return command.lower().strip() in valid_commands\n    \n    @staticmethod\n    def validate_item_name(item: str, max_length: int = 50) -> bool:\n        \"\"\"\n        Validate item name.\n        \n        Args:\n            item: Item name to validate\n            max_length: Maximum allowed length\n            \n        Returns:\n            bool: True if item name is valid\n        \"\"\"\n        if not item or not isinstance(item, str):\n            return False\n            \n        if len(item) > max_length:\n            return False\n            \n        return item.replace('_', '').isalnum()\n\n```\n\n\n",
        "eval_script": "## input_validator.py\nfrom typing import Set\n\nimport logging\n\nclass InputValidator:\n    \"\"\"Centralized input validation functionality.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n    \n    @staticmethod\n    def validate_puzzle_input(input_str: str, \n                            valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\",\n                            min_length: int = 1,\n                            max_length: int = 50) -> bool:\n        \"\"\"\n        Validate input string for puzzles.\n        \n        Args:\n            input_str: Input to validate\n            valid_chars: String of allowed characters\n            min_length: Minimum input length\n            max_length: Maximum input length\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not input_str:\n            return False\n            \n        if not min_length <= len(input_str) <= max_length:\n            return False\n            \n        return all(char in valid_chars for char in input_str.upper())\n    \n    @staticmethod\n    def validate_direction(direction: str, valid_directions: Set[str]) -> bool:\n        \"\"\"\n        Validate movement direction.\n        \n        Args:\n            direction: Direction to validate\n            valid_directions: Set of valid directions\n            \n        Returns:\n            bool: True if direction is valid\n        \"\"\"\n        return direction.lower().strip() in {d.lower() for d in valid_directions}\n    \n    @staticmethod\n    def validate_command(command: str, valid_commands: Set[str]) -> bool:\n        \"\"\"\n        Validate game command.\n        \n        Args:\n            command: Command to validate\n            valid_commands: Set of valid commands\n            \n        Returns:\n            bool: True if command is valid\n        \"\"\"\n        return command.lower().strip() in valid_commands\n    \n    @staticmethod\n    def validate_item_name(item: str, max_length: int = 50) -> bool:\n        \"\"\"\n        Validate item name.\n        \n        Args:\n            item: Item name to validate\n            max_length: Maximum allowed length\n            \n        Returns:\n            bool: True if item name is valid\n        \"\"\"\n        if not item or not isinstance(item, str):\n            return False\n            \n        if len(item) > max_length:\n            return False\n            \n        return item.replace('_', '').isalnum()\n\n\ndef test_validate_item_name():\n    # Test empty string and non-string input\n    assert InputValidator.validate_item_name(\"\") == InputValidator.validate_item_name_new_implementation(\"\")\n    assert InputValidator.validate_item_name(None) == InputValidator.validate_item_name_new_implementation(None)\n\n    # Test string exceeding max length\n    long_string = \"a\" * 51\n    assert InputValidator.validate_item_name(long_string) == InputValidator.validate_item_name_new_implementation(long_string)\n\n    # Test valid item name\n    valid_item = \"item_name_123\"\n    assert InputValidator.validate_item_name(valid_item) == InputValidator.validate_item_name_new_implementation(valid_item)\n\n    # Test string exactly at max length\n    max_length_string = \"a\" * 50\n    assert InputValidator.validate_item_name(max_length_string) == InputValidator.validate_item_name_new_implementation(max_length_string)\n\n    # Test string with special characters\n    special_char_string = \"item@name!\"\n    assert InputValidator.validate_item_name(special_char_string) == InputValidator.validate_item_name_new_implementation(special_char_string)\n\n    # Test string with multiple underscores\n    multiple_underscores = \"item__name__123\"\n    assert InputValidator.validate_item_name(multiple_underscores) == InputValidator.validate_item_name_new_implementation(multiple_underscores)\n\n    # Test purely numeric string\n    numeric_string = \"123456\"\n    assert InputValidator.validate_item_name(numeric_string) == InputValidator.validate_item_name_new_implementation(numeric_string)\n\n    # Test purely alphabetic string\n    alphabetic_string = \"itemname\"\n    assert InputValidator.validate_item_name(alphabetic_string) == InputValidator.validate_item_name_new_implementation(alphabetic_string)\n\n    # Test string with leading whitespace\n    leading_whitespace = \" itemname\"\n    assert InputValidator.validate_item_name(leading_whitespace) == InputValidator.validate_item_name_new_implementation(leading_whitespace)\n\n    # Test string with trailing whitespace\n    trailing_whitespace = \"itemname \"\n    assert InputValidator.validate_item_name(trailing_whitespace) == InputValidator.validate_item_name_new_implementation(trailing_whitespace)\n\n    # Test string with internal whitespace\n    internal_whitespace = \"item name\"\n    assert InputValidator.validate_item_name(internal_whitespace) == InputValidator.validate_item_name_new_implementation(internal_whitespace)\n\n    # Test mixed case string\n    mixed_case_string = \"Item_Name_123\"\n    assert InputValidator.validate_item_name(mixed_case_string) == InputValidator.validate_item_name_new_implementation(mixed_case_string)\n\nif __name__ == \"__main__\":\n    test_validate_item_name()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the ORIGINAL FUNCTION and the REVISED FUNCTION, they are identical in terms of logic and implementation. Both functions perform the same checks: they ensure the input is a non-empty string, check that the length does not exceed the specified maximum length, and verify that the string contains only alphanumeric characters and underscores. The functionality and the code structure are exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `validate_item_name` returns a boolean value, which satisfies the requirement of having return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `validate_item_name` and `validate_item_name_new_implementation`, which means they are checking return values and not printed or logged content.\n\n3. **CONDITION 3**: The test cases are designed to compare the outputs of `validate_item_name` and `validate_item_name_new_implementation` directly. This ensures that `validate_item_name_new_implementation` will pass all tests only if it has the exact same functionality as `validate_item_name`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `validate_item_name` returns a boolean. This is reasonable and aligns with the function's behavior.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including empty strings, non-string inputs, strings exceeding the maximum length, valid item names, strings with special characters, numeric strings, alphabetic strings, strings with whitespace, and mixed-case strings. This variety ensures that the test cases are non-trivial and comprehensive.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "RadioPuzzle._generate_frequencies",
        "idx": "314",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "puzzles/radio_puzzle.py",
        "orig_func": "def _generate_frequencies(self) -> Dict[str, Tuple[int, str]]:\n    \"\"\"\n        Generate random target frequencies within each range with messages.\n        Each band gets a random frequency and message.\n        \"\"\"\n    active = {}\n    with self.error_handler('frequency generation'):\n        for band, (min_freq, max_freq) in self.RADIO_RANGES.items():\n            frequency = random.randint(min_freq, max_freq)\n            message, _ = random.choice(self.RADIO_MESSAGES[band])\n            active[band] = (frequency, message)\n        return active",
        "orig_context": "```python\n## puzzles/base_puzzle.py\nfrom abc import ABC, abstractmethod\n\nfrom typing import Dict, List, Optional, Any\n\nimport logging\n\nfrom contextlib import contextmanager\n\nclass BasePuzzle(ABC):\n    \"\"\"Abstract base class for all puzzles.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.attempts = 0\n        self.max_attempts = 5  # Default value, can be overridden\n        self.solved = False\n\n    @abstractmethod\n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        \"\"\"\n        Attempt to solve the puzzle.\n        \n        Args:\n            inventory: List of items the player has\n            game_state: Current game state dictionary\n            \n        Returns:\n            bool: True if puzzle was solved, False otherwise\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def get_state(self) -> Dict:\n        \"\"\"\n        Get current puzzle state for saving.\n        \n        Returns:\n            Dict containing the puzzle's current state\n        \"\"\"\n        return {\n            \"attempts\": self.attempts,\n            \"max_attempts\": self.max_attempts,\n            \"solved\": self.solved,\n            # Add any additional common state here\n        }\n    \n    @abstractmethod\n    def restore_state(self, state: Dict) -> None:\n        \"\"\"\n        Restore puzzle state from saved game.\n        \n        Args:\n            state: Dictionary containing puzzle state\n        \"\"\"\n        self.attempts = state.get(\"attempts\", 0)\n        self.max_attempts = state.get(\"max_attempts\", 5)\n        self.solved = state.get(\"solved\", False)\n    \n    @property\n    @abstractmethod\n    def requirements(self) -> List[str]:\n        \"\"\"Required items for this puzzle.\"\"\"\n        pass\n    \n    def validate_input(self, user_input: str, \n                      valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\") -> bool:\n        \"\"\"\n        Validate user input.\n        \n        Args:\n            user_input: String to validate\n            valid_chars: String of valid characters\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not user_input:\n            return False\n        return all(char in valid_chars for char in user_input.upper())\n    @contextmanager\n    def error_handler(self, operation: str):\n        \"\"\"\n        Context manager for standardized error handling.\n        \n        Args:\n            operation: Name of the operation being performed\n            \n        Yields:\n            None\n        \"\"\"\n        try:\n            yield\n        except Exception as e:\n            self.logger.error(f\"Error in {operation}: {e}\")\n            print(f\"\\nThere was a problem with {operation}. Please try again.\")\n    \n    def increment_attempts(self) -> bool:\n        \"\"\"\n        Increment attempt counter and check if max attempts reached.\n        \n        Returns:\n            bool: True if more attempts available, False if max reached\n        \"\"\"\n        self.attempts += 1\n        remaining = self.max_attempts - self.attempts\n        \n        if remaining <= 0:\n            print(\"\\nYou've run out of attempts. Try again later.\")\n            return False\n            \n        print(f\"\\n{remaining} attempts remaining.\")\n        return True\n    \n    def reset_attempts(self) -> None:\n        \"\"\"Reset attempt counter.\"\"\"\n        self.attempts = 0\n\n```\n\n\n```python\n## utils.py\nimport os\n\nimport sys\n\nimport time\n\nimport logging\n\nimport shutil\n\nimport textwrap\n\nfrom typing import Tuple, Optional, Dict, Any, List\n\nimport config\n\nclass DisplayManager:\n    \"\"\"Handles all display-related functionality in a centralized way.\"\"\"\n    \n    @staticmethod\n    def get_terminal_size() -> tuple[int, int]:\n        \"\"\"Get current terminal size with fallback values.\"\"\"\n        try:\n            width, height = shutil.get_terminal_size()\n            width = max(config.MIN_TERMINAL_WIDTH, \n                       min(width, config.MAX_TERMINAL_WIDTH))\n            return width, height\n        except Exception:\n            return config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT\n    \n    @staticmethod\n    def wrap_text(text: str, width: Optional[int] = None, indent: int = 0) -> str:\n        \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n        if width is None:\n            width, _ = DisplayManager.get_terminal_size()\n        \n        # Adjust width for indent\n        effective_width = width - indent\n        \n        # Split into paragraphs\n        paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n        wrapped_paragraphs = []\n        \n        for paragraph in paragraphs:\n            # Normalize spaces\n            paragraph = ' '.join(paragraph.split())\n            \n            # Wrap the paragraph\n            wrapped = textwrap.fill(\n                paragraph,\n                width=effective_width,\n                expand_tabs=True,\n                replace_whitespace=True,\n                break_long_words=False,\n                break_on_hyphens=True,\n                initial_indent=' ' * indent,\n                subsequent_indent=' ' * indent\n            )\n            \n            wrapped_paragraphs.append(wrapped)\n        \n        return '\\n\\n'.join(wrapped_paragraphs)\n    \n    @staticmethod\n    def print_text(text: str, delay: Optional[float] = None, \n                  indent: int = 0, wrap: bool = True) -> None:\n        \"\"\"\n        Print text with optional wrapping and slow printing effect.\n        \n        Args:\n            text: Text to display\n            delay: Delay between characters for slow printing\n            indent: Number of spaces to indent text\n            wrap: Whether to wrap text to terminal width\n        \"\"\"\n        try:\n            # Prepare the text\n            display_text = DisplayManager.wrap_text(text, indent=indent) if wrap else text\n            \n            # Print with or without delay\n            if delay:\n                for char in display_text:\n                    sys.stdout.write(char)\n                    sys.stdout.flush()\n                    time.sleep(delay)\n                print()  # Add final newline\n            else:\n                print(display_text)\n                \n        except KeyboardInterrupt:\n            print(\"\\nDisplay interrupted.\")\n        except Exception as e:\n            logging.error(f\"Error displaying text: {e}\")\n            print(\"\\nError displaying text.\")\n    \n    @staticmethod\n    def clear_screen() -> None:\n        \"\"\"Clear the terminal screen.\"\"\"\n        try:\n            # Check if running in IDLE\n            if 'idlelib.run' in sys.modules:\n                print(\"\\n\" * 100)\n                return\n            \n            # Use appropriate clear command based on OS\n            os.system('cls' if os.name == 'nt' else 'clear')\n        except Exception as e:\n            logging.error(f\"Error clearing screen: {e}\")\n            print(\"\\n\" * 100)  # Fallback\n    \n    @staticmethod\n    def format_location_description(description: str, \n                                  exits: list[str], \n                                  items: list[str]) -> str:\n        \"\"\"Format a location description with exits and items.\"\"\"\n        try:\n            if not description:\n                raise ValueError(\"Invalid description\")\n            \n            formatted = description.strip()\n            \n            if exits:\n                formatted += f\"\\n\\nExits: {', '.join(exits)}\"\n            \n            if items:\n                formatted += f\"\\n\\nYou can see: {', '.join(items)}\"\n            \n            return formatted\n            \n        except Exception as e:\n            logging.error(f\"Error formatting location description: {e}\")\n            return description\n\ndef print_text(text: str, delay: Optional[float] = None, \n              indent: int = 0, wrap: bool = True) -> None:\n    \"\"\"Print text using DisplayManager.\"\"\"\n    DisplayManager.print_text(text, delay, indent, wrap)\n\n```\n\n\n```python\n## puzzles/radio_puzzle.py\nfrom typing import Dict, List, Set, Tuple\n\nimport random\n\nfrom .base_puzzle import BasePuzzle\n\nfrom utils import print_text\n\nclass RadioPuzzle(BasePuzzle):\n    \"\"\"\n    Enhanced radio frequency puzzle implementation.\n    Players must tune to correct frequencies to intercept suspicious transmissions.\n    \"\"\"\n\n    def __init__(self):\n        # Initialize base puzzle features\n        super().__init__()\n        \n        # Define frequency ranges for different radio bands\n        self.RADIO_RANGES = {\n            \"emergency\": (1400, 1500),\n            \"police\": (1200, 1300),\n            \"civilian\": (1000, 1100)\n        }\n        \n        # Define possible messages for each band\n        self.RADIO_MESSAGES = {\n            \"emergency\": [\n                (\"...urgent shipment tonight... dock 7... look for red star...\",\n                 \"Emergency broadcast about suspicious shipment\"),\n                (\"...medical supplies... warehouse district... midnight...\",\n                 \"Emergency alert about medical supplies\"),\n            ],\n            \"police\": [\n                (\"...patrol units report to waterfront... suspicious activity...\",\n                 \"Police dispatch about waterfront\"),\n                (\"...all units... warehouse district... maintain surveillance...\",\n                 \"Police alert about warehouse\"),\n            ],\n            \"civilian\": [\n                (\"...weather forecast: heavy rain expected... port closing early...\",\n                 \"Civilian broadcast about weather\"),\n                (\"...dock workers union meeting... discussing night shifts...\",\n                 \"Civilian broadcast about dock workers\"),\n            ]\n        }\n        \n        # Puzzle state variables\n        self.active_frequencies = self._generate_frequencies()\n        self.found_frequencies: Set[str] = set()\n        \n        # Override base puzzle settings\n        self.max_attempts = 8  # More attempts for this puzzle due to its nature\n\n    @property\n    def requirements(self) -> List[str]:\n        \"\"\"Required items for this puzzle.\"\"\"\n        return [\"radio_manual\"]\n\n    def _generate_frequencies(self) -> Dict[str, Tuple[int, str]]:\n        \"\"\"\n        Generate random target frequencies within each range with messages.\n        Each band gets a random frequency and message.\n        \"\"\"\n        active = {}\n        with self.error_handler(\"frequency generation\"):\n            for band, (min_freq, max_freq) in self.RADIO_RANGES.items():\n                frequency = random.randint(min_freq, max_freq)\n                message, _ = random.choice(self.RADIO_MESSAGES[band])\n                active[band] = (frequency, message)\n            return active\n        \n    def _validate_frequency(self, frequency: str) -> bool:\n        \"\"\"\n        Validate user input for frequency.\n        Ensures input is a number within valid ranges.\n        \"\"\"\n        try:\n            freq = int(frequency)\n            min_freq = min(r[0] for r in self.RADIO_RANGES.values())\n            max_freq = max(r[1] for r in self.RADIO_RANGES.values())\n            return min_freq <= freq <= max_freq\n        except ValueError:\n            return False\n\n    def get_signal_strength(self, frequency: int) -> Tuple[str, str, str]:\n        \"\"\"\n        Calculate signal strength and return appropriate message.\n        Returns tuple of (strength, message, band).\n        \"\"\"\n        best_strength = \"NONE\"\n        best_message = \"\"\n        best_band = \"\"\n        \n        for band, (target_freq, message) in self.active_frequencies.items():\n            difference = abs(target_freq - frequency)\n            \n            if difference == 0:\n                return \"STRONG\", message, band\n            elif difference <= 10 and best_strength != \"STRONG\":\n                best_strength = \"MODERATE\"\n                best_message = \"Through the static, you hear fragments of a transmission...\"\n                best_band = band\n            elif difference <= 25 and best_strength not in [\"STRONG\", \"MODERATE\"]:\n                best_strength = \"WEAK\"\n                best_message = \"You hear mostly static, but something's there...\"\n                best_band = band\n                \n        return best_strength, best_message, best_band\n\n    def _display_puzzle_introduction(self) -> None:\n        \"\"\"\n        Display the puzzle introduction and instructions.\n        Provides context and guidance to the player.\n        \"\"\"\n        intro_text = \"\"\"\n        Objective: Locate the emergency frequency being used by the smugglers.\n        The radio manual indicates suspicious activity on emergency channels.\n\n        The radio manual lists several frequency ranges:\n        Emergency Services: 1400-1500 kHz  (Known smuggler activity)\n        Police Band: 1200-1300 kHz        (May contain useful intel)\n        Civilian Band: 1000-1100 kHz      (Dock worker communications)\n        \"\"\"\n        print_text(intro_text)\n    \n    def _handle_strong_signal(self, band: str, message: str, game_state: Dict) -> bool:\n        \"\"\"\n        Process a strong signal detection and update game state.\n        Returns True if puzzle should continue, False if complete.\n        \"\"\"\n        print_text(f\"\\nClear transmission:\")\n        print_text(message)\n        self.found_frequencies.add(band)\n        \n        # Check if found emergency frequency\n        if band == \"emergency\" and not game_state.get(\"solved_radio_puzzle\", False):\n            print_text(\"\\nThis is it! You've found the smugglers' frequency!\")\n            game_state[\"solved_radio_puzzle\"] = True\n        \n        # Check if found all frequencies\n        if len(self.found_frequencies) == len(self.RADIO_RANGES):\n            print_text(\"\\nBy cross-referencing all the transmissions, you've uncovered\")\n            print_text(\"a clear pattern of suspicious activity at the waterfront.\")\n            game_state[\"understood_radio\"] = True\n            return True\n            \n        return False\n    \n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        \"\"\"\n        Main puzzle solving logic.\n        Implements the abstract method from BasePuzzle.\n        \"\"\"\n        with self.error_handler(\"radio puzzle\"):\n            # Check if already solved\n            if game_state.get(\"solved_radio_puzzle\", False):\n                print_text(\"\\nYou've already decoded the critical emergency transmission.\")\n                print_text(\"The radio remains available for scanning other frequencies.\")\n            \n            # Reset frequencies for new attempt\n            self.active_frequencies = self._generate_frequencies()\n            \n            # Display introduction\n            self._display_puzzle_introduction()\n            \n            # Main puzzle loop\n            while self.attempts < self.max_attempts:\n                try:\n                    print_text(f\"\\nAttempts remaining: {self.max_attempts - self.attempts}\")\n                    if self.found_frequencies:\n                        print_text(\"Tuned bands: \" + \", \".join(self.found_frequencies))\n                    \n                    guess = input(\"\\nEnter frequency to tune (or 'quit'): \").lower()\n                    \n                    if guess == \"quit\":\n                        return False\n                    \n                    # Validate input\n                    if not self._validate_frequency(guess):\n                        print_text(\"Please enter a valid frequency number.\")\n                        continue\n                    \n                    frequency = int(guess)\n                    strength, message, band = self.get_signal_strength(frequency)\n                    \n                    # Only increment attempts for actual tuning attempts\n                    if not self.increment_attempts():\n                        break\n                    \n                    print_text(f\"\\nSignal Strength: {strength}\")\n                    \n                    if strength == \"STRONG\":\n                        if self._handle_strong_signal(band, message, game_state):\n                            return True\n                    else:\n                        print_text(message)\n                    \n                    # Give hint after several attempts\n                    if self.attempts == 3:\n                        print_text(\"\\nHint: Try methodically scanning through each band's range.\")\n                    \n                except ValueError:\n                    print_text(\"Please enter a valid frequency number.\")\n                except KeyboardInterrupt:\n                    print_text(\"\\nRadio operation interrupted.\")\n                    return False\n\n            print_text(\"\\nThe radio needs time to cool down. Try again later.\")\n            return False\n\n    def get_state(self) -> Dict:\n        \"\"\"\n        Get current puzzle state for saving.\n        Extends base state from BasePuzzle.\n        \"\"\"\n        state = super().get_state()\n        state.update({\n            \"found_frequencies\": list(self.found_frequencies),\n            \"active_frequencies\": {\n                band: (freq, msg) \n                for band, (freq, msg) in self.active_frequencies.items()\n            }\n        })\n        return state\n\n    def restore_state(self, state: Dict) -> None:\n        \"\"\"\n        Restore puzzle state from saved game.\n        Extends base state restoration from BasePuzzle.\n        \"\"\"\n        super().restore_state(state)\n        self.found_frequencies = set(state.get(\"found_frequencies\", []))\n        if \"active_frequencies\" in state:\n            self.active_frequencies = state[\"active_frequencies\"]\n\n    def get_debug_info(self) -> Dict:\n        \"\"\"\n        Get debug information about current puzzle state.\n        Useful for testing and debugging.\n        \"\"\"\n        return {\n            \"found_frequencies\": list(self.found_frequencies),\n            \"active_frequencies\": {\n                band: freq for band, (freq, _) in self.active_frequencies.items()\n            },\n            \"attempts\": self.attempts,\n            \"max_attempts\": self.max_attempts\n        }\n\n```\n\n\n",
        "eval_script": "import random\nimport logging\nimport sys\nimport time\nimport shutil\nimport textwrap\nfrom typing import Dict, List, Set, Tuple, Optional, Any\nfrom abc import ABC, abstractmethod\nfrom contextlib import contextmanager\n\n# Mock configuration for terminal size\nclass config:\n    MIN_TERMINAL_WIDTH = 40\n    MAX_TERMINAL_WIDTH = 120\n    DEFAULT_TERMINAL_WIDTH = 80\n    DEFAULT_TERMINAL_HEIGHT = 24\n\n# Utility functions\nclass DisplayManager:\n    \"\"\"Handles all display-related functionality in a centralized way.\"\"\"\n    \n    @staticmethod\n    def get_terminal_size() -> tuple[int, int]:\n        \"\"\"Get current terminal size with fallback values.\"\"\"\n        try:\n            width, height = shutil.get_terminal_size()\n            width = max(config.MIN_TERMINAL_WIDTH, \n                       min(width, config.MAX_TERMINAL_WIDTH))\n            return width, height\n        except Exception:\n            return config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT\n    \n    @staticmethod\n    def wrap_text(text: str, width: Optional[int] = None, indent: int = 0) -> str:\n        \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n        if width is None:\n            width, _ = DisplayManager.get_terminal_size()\n        \n        # Adjust width for indent\n        effective_width = width - indent\n        \n        # Split into paragraphs\n        paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n        wrapped_paragraphs = []\n        \n        for paragraph in paragraphs:\n            # Normalize spaces\n            paragraph = ' '.join(paragraph.split())\n            \n            # Wrap the paragraph\n            wrapped = textwrap.fill(\n                paragraph,\n                width=effective_width,\n                expand_tabs=True,\n                replace_whitespace=True,\n                break_long_words=False,\n                break_on_hyphens=True,\n                initial_indent=' ' * indent,\n                subsequent_indent=' ' * indent\n            )\n            \n            wrapped_paragraphs.append(wrapped)\n        \n        return '\\n\\n'.join(wrapped_paragraphs)\n    \n    @staticmethod\n    def print_text(text: str, delay: Optional[float] = None, \n                  indent: int = 0, wrap: bool = True) -> None:\n        \"\"\"\n        Print text with optional wrapping and slow printing effect.\n        \n        Args:\n            text: Text to display\n            delay: Delay between characters for slow printing\n            indent: Number of spaces to indent text\n            wrap: Whether to wrap text to terminal width\n        \"\"\"\n        try:\n            # Prepare the text\n            display_text = DisplayManager.wrap_text(text, indent=indent) if wrap else text\n            \n            # Print with or without delay\n            if delay:\n                for char in display_text:\n                    sys.stdout.write(char)\n                    sys.stdout.flush()\n                    time.sleep(delay)\n                print()  # Add final newline\n            else:\n                print(display_text)\n                \n        except KeyboardInterrupt:\n            print(\"\\nDisplay interrupted.\")\n        except Exception as e:\n            logging.error(f\"Error displaying text: {e}\")\n            print(\"\\nError displaying text.\")\n    \n    @staticmethod\n    def clear_screen() -> None:\n        \"\"\"Clear the terminal screen.\"\"\"\n        try:\n            # Check if running in IDLE\n            if 'idlelib.run' in sys.modules:\n                print(\"\\n\" * 100)\n                return\n            \n            # Use appropriate clear command based on OS\n            os.system('cls' if os.name == 'nt' else 'clear')\n        except Exception as e:\n            logging.error(f\"Error clearing screen: {e}\")\n            print(\"\\n\" * 100)  # Fallback\n    \n    @staticmethod\n    def format_location_description(description: str, \n                                  exits: list[str], \n                                  items: list[str]) -> str:\n        \"\"\"Format a location description with exits and items.\"\"\"\n        try:\n            if not description:\n                raise ValueError(\"Invalid description\")\n            \n            formatted = description.strip()\n            \n            if exits:\n                formatted += f\"\\n\\nExits: {', '.join(exits)}\"\n            \n            if items:\n                formatted += f\"\\n\\nYou can see: {', '.join(items)}\"\n            \n            return formatted\n            \n        except Exception as e:\n            logging.error(f\"Error formatting location description: {e}\")\n            return description\n\ndef print_text(text: str, delay: Optional[float] = None, \n              indent: int = 0, wrap: bool = True) -> None:\n    \"\"\"Print text using DisplayManager.\"\"\"\n    DisplayManager.print_text(text, delay, indent, wrap)\n\n# BasePuzzle class\nclass BasePuzzle(ABC):\n    \"\"\"Abstract base class for all puzzles.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.attempts = 0\n        self.max_attempts = 5  # Default value, can be overridden\n        self.solved = False\n\n    @abstractmethod\n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        \"\"\"\n        Attempt to solve the puzzle.\n        \n        Args:\n            inventory: List of items the player has\n            game_state: Current game state dictionary\n            \n        Returns:\n            bool: True if puzzle was solved, False otherwise\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def get_state(self) -> Dict:\n        \"\"\"\n        Get current puzzle state for saving.\n        \n        Returns:\n            Dict containing the puzzle's current state\n        \"\"\"\n        return {\n            \"attempts\": self.attempts,\n            \"max_attempts\": self.max_attempts,\n            \"solved\": self.solved,\n            # Add any additional common state here\n        }\n    \n    @abstractmethod\n    def restore_state(self, state: Dict) -> None:\n        \"\"\"\n        Restore puzzle state from saved game.\n        \n        Args:\n            state: Dictionary containing puzzle state\n        \"\"\"\n        self.attempts = state.get(\"attempts\", 0)\n        self.max_attempts = state.get(\"max_attempts\", 5)\n        self.solved = state.get(\"solved\", False)\n    \n    @property\n    @abstractmethod\n    def requirements(self) -> List[str]:\n        \"\"\"Required items for this puzzle.\"\"\"\n        pass\n    \n    def validate_input(self, user_input: str, \n                      valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\") -> bool:\n        \"\"\"\n        Validate user input.\n        \n        Args:\n            user_input: String to validate\n            valid_chars: String of valid characters\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not user_input:\n            return False\n        return all(char in valid_chars for char in user_input.upper())\n    \n    @contextmanager\n    def error_handler(self, operation: str):\n        \"\"\"\n        Context manager for standardized error handling.\n        \n        Args:\n            operation: Name of the operation being performed\n            \n        Yields:\n            None\n        \"\"\"\n        try:\n            yield\n        except Exception as e:\n            self.logger.error(f\"Error in {operation}: {e}\")\n            print(f\"\\nThere was a problem with {operation}. Please try again.\")\n    \n    def increment_attempts(self) -> bool:\n        \"\"\"\n        Increment attempt counter and check if max attempts reached.\n        \n        Returns:\n            bool: True if more attempts available, False if max reached\n        \"\"\"\n        self.attempts += 1\n        remaining = self.max_attempts - self.attempts\n        \n        if remaining <= 0:\n            print(\"\\nYou've run out of attempts. Try again later.\")\n            return False\n            \n        print(f\"\\n{remaining} attempts remaining.\")\n        return True\n    \n    def reset_attempts(self) -> None:\n        \"\"\"Reset attempt counter.\"\"\"\n        self.attempts = 0\n\n# RadioPuzzle class\nclass RadioPuzzle(BasePuzzle):\n    \"\"\"\n    Enhanced radio frequency puzzle implementation.\n    Players must tune to correct frequencies to intercept suspicious transmissions.\n    \"\"\"\n\n    def __init__(self):\n        # Initialize base puzzle features\n        super().__init__()\n        \n        # Define frequency ranges for different radio bands\n        self.RADIO_RANGES = {\n            \"emergency\": (1400, 1500),\n            \"police\": (1200, 1300),\n            \"civilian\": (1000, 1100)\n        }\n        \n        # Define possible messages for each band\n        self.RADIO_MESSAGES = {\n            \"emergency\": [\n                (\"...urgent shipment tonight... dock 7... look for red star...\",\n                 \"Emergency broadcast about suspicious shipment\"),\n                (\"...medical supplies... warehouse district... midnight...\",\n                 \"Emergency alert about medical supplies\"),\n            ],\n            \"police\": [\n                (\"...patrol units report to waterfront... suspicious activity...\",\n                 \"Police dispatch about waterfront\"),\n                (\"...all units... warehouse district... maintain surveillance...\",\n                 \"Police alert about warehouse\"),\n            ],\n            \"civilian\": [\n                (\"...weather forecast: heavy rain expected... port closing early...\",\n                 \"Civilian broadcast about weather\"),\n                (\"...dock workers union meeting... discussing night shifts...\",\n                 \"Civilian broadcast about dock workers\"),\n            ]\n        }\n        \n        # Puzzle state variables\n        self.active_frequencies = self._generate_frequencies()\n        self.found_frequencies: Set[str] = set()\n        \n        # Override base puzzle settings\n        self.max_attempts = 8  # More attempts for this puzzle due to its nature\n\n    @property\n    def requirements(self) -> List[str]:\n        \"\"\"Required items for this puzzle.\"\"\"\n        return [\"radio_manual\"]\n\n    def _generate_frequencies(self) -> Dict[str, Tuple[int, str]]:\n        \"\"\"\n        Generate random target frequencies within each range with messages.\n        Each band gets a random frequency and message.\n        \"\"\"\n        active = {}\n        with self.error_handler(\"frequency generation\"):\n            for band, (min_freq, max_freq) in self.RADIO_RANGES.items():\n                frequency = random.randint(min_freq, max_freq)\n                message, _ = random.choice(self.RADIO_MESSAGES[band])\n                active[band] = (frequency, message)\n            return active\n\n\n        \n    def _validate_frequency(self, frequency: str) -> bool:\n        \"\"\"\n        Validate user input for frequency.\n        Ensures input is a number within valid ranges.\n        \"\"\"\n        try:\n            freq = int(frequency)\n            min_freq = min(r[0] for r in self.RADIO_RANGES.values())\n            max_freq = max(r[1] for r in self.RADIO_RANGES.values())\n            return min_freq <= freq <= max_freq\n        except ValueError:\n            return False\n\n    def get_signal_strength(self, frequency: int) -> Tuple[str, str, str]:\n        \"\"\"\n        Calculate signal strength and return appropriate message.\n        Returns tuple of (strength, message, band).\n        \"\"\"\n        best_strength = \"NONE\"\n        best_message = \"\"\n        best_band = \"\"\n        \n        for band, (target_freq, message) in self.active_frequencies.items():\n            difference = abs(target_freq - frequency)\n            \n            if difference == 0:\n                return \"STRONG\", message, band\n            elif difference <= 10 and best_strength != \"STRONG\":\n                best_strength = \"MODERATE\"\n                best_message = \"Through the static, you hear fragments of a transmission...\"\n                best_band = band\n            elif difference <= 25 and best_strength not in [\"STRONG\", \"MODERATE\"]:\n                best_strength = \"WEAK\"\n                best_message = \"You hear mostly static, but something's there...\"\n                best_band = band\n                \n        return best_strength, best_message, best_band\n\n    def _display_puzzle_introduction(self) -> None:\n        \"\"\"\n        Display the puzzle introduction and instructions.\n        Provides context and guidance to the player.\n        \"\"\"\n        intro_text = \"\"\"\n        Objective: Locate the emergency frequency being used by the smugglers.\n        The radio manual indicates suspicious activity on emergency channels.\n\n        The radio manual lists several frequency ranges:\n        Emergency Services: 1400-1500 kHz  (Known smuggler activity)\n        Police Band: 1200-1300 kHz        (May contain useful intel)\n        Civilian Band: 1000-1100 kHz      (Dock worker communications)\n        \"\"\"\n        print_text(intro_text)\n    \n    def _handle_strong_signal(self, band: str, message: str, game_state: Dict) -> bool:\n        \"\"\"\n        Process a strong signal detection and update game state.\n        Returns True if puzzle should continue, False if complete.\n        \"\"\"\n        print_text(f\"\\nClear transmission:\")\n        print_text(message)\n        self.found_frequencies.add(band)\n        \n        # Check if found emergency frequency\n        if band == \"emergency\" and not game_state.get(\"solved_radio_puzzle\", False):\n            print_text(\"\\nThis is it! You've found the smugglers' frequency!\")\n            game_state[\"solved_radio_puzzle\"] = True\n        \n        # Check if found all frequencies\n        if len(self.found_frequencies) == len(self.RADIO_RANGES):\n            print_text(\"\\nBy cross-referencing all the transmissions, you've uncovered\")\n            print_text(\"a clear pattern of suspicious activity at the waterfront.\")\n            game_state[\"understood_radio\"] = True\n            return True\n            \n        return False\n    \n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        \"\"\"\n        Main puzzle solving logic.\n        Implements the abstract method from BasePuzzle.\n        \"\"\"\n        with self.error_handler(\"radio puzzle\"):\n            # Check if already solved\n            if game_state.get(\"solved_radio_puzzle\", False):\n                print_text(\"\\nYou've already decoded the critical emergency transmission.\")\n                print_text(\"The radio remains available for scanning other frequencies.\")\n            \n            # Reset frequencies for new attempt\n            self.active_frequencies = self._generate_frequencies()\n            \n            # Display introduction\n            self._display_puzzle_introduction()\n            \n            # Main puzzle loop\n            while self.attempts < self.max_attempts:\n                try:\n                    print_text(f\"\\nAttempts remaining: {self.max_attempts - self.attempts}\")\n                    if self.found_frequencies:\n                        print_text(\"Tuned bands: \" + \", \".join(self.found_frequencies))\n                    \n                    guess = input(\"\\nEnter frequency to tune (or 'quit'): \").lower()\n                    \n                    if guess == \"quit\":\n                        return False\n                    \n                    # Validate input\n                    if not self._validate_frequency(guess):\n                        print_text(\"Please enter a valid frequency number.\")\n                        continue\n                    \n                    frequency = int(guess)\n                    strength, message, band = self.get_signal_strength(frequency)\n                    \n                    # Only increment attempts for actual tuning attempts\n                    if not self.increment_attempts():\n                        break\n                    \n                    print_text(f\"\\nSignal Strength: {strength}\")\n                    \n                    if strength == \"STRONG\":\n                        if self._handle_strong_signal(band, message, game_state):\n                            return True\n                    else:\n                        print_text(message)\n                    \n                    # Give hint after several attempts\n                    if self.attempts == 3:\n                        print_text(\"\\nHint: Try methodically scanning through each band's range.\")\n                    \n                except ValueError:\n                    print_text(\"Please enter a valid frequency number.\")\n                except KeyboardInterrupt:\n                    print_text(\"\\nRadio operation interrupted.\")\n                    return False\n\n            print_text(\"\\nThe radio needs time to cool down. Try again later.\")\n            return False\n\n    def get_state(self) -> Dict:\n        \"\"\"\n        Get current puzzle state for saving.\n        Extends base state from BasePuzzle.\n        \"\"\"\n        state = super().get_state()\n        state.update({\n            \"found_frequencies\": list(self.found_frequencies),\n            \"active_frequencies\": {\n                band: (freq, msg) \n                for band, (freq, msg) in self.active_frequencies.items()\n            }\n        })\n        return state\n\n    def restore_state(self, state: Dict) -> None:\n        \"\"\"\n        Restore puzzle state from saved game.\n        Extends base state restoration from BasePuzzle.\n        \"\"\"\n        super().restore_state(state)\n        self.found_frequencies = set(state.get(\"found_frequencies\", []))\n        if \"active_frequencies\" in state:\n            self.active_frequencies = state[\"active_frequencies\"]\n\n    def get_debug_info(self) -> Dict:\n        \"\"\"\n        Get debug information about current puzzle state.\n        Useful for testing and debugging.\n        \"\"\"\n        return {\n            \"found_frequencies\": list(self.found_frequencies),\n            \"active_frequencies\": {\n                band: freq for band, (freq, _) in self.active_frequencies.items()\n            },\n            \"attempts\": self.attempts,\n            \"max_attempts\": self.max_attempts\n        }\n\ndef test__generate_frequencies():\n    radio_puzzle = RadioPuzzle()\n    \n    # Set a fixed random seed for reproducibility\n    random.seed(42)\n    # Generate frequencies using the original implementation\n    original_frequencies = radio_puzzle._generate_frequencies()\n    \n    # Set the same fixed random seed again for reproducibility\n    random.seed(42)\n    # Generate frequencies using the new implementation\n    new_frequencies = radio_puzzle._generate_frequencies_new_implementation()\n    \n    # Assert that both implementations produce the same keys (bands)\n    assert original_frequencies.keys() == new_frequencies.keys(), \"Bands mismatch\"\n    \n    # Assert that each band has the same frequency and message\n    for band in original_frequencies:\n        assert original_frequencies[band][0] == new_frequencies[band][0], f\"Frequency mismatch in band {band}\"\n        assert original_frequencies[band][1] == new_frequencies[band][1], f\"Message mismatch in band {band}\"\n\nif __name__ == \"__main__\":\n    test__generate_frequencies()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_generate_frequencies` in the provided code is identical in logic and functionality to the ORIGINAL FUNCTION. Both functions generate random frequencies and select a message for each radio band using the same logic: iterating over `RADIO_RANGES`, generating a random frequency within the specified range, and selecting a random message from `RADIO_MESSAGES`. The context manager `self.error_handler` is used in both functions to handle errors during frequency generation. There are no differences in the implementation or the outcome of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `_generate_frequencies` returns a dictionary, which satisfies the condition of having return values.\n- CONDITION 2: The test function `test__generate_frequencies` checks the return values of the function `_generate_frequencies` and its new implementation. It does not check printed or logged contents.\n- CONDITION 3: The test function uses a fixed random seed to ensure reproducibility and checks that both implementations produce the same keys (bands) and the same frequency and message for each band. This ensures that the new implementation must have the exact same functionality as the original to pass the tests.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_generate_frequencies` returns a dictionary.\n- CONDITION 5: The test cases are non-trivial because they check both the structure (keys) and content (frequency and message) of the returned dictionaries, ensuring a thorough comparison.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "InputValidator.validate_puzzle_input",
        "idx": "317",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "input_validator.py",
        "orig_func": "@staticmethod\ndef validate_puzzle_input(input_str: str, valid_chars: str='ABCDEFGHIJKLMNOPQRSTUVWXYZ', min_length: int=1, max_length: int=50) -> bool:\n    \"\"\"\n        Validate input string for puzzles.\n        \n        Args:\n            input_str: Input to validate\n            valid_chars: String of allowed characters\n            min_length: Minimum input length\n            max_length: Maximum input length\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n    if not input_str:\n        return False\n    if not min_length <= len(input_str) <= max_length:\n        return False\n    return all((char in valid_chars for char in input_str.upper()))",
        "orig_context": "```python\n## input_validator.py\nfrom typing import Set, Dict, Optional, Any\n\nimport logging\n\nclass InputValidator:\n    \"\"\"Centralized input validation functionality.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n    \n    @staticmethod\n    def validate_puzzle_input(input_str: str, \n                            valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\",\n                            min_length: int = 1,\n                            max_length: int = 50) -> bool:\n        \"\"\"\n        Validate input string for puzzles.\n        \n        Args:\n            input_str: Input to validate\n            valid_chars: String of allowed characters\n            min_length: Minimum input length\n            max_length: Maximum input length\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not input_str:\n            return False\n            \n        if not min_length <= len(input_str) <= max_length:\n            return False\n            \n        return all(char in valid_chars for char in input_str.upper())\n    \n    @staticmethod\n    def validate_direction(direction: str, valid_directions: Set[str]) -> bool:\n        \"\"\"\n        Validate movement direction.\n        \n        Args:\n            direction: Direction to validate\n            valid_directions: Set of valid directions\n            \n        Returns:\n            bool: True if direction is valid\n        \"\"\"\n        return direction.lower().strip() in {d.lower() for d in valid_directions}\n    \n    @staticmethod\n    def validate_command(command: str, valid_commands: Set[str]) -> bool:\n        \"\"\"\n        Validate game command.\n        \n        Args:\n            command: Command to validate\n            valid_commands: Set of valid commands\n            \n        Returns:\n            bool: True if command is valid\n        \"\"\"\n        return command.lower().strip() in valid_commands\n    \n    @staticmethod\n    def validate_item_name(item: str, max_length: int = 50) -> bool:\n        \"\"\"\n        Validate item name.\n        \n        Args:\n            item: Item name to validate\n            max_length: Maximum allowed length\n            \n        Returns:\n            bool: True if item name is valid\n        \"\"\"\n        if not item or not isinstance(item, str):\n            return False\n            \n        if len(item) > max_length:\n            return False\n            \n        return item.replace('_', '').isalnum()\n\n```\n\n\n",
        "eval_script": "## input_validator.py\nfrom typing import Set, Dict, Optional, Any\n\nimport logging\n\nclass InputValidator:\n    \"\"\"Centralized input validation functionality.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n    \n    @staticmethod\n    def validate_puzzle_input(input_str: str, \n                            valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\",\n                            min_length: int = 1,\n                            max_length: int = 50) -> bool:\n        \"\"\"\n        Validate input string for puzzles.\n        \n        Args:\n            input_str: Input to validate\n            valid_chars: String of allowed characters\n            min_length: Minimum input length\n            max_length: Maximum input length\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not input_str:\n            return False\n            \n        if not min_length <= len(input_str) <= max_length:\n            return False\n            \n        return all(char in valid_chars for char in input_str.upper())\n    \n    @staticmethod\n    def validate_direction(direction: str, valid_directions: Set[str]) -> bool:\n        \"\"\"\n        Validate movement direction.\n        \n        Args:\n            direction: Direction to validate\n            valid_directions: Set of valid directions\n            \n        Returns:\n            bool: True if direction is valid\n        \"\"\"\n        return direction.lower().strip() in {d.lower() for d in valid_directions}\n    \n    @staticmethod\n    def validate_command(command: str, valid_commands: Set[str]) -> bool:\n        \"\"\"\n        Validate game command.\n        \n        Args:\n            command: Command to validate\n            valid_commands: Set of valid commands\n            \n        Returns:\n            bool: True if command is valid\n        \"\"\"\n        return command.lower().strip() in valid_commands\n    \n    @staticmethod\n    def validate_item_name(item: str, max_length: int = 50) -> bool:\n        \"\"\"\n        Validate item name.\n        \n        Args:\n            item: Item name to validate\n            max_length: Maximum allowed length\n            \n        Returns:\n            bool: True if item name is valid\n        \"\"\"\n        if not item or not isinstance(item, str):\n            return False\n            \n        if len(item) > max_length:\n            return False\n            \n        return item.replace('_', '').isalnum()\n\n\ndef test_validate_puzzle_input():\n    # Test case 1: Empty input string\n    assert InputValidator.validate_puzzle_input(\"\") == InputValidator.validate_puzzle_input_new_implementation(\"\")\n\n    # Test case 2: Input string too short\n    assert InputValidator.validate_puzzle_input(\"A\", min_length=2) == InputValidator.validate_puzzle_input_new_implementation(\"A\", min_length=2)\n\n    # Test case 3: Input string too long\n    assert InputValidator.validate_puzzle_input(\"A\"*51) == InputValidator.validate_puzzle_input_new_implementation(\"A\"*51)\n\n    # Test case 4: Valid input string\n    assert InputValidator.validate_puzzle_input(\"ABC\") == InputValidator.validate_puzzle_input_new_implementation(\"ABC\")\n\n    # Test case 5: Invalid characters in input string\n    assert InputValidator.validate_puzzle_input(\"ABC1\") == InputValidator.validate_puzzle_input_new_implementation(\"ABC1\")\n\n    # Test case 6: Lowercase input string\n    assert InputValidator.validate_puzzle_input(\"abc\") == InputValidator.validate_puzzle_input_new_implementation(\"abc\")\n\n    # Test case 7: Input string at minimum length\n    assert InputValidator.validate_puzzle_input(\"A\", min_length=1) == InputValidator.validate_puzzle_input_new_implementation(\"A\", min_length=1)\n\n    # Test case 8: Input string at maximum length\n    assert InputValidator.validate_puzzle_input(\"A\"*50) == InputValidator.validate_puzzle_input_new_implementation(\"A\"*50)\n\n    # Test case 9: Custom valid characters\n    assert InputValidator.validate_puzzle_input(\"123\", valid_chars=\"123\") == InputValidator.validate_puzzle_input_new_implementation(\"123\", valid_chars=\"123\")\n\n    # Test case 10: Non-ASCII characters\n    assert InputValidator.validate_puzzle_input(\"ABC\u00e9\") == InputValidator.validate_puzzle_input_new_implementation(\"ABC\u00e9\")\n\n    # Test case 11: Leading and trailing whitespace\n    assert InputValidator.validate_puzzle_input(\" ABC \") == InputValidator.validate_puzzle_input_new_implementation(\" ABC \")\n\n    # Test case 12: Empty valid characters\n    assert InputValidator.validate_puzzle_input(\"A\", valid_chars=\"\") == InputValidator.validate_puzzle_input_new_implementation(\"A\", valid_chars=\"\")\n\nif __name__ == \"__main__\":\n    test_validate_puzzle_input()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of logic and functionality. Both functions check if the input string is non-empty, if its length is within the specified minimum and maximum bounds, and if all characters in the input string (converted to uppercase) are within the set of valid characters. The only difference is the context in which the function is placed, which does not affect its functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `validate_puzzle_input` function returns a boolean value, indicating whether the input is valid or not. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `validate_puzzle_input` and `validate_puzzle_input_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `validate_puzzle_input` and `validate_puzzle_input_new_implementation` for various inputs. If `validate_puzzle_input_new_implementation` behaves differently, it will not pass all test cases, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that the function returns a boolean. This satisfies the condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including empty strings, strings that are too short or too long, valid and invalid characters, case sensitivity, and custom valid characters. This makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "BasePuzzle.validate_input",
        "idx": "318",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "puzzles/base_puzzle.py",
        "orig_func": "def validate_input(self, user_input: str, valid_chars: str='ABCDEFGHIJKLMNOPQRSTUVWXYZ') -> bool:\n    \"\"\"\n        Validate user input.\n        \n        Args:\n            user_input: String to validate\n            valid_chars: String of valid characters\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n    if not user_input:\n        return False\n    return all((char in valid_chars for char in user_input.upper()))",
        "orig_context": "```python\n## puzzles/base_puzzle.py\nfrom abc import ABC, abstractmethod\n\nfrom typing import Dict, List, Optional, Any\n\nimport logging\n\nfrom contextlib import contextmanager\n\nclass BasePuzzle(ABC):\n    \"\"\"Abstract base class for all puzzles.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.attempts = 0\n        self.max_attempts = 5  # Default value, can be overridden\n        self.solved = False\n\n    @abstractmethod\n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        \"\"\"\n        Attempt to solve the puzzle.\n        \n        Args:\n            inventory: List of items the player has\n            game_state: Current game state dictionary\n            \n        Returns:\n            bool: True if puzzle was solved, False otherwise\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def get_state(self) -> Dict:\n        \"\"\"\n        Get current puzzle state for saving.\n        \n        Returns:\n            Dict containing the puzzle's current state\n        \"\"\"\n        return {\n            \"attempts\": self.attempts,\n            \"max_attempts\": self.max_attempts,\n            \"solved\": self.solved,\n            # Add any additional common state here\n        }\n    \n    @abstractmethod\n    def restore_state(self, state: Dict) -> None:\n        \"\"\"\n        Restore puzzle state from saved game.\n        \n        Args:\n            state: Dictionary containing puzzle state\n        \"\"\"\n        self.attempts = state.get(\"attempts\", 0)\n        self.max_attempts = state.get(\"max_attempts\", 5)\n        self.solved = state.get(\"solved\", False)\n    \n    @property\n    @abstractmethod\n    def requirements(self) -> List[str]:\n        \"\"\"Required items for this puzzle.\"\"\"\n        pass\n    \n    def validate_input(self, user_input: str, \n                      valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\") -> bool:\n        \"\"\"\n        Validate user input.\n        \n        Args:\n            user_input: String to validate\n            valid_chars: String of valid characters\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not user_input:\n            return False\n        return all(char in valid_chars for char in user_input.upper())\n    @contextmanager\n    def error_handler(self, operation: str):\n        \"\"\"\n        Context manager for standardized error handling.\n        \n        Args:\n            operation: Name of the operation being performed\n            \n        Yields:\n            None\n        \"\"\"\n        try:\n            yield\n        except Exception as e:\n            self.logger.error(f\"Error in {operation}: {e}\")\n            print(f\"\\nThere was a problem with {operation}. Please try again.\")\n    \n    def increment_attempts(self) -> bool:\n        \"\"\"\n        Increment attempt counter and check if max attempts reached.\n        \n        Returns:\n            bool: True if more attempts available, False if max reached\n        \"\"\"\n        self.attempts += 1\n        remaining = self.max_attempts - self.attempts\n        \n        if remaining <= 0:\n            print(\"\\nYou've run out of attempts. Try again later.\")\n            return False\n            \n        print(f\"\\n{remaining} attempts remaining.\")\n        return True\n    \n    def reset_attempts(self) -> None:\n        \"\"\"Reset attempt counter.\"\"\"\n        self.attempts = 0\n\n```\n\n\n",
        "eval_script": "## puzzles/base_puzzle.py\nfrom abc import ABC, abstractmethod\n\nfrom typing import Dict, List, Optional, Any\n\nimport logging\n\nfrom contextlib import contextmanager\n\nclass BasePuzzle(ABC):\n    \"\"\"Abstract base class for all puzzles.\"\"\"\n    \n    def __init__(self):\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.attempts = 0\n        self.max_attempts = 5  # Default value, can be overridden\n        self.solved = False\n\n    @abstractmethod\n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        \"\"\"\n        Attempt to solve the puzzle.\n        \n        Args:\n            inventory: List of items the player has\n            game_state: Current game state dictionary\n            \n        Returns:\n            bool: True if puzzle was solved, False otherwise\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def get_state(self) -> Dict:\n        \"\"\"\n        Get current puzzle state for saving.\n        \n        Returns:\n            Dict containing the puzzle's current state\n        \"\"\"\n        return {\n            \"attempts\": self.attempts,\n            \"max_attempts\": self.max_attempts,\n            \"solved\": self.solved,\n            # Add any additional common state here\n        }\n    \n    @abstractmethod\n    def restore_state(self, state: Dict) -> None:\n        \"\"\"\n        Restore puzzle state from saved game.\n        \n        Args:\n            state: Dictionary containing puzzle state\n        \"\"\"\n        self.attempts = state.get(\"attempts\", 0)\n        self.max_attempts = state.get(\"max_attempts\", 5)\n        self.solved = state.get(\"solved\", False)\n    \n    @property\n    @abstractmethod\n    def requirements(self) -> List[str]:\n        \"\"\"Required items for this puzzle.\"\"\"\n        pass\n    \n    def validate_input(self, user_input: str, \n                      valid_chars: str = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\") -> bool:\n        \"\"\"\n        Validate user input.\n        \n        Args:\n            user_input: String to validate\n            valid_chars: String of valid characters\n            \n        Returns:\n            bool: True if input is valid\n        \"\"\"\n        if not user_input:\n            return False\n        return all(char in valid_chars for char in user_input.upper())\n    \n\n\n    \n    @contextmanager\n    def error_handler(self, operation: str):\n        \"\"\"\n        Context manager for standardized error handling.\n        \n        Args:\n            operation: Name of the operation being performed\n            \n        Yields:\n            None\n        \"\"\"\n        try:\n            yield\n        except Exception as e:\n            self.logger.error(f\"Error in {operation}: {e}\")\n            print(f\"\\nThere was a problem with {operation}. Please try again.\")\n    \n    def increment_attempts(self) -> bool:\n        \"\"\"\n        Increment attempt counter and check if max attempts reached.\n        \n        Returns:\n            bool: True if more attempts available, False if max reached\n        \"\"\"\n        self.attempts += 1\n        remaining = self.max_attempts - self.attempts\n        \n        if remaining <= 0:\n            print(\"\\nYou've run out of attempts. Try again later.\")\n            return False\n            \n        print(f\"\\n{remaining} attempts remaining.\")\n        return True\n    \n    def reset_attempts(self) -> None:\n        \"\"\"Reset attempt counter.\"\"\"\n        self.attempts = 0\n\n# Concrete subclass implementing the abstract methods\nclass ConcretePuzzle(BasePuzzle):\n    def solve(self, inventory: List[str], game_state: Dict) -> bool:\n        # Dummy implementation\n        return True\n    \n    def get_state(self) -> Dict:\n        # Dummy implementation\n        return super().get_state()\n    \n    def restore_state(self, state: Dict) -> None:\n        # Dummy implementation\n        super().restore_state(state)\n    \n    @property\n    def requirements(self) -> List[str]:\n        # Dummy implementation\n        return [\"item1\", \"item2\"]\n\ndef test_validate_input():\n    puzzle = ConcretePuzzle()\n\n    # Test case 1: Valid input\n    assert puzzle.validate_input(\"ABC\") == puzzle.validate_input_new_implementation(\"ABC\")\n\n    # Test case 2: Invalid input\n    assert puzzle.validate_input(\"123\") == puzzle.validate_input_new_implementation(\"123\")\n\n    # Test case 3: Mixed valid and invalid input\n    assert puzzle.validate_input(\"A1B2C3\") == puzzle.validate_input_new_implementation(\"A1B2C3\")\n\n    # Test case 4: Empty input\n    assert puzzle.validate_input(\"\") == puzzle.validate_input_new_implementation(\"\")\n\n    # Test case 5: Valid input with lowercase\n    assert puzzle.validate_input(\"abc\") == puzzle.validate_input_new_implementation(\"abc\")\n\n    # Additional Test case 6: Special characters\n    assert puzzle.validate_input(\"!@#\") == puzzle.validate_input_new_implementation(\"!@#\")\n\n    # Additional Test case 7: Whitespace in input\n    assert puzzle.validate_input(\"A B C\") == puzzle.validate_input_new_implementation(\"A B C\")\n\n    # Additional Test case 8: Different valid characters set\n    valid_chars = \"1234567890\"\n    assert puzzle.validate_input(\"123\", valid_chars) == puzzle.validate_input_new_implementation(\"123\", valid_chars)\n\n    # Additional Test case 9: Very long string input\n    long_input = \"A\" * 1000\n    assert puzzle.validate_input(long_input) == puzzle.validate_input_new_implementation(long_input)\n\n    # Additional Test case 10: Valid input with mixed case\n    assert puzzle.validate_input(\"AbC\") == puzzle.validate_input_new_implementation(\"AbC\")\n\nif __name__ == \"__main__\":\n    test_validate_input()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions take a `user_input` string and a `valid_chars` string, check if the `user_input` is not empty, and then verify that all characters in the `user_input` (converted to uppercase) are present in the `valid_chars`. The logic and implementation are the same, with no changes in functionality or behavior.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `validate_input` function returns a boolean value, satisfying this condition.\n- CONDITION 2: The test cases use assertions to compare return values of `validate_input` and `validate_input_new_implementation`, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `validate_input` and `validate_input_new_implementation` for various inputs. If the outputs match for all test cases, it implies the two implementations have the same functionality, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable given that `validate_input` returns a boolean. The test cases do not use unreasonable assertions, satisfying this condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including valid and invalid inputs, mixed case, special characters, whitespace, different valid character sets, and long strings, making them non-trivial and satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "DisplayManager.wrap_text",
        "idx": "319",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "utils.py",
        "orig_func": "@staticmethod\ndef wrap_text(text: str, width: Optional[int]=None, indent: int=0) -> str:\n    \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n    if width is None:\n        width, _ = DisplayManager.get_terminal_size()\n    effective_width = width - indent\n    paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n    wrapped_paragraphs = []\n    for paragraph in paragraphs:\n        paragraph = ' '.join(paragraph.split())\n        wrapped = textwrap.fill(paragraph, width=effective_width, expand_tabs=True, replace_whitespace=True, break_long_words=False, break_on_hyphens=True, initial_indent=' ' * indent, subsequent_indent=' ' * indent)\n        wrapped_paragraphs.append(wrapped)\n    return '\\n\\n'.join(wrapped_paragraphs)",
        "orig_context": "```python\n## utils.py\nimport os\n\nimport sys\n\nimport time\n\nimport logging\n\nimport shutil\n\nimport textwrap\n\nfrom typing import Tuple, Optional, Dict, Any, List\n\nimport config\n\nclass DisplayManager:\n    \"\"\"Handles all display-related functionality in a centralized way.\"\"\"\n    \n    @staticmethod\n    def get_terminal_size() -> tuple[int, int]:\n        \"\"\"Get current terminal size with fallback values.\"\"\"\n        try:\n            width, height = shutil.get_terminal_size()\n            width = max(config.MIN_TERMINAL_WIDTH, \n                       min(width, config.MAX_TERMINAL_WIDTH))\n            return width, height\n        except Exception:\n            return config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT\n    \n    @staticmethod\n    def wrap_text(text: str, width: Optional[int] = None, indent: int = 0) -> str:\n        \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n        if width is None:\n            width, _ = DisplayManager.get_terminal_size()\n        \n        # Adjust width for indent\n        effective_width = width - indent\n        \n        # Split into paragraphs\n        paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n        wrapped_paragraphs = []\n        \n        for paragraph in paragraphs:\n            # Normalize spaces\n            paragraph = ' '.join(paragraph.split())\n            \n            # Wrap the paragraph\n            wrapped = textwrap.fill(\n                paragraph,\n                width=effective_width,\n                expand_tabs=True,\n                replace_whitespace=True,\n                break_long_words=False,\n                break_on_hyphens=True,\n                initial_indent=' ' * indent,\n                subsequent_indent=' ' * indent\n            )\n            \n            wrapped_paragraphs.append(wrapped)\n        \n        return '\\n\\n'.join(wrapped_paragraphs)\n    \n    @staticmethod\n    def print_text(text: str, delay: Optional[float] = None, \n                  indent: int = 0, wrap: bool = True) -> None:\n        \"\"\"\n        Print text with optional wrapping and slow printing effect.\n        \n        Args:\n            text: Text to display\n            delay: Delay between characters for slow printing\n            indent: Number of spaces to indent text\n            wrap: Whether to wrap text to terminal width\n        \"\"\"\n        try:\n            # Prepare the text\n            display_text = DisplayManager.wrap_text(text, indent=indent) if wrap else text\n            \n            # Print with or without delay\n            if delay:\n                for char in display_text:\n                    sys.stdout.write(char)\n                    sys.stdout.flush()\n                    time.sleep(delay)\n                print()  # Add final newline\n            else:\n                print(display_text)\n                \n        except KeyboardInterrupt:\n            print(\"\\nDisplay interrupted.\")\n        except Exception as e:\n            logging.error(f\"Error displaying text: {e}\")\n            print(\"\\nError displaying text.\")\n    \n    @staticmethod\n    def clear_screen() -> None:\n        \"\"\"Clear the terminal screen.\"\"\"\n        try:\n            # Check if running in IDLE\n            if 'idlelib.run' in sys.modules:\n                print(\"\\n\" * 100)\n                return\n            \n            # Use appropriate clear command based on OS\n            os.system('cls' if os.name == 'nt' else 'clear')\n        except Exception as e:\n            logging.error(f\"Error clearing screen: {e}\")\n            print(\"\\n\" * 100)  # Fallback\n    \n    @staticmethod\n    def format_location_description(description: str, \n                                  exits: list[str], \n                                  items: list[str]) -> str:\n        \"\"\"Format a location description with exits and items.\"\"\"\n        try:\n            if not description:\n                raise ValueError(\"Invalid description\")\n            \n            formatted = description.strip()\n            \n            if exits:\n                formatted += f\"\\n\\nExits: {', '.join(exits)}\"\n            \n            if items:\n                formatted += f\"\\n\\nYou can see: {', '.join(items)}\"\n            \n            return formatted\n            \n        except Exception as e:\n            logging.error(f\"Error formatting location description: {e}\")\n            return description\n\n```\n\n\n",
        "eval_script": "# Mock configuration module\nclass config:\n    MIN_TERMINAL_WIDTH = 40\n    MAX_TERMINAL_WIDTH = 120\n    DEFAULT_TERMINAL_WIDTH = 80\n    DEFAULT_TERMINAL_HEIGHT = 24\n\n# The rest of the code remains unchanged\n\nimport os\nimport sys\nimport time\nimport logging\nimport shutil\nimport textwrap\nfrom typing import Tuple, Optional, Dict, Any, List\n\nclass DisplayManager:\n    \"\"\"Handles all display-related functionality in a centralized way.\"\"\"\n    \n    @staticmethod\n    def get_terminal_size() -> tuple[int, int]:\n        \"\"\"Get current terminal size with fallback values.\"\"\"\n        try:\n            width, height = shutil.get_terminal_size()\n            width = max(config.MIN_TERMINAL_WIDTH, \n                       min(width, config.MAX_TERMINAL_WIDTH))\n            return width, height\n        except Exception:\n            return config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT\n    \n    @staticmethod\n    def wrap_text(text: str, width: Optional[int] = None, indent: int = 0) -> str:\n        \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n        if width is None:\n            width, _ = DisplayManager.get_terminal_size()\n        \n        # Adjust width for indent\n        effective_width = width - indent\n        \n        # Split into paragraphs\n        paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n        wrapped_paragraphs = []\n        \n        for paragraph in paragraphs:\n            # Normalize spaces\n            paragraph = ' '.join(paragraph.split())\n            \n            # Wrap the paragraph\n            wrapped = textwrap.fill(\n                paragraph,\n                width=effective_width,\n                expand_tabs=True,\n                replace_whitespace=True,\n                break_long_words=False,\n                break_on_hyphens=True,\n                initial_indent=' ' * indent,\n                subsequent_indent=' ' * indent\n            )\n            \n            wrapped_paragraphs.append(wrapped)\n        \n        return '\\n\\n'.join(wrapped_paragraphs)\n    \n\n\n    \n    @staticmethod\n    def print_text(text: str, delay: Optional[float] = None, \n                  indent: int = 0, wrap: bool = True) -> None:\n        \"\"\"\n        Print text with optional wrapping and slow printing effect.\n        \n        Args:\n            text: Text to display\n            delay: Delay between characters for slow printing\n            indent: Number of spaces to indent text\n            wrap: Whether to wrap text to terminal width\n        \"\"\"\n        try:\n            # Prepare the text\n            display_text = DisplayManager.wrap_text(text, indent=indent) if wrap else text\n            \n            # Print with or without delay\n            if delay:\n                for char in display_text:\n                    sys.stdout.write(char)\n                    sys.stdout.flush()\n                    time.sleep(delay)\n                print()  # Add final newline\n            else:\n                print(display_text)\n                \n        except KeyboardInterrupt:\n            print(\"\\nDisplay interrupted.\")\n        except Exception as e:\n            logging.error(f\"Error displaying text: {e}\")\n            print(\"\\nError displaying text.\")\n    \n    @staticmethod\n    def clear_screen() -> None:\n        \"\"\"Clear the terminal screen.\"\"\"\n        try:\n            # Check if running in IDLE\n            if 'idlelib.run' in sys.modules:\n                print(\"\\n\" * 100)\n                return\n            \n            # Use appropriate clear command based on OS\n            os.system('cls' if os.name == 'nt' else 'clear')\n        except Exception as e:\n            logging.error(f\"Error clearing screen: {e}\")\n            print(\"\\n\" * 100)  # Fallback\n    \n    @staticmethod\n    def format_location_description(description: str, \n                                  exits: list[str], \n                                  items: list[str]) -> str:\n        \"\"\"Format a location description with exits and items.\"\"\"\n        try:\n            if not description:\n                raise ValueError(\"Invalid description\")\n            \n            formatted = description.strip()\n            \n            if exits:\n                formatted += f\"\\n\\nExits: {', '.join(exits)}\"\n            \n            if items:\n                formatted += f\"\\n\\nYou can see: {', '.join(items)}\"\n            \n            return formatted\n            \n        except Exception as e:\n            logging.error(f\"Error formatting location description: {e}\")\n            return description\n\ndef test_wrap_text():\n    # Test case 1: Default width and no indent\n    text = \"This is a simple test case to check the wrapping functionality.\"\n    assert DisplayManager.wrap_text(text) == DisplayManager.wrap_text_new_implementation(text)\n    \n    # Test case 2: Custom width and no indent\n    text = \"This is a test case with a custom width.\"\n    width = 50\n    assert DisplayManager.wrap_text(text, width) == DisplayManager.wrap_text_new_implementation(text, width)\n    \n    # Test case 3: Custom width and indent\n    text = \"This is a test case with custom width and indent.\"\n    width = 60\n    indent = 4\n    assert DisplayManager.wrap_text(text, width, indent) == DisplayManager.wrap_text_new_implementation(text, width, indent)\n\nif __name__ == \"__main__\":\n    test_wrap_text()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is essentially the same as the ORIGINAL FUNCTION. Both functions aim to wrap text to fit a specified terminal width with proper indentation. They both handle the case where the width is not provided by obtaining the terminal size and adjusting for the indent. The logic for splitting the text into paragraphs, normalizing spaces, and wrapping each paragraph using `textwrap.fill` with the same parameters is identical in both functions. The only difference is the context in which the REVISED FUNCTION is placed, with additional code for handling terminal size and other display functionalities, but this does not affect the functionality of the `wrap_text` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `wrap_text` function returns a string, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `wrap_text` and `wrap_text_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `wrap_text` and `wrap_text_new_implementation` for various inputs. If `wrap_text_new_implementation` has the same functionality as `wrap_text`, it will pass all the test cases. Therefore, this condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `wrap_text` returns a string. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover different scenarios: default width and no indent, custom width and no indent, and custom width with indent. These are non-trivial cases that test different aspects of the `wrap_text` function's behavior, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "DisplayManager.get_terminal_size",
        "idx": "322",
        "repo_name": "red4golf___seattle-noir",
        "func_path": "utils.py",
        "orig_func": "@staticmethod\ndef get_terminal_size() -> tuple[int, int]:\n    \"\"\"Get current terminal size with fallback values.\"\"\"\n    try:\n        width, height = shutil.get_terminal_size()\n        width = max(config.MIN_TERMINAL_WIDTH, min(width, config.MAX_TERMINAL_WIDTH))\n        return (width, height)\n    except Exception:\n        return (config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT)",
        "orig_context": "```python\n## utils.py\nimport os\n\nimport sys\n\nimport time\n\nimport logging\n\nimport shutil\n\nimport textwrap\n\nfrom typing import Tuple, Optional, Dict, Any, List\n\nimport config\n\nclass DisplayManager:\n    \"\"\"Handles all display-related functionality in a centralized way.\"\"\"\n    \n    @staticmethod\n    def get_terminal_size() -> tuple[int, int]:\n        \"\"\"Get current terminal size with fallback values.\"\"\"\n        try:\n            width, height = shutil.get_terminal_size()\n            width = max(config.MIN_TERMINAL_WIDTH, \n                       min(width, config.MAX_TERMINAL_WIDTH))\n            return width, height\n        except Exception:\n            return config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT\n    \n    @staticmethod\n    def wrap_text(text: str, width: Optional[int] = None, indent: int = 0) -> str:\n        \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n        if width is None:\n            width, _ = DisplayManager.get_terminal_size()\n        \n        # Adjust width for indent\n        effective_width = width - indent\n        \n        # Split into paragraphs\n        paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n        wrapped_paragraphs = []\n        \n        for paragraph in paragraphs:\n            # Normalize spaces\n            paragraph = ' '.join(paragraph.split())\n            \n            # Wrap the paragraph\n            wrapped = textwrap.fill(\n                paragraph,\n                width=effective_width,\n                expand_tabs=True,\n                replace_whitespace=True,\n                break_long_words=False,\n                break_on_hyphens=True,\n                initial_indent=' ' * indent,\n                subsequent_indent=' ' * indent\n            )\n            \n            wrapped_paragraphs.append(wrapped)\n        \n        return '\\n\\n'.join(wrapped_paragraphs)\n    \n    @staticmethod\n    def print_text(text: str, delay: Optional[float] = None, \n                  indent: int = 0, wrap: bool = True) -> None:\n        \"\"\"\n        Print text with optional wrapping and slow printing effect.\n        \n        Args:\n            text: Text to display\n            delay: Delay between characters for slow printing\n            indent: Number of spaces to indent text\n            wrap: Whether to wrap text to terminal width\n        \"\"\"\n        try:\n            # Prepare the text\n            display_text = DisplayManager.wrap_text(text, indent=indent) if wrap else text\n            \n            # Print with or without delay\n            if delay:\n                for char in display_text:\n                    sys.stdout.write(char)\n                    sys.stdout.flush()\n                    time.sleep(delay)\n                print()  # Add final newline\n            else:\n                print(display_text)\n                \n        except KeyboardInterrupt:\n            print(\"\\nDisplay interrupted.\")\n        except Exception as e:\n            logging.error(f\"Error displaying text: {e}\")\n            print(\"\\nError displaying text.\")\n    \n    @staticmethod\n    def clear_screen() -> None:\n        \"\"\"Clear the terminal screen.\"\"\"\n        try:\n            # Check if running in IDLE\n            if 'idlelib.run' in sys.modules:\n                print(\"\\n\" * 100)\n                return\n            \n            # Use appropriate clear command based on OS\n            os.system('cls' if os.name == 'nt' else 'clear')\n        except Exception as e:\n            logging.error(f\"Error clearing screen: {e}\")\n            print(\"\\n\" * 100)  # Fallback\n    \n    @staticmethod\n    def format_location_description(description: str, \n                                  exits: list[str], \n                                  items: list[str]) -> str:\n        \"\"\"Format a location description with exits and items.\"\"\"\n        try:\n            if not description:\n                raise ValueError(\"Invalid description\")\n            \n            formatted = description.strip()\n            \n            if exits:\n                formatted += f\"\\n\\nExits: {', '.join(exits)}\"\n            \n            if items:\n                formatted += f\"\\n\\nYou can see: {', '.join(items)}\"\n            \n            return formatted\n            \n        except Exception as e:\n            logging.error(f\"Error formatting location description: {e}\")\n            return description\n\n```\n\n\n",
        "eval_script": "## utils.py\nimport os\n\nimport sys\n\nimport time\n\nimport logging\n\nimport shutil\n\nimport textwrap\n\nfrom typing import Tuple, Optional, Dict, Any, List\n\n# Mock configuration values\nclass config:\n    MIN_TERMINAL_WIDTH = 40\n    MAX_TERMINAL_WIDTH = 120\n    DEFAULT_TERMINAL_WIDTH = 80\n    DEFAULT_TERMINAL_HEIGHT = 24\n\nclass DisplayManager:\n    \"\"\"Handles all display-related functionality in a centralized way.\"\"\"\n    \n    @staticmethod\n    def get_terminal_size() -> tuple[int, int]:\n        \"\"\"Get current terminal size with fallback values.\"\"\"\n        try:\n            width, height = shutil.get_terminal_size()\n            width = max(config.MIN_TERMINAL_WIDTH, \n                       min(width, config.MAX_TERMINAL_WIDTH))\n            return width, height\n        except Exception:\n            return config.DEFAULT_TERMINAL_WIDTH, config.DEFAULT_TERMINAL_HEIGHT\n    \n    @staticmethod\n    def wrap_text(text: str, width: Optional[int] = None, indent: int = 0) -> str:\n        \"\"\"Wrap text to fit terminal width with proper indentation.\"\"\"\n        if width is None:\n            width, _ = DisplayManager.get_terminal_size()\n        \n        # Adjust width for indent\n        effective_width = width - indent\n        \n        # Split into paragraphs\n        paragraphs = [p.strip() for p in text.strip().split('\\n\\n')]\n        wrapped_paragraphs = []\n        \n        for paragraph in paragraphs:\n            # Normalize spaces\n            paragraph = ' '.join(paragraph.split())\n            \n            # Wrap the paragraph\n            wrapped = textwrap.fill(\n                paragraph,\n                width=effective_width,\n                expand_tabs=True,\n                replace_whitespace=True,\n                break_long_words=False,\n                break_on_hyphens=True,\n                initial_indent=' ' * indent,\n                subsequent_indent=' ' * indent\n            )\n            \n            wrapped_paragraphs.append(wrapped)\n        \n        return '\\n\\n'.join(wrapped_paragraphs)\n    \n    @staticmethod\n    def print_text(text: str, delay: Optional[float] = None, \n                  indent: int = 0, wrap: bool = True) -> None:\n        \"\"\"\n        Print text with optional wrapping and slow printing effect.\n        \n        Args:\n            text: Text to display\n            delay: Delay between characters for slow printing\n            indent: Number of spaces to indent text\n            wrap: Whether to wrap text to terminal width\n        \"\"\"\n        try:\n            # Prepare the text\n            display_text = DisplayManager.wrap_text(text, indent=indent) if wrap else text\n            \n            # Print with or without delay\n            if delay:\n                for char in display_text:\n                    sys.stdout.write(char)\n                    sys.stdout.flush()\n                    time.sleep(delay)\n                print()  # Add final newline\n            else:\n                print(display_text)\n                \n        except KeyboardInterrupt:\n            print(\"\\nDisplay interrupted.\")\n        except Exception as e:\n            logging.error(f\"Error displaying text: {e}\")\n            print(\"\\nError displaying text.\")\n    \n    @staticmethod\n    def clear_screen() -> None:\n        \"\"\"Clear the terminal screen.\"\"\"\n        try:\n            # Check if running in IDLE\n            if 'idlelib.run' in sys.modules:\n                print(\"\\n\" * 100)\n                return\n            \n            # Use appropriate clear command based on OS\n            os.system('cls' if os.name == 'nt' else 'clear')\n        except Exception as e:\n            logging.error(f\"Error clearing screen: {e}\")\n            print(\"\\n\" * 100)  # Fallback\n    \n    @staticmethod\n    def format_location_description(description: str, \n                                  exits: list[str], \n                                  items: list[str]) -> str:\n        \"\"\"Format a location description with exits and items.\"\"\"\n        try:\n            if not description:\n                raise ValueError(\"Invalid description\")\n            \n            formatted = description.strip()\n            \n            if exits:\n                formatted += f\"\\n\\nExits: {', '.join(exits)}\"\n            \n            if items:\n                formatted += f\"\\n\\nYou can see: {', '.join(items)}\"\n            \n            return formatted\n            \n        except Exception as e:\n            logging.error(f\"Error formatting location description: {e}\")\n            return description\n\n\ndef test_get_terminal_size():\n    # Test case 1: Normal terminal size within range\n    shutil.get_terminal_size = lambda: (100, 30)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 2: Terminal size smaller than minimum width\n    shutil.get_terminal_size = lambda: (30, 30)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 3: Terminal size larger than maximum width\n    shutil.get_terminal_size = lambda: (150, 30)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 4: Terminal size exactly at minimum width\n    shutil.get_terminal_size = lambda: (40, 30)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 5: Terminal size exactly at maximum width\n    shutil.get_terminal_size = lambda: (120, 30)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 6: Exception handling, simulate an error to trigger default size\n    shutil.get_terminal_size = lambda: (_ for _ in ()).throw(Exception(\"Simulated error\"))\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 7: Terminal size with zero dimensions\n    shutil.get_terminal_size = lambda: (0, 0)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 8: Non-standard terminal height\n    shutil.get_terminal_size = lambda: (80, 10)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\n    # Test case 9: Terminal size with negative dimensions (should fall back to default)\n    shutil.get_terminal_size = lambda: (-10, -10)\n    assert DisplayManager.get_terminal_size() == DisplayManager.get_terminal_size_new_implementation()\n\nif __name__ == \"__main__\":\n    test_get_terminal_size()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `DisplayManager` class is identical to the ORIGINAL FUNCTION. Both functions attempt to get the terminal size using `shutil.get_terminal_size()`, adjust the width to be within a specified minimum and maximum range, and return the width and height. If an exception occurs, both functions return default width and height values specified in the `config` class. The functionality and logic are the same in both implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `get_terminal_size` function returns a tuple of integers representing the terminal size, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `get_terminal_size` and `get_terminal_size_new_implementation`, not printed or logged content.\n- CONDITION 3: The test cases cover various scenarios, including normal, boundary, and exceptional cases, ensuring that `get_terminal_size_new_implementation` must have the same functionality as `get_terminal_size` to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values of the two functions, which is reasonable given that `get_terminal_size` returns a tuple.\n- CONDITION 5: The test cases are non-trivial as they cover a range of scenarios, including normal operation, boundary conditions, and error handling.",
            "answer": "yes"
        },
        "commit_id": "6ce9419bab0194070041498851391070f61e77fb"
    },
    {
        "func_name": "trans_table_to_json",
        "idx": "324",
        "repo_name": "pitlst___dbcenter_server",
        "func_path": "sync/callback_func.py",
        "orig_func": "def trans_table_to_json(__data: pd.DataFrame) -> dict:\n    \"\"\"\u5c06\u8868\u683c\u8f6c\u6362\u6210\u5b57\u5178\"\"\"\n    __data.replace({pd.NaT: None, np.nan: None}, inplace=True)\n    return __data.to_dict(orient='records')",
        "orig_context": "```python\n## sync/callback_func.py\nimport pandas as pd\n\nimport numpy as np\n\ndef trans_table_to_json(__data: pd.DataFrame) -> dict:\n    '''\u5c06\u8868\u683c\u8f6c\u6362\u6210\u5b57\u5178'''\n    __data.replace({pd.NaT: None, np.nan: None}, inplace=True)\n    return __data.to_dict(orient='records')\n\n```\n\n\n",
        "eval_script": "## sync/callback_func.py\nimport pandas as pd\nimport numpy as np\n\ndef trans_table_to_json(__data: pd.DataFrame) -> dict:\n    '''\u5c06\u8868\u683c\u8f6c\u6362\u6210\u5b57\u5178'''\n    __data.replace({pd.NaT: None, np.nan: None}, inplace=True)\n    return __data.to_dict(orient='records')\n\n\ndef test_trans_table_to_json():\n    # Test case 1: Basic functionality with NaN and date values\n    data1 = pd.DataFrame({\n        'A': [1, 2, np.nan],\n        'B': [pd.NaT, '2023-01-01', '2023-01-02'],\n        'C': ['foo', 'bar', None]\n    })\n    assert trans_table_to_json(data1) == trans_table_to_json_new_implementation(data1)\n\n    # Test case 2: DataFrame with only NaN values\n    data2 = pd.DataFrame({\n        'A': [np.nan, np.nan],\n        'B': [pd.NaT, pd.NaT],\n        'C': [None, None]\n    })\n    assert trans_table_to_json(data2) == trans_table_to_json_new_implementation(data2)\n\n    # Test case 3: DataFrame with mixed data types\n    data3 = pd.DataFrame({\n        'A': [1, 'two', 3.0],\n        'B': [pd.Timestamp('2023-01-01'), '2023-01-02', None],\n        'C': [None, 'bar', 'baz']\n    })\n    assert trans_table_to_json(data3) == trans_table_to_json_new_implementation(data3)\n\n    # Test case 4: Empty DataFrame\n    data4 = pd.DataFrame()\n    assert trans_table_to_json(data4) == trans_table_to_json_new_implementation(data4)\n\n    # Test case 5: Single Row DataFrame\n    data5 = pd.DataFrame({\n        'A': [1],\n        'B': [pd.Timestamp('2023-01-01')],\n        'C': ['foo']\n    })\n    assert trans_table_to_json(data5) == trans_table_to_json_new_implementation(data5)\n\n    # Test case 6: Large DataFrame\n    data6 = pd.DataFrame({\n        'A': range(1000),\n        'B': pd.date_range('2023-01-01', periods=1000, freq='D'),\n        'C': ['foo'] * 1000\n    })\n    assert trans_table_to_json(data6) == trans_table_to_json_new_implementation(data6)\n\n    # Test case 7: DataFrame with special characters\n    data7 = pd.DataFrame({\n        'A': ['@', '#', '$'],\n        'B': ['%', '^', '&'],\n        'C': ['*', '(', ')']\n    })\n    assert trans_table_to_json(data7) == trans_table_to_json_new_implementation(data7)\n\n    # Test case 8: MultiIndex DataFrame\n    index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1)])\n    data8 = pd.DataFrame({\n        'C': [1, 2, 3],\n        'D': [4, 5, 6]\n    }, index=index)\n    assert trans_table_to_json(data8.reset_index()) == trans_table_to_json_new_implementation(data8.reset_index())\n\n    # Test case 9: DataFrame with boolean values\n    data9 = pd.DataFrame({\n        'A': [True, False, True],\n        'B': [False, True, False],\n        'C': [True, True, False]\n    })\n    assert trans_table_to_json(data9) == trans_table_to_json_new_implementation(data9)\n\nif __name__ == \"__main__\":\n    test_trans_table_to_json()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions are defined as `trans_table_to_json` and take a pandas DataFrame as input. They replace `pd.NaT` and `np.nan` with `None` in place and return the DataFrame converted to a dictionary with the orientation set to 'records'. There are no changes in the logic or functionality between the two versions of the function. The additional code in the REVISED FUNCTION is for testing purposes and does not alter the function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `trans_table_to_json` returns a dictionary, which is a return value. This condition is satisfied.\n- CONDITION 2: The test cases use assertions to compare the return values of `trans_table_to_json` and `trans_table_to_json_new_implementation`, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `trans_table_to_json` and `trans_table_to_json_new_implementation` for various inputs. If the outputs match for all test cases, it implies that the two functions have the same functionality. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable since `trans_table_to_json` returns a dictionary. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including handling of NaN values, mixed data types, empty DataFrames, large DataFrames, special characters, MultiIndex DataFrames, and boolean values. These are non-trivial and comprehensive. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "5b09c14a37b38ff2f420d9a05f89cbb39fa13ea8"
    },
    {
        "func_name": "compose",
        "idx": "325",
        "repo_name": "pitlst___dbcenter_server",
        "func_path": "sync/callback_func.py",
        "orig_func": "def compose(f, g):\n    \"\"\"\u62fc\u63a5\u51fd\u6570\"\"\"\n\n    def h(x):\n        return f(g(x))\n    return h",
        "orig_context": "```python\n## sync/callback_func.py\ndef compose(f, g):\n    '''\u62fc\u63a5\u51fd\u6570'''\n    def h(x):\n        return f(g(x))\n    return h\n\n```\n\n\n",
        "eval_script": "## sync/callback_func.py\ndef compose(f, g):\n    '''\u62fc\u63a5\u51fd\u6570'''\n    def h(x):\n        return f(g(x))\n    return h\n\n\ndef test_compose():\n    # Test 1: Simple arithmetic functions\n    def add_one(x):\n        return x + 1\n\n    def times_two(x):\n        return x * 2\n\n    composed = compose(add_one, times_two)\n    composed_new = compose_new_implementation(add_one, times_two)\n    assert composed(3) == composed_new(3), \"Test 1 Failed\"\n    assert composed(0) == composed_new(0), \"Test 1 Failed\"\n    \n    # Test 2: String manipulation functions\n    def to_upper(s):\n        return s.upper()\n\n    def add_exclamation(s):\n        return s + \"!\"\n\n    composed = compose(to_upper, add_exclamation)\n    composed_new = compose_new_implementation(to_upper, add_exclamation)\n    assert composed(\"hello\") == composed_new(\"hello\"), \"Test 2 Failed\"\n    \n    # Test 3: Identity and constant functions\n    def identity(x):\n        return x\n\n    def constant(x):\n        return 42\n\n    composed = compose(identity, constant)\n    composed_new = compose_new_implementation(identity, constant)\n    assert composed(10) == composed_new(10), \"Test 3 Failed\"\n\nif __name__ == \"__main__\":\n    test_compose()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are defined as `compose(f, g)` and have the same internal structure, where they define an inner function `h(x)` that returns `f(g(x))`. The functionality of composing two functions remains unchanged. The additional code in the REVISED FUNCTION is a test suite, which does not alter the functionality of the `compose` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `compose` function returns a new function `h`, which is a composition of the two input functions `f` and `g`. Thus, it satisfies the condition of having a return value.\n- CONDITION 2: The test cases use assertions to check the return values of the composed functions, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `compose` and `compose_new_implementation` for various inputs, ensuring that the new implementation must have the same functionality to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the results of the composed functions, which is reasonable given that `compose` returns a function. There are no assertions directly comparing the `compose` functions themselves, which would be inappropriate.\n- CONDITION 5: The test cases cover different scenarios: arithmetic operations, string manipulations, and identity/constant functions, making them non-trivial and comprehensive.",
            "answer": "yes"
        },
        "commit_id": "5b09c14a37b38ff2f420d9a05f89cbb39fa13ea8"
    },
    {
        "func_name": "pipeline.recv",
        "idx": "326",
        "repo_name": "pitlst___dbcenter_server",
        "func_path": "scheduler/general.py",
        "orig_func": "def recv(self, node_name: str) -> list:\n    \"\"\"\u83b7\u53d6\u5bf9\u5e94\u8282\u70b9\u7684\u6d88\u606f\"\"\"\n    temp_doc_list = self.coll_recv.find({'node_name': node_name, 'is_process': False}).to_list()\n    for tdoc in temp_doc_list:\n        self.coll_recv.update_one({'_id': tdoc['_id']}, {'$set': {'is_process': True}})\n    return temp_doc_list",
        "orig_context": "```python\n## scheduler/general.py\nimport os\n\nimport toml\n\nimport pymongo\n\nimport datetime\n\nCONNECT_CONFIG = toml.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"..\", \"source\", \"config\", \"connect.toml\"))[\"\u6570\u636e\u5904\u7406\u670d\u52a1\u5b58\u50a8\"]\n\nurl = \"mongodb://\" + CONNECT_CONFIG[\"ip\"] + \":\" + str(CONNECT_CONFIG[\"port\"])\n\nMONGO_CLIENT = pymongo.MongoClient(url)\n\nclass pipeline:\n    '''\u7ba1\u9053\u7684\u62bd\u8c61\uff0c\u8d1f\u8d23\u53d1\u9001\u548c\u7ef4\u62a4\u5728mongo\u4e2d\u5b9e\u73b0\u7684\u6d88\u606f\u961f\u5217'''\n    def __init__(self) -> None:\n        database = MONGO_CLIENT[\"public\"]\n        coll_list = database.list_collection_names()\n        if \"mq_send\" not in coll_list:\n            self.coll_send = database.create_collection(\"mq_send\")\n        else:\n            self.coll_send = database[\"mq_send\"]\n        if \"mq_recv\" not in coll_list:\n            self.coll_recv = database.create_collection(\"mq_recv\")\n        else:\n            self.coll_recv = database[\"mq_recv\"]\n            \n    def send(self, node_name: str) -> None:\n        self.coll_send.insert_one({\n            \"timestamp\": datetime.datetime.now(),\n            \"node_name\": node_name,\n            \"is_process\": False\n        })\n        \n    def clean_send_history(self, request_time: datetime.datetime) -> int:\n        '''\u6e05\u9664\u5bf9\u5e94\u65f6\u95f4\u4e4b\u524d\u7684\u6d88\u606f'''\n        return self.coll_send.delete_many({'timestamp': {'$lt': request_time}}).deleted_count\n        \n    def clean_recv_history(self, request_time: datetime.datetime) -> int:\n        '''\u6e05\u9664\u5bf9\u5e94\u65f6\u95f4\u4e4b\u524d\u7684\u6d88\u606f'''\n        return self.coll_recv.delete_many({'timestamp': {'$lt': request_time}}).deleted_count\n    \n    def recv(self, node_name: str) -> list:\n        '''\u83b7\u53d6\u5bf9\u5e94\u8282\u70b9\u7684\u6d88\u606f'''\n        temp_doc_list = self.coll_recv.find({\"node_name\": node_name, \"is_process\":False}).to_list()\n        for tdoc in temp_doc_list:\n            self.coll_recv.update_one({\"_id\":tdoc[\"_id\"]}, {\"$set\": {\"is_process\":True}})\n        return temp_doc_list\n\n    def recv_history(self, node_name: str) -> list:\n        '''\u83b7\u53d6\u5bf9\u5e94\u8282\u70b9\u7684\u5386\u53f2\u6d88\u606f'''\n        return self.coll_recv.find({\"node_name\": node_name, \"is_process\":True}).to_list()\n\n```\n\n\n",
        "eval_script": "# The debugged PYTHON CODE in one piece.\n# Mocking pymongo and toml for testing purposes\nimport os\nfrom unittest.mock import MagicMock, patch\nimport datetime\n\n# Mocking the toml.load function to return a fake configuration\nmock_toml_load = MagicMock(return_value={\"\u6570\u636e\u5904\u7406\u670d\u52a1\u5b58\u50a8\": {\"ip\": \"localhost\", \"port\": 27017}})\n\n# Mocking pymongo.MongoClient to avoid real database connections\nmock_mongo_client = MagicMock()\n\n# Mocking the database and collection behavior\nmock_database = MagicMock()\nmock_collection = MagicMock()\n\n# Setting up the mock to return the mock database and collections\nmock_mongo_client.__getitem__.return_value = mock_database\nmock_database.list_collection_names.return_value = []\nmock_database.create_collection.return_value = mock_collection\nmock_database.__getitem__.return_value = mock_collection\n\n# Mocking the find and update_one methods of the collection\nmock_collection.find.return_value.to_list.return_value = [{\"_id\": 1, \"node_name\": \"test_node\", \"is_process\": False}]\nmock_collection.update_one.return_value = None\n\n# Applying the patches\nwith patch('pymongo.MongoClient', return_value=mock_mongo_client):\n    with patch('toml.load', mock_toml_load):\n        # Define the pipeline class as in the original code\n        class pipeline:\n            '''\u7ba1\u9053\u7684\u62bd\u8c61\uff0c\u8d1f\u8d23\u53d1\u9001\u548c\u7ef4\u62a4\u5728mongo\u4e2d\u5b9e\u73b0\u7684\u6d88\u606f\u961f\u5217'''\n            def __init__(self) -> None:\n                # Correctly initialize the MongoClient\n                database = mock_mongo_client[\"public\"]\n                coll_list = database.list_collection_names()\n                if \"mq_send\" not in coll_list:\n                    self.coll_send = database.create_collection(\"mq_send\")\n                else:\n                    self.coll_send = database[\"mq_send\"]\n                if \"mq_recv\" not in coll_list:\n                    self.coll_recv = database.create_collection(\"mq_recv\")\n                else:\n                    self.coll_recv = database[\"mq_recv\"]\n\n            def send(self, node_name: str) -> None:\n                self.coll_send.insert_one({\n                    \"timestamp\": datetime.datetime.now(),\n                    \"node_name\": node_name,\n                    \"is_process\": False\n                })\n\n            def clean_send_history(self, request_time: datetime.datetime) -> int:\n                '''\u6e05\u9664\u5bf9\u5e94\u65f6\u95f4\u4e4b\u524d\u7684\u6d88\u606f'''\n                return self.coll_send.delete_many({'timestamp': {'$lt': request_time}}).deleted_count\n\n            def clean_recv_history(self, request_time: datetime.datetime) -> int:\n                '''\u6e05\u9664\u5bf9\u5e94\u65f6\u95f4\u4e4b\u524d\u7684\u6d88\u606f'''\n                return self.coll_recv.delete_many({'timestamp': {'$lt': request_time}}).deleted_count\n\n            def recv(self, node_name: str) -> list:\n                '''\u83b7\u53d6\u5bf9\u5e94\u8282\u70b9\u7684\u6d88\u606f'''\n                temp_doc_list = self.coll_recv.find({\"node_name\": node_name, \"is_process\":False}).to_list()\n                for tdoc in temp_doc_list:\n                    self.coll_recv.update_one({\"_id\":tdoc[\"_id\"]}, {\"$set\": {\"is_process\":True}})\n                return temp_doc_list\n\n\n            def recv_history(self, node_name: str) -> list:\n                '''\u83b7\u53d6\u5bf9\u5e94\u8282\u70b9\u7684\u5386\u53f2\u6d88\u606f'''\n                return self.coll_recv.find({\"node_name\": node_name, \"is_process\":True}).to_list()\n\n        def test_recv():\n            p = pipeline()\n            # Test case 1: Basic functionality\n            result_original = p.recv(\"test_node\")\n            result_new = p.recv_new_implementation(\"test_node\")\n            assert result_original == result_new, \"Test case 1 failed\"\n\n            # Test case 2: No messages to process\n            mock_collection.find.return_value.to_list.return_value = []\n            result_original = p.recv(\"test_node\")\n            result_new = p.recv_new_implementation(\"test_node\")\n            assert result_original == result_new, \"Test case 2 failed\"\n\n            # Test case 3: Multiple messages\n            mock_collection.find.return_value.to_list.return_value = [\n                {\"_id\": 1, \"node_name\": \"test_node\", \"is_process\": False},\n                {\"_id\": 2, \"node_name\": \"test_node\", \"is_process\": False}\n            ]\n            result_original = p.recv(\"test_node\")\n            result_new = p.recv_new_implementation(\"test_node\")\n            assert result_original == result_new, \"Test case 3 failed\"\n\n        if __name__ == \"__main__\":\n            test_recv()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       0      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  0      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `recv` method within the `pipeline` class. Upon examining the REVISED FUNCTION, it is clear that it performs the same operations as the ORIGINAL FUNCTION. Both functions:\n\n1. Query the `coll_recv` collection for documents where `node_name` matches the provided `node_name` parameter and `is_process` is `False`.\n2. Convert the query result to a list (`to_list()`).\n3. Iterate over each document in the list and update the `is_process` field to `True` for each document.\n4. Return the list of documents.\n\nThe logic and sequence of operations in both the ORIGINAL FUNCTION and the REVISED FUNCTION are identical. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `recv` function returns a list of documents, which satisfies the condition of having return values.\n- [CONDITION 2] The test cases use assertions to compare the return values of `recv` and `recv_new_implementation`, and do not rely on printed or logged content.\n- [CONDITION 3] The test cases compare the outputs of `recv` and `recv_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass all tests.\n- [CONDITION 4] The assertions are reasonable as they compare the return values of the two implementations, which is appropriate given that `recv` returns a list.\n- [CONDITION 5] The test cases cover basic functionality, the scenario with no messages, and the scenario with multiple messages, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "5b09c14a37b38ff2f420d9a05f89cbb39fa13ea8"
    },
    {
        "func_name": "extract_coords",
        "idx": "330",
        "repo_name": "murilob03___sd-batalha-naval",
        "func_path": "client.py",
        "orig_func": "def extract_coords(coords):\n    \"\"\"Convert coordinates from string format to row and column indices.\"\"\"\n    row = int(coords[1]) - 1\n    col = ord(coords[0].upper()) - 65\n    return (row, col)",
        "orig_context": "```python\n## client.py\ndef extract_coords(coords):\n    \"\"\"Convert coordinates from string format to row and column indices.\"\"\"\n    row = int(coords[1]) - 1\n    col = ord(coords[0].upper()) - 65\n    return row, col\n\n```\n\n\n",
        "eval_script": "## client.py\ndef extract_coords(coords):\n    \"\"\"Convert coordinates from string format to row and column indices.\"\"\"\n    row = int(coords[1]) - 1\n    col = ord(coords[0].upper()) - 65\n    return row, col\n\n\ndef test_extract_coords():\n    \"\"\"Test function to compare extract_coords and extract_coords_new_implementation.\"\"\"\n    # Test case 1: Basic input\n    assert extract_coords(\"A1\") == extract_coords_new_implementation(\"A1\"), \"Test case 1 failed\"\n    \n    # Test case 2: Lowercase input\n    assert extract_coords(\"b2\") == extract_coords_new_implementation(\"b2\"), \"Test case 2 failed\"\n    \n    # Test case 3: Edge case with last column\n    assert extract_coords(\"Z9\") == extract_coords_new_implementation(\"Z9\"), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_extract_coords()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `extract_coords` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions take a string representing coordinates, convert the first character to an uppercase letter, calculate the column index by subtracting 65 from its ASCII value, and calculate the row index by converting the second character to an integer and subtracting 1. The return value is a tuple containing the row and column indices. The only difference is in the return statement's formatting, which does not affect functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The original `extract_coords` function returns a tuple of row and column indices, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `extract_coords` and `extract_coords_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of both implementations directly for the same inputs, ensuring that `extract_coords_new_implementation` must have the exact same functionality to pass all tests.\n- CONDITION 4: The assertions are reasonable as they compare the return values of the two implementations, which is appropriate given that `extract_coords` returns values.\n- CONDITION 5: The test cases cover different scenarios: basic input, lowercase input, and an edge case with the last column, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "87e6c5c23f75ec2ca8accd98eaa86d6ee14221ba"
    },
    {
        "func_name": "GameServer.is_game_ready",
        "idx": "332",
        "repo_name": "murilob03___sd-batalha-naval",
        "func_path": "server.py",
        "orig_func": "def is_game_ready(self):\n    \"\"\"Checks if the game has two players ready to play.\"\"\"\n    is_ready = len(self.players) == 2\n    logging.info(f'Game ready status: {is_ready}')\n    return is_ready",
        "orig_context": "```python\n## server.py\nimport logging\n\nclass GameServer:\n    def __init__(self):\n        self.players = {}  # Stores player boards and hits\n        self.turn = None  # Tracks whose turn it is\n        self.game_over = False  # Indicates if the game is over\n\n    def register_player(self, player_name, board_data):\n        \"\"\"Registers a new player with their board.\"\"\"\n        if player_name in self.players:\n            logging.warning(f\"Player {player_name} already registered.\")\n            return \"Player already registered.\"\n\n        if len(self.players) >= 2:\n            logging.warning(f\"Player {player_name} failed to register. Game is full.\")\n            return \"Game is already full.\"\n\n        self.players[player_name] = {\"board\": board_data, \"hits\": 0}\n        logging.info(f\"Player {player_name} successfully registered.\")\n\n        # Assign the first turn to the first player\n        if len(self.players) == 1:\n            self.turn = player_name\n            logging.info(f\"Player {player_name} gets the first turn.\")\n\n        return \"Player registered successfully.\"\n\n    def is_game_ready(self):\n        \"\"\"Checks if the game has two players ready to play.\"\"\"\n        is_ready = len(self.players) == 2\n        logging.info(f\"Game ready status: {is_ready}\")\n        return is_ready\n\n    def whose_turn(self):\n        \"\"\"Returns the name of the player whose turn it is.\"\"\"\n        logging.info(f\"It is {self.turn}'s turn.\")\n        return self.turn\n\n    def make_guess(self, player_name, x, y):\n        \"\"\"Handles a player's guess.\"\"\"\n        if not self.is_game_ready():\n            logging.warning(\n                f\"Player {player_name} tried to guess before the game was ready.\"\n            )\n            return \"Game is not ready yet.\"\n\n        if self.turn != player_name:\n            logging.warning(f\"Player {player_name} tried to guess out of turn.\")\n            return \"Not your turn.\"\n\n        if not (0 <= x < 5 and 0 <= y < 5):\n            logging.warning(\n                f\"Player {player_name} made an invalid guess at ({x}, {y}).\"\n            )\n            return \"Invalid move: Out of bounds.\"\n\n        opponent = self.get_opponent(player_name)\n        opponent_board = self.players[opponent][\"board\"]\n\n        if opponent_board[x][y] == \"1\":\n            opponent_board[x][y] = \"H\"  # Mark as hit\n            self.players[player_name][\"hits\"] += 1\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} hit a ship at ({x}, {y}).\")\n            return \"Hit\"\n\n        elif opponent_board[x][y] == \"0\":\n            opponent_board[x][y] = \"M\"  # Mark as miss\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} missed at ({x}, {y}).\")\n            return \"Miss\"\n\n        logging.warning(f\"Player {player_name} made an invalid move at ({x}, {y}).\")\n        return \"Invalid move: Position already guessed.\"\n\n    def is_game_over(self):\n        \"\"\"Checks if the game is over and declares the winner.\"\"\"\n        for player_name, data in self.players.items():\n            if data[\"hits\"] >= 9:  # Assuming 9 hits to win\n                logging.info(f\"Game over. {player_name} wins!\")\n                if self.game_over:\n                    self._reset_game()\n                self.game_over = True\n                return True\n        return False\n\n    def get_board(self, player_name):\n        \"\"\"Returns the board of the given player.\"\"\"\n        if player_name in self.players:\n            logging.info(f\"Player {player_name} requested their board.\")\n            return (self.players[player_name][\"board\"],)\n        logging.warning(\n            f\"Player {player_name} requested a board but is not registered.\"\n        )\n        return {\n            \"status\": \"error\",\n            \"message\": \"Player not found.\",\n        }\n\n    def get_opponent(self, player_name):\n        \"\"\"Helper method to find the opponent of the given player.\"\"\"\n        return [p for p in self.players if p != player_name][0]\n\n    def _reset_game(self):\n        \"\"\"Resets the game state.\"\"\"\n        self.players = {}\n        self.turn = None\n        self.game_over = False\n        logging.info(\"Game state reset.\")\n        logging.info(\"Battleship server started and awaiting connections...\")\n\n```\n\n\n",
        "eval_script": "## server.py\nimport logging\n\nclass GameServer:\n    def __init__(self):\n        self.players = {}  # Stores player boards and hits\n        self.turn = None  # Tracks whose turn it is\n        self.game_over = False  # Indicates if the game is over\n\n    def register_player(self, player_name, board_data):\n        \"\"\"Registers a new player with their board.\"\"\"\n        if player_name in self.players:\n            logging.warning(f\"Player {player_name} already registered.\")\n            return \"Player already registered.\"\n\n        if len(self.players) >= 2:\n            logging.warning(f\"Player {player_name} failed to register. Game is full.\")\n            return \"Game is already full.\"\n\n        self.players[player_name] = {\"board\": board_data, \"hits\": 0}\n        logging.info(f\"Player {player_name} successfully registered.\")\n\n        # Assign the first turn to the first player\n        if len(self.players) == 1:\n            self.turn = player_name\n            logging.info(f\"Player {player_name} gets the first turn.\")\n\n        return \"Player registered successfully.\"\n\n    def is_game_ready(self):\n        \"\"\"Checks if the game has two players ready to play.\"\"\"\n        is_ready = len(self.players) == 2\n        logging.info(f\"Game ready status: {is_ready}\")\n        return is_ready\n\n    def whose_turn(self):\n        \"\"\"Returns the name of the player whose turn it is.\"\"\"\n        logging.info(f\"It is {self.turn}'s turn.\")\n        return self.turn\n\n    def make_guess(self, player_name, x, y):\n        \"\"\"Handles a player's guess.\"\"\"\n        if not self.is_game_ready():\n            logging.warning(\n                f\"Player {player_name} tried to guess before the game was ready.\"\n            )\n            return \"Game is not ready yet.\"\n\n        if self.turn != player_name:\n            logging.warning(f\"Player {player_name} tried to guess out of turn.\")\n            return \"Not your turn.\"\n\n        if not (0 <= x < 5 and 0 <= y < 5):\n            logging.warning(\n                f\"Player {player_name} made an invalid guess at ({x}, {y}).\"\n            )\n            return \"Invalid move: Out of bounds.\"\n\n        opponent = self.get_opponent(player_name)\n        opponent_board = self.players[opponent][\"board\"]\n\n        if opponent_board[x][y] == \"1\":\n            opponent_board[x][y] = \"H\"  # Mark as hit\n            self.players[player_name][\"hits\"] += 1\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} hit a ship at ({x}, {y}).\")\n            return \"Hit\"\n\n        elif opponent_board[x][y] == \"0\":\n            opponent_board[x][y] = \"M\"  # Mark as miss\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} missed at ({x}, {y}).\")\n            return \"Miss\"\n\n        logging.warning(f\"Player {player_name} made an invalid move at ({x}, {y}).\")\n        return \"Invalid move: Position already guessed.\"\n\n    def is_game_over(self):\n        \"\"\"Checks if the game is over and declares the winner.\"\"\"\n        for player_name, data in self.players.items():\n            if data[\"hits\"] >= 9:  # Assuming 9 hits to win\n                logging.info(f\"Game over. {player_name} wins!\")\n                if self.game_over:\n                    self._reset_game()\n                self.game_over = True\n                return True\n        return False\n\n    def get_board(self, player_name):\n        \"\"\"Returns the board of the given player.\"\"\"\n        if player_name in self.players:\n            logging.info(f\"Player {player_name} requested their board.\")\n            return (self.players[player_name][\"board\"],)\n        logging.warning(\n            f\"Player {player_name} requested a board but is not registered.\"\n        )\n        return {\n            \"status\": \"error\",\n            \"message\": \"Player not found.\",\n        }\n\n    def get_opponent(self, player_name):\n        \"\"\"Helper method to find the opponent of the given player.\"\"\"\n        return [p for p in self.players if p != player_name][0]\n\n    def _reset_game(self):\n        \"\"\"Resets the game state.\"\"\"\n        self.players = {}\n        self.turn = None\n        self.game_over = False\n        logging.info(\"Game state reset.\")\n        logging.info(\"Battleship server started and awaiting connections...\")\n\n\ndef test_is_game_ready():\n    server = GameServer()\n\n    # Test when no players are registered\n    assert server.is_game_ready() == server.is_game_ready_new_implementation()\n\n    # Test when one player is registered\n    server.register_player(\"Player1\", [[0]*5 for _ in range(5)])\n    assert server.is_game_ready() == server.is_game_ready_new_implementation()\n\n    # Test when two players are registered\n    server.register_player(\"Player2\", [[0]*5 for _ in range(5)])\n    assert server.is_game_ready() == server.is_game_ready_new_implementation()\n\n    # Test when attempting to register a third player\n    server.register_player(\"Player3\", [[0]*5 for _ in range(5)])\n    assert server.is_game_ready() == server.is_game_ready_new_implementation()\n\n    # Test after resetting the game\n    server._reset_game()\n    assert server.is_game_ready() == server.is_game_ready_new_implementation()\n\n    # Test game readiness after game over\n    server.register_player(\"Player1\", [[0]*5 for _ in range(5)])\n    server.register_player(\"Player2\", [[0]*5 for _ in range(5)])\n    server.players[\"Player1\"][\"hits\"] = 9  # Simulate Player1 winning\n    server.is_game_over()  # This should trigger game over\n    assert server.is_game_ready() == server.is_game_ready_new_implementation()\n\nif __name__ == \"__main__\":\n    test_is_game_ready()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `is_game_ready` in the `GameServer` class is identical to the ORIGINAL FUNCTION. Both functions check if the number of players is exactly two and log the game readiness status before returning the result. There are no changes in logic or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `is_game_ready` function returns a boolean value indicating whether the game is ready, satisfying this condition.\n- CONDITION 2: The test cases use assertions to compare the return values of `is_game_ready` and `is_game_ready_new_implementation`, not relying on printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases ensure that both implementations return the same result under various scenarios, implying that `is_game_ready_new_implementation` must have the same functionality as `is_game_ready` to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `is_game_ready` returns a value. The assertions are appropriate and correctly structured, satisfying this condition.\n- CONDITION 5: The test cases cover multiple scenarios, including no players, one player, two players, attempting to register a third player, resetting the game, and checking game readiness after a game over. These scenarios are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "87e6c5c23f75ec2ca8accd98eaa86d6ee14221ba"
    },
    {
        "func_name": "GameServer.get_board",
        "idx": "333",
        "repo_name": "murilob03___sd-batalha-naval",
        "func_path": "server.py",
        "orig_func": "def get_board(self, player_name):\n    \"\"\"Returns the board of the given player.\"\"\"\n    if player_name in self.players:\n        logging.info(f'Player {player_name} requested their board.')\n        return (self.players[player_name]['board'],)\n    logging.warning(f'Player {player_name} requested a board but is not registered.')\n    return {'status': 'error', 'message': 'Player not found.'}",
        "orig_context": "```python\n## server.py\nimport logging\n\nclass GameServer:\n    def __init__(self):\n        self.players = {}  # Stores player boards and hits\n        self.turn = None  # Tracks whose turn it is\n        self.game_over = False  # Indicates if the game is over\n\n    def register_player(self, player_name, board_data):\n        \"\"\"Registers a new player with their board.\"\"\"\n        if player_name in self.players:\n            logging.warning(f\"Player {player_name} already registered.\")\n            return \"Player already registered.\"\n\n        if len(self.players) >= 2:\n            logging.warning(f\"Player {player_name} failed to register. Game is full.\")\n            return \"Game is already full.\"\n\n        self.players[player_name] = {\"board\": board_data, \"hits\": 0}\n        logging.info(f\"Player {player_name} successfully registered.\")\n\n        # Assign the first turn to the first player\n        if len(self.players) == 1:\n            self.turn = player_name\n            logging.info(f\"Player {player_name} gets the first turn.\")\n\n        return \"Player registered successfully.\"\n\n    def is_game_ready(self):\n        \"\"\"Checks if the game has two players ready to play.\"\"\"\n        is_ready = len(self.players) == 2\n        logging.info(f\"Game ready status: {is_ready}\")\n        return is_ready\n\n    def whose_turn(self):\n        \"\"\"Returns the name of the player whose turn it is.\"\"\"\n        logging.info(f\"It is {self.turn}'s turn.\")\n        return self.turn\n\n    def make_guess(self, player_name, x, y):\n        \"\"\"Handles a player's guess.\"\"\"\n        if not self.is_game_ready():\n            logging.warning(\n                f\"Player {player_name} tried to guess before the game was ready.\"\n            )\n            return \"Game is not ready yet.\"\n\n        if self.turn != player_name:\n            logging.warning(f\"Player {player_name} tried to guess out of turn.\")\n            return \"Not your turn.\"\n\n        if not (0 <= x < 5 and 0 <= y < 5):\n            logging.warning(\n                f\"Player {player_name} made an invalid guess at ({x}, {y}).\"\n            )\n            return \"Invalid move: Out of bounds.\"\n\n        opponent = self.get_opponent(player_name)\n        opponent_board = self.players[opponent][\"board\"]\n\n        if opponent_board[x][y] == \"1\":\n            opponent_board[x][y] = \"H\"  # Mark as hit\n            self.players[player_name][\"hits\"] += 1\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} hit a ship at ({x}, {y}).\")\n            return \"Hit\"\n\n        elif opponent_board[x][y] == \"0\":\n            opponent_board[x][y] = \"M\"  # Mark as miss\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} missed at ({x}, {y}).\")\n            return \"Miss\"\n\n        logging.warning(f\"Player {player_name} made an invalid move at ({x}, {y}).\")\n        return \"Invalid move: Position already guessed.\"\n\n    def is_game_over(self):\n        \"\"\"Checks if the game is over and declares the winner.\"\"\"\n        for player_name, data in self.players.items():\n            if data[\"hits\"] >= 9:  # Assuming 9 hits to win\n                logging.info(f\"Game over. {player_name} wins!\")\n                if self.game_over:\n                    self._reset_game()\n                self.game_over = True\n                return True\n        return False\n\n    def get_board(self, player_name):\n        \"\"\"Returns the board of the given player.\"\"\"\n        if player_name in self.players:\n            logging.info(f\"Player {player_name} requested their board.\")\n            return (self.players[player_name][\"board\"],)\n        logging.warning(\n            f\"Player {player_name} requested a board but is not registered.\"\n        )\n        return {\n            \"status\": \"error\",\n            \"message\": \"Player not found.\",\n        }\n\n    def get_opponent(self, player_name):\n        \"\"\"Helper method to find the opponent of the given player.\"\"\"\n        return [p for p in self.players if p != player_name][0]\n\n    def _reset_game(self):\n        \"\"\"Resets the game state.\"\"\"\n        self.players = {}\n        self.turn = None\n        self.game_over = False\n        logging.info(\"Game state reset.\")\n        logging.info(\"Battleship server started and awaiting connections...\")\n\n```\n\n\n",
        "eval_script": "## server.py\nimport logging\n\nclass GameServer:\n    def __init__(self):\n        self.players = {}  # Stores player boards and hits\n        self.turn = None  # Tracks whose turn it is\n        self.game_over = False  # Indicates if the game is over\n\n    def register_player(self, player_name, board_data):\n        \"\"\"Registers a new player with their board.\"\"\"\n        if player_name in self.players:\n            logging.warning(f\"Player {player_name} already registered.\")\n            return \"Player already registered.\"\n\n        if len(self.players) >= 2:\n            logging.warning(f\"Player {player_name} failed to register. Game is full.\")\n            return \"Game is already full.\"\n\n        self.players[player_name] = {\"board\": board_data, \"hits\": 0}\n        logging.info(f\"Player {player_name} successfully registered.\")\n\n        # Assign the first turn to the first player\n        if len(self.players) == 1:\n            self.turn = player_name\n            logging.info(f\"Player {player_name} gets the first turn.\")\n\n        return \"Player registered successfully.\"\n\n    def is_game_ready(self):\n        \"\"\"Checks if the game has two players ready to play.\"\"\"\n        is_ready = len(self.players) == 2\n        logging.info(f\"Game ready status: {is_ready}\")\n        return is_ready\n\n    def whose_turn(self):\n        \"\"\"Returns the name of the player whose turn it is.\"\"\"\n        logging.info(f\"It is {self.turn}'s turn.\")\n        return self.turn\n\n    def make_guess(self, player_name, x, y):\n        \"\"\"Handles a player's guess.\"\"\"\n        if not self.is_game_ready():\n            logging.warning(\n                f\"Player {player_name} tried to guess before the game was ready.\"\n            )\n            return \"Game is not ready yet.\"\n\n        if self.turn != player_name:\n            logging.warning(f\"Player {player_name} tried to guess out of turn.\")\n            return \"Not your turn.\"\n\n        if not (0 <= x < 5 and 0 <= y < 5):\n            logging.warning(\n                f\"Player {player_name} made an invalid guess at ({x}, {y}).\"\n            )\n            return \"Invalid move: Out of bounds.\"\n\n        opponent = self.get_opponent(player_name)\n        opponent_board = self.players[opponent][\"board\"]\n\n        if opponent_board[x][y] == \"1\":\n            opponent_board[x][y] = \"H\"  # Mark as hit\n            self.players[player_name][\"hits\"] += 1\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} hit a ship at ({x}, {y}).\")\n            return \"Hit\"\n\n        elif opponent_board[x][y] == \"0\":\n            opponent_board[x][y] = \"M\"  # Mark as miss\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} missed at ({x}, {y}).\")\n            return \"Miss\"\n\n        logging.warning(f\"Player {player_name} made an invalid move at ({x}, {y}).\")\n        return \"Invalid move: Position already guessed.\"\n\n    def is_game_over(self):\n        \"\"\"Checks if the game is over and declares the winner.\"\"\"\n        for player_name, data in self.players.items():\n            if data[\"hits\"] >= 9:  # Assuming 9 hits to win\n                logging.info(f\"Game over. {player_name} wins!\")\n                if self.game_over:\n                    self._reset_game()\n                self.game_over = True\n                return True\n        return False\n\n    def get_board(self, player_name):\n        \"\"\"Returns the board of the given player.\"\"\"\n        if player_name in self.players:\n            logging.info(f\"Player {player_name} requested their board.\")\n            return (self.players[player_name][\"board\"],)\n        logging.warning(\n            f\"Player {player_name} requested a board but is not registered.\"\n        )\n        return {\n            \"status\": \"error\",\n            \"message\": \"Player not found.\",\n        }\n\n\n    def get_opponent(self, player_name):\n        \"\"\"Helper method to find the opponent of the given player.\"\"\"\n        return [p for p in self.players if p != player_name][0]\n\n    def _reset_game(self):\n        \"\"\"Resets the game state.\"\"\"\n        self.players = {}\n        self.turn = None\n        self.game_over = False\n        logging.info(\"Game state reset.\")\n        logging.info(\"Battleship server started and awaiting connections...\")\n\ndef test_get_board():\n    server = GameServer()\n    board_data_empty = [\n        [\"0\", \"0\", \"0\", \"0\", \"0\"],\n        [\"0\", \"0\", \"0\", \"0\", \"0\"],\n        [\"0\", \"0\", \"0\", \"0\", \"0\"],\n        [\"0\", \"0\", \"0\", \"0\", \"0\"],\n        [\"0\", \"0\", \"0\", \"0\", \"0\"],\n    ]\n    board_data_with_hits = [\n        [\"1\", \"0\", \"0\", \"0\", \"0\"],\n        [\"0\", \"H\", \"0\", \"0\", \"0\"],\n        [\"0\", \"0\", \"1\", \"0\", \"0\"],\n        [\"0\", \"0\", \"0\", \"M\", \"0\"],\n        [\"0\", \"0\", \"0\", \"0\", \"1\"],\n    ]\n\n    # Test case 1: Player is registered\n    server.register_player(\"Alice\", board_data_empty)\n    assert server.get_board(\"Alice\") == server.get_board_new_implementation(\"Alice\")\n\n    # Test case 2: Player is not registered\n    assert server.get_board(\"Bob\") == server.get_board_new_implementation(\"Bob\")\n\n    # Test case 3: Another player is registered\n    server.register_player(\"Bob\", board_data_empty)\n    assert server.get_board(\"Bob\") == server.get_board_new_implementation(\"Bob\")\n\n    # Test case 4: Player with different board configuration\n    server.register_player(\"Charlie\", board_data_with_hits)\n    assert server.get_board(\"Charlie\") == server.get_board_new_implementation(\"Charlie\")\n\n    # Test case 5: After game reset\n    server._reset_game()\n    assert server.get_board(\"Alice\") == server.get_board_new_implementation(\"Alice\")\n\n    # Test case 6: Maximum players registered\n    server.register_player(\"Alice\", board_data_empty)\n    server.register_player(\"Bob\", board_data_with_hits)\n    assert server.get_board(\"Alice\") == server.get_board_new_implementation(\"Alice\")\n    assert server.get_board(\"Bob\") == server.get_board_new_implementation(\"Bob\")\n\n    # Test case 7: Invalid player name\n    assert server.get_board(\"\") == server.get_board_new_implementation(\"\")\n    assert server.get_board(None) == server.get_board_new_implementation(None)\n\n    # Test case 8: Game over state\n    server.players[\"Alice\"][\"hits\"] = 9  # Simulate Alice winning\n    server.is_game_over()  # Trigger game over\n    assert server.get_board(\"Alice\") == server.get_board_new_implementation(\"Alice\")\n    assert server.get_board(\"Bob\") == server.get_board_new_implementation(\"Bob\")\n\nif __name__ == \"__main__\":\n    test_get_board()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_board` in the `GameServer` class is identical to the ORIGINAL FUNCTION. Both functions check if the `player_name` is in `self.players` and return the player's board if found, logging an info message. If the player is not found, they log a warning and return an error message. The functionality and logic are the same in both implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_board` function returns a value, specifically the player's board or an error message if the player is not found. This satisfies the condition as it has return values.\n  \n- CONDITION 2: The test cases use assertions to compare the return values of `get_board` and `get_board_new_implementation`. There are no checks for printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `get_board` and `get_board_new_implementation` directly. If `get_board_new_implementation` has the same functionality, it will return the same results for the same inputs, satisfying this condition.\n\n- CONDITION 4: The test cases and assert statements are reasonable. They check the return values of `get_board` and `get_board_new_implementation`, which is appropriate given that `get_board` has return values. This satisfies the condition.\n\n- CONDITION 5: The test cases cover various scenarios, including registered and unregistered players, different board configurations, game reset, maximum players, invalid player names, and game over state. These are non-trivial and cover a wide range of possible states and inputs, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "87e6c5c23f75ec2ca8accd98eaa86d6ee14221ba"
    },
    {
        "func_name": "GameServer._reset_game",
        "idx": "334",
        "repo_name": "murilob03___sd-batalha-naval",
        "func_path": "server.py",
        "orig_func": "def _reset_game(self):\n    \"\"\"Resets the game state.\"\"\"\n    self.players = {}\n    self.turn = None\n    self.game_over = False\n    logging.info('Game state reset.')\n    logging.info('Battleship server started and awaiting connections...')",
        "orig_context": "```python\n## server.py\nimport logging\n\nclass GameServer:\n    def __init__(self):\n        self.players = {}  # Stores player boards and hits\n        self.turn = None  # Tracks whose turn it is\n        self.game_over = False  # Indicates if the game is over\n\n    def register_player(self, player_name, board_data):\n        \"\"\"Registers a new player with their board.\"\"\"\n        if player_name in self.players:\n            logging.warning(f\"Player {player_name} already registered.\")\n            return \"Player already registered.\"\n\n        if len(self.players) >= 2:\n            logging.warning(f\"Player {player_name} failed to register. Game is full.\")\n            return \"Game is already full.\"\n\n        self.players[player_name] = {\"board\": board_data, \"hits\": 0}\n        logging.info(f\"Player {player_name} successfully registered.\")\n\n        # Assign the first turn to the first player\n        if len(self.players) == 1:\n            self.turn = player_name\n            logging.info(f\"Player {player_name} gets the first turn.\")\n\n        return \"Player registered successfully.\"\n\n    def is_game_ready(self):\n        \"\"\"Checks if the game has two players ready to play.\"\"\"\n        is_ready = len(self.players) == 2\n        logging.info(f\"Game ready status: {is_ready}\")\n        return is_ready\n\n    def whose_turn(self):\n        \"\"\"Returns the name of the player whose turn it is.\"\"\"\n        logging.info(f\"It is {self.turn}'s turn.\")\n        return self.turn\n\n    def make_guess(self, player_name, x, y):\n        \"\"\"Handles a player's guess.\"\"\"\n        if not self.is_game_ready():\n            logging.warning(\n                f\"Player {player_name} tried to guess before the game was ready.\"\n            )\n            return \"Game is not ready yet.\"\n\n        if self.turn != player_name:\n            logging.warning(f\"Player {player_name} tried to guess out of turn.\")\n            return \"Not your turn.\"\n\n        if not (0 <= x < 5 and 0 <= y < 5):\n            logging.warning(\n                f\"Player {player_name} made an invalid guess at ({x}, {y}).\"\n            )\n            return \"Invalid move: Out of bounds.\"\n\n        opponent = self.get_opponent(player_name)\n        opponent_board = self.players[opponent][\"board\"]\n\n        if opponent_board[x][y] == \"1\":\n            opponent_board[x][y] = \"H\"  # Mark as hit\n            self.players[player_name][\"hits\"] += 1\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} hit a ship at ({x}, {y}).\")\n            return \"Hit\"\n\n        elif opponent_board[x][y] == \"0\":\n            opponent_board[x][y] = \"M\"  # Mark as miss\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} missed at ({x}, {y}).\")\n            return \"Miss\"\n\n        logging.warning(f\"Player {player_name} made an invalid move at ({x}, {y}).\")\n        return \"Invalid move: Position already guessed.\"\n\n    def is_game_over(self):\n        \"\"\"Checks if the game is over and declares the winner.\"\"\"\n        for player_name, data in self.players.items():\n            if data[\"hits\"] >= 9:  # Assuming 9 hits to win\n                logging.info(f\"Game over. {player_name} wins!\")\n                if self.game_over:\n                    self._reset_game()\n                self.game_over = True\n                return True\n        return False\n\n    def get_board(self, player_name):\n        \"\"\"Returns the board of the given player.\"\"\"\n        if player_name in self.players:\n            logging.info(f\"Player {player_name} requested their board.\")\n            return (self.players[player_name][\"board\"],)\n        logging.warning(\n            f\"Player {player_name} requested a board but is not registered.\"\n        )\n        return {\n            \"status\": \"error\",\n            \"message\": \"Player not found.\",\n        }\n\n    def get_opponent(self, player_name):\n        \"\"\"Helper method to find the opponent of the given player.\"\"\"\n        return [p for p in self.players if p != player_name][0]\n\n    def _reset_game(self):\n        \"\"\"Resets the game state.\"\"\"\n        self.players = {}\n        self.turn = None\n        self.game_over = False\n        logging.info(\"Game state reset.\")\n        logging.info(\"Battleship server started and awaiting connections...\")\n\n```\n\n\n",
        "eval_script": "## server.py\nimport logging\n\nclass GameServer:\n    def __init__(self):\n        self.players = {}  # Stores player boards and hits\n        self.turn = None  # Tracks whose turn it is\n        self.game_over = False  # Indicates if the game is over\n\n    def register_player(self, player_name, board_data):\n        \"\"\"Registers a new player with their board.\"\"\"\n        if player_name in self.players:\n            logging.warning(f\"Player {player_name} already registered.\")\n            return \"Player already registered.\"\n\n        if len(self.players) >= 2:\n            logging.warning(f\"Player {player_name} failed to register. Game is full.\")\n            return \"Game is already full.\"\n\n        self.players[player_name] = {\"board\": board_data, \"hits\": 0}\n        logging.info(f\"Player {player_name} successfully registered.\")\n\n        # Assign the first turn to the first player\n        if len(self.players) == 1:\n            self.turn = player_name\n            logging.info(f\"Player {player_name} gets the first turn.\")\n\n        return \"Player registered successfully.\"\n\n    def is_game_ready(self):\n        \"\"\"Checks if the game has two players ready to play.\"\"\"\n        is_ready = len(self.players) == 2\n        logging.info(f\"Game ready status: {is_ready}\")\n        return is_ready\n\n    def whose_turn(self):\n        \"\"\"Returns the name of the player whose turn it is.\"\"\"\n        logging.info(f\"It is {self.turn}'s turn.\")\n        return self.turn\n\n    def make_guess(self, player_name, x, y):\n        \"\"\"Handles a player's guess.\"\"\"\n        if not self.is_game_ready():\n            logging.warning(\n                f\"Player {player_name} tried to guess before the game was ready.\"\n            )\n            return \"Game is not ready yet.\"\n\n        if self.turn != player_name:\n            logging.warning(f\"Player {player_name} tried to guess out of turn.\")\n            return \"Not your turn.\"\n\n        if not (0 <= x < 5 and 0 <= y < 5):\n            logging.warning(\n                f\"Player {player_name} made an invalid guess at ({x}, {y}).\"\n            )\n            return \"Invalid move: Out of bounds.\"\n\n        opponent = self.get_opponent(player_name)\n        opponent_board = self.players[opponent][\"board\"]\n\n        if opponent_board[x][y] == \"1\":\n            opponent_board[x][y] = \"H\"  # Mark as hit\n            self.players[player_name][\"hits\"] += 1\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} hit a ship at ({x}, {y}).\")\n            return \"Hit\"\n\n        elif opponent_board[x][y] == \"0\":\n            opponent_board[x][y] = \"M\"  # Mark as miss\n            self.turn = opponent  # Switch turn\n            logging.info(f\"Player {player_name} missed at ({x}, {y}).\")\n            return \"Miss\"\n\n        logging.warning(f\"Player {player_name} made an invalid move at ({x}, {y}).\")\n        return \"Invalid move: Position already guessed.\"\n\n    def is_game_over(self):\n        \"\"\"Checks if the game is over and declares the winner.\"\"\"\n        for player_name, data in self.players.items():\n            if data[\"hits\"] >= 9:  # Assuming 9 hits to win\n                logging.info(f\"Game over. {player_name} wins!\")\n                if self.game_over:\n                    self._reset_game()\n                self.game_over = True\n                return True\n        return False\n\n    def get_board(self, player_name):\n        \"\"\"Returns the board of the given player.\"\"\"\n        if player_name in self.players:\n            logging.info(f\"Player {player_name} requested their board.\")\n            return (self.players[player_name][\"board\"],)\n        logging.warning(\n            f\"Player {player_name} requested a board but is not registered.\"\n        )\n        return {\n            \"status\": \"error\",\n            \"message\": \"Player not found.\",\n        }\n\n    def get_opponent(self, player_name):\n        \"\"\"Helper method to find the opponent of the given player.\"\"\"\n        return [p for p in self.players if p != player_name][0]\n\n    def _reset_game(self):\n        \"\"\"Resets the game state.\"\"\"\n        self.players = {}\n        self.turn = None\n        self.game_over = False\n        logging.info(\"Game state reset.\")\n        logging.info(\"Battleship server started and awaiting connections...\")\n\n\ndef test__reset_game():\n    server = GameServer()\n\n    # Test reset on an empty game state\n    server._reset_game()\n    assert server.players == {}\n    assert server.turn is None\n    assert server.game_over is False\n\n    server._reset_game_new_implementation()\n    assert server.players == {}\n    assert server.turn is None\n    assert server.game_over is False\n\n    # Simulate an ongoing game\n    server.players = {\n        \"Alice\": {\"board\": [[0]*5 for _ in range(5)], \"hits\": 5},\n        \"Bob\": {\"board\": [[0]*5 for _ in range(5)], \"hits\": 4}\n    }\n    server.turn = \"Alice\"\n    server.game_over = True\n\n    # Test original reset\n    server._reset_game()\n    assert server.players == {}\n    assert server.turn is None\n    assert server.game_over is False\n\n    # Simulate an ongoing game again with different hits\n    server.players = {\n        \"Alice\": {\"board\": [[1]*5 for _ in range(5)], \"hits\": 9},\n        \"Bob\": {\"board\": [[0]*5 for _ in range(5)], \"hits\": 0}\n    }\n    server.turn = \"Bob\"\n    server.game_over = True\n\n    # Test new reset implementation\n    server._reset_game_new_implementation()\n    assert server.players == {}\n    assert server.turn is None\n    assert server.game_over is False\n\n    # Test multiple resets\n    server.players = {\n        \"Charlie\": {\"board\": [[0]*5 for _ in range(5)], \"hits\": 3},\n        \"Dave\": {\"board\": [[0]*5 for _ in range(5)], \"hits\": 6}\n    }\n    server.turn = \"Charlie\"\n    server.game_over = True\n\n    server._reset_game()\n    server._reset_game()\n    assert server.players == {}\n    assert server.turn is None\n    assert server.game_over is False\n\n    server._reset_game_new_implementation()\n    server._reset_game_new_implementation()\n    assert server.players == {}\n    assert server.turn is None\n    assert server.game_over is False\n\nif __name__ == \"__main__\":\n    test__reset_game()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_reset_game` in the `GameServer` class is identical to the ORIGINAL FUNCTION. Both functions reset the game state by setting `self.players` to an empty dictionary, `self.turn` to `None`, and `self.game_over` to `False`. They also log the same messages: \"Game state reset.\" and \"Battleship server started and awaiting connections...\". There are no differences in the functionality or implementation between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_reset_game` function modifies the instance variables `self.players`, `self.turn`, and `self.game_over`, which are part of the `GameServer` class. This satisfies the condition as it modifies the state of the object, which is a form of modifying global variables or input arguments.\n\n- CONDITION 2: The test cases check the state of the `GameServer` instance variables (`players`, `turn`, `game_over`) after calling `_reset_game` and `_reset_game_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the states of the instance variables after calling both `_reset_game` and `_reset_game_new_implementation`. If the new implementation passes all tests, it must have the same functionality as the original, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to check the state of the instance variables, which is reasonable given that `_reset_game` does not return any values. The assertions are correctly checking the expected state of the object, satisfying this condition.\n\n- CONDITION 5: The test cases cover various scenarios: resetting an empty game state, resetting after simulating an ongoing game, and multiple resets. These are non-trivial scenarios that adequately test the functionality of the reset methods, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "87e6c5c23f75ec2ca8accd98eaa86d6ee14221ba"
    },
    {
        "func_name": "ThreadedWhisperModel.stop",
        "idx": "342",
        "repo_name": "locaal-ai___simpler-whisper",
        "func_path": "simpler_whisper/whisper.py",
        "orig_func": "def stop(self):\n    \"\"\"\n        Stop processing and clean up resources.\n        Any remaining audio will be processed as a final segment.\n        \"\"\"\n    if not self._is_running:\n        return\n    self.model.stop()\n    self._is_running = False",
        "orig_context": "```python\n## simpler_whisper/whisper.py\nimport numpy as np\n\nfrom typing import Callable, List, Union\n\nfrom . import _whisper_cpp\n\nfrom dataclasses import dataclass\n\nclass WhisperToken:\n    \"\"\"A token from the Whisper model with timing and probability information.\"\"\"\n\n    id: int\n    p: float\n    t0: int  # Start time in milliseconds\n    t1: int  # End time in milliseconds\n    text: str\n\nclass WhisperSegment:\n    \"\"\"A segment of transcribed text with timing information and token details.\"\"\"\n\n    text: str\n    start: int  # Start time in milliseconds\n    end: int  # End time in milliseconds\n    tokens: List[WhisperToken]\n\nclass ThreadedWhisperModel:\n    def __init__(\n        self,\n        model_path: str,\n        callback: Callable[[int, List[WhisperSegment], bool], None],\n        use_gpu=False,\n        max_duration_sec=10.0,\n        sample_rate=16000,\n    ):\n        \"\"\"\n        Initialize a threaded Whisper model for continuous audio processing.\n\n        Args:\n            model_path (str): Path to the Whisper model file\n            use_gpu (bool): Whether to use GPU acceleration\n            max_duration_sec (float): Maximum duration in seconds before finalizing a segment\n            sample_rate (int): Audio sample rate (default: 16000)\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (List[WhisperSegment]): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n        \"\"\"\n        self.model = _whisper_cpp.ThreadedWhisperModel(\n            model_path, use_gpu, max_duration_sec, sample_rate\n        )\n        self._is_running = False\n        self.callback = callback\n\n    def handle_result(\n        self, chunk_id: int, segments: List[WhisperSegment], is_partial: bool\n    ):\n        if self.callback is not None:\n            self.callback(chunk_id, segments, is_partial)\n\n    def start(self, result_check_interval_ms=100):\n        \"\"\"\n        Start the processing threads with a callback for results.\n\n        Args:\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (WhisperSegment): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n            result_check_interval_ms (int): How often to check for results\n        \"\"\"\n        if self._is_running:\n            return\n\n        self.model.start(self.handle_result, result_check_interval_ms)\n        self._is_running = True\n\n    def stop(self):\n        \"\"\"\n        Stop processing and clean up resources.\n        Any remaining audio will be processed as a final segment.\n        \"\"\"\n        if not self._is_running:\n            return\n\n        self.model.stop()\n        self._is_running = False\n\n    def queue_audio(self, audio):\n        \"\"\"\n        Queue audio for processing.\n\n        Args:\n            audio: Audio samples as numpy array or array-like object.\n                  Will be converted to float32.\n\n        Returns:\n            chunk_id (int): Unique identifier for this audio chunk\n        \"\"\"\n        # Ensure audio is a numpy array of float32\n        audio = np.array(audio, dtype=np.float32)\n        return self.model.queue_audio(audio)\n\n    def set_max_duration(self, max_duration_sec, sample_rate=16000):\n        \"\"\"\n        Change the maximum duration for partial segments.\n\n        Args:\n            max_duration_sec (float): New maximum duration in seconds\n            sample_rate (int): Audio sample rate (default: 16000)\n        \"\"\"\n        self.model.set_max_duration(max_duration_sec, sample_rate)\n\n    def __del__(self):\n        # Ensure threads are stopped and resources cleaned up\n        if hasattr(self, \"model\"):\n            if self._is_running:\n                self.stop()\n            del self.model\n\n```\n\n\n",
        "eval_script": "## simpler_whisper/whisper.py\nimport numpy as np\n\nfrom typing import Callable, List, Union\n\nfrom dataclasses import dataclass\n\n# Mocking the _whisper_cpp module\nclass MockThreadedWhisperModel:\n    def __init__(self, model_path, use_gpu, max_duration_sec, sample_rate):\n        self.is_running = False\n\n    def start(self, callback, result_check_interval_ms):\n        self.is_running = True\n\n    def stop(self):\n        self.is_running = False\n\n    def queue_audio(self, audio):\n        return 1  # Mock chunk_id\n\n    def set_max_duration(self, max_duration_sec, sample_rate):\n        pass\n\n# Replacing the _whisper_cpp.ThreadedWhisperModel with the mock\n_whisper_cpp = type('mock', (object,), {'ThreadedWhisperModel': MockThreadedWhisperModel})\n\nclass WhisperToken:\n    \"\"\"A token from the Whisper model with timing and probability information.\"\"\"\n\n    id: int\n    p: float\n    t0: int  # Start time in milliseconds\n    t1: int  # End time in milliseconds\n    text: str\n\nclass WhisperSegment:\n    \"\"\"A segment of transcribed text with timing information and token details.\"\"\"\n\n    text: str\n    start: int  # Start time in milliseconds\n    end: int  # End time in milliseconds\n    tokens: List[WhisperToken]\n\nclass ThreadedWhisperModel:\n    def __init__(\n        self,\n        model_path: str,\n        callback: Callable[[int, List[WhisperSegment], bool], None],\n        use_gpu=False,\n        max_duration_sec=10.0,\n        sample_rate=16000,\n    ):\n        \"\"\"\n        Initialize a threaded Whisper model for continuous audio processing.\n\n        Args:\n            model_path (str): Path to the Whisper model file\n            use_gpu (bool): Whether to use GPU acceleration\n            max_duration_sec (float): Maximum duration in seconds before finalizing a segment\n            sample_rate (int): Audio sample rate (default: 16000)\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (List[WhisperSegment]): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n        \"\"\"\n        self.model = _whisper_cpp.ThreadedWhisperModel(\n            model_path, use_gpu, max_duration_sec, sample_rate\n        )\n        self._is_running = False\n        self.callback = callback\n\n    def handle_result(\n        self, chunk_id: int, segments: List[WhisperSegment], is_partial: bool\n    ):\n        if self.callback is not None:\n            self.callback(chunk_id, segments, is_partial)\n\n    def start(self, result_check_interval_ms=100):\n        \"\"\"\n        Start the processing threads with a callback for results.\n\n        Args:\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (WhisperSegment): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n            result_check_interval_ms (int): How often to check for results\n        \"\"\"\n        if self._is_running:\n            return\n\n        self.model.start(self.handle_result, result_check_interval_ms)\n        self._is_running = True\n\n    def stop(self):\n        \"\"\"\n        Stop processing and clean up resources.\n        Any remaining audio will be processed as a final segment.\n        \"\"\"\n        if not self._is_running:\n            return\n\n        self.model.stop()\n        self._is_running = False\n\n\n    def queue_audio(self, audio):\n        \"\"\"\n        Queue audio for processing.\n\n        Args:\n            audio: Audio samples as numpy array or array-like object.\n                  Will be converted to float32.\n\n        Returns:\n            chunk_id (int): Unique identifier for this audio chunk\n        \"\"\"\n        # Ensure audio is a numpy array of float32\n        audio = np.array(audio, dtype=np.float32)\n        return self.model.queue_audio(audio)\n\n    def set_max_duration(self, max_duration_sec, sample_rate=16000):\n        \"\"\"\n        Change the maximum duration for partial segments.\n\n        Args:\n            max_duration_sec (float): New maximum duration in seconds\n            sample_rate (int): Audio sample rate (default: 16000)\n        \"\"\"\n        self.model.set_max_duration(max_duration_sec, sample_rate)\n\n    def __del__(self):\n        # Ensure threads are stopped and resources cleaned up\n        if hasattr(self, \"model\"):\n            if self._is_running:\n                self.stop()\n            del self.model\n\ndef test_stop():\n    def dummy_callback(chunk_id, segments, is_partial):\n        pass\n\n    model_path = \"/path/to/model\"\n    model = ThreadedWhisperModel(model_path, dummy_callback)\n\n    # Test when model is not running\n    model.stop()\n    assert not model._is_running\n    assert not model.model.is_running\n\n    model.stop_new_implementation()\n    assert not model._is_running\n    assert not model.model.is_running\n\n    # Test when model is running\n    model.start()\n    model.stop()\n    assert not model._is_running\n    assert not model.model.is_running\n\n    model.start()\n    model.stop_new_implementation()\n    assert not model._is_running\n    assert not model.model.is_running\n\n    # Test repeated stops\n    model.start()\n    model.stop()\n    model.stop()  # Call stop again\n    assert not model._is_running\n    assert not model.model.is_running\n\n    model.start()\n    model.stop_new_implementation()\n    model.stop_new_implementation()  # Call stop_new_implementation again\n    assert not model._is_running\n    assert not model.model.is_running\n\n    # Test interleaved start and stop\n    model.start()\n    model.stop()\n    model.start()\n    model.stop_new_implementation()\n    assert not model._is_running\n    assert not model.model.is_running\n\n    # Test rapid start and stop\n    model.start()\n    model.stop()\n    model.start()\n    model.stop()\n    assert not model._is_running\n    assert not model.model.is_running\n\n    model.start()\n    model.stop_new_implementation()\n    model.start()\n    model.stop_new_implementation()\n    assert not model._is_running\n    assert not model.model.is_running\n\nif __name__ == \"__main__\":\n    test_stop()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `stop` method within the `ThreadedWhisperModel` class. When comparing the REVISED FUNCTION to the ORIGINAL FUNCTION, both functions perform the same operations: they check if the model is running, stop the model if it is running, and update the `_is_running` attribute to `False`. The functionality of stopping the model and cleaning up resources is preserved in both implementations. There is no additional logic or change in behavior between the two versions. The test cases provided also confirm that the `stop` method behaves as expected in various scenarios.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `stop` function modifies the state of the `ThreadedWhisperModel` instance by setting `_is_running` and `model.is_running` to `False`. This satisfies the condition as it modifies the state of the object.\n  \n- CONDITION 2: The test cases check the state of the variables (`_is_running` and `model.is_running`) after calling the `stop` function, rather than checking printed or logged contents. This satisfies the condition.\n\n- CONDITION 3: The test cases compare the behavior of `stop` and `stop_new_implementation` by checking the state of the variables after each function call. This ensures that `stop_new_implementation` must have the same functionality as `stop` to pass all tests, satisfying the condition.\n\n- CONDITION 4: The test cases use assertions to check the state of the variables, which is reasonable given that `stop` does not return any values. This satisfies the condition.\n\n- CONDITION 5: The test cases cover various scenarios such as stopping when not running, stopping when running, repeated stops, interleaved start and stop, and rapid start and stop. These are non-trivial test cases that thoroughly test the functionality of the `stop` method, satisfying the condition.",
            "answer": "yes"
        },
        "commit_id": "8c17f29ce55518a65eeb688d3a053daf3789a9ee"
    },
    {
        "func_name": "ThreadedWhisperModel.queue_audio",
        "idx": "343",
        "repo_name": "locaal-ai___simpler-whisper",
        "func_path": "simpler_whisper/whisper.py",
        "orig_func": "def queue_audio(self, audio):\n    \"\"\"\n        Queue audio for processing.\n\n        Args:\n            audio: Audio samples as numpy array or array-like object.\n                  Will be converted to float32.\n\n        Returns:\n            chunk_id (int): Unique identifier for this audio chunk\n        \"\"\"\n    audio = np.array(audio, dtype=np.float32)\n    return self.model.queue_audio(audio)",
        "orig_context": "```python\n## simpler_whisper/whisper.py\nimport numpy as np\n\nfrom typing import Callable, List, Union\n\nfrom . import _whisper_cpp\n\nfrom dataclasses import dataclass\n\nclass WhisperToken:\n    \"\"\"A token from the Whisper model with timing and probability information.\"\"\"\n\n    id: int\n    p: float\n    t0: int  # Start time in milliseconds\n    t1: int  # End time in milliseconds\n    text: str\n\nclass WhisperSegment:\n    \"\"\"A segment of transcribed text with timing information and token details.\"\"\"\n\n    text: str\n    start: int  # Start time in milliseconds\n    end: int  # End time in milliseconds\n    tokens: List[WhisperToken]\n\nclass ThreadedWhisperModel:\n    def __init__(\n        self,\n        model_path: str,\n        callback: Callable[[int, List[WhisperSegment], bool], None],\n        use_gpu=False,\n        max_duration_sec=10.0,\n        sample_rate=16000,\n    ):\n        \"\"\"\n        Initialize a threaded Whisper model for continuous audio processing.\n\n        Args:\n            model_path (str): Path to the Whisper model file\n            use_gpu (bool): Whether to use GPU acceleration\n            max_duration_sec (float): Maximum duration in seconds before finalizing a segment\n            sample_rate (int): Audio sample rate (default: 16000)\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (List[WhisperSegment]): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n        \"\"\"\n        self.model = _whisper_cpp.ThreadedWhisperModel(\n            model_path, use_gpu, max_duration_sec, sample_rate\n        )\n        self._is_running = False\n        self.callback = callback\n\n    def handle_result(\n        self, chunk_id: int, segments: List[WhisperSegment], is_partial: bool\n    ):\n        if self.callback is not None:\n            self.callback(chunk_id, segments, is_partial)\n\n    def start(self, result_check_interval_ms=100):\n        \"\"\"\n        Start the processing threads with a callback for results.\n\n        Args:\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (WhisperSegment): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n            result_check_interval_ms (int): How often to check for results\n        \"\"\"\n        if self._is_running:\n            return\n\n        self.model.start(self.handle_result, result_check_interval_ms)\n        self._is_running = True\n\n    def stop(self):\n        \"\"\"\n        Stop processing and clean up resources.\n        Any remaining audio will be processed as a final segment.\n        \"\"\"\n        if not self._is_running:\n            return\n\n        self.model.stop()\n        self._is_running = False\n\n    def queue_audio(self, audio):\n        \"\"\"\n        Queue audio for processing.\n\n        Args:\n            audio: Audio samples as numpy array or array-like object.\n                  Will be converted to float32.\n\n        Returns:\n            chunk_id (int): Unique identifier for this audio chunk\n        \"\"\"\n        # Ensure audio is a numpy array of float32\n        audio = np.array(audio, dtype=np.float32)\n        return self.model.queue_audio(audio)\n\n    def set_max_duration(self, max_duration_sec, sample_rate=16000):\n        \"\"\"\n        Change the maximum duration for partial segments.\n\n        Args:\n            max_duration_sec (float): New maximum duration in seconds\n            sample_rate (int): Audio sample rate (default: 16000)\n        \"\"\"\n        self.model.set_max_duration(max_duration_sec, sample_rate)\n\n    def __del__(self):\n        # Ensure threads are stopped and resources cleaned up\n        if hasattr(self, \"model\"):\n            if self._is_running:\n                self.stop()\n            del self.model\n\n```\n\n\n",
        "eval_script": "## simpler_whisper/whisper.py\nimport numpy as np\n\nfrom typing import Callable, List, Union\n\n# Mock implementation of _whisper_cpp\nclass MockWhisperCppModel:\n    def __init__(self, model_path, use_gpu, max_duration_sec, sample_rate):\n        self.model_path = model_path\n        self.use_gpu = use_gpu\n        self.max_duration_sec = max_duration_sec\n        self.sample_rate = sample_rate\n\n    def start(self, callback, result_check_interval_ms):\n        print(\"Model started with callback and interval:\", result_check_interval_ms)\n\n    def stop(self):\n        print(\"Model stopped.\")\n\n    def queue_audio(self, audio):\n        print(\"Audio queued for processing.\")\n        return 1  # Mock chunk_id\n\n    def set_max_duration(self, max_duration_sec, sample_rate):\n        print(\"Max duration set to:\", max_duration_sec, \"Sample rate:\", sample_rate)\n\n# Mock the _whisper_cpp module\n_whisper_cpp = type('mock', (object,), {'ThreadedWhisperModel': MockWhisperCppModel})()\n\nfrom dataclasses import dataclass\n\nclass WhisperToken:\n    \"\"\"A token from the Whisper model with timing and probability information.\"\"\"\n\n    id: int\n    p: float\n    t0: int  # Start time in milliseconds\n    t1: int  # End time in milliseconds\n    text: str\n\nclass WhisperSegment:\n    \"\"\"A segment of transcribed text with timing information and token details.\"\"\"\n\n    text: str\n    start: int  # Start time in milliseconds\n    end: int  # End time in milliseconds\n    tokens: List[WhisperToken]\n\nclass ThreadedWhisperModel:\n    def __init__(\n        self,\n        model_path: str,\n        callback: Callable[[int, List[WhisperSegment], bool], None],\n        use_gpu=False,\n        max_duration_sec=10.0,\n        sample_rate=16000,\n    ):\n        \"\"\"\n        Initialize a threaded Whisper model for continuous audio processing.\n\n        Args:\n            model_path (str): Path to the Whisper model file\n            use_gpu (bool): Whether to use GPU acceleration\n            max_duration_sec (float): Maximum duration in seconds before finalizing a segment\n            sample_rate (int): Audio sample rate (default: 16000)\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (List[WhisperSegment]): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n        \"\"\"\n        self.model = _whisper_cpp.ThreadedWhisperModel(\n            model_path, use_gpu, max_duration_sec, sample_rate\n        )\n        self._is_running = False\n        self.callback = callback\n\n    def handle_result(\n        self, chunk_id: int, segments: List[WhisperSegment], is_partial: bool\n    ):\n        if self.callback is not None:\n            self.callback(chunk_id, segments, is_partial)\n\n    def start(self, result_check_interval_ms=100):\n        \"\"\"\n        Start the processing threads with a callback for results.\n\n        Args:\n            callback: Function that takes three arguments:\n                     - chunk_id (int): Unique identifier for the audio chunk\n                     - segments (WhisperSegment): Transcribed text for the audio chunk\n                     - is_partial (bool): Whether this is a partial result\n            result_check_interval_ms (int): How often to check for results\n        \"\"\"\n        if self._is_running:\n            return\n\n        self.model.start(self.handle_result, result_check_interval_ms)\n        self._is_running = True\n\n    def stop(self):\n        \"\"\"\n        Stop processing and clean up resources.\n        Any remaining audio will be processed as a final segment.\n        \"\"\"\n        if not self._is_running:\n            return\n\n        self.model.stop()\n        self._is_running = False\n\n    def queue_audio(self, audio):\n        \"\"\"\n        Queue audio for processing.\n\n        Args:\n            audio: Audio samples as numpy array or array-like object.\n                  Will be converted to float32.\n\n        Returns:\n            chunk_id (int): Unique identifier for this audio chunk\n        \"\"\"\n        # Ensure audio is a numpy array of float32\n        audio = np.array(audio, dtype=np.float32)\n        return self.model.queue_audio(audio)\n\n\n    def set_max_duration(self, max_duration_sec, sample_rate=16000):\n        \"\"\"\n        Change the maximum duration for partial segments.\n\n        Args:\n            max_duration_sec (float): New maximum duration in seconds\n            sample_rate (int): Audio sample rate (default: 16000)\n        \"\"\"\n        self.model.set_max_duration(max_duration_sec, sample_rate)\n\n    def __del__(self):\n        # Ensure threads are stopped and resources cleaned up\n        if hasattr(self, \"model\"):\n            if self._is_running:\n                self.stop()\n            del self.model\n\ndef test_queue_audio():\n    def mock_callback(chunk_id, segments, is_partial):\n        pass\n\n    model = ThreadedWhisperModel(\"mock_model_path\", mock_callback)\n\n    # Test case 1: Random audio data\n    audio_data_1 = np.random.rand(16000)\n    chunk_id_1 = model.queue_audio(audio_data_1)\n    chunk_id_1_new = model.queue_audio_new_implementation(audio_data_1)\n    assert chunk_id_1 == chunk_id_1_new, \"Test case 1 failed\"\n\n    # Test case 2: Zero audio data\n    audio_data_2 = np.zeros(16000)\n    chunk_id_2 = model.queue_audio(audio_data_2)\n    chunk_id_2_new = model.queue_audio_new_implementation(audio_data_2)\n    assert chunk_id_2 == chunk_id_2_new, \"Test case 2 failed\"\n\n    # Test case 3: Audio data with different length\n    audio_data_3 = np.random.rand(32000)\n    chunk_id_3 = model.queue_audio(audio_data_3)\n    chunk_id_3_new = model.queue_audio_new_implementation(audio_data_3)\n    assert chunk_id_3 == chunk_id_3_new, \"Test case 3 failed\"\n\n    # Test case 4: Empty audio data\n    audio_data_4 = np.array([])\n    chunk_id_4 = model.queue_audio(audio_data_4)\n    chunk_id_4_new = model.queue_audio_new_implementation(audio_data_4)\n    assert chunk_id_4 == chunk_id_4_new, \"Test case 4 failed\"\n\n    # Test case 5: Single sample audio data\n    audio_data_5 = np.array([0.5])\n    chunk_id_5 = model.queue_audio(audio_data_5)\n    chunk_id_5_new = model.queue_audio_new_implementation(audio_data_5)\n    assert chunk_id_5 == chunk_id_5_new, \"Test case 5 failed\"\n\n    # Test case 6: Non-float audio data (integers)\n    audio_data_6 = np.random.randint(-32768, 32767, 16000)\n    chunk_id_6 = model.queue_audio(audio_data_6)\n    chunk_id_6_new = model.queue_audio_new_implementation(audio_data_6)\n    assert chunk_id_6 == chunk_id_6_new, \"Test case 6 failed\"\n\n    # Test case 7: Max float value audio data\n    audio_data_7 = np.full(16000, np.finfo(np.float32).max)\n    chunk_id_7 = model.queue_audio(audio_data_7)\n    chunk_id_7_new = model.queue_audio_new_implementation(audio_data_7)\n    assert chunk_id_7 == chunk_id_7_new, \"Test case 7 failed\"\n\n    # Test case 8: Negative audio data\n    audio_data_8 = -np.random.rand(16000)\n    chunk_id_8 = model.queue_audio(audio_data_8)\n    chunk_id_8_new = model.queue_audio_new_implementation(audio_data_8)\n    assert chunk_id_8 == chunk_id_8_new, \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test_queue_audio()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `queue_audio` method within the `ThreadedWhisperModel` class. This function takes an audio input, ensures it is a numpy array of type float32, and then calls the `queue_audio` method of the `self.model` object, which is an instance of `MockWhisperCppModel`. This behavior matches the ORIGINAL FUNCTION, which also converts the audio input to a numpy array of type float32 and then calls `self.model.queue_audio(audio)`. The functionality of both functions is the same as they both perform the same operations: converting the audio to a float32 numpy array and queuing it for processing using the model's `queue_audio` method.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `queue_audio` function returns a `chunk_id`, satisfying the condition that it should have return values or modify global variables or input arguments.\n- [CONDITION 2] The test cases use assertions to check the return values of `queue_audio` and `queue_audio_new_implementation`, and do not check printed or logged contents.\n- [CONDITION 3] The test cases compare the outputs of `queue_audio` and `queue_audio_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass.\n- [CONDITION 4] The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `queue_audio` returns a value.\n- [CONDITION 5] The test cases cover a variety of audio data scenarios, including random data, zero data, different lengths, empty data, single sample, integer data, max float values, and negative data, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "8c17f29ce55518a65eeb688d3a053daf3789a9ee"
    },
    {
        "func_name": "encode_problem_details",
        "idx": "346",
        "repo_name": "JAG-UK___ScraPy",
        "func_path": "scrapi/rfc9290.py",
        "orig_func": "def encode_problem_details(data):\n    \"\"\"\n    Encodes a dictionary into a CBOR object according to RFC 9290 using numeric keys.\n\n    Parameters:\n        data (dict): Dictionary containing RFC 9290 problem details.\n\n    Returns:\n        bytes: CBOR-encoded bytes representing the problem details.\n    \"\"\"\n    numeric_key_data = {DEFINED_FIELDS[k]: v for k, v in data.items() if k in DEFINED_FIELDS}\n    return cbor2.dumps(numeric_key_data)",
        "orig_context": "```python\n## scrapi/rfc9290.py\nimport cbor2\n\nDEFINED_FIELDS = {\n    \"title\": -1,\n    \"detail\": -2,\n    \"instance\": -3,\n    \"response-code\": -4,\n    \"base-uri\": -5,\n    \"base-lang\": -6,\n    \"base-rtl\": -7,\n}\n\ndef encode_problem_details(data):\n    \"\"\"\n    Encodes a dictionary into a CBOR object according to RFC 9290 using numeric keys.\n\n    Parameters:\n        data (dict): Dictionary containing RFC 9290 problem details.\n\n    Returns:\n        bytes: CBOR-encoded bytes representing the problem details.\n    \"\"\"\n    # Swap the dictionary keys to their numeric counterparts\n    numeric_key_data = {\n        DEFINED_FIELDS[k]: v for k, v in data.items() if k in DEFINED_FIELDS\n    }\n    return cbor2.dumps(numeric_key_data)\n\n```\n\n\n",
        "eval_script": "## scrapi/rfc9290.py\nimport cbor2\n\nDEFINED_FIELDS = {\n    \"title\": -1,\n    \"detail\": -2,\n    \"instance\": -3,\n    \"response-code\": -4,\n    \"base-uri\": -5,\n    \"base-lang\": -6,\n    \"base-rtl\": -7,\n}\n\ndef encode_problem_details(data):\n    \"\"\"\n    Encodes a dictionary into a CBOR object according to RFC 9290 using numeric keys.\n\n    Parameters:\n        data (dict): Dictionary containing RFC 9290 problem details.\n\n    Returns:\n        bytes: CBOR-encoded bytes representing the problem details.\n    \"\"\"\n    # Swap the dictionary keys to their numeric counterparts\n    numeric_key_data = {\n        DEFINED_FIELDS[k]: v for k, v in data.items() if k in DEFINED_FIELDS\n    }\n    return cbor2.dumps(numeric_key_data)\n\n\ndef test_encode_problem_details():\n    # Test case 1: All defined fields\n    data1 = {\n        \"title\": \"Error Title\",\n        \"detail\": \"Error Detail\",\n        \"instance\": \"Instance Info\",\n        \"response-code\": 404,\n        \"base-uri\": \"http://example.com\",\n        \"base-lang\": \"en\",\n        \"base-rtl\": False\n    }\n    assert encode_problem_details(data1) == encode_problem_details_new_implementation(data1)\n\n    # Test case 2: Subset of defined fields\n    data2 = {\n        \"title\": \"Error Title\",\n        \"response-code\": 500\n    }\n    assert encode_problem_details(data2) == encode_problem_details_new_implementation(data2)\n\n    # Test case 3: Empty dictionary\n    data3 = {}\n    assert encode_problem_details(data3) == encode_problem_details_new_implementation(data3)\n\n    # Test case 4: Undefined fields\n    data4 = {\n        \"undefined-field\": \"Some value\",\n        \"another-undefined\": 123\n    }\n    assert encode_problem_details(data4) == encode_problem_details_new_implementation(data4)\n\n    # Test case 5: Mixed defined and undefined fields\n    data5 = {\n        \"title\": \"Error Title\",\n        \"undefined-field\": \"Some value\"\n    }\n    assert encode_problem_details(data5) == encode_problem_details_new_implementation(data5)\n\n    # Test case 6: Non-string values\n    data6 = {\n        \"title\": 12345,\n        \"detail\": [\"list\", \"of\", \"details\"],\n        \"response-code\": 200\n    }\n    assert encode_problem_details(data6) == encode_problem_details_new_implementation(data6)\n\n    # Test case 7: Boolean values\n    data7 = {\n        \"base-rtl\": True\n    }\n    assert encode_problem_details(data7) == encode_problem_details_new_implementation(data7)\n\n    # Test case 8: Nested structures\n    data8 = {\n        \"detail\": {\"nested\": \"structure\"},\n        \"response-code\": 400\n    }\n    assert encode_problem_details(data8) == encode_problem_details_new_implementation(data8)\n\n    # Test case 9: Special characters\n    data9 = {\n        \"title\": \"Error with special characters: !@#$%^&*()\",\n        \"detail\": \"Details with unicode: \u00fc\u00f1\u00ee\u00e7\u00f8d\u00eb\"\n    }\n    assert encode_problem_details(data9) == encode_problem_details_new_implementation(data9)\n\n    # Test case 10: Large data\n    data10 = {\n        \"title\": \"A\" * 10000,\n        \"detail\": \"B\" * 10000,\n        \"response-code\": 500\n    }\n    assert encode_problem_details(data10) == encode_problem_details_new_implementation(data10)\n\nif __name__ == \"__main__\":\n    test_encode_problem_details()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is the same as the ORIGINAL FUNCTION. Both functions perform the same operation: they take a dictionary, map its keys to numeric values using the `DEFINED_FIELDS` dictionary, and then encode the resulting dictionary using `cbor2.dumps`. The test cases provided in the revised code are used to verify the functionality of the function, but they do not alter the function itself. The functionality and implementation of the `encode_problem_details` function remain unchanged between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `encode_problem_details` function returns CBOR-encoded bytes, which satisfies the requirement of having return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `encode_problem_details` and `encode_problem_details_new_implementation`, without checking printed or logged contents.\n\n3. **CONDITION 3**: The test cases cover a wide range of inputs, including all defined fields, subsets of fields, empty dictionaries, undefined fields, mixed fields, non-string values, boolean values, nested structures, special characters, and large data. This comprehensive coverage ensures that `encode_problem_details_new_implementation` can only pass all tests if it has the same functionality as `encode_problem_details`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the outputs of the two functions, which is reasonable given that `encode_problem_details` returns a value. There are no inappropriate assertions.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover various edge cases and different types of input data, ensuring thorough testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "71547fc4468af3e4c4381125410c57dd302b1488"
    },
    {
        "func_name": "Scrapi.register_signed_statement",
        "idx": "349",
        "repo_name": "JAG-UK___ScraPy",
        "func_path": "scrapi/scrapi.py",
        "orig_func": "def register_signed_statement(self, statement):\n    \"\"\"Wrapper for SCRAPI Register Signed Statement call\n\n        args:\n            Content-Type: application/cose\n\n            18([                            / COSE Sign1         /\n            h'a1013822',                  / Protected Header   /\n            {},                           / Unprotected Header /\n            null,                         / Detached Payload   /\n            h'269cd68f4211dffc...0dcb29c' / Signature          /\n            ])\n\n        returns:\n            application/json\n        \"\"\"\n    self.check_engine()\n    err, result = self.engine.register_signed_statement(statement)\n    if err:\n        problem_details = decode_problem_details(result)\n        print(problem_details)\n        return None\n    operation = cbor2.loads(result)\n    if not 'status' in operation or operation['status'] == 'failed':\n        raise ScrapiException('Statement Registration Failed')\n    if not 'operationID' in operation:\n        raise ScrapiException('No Operation ID for Statement')\n    return operation['operationID']",
        "orig_context": "```python\n## scrapi/scrapi_exception.py\nclass ScrapiException(Exception):\n    \"\"\"Indicate SCRAPI-specific errors\"\"\"\n\n```\n\n\n```python\n## scrapi/scrapi_engine.py\nfrom abc import ABC\n\nfrom abc import abstractmethod\n\nclass ScrapiEngine(ABC):\n    \"\"\"Virtual base class for all SCRAPI providers\"\"\"\n\n    @abstractmethod\n    def initialized(self):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n    @abstractmethod\n    def get_configuration(self):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n    @abstractmethod\n    def register_signed_statement(self, statement):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n    @abstractmethod\n    def check_registration(self, registration_id):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n    @abstractmethod\n    def resolve_receipt(self, entry_id):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n    @abstractmethod\n    def resolve_signed_statement(self, entry_id):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n    @abstractmethod\n    def issue_signed_statement(self, statement):\n        \"\"\"Pure virtual. Use a derived class\"\"\"\n        pass\n\n```\n\n\n```python\n## scrapi/datatrails_engine.py\nimport logging\n\nfrom pprint import pprint\n\nfrom archivist.archivist import Archivist\n\nfrom archivist.logger import set_logger\n\nimport cbor2\n\nfrom pycose.messages import Sign1Message\n\nimport requests\n\nfrom scrapi_engine import ScrapiEngine\n\nfrom scrapi_exception import ScrapiException\n\nclass DatatrailsEngine(ScrapiEngine):\n    \"\"\"DataTrails SCRAPI Engine implementation\"\"\"\n\n    def __init__(self, ts_args):\n        self._url = ts_args[\"url\"]\n        self._client_id = ts_args[\"client_id\"]\n        self._client_secret = ts_args[\"client_secret\"]\n\n        self._archivist = Archivist(self._url, (self._client_id, self._client_secret))\n        set_logger(ts_args[\"log_level\"] or \"INFO\")\n\n        self._initialized = True\n\n    def __str__(self) -> str:\n        return f\"DataTrails Scrapi Engine ({self._url})\"\n\n    def initialized(self):\n        # TODO: need to check more status for emergent errors. Maybe send a NoOp?\n        return self._initialized\n\n    def get_configuration(self):\n        raise NotImplementedError(\"get_configuration\")\n\n    def register_signed_statement(self, statement):\n        # DataTrails SDK doesn't have native SCITT support yet\n        # but we can still use the Archivist object to look after our\n        # authenticated connection to the server\n        # When the SCRAPI standard settles this code will migrate to the\n        # core DataTrails SDK\n        headers = self._archivist._add_headers({})\n        response = requests.post(\n            f\"{self._url}/archivist/v1/publicscitt/entries\",\n            data=statement,\n            headers=headers,\n            timeout=20000,\n        )\n\n        # Three main failure modes:\n        # - General network or API error: non-200 return\n        # - Malformed statement or general errors will not return an operation ID\n        # - Failure of registration policy or somesuch will return a failed operation\n        if response.status_code != 200:\n            logging.debug(\"%s\", str(response))\n            raise ScrapiException(f\"Failed to register statement: {response}\")\n\n        # The DataTrails implementation currently returns JSON.\n        # Fake up CBOR response here so that common code doesn't need to change\n        return None, cbor2.dumps(response.json())\n\n    def check_registration(self, registration_id):\n        logging.debug(\"checking on operation %s\", registration_id)\n        # DataTrails SDK doesn't have native SCITT support yet\n        # but we can still use the general robust machinery to submit\n        # calls to the REST endpoint.\n        # When the SCRAPI standard settles this code will migrate to the\n        # core DataTrails SDK.\n        # Worth using the DataTrails SDK rather than raw requests for\n        # this one because it includes generic error handling like 429s\n        # internally\n        headers = headers = self._archivist._add_headers({})\n        response = requests.get(\n            f\"{self._url}/archivist/v1/publicscitt/operations/{registration_id}\",\n            headers=headers,\n            timeout=20000,\n        )\n        if response.status_code == 400:\n            # Note: The Public SCITT endpoint returns 400 for Events that have not\n            # made it across the sharing boundary yet. Bodge in a non-fatal fix\n            # here but this needs to be made better.\n            # We're not in any position to know if this is just running or a\n            # permanent problem, so return 'unspecified' and let the client app\n            # sort it out for now.\n            logging.debug(\"Suspected temporary propagation 400 error\")\n            return None, cbor2.dumps(\n                {\"operationID\": registration_id, \"status\": \"running\"}\n            )\n\n        if response.status_code not in [200, 202]:\n            logging.debug(\"FAILED to get operation status: %s\", response.status_code)\n            return response.content, None\n\n        # As things stand DataTrails returns JSON. This will change in future releases.\n        # Fake up CBOR response here so that common code doesn't need to change\n        return None, cbor2.dumps(response.json())\n\n    def resolve_receipt(self, entry_id):\n        logging.debug(\"resolving receipt %s\", entry_id)\n        # DataTrails SDK can't process non-JSON responses yet\n        # but we can still use the Archivist object to look after our\n        # authenticated connection to the server then call out to requests\n        # When the SCRAPI standard settles this code will migrate to the\n        # core DataTrails SDK\n        headers = headers = self._archivist._add_headers({})\n        response = requests.get(\n            f\"{self._url}/archivist/v1/publicscitt/entries/{entry_id}/receipt\",\n            headers=headers,\n            timeout=20000,\n        )\n        if response.status_code != 200:\n            logging.debug(\"FAILED to get receipt: %s\", response.status_code)\n            return response.content, None\n\n        return None, response.content\n\n    def resolve_signed_statement(self, entry_id):\n        logging.debug(\"resolving entry %s\", entry_id)\n        # DataTrails SDK can't process non-JSON responses yet\n        # but we can still use the Archivist object to look after our\n        # authenticated connection to the server then call out to requests\n        # When the SCRAPI standard settles this code will migrate to the\n        # core DataTrails SDK\n        headers = headers = self._archivist._add_headers({})\n        response = requests.get(\n            f\"{self._url}/archivist/v1/publicscitt/entries/{entry_id}\",\n            headers=headers,\n            timeout=20000,\n        )\n        if response.status_code != 200:\n            logging.debug(\"FAILED to get entry: %s\", response.status_code)\n            return response.content, None\n\n        # Note: DataTrails currently returns the _counter_SignedStatement\n        # but SCRAPI wants the original SignedStatement exactly as submitted\n        # by the Issuer, so strip off the outer envelope\n        decoded_statement = Sign1Message.decode(response.content)\n        inner_statement = Sign1Message.decode(decoded_statement.payload)\n\n        print(\"\\ncbor decoded cose sign1 statement:\\n\")\n        print(\"protected headers:\")\n        pprint(inner_statement.phdr)\n        print(\"\\nunprotected headers: \")\n        pprint(inner_statement.uhdr)\n        print(\"\\npayload: \", inner_statement.payload)\n        print(\"payload hex: \", inner_statement.payload.hex())\n\n        return None, inner_statement\n\n    def issue_signed_statement(self, statement):\n        return (None, None)\n\n```\n\n\n```python\n## scrapi/rfc9290.py\nimport cbor2\n\nDEFINED_FIELDS = {\n    \"title\": -1,\n    \"detail\": -2,\n    \"instance\": -3,\n    \"response-code\": -4,\n    \"base-uri\": -5,\n    \"base-lang\": -6,\n    \"base-rtl\": -7,\n}\n\ndef decode_problem_details(cbor_data):\n    \"\"\"\n    Decodes a CBOR-encoded problem details object with numeric keys into a dictionary.\n\n    Parameters:\n        cbor_data (bytes): CBOR-encoded bytes representing the problem details.\n\n    Returns:\n        dict: Dictionary containing RFC 9290 problem details with textual keys.\n    \"\"\"\n    # Decode the CBOR data\n    decoded_data = cbor2.loads(cbor_data)\n    # Convert numeric keys back to their textual representation\n    text_key_data = {\n        key: decoded_data.get(DEFINED_FIELDS[key])\n        for key in DEFINED_FIELDS\n        if DEFINED_FIELDS[key] in decoded_data\n    }\n    return text_key_data\n\n```\n\n\n```python\n## scrapi/scrapi.py\nimport logging\n\nfrom time import time\n\nimport cbor2\n\nfrom datatrails_engine import DatatrailsEngine\n\nfrom scrapi_exception import ScrapiException\n\nfrom rfc9290 import decode_problem_details, encode_problem_details\n\nclass Scrapi:  # pylint: disable=too-many-instance-attributes\n    \"\"\"Portable class for all Scrapi implementations.\n\n    args:\n        ts_type (str): Type of transparency service\n        ts_args (dict): TS-specific initialization params\n\n    \"\"\"\n\n    def __init__(self, ts_type: str, ts_args: dict):\n        match ts_type:\n            case \"DataTrails\":\n                self.engine = DatatrailsEngine(ts_args)\n\n            case _:\n                raise ScrapiException(f\"Unknown engine type: {ts_type}'\")\n\n    def __str__(self) -> str:\n        if self.engine:\n            return self.engine.__str__()\n\n        return \"Scrapi (uninitialized)\"\n\n    # The following methods require a Transparency Service and so require the\n    # engine to be initialized\n\n    def check_engine(self):\n        \"\"\"Helper to protect all calls that need a valid TS connection\"\"\"\n\n        logging.debug(\"Scrapi checking engine liveness...\")\n\n        if not self.engine:\n            raise ScrapiException(\"No Transparency Service engine specified\")\n\n        if not self.engine.initialized():\n            raise ScrapiException(\"Transparency Service engine malfunction\")\n\n        logging.debug(\"Scrapi engine check SUCCESS\")\n\n    def get_configuration(self):\n        \"\"\"Wrapper for SCRAPI Transparency Configuration call\n\n        args:\n            none\n\n        returns:\n            application/json\n        \"\"\"\n\n        self.check_engine()\n\n        return self.engine.get_configuration()\n\n    def register_signed_statement(self, statement):\n        \"\"\"Wrapper for SCRAPI Register Signed Statement call\n\n        args:\n            Content-Type: application/cose\n\n            18([                            / COSE Sign1         /\n            h'a1013822',                  / Protected Header   /\n            {},                           / Unprotected Header /\n            null,                         / Detached Payload   /\n            h'269cd68f4211dffc...0dcb29c' / Signature          /\n            ])\n\n        returns:\n            application/json\n        \"\"\"\n\n        self.check_engine()\n\n        err, result = self.engine.register_signed_statement(statement)\n        if err:\n            # Decode and log the RFC9290 Problem Details.\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        # Pull the registration ID out\n        operation = cbor2.loads(result)\n\n        # Check for common errors\n        if not \"status\" in operation or operation[\"status\"] == \"failed\":\n            raise ScrapiException(\"Statement Registration Failed\")\n\n        if not \"operationID\" in operation:\n            raise ScrapiException(\"No Operation ID for Statement\")\n\n        # Seems legit, send it back\n        return operation[\"operationID\"]\n\n    def check_registration(self, registration_id):\n        \"\"\"Wrapper for SCRAPI Check Registration call\n\n        args:\n            registration_id (str):\n\n        returns:\n            application/json\n        \"\"\"\n\n        self.check_engine()\n\n        err, result = self.engine.check_registration(registration_id)\n        if err:\n            # Decode and log the RFC9290 Problem Details.\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        # Pull the registration ID out\n        operation = cbor2.loads(result)\n\n        # Check for common errors\n        if not \"operationID\" in operation:\n            raise ScrapiException(\"Operation ID link lost\")\n\n        if not \"status\" in operation:\n            raise ScrapiException(\"No LRO status for Operation ID\")\n\n        # Seems legit, send it back\n        return cbor2.loads(result)\n\n    def resolve_receipt(self, entry_id):\n        \"\"\"Wrapper for SCRAPI Resolve Receipt call\n\n        args:\n            entry_id (str):\n\n        returns:\n            application/cose\n        \"\"\"\n        self.check_engine()\n\n        err, result = self.engine.resolve_receipt(entry_id)\n\n        if err:\n            # Decode and log the RFC9290 Problem Details.\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        return result\n\n    def resolve_signed_statement(self, entry_id):\n        \"\"\"Wrapper for SCRAPI Resolve Signed Statement call\n\n        args:\n            entry_id (str):\n\n        returns:\n            application/cose\n        \"\"\"\n        self.check_engine()\n\n        err, result = self.engine.resolve_signed_statement(entry_id)\n\n        if err:\n            # Decode and log the RFC9290 Problem Details.\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        return result\n\n    def issue_signed_statement(self, statement):\n        \"\"\"Sign a statement using a key held on the remote server\n\n        args:\n            statement (bstr): the to-be-signed bytes of a COSE Sign1 input\n\n        returns:\n            application/cose\n        \"\"\"\n\n        self.check_engine()\n\n        err, result = self.engine.issue_signed_statement(statement)\n\n        if err:\n            # Decode and log the RFC9290 Problem Details.\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        return result\n\n    def register_signed_statement_sync(self, statement):\n        \"\"\"Utility function for synchronous receipt generation.\n\n        CAUTION! On some Transparency Service implementations this call may block\n        for a *very* long time!\n\n        args:\n            Content-Type: application/cose\n\n            18([                            / COSE Sign1         /\n            h'a1013822',                  / Protected Header   /\n            {},                           / Unprotected Header /\n            null,                         / Detached Payload   /\n            h'269cd68f4211dffc...0dcb29c' / Signature          /\n            ])\n\n        returns:\n            application/cose\n        \"\"\"\n\n        res = self.register_signed_statement(statement)\n        rid = res[\"registration_id\"]\n        while True:\n            res = self.check_registration(rid)\n            if res[\"status\"] == \"running\":\n                # Wait a moment then go back around\n                logging.info(\"Registration operation %s still running. Waiting...\", rid)\n                time.sleep(2)\n            elif res[\"status\"] == \"failed\":\n                # Fatal. Return\n                logging.info(\"Registration operation %s FAILED.\", rid)\n                return None\n            elif res[\"status\"] == \"success\":\n                # All done. Extract COSE and return the receipt\n                logging.info(\"Registration operation %s SUCCESS.\", rid)\n                return \"COSE goes here!\"\n            else:\n                logging.error(\n                    \"Malformed response from check_registration: %s\", str(res)\n                )\n                return None\n\n```\n\n\n",
        "eval_script": "import logging\nfrom time import time\nimport cbor2\nfrom abc import ABC, abstractmethod\nfrom pprint import pprint\n\n# Mocking external dependencies\nclass Archivist:\n    def __init__(self, url, credentials):\n        self.url = url\n        self.credentials = credentials\n\n    def _add_headers(self, headers):\n        return headers\n\ndef set_logger(log_level):\n    logging.basicConfig(level=log_level)\n\n# Mocking requests module\nclass MockResponse:\n    def __init__(self, status_code, json_data=None, content=None):\n        self.status_code = status_code\n        self._json_data = json_data\n        self.content = content\n\n    def json(self):\n        return self._json_data\n\nclass requests:\n    @staticmethod\n    def post(url, data, headers, timeout):\n        # Simulate a successful response\n        return MockResponse(200, {\"operationID\": \"12345\", \"status\": \"success\"})\n\n    @staticmethod\n    def get(url, headers, timeout):\n        # Simulate a successful response\n        return MockResponse(200, {\"operationID\": \"12345\", \"status\": \"success\"})\n\n# scrapi_exception.py\nclass ScrapiException(Exception):\n    \"\"\"Indicate SCRAPI-specific errors\"\"\"\n\n# scrapi_engine.py\nclass ScrapiEngine(ABC):\n    \"\"\"Virtual base class for all SCRAPI providers\"\"\"\n\n    @abstractmethod\n    def initialized(self):\n        pass\n\n    @abstractmethod\n    def get_configuration(self):\n        pass\n\n    @abstractmethod\n    def register_signed_statement(self, statement):\n        pass\n\n    @abstractmethod\n    def check_registration(self, registration_id):\n        pass\n\n    @abstractmethod\n    def resolve_receipt(self, entry_id):\n        pass\n\n    @abstractmethod\n    def resolve_signed_statement(self, entry_id):\n        pass\n\n    @abstractmethod\n    def issue_signed_statement(self, statement):\n        pass\n\n# datatrails_engine.py\nclass DatatrailsEngine(ScrapiEngine):\n    \"\"\"DataTrails SCRAPI Engine implementation\"\"\"\n\n    def __init__(self, ts_args):\n        self._url = ts_args[\"url\"]\n        self._client_id = ts_args[\"client_id\"]\n        self._client_secret = ts_args[\"client_secret\"]\n\n        self._archivist = Archivist(self._url, (self._client_id, self._client_secret))\n        set_logger(ts_args.get(\"log_level\", \"INFO\"))\n\n        self._initialized = True\n\n    def __str__(self) -> str:\n        return f\"DataTrails Scrapi Engine ({self._url})\"\n\n    def initialized(self):\n        return self._initialized\n\n    def get_configuration(self):\n        raise NotImplementedError(\"get_configuration\")\n\n    def register_signed_statement(self, statement):\n        headers = self._archivist._add_headers({})\n        response = requests.post(\n            f\"{self._url}/archivist/v1/publicscitt/entries\",\n            data=statement,\n            headers=headers,\n            timeout=20000,\n        )\n\n        if response.status_code != 200:\n            logging.debug(\"%s\", str(response))\n            raise ScrapiException(f\"Failed to register statement: {response}\")\n\n        return None, cbor2.dumps(response.json())\n\n    def check_registration(self, registration_id):\n        logging.debug(\"checking on operation %s\", registration_id)\n        headers = self._archivist._add_headers({})\n        response = requests.get(\n            f\"{self._url}/archivist/v1/publicscitt/operations/{registration_id}\",\n            headers=headers,\n            timeout=20000,\n        )\n        if response.status_code == 400:\n            logging.debug(\"Suspected temporary propagation 400 error\")\n            return None, cbor2.dumps(\n                {\"operationID\": registration_id, \"status\": \"running\"}\n            )\n\n        if response.status_code not in [200, 202]:\n            logging.debug(\"FAILED to get operation status: %s\", response.status_code)\n            return response.content, None\n\n        return None, cbor2.dumps(response.json())\n\n    def resolve_receipt(self, entry_id):\n        logging.debug(\"resolving receipt %s\", entry_id)\n        headers = self._archivist._add_headers({})\n        response = requests.get(\n            f\"{self._url}/archivist/v1/publicscitt/entries/{entry_id}/receipt\",\n            headers=headers,\n            timeout=20000,\n        )\n        if response.status_code != 200:\n            logging.debug(\"FAILED to get receipt: %s\", response.status_code)\n            return response.content, None\n\n        return None, response.content\n\n    def resolve_signed_statement(self, entry_id):\n        logging.debug(\"resolving entry %s\", entry_id)\n        headers = self._archivist._add_headers({})\n        response = requests.get(\n            f\"{self._url}/archivist/v1/publicscitt/entries/{entry_id}\",\n            headers=headers,\n            timeout=20000,\n        )\n        if response.status_code != 200:\n            logging.debug(\"FAILED to get entry: %s\", response.status_code)\n            return response.content, None\n\n        decoded_statement = Sign1Message.decode(response.content)\n        inner_statement = Sign1Message.decode(decoded_statement.payload)\n\n        print(\"\\ncbor decoded cose sign1 statement:\\n\")\n        print(\"protected headers:\")\n        pprint(inner_statement.phdr)\n        print(\"\\nunprotected headers: \")\n        pprint(inner_statement.uhdr)\n        print(\"\\npayload: \", inner_statement.payload)\n        print(\"payload hex: \", inner_statement.payload.hex())\n\n        return None, inner_statement\n\n    def issue_signed_statement(self, statement):\n        return (None, None)\n\n# rfc9290.py\nDEFINED_FIELDS = {\n    \"title\": -1,\n    \"detail\": -2,\n    \"instance\": -3,\n    \"response-code\": -4,\n    \"base-uri\": -5,\n    \"base-lang\": -6,\n    \"base-rtl\": -7,\n}\n\ndef decode_problem_details(cbor_data):\n    decoded_data = cbor2.loads(cbor_data)\n    text_key_data = {\n        key: decoded_data.get(DEFINED_FIELDS[key])\n        for key in DEFINED_FIELDS\n        if DEFINED_FIELDS[key] in decoded_data\n    }\n    return text_key_data\n\n# scrapi.py\nclass Scrapi:  # pylint: disable=too-many-instance-attributes\n    \"\"\"Portable class for all Scrapi implementations.\"\"\"\n\n    def __init__(self, ts_type: str, ts_args: dict):\n        match ts_type:\n            case \"DataTrails\":\n                self.engine = DatatrailsEngine(ts_args)\n            case _:\n                raise ScrapiException(f\"Unknown engine type: {ts_type}'\")\n\n    def __str__(self) -> str:\n        if self.engine:\n            return self.engine.__str__()\n        return \"Scrapi (uninitialized)\"\n\n    def check_engine(self):\n        logging.debug(\"Scrapi checking engine liveness...\")\n        if not self.engine:\n            raise ScrapiException(\"No Transparency Service engine specified\")\n        if not self.engine.initialized():\n            raise ScrapiException(\"Transparency Service engine malfunction\")\n        logging.debug(\"Scrapi engine check SUCCESS\")\n\n    def get_configuration(self):\n        self.check_engine()\n        return self.engine.get_configuration()\n\n    def register_signed_statement(self, statement):\n        self.check_engine()\n        err, result = self.engine.register_signed_statement(statement)\n        if err:\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        operation = cbor2.loads(result)\n        if not \"status\" in operation or operation[\"status\"] == \"failed\":\n            raise ScrapiException(\"Statement Registration Failed\")\n        if not \"operationID\" in operation:\n            raise ScrapiException(\"No Operation ID for Statement\")\n        return operation[\"operationID\"]\n\n\n    def check_registration(self, registration_id):\n        self.check_engine()\n        err, result = self.engine.check_registration(registration_id)\n        if err:\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n\n        operation = cbor2.loads(result)\n        if not \"operationID\" in operation:\n            raise ScrapiException(\"Operation ID link lost\")\n        if not \"status\" in operation:\n            raise ScrapiException(\"No LRO status for Operation ID\")\n        return cbor2.loads(result)\n\n    def resolve_receipt(self, entry_id):\n        self.check_engine()\n        err, result = self.engine.resolve_receipt(entry_id)\n        if err:\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n        return result\n\n    def resolve_signed_statement(self, entry_id):\n        self.check_engine()\n        err, result = self.engine.resolve_signed_statement(entry_id)\n        if err:\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n        return result\n\n    def issue_signed_statement(self, statement):\n        self.check_engine()\n        err, result = self.engine.issue_signed_statement(statement)\n        if err:\n            problem_details = decode_problem_details(result)\n            print(problem_details)\n            return None\n        return result\n\n    def register_signed_statement_sync(self, statement):\n        res = self.register_signed_statement(statement)\n        rid = res[\"registration_id\"]\n        while True:\n            res = self.check_registration(rid)\n            if res[\"status\"] == \"running\":\n                logging.info(\"Registration operation %s still running. Waiting...\", rid)\n                time.sleep(2)\n            elif res[\"status\"] == \"failed\":\n                logging.info(\"Registration operation %s FAILED.\", rid)\n                return None\n            elif res[\"status\"] == \"success\":\n                logging.info(\"Registration operation %s SUCCESS.\", rid)\n                return \"COSE goes here!\"\n            else:\n                logging.error(\n                    \"Malformed response from check_registration: %s\", str(res)\n                )\n                return None\n\ndef test_register_signed_statement():\n    ts_args = {\n        \"url\": \"http://example.com\",\n        \"client_id\": \"client_id\",\n        \"client_secret\": \"client_secret\",\n        \"log_level\": \"DEBUG\"\n    }\n    scrapi = Scrapi(\"DataTrails\", ts_args)\n    statement = \"test_statement\"\n\n    # Test 1: Check if both implementations return the same operation ID\n    original_result = scrapi.register_signed_statement(statement)\n    new_result = scrapi.register_signed_statement_new_implementation(statement)\n    assert original_result == new_result, \"Operation IDs do not match\"\n\n    # Test 2: Check if both implementations handle a successful registration\n    assert original_result == \"12345\", \"Original implementation failed to register\"\n    assert new_result == \"12345\", \"New implementation failed to register\"\n\n    # Test 3: Check if both implementations raise an exception for failed registration\n    try:\n        scrapi.engine.register_signed_statement = lambda x: (None, cbor2.dumps({\"status\": \"failed\"}))\n        scrapi.register_signed_statement(statement)\n    except ScrapiException:\n        pass\n    else:\n        assert False, \"Original implementation did not raise exception on failure\"\n\n    try:\n        scrapi.register_signed_statement_new_implementation(statement)\n    except ScrapiException:\n        pass\n    else:\n        assert False, \"New implementation did not raise exception on failure\"\n\nif __name__ == \"__main__\":\n    test_register_signed_statement()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, we can see that they perform the same operations. Both functions call `self.check_engine()` to ensure the engine is ready, then call `self.engine.register_signed_statement(statement)` to register the statement. They both handle errors by decoding problem details and printing them, and they both check the operation's status and operation ID, raising exceptions if necessary. The revised function is embedded in a larger context with additional classes and methods, but the core logic of `register_signed_statement` remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `register_signed_statement` function in the `Scrapi` class does not have a visible implementation in the provided code, so we cannot determine if it returns values or modifies global variables or input arguments. However, based on the test function, it seems to return an operation ID, which suggests it has a return value.\n\n2. **CONDITION 2**: The test cases in `test_register_signed_statement` check the return values of the function and do not rely on printed or logged contents. The assertions compare the results of the original and new implementations, and they also check for exceptions, which aligns with this condition.\n\n3. **CONDITION 3**: The test cases compare the results of the original and new implementations and check for exceptions, which implies they are designed to ensure both implementations have the same functionality. However, without the actual implementation of `register_signed_statement`, we cannot definitively verify this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values and check for exceptions, which is reasonable. However, since the actual implementation of `register_signed_statement` is hidden, we cannot fully assess if the assertions are appropriate for the function's behavior.\n\n5. **CONDITION 5**: The test cases cover different scenarios, such as successful registration and handling of failed registration, which makes them non-trivial.\n\nGiven the hidden implementation of `register_signed_statement`, it is challenging to fully evaluate conditions 1, 3, and 4. However, based on the available information and the structure of the test cases, it seems likely that the test function satisfies the conditions.",
            "answer": "yes"
        },
        "commit_id": "71547fc4468af3e4c4381125410c57dd302b1488"
    },
    {
        "func_name": "format_file_size",
        "idx": "365",
        "repo_name": "kevin-claneo___vector-embedding-calculation",
        "func_path": "utils/file_config.py",
        "orig_func": "def format_file_size(size_bytes: int) -> str:\n    \"\"\"Format file size in bytes to human readable format\"\"\"\n    for unit in ['B', 'KB', 'MB', 'GB']:\n        if size_bytes < 1024:\n            return f'{size_bytes:.2f} {unit}'\n        size_bytes /= 1024\n    return f'{size_bytes:.2f} TB'",
        "orig_context": "```python\n## utils/file_config.py\ndef format_file_size(size_bytes: int) -> str:\n    \"\"\"Format file size in bytes to human readable format\"\"\"\n    for unit in ['B', 'KB', 'MB', 'GB']:\n        if size_bytes < 1024:\n            return f\"{size_bytes:.2f} {unit}\"\n        size_bytes /= 1024\n    return f\"{size_bytes:.2f} TB\"\n\n```\n\n\n",
        "eval_script": "## utils/file_config.py\ndef format_file_size(size_bytes: int) -> str:\n    \"\"\"Format file size in bytes to human readable format\"\"\"\n    for unit in ['B', 'KB', 'MB', 'GB']:\n        if size_bytes < 1024:\n            return f\"{size_bytes:.2f} {unit}\"\n        size_bytes /= 1024\n    return f\"{size_bytes:.2f} TB\"\n\n\ndef test_format_file_size():\n    # Test for bytes\n    assert format_file_size(500) == format_file_size_new_implementation(500), \"Test failed for bytes\"\n\n    # Test for kilobytes\n    assert format_file_size(2048) == format_file_size_new_implementation(2048), \"Test failed for kilobytes\"\n\n    # Test for megabytes\n    assert format_file_size(2**20) == format_file_size_new_implementation(2**20), \"Test failed for megabytes\"\n\n    # Test for gigabytes\n    assert format_file_size(2**30) == format_file_size_new_implementation(2**30), \"Test failed for gigabytes\"\n\n    # Test for terabytes\n    assert format_file_size(2**40) == format_file_size_new_implementation(2**40), \"Test failed for terabytes\"\n\n    # Test for zero bytes\n    assert format_file_size(0) == format_file_size_new_implementation(0), \"Test failed for zero bytes\"\n\n    # Test for boundary conditions\n    assert format_file_size(1023) == format_file_size_new_implementation(1023), \"Test failed for 1023 bytes\"\n    assert format_file_size(1024) == format_file_size_new_implementation(1024), \"Test failed for 1024 bytes\"\n    assert format_file_size(1025) == format_file_size_new_implementation(1025), \"Test failed for 1025 bytes\"\n\n    # Test for large numbers\n    assert format_file_size(10**12) == format_file_size_new_implementation(10**12), \"Test failed for large number\"\n\n    # Test for negative numbers\n    assert format_file_size(-500) == format_file_size_new_implementation(-500), \"Test failed for negative bytes\"\n\nif __name__ == \"__main__\":\n    test_format_file_size()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. The only difference is the use of double quotes instead of single quotes for string formatting, which does not affect the functionality of the code. Both functions iterate over the units ['B', 'KB', 'MB', 'GB'] and format the file size accordingly, returning the size in a human-readable format. The logic and flow of the function remain unchanged. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `format_file_size` returns a formatted string representing the file size in a human-readable format. It does not modify global variables or input arguments. Therefore, this condition is satisfied as the function has return values.\n\n- CONDITION 2: The test cases use assertions to check the return values of the `format_file_size` function against the `format_file_size_new_implementation` function. There are no checks for printed or logged contents. This condition is satisfied.\n\n- CONDITION 3: The test cases compare the outputs of `format_file_size` and `format_file_size_new_implementation` for various inputs, ensuring that the new implementation must have exactly the same functionality to pass all tests. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two functions, which is appropriate since `format_file_size` returns a value. The assertions are reasonable and correctly structured. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a range of scenarios, including bytes, kilobytes, megabytes, gigabytes, terabytes, zero bytes, boundary conditions, large numbers, and negative numbers. This provides a comprehensive test suite, making the test cases non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "2d43f81cd8a572265cd0f81170eee283d56307d4"
    },
    {
        "func_name": "duration_to_seconds",
        "idx": "370",
        "repo_name": "desh0cc___seikatsu-logger",
        "func_path": "utils.py",
        "orig_func": "def duration_to_seconds(duration: str) -> int:\n    \"\"\"\u041f\u0435\u0440\u0435\u0432\u043e\u0434 \u0447\u0430\u0441\u0443 \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0438\"\"\"\n    times = duration.split(':')\n    return int(times[0]) * 3600 + int(times[1]) * 60 + int(times[2])",
        "orig_context": "```python\n## utils.py\ndef duration_to_seconds(duration: str) -> int:\n    \"\"\"\u041f\u0435\u0440\u0435\u0432\u043e\u0434 \u0447\u0430\u0441\u0443 \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0438\"\"\"\n    times = duration.split(\":\")\n    return (int(times[0]) * 3600) + (int(times[1]) * 60) + int(times[2])\n\n```\n\n\n",
        "eval_script": "## utils.py\ndef duration_to_seconds(duration: str) -> int:\n    \"\"\"\u041f\u0435\u0440\u0435\u0432\u043e\u0434 \u0447\u0430\u0441\u0443 \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0438\"\"\"\n    times = duration.split(\":\")\n    return (int(times[0]) * 3600) + (int(times[1]) * 60) + int(times[2])\n\n\ndef test_duration_to_seconds():\n    # Test case 1: Typical duration\n    assert duration_to_seconds(\"01:02:03\") == duration_to_seconds_new_implementation(\"01:02:03\")\n    # Test case 2: Zero hours\n    assert duration_to_seconds(\"00:45:30\") == duration_to_seconds_new_implementation(\"00:45:30\")\n    # Test case 3: Zero minutes and seconds\n    assert duration_to_seconds(\"02:00:00\") == duration_to_seconds_new_implementation(\"02:00:00\")\n    # Test case 4: Maximum values\n    assert duration_to_seconds(\"23:59:59\") == duration_to_seconds_new_implementation(\"23:59:59\")\n    # Test case 5: Single digit values\n    assert duration_to_seconds(\"1:2:3\") == duration_to_seconds_new_implementation(\"1:2:3\")\n    # Test case 6: Midnight edge case\n    assert duration_to_seconds(\"00:00:00\") == duration_to_seconds_new_implementation(\"00:00:00\")\n    # Test case 7: Boundary value just before midnight\n    assert duration_to_seconds(\"23:59:59\") == duration_to_seconds_new_implementation(\"23:59:59\")\n\nif __name__ == \"__main__\":\n    test_duration_to_seconds()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is essentially the same as the ORIGINAL FUNCTION. The only difference is the addition of parentheses around the arithmetic operations, which does not affect the functionality or the result of the function. Both functions split the input string by colons, convert the resulting components to integers, and calculate the total number of seconds by multiplying hours by 3600, minutes by 60, and adding the seconds. The logic and operations performed are identical.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `duration_to_seconds` returns an integer value, which satisfies this condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the return values of `duration_to_seconds` and `duration_to_seconds_new_implementation` for various inputs. If `duration_to_seconds_new_implementation` passes all these tests, it must have the same functionality as `duration_to_seconds`, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable since `duration_to_seconds` has a return value. This condition is satisfied.\n- CONDITION 5: The test cases cover a range of scenarios including typical durations, edge cases, and boundary values, making them non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "3d1d6b32a3900c00b8df7c8346c9d7e4d1520cfe"
    },
    {
        "func_name": "ProcessInputFromSpec",
        "idx": "378",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_go.py",
        "orig_func": "def ProcessInputFromSpec(config, input, widget_id, spec, depth=0):\n    \"\"\"We are given input, and we match it to the spec, so we can organize it into output\"\"\"\n    result = {}\n    for spec_item in spec:\n        if 'import_data' in spec_item:\n            sub_result = ProcessInputFromSpec(config, input, widget_id, spec_item['import_data'], depth=depth + 1)\n            result.update(sub_result)\n        elif 'list_data' in spec_item:\n            sub_result = ProcessInputFromSpec(config, input, widget_id, spec_item['list_data'], depth=depth + 1)\n            result.update(sub_result)\n        else:\n            ProcessInput_SpecificType(config, input, widget_id, spec_item, result)\n    return result",
        "orig_context": "```python\n## logic/command_go.py\nfrom logic.log import LOG\n\nWIDGET_WIDTH_OPTIONS = ['w-min', 'w-24', 'w-32', 'w-40', 'w-48', 'w-64', 'w-72', 'w-90', 'w-96', 'w-full']\n\nWIDGET_HEIGHT_OPTIONS = ['h-min', 'h-24', 'h-32', 'h-40', 'h-48', 'h-64', 'h-72', 'h-90', 'h-96', 'h-full']\n\nCOLOR_VALUE_OPTIONS = ['50', '100', '200', '300', '400', '500', '600', '700', '800', '900']\n\nROUNDED_OPTIONS = ['rounded-none', 'rounded-sm', 'rounded', 'rounded-md', 'rounded-lg', 'rounded-xl', 'rounded-2xl', 'rounded-full']\n\nWIDGET_MARGIN_OPTIONS = ['m-0', 'm-2', 'm-4', 'm-6', 'm-8', 'm-12', 'm-16', 'm-24', 'm-32']\n\nBOLD_OPTIONS = ['', 'font-thin', 'font-normal', 'font-medium', 'font-semibold', 'font-bold', 'font-extrabold', 'font-black']\n\nTEXT_ALIGNMENT_OPTIONS = ['text-left', 'text-left', 'text-center', 'text-justify', 'text-right']\n\nTEXT_SIZE_OPTIONS = ['text-xs', 'text-sm', 'text-base', 'text-lg', 'text-xl', 'text-2xl', 'text-3xl', 'text-4xl', 'text-5xl', 'text-6xl', 'text-7xl', 'text-8xl', 'text-9xl']\n\nDEFAULT_COLOR_VALUE = 50\n\ndef ProcessInput_SpecificType(config, input, widget_id, spec_item, target_dict, depth=0):\n  \"\"\"`input_type_record` is a single spec item, with no children, that results in a single value set by `key_full` into `target_dict`\n  \n  `target_dict` is actually the input we are processing, thus ultimately it is our \"output\"\n  \"\"\"\n  # key = widget_spec_key\n\n  # LOG.info(f'Input: {input}')\n  # LOG.debug(f'For Widget: {widget_id}  Spec Item: {spec_item}')\n\n  # widget_edit_key = f'''__edit.{spec_item['key_full']}'''\n  widget_spec_key = f'''{widget_id}.{spec_item['key_full']}'''\n\n  # Set the raw input value in 1 place, because we need to set the default too\n  raw_input_value = input.get(widget_spec_key, spec_item.get('default', ''))\n\n  # Text\n  if spec_item['type'] == 'text':\n    target_dict[widget_spec_key] = raw_input_value\n\n  # Int\n  elif spec_item['type'] == 'int':\n    # Lookup: Width\n    if 'lookup' in spec_item and spec_item['lookup'] == 'width':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, WIDGET_WIDTH_OPTIONS, int(raw_input_value))\n    \n    # Lookup: Height\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'height':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, WIDGET_HEIGHT_OPTIONS, int(raw_input_value))\n    \n    # Lookup: Rounded\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'rounded':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, ROUNDED_OPTIONS, int(raw_input_value))\n    \n    # Lookup: Margin\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'margin':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, WIDGET_MARGIN_OPTIONS, int(raw_input_value))\n\n    # Lookup: Bold\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'bold':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, BOLD_OPTIONS, int(raw_input_value))\n\n    # Lookup: Align\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'text_align':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, TEXT_ALIGNMENT_OPTIONS, int(raw_input_value))\n\n    # Lookup: Align\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'text_size':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, TEXT_SIZE_OPTIONS, int(raw_input_value))\n\n    # Else, just direct assignment\n    else:\n      target_dict[widget_spec_key] = int(raw_input_value)\n\n  # Checkbox\n  elif spec_item['type'] == 'checkbox':\n    # LOG.info(f'For Widget Checkbox: {widget_id}  Spec Item: {spec_item}')\n\n    if raw_input_value == 'true':\n      if 'value_if_true' in spec_item:\n        target_dict[widget_spec_key] = spec_item['value_if_true']\n      else:\n        target_dict[widget_spec_key] = True\n    else:\n      if 'value_if_false' in spec_item:\n        target_dict[widget_spec_key] = spec_item['value_if_false']\n      else:\n        target_dict[widget_spec_key] = False\n\n  # Color\n  elif spec_item['type'] == 'color':\n    value_key = f'''{widget_spec_key}_value'''\n\n    # Get the raw value color value, or default back to 50\n    raw_color_value = input.get(value_key, DEFAULT_COLOR_VALUE)\n\n    color_value = GetOptionByPercent(value_key, COLOR_VALUE_OPTIONS, int(raw_color_value))\n    target_dict[widget_spec_key] = f'''text-{raw_input_value}-{color_value} raw-color-{raw_color_value} value-key-{value_key}'''\n\n  # Color Background\n  elif spec_item['type'] == 'color_background':\n    value_key = f'''{widget_spec_key}_value'''\n\n    # Get the raw value color value, or default back to 50\n    raw_color_value = input.get(value_key, DEFAULT_COLOR_VALUE)\n\n    color_value = GetOptionByPercent(value_key, COLOR_VALUE_OPTIONS, int(raw_color_value))\n    target_dict[widget_spec_key] = f'''bg-{raw_input_value}-{color_value}'''\n\n  # Icon\n  elif spec_item['type'] == 'icon':\n    target_dict[widget_spec_key] = raw_input_value\n\n  # Unknown: Error\n  else:\n    LOG.error(f'''ProcessInput_SpecificType: Unknown type: {spec_item['type']}  Spec Item: {spec_item}''')\n\ndef Clamp(value, clamp_min, clamp_max):\n  \"\"\"Clamp value between min and max values\"\"\"\n  return min(clamp_max, max(clamp_min, value))\n\ndef GetOptionByPercent(name_for_error, options, value_0_100):\n  \"\"\"\"\"\"\n  try:\n    percent = value_0_100 / 100.0\n\n    index = int(len(options) * percent)\n    index = Clamp(index, 0, len(options) - 1)\n\n    return options[index]\n  except ValueError as e:\n    LOG.error(f'''Site Editor: {name_for_error} input is not an integer: {value_0_100}''')\n    return None\n\ndef ProcessInputFromSpec(config, input, widget_id, spec, depth=0):\n  \"\"\"We are given input, and we match it to the spec, so we can organize it into output\"\"\"\n  result = {}\n\n  # LOG.debug(f'Widget: {widget_id}')\n  # LOG.debug(f'Input: {pprint.pformat(input)}')\n  # LOG.debug(f'Spec: {pprint.pformat(spec)}')\n\n  for spec_item in spec:\n    # If this is an include of some type, then we recurse with this data as the new root item\n    if 'import_data' in spec_item:\n      sub_result = ProcessInputFromSpec(config, input, widget_id, spec_item['import_data'], depth=depth+1)\n\n      result.update(sub_result)\n    elif 'list_data' in spec_item:\n      sub_result = ProcessInputFromSpec(config, input, widget_id, spec_item['list_data'], depth=depth+1)\n      result.update(sub_result)\n\n    # Else, this is a specific type, we process it here\n    else:\n      ProcessInput_SpecificType(config, input, widget_id, spec_item, result)\n\n  return result\n\n```\n\n\n",
        "eval_script": "## logic/command_go.py\nimport logging\n\n# Mock logging setup\nlogging.basicConfig(level=logging.DEBUG)\nLOG = logging.getLogger(__name__)\n\nWIDGET_WIDTH_OPTIONS = ['w-min', 'w-24', 'w-32', 'w-40', 'w-48', 'w-64', 'w-72', 'w-90', 'w-96', 'w-full']\nWIDGET_HEIGHT_OPTIONS = ['h-min', 'h-24', 'h-32', 'h-40', 'h-48', 'h-64', 'h-72', 'h-90', 'h-96', 'h-full']\nCOLOR_VALUE_OPTIONS = ['50', '100', '200', '300', '400', '500', '600', '700', '800', '900']\nROUNDED_OPTIONS = ['rounded-none', 'rounded-sm', 'rounded', 'rounded-md', 'rounded-lg', 'rounded-xl', 'rounded-2xl', 'rounded-full']\nWIDGET_MARGIN_OPTIONS = ['m-0', 'm-2', 'm-4', 'm-6', 'm-8', 'm-12', 'm-16', 'm-24', 'm-32']\nBOLD_OPTIONS = ['', 'font-thin', 'font-normal', 'font-medium', 'font-semibold', 'font-bold', 'font-extrabold', 'font-black']\nTEXT_ALIGNMENT_OPTIONS = ['text-left', 'text-left', 'text-center', 'text-justify', 'text-right']\nTEXT_SIZE_OPTIONS = ['text-xs', 'text-sm', 'text-base', 'text-lg', 'text-xl', 'text-2xl', 'text-3xl', 'text-4xl', 'text-5xl', 'text-6xl', 'text-7xl', 'text-8xl', 'text-9xl']\nDEFAULT_COLOR_VALUE = 50\n\ndef ProcessInput_SpecificType(config, input, widget_id, spec_item, target_dict, depth=0):\n  widget_spec_key = f'''{widget_id}.{spec_item['key_full']}'''\n  raw_input_value = input.get(widget_spec_key, spec_item.get('default', ''))\n\n  if spec_item['type'] == 'text':\n    target_dict[widget_spec_key] = raw_input_value\n  elif spec_item['type'] == 'int':\n    if 'lookup' in spec_item and spec_item['lookup'] == 'width':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, WIDGET_WIDTH_OPTIONS, int(raw_input_value))\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'height':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, WIDGET_HEIGHT_OPTIONS, int(raw_input_value))\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'rounded':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, ROUNDED_OPTIONS, int(raw_input_value))\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'margin':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, WIDGET_MARGIN_OPTIONS, int(raw_input_value))\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'bold':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, BOLD_OPTIONS, int(raw_input_value))\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'text_align':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, TEXT_ALIGNMENT_OPTIONS, int(raw_input_value))\n    elif 'lookup' in spec_item and spec_item['lookup'] == 'text_size':\n      target_dict[widget_spec_key] = GetOptionByPercent(widget_spec_key, TEXT_SIZE_OPTIONS, int(raw_input_value))\n    else:\n      target_dict[widget_spec_key] = int(raw_input_value)\n  elif spec_item['type'] == 'checkbox':\n    if raw_input_value == 'true':\n      if 'value_if_true' in spec_item:\n        target_dict[widget_spec_key] = spec_item['value_if_true']\n      else:\n        target_dict[widget_spec_key] = True\n    else:\n      if 'value_if_false' in spec_item:\n        target_dict[widget_spec_key] = spec_item['value_if_false']\n      else:\n        target_dict[widget_spec_key] = False\n  elif spec_item['type'] == 'color':\n    value_key = f'''{widget_spec_key}_value'''\n    raw_color_value = input.get(value_key, DEFAULT_COLOR_VALUE)\n    color_value = GetOptionByPercent(value_key, COLOR_VALUE_OPTIONS, int(raw_color_value))\n    target_dict[widget_spec_key] = f'''text-{raw_input_value}-{color_value} raw-color-{raw_color_value} value-key-{value_key}'''\n  elif spec_item['type'] == 'color_background':\n    value_key = f'''{widget_spec_key}_value'''\n    raw_color_value = input.get(value_key, DEFAULT_COLOR_VALUE)\n    color_value = GetOptionByPercent(value_key, COLOR_VALUE_OPTIONS, int(raw_color_value))\n    target_dict[widget_spec_key] = f'''bg-{raw_input_value}-{color_value}'''\n  elif spec_item['type'] == 'icon':\n    target_dict[widget_spec_key] = raw_input_value\n  else:\n    LOG.error(f'''ProcessInput_SpecificType: Unknown type: {spec_item['type']}  Spec Item: {spec_item}''')\n\ndef Clamp(value, clamp_min, clamp_max):\n  return min(clamp_max, max(clamp_min, value))\n\ndef GetOptionByPercent(name_for_error, options, value_0_100):\n  try:\n    percent = value_0_100 / 100.0\n    index = int(len(options) * percent)\n    index = Clamp(index, 0, len(options) - 1)\n    return options[index]\n  except ValueError as e:\n    LOG.error(f'''Site Editor: {name_for_error} input is not an integer: {value_0_100}''')\n    return None\n\ndef ProcessInputFromSpec(config, input, widget_id, spec, depth=0):\n  result = {}\n  for spec_item in spec:\n    if 'import_data' in spec_item:\n      sub_result = ProcessInputFromSpec(config, input, widget_id, spec_item['import_data'], depth=depth+1)\n      result.update(sub_result)\n    elif 'list_data' in spec_item:\n      sub_result = ProcessInputFromSpec(config, input, widget_id, spec_item['list_data'], depth=depth+1)\n      result.update(sub_result)\n    else:\n      ProcessInput_SpecificType(config, input, widget_id, spec_item, result)\n  return result\n\n\ndef test_ProcessInputFromSpec():\n  config = {}\n  widget_id = 'widget1'\n\n  # Test case 1: Text type\n  input1 = {'widget1.text_key': 'sample text'}\n  spec1 = [{'key_full': 'text_key', 'type': 'text'}]\n  assert ProcessInputFromSpec(config, input1, widget_id, spec1) == ProcessInputFromSpec_new_implementation(config, input1, widget_id, spec1)\n\n  # Test case 2: Int type with lookup\n  input2 = {'widget1.int_key': '50'}\n  spec2 = [{'key_full': 'int_key', 'type': 'int', 'lookup': 'width'}]\n  assert ProcessInputFromSpec(config, input2, widget_id, spec2) == ProcessInputFromSpec_new_implementation(config, input2, widget_id, spec2)\n\n  # Test case 3: Checkbox type\n  input3 = {'widget1.checkbox_key': 'true'}\n  spec3 = [{'key_full': 'checkbox_key', 'type': 'checkbox', 'value_if_true': 'checked'}]\n  assert ProcessInputFromSpec(config, input3, widget_id, spec3) == ProcessInputFromSpec_new_implementation(config, input3, widget_id, spec3)\n\n  # Test case 4: Import data\n  input4 = {'widget1.import_key': 'imported text'}\n  spec4 = [{'key_full': 'import_key', 'type': 'text', 'import_data': [{'key_full': 'import_key', 'type': 'text'}]}]\n  assert ProcessInputFromSpec(config, input4, widget_id, spec4) == ProcessInputFromSpec_new_implementation(config, input4, widget_id, spec4)\n\n  # Test case 5: List data\n  input5 = {'widget1.list_key': 'list text'}\n  spec5 = [{'key_full': 'list_key', 'type': 'text', 'list_data': [{'key_full': 'list_key', 'type': 'text'}]}]\n  assert ProcessInputFromSpec(config, input5, widget_id, spec5) == ProcessInputFromSpec_new_implementation(config, input5, widget_id, spec5)\n\nif __name__ == \"__main__\":\n  test_ProcessInputFromSpec()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions have the same name, `ProcessInputFromSpec`, and the logic within the function is unchanged. They both iterate over the `spec` list, check for the presence of 'import_data' or 'list_data' keys, and recursively call themselves if those keys are present. If neither key is present, they call `ProcessInput_SpecificType` with the same arguments. The structure, logic, and flow of the function remain consistent between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `ProcessInputFromSpec` function returns a dictionary (`result`), which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `ProcessInputFromSpec` and `ProcessInputFromSpec_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `ProcessInputFromSpec` and `ProcessInputFromSpec_new_implementation` directly. This ensures that `ProcessInputFromSpec_new_implementation` can pass all the test cases only if it has the exact same functionality as `ProcessInputFromSpec`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `ProcessInputFromSpec` returns a dictionary. The test cases do not use inappropriate assertions.\n\n5. **CONDITION 5**: The test cases cover various scenarios, including different types of inputs (`text`, `int` with lookup, `checkbox`, `import_data`, and `list_data`). This variety makes the test cases non-trivial, as they test different branches and functionalities of the `ProcessInputFromSpec` function.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "Site_Content_Admin",
        "idx": "381",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_go.py",
        "orig_func": "def Site_Content_Admin(config):\n    \"\"\"This is the data for ALL the content, and it edits data fields, or adds/removes, whatever.  Label management as well, since `limit` must be upheld\n\n  `limit` and `limit_queue`, where we insert new elements into the front of the queue, so it bumps the old.  `limit` causes additions to fail until 1 is removed.  Make that easy, viewer-select\n\n  \"\"\"\n    result = config.input['existing']\n    return result",
        "orig_context": "```python\n## logic/command_go.py\ndef Site_Content_Admin(config):\n  \"\"\"This is the data for ALL the content, and it edits data fields, or adds/removes, whatever.  Label management as well, since `limit` must be upheld\n\n  `limit` and `limit_queue`, where we insert new elements into the front of the queue, so it bumps the old.  `limit` causes additions to fail until 1 is removed.  Make that easy, viewer-select\n\n  \"\"\"\n  # Start with the pass through and mutate\n  result = config.input['existing']\n  \n  return result\n\n```\n\n\n",
        "eval_script": "## logic/command_go.py\ndef Site_Content_Admin(config):\n    \"\"\"This is the data for ALL the content, and it edits data fields, or adds/removes, whatever.  Label management as well, since `limit` must be upheld\n\n    `limit` and `limit_queue`, where we insert new elements into the front of the queue, so it bumps the old.  `limit` causes additions to fail until 1 is removed.  Make that easy, viewer-select\n\n    \"\"\"\n    # Start with the pass through and mutate\n    result = config.input['existing']\n    \n    return result\n\n\n# Mock configuration object\nclass MockConfig:\n    def __init__(self, existing_value):\n        self.input = {'existing': existing_value}\n\ndef test_Site_Content_Admin():\n    # Test case 1: Basic functionality\n    config1 = MockConfig('mock_data')\n    assert Site_Content_Admin(config1) == Site_Content_Admin_new_implementation(config1)\n\n    # Test case 2: Different data\n    config2 = MockConfig('different_data')\n    assert Site_Content_Admin(config2) == Site_Content_Admin_new_implementation(config2)\n\n    # Test case 3: Empty data\n    config3 = MockConfig('')\n    assert Site_Content_Admin(config3) == Site_Content_Admin_new_implementation(config3)\n\n    # Test case 4: None as data\n    config4 = MockConfig(None)\n    assert Site_Content_Admin(config4) == Site_Content_Admin_new_implementation(config4)\n\n    # Test case 5: Non-string data (integer)\n    config5 = MockConfig(12345)\n    assert Site_Content_Admin(config5) == Site_Content_Admin_new_implementation(config5)\n\n    # Test case 6: Non-string data (list)\n    config6 = MockConfig([1, 2, 3, 4, 5])\n    assert Site_Content_Admin(config6) == Site_Content_Admin_new_implementation(config6)\n\n    # Test case 7: Non-string data (dictionary)\n    config7 = MockConfig({'key': 'value'})\n    assert Site_Content_Admin(config7) == Site_Content_Admin_new_implementation(config7)\n\n    # Test case 8: Large data\n    large_data = 'a' * 10000  # String of 10,000 'a' characters\n    config8 = MockConfig(large_data)\n    assert Site_Content_Admin(config8) == Site_Content_Admin_new_implementation(config8)\n\n    # Test case 9: Special characters\n    special_chars = '!@#$%^&*()_+-=[]{}|;:\\'\",.<>?/~`'\n    config9 = MockConfig(special_chars)\n    assert Site_Content_Admin(config9) == Site_Content_Admin_new_implementation(config9)\n\nif __name__ == \"__main__\":\n    test_Site_Content_Admin()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `Site_Content_Admin` simply returns the value associated with the key `'existing'` from the `input` attribute of the `config` object. The revised function in the provided code does exactly the same thing. The additional code in the revised version, such as the mock configuration class and test cases, does not alter the functionality of the `Site_Content_Admin` function itself. These additions are for testing purposes only and do not affect the logic of the function. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `Site_Content_Admin` returns a value, specifically `result`, which is derived from `config.input['existing']`. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to check the return values of `Site_Content_Admin` and `Site_Content_Admin_new_implementation`. There is no checking of printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the return values of `Site_Content_Admin` and `Site_Content_Admin_new_implementation` for various inputs. This ensures that `Site_Content_Admin_new_implementation` can only pass all tests if it has the exact same functionality as `Site_Content_Admin`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `Site_Content_Admin` returns a value. There are no unreasonable assertions present.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including different data types (strings, integers, lists, dictionaries), empty data, large data, and special characters. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "GetLinkHtml",
        "idx": "382",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/utility.py",
        "orig_func": "def GetLinkHtml(url, name=None):\n    \"\"\"Create an HTML URL\"\"\"\n    if not name:\n        name = url\n    link = f'<a href=\"{url}\" class=\"font-medium text-blue-600 dark:text-blue-500 hover:underline\">{name}</a>'\n    return link",
        "orig_context": "```python\n## logic/utility.py\ndef GetLinkHtml(url, name=None):\n  \"\"\"Create an HTML URL\"\"\"\n  if not name:\n    name = url\n\n  link = f'''<a href=\"{url}\" class=\"font-medium text-blue-600 dark:text-blue-500 hover:underline\">{name}</a>'''\n  \n  return link\n\n```\n\n\n",
        "eval_script": "## logic/utility.py\ndef GetLinkHtml(url, name=None):\n  \"\"\"Create an HTML URL\"\"\"\n  if not name:\n    name = url\n\n  link = f'''<a href=\"{url}\" class=\"font-medium text-blue-600 dark:text-blue-500 hover:underline\">{name}</a>'''\n  \n  return link\n\n\ndef test_GetLinkHtml():\n  # Test when both url and name are provided\n  url = \"http://example.com\"\n  name = \"Example\"\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test when only url is provided\n  url = \"http://example.com\"\n  assert GetLinkHtml(url) == GetLinkHtml_new_implementation(url)\n\n  # Test when name is None\n  url = \"http://example.com\"\n  name = None\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test with empty URL\n  url = \"\"\n  name = \"Empty URL\"\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test with empty name\n  url = \"http://example.com\"\n  name = \"\"\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test with special characters in URL and name\n  url = \"http://example.com/?query=1&other=2\"\n  name = \"Example & Test\"\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test with long URL and name\n  url = \"http://\" + \"a\" * 1000 + \".com\"\n  name = \"Name \" + \"b\" * 1000\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test URL with query parameters\n  url = \"http://example.com/page?param=value\"\n  name = \"Example with Query\"\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\n  # Test URL with fragment identifier\n  url = \"http://example.com/page#section\"\n  name = \"Example with Fragment\"\n  assert GetLinkHtml(url, name) == GetLinkHtml_new_implementation(url, name)\n\nif __name__ == \"__main__\":\n  test_GetLinkHtml()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The revised function `GetLinkHtml` is identical to the original function in terms of functionality. The only difference is the use of triple quotes (`'''`) instead of double quotes (`\"`) for the string that defines the HTML link. This change does not affect the functionality or output of the function, as both types of quotes can be used interchangeably for string literals in Python. The rest of the code, including the logic for handling the `name` parameter and constructing the HTML link, remains unchanged. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `GetLinkHtml` function returns a string, which is an HTML link. It does not modify global variables or input arguments. This condition is satisfied.\n- CONDITION 2: The test cases use assertions to check the return values of `GetLinkHtml` and `GetLinkHtml_new_implementation`. They do not check printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `GetLinkHtml` and `GetLinkHtml_new_implementation` for various inputs. If `GetLinkHtml_new_implementation` has the same functionality, it will pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable since `GetLinkHtml` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including different combinations of `url` and `name`, empty strings, special characters, long strings, and URLs with query parameters and fragments. This makes the test cases non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "IsDataEqual",
        "idx": "383",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/utility.py",
        "orig_func": "def IsDataEqual(a, b):\n    \"\"\"Uses JSON to ensure any data type that is serializable can be compared\"\"\"\n    a_json = json.dumps(a)\n    b_json = json.dumps(b)\n    return a_json == b_json",
        "orig_context": "```python\n## logic/utility.py\nimport json\n\ndef IsDataEqual(a, b):\n  \"\"\"Uses JSON to ensure any data type that is serializable can be compared\"\"\"\n  a_json = json.dumps(a)\n  b_json = json.dumps(b)\n\n  # Return if they are equal\n  return a_json == b_json\n\n```\n\n\n",
        "eval_script": "## logic/utility.py\nimport json\n\ndef IsDataEqual(a, b):\n  \"\"\"Uses JSON to ensure any data type that is serializable can be compared\"\"\"\n  a_json = json.dumps(a)\n  b_json = json.dumps(b)\n\n  # Return if they are equal\n  return a_json == b_json\n\n\ndef test_IsDataEqual():\n  # Test case 1: Identical simple data\n  assert IsDataEqual(1, 1) == IsDataEqual_new_implementation(1, 1)\n\n  # Test case 2: Different simple data\n  assert IsDataEqual(1, 2) == IsDataEqual_new_implementation(1, 2)\n\n  # Test case 3: Identical complex data\n  complex_data = {\"key\": [1, 2, 3], \"another_key\": {\"nested_key\": \"value\"}}\n  assert IsDataEqual(complex_data, complex_data) == IsDataEqual_new_implementation(complex_data, complex_data)\n\n  # Test case 4: Different complex data\n  complex_data_1 = {\"key\": [1, 2, 3], \"another_key\": {\"nested_key\": \"value\"}}\n  complex_data_2 = {\"key\": [1, 2, 3], \"another_key\": {\"nested_key\": \"different_value\"}}\n  assert IsDataEqual(complex_data_1, complex_data_2) == IsDataEqual_new_implementation(complex_data_1, complex_data_2)\n\n  # Test case 5: Identical data with different types\n  assert IsDataEqual(1, 1.0) == IsDataEqual_new_implementation(1, 1.0)\n\n  # Test case 6: Empty data structures\n  assert IsDataEqual([], []) == IsDataEqual_new_implementation([], [])\n  assert IsDataEqual({}, {}) == IsDataEqual_new_implementation({}, {})\n\n  # Test case 7: Nested data structures\n  nested_data_1 = {\"key\": {\"nested_key\": {\"deep_key\": \"value\"}}}\n  nested_data_2 = {\"key\": {\"nested_key\": {\"deep_key\": \"value\"}}}\n  assert IsDataEqual(nested_data_1, nested_data_2) == IsDataEqual_new_implementation(nested_data_1, nested_data_2)\n\n  # Test case 8: Data with different orders\n  list_1 = [1, 2, 3]\n  list_2 = [3, 2, 1]\n  assert IsDataEqual(list_1, list_2) == IsDataEqual_new_implementation(list_1, list_2)\n\n  # Test case 9: Special characters and Unicode\n  special_str_1 = \"hello\\nworld\"\n  special_str_2 = \"hello\\nworld\"\n  assert IsDataEqual(special_str_1, special_str_2) == IsDataEqual_new_implementation(special_str_1, special_str_2)\n\n  unicode_str_1 = \"caf\u00e9\"\n  unicode_str_2 = \"caf\u00e9\"\n  assert IsDataEqual(unicode_str_1, unicode_str_2) == IsDataEqual_new_implementation(unicode_str_1, unicode_str_2)\n\n  # Test case 10: Null and Boolean values\n  assert IsDataEqual(None, None) == IsDataEqual_new_implementation(None, None)\n  assert IsDataEqual(True, True) == IsDataEqual_new_implementation(True, True)\n  assert IsDataEqual(False, False) == IsDataEqual_new_implementation(False, False)\n\n  # Test case 11: Floating point precision\n  assert IsDataEqual(1.0000001, 1.0000001) == IsDataEqual_new_implementation(1.0000001, 1.0000001)\n\n  # Test case 12: Large data structures\n  large_data_1 = {\"key\": list(range(1000))}\n  large_data_2 = {\"key\": list(range(1000))}\n  assert IsDataEqual(large_data_1, large_data_2) == IsDataEqual_new_implementation(large_data_1, large_data_2)\n\nif __name__ == \"__main__\":\n  test_IsDataEqual()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `IsDataEqual` is identical to the ORIGINAL FUNCTION in terms of implementation. Both functions import the `json` module, convert the inputs `a` and `b` to JSON strings using `json.dumps`, and then compare these strings for equality. The additional test cases provided in the revised code do not alter the functionality of the `IsDataEqual` function itself; they merely serve to validate its behavior. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The original `IsDataEqual` function returns a boolean value indicating whether the JSON representations of the inputs are equal. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents. This satisfies the condition.\n- CONDITION 3: The test cases compare the return values of `IsDataEqual` and `IsDataEqual_new_implementation` for various inputs. If the new implementation has the same functionality, it will return the same results for all test cases. This satisfies the condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that the function returns a boolean value. This satisfies the condition.\n- CONDITION 5: The test cases cover a wide range of scenarios, including simple and complex data, different data types, nested structures, special characters, and large data structures. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "CloseDifference",
        "idx": "384",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_go.py",
        "orig_func": "def CloseDifference(value, test_value, tolerance=0.04):\n    \"\"\"\"\"\"\n    diff = value - test_value\n    return abs(diff) < tolerance",
        "orig_context": "```python\n## logic/command_go.py\ndef CloseDifference(value, test_value, tolerance=0.04):\n  \"\"\"\"\"\"\n  diff = value - test_value\n  \n  return abs(diff) < tolerance\n\n```\n\n\n",
        "eval_script": "## logic/command_go.py\ndef CloseDifference(value, test_value, tolerance=0.04):\n  \"\"\"\"\"\"\n  diff = value - test_value\n  \n  return abs(diff) < tolerance\n\n\ndef test_CloseDifference():\n    # Test case 1: Difference is less than tolerance\n    assert CloseDifference(1.0, 0.97) == CloseDifference_new_implementation(1.0, 0.97)\n\n    # Test case 2: Difference is exactly equal to tolerance\n    assert CloseDifference(1.0, 0.96) == CloseDifference_new_implementation(1.0, 0.96)\n\n    # Test case 3: Difference is greater than tolerance\n    assert CloseDifference(1.0, 0.95) == CloseDifference_new_implementation(1.0, 0.95)\n\n    # Test case 4: Negative values\n    assert CloseDifference(-1.0, -1.03) == CloseDifference_new_implementation(-1.0, -1.03)\n\n    # Test case 5: Zero values\n    assert CloseDifference(0.0, 0.0) == CloseDifference_new_implementation(0.0, 0.0)\n    assert CloseDifference(0.0, 0.03) == CloseDifference_new_implementation(0.0, 0.03)\n\n    # Test case 6: Custom tolerance\n    assert CloseDifference(1.0, 0.90, tolerance=0.1) == CloseDifference_new_implementation(1.0, 0.90, tolerance=0.1)\n\n    # Test case 7: Large values\n    assert CloseDifference(1000000.0, 999999.96) == CloseDifference_new_implementation(1000000.0, 999999.96)\n\n    # Test case 8: Identical values\n    assert CloseDifference(1.0, 1.0) == CloseDifference_new_implementation(1.0, 1.0)\n\n    # Test case 9: Negative tolerance\n    assert CloseDifference(1.0, 0.97, tolerance=-0.04) == CloseDifference_new_implementation(1.0, 0.97, tolerance=-0.04)\n\n    # Test case 10: Boundary condition\n    assert CloseDifference(1.0, 0.96 + 1e-10) == CloseDifference_new_implementation(1.0, 0.96 + 1e-10)\n\nif __name__ == \"__main__\":\n    test_CloseDifference()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions are named `CloseDifference`, take the same parameters (`value`, `test_value`, `tolerance`), and contain the same logic: calculating the difference between `value` and `test_value`, taking the absolute value of this difference, and checking if it is less than `tolerance`. The additional test cases in the CODE do not alter the function itself; they merely test its behavior. Therefore, the functionality and implementation of the REVISED FUNCTION are exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `CloseDifference` returns a boolean value, satisfying this condition.\n- CONDITION 2: The test cases use assert statements to check return values, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the return values of `CloseDifference` and `CloseDifference_new_implementation`, ensuring that the new implementation must have the same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use assert statements to compare return values, which is appropriate given that `CloseDifference` returns a value, satisfying this condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including differences less than, equal to, and greater than the tolerance, negative values, zero values, custom tolerance, large values, identical values, negative tolerance, and boundary conditions. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "Space_Content_Derived",
        "idx": "385",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_go.py",
        "orig_func": "def Space_Content_Derived(config):\n    \"\"\"Register content\"\"\"\n    request_input = utility.LoadJsonFromString(config.input['request']['request_input'])\n    LOG.info(f'Input request: {pprint.pformat(request_input)}')\n    result = config.input.get('existing', {})\n    if not result:\n        result = {}\n    result['request_input'] = request_input\n    return result",
        "orig_context": "```python\n## logic/command_go.py\nimport pprint\n\nfrom logic import utility\n\nfrom logic.log import LOG\n\ndef Space_Content_Derived(config):\n  \"\"\"Register content\"\"\"\n  request_input = utility.LoadJsonFromString(config.input['request']['request_input'])\n\n  LOG.info(f'Input request: {pprint.pformat(request_input)}')\n\n  # Start with the pass through and mutate\n  result = config.input.get('existing', {})\n  if not result: result = {}\n\n  # Pass this through\n  result['request_input'] = request_input\n\n  return result\n\n```\n\n\n",
        "eval_script": "# Mocking the necessary components to run Space_Content_Derived\n\nimport pprint  # Importing pprint module for pretty-printing\n\n# Mocking the utility module\nclass utility:\n    @staticmethod\n    def LoadJsonFromString(json_string):\n        import json\n        return json.loads(json_string)\n\n# Mocking the LOG class\nclass LOG:\n    @staticmethod\n    def info(message):\n        print(f\"INFO: {message}\")\n\n# Mocking the config object\nclass MockConfig:\n    def __init__(self, input_data):\n        self.input = input_data\n\n# Example usage of the Space_Content_Derived function\ndef Space_Content_Derived(config):\n    \"\"\"Register content\"\"\"\n    request_input = utility.LoadJsonFromString(config.input['request']['request_input'])\n\n    LOG.info(f'Input request: {pprint.pformat(request_input)}')\n\n    # Start with the pass through and mutate\n    result = config.input.get('existing', {})\n    if not result: result = {}\n\n    # Pass this through\n    result['request_input'] = request_input\n\n    return result\n\n\ndef test_Space_Content_Derived():\n    # Test case 1: existing key is present\n    config1 = MockConfig({\n        'request': {'request_input': '{\"key1\": \"value1\"}'},\n        'existing': {'key2': 'value2'}\n    })\n    assert Space_Content_Derived(config1) == Space_Content_Derived_new_implementation(config1)\n\n    # Test case 2: existing key is absent\n    config2 = MockConfig({\n        'request': {'request_input': '{\"key3\": \"value3\"}'}\n    })\n    assert Space_Content_Derived(config2) == Space_Content_Derived_new_implementation(config2)\n\n    # Test case 3: different JSON structure\n    config3 = MockConfig({\n        'request': {'request_input': '{\"key4\": {\"subkey\": \"subvalue\"}}'},\n        'existing': {}\n    })\n    assert Space_Content_Derived(config3) == Space_Content_Derived_new_implementation(config3)\n\nif __name__ == \"__main__\":\n    test_Space_Content_Derived()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is essentially the same as the ORIGINAL FUNCTION. Both functions perform the following steps:\n\n1. They load a JSON string from the `request_input` field of the `config.input['request']` dictionary using the `utility.LoadJsonFromString` method.\n2. They log the loaded JSON object using the `LOG.info` method with pretty-print formatting.\n3. They attempt to retrieve the `existing` dictionary from `config.input`. If it doesn't exist, they initialize it as an empty dictionary.\n4. They add the loaded JSON object to the `result` dictionary under the key `request_input`.\n5. They return the `result` dictionary.\n\nThe REVISED FUNCTION includes additional code for mocking necessary components and testing, but the core functionality of the `Space_Content_Derived` function remains unchanged. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `Space_Content_Derived` returns a dictionary, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `Space_Content_Derived` and `Space_Content_Derived_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that the function returns a value.\n- CONDITION 5: The test cases cover different scenarios: when the 'existing' key is present, absent, and when the JSON structure is different. These are non-trivial cases that test the functionality of the function.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "CreateNewDomain",
        "idx": "387",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_go.py",
        "orig_func": "def CreateNewDomain(new_uuid, domain_name):\n    \"\"\"\"\"\"\n    data = {'uuid': new_uuid, 'name': domain_name, 'theme': 'default', 'paths': ['/', '/404']}\n    return data",
        "orig_context": "```python\n## logic/command_go.py\ndef CreateNewDomain(new_uuid, domain_name):\n  \"\"\"\"\"\"\n  data = {'uuid': new_uuid, 'name': domain_name, 'theme': 'default', 'paths': ['/', '/404']}\n\n  return data\n\n```\n\n\n",
        "eval_script": "## logic/command_go.py\ndef CreateNewDomain(new_uuid, domain_name):\n  \"\"\"\"\"\"\n  data = {'uuid': new_uuid, 'name': domain_name, 'theme': 'default', 'paths': ['/', '/404']}\n\n  return data\n\n\ndef test_CreateNewDomain():\n    # Test case 1: Check if both functions return the same dictionary for given inputs\n    assert CreateNewDomain('1234', 'example.com') == CreateNewDomain_new_implementation('1234', 'example.com')\n\n    # Test case 2: Check with different UUID and domain name\n    assert CreateNewDomain('5678', 'test.com') == CreateNewDomain_new_implementation('5678', 'test.com')\n\n    # Test case 3: Check with another set of UUID and domain name\n    assert CreateNewDomain('abcd', 'mydomain.com') == CreateNewDomain_new_implementation('abcd', 'mydomain.com')\n\n    # Test case 4: Check with empty strings\n    assert CreateNewDomain('', '') == CreateNewDomain_new_implementation('', '')\n\n    # Test case 5: Check with special characters\n    assert CreateNewDomain('!@#$%', '^&*()') == CreateNewDomain_new_implementation('!@#$%', '^&*()')\n\n    # Test case 6: Check with long strings\n    long_uuid = 'a' * 1000\n    long_domain = 'b' * 1000 + '.com'\n    assert CreateNewDomain(long_uuid, long_domain) == CreateNewDomain_new_implementation(long_uuid, long_domain)\n\n    # Test case 7: Check with numeric strings\n    assert CreateNewDomain('1234567890', '0987654321.com') == CreateNewDomain_new_implementation('1234567890', '0987654321.com')\n\n    # Test case 8: Check with Unicode characters\n    assert CreateNewDomain('uuid123', '\u4f8b\u5b50.com') == CreateNewDomain_new_implementation('uuid123', '\u4f8b\u5b50.com')\n\nif __name__ == \"__main__\":\n    test_CreateNewDomain()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions take two parameters, `new_uuid` and `domain_name`, and return a dictionary with the same structure and values. The additional code in the REVISED FUNCTION is a set of test cases that compare the output of the `CreateNewDomain` function with a non-existent `CreateNewDomain_new_implementation` function. However, these test cases do not affect the functionality of the `CreateNewDomain` function itself. Since the function's implementation has not changed, the functionality remains the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The CreateNewDomain function returns a dictionary, satisfying the condition that it should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases check the return values of the function, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of CreateNewDomain and CreateNewDomain_new_implementation for various inputs, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- CONDITION 4: The test cases use assert statements to compare the return values of the two implementations, which is reasonable given that the function returns a dictionary.\n- CONDITION 5: The test cases cover a variety of inputs, including different UUIDs and domain names, empty strings, special characters, long strings, numeric strings, and Unicode characters, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "CreateNewProductStock",
        "idx": "388",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_product_stock.py",
        "orig_func": "def CreateNewProductStock(new_uuid, product_stock_name):\n    \"\"\"\"\"\"\n    data = {'uuid': new_uuid, 'name': product_stock_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n    return data",
        "orig_context": "```python\n## logic/command_product_stock.py\ndef CreateNewProductStock(new_uuid, product_stock_name):\n  \"\"\"\"\"\"\n  data = {'uuid': new_uuid, 'name': product_stock_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n\n  return data\n\n```\n\n\n",
        "eval_script": "## logic/command_product_stock.py\ndef CreateNewProductStock(new_uuid, product_stock_name):\n  \"\"\"\"\"\"\n  data = {'uuid': new_uuid, 'name': product_stock_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n\n  return data\n\n\ndef test_CreateNewProductStock():\n  # Test case 1\n  result_original = CreateNewProductStock(\"123\", \"Product A\")\n  result_new = CreateNewProductStock_new_implementation(\"123\", \"Product A\")\n  assert result_original == result_new, \"Test case 1 failed\"\n\n  # Test case 2\n  result_original = CreateNewProductStock(\"456\", \"Product B\")\n  result_new = CreateNewProductStock_new_implementation(\"456\", \"Product B\")\n  assert result_original == result_new, \"Test case 2 failed\"\n\n  # Test case 3\n  result_original = CreateNewProductStock(\"789\", \"Product C\")\n  result_new = CreateNewProductStock_new_implementation(\"789\", \"Product C\")\n  assert result_original == result_new, \"Test case 3 failed\"\n\n  # Test case 4: Empty strings\n  result_original = CreateNewProductStock(\"\", \"\")\n  result_new = CreateNewProductStock_new_implementation(\"\", \"\")\n  assert result_original == result_new, \"Test case 4 failed\"\n\n  # Test case 5: Special characters\n  result_original = CreateNewProductStock(\"!@#$%^\", \"&*()_+\")\n  result_new = CreateNewProductStock_new_implementation(\"!@#$%^\", \"&*()_+\")\n  assert result_original == result_new, \"Test case 5 failed\"\n\n  # Test case 6: Long strings\n  long_uuid = \"a\" * 1000\n  long_name = \"b\" * 1000\n  result_original = CreateNewProductStock(long_uuid, long_name)\n  result_new = CreateNewProductStock_new_implementation(long_uuid, long_name)\n  assert result_original == result_new, \"Test case 6 failed\"\n\n  # Test case 7: Numeric strings\n  result_original = CreateNewProductStock(\"1234567890\", \"0987654321\")\n  result_new = CreateNewProductStock_new_implementation(\"1234567890\", \"0987654321\")\n  assert result_original == result_new, \"Test case 7 failed\"\n\n  # Test case 8: UUID format\n  uuid_format = \"550e8400-e29b-41d4-a716-446655440000\"\n  result_original = CreateNewProductStock(uuid_format, \"UUID Product\")\n  result_new = CreateNewProductStock_new_implementation(uuid_format, \"UUID Product\")\n  assert result_original == result_new, \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n  test_CreateNewProductStock()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions have the same name, parameters, and logic. They both create a dictionary with the same keys and values based on the input parameters and return this dictionary. The additional code in the revised version is a set of test cases, which do not alter the functionality of the CreateNewProductStock function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `CreateNewProductStock` returns a dictionary containing the product stock details. This satisfies the condition as it has a return value.\n  \n- CONDITION 2: The test cases in `test_CreateNewProductStock` compare the return values of `CreateNewProductStock` and `CreateNewProductStock_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `CreateNewProductStock` and `CreateNewProductStock_new_implementation` for various inputs. If `CreateNewProductStock_new_implementation` passes all these tests, it must have the same functionality as `CreateNewProductStock`. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations. Since `CreateNewProductStock` returns a dictionary, this is a reasonable way to test the functionality. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including normal strings, empty strings, special characters, long strings, numeric strings, and UUID format strings. This variety ensures that the test cases are non-trivial and robust. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "CreateNewPurchaseUsage",
        "idx": "390",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_purchase_usage.py",
        "orig_func": "def CreateNewPurchaseUsage(new_uuid, purchase_usage_name):\n    \"\"\"\"\"\"\n    data = {'uuid': new_uuid, 'name': purchase_usage_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n    return data",
        "orig_context": "```python\n## logic/command_purchase_usage.py\ndef CreateNewPurchaseUsage(new_uuid, purchase_usage_name):\n  \"\"\"\"\"\"\n  data = {'uuid': new_uuid, 'name': purchase_usage_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n\n  return data\n\n```\n\n\n",
        "eval_script": "## logic/command_purchase_usage.py\ndef CreateNewPurchaseUsage(new_uuid, purchase_usage_name):\n  \"\"\"\"\"\"\n  data = {'uuid': new_uuid, 'name': purchase_usage_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n\n  return data\n\n\ndef test_CreateNewPurchaseUsage():\n  # Test case 1: Standard input\n  uuid1 = \"1234\"\n  name1 = \"Purchase1\"\n  assert CreateNewPurchaseUsage(uuid1, name1) == CreateNewPurchaseUsage_new_implementation(uuid1, name1), \"Test case 1 failed\"\n\n  # Test case 2: Empty name\n  uuid2 = \"5678\"\n  name2 = \"\"\n  assert CreateNewPurchaseUsage(uuid2, name2) == CreateNewPurchaseUsage_new_implementation(uuid2, name2), \"Test case 2 failed\"\n\n  # Test case 3: Special characters in name\n  uuid3 = \"91011\"\n  name3 = \"@#$%^&*\"\n  assert CreateNewPurchaseUsage(uuid3, name3) == CreateNewPurchaseUsage_new_implementation(uuid3, name3), \"Test case 3 failed\"\n\n  # Test case 4: Empty UUID\n  uuid4 = \"\"\n  name4 = \"Purchase4\"\n  assert CreateNewPurchaseUsage(uuid4, name4) == CreateNewPurchaseUsage_new_implementation(uuid4, name4), \"Test case 4 failed\"\n\n  # Test case 5: Long strings\n  uuid5 = \"a\" * 1000\n  name5 = \"b\" * 1000\n  assert CreateNewPurchaseUsage(uuid5, name5) == CreateNewPurchaseUsage_new_implementation(uuid5, name5), \"Test case 5 failed\"\n\n  # Test case 6: Numeric strings\n  uuid6 = \"1234567890\"\n  name6 = \"9876543210\"\n  assert CreateNewPurchaseUsage(uuid6, name6) == CreateNewPurchaseUsage_new_implementation(uuid6, name6), \"Test case 6 failed\"\n\n  # Test case 7: None values\n  uuid7 = None\n  name7 = None\n  try:\n      assert CreateNewPurchaseUsage(uuid7, name7) == CreateNewPurchaseUsage_new_implementation(uuid7, name7), \"Test case 7 failed\"\n  except TypeError:\n      pass  # Assuming the function should raise a TypeError for None inputs\n\n  # Test case 8: Unicode characters in name\n  uuid8 = \"1234\"\n  name8 = \"\u6d4b\u8bd5\"\n  assert CreateNewPurchaseUsage(uuid8, name8) == CreateNewPurchaseUsage_new_implementation(uuid8, name8), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n  test_CreateNewPurchaseUsage()\n  print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of code and functionality. Both functions take two parameters, `new_uuid` and `purchase_usage_name`, and return a dictionary with the same structure and default values. The additional code in the revised version includes a test suite to verify the function's behavior under various conditions, but this does not alter the function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `CreateNewPurchaseUsage` returns a dictionary, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `CreateNewPurchaseUsage` and `CreateNewPurchaseUsage_new_implementation` directly. If the new implementation has the same functionality, it will pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of both implementations, which is reasonable given that the function returns a dictionary. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including standard inputs, empty strings, special characters, long strings, numeric strings, `None` values, and Unicode characters. This variety makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "CreateNewUser",
        "idx": "391",
        "repo_name": "ghowland___opsland-example",
        "func_path": "logic/command_user.py",
        "orig_func": "def CreateNewUser(new_uuid, user_name):\n    \"\"\"\"\"\"\n    data = {'uuid': new_uuid, 'name': user_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n    return data",
        "orig_context": "```python\n## logic/command_user.py\ndef CreateNewUser(new_uuid, user_name):\n  \"\"\"\"\"\"\n  #TODO: What if we used the parent_uuid for something interesting?  But can have a better name: referrer_user_uuid, ...\n  data = {'uuid': new_uuid, 'name': user_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n\n  return data\n\n```\n\n\n",
        "eval_script": "## logic/command_user.py\ndef CreateNewUser(new_uuid, user_name):\n  \"\"\"\"\"\"\n  #TODO: What if we used the parent_uuid for something interesting?  But can have a better name: referrer_user_uuid, ...\n  data = {'uuid': new_uuid, 'name': user_name, 'theme': 'default', 'details': [], 'parent_uuid': None}\n\n  return data\n\n\ndef test_CreateNewUser():\n    # Test case 1: Standard input\n    uuid1 = \"12345\"\n    name1 = \"Alice\"\n    assert CreateNewUser(uuid1, name1) == CreateNewUser_new_implementation(uuid1, name1)\n\n    # Test case 2: Different UUID and name\n    uuid2 = \"67890\"\n    name2 = \"Bob\"\n    assert CreateNewUser(uuid2, name2) == CreateNewUser_new_implementation(uuid2, name2)\n\n    # Test case 3: Empty name\n    uuid3 = \"54321\"\n    name3 = \"\"\n    assert CreateNewUser(uuid3, name3) == CreateNewUser_new_implementation(uuid3, name3)\n\n    # Test case 4: Empty UUID\n    uuid4 = \"\"\n    name4 = \"Charlie\"\n    assert CreateNewUser(uuid4, name4) == CreateNewUser_new_implementation(uuid4, name4)\n\n    # Test case 5: Special characters in name\n    uuid5 = \"11223\"\n    name5 = \"D@ve!\"\n    assert CreateNewUser(uuid5, name5) == CreateNewUser_new_implementation(uuid5, name5)\n\n    # Test case 6: Long strings\n    uuid6 = \"a\" * 1000\n    name6 = \"b\" * 1000\n    assert CreateNewUser(uuid6, name6) == CreateNewUser_new_implementation(uuid6, name6)\n\n    # Test case 7: Numeric name\n    uuid7 = \"33445\"\n    name7 = \"123456\"\n    assert CreateNewUser(uuid7, name7) == CreateNewUser_new_implementation(uuid7, name7)\n\n    # Test case 8: UUID with special characters\n    uuid8 = \"uuid-1234-5678-!@#$\"\n    name8 = \"Eve\"\n    assert CreateNewUser(uuid8, name8) == CreateNewUser_new_implementation(uuid8, name8)\n\n    # Test case 9: Both UUID and name empty\n    uuid9 = \"\"\n    name9 = \"\"\n    assert CreateNewUser(uuid9, name9) == CreateNewUser_new_implementation(uuid9, name9)\n\nif __name__ == \"__main__\":\n    test_CreateNewUser()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `CreateNewUser` is identical to the ORIGINAL FUNCTION. Both functions take two parameters, `new_uuid` and `user_name`, and return a dictionary with the same structure and default values. The additional comments and test cases in the revised code do not alter the functionality of the `CreateNewUser` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `CreateNewUser` function returns a dictionary containing user details, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `CreateNewUser` and `CreateNewUser_new_implementation`, not relying on printed or logged output.\n- CONDITION 3: The test cases compare the return values of both implementations for various inputs, ensuring that `CreateNewUser_new_implementation` must have the exact same functionality to pass all tests.\n- CONDITION 4: The assertions are reasonable as they compare the return values of the two implementations, which is appropriate given that `CreateNewUser` returns a dictionary.\n- CONDITION 5: The test cases cover a range of scenarios, including standard input, empty strings, special characters, and long strings, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "34ef79a774bee8586a8f472aebe49b312669d33f"
    },
    {
        "func_name": "bytes_num",
        "idx": "395",
        "repo_name": "BcnBitcoinOnly___dust-raffle",
        "func_path": "examples/utils.py",
        "orig_func": "def bytes_num(n: int) -> int:\n    \"\"\"\n    Returns the number of bytes required to represent n as bytes\n    \"\"\"\n    if n == 0:\n        return 1\n    return (n.bit_length() + 7) // 8",
        "orig_context": "```python\n## examples/utils.py\ndef bytes_num(n: int) -> int:\n    \"\"\"\n    Returns the number of bytes required to represent n as bytes\n    \"\"\"\n    if n == 0:\n        return 1\n    return (n.bit_length() + 7) // 8\n\n```\n\n\n",
        "eval_script": "## examples/utils.py\ndef bytes_num(n: int) -> int:\n    \"\"\"\n    Returns the number of bytes required to represent n as bytes\n    \"\"\"\n    if n == 0:\n        return 1\n    return (n.bit_length() + 7) // 8\n\n\ndef test_bytes_num():\n    # Test when n is zero\n    assert bytes_num(0) == bytes_num_new_implementation(0)\n\n    # Test when n is a small positive number\n    assert bytes_num(1) == bytes_num_new_implementation(1)\n\n    # Test when n is a larger positive number\n    assert bytes_num(1024) == bytes_num_new_implementation(1024)\n\n    # Test when n is a very large number\n    assert bytes_num(2**100) == bytes_num_new_implementation(2**100)\n\n    # Test when n is a negative number\n    assert bytes_num(-1) == bytes_num_new_implementation(-1)\n\n    # Additional test cases:\n    # Test edge cases around byte boundaries\n    assert bytes_num(255) == bytes_num_new_implementation(255)  # 8 bits\n    assert bytes_num(256) == bytes_num_new_implementation(256)  # 9 bits\n    assert bytes_num(65535) == bytes_num_new_implementation(65535)  # 16 bits\n    assert bytes_num(65536) == bytes_num_new_implementation(65536)  # 17 bits\n\n    # Test negative numbers around byte boundaries\n    assert bytes_num(-255) == bytes_num_new_implementation(-255)\n    assert bytes_num(-256) == bytes_num_new_implementation(-256)\n    assert bytes_num(-65535) == bytes_num_new_implementation(-65535)\n    assert bytes_num(-65536) == bytes_num_new_implementation(-65536)\n\n    # Test very large negative number\n    assert bytes_num(-2**100) == bytes_num_new_implementation(-2**100)\n\nif __name__ == \"__main__\":\n    test_bytes_num()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       4      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  4      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions are named `bytes_num`, take an integer `n` as input, and return the number of bytes required to represent `n` as bytes. The logic within the function is the same: if `n` is zero, it returns 1; otherwise, it calculates `(n.bit_length() + 7) // 8`. The additional code in the REVISED FUNCTION is a test suite that compares the output of `bytes_num` with another function `bytes_num_new_implementation`, which is not provided. However, the presence of this test suite does not alter the functionality of the `bytes_num` function itself. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The function `bytes_num` returns an integer, which is the number of bytes required to represent the input integer `n`. This satisfies the condition as it has a return value.\n  \n- [CONDITION 2] The test cases use assertions to compare the return values of `bytes_num` and `bytes_num_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n- [CONDITION 3] The test cases cover a variety of inputs, including zero, small positive numbers, large positive numbers, negative numbers, and edge cases around byte boundaries. This comprehensive coverage ensures that `bytes_num_new_implementation` must have the same functionality as `bytes_num` to pass all tests, satisfying this condition.\n\n- [CONDITION 4] The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `bytes_num` returns a value. This satisfies the condition.\n\n- [CONDITION 5] The test cases are non-trivial as they cover a range of scenarios, including edge cases and large numbers, ensuring thorough testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "7357e1b329f74b99bc852a7262b94941732a325d"
    },
    {
        "func_name": "op_return_script",
        "idx": "396",
        "repo_name": "BcnBitcoinOnly___dust-raffle",
        "func_path": "examples/utils.py",
        "orig_func": "def op_return_script(blockheight: int) -> Script:\n    \"\"\"\n    The OP_RETURN Script to be appended to the OP_RETURN output.\n\n    It's a raffle version number followed by more data.\n    For version 0x00, what follows is a block height number represented as a big endian byte sequence.\n    The data is returned as a hex string because that's what the bitcoinutils library expects.\n    \"\"\"\n    payload = (RAFFLE_VERSION + blockheight.to_bytes(bytes_num(blockheight), 'big')).hex()\n    return Script(['OP_RETURN', payload])",
        "orig_context": "```python\n## examples/utils.py\nfrom bitcoinutils.script import Script\n\nRAFFLE_VERSION = b'\\x00'\n\ndef bytes_num(n: int) -> int:\n    \"\"\"\n    Returns the number of bytes required to represent n as bytes\n    \"\"\"\n    if n == 0:\n        return 1\n    return (n.bit_length() + 7) // 8\n\ndef op_return_script(blockheight: int) -> Script:\n    \"\"\"\n    The OP_RETURN Script to be appended to the OP_RETURN output.\n\n    It's a raffle version number followed by more data.\n    For version 0x00, what follows is a block height number represented as a big endian byte sequence.\n    The data is returned as a hex string because that's what the bitcoinutils library expects.\n    \"\"\"\n    payload = (RAFFLE_VERSION + blockheight.to_bytes(bytes_num(blockheight), 'big')).hex()\n    return Script(['OP_RETURN', payload])\n\n```\n\n\n",
        "eval_script": "# Mock implementation of the Script class from bitcoinutils\nclass Script:\n    def __init__(self, data):\n        self.data = data\n\n    def __repr__(self):\n        return f\"Script({self.data})\"\n\nRAFFLE_VERSION = b'\\x00'\n\ndef bytes_num(n: int) -> int:\n    \"\"\"\n    Returns the number of bytes required to represent n as bytes\n    \"\"\"\n    if n == 0:\n        return 1\n    return (n.bit_length() + 7) // 8\n\ndef op_return_script(blockheight: int) -> Script:\n    \"\"\"\n    The OP_RETURN Script to be appended to the OP_RETURN output.\n\n    It's a raffle version number followed by more data.\n    For version 0x00, what follows is a block height number represented as a big endian byte sequence.\n    The data is returned as a hex string because that's what the bitcoinutils library expects.\n    \"\"\"\n    payload = (RAFFLE_VERSION + blockheight.to_bytes(bytes_num(blockheight), 'big')).hex()\n    return Script(['OP_RETURN', payload])\n\n\ndef test_op_return_script():\n    # Test with blockheight 0\n    assert op_return_script(0).data == op_return_script_new_implementation(0).data, \"Test failed for blockheight 0\"\n\n    # Test with a small blockheight\n    assert op_return_script(1).data == op_return_script_new_implementation(1).data, \"Test failed for blockheight 1\"\n\n    # Test with a larger blockheight\n    assert op_return_script(100000).data == op_return_script_new_implementation(100000).data, \"Test failed for blockheight 100000\"\n\nif __name__ == \"__main__\":\n    test_op_return_script()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `op_return_script` is identical to the ORIGINAL FUNCTION in terms of both code and functionality. The function takes an integer `blockheight`, converts it to a byte sequence in big-endian format, prepends a raffle version byte, and then converts the entire sequence to a hex string. This hex string is then used to create a `Script` object with an `OP_RETURN` operation. The mock implementation of the `Script` class and the `bytes_num` function are consistent with the expected behavior described in the original function's docstring. The test cases provided in the CODE verify that the function behaves as expected for different blockheight values. Since the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `op_return_script` returns a `Script` object, which satisfies the condition of having a return value.\n- CONDITION 2: The test cases use assertions to compare the `data` attribute of the `Script` objects returned by `op_return_script` and `op_return_script_new_implementation`. This checks the return values, not printed or logged content.\n- CONDITION 3: The test cases compare the `data` attribute of the `Script` objects returned by both implementations for various inputs. This ensures that `op_return_script_new_implementation` must have the same functionality as `op_return_script` to pass the tests.\n- CONDITION 4: The test cases use the `data` attribute of the returned `Script` objects for comparison, which is reasonable since `op_return_script` returns a `Script` object. The assertions are correctly structured.\n- CONDITION 5: The test cases cover multiple scenarios: a blockheight of 0, a small blockheight (1), and a larger blockheight (100000). These are non-trivial and cover a range of possible inputs.",
            "answer": "yes"
        },
        "commit_id": "7357e1b329f74b99bc852a7262b94941732a325d"
    },
    {
        "func_name": "canonize_outpoint",
        "idx": "397",
        "repo_name": "BcnBitcoinOnly___dust-raffle",
        "func_path": "examples/utils.py",
        "orig_func": "def canonize_outpoint(outpoint: tuple[str, int]) -> bytes:\n    \"\"\"\n    Returns an outpoint in its canonical form.\n    The canonical form is defined as the serialization format in a real bitcoin transaction.\n\n    It consists of 32 bytes of the txid in little endian, followed by 4 bytes for the vout, also in little endian.\n    \"\"\"\n    canonical_txid = bytes.fromhex(outpoint[0])[::-1]\n    canonical_vout = outpoint[1].to_bytes(4, 'little')\n    assert len(canonical_txid) == 32\n    return canonical_txid + canonical_vout",
        "orig_context": "```python\n## examples/utils.py\ndef canonize_outpoint(outpoint: tuple[str, int]) -> bytes:\n    \"\"\"\n    Returns an outpoint in its canonical form.\n    The canonical form is defined as the serialization format in a real bitcoin transaction.\n\n    It consists of 32 bytes of the txid in little endian, followed by 4 bytes for the vout, also in little endian.\n    \"\"\"\n    canonical_txid = bytes.fromhex(outpoint[0])[::-1]\n    canonical_vout = outpoint[1].to_bytes(4, 'little')\n\n    assert len(canonical_txid) == 32\n    return canonical_txid + canonical_vout\n\n```\n\n\n",
        "eval_script": "## examples/utils.py\ndef canonize_outpoint(outpoint: tuple[str, int]) -> bytes:\n    \"\"\"\n    Returns an outpoint in its canonical form.\n    The canonical form is defined as the serialization format in a real bitcoin transaction.\n\n    It consists of 32 bytes of the txid in little endian, followed by 4 bytes for the vout, also in little endian.\n    \"\"\"\n    canonical_txid = bytes.fromhex(outpoint[0])[::-1]\n    canonical_vout = outpoint[1].to_bytes(4, 'little')\n\n    assert len(canonical_txid) == 32\n    return canonical_txid + canonical_vout\n\n\ndef test_canonize_outpoint():\n    # Test case 1: Standard transaction ID and output index\n    outpoint1 = (\"a3e1f3d5b2c4e6f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1f2\", 1)\n    assert canonize_outpoint(outpoint1) == canonize_outpoint_new_implementation(outpoint1)\n\n    # Test case 2: Transaction ID with leading zeros\n    outpoint2 = (\"0000000000000000000000000000000000000000000000000000000000000001\", 0)\n    assert canonize_outpoint(outpoint2) == canonize_outpoint_new_implementation(outpoint2)\n\n    # Test case 3: Maximum output index\n    outpoint3 = (\"ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\", 4294967295)\n    assert canonize_outpoint(outpoint3) == canonize_outpoint_new_implementation(outpoint3)\n\n    # Test case 4: Empty transaction ID\n    outpoint4 = (\"\", 1)\n    try:\n        canonize_outpoint(outpoint4)\n        assert False, \"Expected an exception for empty transaction ID\"\n    except Exception:\n        pass\n\n    # Test case 5: Short transaction ID\n    outpoint5 = (\"a3e1f3d5b2c4e6f8\", 1)\n    try:\n        canonize_outpoint(outpoint5)\n        assert False, \"Expected an exception for short transaction ID\"\n    except Exception:\n        pass\n\n    # Test case 6: Non-hexadecimal characters in transaction ID\n    outpoint6 = (\"g3e1f3d5b2c4e6f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1f2\", 1)\n    try:\n        canonize_outpoint(outpoint6)\n        assert False, \"Expected an exception for non-hexadecimal characters\"\n    except Exception:\n        pass\n\n    # Test case 7: Negative output index\n    outpoint7 = (\"a3e1f3d5b2c4e6f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1f2\", -1)\n    try:\n        canonize_outpoint(outpoint7)\n        assert False, \"Expected an exception for negative output index\"\n    except Exception:\n        pass\n\n    # Test case 8: Non-integer output index\n    outpoint8 = (\"a3e1f3d5b2c4e6f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1f2\", \"one\")\n    try:\n        canonize_outpoint(outpoint8)\n        assert False, \"Expected an exception for non-integer output index\"\n    except Exception:\n        pass\n\n    # Test case 9: Output index just above maximum\n    outpoint9 = (\"ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\", 4294967296)\n    try:\n        canonize_outpoint(outpoint9)\n        assert False, \"Expected an exception for output index above maximum\"\n    except Exception:\n        pass\n\nif __name__ == \"__main__\":\n    test_canonize_outpoint()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions take a tuple consisting of a transaction ID (txid) as a string and an output index (vout) as an integer. They convert the txid from a hexadecimal string to bytes, reverse the byte order to little-endian, and convert the vout to a 4-byte little-endian representation. Both functions assert that the length of the canonical_txid is 32 bytes and return the concatenated result of the canonical_txid and canonical_vout. The test cases provided in the code are for verifying the correctness of the function but do not alter the function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `canonize_outpoint` returns a value of type `bytes`, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `canonize_outpoint` and `canonize_outpoint_new_implementation`, and they also check for exceptions, not printed or logged content.\n- CONDITION 3: The test cases compare the output of `canonize_outpoint` with `canonize_outpoint_new_implementation` for valid inputs and check for exceptions for invalid inputs. This ensures that `canonize_outpoint_new_implementation` must have the exact same functionality to pass all tests.\n- CONDITION 4: The test cases use assertions appropriately to check the return values and exceptions, which is reasonable given the function's behavior.\n- CONDITION 5: The test cases cover a variety of scenarios, including normal cases, edge cases, and invalid inputs, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "7357e1b329f74b99bc852a7262b94941732a325d"
    },
    {
        "func_name": "outpoints_merkle_root",
        "idx": "398",
        "repo_name": "BcnBitcoinOnly___dust-raffle",
        "func_path": "examples/utils.py",
        "orig_func": "def outpoints_merkle_root(sorted_outpoints: list[tuple[str, int]]) -> bytes:\n    \"\"\"\n    Calculates the Merkle Root Hash of a list of outpoints.\n\n    Pre: The list of outpoints is not empty\n    Pre: The outpoints are sorted according to their canonical representation\n    \"\"\"\n    assert len(sorted_outpoints) > 0\n    assert sorted(sorted_outpoints, key=canonize_outpoint) == sorted_outpoints\n    hashes = [hashlib.sha256(leaf).digest() for leaf in [canonize_outpoint(outpoint) for outpoint in sorted_outpoints]]\n    while len(hashes) > 1:\n        if len(hashes) % 2 == 1:\n            hashes.append(hashes[-1])\n        next_hashes: list[bytes] = []\n        for i in range(0, len(hashes), 2):\n            next_hashes.append(hashlib.sha256(hashes[i] + hashes[i + 1]).digest())\n        hashes = next_hashes\n    return hashes[0]",
        "orig_context": "```python\n## examples/utils.py\nimport hashlib\n\ndef canonize_outpoint(outpoint: tuple[str, int]) -> bytes:\n    \"\"\"\n    Returns an outpoint in its canonical form.\n    The canonical form is defined as the serialization format in a real bitcoin transaction.\n\n    It consists of 32 bytes of the txid in little endian, followed by 4 bytes for the vout, also in little endian.\n    \"\"\"\n    canonical_txid = bytes.fromhex(outpoint[0])[::-1]\n    canonical_vout = outpoint[1].to_bytes(4, 'little')\n\n    assert len(canonical_txid) == 32\n    return canonical_txid + canonical_vout\n\ndef outpoints_merkle_root(sorted_outpoints: list[tuple[str, int]]) -> bytes:\n    \"\"\"\n    Calculates the Merkle Root Hash of a list of outpoints.\n\n    Pre: The list of outpoints is not empty\n    Pre: The outpoints are sorted according to their canonical representation\n    \"\"\"\n    assert len(sorted_outpoints) > 0\n    assert sorted(sorted_outpoints, key=canonize_outpoint) == sorted_outpoints\n\n    hashes = [hashlib.sha256(leaf).digest() for leaf in\n              [canonize_outpoint(outpoint) for outpoint in sorted_outpoints]]\n\n    while len(hashes) > 1:\n        if len(hashes) % 2 == 1:\n            hashes.append(hashes[-1])\n\n        next_hashes: list[bytes] = []\n        for i in range(0, len(hashes), 2):\n            next_hashes.append(hashlib.sha256(hashes[i] + hashes[i + 1]).digest())\n\n        hashes = next_hashes\n    return hashes[0]\n\n```\n\n\n",
        "eval_script": "## examples/utils.py\nimport hashlib\n\ndef canonize_outpoint(outpoint: tuple[str, int]) -> bytes:\n    \"\"\"\n    Returns an outpoint in its canonical form.\n    The canonical form is defined as the serialization format in a real bitcoin transaction.\n\n    It consists of 32 bytes of the txid in little endian, followed by 4 bytes for the vout, also in little endian.\n    \"\"\"\n    canonical_txid = bytes.fromhex(outpoint[0])[::-1]\n    canonical_vout = outpoint[1].to_bytes(4, 'little')\n\n    assert len(canonical_txid) == 32\n    return canonical_txid + canonical_vout\n\ndef outpoints_merkle_root(sorted_outpoints: list[tuple[str, int]]) -> bytes:\n    \"\"\"\n    Calculates the Merkle Root Hash of a list of outpoints.\n\n    Pre: The list of outpoints is not empty\n    Pre: The outpoints are sorted according to their canonical representation\n    \"\"\"\n    assert len(sorted_outpoints) > 0\n    assert sorted(sorted_outpoints, key=canonize_outpoint) == sorted_outpoints\n\n    hashes = [hashlib.sha256(leaf).digest() for leaf in\n              [canonize_outpoint(outpoint) for outpoint in sorted_outpoints]]\n\n    while len(hashes) > 1:\n        if len(hashes) % 2 == 1:\n            hashes.append(hashes[-1])\n\n        next_hashes: list[bytes] = []\n        for i in range(0, len(hashes), 2):\n            next_hashes.append(hashlib.sha256(hashes[i] + hashes[i + 1]).digest())\n\n        hashes = next_hashes\n    return hashes[0]\n\n\ndef test_outpoints_merkle_root():\n    # Test case 1: Single outpoint\n    outpoints1 = [(\"a\" * 64, 0)]\n    assert outpoints_merkle_root(outpoints1) == outpoints_merkle_root_new_implementation(outpoints1)\n\n    # Test case 2: Two outpoints\n    outpoints2 = [(\"a\" * 64, 0), (\"b\" * 64, 1)]\n    assert outpoints_merkle_root(outpoints2) == outpoints_merkle_root_new_implementation(outpoints2)\n\n    # Test case 3: Three outpoints (odd number, requires duplication)\n    outpoints3 = [(\"a\" * 64, 0), (\"b\" * 64, 1), (\"c\" * 64, 2)]\n    assert outpoints_merkle_root(outpoints3) == outpoints_merkle_root_new_implementation(outpoints3)\n\n    # Test case 4: Empty list (should raise an assertion error)\n    try:\n        outpoints_merkle_root([])\n    except AssertionError:\n        pass\n    else:\n        assert False, \"Expected an assertion error for empty input\"\n\n    # Test case 5: Multiple outpoints\n    outpoints5 = [(\"a\" * 64, i) for i in range(10)]\n    assert outpoints_merkle_root(outpoints5) == outpoints_merkle_root_new_implementation(outpoints5)\n\n    # Test case 6: Duplicate outpoints\n    outpoints6 = [(\"a\" * 64, 0), (\"a\" * 64, 0)]\n    assert outpoints_merkle_root(outpoints6) == outpoints_merkle_root_new_implementation(outpoints6)\n\n    # Test case 7: Outpoints with maximum vout value\n    outpoints7 = [(\"a\" * 64, 2**32 - 1), (\"b\" * 64, 2**32 - 1)]\n    assert outpoints_merkle_root(outpoints7) == outpoints_merkle_root_new_implementation(outpoints7)\n\n    # Test case 8: Outpoints with minimum vout value\n    outpoints8 = [(\"a\" * 64, 0), (\"b\" * 64, 0)]\n    assert outpoints_merkle_root(outpoints8) == outpoints_merkle_root_new_implementation(outpoints8)\n\n    # Test case 9: Unsorted outpoints (should raise an assertion error)\n    try:\n        outpoints_merkle_root([(\"b\" * 64, 1), (\"a\" * 64, 0)])\n    except AssertionError:\n        pass\n    else:\n        assert False, \"Expected an assertion error for unsorted input\"\n\nif __name__ == \"__main__\":\n    test_outpoints_merkle_root()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      12      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 12      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of logic and implementation. Both functions calculate the Merkle Root Hash of a list of outpoints, ensuring that the list is not empty and is sorted according to their canonical representation. The process of hashing and pairing is the same in both functions, including handling odd numbers of hashes by duplicating the last hash. The revised function is placed within a larger code context that includes additional utility functions and tests, but the core functionality of `outpoints_merkle_root` remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `outpoints_merkle_root` returns a value, specifically a `bytes` object representing the Merkle root hash. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases in `test_outpoints_merkle_root` use assertions to check the return values of the function `outpoints_merkle_root` against `outpoints_merkle_root_new_implementation`. There are no checks for printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases are designed to compare the outputs of `outpoints_merkle_root` and `outpoints_merkle_root_new_implementation` for various inputs. They ensure that both implementations produce the same results, which implies that the new implementation must have the exact same functionality as the original to pass all tests. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations. There are also checks for assertion errors in cases of invalid inputs (empty list and unsorted list), which are reasonable given the preconditions of the function. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including single outpoint, multiple outpoints, odd number of outpoints (requiring duplication), empty list, duplicate outpoints, maximum and minimum `vout` values, and unsorted input. These tests are non-trivial and cover edge cases and typical use cases, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "7357e1b329f74b99bc852a7262b94941732a325d"
    },
    {
        "func_name": "Client.with_headers",
        "idx": "417",
        "repo_name": "derapi___derapi-python",
        "func_path": "derapi/client.py",
        "orig_func": "def with_headers(self, headers: Dict[str, str]) -> 'Client':\n    \"\"\"Get a new client matching this one with additional headers\"\"\"\n    if self._client is not None:\n        self._client.headers.update(headers)\n    if self._async_client is not None:\n        self._async_client.headers.update(headers)\n    return evolve(self, headers={**self._headers, **headers})",
        "orig_context": "```python\n## derapi/client.py\nimport ssl\n\nfrom typing import Any, Dict, Optional, Union\n\nimport httpx\n\nfrom attrs import define, evolve, field\n\nclass Client:\n    \"\"\"A class for keeping track of data related to the API\n\n    The following are accepted as keyword arguments and will be used to construct httpx Clients internally:\n\n        ``base_url``: The base URL for the API, all requests are made to a relative path to this URL\n\n        ``cookies``: A dictionary of cookies to be sent with every request\n\n        ``headers``: A dictionary of headers to be sent with every request\n\n        ``timeout``: The maximum amount of a time a request can take. API functions will raise\n        httpx.TimeoutException if this is exceeded.\n\n        ``verify_ssl``: Whether or not to verify the SSL certificate of the API server. This should be True in production,\n        but can be set to False for testing purposes.\n\n        ``follow_redirects``: Whether or not to follow redirects. Default value is False.\n\n        ``httpx_args``: A dictionary of additional arguments to be passed to the ``httpx.Client`` and ``httpx.AsyncClient`` constructor.\n\n\n    Attributes:\n        raise_on_unexpected_status: Whether or not to raise an errors.UnexpectedStatus if the API returns a\n            status code that was not documented in the source OpenAPI document. Can also be provided as a keyword\n            argument to the constructor.\n    \"\"\"\n\n    raise_on_unexpected_status: bool = field(default=False, kw_only=True)\n    _base_url: str = field(alias=\"base_url\")\n    _cookies: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"cookies\")\n    _headers: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"headers\")\n    _timeout: Optional[httpx.Timeout] = field(default=None, kw_only=True, alias=\"timeout\")\n    _verify_ssl: Union[str, bool, ssl.SSLContext] = field(default=True, kw_only=True, alias=\"verify_ssl\")\n    _follow_redirects: bool = field(default=False, kw_only=True, alias=\"follow_redirects\")\n    _httpx_args: Dict[str, Any] = field(factory=dict, kw_only=True, alias=\"httpx_args\")\n    _client: Optional[httpx.Client] = field(default=None, init=False)\n    _async_client: Optional[httpx.AsyncClient] = field(default=None, init=False)\n\n    def with_headers(self, headers: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional headers\"\"\"\n        if self._client is not None:\n            self._client.headers.update(headers)\n        if self._async_client is not None:\n            self._async_client.headers.update(headers)\n        return evolve(self, headers={**self._headers, **headers})\n\n    def with_cookies(self, cookies: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional cookies\"\"\"\n        if self._client is not None:\n            self._client.cookies.update(cookies)\n        if self._async_client is not None:\n            self._async_client.cookies.update(cookies)\n        return evolve(self, cookies={**self._cookies, **cookies})\n\n    def with_timeout(self, timeout: httpx.Timeout) -> \"Client\":\n        \"\"\"Get a new client matching this one with a new timeout (in seconds)\"\"\"\n        if self._client is not None:\n            self._client.timeout = timeout\n        if self._async_client is not None:\n            self._async_client.timeout = timeout\n        return evolve(self, timeout=timeout)\n\n    def set_httpx_client(self, client: httpx.Client) -> \"Client\":\n        \"\"\"Manually set the underlying httpx.Client\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._client = client\n        return self\n\n    def get_httpx_client(self) -> httpx.Client:\n        \"\"\"Get the underlying httpx.Client, constructing a new one if not previously set\"\"\"\n        if self._client is None:\n            self._client = httpx.Client(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._client\n\n    def __enter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for self.client\u2014you cannot enter twice (see httpx docs)\"\"\"\n        self.get_httpx_client().__enter__()\n        return self\n\n    def __exit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for internal httpx.Client (see httpx docs)\"\"\"\n        self.get_httpx_client().__exit__(*args, **kwargs)\n\n    def set_async_httpx_client(self, async_client: httpx.AsyncClient) -> \"Client\":\n        \"\"\"Manually the underlying httpx.AsyncClient\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._async_client = async_client\n        return self\n\n    def get_async_httpx_client(self) -> httpx.AsyncClient:\n        \"\"\"Get the underlying httpx.AsyncClient, constructing a new one if not previously set\"\"\"\n        if self._async_client is None:\n            self._async_client = httpx.AsyncClient(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._async_client\n\n    async def __aenter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for underlying httpx.AsyncClient\u2014you cannot enter twice (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aenter__()\n        return self\n\n    async def __aexit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for underlying httpx.AsyncClient (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aexit__(*args, **kwargs)\n\n```\n\n\n",
        "eval_script": "## derapi/client.py\nimport ssl\n\nfrom typing import Any, Dict, Optional, Union\n\nimport httpx\n\nfrom attrs import define, evolve, field\n\n@define\nclass Client:\n    \"\"\"A class for keeping track of data related to the API\n\n    The following are accepted as keyword arguments and will be used to construct httpx Clients internally:\n\n        ``base_url``: The base URL for the API, all requests are made to a relative path to this URL\n\n        ``cookies``: A dictionary of cookies to be sent with every request\n\n        ``headers``: A dictionary of headers to be sent with every request\n\n        ``timeout``: The maximum amount of a time a request can take. API functions will raise\n        httpx.TimeoutException if this is exceeded.\n\n        ``verify_ssl``: Whether or not to verify the SSL certificate of the API server. This should be True in production,\n        but can be set to False for testing purposes.\n\n        ``follow_redirects``: Whether or not to follow redirects. Default value is False.\n\n        ``httpx_args``: A dictionary of additional arguments to be passed to the ``httpx.Client`` and ``httpx.AsyncClient`` constructor.\n\n\n    Attributes:\n        raise_on_unexpected_status: Whether or not to raise an errors.UnexpectedStatus if the API returns a\n            status code that was not documented in the source OpenAPI document. Can also be provided as a keyword\n            argument to the constructor.\n    \"\"\"\n\n    raise_on_unexpected_status: bool = field(default=False, kw_only=True)\n    _base_url: str = field(alias=\"base_url\")\n    _cookies: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"cookies\")\n    _headers: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"headers\")\n    _timeout: Optional[httpx.Timeout] = field(default=None, kw_only=True, alias=\"timeout\")\n    _verify_ssl: Union[str, bool, ssl.SSLContext] = field(default=True, kw_only=True, alias=\"verify_ssl\")\n    _follow_redirects: bool = field(default=False, kw_only=True, alias=\"follow_redirects\")\n    _httpx_args: Dict[str, Any] = field(factory=dict, kw_only=True, alias=\"httpx_args\")\n    _client: Optional[httpx.Client] = field(default=None, init=False)\n    _async_client: Optional[httpx.AsyncClient] = field(default=None, init=False)\n\n    def with_headers(self, headers: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional headers\"\"\"\n        if self._client is not None:\n            self._client.headers.update(headers)\n        if self._async_client is not None:\n            self._async_client.headers.update(headers)\n        return evolve(self, headers={**self._headers, **headers})\n\n\n    def with_cookies(self, cookies: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional cookies\"\"\"\n        if self._client is not None:\n            self._client.cookies.update(cookies)\n        if self._async_client is not None:\n            self._async_client.cookies.update(cookies)\n        return evolve(self, cookies={**self._cookies, **cookies})\n\n    def with_timeout(self, timeout: httpx.Timeout) -> \"Client\":\n        \"\"\"Get a new client matching this one with a new timeout (in seconds)\"\"\"\n        if self._client is not None:\n            self._client.timeout = timeout\n        if self._async_client is not None:\n            self._async_client.timeout = timeout\n        return evolve(self, timeout=timeout)\n\n    def set_httpx_client(self, client: httpx.Client) -> \"Client\":\n        \"\"\"Manually set the underlying httpx.Client\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._client = client\n        return self\n\n    def get_httpx_client(self) -> httpx.Client:\n        \"\"\"Get the underlying httpx.Client, constructing a new one if not previously set\"\"\"\n        if self._client is None:\n            self._client = httpx.Client(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._client\n\n    def __enter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for self.client\u2014you cannot enter twice (see httpx docs)\"\"\"\n        self.get_httpx_client().__enter__()\n        return self\n\n    def __exit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for internal httpx.Client (see httpx docs)\"\"\"\n        self.get_httpx_client().__exit__(*args, **kwargs)\n\n    def set_async_httpx_client(self, async_client: httpx.AsyncClient) -> \"Client\":\n        \"\"\"Manually the underlying httpx.AsyncClient\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._async_client = async_client\n        return self\n\n    def get_async_httpx_client(self) -> httpx.AsyncClient:\n        \"\"\"Get the underlying httpx.AsyncClient, constructing a new one if not previously set\"\"\"\n        if self._async_client is None:\n            self._async_client = httpx.AsyncClient(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._async_client\n\n    async def __aenter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for underlying httpx.AsyncClient\u2014you cannot enter twice (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aenter__()\n        return self\n\n    async def __aexit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for underlying httpx.AsyncClient (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aexit__(*args, **kwargs)\n\ndef test_with_headers():\n    client = Client(base_url=\"https://api.example.com\")\n\n    # Test 1: Adding new headers to an empty headers dictionary\n    headers1 = {\"Authorization\": \"Bearer token\"}\n    client1 = client.with_headers(headers1)\n    client1_new = client.with_headers_new_implementation(headers1)\n    assert client1._headers == client1_new._headers\n\n    # Test 2: Adding new headers to a non-empty headers dictionary\n    client2 = Client(base_url=\"https://api.example.com\", headers={\"User-Agent\": \"test-agent\"})\n    headers2 = {\"Authorization\": \"Bearer token\"}\n    client2_updated = client2.with_headers(headers2)\n    client2_new_updated = client2.with_headers_new_implementation(headers2)\n    assert client2_updated._headers == client2_new_updated._headers\n\n    # Test 3: Overwriting existing headers with new values\n    client3 = Client(base_url=\"https://api.example.com\", headers={\"Authorization\": \"Old token\"})\n    headers3 = {\"Authorization\": \"New token\"}\n    client3_updated = client3.with_headers(headers3)\n    client3_new_updated = client3.with_headers_new_implementation(headers3)\n    assert client3_updated._headers == client3_new_updated._headers\n\n    # Test 4: Adding empty headers\n    headers4 = {}\n    client4 = client.with_headers(headers4)\n    client4_new = client.with_headers_new_implementation(headers4)\n    assert client4._headers == client4_new._headers\n\n    # Test 5: Adding multiple headers\n    headers5 = {\"Authorization\": \"Bearer token\", \"Content-Type\": \"application/json\"}\n    client5 = client.with_headers(headers5)\n    client5_new = client.with_headers_new_implementation(headers5)\n    assert client5._headers == client5_new._headers\n\n    # Test 6: Case sensitivity of headers\n    client6 = Client(base_url=\"https://api.example.com\", headers={\"authorization\": \"Bearer token\"})\n    headers6 = {\"Authorization\": \"New token\"}\n    client6_updated = client6.with_headers(headers6)\n    client6_new_updated = client6.with_headers_new_implementation(headers6)\n    assert client6_updated._headers == client6_new_updated._headers\n\n    # Test 7: Adding the same headers again\n    client7 = Client(base_url=\"https://api.example.com\", headers={\"Authorization\": \"Bearer token\"})\n    headers7 = {\"Authorization\": \"Bearer token\"}\n    client7_updated = client7.with_headers(headers7)\n    client7_new_updated = client7.with_headers_new_implementation(headers7)\n    assert client7_updated._headers == client7_new_updated._headers\n\n    # Test 8: Complex headers with special characters\n    headers8 = {\"X-Custom-Header\": \"Value with spaces and !@#$%^&*()\"}\n    client8 = client.with_headers(headers8)\n    client8_new = client.with_headers_new_implementation(headers8)\n    assert client8._headers == client8_new._headers\n\nif __name__ == \"__main__\":\n    test_with_headers()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they update the headers of the `_client` and `_async_client` if they are not `None`, and then return a new instance of the `Client` class with the updated headers using the `evolve` function. The logic and sequence of operations are the same in both versions. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `with_headers` function returns a new `Client` object with updated headers, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the `_headers` attribute of the returned `Client` objects, which checks the state of the objects rather than printed or logged content.\n- CONDITION 3: The test cases compare the results of `with_headers` and `with_headers_new_implementation` by checking if the `_headers` attributes of the resulting `Client` objects are equal. This ensures that `with_headers_new_implementation` must have the exact same functionality as `with_headers` to pass all tests.\n- CONDITION 4: The test cases and assert statements are reasonable as they compare the `_headers` attributes of the `Client` objects returned by both implementations, which is appropriate given that `with_headers` returns a new `Client` object.\n- CONDITION 5: The test cases cover various scenarios, including adding new headers, overwriting existing headers, handling empty headers, and dealing with case sensitivity and special characters. These are non-trivial and comprehensive tests.",
            "answer": "yes"
        },
        "commit_id": "a9e6b778f0c214a4a03682d47a987d13883b7611"
    },
    {
        "func_name": "Client.with_timeout",
        "idx": "419",
        "repo_name": "derapi___derapi-python",
        "func_path": "derapi/client.py",
        "orig_func": "def with_timeout(self, timeout: httpx.Timeout) -> 'Client':\n    \"\"\"Get a new client matching this one with a new timeout (in seconds)\"\"\"\n    if self._client is not None:\n        self._client.timeout = timeout\n    if self._async_client is not None:\n        self._async_client.timeout = timeout\n    return evolve(self, timeout=timeout)",
        "orig_context": "```python\n## derapi/client.py\nimport ssl\n\nfrom typing import Any, Dict, Optional, Union\n\nimport httpx\n\nfrom attrs import define, evolve, field\n\nclass Client:\n    \"\"\"A class for keeping track of data related to the API\n\n    The following are accepted as keyword arguments and will be used to construct httpx Clients internally:\n\n        ``base_url``: The base URL for the API, all requests are made to a relative path to this URL\n\n        ``cookies``: A dictionary of cookies to be sent with every request\n\n        ``headers``: A dictionary of headers to be sent with every request\n\n        ``timeout``: The maximum amount of a time a request can take. API functions will raise\n        httpx.TimeoutException if this is exceeded.\n\n        ``verify_ssl``: Whether or not to verify the SSL certificate of the API server. This should be True in production,\n        but can be set to False for testing purposes.\n\n        ``follow_redirects``: Whether or not to follow redirects. Default value is False.\n\n        ``httpx_args``: A dictionary of additional arguments to be passed to the ``httpx.Client`` and ``httpx.AsyncClient`` constructor.\n\n\n    Attributes:\n        raise_on_unexpected_status: Whether or not to raise an errors.UnexpectedStatus if the API returns a\n            status code that was not documented in the source OpenAPI document. Can also be provided as a keyword\n            argument to the constructor.\n    \"\"\"\n\n    raise_on_unexpected_status: bool = field(default=False, kw_only=True)\n    _base_url: str = field(alias=\"base_url\")\n    _cookies: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"cookies\")\n    _headers: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"headers\")\n    _timeout: Optional[httpx.Timeout] = field(default=None, kw_only=True, alias=\"timeout\")\n    _verify_ssl: Union[str, bool, ssl.SSLContext] = field(default=True, kw_only=True, alias=\"verify_ssl\")\n    _follow_redirects: bool = field(default=False, kw_only=True, alias=\"follow_redirects\")\n    _httpx_args: Dict[str, Any] = field(factory=dict, kw_only=True, alias=\"httpx_args\")\n    _client: Optional[httpx.Client] = field(default=None, init=False)\n    _async_client: Optional[httpx.AsyncClient] = field(default=None, init=False)\n\n    def with_headers(self, headers: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional headers\"\"\"\n        if self._client is not None:\n            self._client.headers.update(headers)\n        if self._async_client is not None:\n            self._async_client.headers.update(headers)\n        return evolve(self, headers={**self._headers, **headers})\n\n    def with_cookies(self, cookies: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional cookies\"\"\"\n        if self._client is not None:\n            self._client.cookies.update(cookies)\n        if self._async_client is not None:\n            self._async_client.cookies.update(cookies)\n        return evolve(self, cookies={**self._cookies, **cookies})\n\n    def with_timeout(self, timeout: httpx.Timeout) -> \"Client\":\n        \"\"\"Get a new client matching this one with a new timeout (in seconds)\"\"\"\n        if self._client is not None:\n            self._client.timeout = timeout\n        if self._async_client is not None:\n            self._async_client.timeout = timeout\n        return evolve(self, timeout=timeout)\n\n    def set_httpx_client(self, client: httpx.Client) -> \"Client\":\n        \"\"\"Manually set the underlying httpx.Client\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._client = client\n        return self\n\n    def get_httpx_client(self) -> httpx.Client:\n        \"\"\"Get the underlying httpx.Client, constructing a new one if not previously set\"\"\"\n        if self._client is None:\n            self._client = httpx.Client(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._client\n\n    def __enter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for self.client\u2014you cannot enter twice (see httpx docs)\"\"\"\n        self.get_httpx_client().__enter__()\n        return self\n\n    def __exit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for internal httpx.Client (see httpx docs)\"\"\"\n        self.get_httpx_client().__exit__(*args, **kwargs)\n\n    def set_async_httpx_client(self, async_client: httpx.AsyncClient) -> \"Client\":\n        \"\"\"Manually the underlying httpx.AsyncClient\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._async_client = async_client\n        return self\n\n    def get_async_httpx_client(self) -> httpx.AsyncClient:\n        \"\"\"Get the underlying httpx.AsyncClient, constructing a new one if not previously set\"\"\"\n        if self._async_client is None:\n            self._async_client = httpx.AsyncClient(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._async_client\n\n    async def __aenter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for underlying httpx.AsyncClient\u2014you cannot enter twice (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aenter__()\n        return self\n\n    async def __aexit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for underlying httpx.AsyncClient (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aexit__(*args, **kwargs)\n\n```\n\n\n",
        "eval_script": "## derapi/client.py\nimport ssl\n\nfrom typing import Any, Dict, Optional, Union\n\nimport httpx\n\nfrom attrs import define, evolve, field\n\n@define\nclass Client:\n    \"\"\"A class for keeping track of data related to the API\n\n    The following are accepted as keyword arguments and will be used to construct httpx Clients internally:\n\n        ``base_url``: The base URL for the API, all requests are made to a relative path to this URL\n\n        ``cookies``: A dictionary of cookies to be sent with every request\n\n        ``headers``: A dictionary of headers to be sent with every request\n\n        ``timeout``: The maximum amount of a time a request can take. API functions will raise\n        httpx.TimeoutException if this is exceeded.\n\n        ``verify_ssl``: Whether or not to verify the SSL certificate of the API server. This should be True in production,\n        but can be set to False for testing purposes.\n\n        ``follow_redirects``: Whether or not to follow redirects. Default value is False.\n\n        ``httpx_args``: A dictionary of additional arguments to be passed to the ``httpx.Client`` and ``httpx.AsyncClient`` constructor.\n\n\n    Attributes:\n        raise_on_unexpected_status: Whether or not to raise an errors.UnexpectedStatus if the API returns a\n            status code that was not documented in the source OpenAPI document. Can also be provided as a keyword\n            argument to the constructor.\n    \"\"\"\n\n    raise_on_unexpected_status: bool = field(default=False, kw_only=True)\n    _base_url: str = field(alias=\"base_url\")\n    _cookies: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"cookies\")\n    _headers: Dict[str, str] = field(factory=dict, kw_only=True, alias=\"headers\")\n    _timeout: Optional[httpx.Timeout] = field(default=None, kw_only=True, alias=\"timeout\")\n    _verify_ssl: Union[str, bool, ssl.SSLContext] = field(default=True, kw_only=True, alias=\"verify_ssl\")\n    _follow_redirects: bool = field(default=False, kw_only=True, alias=\"follow_redirects\")\n    _httpx_args: Dict[str, Any] = field(factory=dict, kw_only=True, alias=\"httpx_args\")\n    _client: Optional[httpx.Client] = field(default=None, init=False)\n    _async_client: Optional[httpx.AsyncClient] = field(default=None, init=False)\n\n    def with_headers(self, headers: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional headers\"\"\"\n        if self._client is not None:\n            self._client.headers.update(headers)\n        if self._async_client is not None:\n            self._async_client.headers.update(headers)\n        return evolve(self, headers={**self._headers, **headers})\n\n    def with_cookies(self, cookies: Dict[str, str]) -> \"Client\":\n        \"\"\"Get a new client matching this one with additional cookies\"\"\"\n        if self._client is not None:\n            self._client.cookies.update(cookies)\n        if self._async_client is not None:\n            self._async_client.cookies.update(cookies)\n        return evolve(self, cookies={**self._cookies, **cookies})\n\n    def with_timeout(self, timeout: httpx.Timeout) -> \"Client\":\n        \"\"\"Get a new client matching this one with a new timeout (in seconds)\"\"\"\n        if self._client is not None:\n            self._client.timeout = timeout\n        if self._async_client is not None:\n            self._async_client.timeout = timeout\n        return evolve(self, timeout=timeout)\n\n\n    def set_httpx_client(self, client: httpx.Client) -> \"Client\":\n        \"\"\"Manually set the underlying httpx.Client\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._client = client\n        return self\n\n    def get_httpx_client(self) -> httpx.Client:\n        \"\"\"Get the underlying httpx.Client, constructing a new one if not previously set\"\"\"\n        if self._client is None:\n            self._client = httpx.Client(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._client\n\n    def __enter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for self.client\u2014you cannot enter twice (see httpx docs)\"\"\"\n        self.get_httpx_client().__enter__()\n        return self\n\n    def __exit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for internal httpx.Client (see httpx docs)\"\"\"\n        self.get_httpx_client().__exit__(*args, **kwargs)\n\n    def set_async_httpx_client(self, async_client: httpx.AsyncClient) -> \"Client\":\n        \"\"\"Manually the underlying httpx.AsyncClient\n\n        **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n        \"\"\"\n        self._async_client = async_client\n        return self\n\n    def get_async_httpx_client(self) -> httpx.AsyncClient:\n        \"\"\"Get the underlying httpx.AsyncClient, constructing a new one if not previously set\"\"\"\n        if self._async_client is None:\n            self._async_client = httpx.AsyncClient(\n                base_url=self._base_url,\n                cookies=self._cookies,\n                headers=self._headers,\n                timeout=self._timeout,\n                verify=self._verify_ssl,\n                follow_redirects=self._follow_redirects,\n                **self._httpx_args,\n            )\n        return self._async_client\n\n    async def __aenter__(self) -> \"Client\":\n        \"\"\"Enter a context manager for underlying httpx.AsyncClient\u2014you cannot enter twice (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aenter__()\n        return self\n\n    async def __aexit__(self, *args: Any, **kwargs: Any) -> None:\n        \"\"\"Exit a context manager for underlying httpx.AsyncClient (see httpx docs)\"\"\"\n        await self.get_async_httpx_client().__aexit__(*args, **kwargs)\n\ndef test_with_timeout():\n    client = Client(base_url=\"https://example.com\")\n    timeout = httpx.Timeout(5.0)\n\n    # Test with original implementation\n    client_with_timeout = client.with_timeout(timeout)\n    assert client_with_timeout._timeout == timeout\n\n    # Test with new implementation\n    client_with_timeout_new = client.with_timeout_new_implementation(timeout)\n    assert client_with_timeout_new._timeout == timeout\n\n    # Test that both implementations produce the same result\n    assert client_with_timeout._timeout == client_with_timeout_new._timeout\n\nif __name__ == \"__main__\":\n    test_with_timeout()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are named `with_timeout` and perform the same operations: they check if `_client` and `_async_client` are not `None`, and if so, they set their `timeout` attributes to the provided `timeout` value. Finally, they return a new instance of the `Client` class with the updated `timeout` using the `evolve` function. There are no differences in the logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `with_timeout` function returns a new `Client` object with the updated timeout, satisfying the condition that it either returns a value or modifies global variables or input arguments.\n\n2. **CONDITION 2**: The test function checks the return values by asserting the `_timeout` attribute of the returned `Client` objects. It does not rely on printed or logged contents.\n\n3. **CONDITION 3**: The test function checks both the original and new implementations by comparing the `_timeout` attribute of the `Client` objects returned by both implementations. This ensures that the new implementation must have the exact same functionality as the original to pass the test.\n\n4. **CONDITION 4**: The test cases and assert statements are reasonable. They correctly check the `_timeout` attribute of the `Client` objects returned by both implementations, which is the expected behavior of the `with_timeout` function.\n\n5. **CONDITION 5**: The test cases are non-trivial as they not only check the functionality of both implementations individually but also compare the results of both to ensure they are identical.",
            "answer": "yes"
        },
        "commit_id": "a9e6b778f0c214a4a03682d47a987d13883b7611"
    },
    {
        "func_name": "AbstractDarkHex.win_check",
        "idx": "435",
        "repo_name": "JimmyR714___darkhex",
        "func_path": "game/darkhex.py",
        "orig_func": "def win_check(self) -> str:\n    \"\"\"\n        Check if the board is in a winning position for some player.\n        Returns:\n            \"black_win\" if black has won\n            \"white_win\" if white has won\n            \"none\" if nobody has won\n        \"\"\"\n    logging.info('Performing a win check')\n    if self.black_components.connected((1, 0), (1, self.rows + 1)):\n        return 'black_win'\n    elif self.white_components.connected((0, 1), (self.cols + 1, 1)):\n        return 'white_win'\n    else:\n        return 'none'",
        "orig_context": "```python\n## game/darkhex.py\nimport logging\n\nfrom scipy.cluster.hierarchy import DisjointSet\n\nimport game.util as util\n\nclass AbstractDarkHex:\n    \"\"\"\n    Class that can run a game of Dark Hex. \n    \n    Initialise with the dimensions and an empty board will be created. \n    \n    By default, white moves first.\n    \n    Assume that position (1,1) is the top left cell, and (2,1) is the top row, 2nd column etc.\n    \n    We have black \"goals\" at the top and bottom, and white \"goals\" at the left and right.\n    \"\"\"\n\n    def __init__(self, _cols : int, _rows : int, _first_turn=\"w\"):\n        self.cols = _cols\n        self.rows = _rows\n        self.board = []\n        self.black_board = []\n        self.white_board = []\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        self.turn = _first_turn  # for now, default first turn is white's\n        self.first_turn = _first_turn  # save in case of reset\n        logging.info(\"Board parameters defined\")\n        self.reset_board()  # set starting state of board and components\n\n    def move(self, row : int, col : int, colour : str) -> str:\n        \"\"\"\n        Attempt to place a piece of a given colour into a given cell\n        Parameters:\n            colour: \"b\" or \"w\" representing black and white respectively\n            row: The row to insert on, with 1 being at the top\n            col: The column to insert on, with 1 being at the left\n        Returns:\n            \"black_win\" if the cell is placed and this wins the game for black\n            \"white_win\" if the cell is placed and this wins the game for white\n            \"placed\" if the cell is placed and the game continues\n            \"full_white\" if the cell is occupied with a white tile\n            \"full_black\" if the cell is occupied with a black tile\n        \"\"\"\n        # check that this player is allowed to move\n        assert colour == self.turn\n\n        cell = self.board[row][col]\n        if cell != \"e\":  # if the chosen cell is not empty\n            logging.info(\"Non-empty cell %s at (%s, %s)\", cell, col, row)\n            # update our view, since we know where their piece is now\n            self._get_board(colour)[row][col] = self.board[row][col]  # view update\n            return \"full_\" + util.colour_map[self.board[row][col]]\n        else:\n            # update global board and our view\n            self.board[row][col] = colour\n            self._get_board(colour)[row][col] = colour\n            self.turn = util.swap_colour(colour)  # swap turn\n            self.update_components(row, col, colour)  # update components\n            logging.info(\"%s played at (%s, %s)\",\n                         util.colour_map[colour], col, row)\n            win_check = self.win_check()\n            logging.info(\"Result of win_check is: %s\", win_check)\n            if win_check != \"none\":\n                return win_check\n            else:\n                return \"placed\"\n    #TODO bug where black wins but it isnt registered. shape like <\n\n    def win_check(self) -> str:\n        \"\"\"\n        Check if the board is in a winning position for some player.\n        Returns:\n            \"black_win\" if black has won\n            \"white_win\" if white has won\n            \"none\" if nobody has won\n        \"\"\"\n        #check if the two black rows are connected or the two white columns are connected\n        logging.info(\"Performing a win check\")\n        if self.black_components.connected((1, 0), (1, self.rows+1)):\n            return \"black_win\"\n        elif self.white_components.connected((0, 1), (self.cols+1, 1)):\n            return \"white_win\"\n        else:\n            return \"none\"\n\n    def update_components(self, row : int, col : int, colour : str) -> None:\n        \"\"\"\n        Update the connected components of the given colour to include the new cell (col,row)\n        \"\"\"\n        logging.debug(\"Attempting to merge components surrounding (%s, %s)\", col, row)\n        match colour:\n            case \"w\":\n                components = self.white_components\n            case \"b\":\n                components = self.black_components\n            case _:\n                raise ValueError(\"Invalid colour given to update_components\")\n\n        components.add((col,row))\n        # attempt to connect to each matching colour in surrounding hex\n        adj = [\n            (col-1, row), (col, row-1), (col+1, row-1),\n            (col+1, row), (col, row+1), (col-1,row+1)\n        ]\n        for cell in adj:\n            # if adjacent cell is of the same colour\n            if self.board[cell[1]][cell[0]] == colour:\n                # connect the components\n                components.merge((col,row),cell)\n                logging.debug(\"(%s, %s) and %s %s components merged\", \n                              col, row, cell, util.colour_map[colour])\n\n\n    def reset_board(self) -> None:\n        \"\"\"\n        Restart the game with the same board dimensions\n        \"\"\"\n        # reset board contents\n        self.board = self._create_board()\n        self.black_board = self._create_board()\n        self.white_board = self._create_board()\n\n        # revert to correct first turn\n        self.turn = self.first_turn\n\n        # set starting components\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        # initial black components are top and bottom rows\n        for x in range(1,self.cols+1):\n            self.black_components.add((x, 0))\n            self.black_components.merge((1,0), (x,0))\n            self.black_components.add((x, self.rows+1))\n            self.black_components.merge((1, self.rows+1), (x, self.rows+1))\n\n        # initial white components are left and right columns\n        for y in range(self.rows+2):\n            self.white_components.add((0,y))\n            self.white_components.merge((0,0), (0,y))\n            self.white_components.add((self.cols+1, y))\n            self.white_components.merge((self.cols+1,0), (self.cols+1, y))\n\n        logging.info(\"Board reset\")\n\n\n    def _create_board(self) -> list[list[str]]:\n        \"\"\"\n        Create a starting board of size cols+1 x rows+1\n        The top and bottom rows are black, the left and right columns are white\n        \"\"\"\n        row = [\"w\"] + [\"b\" for i in range(self.cols)] + [\"w\"]\n        return [row] + [\n            [\"w\"] + [\"e\" for i in range(self.cols)] + [\"w\"] for j in range(self.rows)\n        ] + [row]\n\n\n    def _get_board(self, colour : str) -> list[list[str]]:\n        \"\"\"\n        Returns the board for a given colour\n        \"\"\"\n        match colour:\n            case \"b\":\n                return self.black_board\n            case \"w\":\n                return self.white_board\n            case _:\n                raise ValueError(\"Invalid colour given to _get_board\")\n\n```\n\n\n",
        "eval_script": "# Mock game/util.py\ncolour_map = {\n    \"b\": \"black\",\n    \"w\": \"white\",\n    \"e\": \"empty\"\n}\n\ndef swap_colour(colour):\n    return \"b\" if colour == \"w\" else \"w\"\n\n# Revised game/darkhex.py\nimport logging\nfrom scipy.cluster.hierarchy import DisjointSet\n\n# Mock import for game.util\nimport sys\nimport types\n\nutil = types.ModuleType(\"util\")\nutil.colour_map = {\n    \"b\": \"black\",\n    \"w\": \"white\",\n    \"e\": \"empty\"\n}\nutil.swap_colour = lambda colour: \"b\" if colour == \"w\" else \"w\"\nsys.modules[\"game.util\"] = util\n\nclass AbstractDarkHex:\n    \"\"\"\n    Class that can run a game of Dark Hex. \n    \n    Initialise with the dimensions and an empty board will be created. \n    \n    By default, white moves first.\n    \n    Assume that position (1,1) is the top left cell, and (2,1) is the top row, 2nd column etc.\n    \n    We have black \"goals\" at the top and bottom, and white \"goals\" at the left and right.\n    \"\"\"\n\n    def __init__(self, _cols : int, _rows : int, _first_turn=\"w\"):\n        self.cols = _cols\n        self.rows = _rows\n        self.board = []\n        self.black_board = []\n        self.white_board = []\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        self.turn = _first_turn  # for now, default first turn is white's\n        self.first_turn = _first_turn  # save in case of reset\n        logging.info(\"Board parameters defined\")\n        self.reset_board()  # set starting state of board and components\n\n    def move(self, row : int, col : int, colour : str) -> str:\n        \"\"\"\n        Attempt to place a piece of a given colour into a given cell\n        Parameters:\n            colour: \"b\" or \"w\" representing black and white respectively\n            row: The row to insert on, with 1 being at the top\n            col: The column to insert on, with 1 being at the left\n        Returns:\n            \"black_win\" if the cell is placed and this wins the game for black\n            \"white_win\" if the cell is placed and this wins the game for white\n            \"placed\" if the cell is placed and the game continues\n            \"full_white\" if the cell is occupied with a white tile\n            \"full_black\" if the cell is occupied with a black tile\n        \"\"\"\n        # check that this player is allowed to move\n        assert colour == self.turn\n\n        cell = self.board[row][col]\n        if cell != \"e\":  # if the chosen cell is not empty\n            logging.info(\"Non-empty cell %s at (%s, %s)\", cell, col, row)\n            # update our view, since we know where their piece is now\n            self._get_board(colour)[row][col] = self.board[row][col]  # view update\n            return \"full_\" + util.colour_map[self.board[row][col]]\n        else:\n            # update global board and our view\n            self.board[row][col] = colour\n            self._get_board(colour)[row][col] = colour\n            self.turn = util.swap_colour(colour)  # swap turn\n            self.update_components(row, col, colour)  # update components\n            logging.info(\"%s played at (%s, %s)\",\n                         util.colour_map[colour], col, row)\n            win_check = self.win_check()\n            logging.info(\"Result of win_check is: %s\", win_check)\n            if win_check != \"none\":\n                return win_check\n            else:\n                return \"placed\"\n    #TODO bug where black wins but it isnt registered. shape like <\n\n    def win_check(self) -> str:\n        \"\"\"\n        Check if the board is in a winning position for some player.\n        Returns:\n            \"black_win\" if black has won\n            \"white_win\" if white has won\n            \"none\" if nobody has won\n        \"\"\"\n        #check if the two black rows are connected or the two white columns are connected\n        logging.info(\"Performing a win check\")\n        if self.black_components.connected((1, 0), (1, self.rows+1)):\n            return \"black_win\"\n        elif self.white_components.connected((0, 1), (self.cols+1, 1)):\n            return \"white_win\"\n        else:\n            return \"none\"\n\n\n    def update_components(self, row : int, col : int, colour : str) -> None:\n        \"\"\"\n        Update the connected components of the given colour to include the new cell (col,row)\n        \"\"\"\n        logging.debug(\"Attempting to merge components surrounding (%s, %s)\", col, row)\n        match colour:\n            case \"w\":\n                components = self.white_components\n            case \"b\":\n                components = self.black_components\n            case _:\n                raise ValueError(\"Invalid colour given to update_components\")\n\n        components.add((col,row))\n        # attempt to connect to each matching colour in surrounding hex\n        adj = [\n            (col-1, row), (col, row-1), (col+1, row-1),\n            (col+1, row), (col, row+1), (col-1,row+1)\n        ]\n        for cell in adj:\n            # if adjacent cell is of the same colour\n            if self.board[cell[1]][cell[0]] == colour:\n                # connect the components\n                components.merge((col,row),cell)\n                logging.debug(\"(%s, %s) and %s %s components merged\", \n                              col, row, cell, util.colour_map[colour])\n\n\n    def reset_board(self) -> None:\n        \"\"\"\n        Restart the game with the same board dimensions\n        \"\"\"\n        # reset board contents\n        self.board = self._create_board()\n        self.black_board = self._create_board()\n        self.white_board = self._create_board()\n\n        # revert to correct first turn\n        self.turn = self.first_turn\n\n        # set starting components\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        # initial black components are top and bottom rows\n        for x in range(1,self.cols+1):\n            self.black_components.add((x, 0))\n            self.black_components.merge((1,0), (x,0))\n            self.black_components.add((x, self.rows+1))\n            self.black_components.merge((1, self.rows+1), (x, self.rows+1))\n\n        # initial white components are left and right columns\n        for y in range(self.rows+2):\n            self.white_components.add((0,y))\n            self.white_components.merge((0,0), (0,y))\n            self.white_components.add((self.cols+1, y))\n            self.white_components.merge((self.cols+1,0), (self.cols+1, y))\n\n        logging.info(\"Board reset\")\n\n\n    def _create_board(self) -> list[list[str]]:\n        \"\"\"\n        Create a starting board of size cols+1 x rows+1\n        The top and bottom rows are black, the left and right columns are white\n        \"\"\"\n        row = [\"w\"] + [\"b\" for i in range(self.cols)] + [\"w\"]\n        return [row] + [\n            [\"w\"] + [\"e\" for i in range(self.cols)] + [\"w\"] for j in range(self.rows)\n        ] + [row]\n\n\n    def _get_board(self, colour : str) -> list[list[str]]:\n        \"\"\"\n        Returns the board for a given colour\n        \"\"\"\n        match colour:\n            case \"b\":\n                return self.black_board\n            case \"w\":\n                return self.white_board\n            case _:\n                raise ValueError(\"Invalid colour given to _get_board\")\n\ndef test_win_check():\n    # Test case 1: No win\n    game = AbstractDarkHex(3, 3)\n    game.reset_board()\n    assert game.win_check() == game.win_check_new_implementation()\n\n    # Test case 2: Black win\n    game.black_components.merge((1, 0), (1, 4))\n    assert game.win_check() == game.win_check_new_implementation()\n\n    # Test case 3: White win\n    game.reset_board()\n    game.white_components.merge((0, 1), (4, 1))\n    assert game.win_check() == game.win_check_new_implementation()\n\nif __name__ == \"__main__\":\n    test_win_check()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, it is clear that they perform the same operations. Both functions check if the black components are connected from the top to the bottom of the board and if the white components are connected from the left to the right of the board. They return \"black_win\" if black has won, \"white_win\" if white has won, and \"none\" if neither has won. The logic and conditions used in both functions are identical, and the revised function does not introduce any changes in functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `win_check` function returns a string indicating the game's status (\"black_win\", \"white_win\", or \"none\"). Therefore, it satisfies the condition of having return values.\n  \n- **CONDITION 2**: The test cases in `test_win_check` use assertions to compare the return values of `win_check` and `win_check_new_implementation`. They do not rely on printed or logged outputs, satisfying this condition.\n\n- **CONDITION 3**: The test cases check the equivalence of the outputs of `win_check` and `win_check_new_implementation` under different scenarios (no win, black win, white win). This ensures that `win_check_new_implementation` must have the same functionality as `win_check` to pass all tests, satisfying this condition.\n\n- **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations of `win_check`, which is appropriate given that `win_check` returns a value. The assertions are reasonable and correctly implemented.\n\n- **CONDITION 5**: The test cases cover different scenarios: no win, a win for black, and a win for white. These are non-trivial and relevant scenarios for a game-winning check function, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "eed78380bbf1f83a1e0752fcef1b6dda057988ad"
    },
    {
        "func_name": "AbstractDarkHex.reset_board",
        "idx": "437",
        "repo_name": "JimmyR714___darkhex",
        "func_path": "game/darkhex.py",
        "orig_func": "def reset_board(self) -> None:\n    \"\"\"\n        Restart the game with the same board dimensions\n        \"\"\"\n    self.board = self._create_board()\n    self.black_board = self._create_board()\n    self.white_board = self._create_board()\n    self.turn = self.first_turn\n    self.black_components = DisjointSet([])\n    self.white_components = DisjointSet([])\n    for x in range(1, self.cols + 1):\n        self.black_components.add((x, 0))\n        self.black_components.merge((1, 0), (x, 0))\n        self.black_components.add((x, self.rows + 1))\n        self.black_components.merge((1, self.rows + 1), (x, self.rows + 1))\n    for y in range(self.rows + 2):\n        self.white_components.add((0, y))\n        self.white_components.merge((0, 0), (0, y))\n        self.white_components.add((self.cols + 1, y))\n        self.white_components.merge((self.cols + 1, 0), (self.cols + 1, y))\n    logging.info('Board reset')",
        "orig_context": "```python\n## game/darkhex.py\nimport logging\n\nfrom scipy.cluster.hierarchy import DisjointSet\n\nimport game.util as util\n\nclass AbstractDarkHex:\n    \"\"\"\n    Class that can run a game of Dark Hex. \n    \n    Initialise with the dimensions and an empty board will be created. \n    \n    By default, white moves first.\n    \n    Assume that position (1,1) is the top left cell, and (2,1) is the top row, 2nd column etc.\n    \n    We have black \"goals\" at the top and bottom, and white \"goals\" at the left and right.\n    \"\"\"\n\n    def __init__(self, _cols : int, _rows : int, _first_turn=\"w\"):\n        self.cols = _cols\n        self.rows = _rows\n        self.board = []\n        self.black_board = []\n        self.white_board = []\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        self.turn = _first_turn  # for now, default first turn is white's\n        self.first_turn = _first_turn  # save in case of reset\n        logging.info(\"Board parameters defined\")\n        self.reset_board()  # set starting state of board and components\n\n    def move(self, row : int, col : int, colour : str) -> str:\n        \"\"\"\n        Attempt to place a piece of a given colour into a given cell\n        Parameters:\n            colour: \"b\" or \"w\" representing black and white respectively\n            row: The row to insert on, with 1 being at the top\n            col: The column to insert on, with 1 being at the left\n        Returns:\n            \"black_win\" if the cell is placed and this wins the game for black\n            \"white_win\" if the cell is placed and this wins the game for white\n            \"placed\" if the cell is placed and the game continues\n            \"full_white\" if the cell is occupied with a white tile\n            \"full_black\" if the cell is occupied with a black tile\n        \"\"\"\n        # check that this player is allowed to move\n        assert colour == self.turn\n\n        cell = self.board[row][col]\n        if cell != \"e\":  # if the chosen cell is not empty\n            logging.info(\"Non-empty cell %s at (%s, %s)\", cell, col, row)\n            # update our view, since we know where their piece is now\n            self._get_board(colour)[row][col] = self.board[row][col]  # view update\n            return \"full_\" + util.colour_map[self.board[row][col]]\n        else:\n            # update global board and our view\n            self.board[row][col] = colour\n            self._get_board(colour)[row][col] = colour\n            self.turn = util.swap_colour(colour)  # swap turn\n            self.update_components(row, col, colour)  # update components\n            logging.info(\"%s played at (%s, %s)\",\n                         util.colour_map[colour], col, row)\n            win_check = self.win_check()\n            logging.info(\"Result of win_check is: %s\", win_check)\n            if win_check != \"none\":\n                return win_check\n            else:\n                return \"placed\"\n    #TODO bug where black wins but it isnt registered. shape like <\n\n    def win_check(self) -> str:\n        \"\"\"\n        Check if the board is in a winning position for some player.\n        Returns:\n            \"black_win\" if black has won\n            \"white_win\" if white has won\n            \"none\" if nobody has won\n        \"\"\"\n        #check if the two black rows are connected or the two white columns are connected\n        logging.info(\"Performing a win check\")\n        if self.black_components.connected((1, 0), (1, self.rows+1)):\n            return \"black_win\"\n        elif self.white_components.connected((0, 1), (self.cols+1, 1)):\n            return \"white_win\"\n        else:\n            return \"none\"\n\n    def update_components(self, row : int, col : int, colour : str) -> None:\n        \"\"\"\n        Update the connected components of the given colour to include the new cell (col,row)\n        \"\"\"\n        logging.debug(\"Attempting to merge components surrounding (%s, %s)\", col, row)\n        match colour:\n            case \"w\":\n                components = self.white_components\n            case \"b\":\n                components = self.black_components\n            case _:\n                raise ValueError(\"Invalid colour given to update_components\")\n\n        components.add((col,row))\n        # attempt to connect to each matching colour in surrounding hex\n        adj = [\n            (col-1, row), (col, row-1), (col+1, row-1),\n            (col+1, row), (col, row+1), (col-1,row+1)\n        ]\n        for cell in adj:\n            # if adjacent cell is of the same colour\n            if self.board[cell[1]][cell[0]] == colour:\n                # connect the components\n                components.merge((col,row),cell)\n                logging.debug(\"(%s, %s) and %s %s components merged\", \n                              col, row, cell, util.colour_map[colour])\n\n\n    def reset_board(self) -> None:\n        \"\"\"\n        Restart the game with the same board dimensions\n        \"\"\"\n        # reset board contents\n        self.board = self._create_board()\n        self.black_board = self._create_board()\n        self.white_board = self._create_board()\n\n        # revert to correct first turn\n        self.turn = self.first_turn\n\n        # set starting components\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        # initial black components are top and bottom rows\n        for x in range(1,self.cols+1):\n            self.black_components.add((x, 0))\n            self.black_components.merge((1,0), (x,0))\n            self.black_components.add((x, self.rows+1))\n            self.black_components.merge((1, self.rows+1), (x, self.rows+1))\n\n        # initial white components are left and right columns\n        for y in range(self.rows+2):\n            self.white_components.add((0,y))\n            self.white_components.merge((0,0), (0,y))\n            self.white_components.add((self.cols+1, y))\n            self.white_components.merge((self.cols+1,0), (self.cols+1, y))\n\n        logging.info(\"Board reset\")\n\n\n    def _create_board(self) -> list[list[str]]:\n        \"\"\"\n        Create a starting board of size cols+1 x rows+1\n        The top and bottom rows are black, the left and right columns are white\n        \"\"\"\n        row = [\"w\"] + [\"b\" for i in range(self.cols)] + [\"w\"]\n        return [row] + [\n            [\"w\"] + [\"e\" for i in range(self.cols)] + [\"w\"] for j in range(self.rows)\n        ] + [row]\n\n\n    def _get_board(self, colour : str) -> list[list[str]]:\n        \"\"\"\n        Returns the board for a given colour\n        \"\"\"\n        match colour:\n            case \"b\":\n                return self.black_board\n            case \"w\":\n                return self.white_board\n            case _:\n                raise ValueError(\"Invalid colour given to _get_board\")\n\n```\n\n\n",
        "eval_script": "# Import necessary modules\nimport logging\nfrom scipy.cluster.hierarchy import DisjointSet\n\n# Mock the util module with necessary functions\nclass util:\n    colour_map = {'b': 'black', 'w': 'white'}\n    \n    @staticmethod\n    def swap_colour(colour):\n        return 'b' if colour == 'w' else 'w'\n\n# Define the AbstractDarkHex class as provided\nclass AbstractDarkHex:\n    def __init__(self, _cols: int, _rows: int, _first_turn=\"w\"):\n        self.cols = _cols\n        self.rows = _rows\n        self.board = []\n        self.black_board = []\n        self.white_board = []\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        self.turn = _first_turn\n        self.first_turn = _first_turn\n        logging.info(\"Board parameters defined\")\n        self.reset_board()\n\n    def move(self, row: int, col: int, colour: str) -> str:\n        assert colour == self.turn\n        cell = self.board[row][col]\n        if cell != \"e\":\n            logging.info(\"Non-empty cell %s at (%s, %s)\", cell, col, row)\n            self._get_board(colour)[row][col] = self.board[row][col]\n            return \"full_\" + util.colour_map[self.board[row][col]]\n        else:\n            self.board[row][col] = colour\n            self._get_board(colour)[row][col] = colour\n            self.turn = util.swap_colour(colour)\n            self.update_components(row, col, colour)\n            logging.info(\"%s played at (%s, %s)\", util.colour_map[colour], col, row)\n            win_check = self.win_check()\n            logging.info(\"Result of win_check is: %s\", win_check)\n            if win_check != \"none\":\n                return win_check\n            else:\n                return \"placed\"\n\n    def win_check(self) -> str:\n        logging.info(\"Performing a win check\")\n        if self.black_components.connected((1, 0), (1, self.rows + 1)):\n            return \"black_win\"\n        elif self.white_components.connected((0, 1), (self.cols + 1, 1)):\n            return \"white_win\"\n        else:\n            return \"none\"\n\n    def update_components(self, row: int, col: int, colour: str) -> None:\n        logging.debug(\"Attempting to merge components surrounding (%s, %s)\", col, row)\n        match colour:\n            case \"w\":\n                components = self.white_components\n            case \"b\":\n                components = self.black_components\n            case _:\n                raise ValueError(\"Invalid colour given to update_components\")\n\n        components.add((col, row))\n        adj = [\n            (col - 1, row), (col, row - 1), (col + 1, row - 1),\n            (col + 1, row), (col, row + 1), (col - 1, row + 1)\n        ]\n        for cell in adj:\n            if self.board[cell[1]][cell[0]] == colour:\n                components.merge((col, row), cell)\n                logging.debug(\"(%s, %s) and %s %s components merged\",\n                              col, row, cell, util.colour_map[colour])\n\n    def reset_board(self) -> None:\n        self.board = self._create_board()\n        self.black_board = self._create_board()\n        self.white_board = self._create_board()\n        self.turn = self.first_turn\n        self.black_components = DisjointSet([])\n        self.white_components = DisjointSet([])\n        for x in range(1, self.cols + 1):\n            self.black_components.add((x, 0))\n            self.black_components.merge((1, 0), (x, 0))\n            self.black_components.add((x, self.rows + 1))\n            self.black_components.merge((1, self.rows + 1), (x, self.rows + 1))\n        for y in range(self.rows + 2):\n            self.white_components.add((0, y))\n            self.white_components.merge((0, 0), (0, y))\n            self.white_components.add((self.cols + 1, y))\n            self.white_components.merge((self.cols + 1, 0), (self.cols + 1, y))\n        logging.info(\"Board reset\")\n\n\n    def _create_board(self) -> list[list[str]]:\n        row = [\"w\"] + [\"b\" for i in range(self.cols)] + [\"w\"]\n        return [row] + [\n            [\"w\"] + [\"e\" for i in range(self.cols)] + [\"w\"] for j in range(self.rows)\n        ] + [row]\n\n    def _get_board(self, colour: str) -> list[list[str]]:\n        match colour:\n            case \"b\":\n                return self.black_board\n            case \"w\":\n                return self.white_board\n            case _:\n                raise ValueError(\"Invalid colour given to _get_board\")\n\n\ndef test_reset_board():\n    game_original = AbstractDarkHex(5, 5)\n    game_new = AbstractDarkHex(5, 5)\n    \n    # Reset using the new implementation\n    game_new.reset_board_new_implementation()\n    \n    # Assert that both boards are the same\n    assert game_original.board == game_new.board, \"Board states do not match\"\n    assert game_original.black_board == game_new.black_board, \"Black board states do not match\"\n    assert game_original.white_board == game_new.white_board, \"White board states do not match\"\n    assert game_original.turn == game_new.turn, \"Turn states do not match\"\n    \n    # Compare the sets of connected components\n    assert set(game_original.black_components) == set(game_new.black_components), \"Black components do not match\"\n    assert set(game_original.white_components) == set(game_new.white_components), \"White components do not match\"\n\nif __name__ == \"__main__\":\n    test_reset_board()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing both the original and revised `reset_board` functions, they are identical in terms of functionality. Both functions initialize the board, black_board, and white_board using the `_create_board` method, set the turn to `first_turn`, and initialize the `black_components` and `white_components` as empty `DisjointSet` objects. They then add and merge components for the black and white components in the same manner. The logging statement at the end of both functions is also the same. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `reset_board` function modifies the state of the object by resetting the board and other attributes. It does not return a value but modifies instance variables such as `board`, `black_board`, `white_board`, `turn`, `black_components`, and `white_components`.\n- CONDITION 2: The test function `test_reset_board` checks the states of the variables after calling `reset_board_new_implementation`, not printed or logged contents. It uses assertions to compare the states of the boards, turn, and components.\n- CONDITION 3: The test cases compare all relevant attributes of the `AbstractDarkHex` class that are affected by the `reset_board` function. If `reset_board_new_implementation` has the same functionality, it will pass these tests.\n- CONDITION 4: The test cases and assert statements are reasonable. They compare the states of the boards, turn, and components, which are the expected outcomes of the `reset_board` function.\n- CONDITION 5: The test cases are non-trivial as they cover all the attributes that `reset_board` modifies, ensuring a comprehensive check of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "eed78380bbf1f83a1e0752fcef1b6dda057988ad"
    },
    {
        "func_name": "is_object_detected",
        "idx": "442",
        "repo_name": "WhyXic___studify-eng-project",
        "func_path": "ultrasonic.py",
        "orig_func": "def is_object_detected(threshold=10):\n    \"\"\"Check if an object is within the threshold distance.\"\"\"\n    distance = measure_distance()\n    return distance < threshold",
        "orig_context": "```python\n## ultrasonic.py\nimport RPi.GPIO as GPIO\n\nfrom time import sleep, time\n\nTRIG_PIN = 23\n\nECHO_PIN = 24\n\ndef measure_distance():\n    \"\"\"Measure the distance using the ultrasonic sensor.\"\"\"\n    # Send pulse\n    GPIO.output(TRIG_PIN, GPIO.LOW)\n    sleep(0.1)\n    GPIO.output(TRIG_PIN, GPIO.HIGH)\n    sleep(0.00001)\n    GPIO.output(TRIG_PIN, GPIO.LOW)\n\n    # Wait for echo response\n    pulse_start = time()\n    while GPIO.input(ECHO_PIN) == GPIO.LOW:\n        pulse_start = time()\n\n    pulse_end = time()\n    while GPIO.input(ECHO_PIN) == GPIO.HIGH:\n        pulse_end = time()\n\n    pulse_duration = pulse_end - pulse_start\n    distance = pulse_duration * 17150  # Calculate distance in cm\n    return distance\n\ndef is_object_detected(threshold=10):\n    \"\"\"Check if an object is within the threshold distance.\"\"\"\n    distance = measure_distance()\n    return distance < threshold\n\n```\n\n\n",
        "eval_script": "# Mocking the RPi.GPIO library for testing purposes\nclass MockGPIO:\n    LOW = 0\n    HIGH = 1\n    OUT = 0\n    IN = 1\n\n    @staticmethod\n    def output(pin, state):\n        pass\n\n    @staticmethod\n    def input(pin):\n        # Simulate the echo pin behavior\n        return MockGPIO.LOW\n\n    @staticmethod\n    def setmode(mode):\n        pass\n\n    @staticmethod\n    def setup(pin, mode):\n        pass\n\n    @staticmethod\n    def cleanup():\n        pass\n\n# Replace RPi.GPIO with our mock\nGPIO = MockGPIO\n\nfrom time import sleep, time\n\nTRIG_PIN = 23\nECHO_PIN = 24\n\ndef measure_distance():\n    \"\"\"Measure the distance using the ultrasonic sensor.\"\"\"\n    # Send pulse\n    GPIO.output(TRIG_PIN, GPIO.LOW)\n    sleep(0.1)\n    GPIO.output(TRIG_PIN, GPIO.HIGH)\n    sleep(0.00001)\n    GPIO.output(TRIG_PIN, GPIO.LOW)\n\n    # Wait for echo response\n    pulse_start = time()\n    while GPIO.input(ECHO_PIN) == GPIO.LOW:\n        pulse_start = time()\n\n    pulse_end = time()\n    while GPIO.input(ECHO_PIN) == GPIO.HIGH:\n        pulse_end = time()\n\n    pulse_duration = pulse_end - pulse_start\n    distance = pulse_duration * 17150  # Calculate distance in cm\n    return distance\n\ndef is_object_detected(threshold=10):\n    \"\"\"Check if an object is within the threshold distance.\"\"\"\n    distance = measure_distance()\n    return distance < threshold\n\n\ndef test_is_object_detected():\n    \"\"\"Test function to compare the two implementations.\"\"\"\n    # Mock measure_distance to return specific values\n    original_measure_distance = measure_distance\n\n    def mock_measure_distance_5():\n        return 5\n\n    def mock_measure_distance_10():\n        return 10\n\n    def mock_measure_distance_15():\n        return 15\n\n    def mock_measure_distance_0():\n        return 0\n\n    def mock_measure_distance_negative():\n        return -5\n\n    # Test when distance is less than threshold\n    globals()['measure_distance'] = mock_measure_distance_5\n    assert is_object_detected(10) == is_object_detected_new_implementation(10)\n\n    # Test when distance is equal to threshold\n    globals()['measure_distance'] = mock_measure_distance_10\n    assert is_object_detected(10) == is_object_detected_new_implementation(10)\n\n    # Test when distance is greater than threshold\n    globals()['measure_distance'] = mock_measure_distance_15\n    assert is_object_detected(10) == is_object_detected_new_implementation(10)\n\n    # Test with zero threshold\n    globals()['measure_distance'] = mock_measure_distance_0\n    assert is_object_detected(0) == is_object_detected_new_implementation(0)\n\n    # Test with negative threshold\n    globals()['measure_distance'] = mock_measure_distance_negative\n    assert is_object_detected(-10) == is_object_detected_new_implementation(-10)\n\n    # Test with different threshold values\n    assert is_object_detected(5) == is_object_detected_new_implementation(5)\n    assert is_object_detected(15) == is_object_detected_new_implementation(15)\n\n    # Restore the original measure_distance function\n    globals()['measure_distance'] = original_measure_distance\n\nif __name__ == \"__main__\":\n    test_is_object_detected()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `is_object_detected` is identical to the ORIGINAL FUNCTION in terms of its definition and logic. Both functions take a `threshold` parameter with a default value of 10, call the `measure_distance` function to get a distance measurement, and return `True` if the measured distance is less than the threshold, otherwise `False`. The additional code in the revised version, such as the `MockGPIO` class and the `test_is_object_detected` function, is for testing purposes and does not alter the functionality of the `is_object_detected` function itself. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `is_object_detected` function returns a boolean value indicating whether an object is within the threshold distance. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to check the return values of `is_object_detected` and `is_object_detected_new_implementation`, not printed or logged contents. This satisfies the condition.\n- CONDITION 3: The test cases compare the return values of `is_object_detected` and `is_object_detected_new_implementation` for various mocked distances and thresholds. This ensures that `is_object_detected_new_implementation` must have the exact same functionality to pass all tests, satisfying the condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable since `is_object_detected` returns a boolean. This satisfies the condition.\n- CONDITION 5: The test cases cover a range of scenarios, including distances less than, equal to, and greater than the threshold, as well as zero and negative thresholds. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "716f266df86c35cac337dc2bfde0abd17608782b"
    },
    {
        "func_name": "skip_submodules",
        "idx": "444",
        "repo_name": "Entalpic___entaldocs",
        "func_path": "docs/source/conf.py",
        "orig_func": "def skip_submodules(app, what, name, obj, skip, options):\n    \"\"\"Function used by ``autoapi-skip-member`` event to skip submodules.\n\n    Parameters\n    ----------\n    app : Sphinx\n        The Sphinx application object.\n    what : str\n        The type of the member.\n    name : str\n        The name of the member (like ``module.Class.attribute`` for instance).\n    obj : object\n        The Sphinx object representing the member.\n    skip : bool\n        Whether the member should be skipped (at this point, from the configuration).\n    options : list[str]\n        The options passed to the directive from ``conf.py``.\n\n    Returns\n    -------\n    bool\n        Whether the member should be skipped.\n    \"\"\"\n    if what == 'attribute':\n        if obj.is_undoc_member:\n            print(f'  \u2022 Skipping {what} {name} because it is not documented.')\n            return True\n    return skip",
        "orig_context": "```python\n## docs/source/conf.py\ndef skip_submodules(app, what, name, obj, skip, options):\n    \"\"\"Function used by ``autoapi-skip-member`` event to skip submodules.\n\n    Parameters\n    ----------\n    app : Sphinx\n        The Sphinx application object.\n    what : str\n        The type of the member.\n    name : str\n        The name of the member (like ``module.Class.attribute`` for instance).\n    obj : object\n        The Sphinx object representing the member.\n    skip : bool\n        Whether the member should be skipped (at this point, from the configuration).\n    options : list[str]\n        The options passed to the directive from ``conf.py``.\n\n    Returns\n    -------\n    bool\n        Whether the member should be skipped.\n    \"\"\"\n    if what == \"attribute\":\n        if obj.is_undoc_member:\n            print(f\"  \u2022 Skipping {what} {name} because it is not documented.\")\n            return True\n    return skip\n\n```\n\n\n",
        "eval_script": "# Mock classes to simulate the Sphinx environment\nclass MockApp:\n    pass\n\nclass MockObj:\n    def __init__(self, is_undoc_member):\n        self.is_undoc_member = is_undoc_member\n\n# The skip_submodules function as provided\ndef skip_submodules(app, what, name, obj, skip, options):\n    \"\"\"Function used by ``autoapi-skip-member`` event to skip submodules.\n\n    Parameters\n    ----------\n    app : Sphinx\n        The Sphinx application object.\n    what : str\n        The type of the member.\n    name : str\n        The name of the member (like ``module.Class.attribute`` for instance).\n    obj : object\n        The Sphinx object representing the member.\n    skip : bool\n        Whether the member should be skipped (at this point, from the configuration).\n    options : list[str]\n        The options passed to the directive from ``conf.py``.\n\n    Returns\n    -------\n    bool\n        Whether the member should be skipped.\n    \"\"\"\n    if what == \"attribute\":\n        if obj.is_undoc_member:\n            print(f\"  \u2022 Skipping {what} {name} because it is not documented.\")\n            return True\n    return skip\n\n\ndef test_skip_submodules():\n    app = MockApp()\n    options = []\n\n    # Test case 1: what is \"attribute\" and obj.is_undoc_member is True\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, False, options)\n\n    # Test case 2: what is \"attribute\" and obj.is_undoc_member is False\n    obj = MockObj(is_undoc_member=False)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, False, options)\n\n    # Test case 3: what is not \"attribute\"\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"module\", \"name\", obj, True, options) == skip_submodules_new_implementation(app, \"module\", \"name\", obj, True, options)\n\n    # Additional Test case 4: what is \"attribute\", obj.is_undoc_member is True, skip is True\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, True, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, True, options)\n\n    # Additional Test case 5: what is \"attribute\", obj.is_undoc_member is False, skip is True\n    obj = MockObj(is_undoc_member=False)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, True, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, True, options)\n\n    # Additional Test case 6: what is an empty string\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"\", \"name\", obj, False, options)\n\n    # Additional Test case 7: name is an empty string\n    obj = MockObj(is_undoc_member=False)\n    assert skip_submodules(app, \"attribute\", \"\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"\", obj, False, options)\n\n    # Additional Test case 8: options is not empty\n    obj = MockObj(is_undoc_member=True)\n    options = [\"option1\", \"option2\"]\n    assert skip_submodules(app, \"attribute\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, False, options)\n\nif __name__ == \"__main__\":\n    test_skip_submodules()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions check if the `what` parameter is \"attribute\" and if the `obj.is_undoc_member` is True, then they print a message and return True. Otherwise, they return the value of `skip`. The additional code provided in the REVISED FUNCTION is for testing purposes and does not alter the functionality of the `skip_submodules` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `skip_submodules` function returns a boolean value, satisfying the condition that it should have return values or modify global variables or input arguments.\n- [CONDITION 2] The test cases use assertions to check the return values of `skip_submodules` and `skip_submodules_new_implementation`, not printed or logged contents.\n- [CONDITION 3] The test cases compare the return values of `skip_submodules` and `skip_submodules_new_implementation` for various inputs, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- [CONDITION 4] The test cases and assert statements are reasonable as they compare the return values directly, which is appropriate given that `skip_submodules` returns a boolean.\n- [CONDITION 5] The test cases cover a variety of scenarios, including different values for `what`, `obj.is_undoc_member`, `skip`, and `options`, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "9933225ced43b5b09b14f709c61c9ed073a333a8"
    },
    {
        "func_name": "skip_submodules",
        "idx": "445",
        "repo_name": "Entalpic___entaldocs",
        "func_path": "boilerplate/source/conf.py",
        "orig_func": "def skip_submodules(app, what, name, obj, skip, options):\n    \"\"\"Function used by ``autoapi-skip-member`` event to skip submodules.\n\n    Parameters\n    ----------\n    app : Sphinx\n        The Sphinx application object.\n    what : str\n        The type of the member.\n    name : str\n        The name of the member (like ``module.Class.attribute`` for instance).\n    obj : object\n        The Sphinx object representing the member.\n    skip : bool\n        Whether the member should be skipped (at this point, from the configuration).\n    options : list[str]\n        The options passed to the directive from ``conf.py``.\n\n    Returns\n    -------\n    bool\n        Whether the member should be skipped.\n    \"\"\"\n    if what == 'attribute':\n        if obj.is_undoc_member:\n            print(f'  \u2022 Skipping {what} {name} because it is not documented.')\n            return True\n    return skip",
        "orig_context": "```python\n## boilerplate/source/conf.py\ndef skip_submodules(app, what, name, obj, skip, options):\n    \"\"\"Function used by ``autoapi-skip-member`` event to skip submodules.\n\n    Parameters\n    ----------\n    app : Sphinx\n        The Sphinx application object.\n    what : str\n        The type of the member.\n    name : str\n        The name of the member (like ``module.Class.attribute`` for instance).\n    obj : object\n        The Sphinx object representing the member.\n    skip : bool\n        Whether the member should be skipped (at this point, from the configuration).\n    options : list[str]\n        The options passed to the directive from ``conf.py``.\n\n    Returns\n    -------\n    bool\n        Whether the member should be skipped.\n    \"\"\"\n    if what == \"attribute\":\n        if obj.is_undoc_member:\n            print(f\"  \u2022 Skipping {what} {name} because it is not documented.\")\n            return True\n    return skip\n\n```\n\n\n",
        "eval_script": "# Mock classes to simulate the expected parameters for skip_submodules\nclass MockApp:\n    pass\n\nclass MockObj:\n    def __init__(self, is_undoc_member):\n        self.is_undoc_member = is_undoc_member\n\ndef skip_submodules(app, what, name, obj, skip, options):\n    \"\"\"Function used by ``autoapi-skip-member`` event to skip submodules.\n\n    Parameters\n    ----------\n    app : Sphinx\n        The Sphinx application object.\n    what : str\n        The type of the member.\n    name : str\n        The name of the member (like ``module.Class.attribute`` for instance).\n    obj : object\n        The Sphinx object representing the member.\n    skip : bool\n        Whether the member should be skipped (at this point, from the configuration).\n    options : list[str]\n        The options passed to the directive from ``conf.py``.\n\n    Returns\n    -------\n    bool\n        Whether the member should be skipped.\n    \"\"\"\n    if what == \"attribute\":\n        if obj.is_undoc_member:\n            print(f\"  \u2022 Skipping {what} {name} because it is not documented.\")\n            return True\n    return skip\n\n\ndef test_skip_submodules():\n    app = MockApp()\n    options = []\n\n    # Test case 1: what is \"attribute\" and obj.is_undoc_member is True\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, False, options)\n\n    # Test case 2: what is \"attribute\" and obj.is_undoc_member is False\n    obj = MockObj(is_undoc_member=False)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, False, options)\n\n    # Test case 3: what is not \"attribute\"\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"module\", \"name\", obj, True, options) == skip_submodules_new_implementation(app, \"module\", \"name\", obj, True, options)\n\n    # Test case 4: what is \"attribute\", obj.is_undoc_member is True, and skip is True\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, True, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, True, options)\n\n    # Test case 5: what is \"attribute\", obj.is_undoc_member is False, and skip is True\n    obj = MockObj(is_undoc_member=False)\n    assert skip_submodules(app, \"attribute\", \"name\", obj, True, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, True, options)\n\n    # Test case 6: what is \"class\" and obj.is_undoc_member is True\n    obj = MockObj(is_undoc_member=True)\n    assert skip_submodules(app, \"class\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"class\", \"name\", obj, False, options)\n\n    # Test case 7: what is \"method\" and obj.is_undoc_member is False\n    obj = MockObj(is_undoc_member=False)\n    assert skip_submodules(app, \"method\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"method\", \"name\", obj, False, options)\n\n    # Test case 8: what is \"attribute\", obj.is_undoc_member is True, and options is not empty\n    obj = MockObj(is_undoc_member=True)\n    options = [\"option1\", \"option2\"]\n    assert skip_submodules(app, \"attribute\", \"name\", obj, False, options) == skip_submodules_new_implementation(app, \"attribute\", \"name\", obj, False, options)\n\nif __name__ == \"__main__\":\n    test_skip_submodules()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `skip_submodules` is identical to the ORIGINAL FUNCTION in terms of its logic and implementation. Both functions check if the `what` parameter is \"attribute\" and if the `obj.is_undoc_member` is `True`, in which case they print a message and return `True`. Otherwise, they return the value of `skip`. The additional code in the revised version, such as the `MockApp` and `MockObj` classes and the `test_skip_submodules` function, is used for testing purposes and does not alter the functionality of the `skip_submodules` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `skip_submodules` function returns a boolean value, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `skip_submodules` and `skip_submodules_new_implementation`, which means they check the return values and not printed or logged contents.\n\n- CONDITION 3: The test cases are designed to compare the return values of `skip_submodules` and `skip_submodules_new_implementation` directly. This means that `skip_submodules_new_implementation` can only pass all test cases if it has the same functionality as `skip_submodules`.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `skip_submodules` returns a boolean value. The assertions are correctly structured to compare the outputs of the two functions.\n\n- CONDITION 5: The test cases cover various scenarios, including different values for `what`, `obj.is_undoc_member`, and `skip`, as well as different states of the `options` list. This variety ensures that the test cases are non-trivial and cover a range of possible inputs.",
            "answer": "yes"
        },
        "commit_id": "9933225ced43b5b09b14f709c61c9ed073a333a8"
    },
    {
        "func_name": "get_project_name",
        "idx": "448",
        "repo_name": "Entalpic___entaldocs",
        "func_path": "src/entaldocs/utils.py",
        "orig_func": "def get_project_name(with_defaults) -> str:\n    \"\"\"Get the current project's name from the user.\n\n    Prompts the user for the project name, with the default being the current\n    directory's name.\n\n    Parameters\n    ----------\n    with_defaults : bool\n        Whether to trust the defaults and skip all prompts.\n\n    Returns\n    -------\n    str\n        The project name.\n    \"\"\"\n    default = resolve_path('.').name\n    return default if with_defaults else logger.prompt('Project name', default=default)",
        "orig_context": "```python\n## src/entaldocs/logger.py\nimport sys\n\nfrom datetime import datetime\n\nclass BaseLogger:\n    \"\"\"A dummy class for documentation purposes\"\"\"\n\n    ...\n\nclass Logger(BaseLogger):\n    \"\"\"A class to log messages to the console.\"\"\"\n\n    def __init__(self, name: str, with_time: bool = True):\n        \"\"\"Initialize the Logger.\n\n        Parameters\n        ----------\n        name : str\n            The name of the logger.\n        with_time : bool, optional\n            Whether to include the time in the log messages, by default True.\n        \"\"\"\n        self.name = name\n        self.with_time = with_time\n\n    def now(self):\n        \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n        return datetime.now().strftime(\"%H:%M:%S\")\n\n    @property\n    def prefix(self):\n        \"\"\"Get the prefix for the log messages.\n\n        The prefix includes the name of the logger and the current time.\n\n        Returns:\n        --------\n        str\n            The prefix.\n        \"\"\"\n        prefix = \"\"\n        if self.name:\n            prefix += f\"{self.name}\"\n            if self.with_time:\n                prefix += f\" | {self.now()}\"\n        else:\n            prefix += self.now()\n        if prefix:\n            return f\"[grey50 bold]\\\\[{prefix}][/grey50 bold] \"\n        return prefix\n\n    def prompt(self, message: str, default: str = None) -> str:\n        \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n        text = (\n            f\"{self.prefix}{message} \\\\[default: {default}]\"\n            if default\n            else f\"{self.prefix}{message}\"\n        )\n        print(text, end=\"\")\n        return input(\":\").strip() or default\n\n    def confirm(self, message: str) -> bool:\n        \"\"\"Confirm a message with the user.\n\n        Parameters\n        ----------\n        message : str\n            The message to confirm.\n\n        Returns:\n        --------\n        bool\n            Whether the user confirmed the message.\n        \"\"\"\n        return self.prompt(f\"{message} (y/N)\", \"N\").lower() == \"y\"\n\n    def abort(self, message: str, exit=1):\n        \"\"\"Abort the program with a message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print before aborting.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n        sys.exit(exit)\n\n    def success(self, message: str):\n        \"\"\"Print a success message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[green]{message}[/green]\")\n\n    def warning(self, message: str):\n        \"\"\"Print a warning message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[yellow]{message}[/yellow]\")\n\n    def error(self, message: str):\n        \"\"\"Print an error message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n\n    def info(self, message: str):\n        \"\"\"Print an info message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[blue]{message}[/blue]\")\n\n    def clear_line(self):\n        \"\"\"Clear the current line.\"\"\"\n        import shutil\n\n        cols = shutil.get_terminal_size().columns\n        print(\" \" * cols, end=\"\\r\")\n        print(\" \" * cols, end=\"\\r\")\n\n```\n\n\n```python\n## src/entaldocs/utils.py\nfrom os.path import expandvars, relpath\n\nfrom pathlib import Path\n\nfrom entaldocs.logger import Logger\n\nlogger = Logger(\"entaldocs\")\n\ndef resolve_path(path: str | Path) -> Path:\n    \"\"\"Resolve a path and expand environment variables.\n\n    Parameters\n    ----------\n    path : str | Path\n        The path to resolve.\n\n    Returns:\n    --------\n    Path\n        The resolved path.\n    \"\"\"\n    return Path(expandvars(path)).expanduser().resolve()\n\ndef get_project_name(with_defaults) -> str:\n    \"\"\"Get the current project's name from the user.\n\n    Prompts the user for the project name, with the default being the current\n    directory's name.\n\n    Parameters\n    ----------\n    with_defaults : bool\n        Whether to trust the defaults and skip all prompts.\n\n    Returns\n    -------\n    str\n        The project name.\n    \"\"\"\n    default = resolve_path(\".\").name\n    return default if with_defaults else logger.prompt(\"Project name\", default=default)\n\n```\n\n\n",
        "eval_script": "# Import necessary modules\nimport sys\nfrom datetime import datetime\nfrom os.path import expandvars\nfrom pathlib import Path\nfrom unittest.mock import patch\n\n# Define the BaseLogger class\nclass BaseLogger:\n    \"\"\"A dummy class for documentation purposes\"\"\"\n    ...\n\n# Define the Logger class\nclass Logger(BaseLogger):\n    \"\"\"A class to log messages to the console.\"\"\"\n\n    def __init__(self, name: str, with_time: bool = True):\n        \"\"\"Initialize the Logger.\n\n        Parameters\n        ----------\n        name : str\n            The name of the logger.\n        with_time : bool, optional\n            Whether to include the time in the log messages, by default True.\n        \"\"\"\n        self.name = name\n        self.with_time = with_time\n\n    def now(self):\n        \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n        return datetime.now().strftime(\"%H:%M:%S\")\n\n    @property\n    def prefix(self):\n        \"\"\"Get the prefix for the log messages.\n\n        The prefix includes the name of the logger and the current time.\n\n        Returns:\n        --------\n        str\n            The prefix.\n        \"\"\"\n        prefix = \"\"\n        if self.name:\n            prefix += f\"{self.name}\"\n            if self.with_time:\n                prefix += f\" | {self.now()}\"\n        else:\n            prefix += self.now()\n        if prefix:\n            return f\"[grey50 bold]\\\\[{prefix}][/grey50 bold] \"\n        return prefix\n\n    def prompt(self, message: str, default: str = None) -> str:\n        \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n        text = (\n            f\"{self.prefix}{message} \\\\[default: {default}]\"\n            if default\n            else f\"{self.prefix}{message}\"\n        )\n        print(text, end=\"\")\n        return input(\":\").strip() or default\n\n    def confirm(self, message: str) -> bool:\n        \"\"\"Confirm a message with the user.\n\n        Parameters\n        ----------\n        message : str\n            The message to confirm.\n\n        Returns:\n        --------\n        bool\n            Whether the user confirmed the message.\n        \"\"\"\n        return self.prompt(f\"{message} (y/N)\", \"N\").lower() == \"y\"\n\n    def abort(self, message: str, exit=1):\n        \"\"\"Abort the program with a message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print before aborting.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n        sys.exit(exit)\n\n    def success(self, message: str):\n        \"\"\"Print a success message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[green]{message}[/green]\")\n\n    def warning(self, message: str):\n        \"\"\"Print a warning message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[yellow]{message}[/yellow]\")\n\n    def error(self, message: str):\n        \"\"\"Print an error message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n\n    def info(self, message: str):\n        \"\"\"Print an info message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[blue]{message}[/blue]\")\n\n    def clear_line(self):\n        \"\"\"Clear the current line.\"\"\"\n        import shutil\n\n        cols = shutil.get_terminal_size().columns\n        print(\" \" * cols, end=\"\\r\")\n        print(\" \" * cols, end=\"\\r\")\n\n# Initialize the logger\nlogger = Logger(\"entaldocs\")\n\n# Define the resolve_path function\ndef resolve_path(path: str | Path) -> Path:\n    \"\"\"Resolve a path and expand environment variables.\n\n    Parameters\n    ----------\n    path : str | Path\n        The path to resolve.\n\n    Returns:\n    --------\n    Path\n        The resolved path.\n    \"\"\"\n    return Path(expandvars(path)).expanduser().resolve()\n\n# Define the get_project_name function\ndef get_project_name(with_defaults) -> str:\n    \"\"\"Get the current project's name from the user.\n\n    Prompts the user for the project name, with the default being the current\n    directory's name.\n\n    Parameters\n    ----------\n    with_defaults : bool\n        Whether to trust the defaults and skip all prompts.\n\n    Returns\n    -------\n    str\n        The project name.\n    \"\"\"\n    default = resolve_path(\".\").name\n    return default if with_defaults else logger.prompt(\"Project name\", default=default)\n\n\n# Your revised test_get_project_name function\ndef test_get_project_name():\n    # Test case 1: with_defaults is True\n    assert get_project_name(True) == get_project_name_new_implementation(True)\n\n    # Test case 2: with_defaults is False, simulate user input as empty\n    with patch('builtins.input', return_value=''):\n        assert get_project_name(False) == get_project_name_new_implementation(False)\n\n    # Test case 3: with_defaults is False, simulate user input as a specific name\n    with patch('builtins.input', return_value='MyProject'):\n        assert get_project_name(False) == get_project_name_new_implementation(False)\n\n    # Test case 4: with_defaults is False, simulate user input with leading/trailing spaces\n    with patch('builtins.input', return_value='  MyProject  '):\n        assert get_project_name(False) == get_project_name_new_implementation(False)\n\n    # Test case 5: with_defaults is False, simulate user input with special characters\n    with patch('builtins.input', return_value='My_Project!@#'):\n        assert get_project_name(False) == get_project_name_new_implementation(False)\n\n    # Test case 6: with_defaults is False, simulate user input with a very long name\n    long_name = 'A' * 256\n    with patch('builtins.input', return_value=long_name):\n        assert get_project_name(False) == get_project_name_new_implementation(False)\n\n    # Test case 7: Change current directory and test with_defaults is True\n    with patch('os.getcwd', return_value='/home/user/tmp/testdir'):\n        assert get_project_name(True) == get_project_name_new_implementation(True)\n\n    # Test case 8: Change current directory and test with_defaults is False\n    with patch('os.getcwd', return_value='/home/user/tmp/testdir'), patch('builtins.input', return_value=''):\n        assert get_project_name(False) == get_project_name_new_implementation(False)\n\n# Define the __main__ function\nif __name__ == \"__main__\":\n    test_get_project_name()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_project_name` is identical to the ORIGINAL FUNCTION. Both functions have the same logic: they resolve the current directory's name using `resolve_path('.')` and return it if `with_defaults` is True. Otherwise, they prompt the user for a project name using `logger.prompt`, with the default being the resolved directory name. The additional code in the revised version, such as the `Logger` class and the `resolve_path` function, supports the functionality of `get_project_name` but does not alter its logic or behavior. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_project_name` function returns a string, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare return values of `get_project_name` and `get_project_name_new_implementation`, not printed or logged content.\n- CONDITION 3: The test cases compare the outputs of `get_project_name` and `get_project_name_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable given that `get_project_name` returns a value.\n- CONDITION 5: The test cases cover various scenarios, including default behavior, user input, special characters, long names, and changing directories, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "9933225ced43b5b09b14f709c61c9ed073a333a8"
    },
    {
        "func_name": "Logger.now",
        "idx": "450",
        "repo_name": "Entalpic___entaldocs",
        "func_path": "src/entaldocs/logger.py",
        "orig_func": "def now(self):\n    \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n    return datetime.now().strftime('%H:%M:%S')",
        "orig_context": "```python\n## src/entaldocs/logger.py\nimport sys\n\nfrom datetime import datetime\n\nclass BaseLogger:\n    \"\"\"A dummy class for documentation purposes\"\"\"\n\n    ...\n\nclass Logger(BaseLogger):\n    \"\"\"A class to log messages to the console.\"\"\"\n\n    def __init__(self, name: str, with_time: bool = True):\n        \"\"\"Initialize the Logger.\n\n        Parameters\n        ----------\n        name : str\n            The name of the logger.\n        with_time : bool, optional\n            Whether to include the time in the log messages, by default True.\n        \"\"\"\n        self.name = name\n        self.with_time = with_time\n\n    def now(self):\n        \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n        return datetime.now().strftime(\"%H:%M:%S\")\n\n    @property\n    def prefix(self):\n        \"\"\"Get the prefix for the log messages.\n\n        The prefix includes the name of the logger and the current time.\n\n        Returns:\n        --------\n        str\n            The prefix.\n        \"\"\"\n        prefix = \"\"\n        if self.name:\n            prefix += f\"{self.name}\"\n            if self.with_time:\n                prefix += f\" | {self.now()}\"\n        else:\n            prefix += self.now()\n        if prefix:\n            return f\"[grey50 bold]\\\\[{prefix}][/grey50 bold] \"\n        return prefix\n\n    def prompt(self, message: str, default: str = None) -> str:\n        \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n        text = (\n            f\"{self.prefix}{message} \\\\[default: {default}]\"\n            if default\n            else f\"{self.prefix}{message}\"\n        )\n        print(text, end=\"\")\n        return input(\":\").strip() or default\n\n    def confirm(self, message: str) -> bool:\n        \"\"\"Confirm a message with the user.\n\n        Parameters\n        ----------\n        message : str\n            The message to confirm.\n\n        Returns:\n        --------\n        bool\n            Whether the user confirmed the message.\n        \"\"\"\n        return self.prompt(f\"{message} (y/N)\", \"N\").lower() == \"y\"\n\n    def abort(self, message: str, exit=1):\n        \"\"\"Abort the program with a message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print before aborting.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n        sys.exit(exit)\n\n    def success(self, message: str):\n        \"\"\"Print a success message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[green]{message}[/green]\")\n\n    def warning(self, message: str):\n        \"\"\"Print a warning message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[yellow]{message}[/yellow]\")\n\n    def error(self, message: str):\n        \"\"\"Print an error message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n\n    def info(self, message: str):\n        \"\"\"Print an info message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[blue]{message}[/blue]\")\n\n    def clear_line(self):\n        \"\"\"Clear the current line.\"\"\"\n        import shutil\n\n        cols = shutil.get_terminal_size().columns\n        print(\" \" * cols, end=\"\\r\")\n        print(\" \" * cols, end=\"\\r\")\n\n```\n\n\n",
        "eval_script": "## src/entaldocs/logger.py\nimport sys\n\nfrom datetime import datetime\n\nclass BaseLogger:\n    \"\"\"A dummy class for documentation purposes\"\"\"\n\n    ...\n\nclass Logger(BaseLogger):\n    \"\"\"A class to log messages to the console.\"\"\"\n\n    def __init__(self, name: str, with_time: bool = True):\n        \"\"\"Initialize the Logger.\n\n        Parameters\n        ----------\n        name : str\n            The name of the logger.\n        with_time : bool, optional\n            Whether to include the time in the log messages, by default True.\n        \"\"\"\n        self.name = name\n        self.with_time = with_time\n\n    @staticmethod\n    def now():\n        \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n        return datetime.now().strftime(\"%H:%M:%S\")\n\n\n    @property\n    def prefix(self):\n        \"\"\"Get the prefix for the log messages.\n\n        The prefix includes the name of the logger and the current time.\n\n        Returns:\n        --------\n        str\n            The prefix.\n        \"\"\"\n        prefix = \"\"\n        if self.name:\n            prefix += f\"{self.name}\"\n            if self.with_time:\n                prefix += f\" | {self.now()}\"\n        else:\n            prefix += self.now()\n        if prefix:\n            return f\"[grey50 bold]\\\\[{prefix}][/grey50 bold] \"\n        return prefix\n\n    def prompt(self, message: str, default: str = None) -> str:\n        \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n        text = (\n            f\"{self.prefix}{message} \\\\[default: {default}]\"\n            if default\n            else f\"{self.prefix}{message}\"\n        )\n        print(text, end=\"\")\n        return input(\":\").strip() or default\n\n    def confirm(self, message: str) -> bool:\n        \"\"\"Confirm a message with the user.\n\n        Parameters\n        ----------\n        message : str\n            The message to confirm.\n\n        Returns:\n        --------\n        bool\n            Whether the user confirmed the message.\n        \"\"\"\n        return self.prompt(f\"{message} (y/N)\", \"N\").lower() == \"y\"\n\n    def abort(self, message: str, exit=1):\n        \"\"\"Abort the program with a message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print before aborting.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n        sys.exit(exit)\n\n    def success(self, message: str):\n        \"\"\"Print a success message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[green]{message}[/green]\")\n\n    def warning(self, message: str):\n        \"\"\"Print a warning message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[yellow]{message}[/yellow]\")\n\n    def error(self, message: str):\n        \"\"\"Print an error message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n\n    def info(self, message: str):\n        \"\"\"Print an info message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[blue]{message}[/blue]\")\n\n    def clear_line(self):\n        \"\"\"Clear the current line.\"\"\"\n        import shutil\n\n        cols = shutil.get_terminal_size().columns\n        print(\" \" * cols, end=\"\\r\")\n        print(\" \" * cols, end=\"\\r\")\n\ndef test_now():\n    \"\"\"Test function to compare Logger.now and Logger.now_new_implementation.\"\"\"\n    import time\n    import re\n\n    # Define a regex pattern for the expected time format\n    time_pattern = re.compile(r\"^\\d{2}:\\d{2}:\\d{2}$\")\n\n    for _ in range(5):  # Repeat the test multiple times\n        now1 = Logger.now()\n        now2 = Logger.now_new_implementation()\n\n        # Assert both return a string\n        assert isinstance(now1, str), \"Logger.now() did not return a string\"\n        assert isinstance(now2, str), \"Logger.now_new_implementation() did not return a string\"\n\n        # Assert both return the same formatted time\n        assert now1 == now2, \"Logger.now() and Logger.now_new_implementation() returned different times\"\n\n        # Assert both return the same value at the same instance\n        assert Logger.now() == Logger.now_new_implementation(), \"Logger.now() and Logger.now_new_implementation() returned different values at the same instance\"\n\n        # Assert the time format is correct\n        assert time_pattern.match(now1), \"Logger.now() returned time in incorrect format\"\n        assert time_pattern.match(now2), \"Logger.now_new_implementation() returned time in incorrect format\"\n\n        # Introduce a small delay and check again\n        time.sleep(0.1)\n\nif __name__ == \"__main__\":\n    test_now()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `now` is a method that returns the current time formatted as a string in the format '%H:%M:%S'. The revised function `now` in the `Logger` class is a static method that performs the same operation: it returns the current time formatted as a string in the format \"%H:%M:%S\". Both functions use `datetime.now().strftime()` to achieve this, and there is no change in the functionality or output format between the original and revised versions. The only difference is that the revised function is a static method within a class, but this does not affect its functionality or output.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `now` function returns a string representing the current time, so it satisfies the condition of having return values.\n\n2. **CONDITION 2**: The test function `test_now` checks the return values of `Logger.now()` and `Logger.now_new_implementation()`, ensuring they are strings and formatted correctly. It does not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `Logger.now()` and `Logger.now_new_implementation()` multiple times, ensuring they return the same value and format. This implies that `now_new_implementation` must have the exact same functionality as `now` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to check the return values and their format, which is appropriate given that `now` returns a string. The assertions are reasonable and relevant to the function's purpose, satisfying this condition.\n\n5. **CONDITION 5**: The test cases are non-trivial as they not only check for type and format but also ensure consistency between multiple calls and implementations. The use of a regex pattern to validate the time format adds to the robustness of the tests.",
            "answer": "yes"
        },
        "commit_id": "9933225ced43b5b09b14f709c61c9ed073a333a8"
    },
    {
        "func_name": "Logger.prompt",
        "idx": "451",
        "repo_name": "Entalpic___entaldocs",
        "func_path": "src/entaldocs/logger.py",
        "orig_func": "def prompt(self, message: str, default: str=None) -> str:\n    \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n    text = f'{self.prefix}{message} \\\\[default: {default}]' if default else f'{self.prefix}{message}'\n    print(text, end='')\n    return input(':').strip() or default",
        "orig_context": "```python\n## src/entaldocs/logger.py\nimport sys\n\nfrom datetime import datetime\n\nclass BaseLogger:\n    \"\"\"A dummy class for documentation purposes\"\"\"\n\n    ...\n\nclass Logger(BaseLogger):\n    \"\"\"A class to log messages to the console.\"\"\"\n\n    def __init__(self, name: str, with_time: bool = True):\n        \"\"\"Initialize the Logger.\n\n        Parameters\n        ----------\n        name : str\n            The name of the logger.\n        with_time : bool, optional\n            Whether to include the time in the log messages, by default True.\n        \"\"\"\n        self.name = name\n        self.with_time = with_time\n\n    def now(self):\n        \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n        return datetime.now().strftime(\"%H:%M:%S\")\n\n    @property\n    def prefix(self):\n        \"\"\"Get the prefix for the log messages.\n\n        The prefix includes the name of the logger and the current time.\n\n        Returns:\n        --------\n        str\n            The prefix.\n        \"\"\"\n        prefix = \"\"\n        if self.name:\n            prefix += f\"{self.name}\"\n            if self.with_time:\n                prefix += f\" | {self.now()}\"\n        else:\n            prefix += self.now()\n        if prefix:\n            return f\"[grey50 bold]\\\\[{prefix}][/grey50 bold] \"\n        return prefix\n\n    def prompt(self, message: str, default: str = None) -> str:\n        \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n        text = (\n            f\"{self.prefix}{message} \\\\[default: {default}]\"\n            if default\n            else f\"{self.prefix}{message}\"\n        )\n        print(text, end=\"\")\n        return input(\":\").strip() or default\n\n    def confirm(self, message: str) -> bool:\n        \"\"\"Confirm a message with the user.\n\n        Parameters\n        ----------\n        message : str\n            The message to confirm.\n\n        Returns:\n        --------\n        bool\n            Whether the user confirmed the message.\n        \"\"\"\n        return self.prompt(f\"{message} (y/N)\", \"N\").lower() == \"y\"\n\n    def abort(self, message: str, exit=1):\n        \"\"\"Abort the program with a message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print before aborting.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n        sys.exit(exit)\n\n    def success(self, message: str):\n        \"\"\"Print a success message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[green]{message}[/green]\")\n\n    def warning(self, message: str):\n        \"\"\"Print a warning message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[yellow]{message}[/yellow]\")\n\n    def error(self, message: str):\n        \"\"\"Print an error message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n\n    def info(self, message: str):\n        \"\"\"Print an info message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[blue]{message}[/blue]\")\n\n    def clear_line(self):\n        \"\"\"Clear the current line.\"\"\"\n        import shutil\n\n        cols = shutil.get_terminal_size().columns\n        print(\" \" * cols, end=\"\\r\")\n        print(\" \" * cols, end=\"\\r\")\n\n```\n\n\n",
        "eval_script": "## src/entaldocs/logger.py\nimport sys\nfrom datetime import datetime\nfrom unittest.mock import patch\n\nclass BaseLogger:\n    \"\"\"A dummy class for documentation purposes\"\"\"\n\n    ...\n\nclass Logger(BaseLogger):\n    \"\"\"A class to log messages to the console.\"\"\"\n\n    def __init__(self, name: str, with_time: bool = True):\n        \"\"\"Initialize the Logger.\n\n        Parameters\n        ----------\n        name : str\n            The name of the logger.\n        with_time : bool, optional\n            Whether to include the time in the log messages, by default True.\n        \"\"\"\n        self.name = name\n        self.with_time = with_time\n\n    def now(self):\n        \"\"\"Get the current time.\n\n        Returns:\n        --------\n        str\n            The current time.\n        \"\"\"\n        return datetime.now().strftime(\"%H:%M:%S\")\n\n    @property\n    def prefix(self):\n        \"\"\"Get the prefix for the log messages.\n\n        The prefix includes the name of the logger and the current time.\n\n        Returns:\n        --------\n        str\n            The prefix.\n        \"\"\"\n        prefix = \"\"\n        if self.name:\n            prefix += f\"{self.name}\"\n            if self.with_time:\n                prefix += f\" | {self.now()}\"\n        else:\n            prefix += self.now()\n        if prefix:\n            return f\"[grey50 bold]\\\\[{prefix}][/grey50 bold] \"\n        return prefix\n\n    def prompt(self, message: str, default: str = None) -> str:\n        \"\"\"Prompt the user for a value.\n\n        Parameters\n        ----------\n        message : str\n            The message to prompt the user with.\n        default : str, optional\n            The default value, by default None.\n\n        Returns:\n        --------\n        str\n            The value entered by the user.\n        \"\"\"\n        text = (\n            f\"{self.prefix}{message} \\\\[default: {default}]\"\n            if default\n            else f\"{self.prefix}{message}\"\n        )\n        print(text, end=\"\")\n        return input(\":\").strip() or default\n\n\n    def confirm(self, message: str) -> bool:\n        \"\"\"Confirm a message with the user.\n\n        Parameters\n        ----------\n        message : str\n            The message to confirm.\n\n        Returns:\n        --------\n        bool\n            Whether the user confirmed the message.\n        \"\"\"\n        return self.prompt(f\"{message} (y/N)\", \"N\").lower() == \"y\"\n\n    def abort(self, message: str, exit=1):\n        \"\"\"Abort the program with a message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print before aborting.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n        sys.exit(exit)\n\n    def success(self, message: str):\n        \"\"\"Print a success message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[green]{message}[/green]\")\n\n    def warning(self, message: str):\n        \"\"\"Print a warning message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[yellow]{message}[/yellow]\")\n\n    def error(self, message: str):\n        \"\"\"Print an error message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[red]{message}[/red]\")\n\n    def info(self, message: str):\n        \"\"\"Print an info message.\n\n        Parameters\n        ----------\n        message : str\n            The message to print.\n        \"\"\"\n        print(f\"{self.prefix}[blue]{message}[/blue]\")\n\n    def clear_line(self):\n        \"\"\"Clear the current line.\"\"\"\n        import shutil\n\n        cols = shutil.get_terminal_size().columns\n        print(\" \" * cols, end=\"\\r\")\n        print(\" \" * cols, end=\"\\r\")\n\ndef test_prompt():\n    logger = Logger(name=\"TestLogger\")\n\n    # Test case 1: User provides input\n    with patch('builtins.input', return_value='user_input'):\n        assert logger.prompt(\"Enter something\") == logger.prompt_new_implementation(\"Enter something\")\n\n    # Test case 2: User provides no input, default is used\n    with patch('builtins.input', return_value=''):\n        assert logger.prompt(\"Enter something\", default=\"default_value\") == logger.prompt_new_implementation(\"Enter something\", default=\"default_value\")\n\n    # Test case 3: User provides input, default is ignored\n    with patch('builtins.input', return_value='user_input'):\n        assert logger.prompt(\"Enter something\", default=\"default_value\") == logger.prompt_new_implementation(\"Enter something\", default=\"default_value\")\n\n    # Test case 4: User provides empty string as input, default is used\n    with patch('builtins.input', return_value=''):\n        assert logger.prompt(\"Enter something\", default=\"\") == logger.prompt_new_implementation(\"Enter something\", default=\"\")\n\n    # Test case 5: User provides whitespace as input, default is used\n    with patch('builtins.input', return_value='   '):\n        assert logger.prompt(\"Enter something\", default=\"default_value\") == logger.prompt_new_implementation(\"Enter something\", default=\"default_value\")\n\n    # Test case 6: Default is None, user provides no input\n    with patch('builtins.input', return_value=''):\n        assert logger.prompt(\"Enter something\", default=None) == logger.prompt_new_implementation(\"Enter something\", default=None)\n\n    # Test case 7: Default is a number, user provides no input\n    with patch('builtins.input', return_value=''):\n        assert logger.prompt(\"Enter something\", default=123) == logger.prompt_new_implementation(\"Enter something\", default=123)\n\n    # Test case 8: User provides input that is a number\n    with patch('builtins.input', return_value='456'):\n        assert logger.prompt(\"Enter something\", default=\"default_value\") == logger.prompt_new_implementation(\"Enter something\", default=\"default_value\")\n\n    # Test case 9: User provides input with special characters\n    with patch('builtins.input', return_value='!@#$%^&*()'):\n        assert logger.prompt(\"Enter something\", default=\"default_value\") == logger.prompt_new_implementation(\"Enter something\", default=\"default_value\")\n\nif __name__ == \"__main__\":\n    test_prompt()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions construct a prompt message using a prefix, message, and optional default value, then print this message and return the user's input or the default value if no input is provided. The test cases in the code are designed to compare the `prompt` function with a non-existent `prompt_new_implementation` function, which suggests that the revised function is intended to be the same as the original. Since there is no `prompt_new_implementation` function defined, the test cases are not executable, but they imply that the functionality is expected to be the same. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `prompt` function has a return value, which is the user input or the default value if no input is provided. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases check the return values of the `prompt` function and do not rely on printed or logged contents. They use assertions to compare the return values of `prompt` and `prompt_new_implementation`.\n\n3. **CONDITION 3**: The test cases cover various scenarios, including user input, default values, empty input, whitespace input, and special characters. These tests ensure that `prompt_new_implementation` must have the same functionality as `prompt` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases and assert statements are reasonable. They compare the return values of the two implementations directly, which is appropriate since `prompt` has a return value.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover a wide range of input scenarios, including edge cases like empty input, whitespace input, and special characters. This ensures comprehensive testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "9933225ced43b5b09b14f709c61c9ed073a333a8"
    },
    {
        "func_name": "supports_isinstance",
        "idx": "455",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "liteswarm/types/typing.py",
        "orig_func": "def supports_isinstance(type_: type) -> bool:\n    \"\"\"Check if a type supports isinstance checks.\n\n    This is used for checking if a type supports isinstance checks, like checking\n    if a tool's params_type supports isinstance checks.\n\n    Args:\n        type_: The type to check.\n\n    Returns:\n        True if the type supports isinstance checks.\n    \"\"\"\n    metaclass = type(type_)\n    return hasattr(metaclass, '__instancecheck__') or hasattr(metaclass, '__subclasscheck__')",
        "orig_context": "```python\n## liteswarm/types/typing.py\ndef supports_isinstance(type_: type) -> bool:\n    \"\"\"Check if a type supports isinstance checks.\n\n    This is used for checking if a type supports isinstance checks, like checking\n    if a tool's params_type supports isinstance checks.\n\n    Args:\n        type_: The type to check.\n\n    Returns:\n        True if the type supports isinstance checks.\n    \"\"\"\n    metaclass = type(type_)\n    return hasattr(metaclass, \"__instancecheck__\") or hasattr(metaclass, \"__subclasscheck__\")\n\n```\n\n\n",
        "eval_script": "## liteswarm/types/typing.py\ndef supports_isinstance(type_: type) -> bool:\n    \"\"\"Check if a type supports isinstance checks.\n\n    This is used for checking if a type supports isinstance checks, like checking\n    if a tool's params_type supports isinstance checks.\n\n    Args:\n        type_: The type to check.\n\n    Returns:\n        True if the type supports isinstance checks.\n    \"\"\"\n    metaclass = type(type_)\n    return hasattr(metaclass, \"__instancecheck__\") or hasattr(metaclass, \"__subclasscheck__\")\n\n\ndef test_supports_isinstance():\n    # Test with a regular class\n    class RegularClass:\n        pass\n    assert supports_isinstance(RegularClass) == supports_isinstance_new_implementation(RegularClass)\n\n    # Test with a built-in type\n    assert supports_isinstance(int) == supports_isinstance_new_implementation(int)\n\n    # Test with a custom metaclass without __instancecheck__ or __subclasscheck__\n    class CustomMeta(type):\n        pass\n\n    class CustomClass(metaclass=CustomMeta):\n        pass\n\n    assert supports_isinstance(CustomClass) == supports_isinstance_new_implementation(CustomClass)\n\n    # Test with a custom metaclass with __instancecheck__ and __subclasscheck__\n    class CustomMetaWithChecks(type):\n        def __instancecheck__(cls, instance):\n            return True\n\n        def __subclasscheck__(cls, subclass):\n            return True\n\n    class CustomClassWithChecks(metaclass=CustomMetaWithChecks):\n        pass\n\n    assert supports_isinstance(CustomClassWithChecks) == supports_isinstance_new_implementation(CustomClassWithChecks)\n\n    # Test with an abstract base class\n    from abc import ABC\n\n    class AbstractBase(ABC):\n        pass\n\n    assert supports_isinstance(AbstractBase) == supports_isinstance_new_implementation(AbstractBase)\n\n    # Test with a protocol (Python 3.8+)\n    try:\n        from typing import Protocol\n\n        class ExampleProtocol(Protocol):\n            def example_method(self) -> int:\n                pass\n\n        assert supports_isinstance(ExampleProtocol) == supports_isinstance_new_implementation(ExampleProtocol)\n    except ImportError:\n        pass  # Protocol is not available in this Python version\n\n    # Test with None (edge case)\n    try:\n        assert supports_isinstance(None) == supports_isinstance_new_implementation(None)\n    except TypeError:\n        pass  # Expecting a TypeError because None is not a type\n\n    # Test with the type of 'type' itself\n    assert supports_isinstance(type) == supports_isinstance_new_implementation(type)\n\nif __name__ == \"__main__\":\n    test_supports_isinstance()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function are identical in terms of their logic and implementation. Both functions take a type as input, determine its metaclass, and check if the metaclass has the `__instancecheck__` or `__subclasscheck__` attributes. The revised code includes a test suite to verify the function's behavior, but this does not alter the function itself. The test suite is an addition for validation purposes and does not change the functionality of the `supports_isinstance` function. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `supports_isinstance` returns a boolean value indicating whether a type supports `isinstance` checks. Therefore, it satisfies this condition as it has return values.\n- CONDITION 2: The test cases use assertions to check the return values of `supports_isinstance` and `supports_isinstance_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `supports_isinstance` and `supports_isinstance_new_implementation` for various types, ensuring that the new implementation must have the exact same functionality to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `supports_isinstance` returns a boolean. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including regular classes, built-in types, custom metaclasses with and without `__instancecheck__` and `__subclasscheck__`, abstract base classes, protocols, `None`, and the type of `type` itself. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "combine_usage",
        "idx": "457",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "liteswarm/utils/usage.py",
        "orig_func": "def combine_usage(left: Usage | None, right: Usage | None) -> Usage | None:\n    \"\"\"Merge two LiteLLM usage statistics.\n\n    Combines token counts and details from two Usage objects:\n    - Adds all token counts (prompt, completion, total)\n    - Merges token details dictionaries\n    - Handles optional fields and None values\n\n    Args:\n        left: First Usage object to merge.\n        right: Second Usage object to merge.\n\n    Returns:\n        Combined Usage object or None if both inputs are None.\n\n    Examples:\n        Basic merge:\n            ```python\n            usage1 = Usage(\n                prompt_tokens=10,\n                completion_tokens=5,\n                total_tokens=15,\n            )\n            usage2 = Usage(\n                prompt_tokens=20,\n                completion_tokens=10,\n                total_tokens=30,\n            )\n            total = combine_usage(usage1, usage2)\n            assert total.prompt_tokens == 30\n            assert total.completion_tokens == 15\n            assert total.total_tokens == 45\n            ```\n\n        With details:\n            ```python\n            usage1 = Usage(\n                prompt_tokens=10,\n                completion_tokens=5,\n                prompt_tokens_details={\"system\": 3, \"user\": 7},\n            )\n            usage2 = Usage(\n                prompt_tokens=15,\n                completion_tokens=8,\n                prompt_tokens_details={\"system\": 5, \"user\": 10},\n            )\n            total = combine_usage(usage1, usage2)\n            assert total.prompt_tokens_details == {\"system\": 8, \"user\": 17}\n            ```\n    \"\"\"\n    if left is None:\n        return right\n    if right is None:\n        return left\n    prompt_tokens = (left.prompt_tokens or 0) + (right.prompt_tokens or 0)\n    completion_tokens = (left.completion_tokens or 0) + (right.completion_tokens or 0)\n    total_tokens = (left.total_tokens or 0) + (right.total_tokens or 0)\n    lhs_reasoning_tokens = safe_get_attr(left, 'reasoning_tokens', int, default=0)\n    rhs_reasoning_tokens = safe_get_attr(right, 'reasoning_tokens', int, default=0)\n    reasoning_tokens = lhs_reasoning_tokens + rhs_reasoning_tokens\n    lhs_completion_tokens_details = safe_get_attr(left, 'completion_tokens_details', dict)\n    rhs_completion_tokens_details = safe_get_attr(right, 'completion_tokens_details', dict)\n    completion_tokens_details = combine_dicts(lhs_completion_tokens_details, rhs_completion_tokens_details)\n    lhs_prompt_tokens_details = safe_get_attr(left, 'prompt_tokens_details', dict)\n    rhs_prompt_tokens_details = safe_get_attr(right, 'prompt_tokens_details', dict)\n    prompt_tokens_details = combine_dicts(lhs_prompt_tokens_details, rhs_prompt_tokens_details)\n    return Usage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, reasoning_tokens=reasoning_tokens, completion_tokens_details=completion_tokens_details, prompt_tokens_details=prompt_tokens_details)",
        "orig_context": "```python\n## liteswarm/utils/misc.py\nfrom typing import Any, Concatenate, ParamSpec, TypeVar, cast, overload\n\n_AttributeType = TypeVar(\"_AttributeType\")\n\n_AttributeDefaultType = TypeVar(\"_AttributeDefaultType\")\n\ndef safe_get_attr(\n    obj: Any,\n    attr: str,\n    expected_type: type[_AttributeType],\n    default: _AttributeDefaultType = None,  # type: ignore\n) -> _AttributeType | _AttributeDefaultType:\n    \"\"\"Safely retrieves and validates an attribute of an object.\n\n    This function attempts to access the specified attribute from the given object.\n    If the attribute exists and its value matches the expected type, the value is returned.\n    Otherwise, the `default` value is returned.\n\n    If the `default` is not provided, it defaults to `None`. The return type will be inferred\n    as a union of the expected type and the type of the default value.\n\n    Args:\n        obj: The object from which to retrieve the attribute.\n        attr: The name of the attribute to retrieve.\n        expected_type: The expected type of the attribute's value.\n        default: The value to return if the attribute does not exist\n            or its value does not match the expected type. Defaults to `None`.\n\n    Returns:\n        The attribute's value if it exists and matches the expected type,\n        or the `default` value otherwise.\n\n    Examples:\n        Basic usage:\n            ```python\n            class Example:\n                attribute: int = 42\n\n\n            instance = Example()\n\n            # Attribute exists and matches expected type\n            value1: int = safe_get_attr(instance, \"attribute\", int, default=0)\n            print(value1)  # Output: 42\n\n            # Attribute exists but does not match expected type\n            value2: str = safe_get_attr(instance, \"attribute\", str, default=\"default_value\")\n            print(value2)  # Output: \"default_value\"\n\n            # Attribute does not exist, returns default\n            value3: int = safe_get_attr(instance, \"nonexistent\", int, default=100)\n            print(value3)  # Output: 100\n\n            # Attribute does not exist, no default provided\n            value4: int | None = safe_get_attr(instance, \"nonexistent\", int, default=100)\n            print(value4)  # Output: 100\n            ```\n    \"\"\"\n    value = getattr(obj, attr, default)\n    if isinstance(value, expected_type):\n        return value\n\n    return default\n\n```\n\n\n```python\n## liteswarm/utils/usage.py\nfrom numbers import Number\n\nfrom typing import Any\n\nfrom litellm import Usage\n\nfrom liteswarm.utils.misc import safe_get_attr\n\ndef combine_dicts(\n    left: dict[str, Any] | None,\n    right: dict[str, Any] | None,\n) -> dict[str, Any] | None:\n    \"\"\"Merge dictionaries with special handling for numbers.\n\n    Creates a new dictionary by combining two input dictionaries:\n    - Adds numeric values when keys overlap\n    - Preserves non-numeric values from left dictionary\n    - Includes unique keys from both dictionaries\n\n    Args:\n        left: First dictionary to merge.\n        right: Second dictionary to merge.\n\n    Returns:\n        Combined dictionary or None if both inputs are None.\n\n    Examples:\n        Basic merge:\n            ```python\n            result = combine_dicts(\n                {\"a\": 1, \"b\": \"text\"},\n                {\"a\": 2, \"c\": 3},\n            )\n            assert result == {\n                \"a\": 3,  # Numbers added\n                \"b\": \"text\",  # Non-numeric preserved\n                \"c\": 3,  # Unique key included\n            }\n            ```\n\n        None handling:\n            ```python\n            assert combine_dicts(None, None) is None\n            assert combine_dicts({\"a\": 1}, None) == {\"a\": 1}\n            assert combine_dicts(None, {\"b\": 2}) == {\"b\": 2}\n            ```\n\n        Mixed types:\n            ```python\n            result = combine_dicts(\n                {\"count\": 1, \"name\": \"test\"},\n                {\"count\": 2, \"name\": \"other\"},\n            )\n            assert result == {\n                \"count\": 3,  # Numbers added\n                \"name\": \"test\",  # Left value preserved\n            }\n            ```\n    \"\"\"\n    if left is None:\n        return right\n\n    if right is None:\n        return left\n\n    result = {}\n\n    all_keys = set(left) | set(right)\n\n    for key in all_keys:\n        left_value = left.get(key)\n        right_value = right.get(key)\n\n        if isinstance(left_value, Number) and isinstance(right_value, Number):\n            result[key] = left_value + right_value  # type: ignore\n        elif key in left:\n            result[key] = left_value\n        else:\n            result[key] = right_value\n\n    return result\n\ndef combine_usage(left: Usage | None, right: Usage | None) -> Usage | None:\n    \"\"\"Merge two LiteLLM usage statistics.\n\n    Combines token counts and details from two Usage objects:\n    - Adds all token counts (prompt, completion, total)\n    - Merges token details dictionaries\n    - Handles optional fields and None values\n\n    Args:\n        left: First Usage object to merge.\n        right: Second Usage object to merge.\n\n    Returns:\n        Combined Usage object or None if both inputs are None.\n\n    Examples:\n        Basic merge:\n            ```python\n            usage1 = Usage(\n                prompt_tokens=10,\n                completion_tokens=5,\n                total_tokens=15,\n            )\n            usage2 = Usage(\n                prompt_tokens=20,\n                completion_tokens=10,\n                total_tokens=30,\n            )\n            total = combine_usage(usage1, usage2)\n            assert total.prompt_tokens == 30\n            assert total.completion_tokens == 15\n            assert total.total_tokens == 45\n            ```\n\n        With details:\n            ```python\n            usage1 = Usage(\n                prompt_tokens=10,\n                completion_tokens=5,\n                prompt_tokens_details={\"system\": 3, \"user\": 7},\n            )\n            usage2 = Usage(\n                prompt_tokens=15,\n                completion_tokens=8,\n                prompt_tokens_details={\"system\": 5, \"user\": 10},\n            )\n            total = combine_usage(usage1, usage2)\n            assert total.prompt_tokens_details == {\"system\": 8, \"user\": 17}\n            ```\n    \"\"\"\n    if left is None:\n        return right\n\n    if right is None:\n        return left\n\n    prompt_tokens = (left.prompt_tokens or 0) + (right.prompt_tokens or 0)\n    completion_tokens = (left.completion_tokens or 0) + (right.completion_tokens or 0)\n    total_tokens = (left.total_tokens or 0) + (right.total_tokens or 0)\n\n    lhs_reasoning_tokens = safe_get_attr(left, \"reasoning_tokens\", int, default=0)\n    rhs_reasoning_tokens = safe_get_attr(right, \"reasoning_tokens\", int, default=0)\n    reasoning_tokens = lhs_reasoning_tokens + rhs_reasoning_tokens\n\n    lhs_completion_tokens_details = safe_get_attr(left, \"completion_tokens_details\", dict)\n    rhs_completion_tokens_details = safe_get_attr(right, \"completion_tokens_details\", dict)\n    completion_tokens_details = combine_dicts(\n        lhs_completion_tokens_details,\n        rhs_completion_tokens_details,\n    )\n\n    lhs_prompt_tokens_details = safe_get_attr(left, \"prompt_tokens_details\", dict)\n    rhs_prompt_tokens_details = safe_get_attr(right, \"prompt_tokens_details\", dict)\n    prompt_tokens_details = combine_dicts(\n        lhs_prompt_tokens_details,\n        rhs_prompt_tokens_details,\n    )\n\n    return Usage(\n        prompt_tokens=prompt_tokens,\n        completion_tokens=completion_tokens,\n        total_tokens=total_tokens,\n        reasoning_tokens=reasoning_tokens,\n        completion_tokens_details=completion_tokens_details,\n        prompt_tokens_details=prompt_tokens_details,\n    )\n\n```\n\n\n",
        "eval_script": "from numbers import Number\nfrom typing import Any, Optional\n\n# Mock Usage class to simulate the behavior of the actual Usage class from litellm\nclass Usage:\n    def __init__(\n        self,\n        prompt_tokens: Optional[int] = None,\n        completion_tokens: Optional[int] = None,\n        total_tokens: Optional[int] = None,\n        reasoning_tokens: Optional[int] = None,\n        completion_tokens_details: Optional[dict[str, Any]] = None,\n        prompt_tokens_details: Optional[dict[str, Any]] = None,\n    ):\n        self.prompt_tokens = prompt_tokens\n        self.completion_tokens = completion_tokens\n        self.total_tokens = total_tokens\n        self.reasoning_tokens = reasoning_tokens\n        self.completion_tokens_details = completion_tokens_details\n        self.prompt_tokens_details = prompt_tokens_details\n\n# Function from liteswarm/utils/misc.py\ndef safe_get_attr(\n    obj: Any,\n    attr: str,\n    expected_type: type,\n    default: Any = None,\n) -> Any:\n    value = getattr(obj, attr, default)\n    if isinstance(value, expected_type):\n        return value\n    return default\n\n# Function from liteswarm/utils/usage.py\ndef combine_dicts(\n    left: dict[str, Any] | None,\n    right: dict[str, Any] | None,\n) -> dict[str, Any] | None:\n    if left is None:\n        return right\n    if right is None:\n        return left\n    result = {}\n    all_keys = set(left) | set(right)\n    for key in all_keys:\n        left_value = left.get(key)\n        right_value = right.get(key)\n        if isinstance(left_value, Number) and isinstance(right_value, Number):\n            result[key] = left_value + right_value  # type: ignore\n        elif key in left:\n            result[key] = left_value\n        else:\n            result[key] = right_value\n    return result\n\ndef combine_usage(left: Usage | None, right: Usage | None) -> Usage | None:\n    if left is None:\n        return right\n    if right is None:\n        return left\n    prompt_tokens = (left.prompt_tokens or 0) + (right.prompt_tokens or 0)\n    completion_tokens = (left.completion_tokens or 0) + (right.completion_tokens or 0)\n    total_tokens = (left.total_tokens or 0) + (right.total_tokens or 0)\n    lhs_reasoning_tokens = safe_get_attr(left, \"reasoning_tokens\", int, default=0)\n    rhs_reasoning_tokens = safe_get_attr(right, \"reasoning_tokens\", int, default=0)\n    reasoning_tokens = lhs_reasoning_tokens + rhs_reasoning_tokens\n    lhs_completion_tokens_details = safe_get_attr(left, \"completion_tokens_details\", dict)\n    rhs_completion_tokens_details = safe_get_attr(right, \"completion_tokens_details\", dict)\n    completion_tokens_details = combine_dicts(\n        lhs_completion_tokens_details,\n        rhs_completion_tokens_details,\n    )\n    lhs_prompt_tokens_details = safe_get_attr(left, \"prompt_tokens_details\", dict)\n    rhs_prompt_tokens_details = safe_get_attr(right, \"prompt_tokens_details\", dict)\n    prompt_tokens_details = combine_dicts(\n        lhs_prompt_tokens_details,\n        rhs_prompt_tokens_details,\n    )\n    return Usage(\n        prompt_tokens=prompt_tokens,\n        completion_tokens=completion_tokens,\n        total_tokens=total_tokens,\n        reasoning_tokens=reasoning_tokens,\n        completion_tokens_details=completion_tokens_details,\n        prompt_tokens_details=prompt_tokens_details,\n    )\n\n\ndef test_combine_usage():\n    # Test case 1: Both usages are None\n    assert combine_usage(None, None) == combine_usage_new_implementation(None, None)\n\n    # Test case 2: One usage is None\n    usage1 = Usage(prompt_tokens=10, completion_tokens=5)\n    assert combine_usage(usage1, None) == combine_usage_new_implementation(usage1, None)\n    assert combine_usage(None, usage1) == combine_usage_new_implementation(None, usage1)\n\n    # Test case 3: Both usages have values\n    usage2 = Usage(prompt_tokens=20, completion_tokens=10, reasoning_tokens=5)\n    expected = combine_usage(usage1, usage2)\n    result = combine_usage_new_implementation(usage1, usage2)\n    assert expected.prompt_tokens == result.prompt_tokens\n    assert expected.completion_tokens == result.completion_tokens\n    assert expected.reasoning_tokens == result.reasoning_tokens\n\nif __name__ == \"__main__\":\n    test_combine_usage()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      18      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 18      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing both the original and revised functions, the logic and operations performed in both versions are identical. Both functions handle `None` values, sum the token counts, and merge the token details using the same helper functions (`safe_get_attr` and `combine_dicts`). The revised function includes a mock `Usage` class and test cases, but these do not alter the functionality of the `combine_usage` function itself. The core logic of combining usage statistics remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `combine_usage` function returns a `Usage` object or `None`, satisfying the requirement of having return values.\n- **CONDITION 2**: The test cases use assertions to compare return values and attributes of the `Usage` objects, not printed or logged content.\n- **CONDITION 3**: The test cases compare the results of `combine_usage` and `combine_usage_new_implementation` directly, ensuring that both implementations must have the same functionality to pass.\n- **CONDITION 4**: The test cases use appropriate assertions to compare the attributes of the `Usage` objects returned by both implementations, which is reasonable given that `combine_usage` returns a `Usage` object.\n- **CONDITION 5**: The test cases cover various scenarios, including both inputs being `None`, one input being `None`, and both inputs having values. This provides a non-trivial set of test cases that effectively test the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "create_response_parser",
        "idx": "461",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "examples/advanced/structured_outputs/strategies/openai_pydantic.py",
        "orig_func": "def create_response_parser(response_format: type[T], patched_response_format: type[BaseModel]) -> Callable[[str, ContextVariables], T]:\n    \"\"\"Create a response parser for OpenAI models.\n\n    Args:\n        response_format: Original response format type.\n        patched_response_format: Modified response format with defaults removed.\n\n    Returns:\n        A callable that parses OpenAI responses into the specified format.\n\n    Notes:\n        The parser handles OpenAI's response format and restores default values\n        that were removed to improve response accuracy.\n    \"\"\"\n\n    def response_parser(response: str, _: ContextVariables) -> T:\n        patched_response_content = patched_response_format.model_validate_json(response)\n        return restore_default_values(patched_response_content, response_format)\n    return response_parser",
        "orig_context": "```python\n## examples/advanced/structured_outputs/strategies/openai_pydantic.py\nfrom collections.abc import Callable\n\nfrom typing import Literal, TypeAlias, TypeVar, get_args\n\nfrom pydantic import BaseModel\n\nfrom liteswarm import LLM, Agent, ContextVariables\n\nfrom liteswarm.utils.pydantic import (\n    remove_default_values,\n    replace_default_values,\n    restore_default_values,\n)\n\nT = TypeVar(\"T\", bound=BaseModel)\n\ndef create_response_parser(\n    response_format: type[T],\n    patched_response_format: type[BaseModel],\n) -> Callable[[str, ContextVariables], T]:\n    \"\"\"Create a response parser for OpenAI models.\n\n    Args:\n        response_format: Original response format type.\n        patched_response_format: Modified response format with defaults removed.\n\n    Returns:\n        A callable that parses OpenAI responses into the specified format.\n\n    Notes:\n        The parser handles OpenAI's response format and restores default values\n        that were removed to improve response accuracy.\n    \"\"\"\n\n    def response_parser(response: str, _: ContextVariables) -> T:\n        patched_response_content = patched_response_format.model_validate_json(response)\n        return restore_default_values(patched_response_content, response_format)\n\n    return response_parser\n\n```\n\n\n",
        "eval_script": "# Mock implementations to replace missing dependencies\n\nfrom collections.abc import Callable\nfrom typing import TypeVar\nfrom pydantic import BaseModel\n\n# Mock LLM, Agent, ContextVariables, and utility functions\nclass LLM:\n    pass\n\nclass Agent:\n    pass\n\nclass ContextVariables:\n    pass\n\ndef remove_default_values(model_instance):\n    return model_instance\n\ndef replace_default_values(model_instance, defaults):\n    return model_instance\n\ndef restore_default_values(patched_instance, original_format):\n    return original_format()\n\nT = TypeVar(\"T\", bound=BaseModel)\n\ndef create_response_parser(\n    response_format: type[T],\n    patched_response_format: type[BaseModel],\n) -> Callable[[str, ContextVariables], T]:\n    \"\"\"Create a response parser for OpenAI models.\n\n    Args:\n        response_format: Original response format type.\n        patched_response_format: Modified response format with defaults removed.\n\n    Returns:\n        A callable that parses OpenAI responses into the specified format.\n\n    Notes:\n        The parser handles OpenAI's response format and restores default values\n        that were removed to improve response accuracy.\n    \"\"\"\n\n    def response_parser(response: str, _: ContextVariables) -> T:\n        patched_response_content = patched_response_format.model_validate_json(response)\n        return restore_default_values(patched_response_content, response_format)\n\n    return response_parser\n\n# Example usage with mock data\nclass ExampleResponseFormat(BaseModel):\n    field1: str = \"default_value\"\n    field2: int = 0\n\nclass PatchedExampleResponseFormat(BaseModel):\n    field1: str\n    field2: int\n\n\ndef test_create_response_parser():\n    # Create parsers using both implementations\n    original_parser = create_response_parser(ExampleResponseFormat, PatchedExampleResponseFormat)\n    new_parser = create_response_parser_new_implementation(ExampleResponseFormat, PatchedExampleResponseFormat)\n    \n    # Test case 1\n    response1 = '{\"field1\": \"value1\", \"field2\": 42}'\n    context = ContextVariables()\n    assert original_parser(response1, context) == new_parser(response1, context)\n    \n    # Test case 2\n    response2 = '{\"field1\": \"another_value\", \"field2\": 100}'\n    assert original_parser(response2, context) == new_parser(response2, context)\n    \n    # Test case 3\n    response3 = '{\"field1\": \"default_value\", \"field2\": 0}'\n    assert original_parser(response3, context) == new_parser(response3, context)\n\nif __name__ == \"__main__\":\n    test_create_response_parser()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions have the same signature, logic, and implementation. They both define a nested function `response_parser` that takes a response string and a `ContextVariables` object, validates the response using `patched_response_format.model_validate_json`, and then calls `restore_default_values` with the validated response and the original response format. There are no changes in the logic or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `create_response_parser` function returns a callable (a function) that processes a string response and returns an instance of a specified format. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of the parsers created by the original and new implementations. There is no checking of printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of the original and new implementations for equality. This ensures that the new implementation must have the exact same functionality as the original to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two parser functions, which is reasonable given that `create_response_parser` returns a callable that produces output. This satisfies the condition.\n- CONDITION 5: The test cases cover different scenarios with varying input values, including default values, which makes them non-trivial and satisfies this condition.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "find_tag",
        "idx": "462",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "examples/advanced/structured_outputs/strategies/llm_json_tags.py",
        "orig_func": "def find_tag(text: str, tag: str) -> str | None:\n    \"\"\"Find and extract content from a tagged section.\n\n    Args:\n        text: Text containing tagged sections.\n        tag: Name of the tag to find.\n\n    Returns:\n        Content between the specified tags, or None if not found.\n    \"\"\"\n    pattern = re.compile(f'<{tag}>(.*?)</{tag}>', re.DOTALL)\n    match = pattern.search(text)\n    return match.group(1) if match else None",
        "orig_context": "```python\n## examples/advanced/structured_outputs/strategies/llm_json_tags.py\nimport re\n\ndef find_tag(text: str, tag: str) -> str | None:\n    \"\"\"Find and extract content from a tagged section.\n\n    Args:\n        text: Text containing tagged sections.\n        tag: Name of the tag to find.\n\n    Returns:\n        Content between the specified tags, or None if not found.\n    \"\"\"\n    pattern = re.compile(rf\"<{tag}>(.*?)</{tag}>\", re.DOTALL)\n    match = pattern.search(text)\n    return match.group(1) if match else None\n\n```\n\n\n",
        "eval_script": "import re\n\ndef find_tag(text: str, tag: str) -> str | None:\n    \"\"\"Find and extract content from a tagged section.\n\n    Args:\n        text: Text containing tagged sections.\n        tag: Name of the tag to find.\n\n    Returns:\n        Content between the specified tags, or None if not found.\n    \"\"\"\n    pattern = re.compile(rf\"<{tag}>(.*?)</{tag}>\", re.DOTALL)\n    match = pattern.search(text)\n    return match.group(1) if match else None\n\n\ndef test_find_tag():\n    # Test case 1: Tag is found\n    text1 = \"<tag>Hello World</tag>\"\n    tag1 = \"tag\"\n    assert find_tag(text1, tag1) == find_tag_new_implementation(text1, tag1)\n\n    # Test case 2: Tag is not found\n    text2 = \"<tag>Hello World</tag>\"\n    tag2 = \"notag\"\n    assert find_tag(text2, tag2) == find_tag_new_implementation(text2, tag2)\n\n    # Test case 3: Multiple tags\n    text3 = \"<tag>Hello</tag> <tag>World</tag>\"\n    tag3 = \"tag\"\n    assert find_tag(text3, tag3) == find_tag_new_implementation(text3, tag3)\n\n    # Test case 4: Nested tags\n    text4 = \"<tag><inner>Content</inner></tag>\"\n    tag4 = \"tag\"\n    assert find_tag(text4, tag4) == find_tag_new_implementation(text4, tag4)\n\n    # Test case 5: Tags with attributes (should not match)\n    text5 = \"<tag attr='value'>Content</tag>\"\n    tag5 = \"tag\"\n    assert find_tag(text5, tag5) == find_tag_new_implementation(text5, tag5)\n\n    # Test case 6: Empty content\n    text6 = \"<tag></tag>\"\n    tag6 = \"tag\"\n    assert find_tag(text6, tag6) == find_tag_new_implementation(text6, tag6)\n\n    # Test case 7: Case sensitivity\n    text7 = \"<Tag>Content</Tag>\"\n    tag7 = \"tag\"\n    assert find_tag(text7, tag7) == find_tag_new_implementation(text7, tag7)\n\n    # Test case 8: Malformed tags\n    text8 = \"<tag>Content<tag>\"\n    tag8 = \"tag\"\n    assert find_tag(text8, tag8) == find_tag_new_implementation(text8, tag8)\n\n    # Test case 9: Whitespace handling\n    text9 = \"<tag>   Content   </tag>\"\n    tag9 = \"tag\"\n    assert find_tag(text9, tag9) == find_tag_new_implementation(text9, tag9)\n\n    # Test case 10: Special characters\n    text10 = \"<tag>Content &amp; More</tag>\"\n    tag10 = \"tag\"\n    assert find_tag(text10, tag10) == find_tag_new_implementation(text10, tag10)\n\nif __name__ == \"__main__\":\n    test_find_tag()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function are essentially the same in terms of functionality. The only difference is in the way the regular expression pattern is defined. In the original function, the pattern is defined using an f-string: `f'<{tag}>(.*?)</{tag}>'`, while in the revised function, it is defined using a raw f-string: `rf\"<{tag}>(.*?)</{tag}>\"`. Both methods achieve the same result, as the content within the tags is extracted using the same regular expression logic. The use of a raw string (`r`) is typically to avoid escaping backslashes, but in this context, it does not affect the functionality since there are no backslashes in the pattern. Therefore, the functionality of both functions is the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `find_tag` function returns a value (either a string or None), satisfying this condition.\n- CONDITION 2: The test cases use assertions to compare return values, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `find_tag` and `find_tag_new_implementation` for various inputs. If `find_tag_new_implementation` produces the same outputs for all these inputs, it must have the same functionality, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable given that `find_tag` returns a value. This satisfies the condition.\n- CONDITION 5: The test cases cover various scenarios, including finding tags, not finding tags, multiple tags, nested tags, tags with attributes, empty content, case sensitivity, malformed tags, whitespace handling, and special characters. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "StateManager.get_chat_state",
        "idx": "469",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "examples/advanced/chat_app_server/app.py",
        "orig_func": "@classmethod\ndef get_chat_state(cls) -> ChatState:\n    \"\"\"Get or create chat state.\"\"\"\n    if cls._chat_state is None:\n        cls._chat_state = ChatState(user_store=UserStore())\n    return cls._chat_state",
        "orig_context": "```python\n## examples/advanced/chat_app_server/app.py\nfrom dataclasses import dataclass, field\n\nfrom typing import Annotated, Any\n\nfrom uuid import uuid4\n\nfrom pydantic import BaseModel, ConfigDict\n\nfrom liteswarm import SwarmChat, set_verbose\n\nclass ChatSession:\n    \"\"\"Chat session container.\"\"\"\n\n    session_id: str\n    user_id: str\n    chat: SwarmChat\n    last_agent_id: str | None = None\n\nclass ChatState:\n    \"\"\"State container for chat components.\"\"\"\n\n    user_store: \"UserStore\"\n    sessions: dict[str, ChatSession] = field(default_factory=dict)\n\n    async def create_session(self, user_id: str) -> ChatSession:\n        \"\"\"Create a new chat session.\n\n        Args:\n            user_id: ID of the user creating the session.\n\n        Returns:\n            New chat session.\n        \"\"\"\n        chat = SwarmChat()\n        session_id = str(uuid4())\n        session = ChatSession(\n            session_id=session_id,\n            user_id=user_id,\n            chat=chat,\n        )\n\n        self.sessions[session_id] = session\n        return session\n\n    async def get_session(self, session_id: str) -> ChatSession | None:\n        \"\"\"Get a chat session by ID.\n\n        Args:\n            session_id: ID of the session to get.\n\n        Returns:\n            Chat session if found, None otherwise.\n        \"\"\"\n        return self.sessions.get(session_id)\n\n    async def delete_session(self, session_id: str) -> None:\n        \"\"\"Delete a chat session.\n\n        Args:\n            session_id: ID of the session to delete.\n        \"\"\"\n        if session_id in self.sessions:\n            self.sessions.pop(session_id)\n\n    async def get_user_sessions(self, user_id: str) -> list[ChatSession]:\n        \"\"\"Get all sessions for a user.\n\n        Args:\n            user_id: ID of the user to get sessions for.\n\n        Returns:\n            List of user's chat sessions.\n        \"\"\"\n        return [s for s in self.sessions.values() if s.user_id == user_id]\n\n    async def delete_user_sessions(self, user_id: str) -> None:\n        \"\"\"Delete all sessions for a user.\n\n        Args:\n            user_id: ID of the user to delete sessions for.\n        \"\"\"\n        session_ids = [s.session_id for s in self.sessions.values() if s.user_id == user_id]\n        for session_id in session_ids:\n            await self.delete_session(session_id)\n\nclass UserStore:\n    \"\"\"In-memory user management for chat server.\"\"\"\n\n    _users: dict[str, \"User\"] = field(default_factory=dict)\n\n    async def add_user(\n        self,\n        user_id: str,\n        name: str | None = None,\n        metadata: dict[str, Any] | None = None,\n    ) -> None:\n        \"\"\"Add new user.\"\"\"\n        if user_id in self._users:\n            raise ValueError(\"User already exists\")\n\n        self._users[user_id] = User(\n            user_id=user_id,\n            name=name,\n            metadata=metadata,\n        )\n\n    async def get_user(self, user_id: str) -> \"User | None\":\n        \"\"\"Get user by ID.\"\"\"\n        return self._users.get(user_id)\n\n    async def list_users(self) -> dict[str, \"User\"]:\n        \"\"\"Get all users.\"\"\"\n        return self._users.copy()\n\n    async def delete_user(self, user_id: str) -> None:\n        \"\"\"Delete user.\"\"\"\n        if user_id not in self._users:\n            return\n        self._users.pop(user_id)\n\nclass PydanticModel(BaseModel):\n    \"\"\"Base model for Pydantic models.\"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\nclass User(PydanticModel):\n    \"\"\"User model for chat server.\"\"\"\n\n    user_id: str\n    name: str | None = None\n    metadata: dict[str, Any] | None = None\n\nclass StateManager:\n    \"\"\"Singleton manager for application state.\"\"\"\n\n    _chat_state: ChatState | None = None\n\n    @classmethod\n    def get_chat_state(cls) -> ChatState:\n        \"\"\"Get or create chat state.\"\"\"\n        if cls._chat_state is None:\n            cls._chat_state = ChatState(user_store=UserStore())\n        return cls._chat_state\n\n```\n\n\n",
        "eval_script": "from dataclasses import dataclass, field\n\nfrom typing import Annotated, Any\n\nfrom uuid import uuid4\n\nfrom pydantic import BaseModel, ConfigDict\n\nfrom liteswarm import SwarmChat, set_verbose\n\nclass ChatSession:\n    \"\"\"Chat session container.\"\"\"\n\n    session_id: str\n    user_id: str\n    chat: SwarmChat\n    last_agent_id: str | None = None\n\n@dataclass\nclass ChatState:\n    \"\"\"State container for chat components.\"\"\"\n\n    user_store: \"UserStore\"\n    sessions: dict[str, ChatSession] = field(default_factory=dict)\n\n    async def create_session(self, user_id: str) -> ChatSession:\n        \"\"\"Create a new chat session.\n\n        Args:\n            user_id: ID of the user creating the session.\n\n        Returns:\n            New chat session.\n        \"\"\"\n        chat = SwarmChat()\n        session_id = str(uuid4())\n        session = ChatSession(\n            session_id=session_id,\n            user_id=user_id,\n            chat=chat,\n        )\n\n        self.sessions[session_id] = session\n        return session\n\n    async def get_session(self, session_id: str) -> ChatSession | None:\n        \"\"\"Get a chat session by ID.\n\n        Args:\n            session_id: ID of the session to get.\n\n        Returns:\n            Chat session if found, None otherwise.\n        \"\"\"\n        return self.sessions.get(session_id)\n\n    async def delete_session(self, session_id: str) -> None:\n        \"\"\"Delete a chat session.\n\n        Args:\n            session_id: ID of the session to delete.\n        \"\"\"\n        if session_id in self.sessions:\n            self.sessions.pop(session_id)\n\n    async def get_user_sessions(self, user_id: str) -> list[ChatSession]:\n        \"\"\"Get all sessions for a user.\n\n        Args:\n            user_id: ID of the user to get sessions for.\n\n        Returns:\n            List of user's chat sessions.\n        \"\"\"\n        return [s for s in self.sessions.values() if s.user_id == user_id]\n\n    async def delete_user_sessions(self, user_id: str) -> None:\n        \"\"\"Delete all sessions for a user.\n\n        Args:\n            user_id: ID of the user to delete sessions for.\n        \"\"\"\n        session_ids = [s.session_id for s in self.sessions.values() if s.user_id == user_id]\n        for session_id in session_ids:\n            await self.delete_session(session_id)\n\n@dataclass\nclass UserStore:\n    \"\"\"In-memory user management for chat server.\"\"\"\n\n    _users: dict[str, \"User\"] = field(default_factory=dict)\n\n    async def add_user(\n        self,\n        user_id: str,\n        name: str | None = None,\n        metadata: dict[str, Any] | None = None,\n    ) -> None:\n        \"\"\"Add new user.\"\"\"\n        if user_id in self._users:\n            raise ValueError(\"User already exists\")\n\n        self._users[user_id] = User(\n            user_id=user_id,\n            name=name,\n            metadata=metadata,\n        )\n\n    async def get_user(self, user_id: str) -> \"User | None\":\n        \"\"\"Get user by ID.\"\"\"\n        return self._users.get(user_id)\n\n    async def list_users(self) -> dict[str, \"User\"]:\n        \"\"\"Get all users.\"\"\"\n        return self._users.copy()\n\n    async def delete_user(self, user_id: str) -> None:\n        \"\"\"Delete user.\"\"\"\n        if user_id not in self._users:\n            return\n        self._users.pop(user_id)\n\nclass PydanticModel(BaseModel):\n    \"\"\"Base model for Pydantic models.\"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\nclass User(PydanticModel):\n    \"\"\"User model for chat server.\"\"\"\n\n    user_id: str\n    name: str | None = None\n    metadata: dict[str, Any] | None = None\n\nclass StateManager:\n    \"\"\"Singleton manager for application state.\"\"\"\n\n    _chat_state: ChatState | None = None\n\n    @classmethod\n    def get_chat_state(cls) -> ChatState:\n        \"\"\"Get or create chat state.\"\"\"\n        if cls._chat_state is None:\n            cls._chat_state = ChatState(user_store=UserStore())\n        return cls._chat_state\n\n\ndef test_get_chat_state():\n    # Test 1: Check if both implementations return the same instance\n    state1 = StateManager.get_chat_state()\n    state2 = StateManager.get_chat_state_new_implementation()\n    assert state1 is state2, \"Both implementations should return the same instance\"\n\n    # Test 2: Check if the chat state is initialized correctly\n    assert isinstance(state1, ChatState), \"The chat state should be an instance of ChatState\"\n    assert isinstance(state2, ChatState), \"The chat state should be an instance of ChatState\"\n\n    # Test 3: Check if user_store is initialized correctly\n    assert isinstance(state1.user_store, UserStore), \"The user_store should be an instance of UserStore\"\n    assert isinstance(state2.user_store, UserStore), \"The user_store should be an instance of UserStore\"\n\n    # Test 4: Check singleton behavior on multiple calls\n    state3 = StateManager.get_chat_state()\n    state4 = StateManager.get_chat_state_new_implementation()\n    assert state1 is state3, \"Subsequent calls should return the same instance\"\n    assert state2 is state4, \"Subsequent calls should return the same instance\"\n\n    # Test 5: Check if sessions are initialized as empty\n    assert state1.sessions == {}, \"Sessions should be initialized as an empty dictionary\"\n    assert state2.sessions == {}, \"Sessions should be initialized as an empty dictionary\"\n\n    # Test 6: Check if user_store is initialized with no users\n    assert state1.user_store._users == {}, \"UserStore should be initialized with no users\"\n    assert state2.user_store._users == {}, \"UserStore should be initialized with no users\"\n\nif __name__ == \"__main__\":\n    test_get_chat_state()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the same as the ORIGINAL FUNCTION. Both functions are class methods of the `StateManager` class and are responsible for returning the `_chat_state` attribute. If `_chat_state` is `None`, both functions initialize it with a new `ChatState` object that has a `UserStore` instance as its `user_store`. The functionality of getting or creating the chat state is preserved in both implementations, and they both ensure that the `StateManager` class maintains a singleton instance of `ChatState`. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_chat_state` function returns a `ChatState` object, satisfying the condition that it should have return values or modify global variables or input arguments.\n  \n- CONDITION 2: The test cases check the return values and states of variables, such as checking if the returned object is an instance of `ChatState`, if the `user_store` is an instance of `UserStore`, and if the `sessions` and `_users` dictionaries are initialized correctly. There is no checking of printed or logged content.\n\n- CONDITION 3: The test cases are designed to ensure that both `get_chat_state` and `get_chat_state_new_implementation` return the same instance and that the state is initialized correctly. This implies that `get_chat_state_new_implementation` must have exactly the same functionality as `get_chat_state` to pass all tests.\n\n- CONDITION 4: The test cases and assert statements are reasonable. They check for singleton behavior, correct initialization of `ChatState` and `UserStore`, and ensure that the sessions and users are initialized as empty. There is no use of inappropriate assertions like comparing return values directly when there are none.\n\n- CONDITION 5: The test cases are non-trivial as they check for singleton behavior, correct class instantiation, and proper initialization of internal states, which are essential aspects of the `get_chat_state` function's functionality.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "TaskBase.build_prompt",
        "idx": "471",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "examples/advanced/agent_team/run.py",
        "orig_func": "def build_prompt(self) -> str:\n    \"\"\"Build a prompt for this task.\"\"\"\n    raise NotImplementedError('Subclasses must implement build_prompt')",
        "orig_context": "```python\n## examples/advanced/agent_team/run.py\nfrom typing import Any, Literal, get_args, get_origin\n\nfrom pydantic import BaseModel\n\nclass TaskBase(BaseModel):\n    \"\"\"Base class for all task types.\"\"\"\n\n    type: str\n    id: str\n    title: str\n    description: str\n\n    @classmethod\n    def task_type(cls) -> str:\n        \"\"\"Get the task type.\"\"\"\n        type_field = cls.model_fields[\"type\"]\n        origin = get_origin(type_field.annotation)\n        if origin is Literal:\n            return get_args(type_field.annotation)[0]\n\n        raise ValueError(\"Task type is not set\")\n\n    def build_prompt(self) -> str:\n        \"\"\"Build a prompt for this task.\"\"\"\n        raise NotImplementedError(\"Subclasses must implement build_prompt\")\n\n```\n\n\n",
        "eval_script": "## examples/advanced/agent_team/run.py\nfrom typing import Any, Literal, get_args, get_origin\n\nfrom pydantic import BaseModel\n\nclass TaskBase(BaseModel):\n    \"\"\"Base class for all task types.\"\"\"\n\n    type: str\n    id: str\n    title: str\n    description: str\n\n    @classmethod\n    def task_type(cls) -> str:\n        \"\"\"Get the task type.\"\"\"\n        type_field = cls.model_fields[\"type\"]\n        origin = get_origin(type_field.annotation)\n        if origin is Literal:\n            return get_args(type_field.annotation)[0]\n\n        raise ValueError(\"Task type is not set\")\n\n    def build_prompt(self) -> str:\n        \"\"\"Build a prompt for this task.\"\"\"\n        raise NotImplementedError(\"Subclasses must implement build_prompt\")\n\n# Subclass of TaskBase that implements build_prompt\nclass ConcreteTask(TaskBase):\n    def build_prompt(self) -> str:\n        return f\"Task ID: {self.id}, Title: {self.title}, Description: {self.description}\"\n\n    def build_prompt_new_implementation(self) -> str:\n        # Assuming the new implementation is supposed to be the same\n        return f\"Task ID: {self.id}, Title: {self.title}, Description: {self.description}\"\n\ndef test_build_prompt():\n    # Test case 1: Normal case\n    task1 = ConcreteTask(type=\"type1\", id=\"1\", title=\"Title1\", description=\"Description1\")\n    assert task1.build_prompt() == task1.build_prompt_new_implementation()\n\n    # Test case 2: Empty title\n    task2 = ConcreteTask(type=\"type2\", id=\"2\", title=\"\", description=\"Description2\")\n    assert task2.build_prompt() == task2.build_prompt_new_implementation()\n\n    # Test case 3: Empty description\n    task3 = ConcreteTask(type=\"type3\", id=\"3\", title=\"Title3\", description=\"\")\n    assert task3.build_prompt() == task3.build_prompt_new_implementation()\n\n    # Test case 4: Empty ID\n    task4 = ConcreteTask(type=\"type4\", id=\"\", title=\"Title4\", description=\"Description4\")\n    assert task4.build_prompt() == task4.build_prompt_new_implementation()\n\n    # Test case 5: Empty type\n    task5 = ConcreteTask(type=\"\", id=\"5\", title=\"Title5\", description=\"Description5\")\n    assert task5.build_prompt() == task5.build_prompt_new_implementation()\n\n    # Test case 6: Special characters in title and description\n    task6 = ConcreteTask(type=\"type6\", id=\"6\", title=\"!@#$%^&*()\", description=\"<>?:\\\"{}|\")\n    assert task6.build_prompt() == task6.build_prompt_new_implementation()\n\n    # Test case 7: Long strings\n    long_string = \"a\" * 1000\n    task7 = ConcreteTask(type=\"type7\", id=\"7\", title=long_string, description=long_string)\n    assert task7.build_prompt() == task7.build_prompt_new_implementation()\n\n    # Test case 8: Whitespace handling\n    task8 = ConcreteTask(type=\"type8\", id=\"8\", title=\"  Title with spaces  \", description=\"  Description with spaces  \")\n    assert task8.build_prompt() == task8.build_prompt_new_implementation()\n\nif __name__ == \"__main__\":\n    test_build_prompt()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `build_prompt` is an abstract method that raises a `NotImplementedError`, indicating that subclasses must implement this method. The revised function in the `TaskBase` class also raises a `NotImplementedError`, maintaining the same abstract behavior. The `ConcreteTask` subclass provides an implementation for `build_prompt`, which is consistent with the requirement that subclasses must implement the method. The functionality of the `build_prompt` method in the `ConcreteTask` class is not part of the original function's specification, as the original function is abstract. Therefore, the revised function maintains the same functionality as the original function by requiring subclasses to implement the method.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `build_prompt` function in `ConcreteTask` returns a string, which satisfies the condition of having return values.\n- [CONDITION 2] The test cases use assertions to compare the return values of `build_prompt` and `build_prompt_new_implementation`, not printed or logged content.\n- [CONDITION 3] The test cases compare the outputs of `build_prompt` and `build_prompt_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass.\n- [CONDITION 4] The assertions are reasonable as they compare the return values of the two implementations, which is appropriate given that `build_prompt` returns a string.\n- [CONDITION 5] The test cases cover a variety of scenarios, including normal cases, empty fields, special characters, long strings, and whitespace handling, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "Delta.from_delta",
        "idx": "474",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "liteswarm/types/llm.py",
        "orig_func": "@classmethod\ndef from_delta(cls, delta: LiteDelta) -> 'Delta':\n    \"\"\"Create a Delta from a LiteLLM delta object.\n\n        Args:\n            delta: LiteLLM delta to convert.\n\n        Returns:\n            New Delta instance with copied attributes.\n        \"\"\"\n    return cls(content=delta.content, role=delta.role, function_call=delta.function_call, tool_calls=delta.tool_calls, audio=delta.audio)",
        "orig_context": "```python\n## liteswarm/types/typing.py\nfrom typing import TYPE_CHECKING, Any, TypeAlias, TypeGuard, TypeVar, Union, get_args, get_origin\n\n```\n\n\n```python\n## liteswarm/types/base.py\nfrom pydantic import BaseModel, ConfigDict\n\nclass SwarmBaseModel(BaseModel):\n    \"\"\"Base model configuration for Swarm types.\n\n    Enables arbitrary types, docstrings as descriptions, and strict field checking.\n    \"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\n```\n\n\n```python\n## liteswarm/types/llm.py\nfrom typing import Any, Generic, Literal, Self, TypeAlias\n\nfrom litellm.types.utils import ChatCompletionAudioResponse, ChatCompletionDeltaToolCall, FunctionCall\n\nfrom litellm.types.utils import Delta as LiteDelta\n\nfrom liteswarm.types.base import SwarmBaseModel\n\nMessageRole: TypeAlias = Literal[\"assistant\", \"user\", \"system\", \"tool\", \"function\", \"developer\"]\n\nToolCall: TypeAlias = ChatCompletionDeltaToolCall\n\nAudioResponse: TypeAlias = ChatCompletionAudioResponse\n\nclass Delta(SwarmBaseModel):\n    \"\"\"Streaming update from language model generation.\n\n    Contains new content and changes since the last update during\n    streaming. Updates can include text content, role changes,\n    tool calls, or audio responses.\n\n    Note:\n        Any field may be empty or partially complete.\n    \"\"\"\n\n    content: str | None = None\n    \"\"\"New text content.\"\"\"\n\n    role: MessageRole | None = None\n    \"\"\"Role of the message author.\"\"\"\n\n    function_call: FunctionCall | None = None\n    \"\"\"Function call update (deprecated).\"\"\"\n\n    tool_calls: list[ToolCall] | None = None\n    \"\"\"Tool calls being made.\"\"\"\n\n    audio: AudioResponse | None = None\n    \"\"\"Audio response if available.\"\"\"\n\n    @property\n    def is_empty(self) -> bool:\n        \"\"\"Check if the delta is empty.\n\n        Returns:\n            True if the delta is empty, False otherwise.\n        \"\"\"\n        return all(not getattr(self, attr) for attr in self.model_fields)\n\n    @classmethod\n    def from_delta(cls, delta: LiteDelta) -> \"Delta\":\n        \"\"\"Create a Delta from a LiteLLM delta object.\n\n        Args:\n            delta: LiteLLM delta to convert.\n\n        Returns:\n            New Delta instance with copied attributes.\n        \"\"\"\n        return cls(\n            content=delta.content,\n            role=delta.role,\n            function_call=delta.function_call,\n            tool_calls=delta.tool_calls,\n            audio=delta.audio,\n        )\n\n```\n\n\n",
        "eval_script": "# Mocking the necessary imports from litellm.types.utils\nfrom typing import Any, Literal, TypeAlias\n\nclass ChatCompletionAudioResponse:\n    pass\n\nclass ChatCompletionDeltaToolCall:\n    pass\n\nclass FunctionCall:\n    pass\n\nclass LiteDelta:\n    def __init__(self, content=None, role=None, function_call=None, tool_calls=None, audio=None):\n        self.content = content\n        self.role = role\n        self.function_call = function_call\n        self.tool_calls = tool_calls\n        self.audio = audio\n\n# Importing SwarmBaseModel from liteswarm/types/base.py\nfrom pydantic import BaseModel, ConfigDict\n\nclass SwarmBaseModel(BaseModel):\n    \"\"\"Base model configuration for Swarm types.\n\n    Enables arbitrary types, docstrings as descriptions, and strict field checking.\n    \"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\n# Defining the Delta class with the from_delta method\nMessageRole: TypeAlias = Literal[\"assistant\", \"user\", \"system\", \"tool\", \"function\", \"developer\"]\n\nToolCall: TypeAlias = ChatCompletionDeltaToolCall\n\nAudioResponse: TypeAlias = ChatCompletionAudioResponse\n\nclass Delta(SwarmBaseModel):\n    \"\"\"Streaming update from language model generation.\n\n    Contains new content and changes since the last update during\n    streaming. Updates can include text content, role changes,\n    tool calls, or audio responses.\n\n    Note:\n        Any field may be empty or partially complete.\n    \"\"\"\n\n    content: str | None = None\n    \"\"\"New text content.\"\"\"\n\n    role: MessageRole | None = None\n    \"\"\"Role of the message author.\"\"\"\n\n    function_call: FunctionCall | None = None\n    \"\"\"Function call update (deprecated).\"\"\"\n\n    tool_calls: list[ToolCall] | None = None\n    \"\"\"Tool calls being made.\"\"\"\n\n    audio: AudioResponse | None = None\n    \"\"\"Audio response if available.\"\"\"\n\n    @property\n    def is_empty(self) -> bool:\n        \"\"\"Check if the delta is empty.\n\n        Returns:\n            True if the delta is empty, False otherwise.\n        \"\"\"\n        return all(not getattr(self, attr) for attr in self.model_fields)\n\n    @classmethod\n    def from_delta(cls, delta: LiteDelta) -> \"Delta\":\n        \"\"\"Create a Delta from a LiteLLM delta object.\n\n        Args:\n            delta: LiteLLM delta to convert.\n\n        Returns:\n            New Delta instance with copied attributes.\n        \"\"\"\n        return cls(\n            content=delta.content,\n            role=delta.role,\n            function_call=delta.function_call,\n            tool_calls=delta.tool_calls,\n            audio=delta.audio,\n        )\n\n\ndef test_from_delta():\n    # Test case 1: All attributes are None\n    delta1 = LiteDelta()\n    assert Delta.from_delta(delta1) == Delta.from_delta_new_implementation(delta1)\n\n    # Test case 2: Some attributes are set\n    delta2 = LiteDelta(content=\"Hello\", role=\"assistant\")\n    assert Delta.from_delta(delta2) == Delta.from_delta_new_implementation(delta2)\n\n    # Test case 3: All attributes are set\n    delta3 = LiteDelta(content=\"Hello\", role=\"assistant\", function_call=FunctionCall(), tool_calls=[ChatCompletionDeltaToolCall()], audio=ChatCompletionAudioResponse())\n    assert Delta.from_delta(delta3) == Delta.from_delta_new_implementation(delta3)\n\nif __name__ == \"__main__\":\n    test_from_delta()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions are class methods of a class named `Delta` and take a `LiteDelta` object as an argument. They both return a new instance of the `Delta` class with attributes copied from the `LiteDelta` object: `content`, `role`, `function_call`, `tool_calls`, and `audio`. The test cases provided in the code are meant to compare the output of the `from_delta` method with a non-existent `from_delta_new_implementation` method, which seems to be an error in the test code. However, this does not affect the comparison of the two `from_delta` methods themselves, which are functionally the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `from_delta` function returns a new `Delta` instance, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `from_delta` and `from_delta_new_implementation`, not printed or logged outputs.\n- CONDITION 3: The test cases compare the outputs of `from_delta` and `from_delta_new_implementation` directly. If both implementations produce the same outputs for all test cases, they must have the same functionality.\n- CONDITION 4: The test cases use assertions to compare the results of the two implementations, which is reasonable given that `from_delta` returns a value.\n- CONDITION 5: The test cases cover scenarios where all attributes are `None`, some attributes are set, and all attributes are set, which are non-trivial and cover a range of possible inputs.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "ChatMessage.from_message",
        "idx": "477",
        "repo_name": "GlyphyAI___liteswarm",
        "func_path": "liteswarm/types/chat.py",
        "orig_func": "@classmethod\ndef from_message(cls, message: Message, *, id: str | None=None, created_at: datetime | None=None, metadata: dict[str, Any] | None=None) -> 'ChatMessage':\n    \"\"\"Create a ChatMessage from a base Message.\n\n        Args:\n            message: Base Message to convert.\n            id: Optional message identifier.\n            created_at: Optional creation timestamp.\n            metadata: Optional message metadata.\n\n        Returns:\n            New ChatMessage with added fields.\n        \"\"\"\n    return cls(id=id or str(uuid.uuid4()), role=message.role, content=message.content, tool_calls=message.tool_calls, tool_call_id=message.tool_call_id, audio=message.audio, created_at=created_at or datetime.now(), metadata=metadata)",
        "orig_context": "```python\n## liteswarm/types/typing.py\nfrom typing import TYPE_CHECKING, Any, TypeAlias, TypeGuard, TypeVar, Union, get_args, get_origin\n\n```\n\n\n```python\n## liteswarm/types/base.py\nfrom pydantic import BaseModel, ConfigDict\n\nclass SwarmBaseModel(BaseModel):\n    \"\"\"Base model configuration for Swarm types.\n\n    Enables arbitrary types, docstrings as descriptions, and strict field checking.\n    \"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\n```\n\n\n```python\n## liteswarm/types/llm.py\nfrom typing import Any, Generic, Literal, Self, TypeAlias\n\nfrom litellm.types.utils import ChatCompletionAudioResponse, ChatCompletionDeltaToolCall, FunctionCall\n\nfrom liteswarm.types.base import SwarmBaseModel\n\nMessageRole: TypeAlias = Literal[\"assistant\", \"user\", \"system\", \"tool\", \"function\", \"developer\"]\n\nToolCall: TypeAlias = ChatCompletionDeltaToolCall\n\nAudioResponse: TypeAlias = ChatCompletionAudioResponse\n\nclass Message(SwarmBaseModel):\n    \"\"\"Message in a conversation between user, assistant, and tools.\n\n    Represents a message in a conversation between participants\n    with content and optional tool interactions. Each message has\n    a specific role and may include tool calls or responses.\n\n    Examples:\n        System message:\n            ```python\n            system_msg = Message(\n                role=\"system\",\n                content=\"You are a helpful assistant.\",\n            )\n            ```\n\n        Assistant with tool:\n            ```python\n            assistant_msg = Message(\n                role=\"assistant\",\n                content=\"Let me calculate that.\",\n                tool_calls=[\n                    ToolCall(\n                        id=\"calc_1\",\n                        function={\"name\": \"add\", \"arguments\": '{\"a\": 2, \"b\": 2}'},\n                        type=\"function\",\n                        index=0,\n                    )\n                ],\n            )\n            ```\n\n        Tool response:\n            ```python\n            tool_msg = Message(\n                role=\"tool\",\n                content=\"4\",\n                tool_call_id=\"calc_1\",\n            )\n            ```\n    \"\"\"\n\n    role: MessageRole\n    \"\"\"Role of the message author.\"\"\"\n\n    content: str | None = None\n    \"\"\"Text content of the message.\"\"\"\n\n    tool_calls: list[ToolCall] | None = None\n    \"\"\"Tool calls made in this message.\"\"\"\n\n    tool_call_id: str | None = None\n    \"\"\"ID of the tool call this message responds to.\"\"\"\n\n    audio: AudioResponse | None = None\n    \"\"\"Audio response data if available.\"\"\"\n\n```\n\n\n```python\n## liteswarm/types/chat.py\nimport uuid\n\nfrom datetime import datetime\n\nfrom typing import Annotated, Any, Generic, Literal\n\nfrom pydantic import BaseModel, ConfigDict, Discriminator, field_serializer\n\nfrom liteswarm.types.llm import AudioResponse, Message, MessageRole, ToolCall\n\nclass ChatMessage(BaseModel):\n    \"\"\"Message type for chat applications with metadata support.\n\n    Extends the base Message type with fields for identification, timestamps,\n    and application-specific metadata. Maintains compatibility with base Message\n    while adding features needed for chat applications.\n\n    Examples:\n        ```python\n        # Create from scratch\n        message = ChatMessage(\n            id=\"msg_123\",\n            role=\"user\",\n            content=\"Hello!\",\n            metadata={\"client_id\": \"web_1\"},\n        )\n\n        # Convert from base Message\n        base_msg = Message(role=\"assistant\", content=\"Hi!\")\n        chat_msg = ChatMessage.from_message(\n            base_msg,\n            metadata={\"source\": \"chat\"},\n        )\n        ```\n    \"\"\"\n\n    id: str\n    \"\"\"Unique message identifier.\"\"\"\n\n    role: MessageRole\n    \"\"\"Role of the message author.\"\"\"\n\n    content: str | None = None\n    \"\"\"Text content of the message.\"\"\"\n\n    tool_calls: list[ToolCall] | None = None\n    \"\"\"Tool calls made in this message.\"\"\"\n\n    tool_call_id: str | None = None\n    \"\"\"ID of the tool call this message responds to.\"\"\"\n\n    audio: AudioResponse | None = None\n    \"\"\"Audio response data if available.\"\"\"\n\n    created_at: datetime = datetime.now()\n    \"\"\"Message creation timestamp.\"\"\"\n\n    metadata: dict[str, Any] | None = None\n    \"\"\"Application-specific message data.\"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\n    @classmethod\n    def from_message(\n        cls,\n        message: Message,\n        *,\n        id: str | None = None,\n        created_at: datetime | None = None,\n        metadata: dict[str, Any] | None = None,\n    ) -> \"ChatMessage\":\n        \"\"\"Create a ChatMessage from a base Message.\n\n        Args:\n            message: Base Message to convert.\n            id: Optional message identifier.\n            created_at: Optional creation timestamp.\n            metadata: Optional message metadata.\n\n        Returns:\n            New ChatMessage with added fields.\n        \"\"\"\n        return cls(\n            id=id or str(uuid.uuid4()),\n            role=message.role,\n            content=message.content,\n            tool_calls=message.tool_calls,\n            tool_call_id=message.tool_call_id,\n            audio=message.audio,\n            created_at=created_at or datetime.now(),\n            metadata=metadata,\n        )\n\n    def to_message(self) -> Message:\n        \"\"\"Convert to base Message type.\n\n        Returns:\n            Message without chat-specific fields.\n        \"\"\"\n        return Message(\n            role=self.role,\n            content=self.content,\n            tool_calls=self.tool_calls,\n            tool_call_id=self.tool_call_id,\n            audio=self.audio,\n        )\n\n    @field_serializer(\"created_at\")\n    def serialize_created_at(self, created_at: datetime) -> str:\n        \"\"\"Serialize created_at field to ISO format.\"\"\"\n        return created_at.isoformat()\n\n```\n\n\n",
        "eval_script": "import uuid\nfrom datetime import datetime\nfrom typing import Any, Literal, TypeAlias\nfrom pydantic import BaseModel, ConfigDict, field_serializer\n\n# Mock implementations for missing imports\nclass ChatCompletionAudioResponse:\n    pass\n\nclass ChatCompletionDeltaToolCall:\n    pass\n\n# Base model configuration for Swarm types\nclass SwarmBaseModel(BaseModel):\n    \"\"\"Base model configuration for Swarm types.\n\n    Enables arbitrary types, docstrings as descriptions, and strict field checking.\n    \"\"\"\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\n# Type aliases\nMessageRole: TypeAlias = Literal[\"assistant\", \"user\", \"system\", \"tool\", \"function\", \"developer\"]\nToolCall: TypeAlias = ChatCompletionDeltaToolCall\nAudioResponse: TypeAlias = ChatCompletionAudioResponse\n\n# Message class definition\nclass Message(SwarmBaseModel):\n    \"\"\"Message in a conversation between user, assistant, and tools.\n\n    Represents a message in a conversation between participants\n    with content and optional tool interactions. Each message has\n    a specific role and may include tool calls or responses.\n    \"\"\"\n    role: MessageRole\n    \"\"\"Role of the message author.\"\"\"\n\n    content: str | None = None\n    \"\"\"Text content of the message.\"\"\"\n\n    tool_calls: list[ToolCall] | None = None\n    \"\"\"Tool calls made in this message.\"\"\"\n\n    tool_call_id: str | None = None\n    \"\"\"ID of the tool call this message responds to.\"\"\"\n\n    audio: AudioResponse | None = None\n    \"\"\"Audio response data if available.\"\"\"\n\n# ChatMessage class definition\nclass ChatMessage(BaseModel):\n    \"\"\"Message type for chat applications with metadata support.\n\n    Extends the base Message type with fields for identification, timestamps,\n    and application-specific metadata. Maintains compatibility with base Message\n    while adding features needed for chat applications.\n    \"\"\"\n    id: str\n    \"\"\"Unique message identifier.\"\"\"\n\n    role: MessageRole\n    \"\"\"Role of the message author.\"\"\"\n\n    content: str | None = None\n    \"\"\"Text content of the message.\"\"\"\n\n    tool_calls: list[ToolCall] | None = None\n    \"\"\"Tool calls made in this message.\"\"\"\n\n    tool_call_id: str | None = None\n    \"\"\"ID of the tool call this message responds to.\"\"\"\n\n    audio: AudioResponse | None = None\n    \"\"\"Audio response data if available.\"\"\"\n\n    created_at: datetime = datetime.now()\n    \"\"\"Message creation timestamp.\"\"\"\n\n    metadata: dict[str, Any] | None = None\n    \"\"\"Application-specific message data.\"\"\"\n\n    model_config = ConfigDict(\n        arbitrary_types_allowed=True,\n        use_attribute_docstrings=True,\n        extra=\"forbid\",\n    )\n\n    @classmethod\n    def from_message(\n        cls,\n        message: Message,\n        *,\n        id: str | None = None,\n        created_at: datetime | None = None,\n        metadata: dict[str, Any] | None = None,\n    ) -> \"ChatMessage\":\n        \"\"\"Create a ChatMessage from a base Message.\n\n        Args:\n            message: Base Message to convert.\n            id: Optional message identifier.\n            created_at: Optional creation timestamp.\n            metadata: Optional message metadata.\n\n        Returns:\n            New ChatMessage with added fields.\n        \"\"\"\n        return cls(\n            id=id or str(uuid.uuid4()),\n            role=message.role,\n            content=message.content,\n            tool_calls=message.tool_calls,\n            tool_call_id=message.tool_call_id,\n            audio=message.audio,\n            created_at=created_at or datetime.now(),\n            metadata=metadata,\n        )\n\n\n    def to_message(self) -> Message:\n        \"\"\"Convert to base Message type.\n\n        Returns:\n            Message without chat-specific fields.\n        \"\"\"\n        return Message(\n            role=self.role,\n            content=self.content,\n            tool_calls=self.tool_calls,\n            tool_call_id=self.tool_call_id,\n            audio=self.audio,\n        )\n\n    @field_serializer(\"created_at\")\n    def serialize_created_at(self, created_at: datetime) -> str:\n        \"\"\"Serialize created_at field to ISO format.\"\"\"\n        return created_at.isoformat()\n\ndef test_from_message():\n    # Test case 1: Basic message with only role\n    message1 = Message(role=\"user\")\n    id1 = str(uuid.uuid4())\n    chat_message1 = ChatMessage.from_message(message1, id=id1)\n    chat_message1_new = ChatMessage.from_message_new_implementation(message1, id=id1)\n    assert chat_message1.id == chat_message1_new.id\n    assert chat_message1.role == chat_message1_new.role\n    assert chat_message1.content == chat_message1_new.content\n\n    # Test case 2: Message with content and tool_calls\n    message2 = Message(role=\"assistant\", content=\"Hello\", tool_calls=[ToolCall()])\n    id2 = str(uuid.uuid4())\n    chat_message2 = ChatMessage.from_message(message2, id=id2)\n    chat_message2_new = ChatMessage.from_message_new_implementation(message2, id=id2)\n    assert chat_message2.id == chat_message2_new.id\n    assert chat_message2.role == chat_message2_new.role\n    assert chat_message2.tool_calls == chat_message2_new.tool_calls\n\n    # Test case 3: Message with all fields\n    message3 = Message(role=\"system\", content=\"System message\", tool_calls=[ToolCall()], tool_call_id=\"123\", audio=AudioResponse())\n    id3 = str(uuid.uuid4())\n    chat_message3 = ChatMessage.from_message(message3, id=id3)\n    chat_message3_new = ChatMessage.from_message_new_implementation(message3, id=id3)\n    assert chat_message3.id == chat_message3_new.id\n    assert chat_message3.role == chat_message3_new.role\n    assert chat_message3.audio == chat_message3_new.audio\n\nif __name__ == \"__main__\":\n    test_from_message()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing both the original and revised functions, the functionality remains unchanged. Both functions are class methods of the `ChatMessage` class and are designed to create a `ChatMessage` instance from a `Message` instance. They both accept the same parameters: `message`, `id`, `created_at`, and `metadata`. The logic within the function is identical, as they both assign values to the `ChatMessage` fields using the same logic, including generating a new UUID if `id` is not provided and using the current datetime if `created_at` is not provided. The test cases provided also confirm that the functionality is preserved. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n1. **CONDITION 1**: The `from_message` function returns a `ChatMessage` object, which satisfies the condition of having return values.\n2. **CONDITION 2**: The test cases use assertions to compare attributes of the returned `ChatMessage` objects, ensuring they check return values or variable states rather than printed or logged content.\n3. **CONDITION 3**: The test cases compare the attributes of `ChatMessage` objects created by `from_message` and `from_message_new_implementation`. This ensures that `from_message_new_implementation` must have the same functionality as `from_message` to pass all tests.\n4. **CONDITION 4**: The test cases use reasonable assertions to compare the attributes of the `ChatMessage` objects. They do not make unreasonable assumptions or use invalid comparisons.\n5. **CONDITION 5**: The test cases cover various scenarios, including messages with different roles, content, tool calls, and other fields, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "e10aa9bc3d56371ec04cc9b02aa2d87a64a7a419"
    },
    {
        "func_name": "convert_to_dict",
        "idx": "484",
        "repo_name": "abhishekpaul11___4111-Project1-HomeLink",
        "func_path": "src/utils/dbcon.py",
        "orig_func": "def convert_to_dict(sql_result):\n    \"\"\"\n    Converts the results of a query to a JSON format.\n\n    Parameters:\n    results (ResultProxy): The result of a query.\n\n    Returns:\n    list: A list of dictionaries, where each dictionary represents a row in the query result.\n    \"\"\"\n    raw_results = sql_result.all()\n    return [dict(row._mapping) for row in raw_results]",
        "orig_context": "```python\n## src/utils/dbcon.py\ndef convert_to_dict(sql_result):\n    \"\"\"\n    Converts the results of a query to a JSON format.\n\n    Parameters:\n    results (ResultProxy): The result of a query.\n\n    Returns:\n    list: A list of dictionaries, where each dictionary represents a row in the query result.\n    \"\"\"\n    raw_results = sql_result.all()\n\n    # Convert each row to a dictionary\n    return [dict(row._mapping) for row in raw_results]\n\n```\n\n\n",
        "eval_script": "# Mock class to simulate the SQL result object\nclass MockRow:\n    def __init__(self, **kwargs):\n        self._mapping = kwargs\n\nclass MockResultProxy:\n    def __init__(self, *rows):\n        self.rows = rows\n\n    def all(self):\n        return self.rows\n\n# The original function\ndef convert_to_dict(sql_result):\n    \"\"\"\n    Converts the results of a query to a JSON format.\n\n    Parameters:\n    results (ResultProxy): The result of a query.\n\n    Returns:\n    list: A list of dictionaries, where each dictionary represents a row in the query result.\n    \"\"\"\n    raw_results = sql_result.all()\n\n    # Convert each row to a dictionary\n    return [dict(row._mapping) for row in raw_results]\n\n\ndef test_convert_to_dict():\n    # Test case 1: Multiple rows\n    rows = MockResultProxy(\n        MockRow(id=1, name='Alice'),\n        MockRow(id=2, name='Bob')\n    )\n    assert convert_to_dict(rows) == convert_to_dict_new_implementation(rows)\n\n    # Test case 2: Empty result\n    empty_rows = MockResultProxy()\n    assert convert_to_dict(empty_rows) == convert_to_dict_new_implementation(empty_rows)\n\n    # Test case 3: Rows with various data types\n    mixed_rows = MockResultProxy(\n        MockRow(id=1, name='Alice', age=30, active=True),\n        MockRow(id=2, name='Bob', age=None, active=False)\n    )\n    assert convert_to_dict(mixed_rows) == convert_to_dict_new_implementation(mixed_rows)\n\n    # Test case 4: Single row\n    single_row = MockResultProxy(\n        MockRow(id=1, name='Charlie')\n    )\n    assert convert_to_dict(single_row) == convert_to_dict_new_implementation(single_row)\n\n    # Test case 5: Special characters\n    special_characters = MockResultProxy(\n        MockRow(id=1, name='@lice!', description='Hello, World!')\n    )\n    assert convert_to_dict(special_characters) == convert_to_dict_new_implementation(special_characters)\n\n    # Test case 6: Nested structures\n    nested_structures = MockResultProxy(\n        MockRow(id=1, data={'key': 'value'})\n    )\n    assert convert_to_dict(nested_structures) == convert_to_dict_new_implementation(nested_structures)\n\n    # Test case 7: Large dataset\n    large_dataset = MockResultProxy(\n        *[MockRow(id=i, name=f'Name{i}') for i in range(1000)]\n    )\n    assert convert_to_dict(large_dataset) == convert_to_dict_new_implementation(large_dataset)\n\n    # Test case 8: Unicode characters\n    unicode_characters = MockResultProxy(\n        MockRow(id=1, name='Jos\u00e9', city='M\u00fcnchen')\n    )\n    assert convert_to_dict(unicode_characters) == convert_to_dict_new_implementation(unicode_characters)\n\n# Main function\nif __name__ == \"__main__\":\n    test_convert_to_dict()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions take a SQL result object, retrieve all rows using the `all()` method, and convert each row's `_mapping` attribute to a dictionary. The additional code in the REVISED FUNCTION is for testing purposes and does not alter the functionality of the `convert_to_dict` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `convert_to_dict` returns a list of dictionaries, which satisfies the condition of having return values.\n\n- CONDITION 2: The test cases use assertions to check the return values of `convert_to_dict` and `convert_to_dict_new_implementation`, not printed or logged contents.\n\n- CONDITION 3: The test cases compare the outputs of `convert_to_dict` and `convert_to_dict_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass all tests.\n\n- CONDITION 4: The test cases use assertions to compare the return values of both implementations, which is appropriate given that `convert_to_dict` returns a list of dictionaries.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including multiple rows, empty results, various data types, special characters, nested structures, large datasets, and unicode characters. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "99f30c8eebcc7ad64efbfa03ec984ea08f814164"
    },
    {
        "func_name": "family_feat_eng",
        "idx": "486",
        "repo_name": "15jkw205___titanic_machine_learning",
        "func_path": "models/features.py",
        "orig_func": "def family_feat_eng(data):\n    \"\"\"Add family-related features to the dataset: Family_Size and Family_category.\"\"\"\n    data['Family_Size'] = data['Parch'] + data['SibSp']\n\n    def categorize_family_size(size):\n        if size == 0:\n            return 'no family'\n        elif size <= 3:\n            return 'small family'\n        else:\n            return 'large family'\n    data['Family_category'] = data['Family_Size'].apply(categorize_family_size)\n    family_encoder = LabelEncoder()\n    data['Family_category'] = family_encoder.fit_transform(data['Family_category'])\n    return data",
        "orig_context": "```python\n## models/features.py\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n\ndef family_feat_eng(data):\n    \"\"\"Add family-related features to the dataset: Family_Size and Family_category.\"\"\"\n\n    # combine parch and sibsp into 'Family_size'\n    data[\"Family_Size\"] = data[\"Parch\"] + data[\"SibSp\"]\n\n    # categorize 'Family_size' into 'family_category'\n    def categorize_family_size(size):\n        if size == 0:\n            return \"no family\"\n        elif size <= 3:\n            return \"small family\"\n        else:\n            return \"large family\"\n\n    data[\"Family_category\"] = data[\"Family_Size\"].apply(categorize_family_size)\n\n    # encode family_category with numbers\n    family_encoder = LabelEncoder()\n    data[\"Family_category\"] = family_encoder.fit_transform(data[\"Family_category\"])\n\n    return data\n\n```\n\n\n",
        "eval_script": "import pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\n\ndef family_feat_eng(data):\n    \"\"\"Add family-related features to the dataset: Family_Size and Family_category.\"\"\"\n\n    # combine parch and sibsp into 'Family_size'\n    data[\"Family_Size\"] = data[\"Parch\"] + data[\"SibSp\"]\n\n    # categorize 'Family_size' into 'family_category'\n    def categorize_family_size(size):\n        if size == 0:\n            return \"no family\"\n        elif size <= 3:\n            return \"small family\"\n        else:\n            return \"large family\"\n\n    data[\"Family_category\"] = data[\"Family_Size\"].apply(categorize_family_size)\n\n    # encode family_category with numbers\n    family_encoder = LabelEncoder()\n    data[\"Family_category\"] = family_encoder.fit_transform(data[\"Family_category\"])\n\n    return data\n\n\ndef test_family_feat_eng():\n    # Test case 1: No family\n    data_no_family = pd.DataFrame({'Parch': [0], 'SibSp': [0]})\n    result_original = family_feat_eng(data_no_family.copy())\n    result_new = family_feat_eng_new_implementation(data_no_family.copy())\n    assert result_original.equals(result_new), \"Test case 1 failed\"\n\n    # Test case 2: Small family\n    data_small_family = pd.DataFrame({'Parch': [1, 2], 'SibSp': [1, 0]})\n    result_original = family_feat_eng(data_small_family.copy())\n    result_new = family_feat_eng_new_implementation(data_small_family.copy())\n    assert result_original.equals(result_new), \"Test case 2 failed\"\n\n    # Test case 3: Large family\n    data_large_family = pd.DataFrame({'Parch': [3], 'SibSp': [2]})\n    result_original = family_feat_eng(data_large_family.copy())\n    result_new = family_feat_eng_new_implementation(data_large_family.copy())\n    assert result_original.equals(result_new), \"Test case 3 failed\"\n\n    # Test case 4: Empty DataFrame\n    data_empty = pd.DataFrame({'Parch': [], 'SibSp': []})\n    result_original = family_feat_eng(data_empty.copy())\n    result_new = family_feat_eng_new_implementation(data_empty.copy())\n    assert result_original.equals(result_new), \"Test case 4 failed\"\n\n    # Test case 5: Single Row with Maximum Values\n    data_max_values = pd.DataFrame({'Parch': [100], 'SibSp': [100]})\n    result_original = family_feat_eng(data_max_values.copy())\n    result_new = family_feat_eng_new_implementation(data_max_values.copy())\n    assert result_original.equals(result_new), \"Test case 5 failed\"\n\n    # Test case 6: Multiple Rows with Mixed Family Sizes\n    data_mixed_families = pd.DataFrame({'Parch': [0, 1, 4], 'SibSp': [0, 2, 3]})\n    result_original = family_feat_eng(data_mixed_families.copy())\n    result_new = family_feat_eng_new_implementation(data_mixed_families.copy())\n    assert result_original.equals(result_new), \"Test case 6 failed\"\n\n    # Test case 7: Negative Values\n    data_negative_values = pd.DataFrame({'Parch': [-1, -2], 'SibSp': [-1, -2]})\n    result_original = family_feat_eng(data_negative_values.copy())\n    result_new = family_feat_eng_new_implementation(data_negative_values.copy())\n    assert result_original.equals(result_new), \"Test case 7 failed\"\n\n    # Test case 8: Non-integer Values\n    data_float_values = pd.DataFrame({'Parch': [0.5, 1.5], 'SibSp': [0.5, 1.5]})\n    result_original = family_feat_eng(data_float_values.copy())\n    result_new = family_feat_eng_new_implementation(data_float_values.copy())\n    assert result_original.equals(result_new), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test_family_feat_eng()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      12      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 12      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of logic and functionality. Both functions add a 'Family_Size' column by summing 'Parch' and 'SibSp', categorize this size into 'Family_category', and then encode this category using LabelEncoder. The test cases provided in the revised code further confirm that the functionality remains consistent across various scenarios. The only differences are in comments and formatting, which do not affect functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `family_feat_eng` modifies the input DataFrame by adding new columns and returns the modified DataFrame. This satisfies the condition as it has return values.\n- CONDITION 2: The test cases use the `equals` method to compare DataFrames, which checks the state of the DataFrames rather than any printed or logged content. This satisfies the condition.\n- CONDITION 3: The test cases compare the results of `family_feat_eng` and `family_feat_eng_new_implementation` using the `equals` method on DataFrames, ensuring that they must have exactly the same functionality to pass. This satisfies the condition.\n- CONDITION 4: The test cases use `assert` statements to compare the output DataFrames, which is reasonable given that `family_feat_eng` returns a DataFrame. This satisfies the condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including no family, small family, large family, empty DataFrame, maximum values, mixed family sizes, negative values, and non-integer values. These are non-trivial and comprehensive, satisfying the condition.",
            "answer": "yes"
        },
        "commit_id": "97fffa4144046f9b8d4b75e2ba646b1b085bcdfe"
    },
    {
        "func_name": "age_feat_eng",
        "idx": "487",
        "repo_name": "15jkw205___titanic_machine_learning",
        "func_path": "models/features.py",
        "orig_func": "def age_feat_eng(data):\n    \"\"\"Add age-related categories to the dataset: Age_band.\"\"\"\n    bins = [-float('inf'), 2, 4, 12, 18, 30, 45, 60, float('inf')]\n    labels = ['baby', 'infant', 'child', 'teenager', 'youngadult', 'adult', 'oldadult', 'elder']\n    data['Age_band'] = pd.cut(data['Age'], bins=bins, labels=labels)\n    age_band_encoder = LabelEncoder()\n    data['Age_band'] = age_band_encoder.fit_transform(data['Age_band'])\n    return data",
        "orig_context": "```python\n## models/features.py\nimport pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n\ndef age_feat_eng(data):\n    \"\"\"Add age-related categories to the dataset: Age_band.\"\"\"\n\n    # define bins for age categories and corresponding labels\n    bins = [-float(\"inf\"), 2, 4, 12, 18, 30, 45, 60, float(\"inf\")]\n    labels = [\n        \"baby\",\n        \"infant\",\n        \"child\",\n        \"teenager\",\n        \"youngadult\",\n        \"adult\",\n        \"oldadult\",\n        \"elder\",\n    ]\n\n    # Cut the 'Age' column into bins and add 'Age_band'\n    data[\"Age_band\"] = pd.cut(data[\"Age\"], bins=bins, labels=labels)\n\n    # encode Age_band with numbers\n    age_band_encoder = LabelEncoder()\n    data[\"Age_band\"] = age_band_encoder.fit_transform(data[\"Age_band\"])\n\n    return data\n\n```\n\n\n",
        "eval_script": "## models/features.py\nimport pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n\ndef age_feat_eng(data):\n    \"\"\"Add age-related categories to the dataset: Age_band.\"\"\"\n\n    # define bins for age categories and corresponding labels\n    bins = [-float(\"inf\"), 2, 4, 12, 18, 30, 45, 60, float(\"inf\")]\n    labels = [\n        \"baby\",\n        \"infant\",\n        \"child\",\n        \"teenager\",\n        \"youngadult\",\n        \"adult\",\n        \"oldadult\",\n        \"elder\",\n    ]\n\n    # Cut the 'Age' column into bins and add 'Age_band'\n    data[\"Age_band\"] = pd.cut(data[\"Age\"], bins=bins, labels=labels)\n\n    # encode Age_band with numbers\n    age_band_encoder = LabelEncoder()\n    data[\"Age_band\"] = age_band_encoder.fit_transform(data[\"Age_band\"])\n\n    return data\n\n\ndef test_age_feat_eng():\n    # Test case 1: Check for a range of ages\n    data1 = pd.DataFrame({\"Age\": [1, 3, 10, 17, 25, 40, 55, 70]})\n    result1_old = age_feat_eng(data1.copy())\n    result1_new = age_feat_eng_new_implementation(data1.copy())\n    assert result1_old.equals(result1_new), \"Test case 1 failed\"\n\n    # Test case 2: Check for edge cases at the boundaries\n    data2 = pd.DataFrame({\"Age\": [2, 4, 12, 18, 30, 45, 60]})\n    result2_old = age_feat_eng(data2.copy())\n    result2_new = age_feat_eng_new_implementation(data2.copy())\n    assert result2_old.equals(result2_new), \"Test case 2 failed\"\n\n    # Test case 3: Check for negative and very high ages\n    data3 = pd.DataFrame({\"Age\": [-1, 100]})\n    result3_old = age_feat_eng(data3.copy())\n    result3_new = age_feat_eng_new_implementation(data3.copy())\n    assert result3_old.equals(result3_new), \"Test case 3 failed\"\n\n    # Test case 4: Check for an empty DataFrame\n    data4 = pd.DataFrame({\"Age\": []})\n    result4_old = age_feat_eng(data4.copy())\n    result4_new = age_feat_eng_new_implementation(data4.copy())\n    assert result4_old.equals(result4_new), \"Test case 4 failed\"\n\n    # Test case 5: Check for a single age group\n    data5 = pd.DataFrame({\"Age\": [5, 5, 5, 5]})\n    result5_old = age_feat_eng(data5.copy())\n    result5_new = age_feat_eng_new_implementation(data5.copy())\n    assert result5_old.equals(result5_new), \"Test case 5 failed\"\n\n    # Test case 6: Check for missing age values\n    data6 = pd.DataFrame({\"Age\": [10, None, 20]})\n    result6_old = age_feat_eng(data6.copy())\n    result6_new = age_feat_eng_new_implementation(data6.copy())\n    assert result6_old.equals(result6_new), \"Test case 6 failed\"\n\n    # Test case 7: Check for non-integer ages\n    data7 = pd.DataFrame({\"Age\": [1.5, 3.7, 10.1, 17.9]})\n    result7_old = age_feat_eng(data7.copy())\n    result7_new = age_feat_eng_new_implementation(data7.copy())\n    assert result7_old.equals(result7_new), \"Test case 7 failed\"\n\n    # Test case 8: Check for a large dataset\n    data8 = pd.DataFrame({\"Age\": list(range(1000))})\n    result8_old = age_feat_eng(data8.copy())\n    result8_new = age_feat_eng_new_implementation(data8.copy())\n    assert result8_old.equals(result8_new), \"Test case 8 failed\"\n\n    # Test case 9: Check for non-numeric data\n    try:\n        data9 = pd.DataFrame({\"Age\": [\"a\", \"b\", \"c\"]})\n        age_feat_eng(data9.copy())\n        assert False, \"Test case 9 failed - should have raised an error\"\n    except Exception:\n        pass\n\n    try:\n        data9 = pd.DataFrame({\"Age\": [\"a\", \"b\", \"c\"]})\n        age_feat_eng_new_implementation(data9.copy())\n        assert False, \"Test case 9 failed - should have raised an error\"\n    except Exception:\n        pass\n\nif __name__ == \"__main__\":\n    test_age_feat_eng()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they define the same bins and labels for age categories, use `pd.cut` to categorize ages into these bins, and then use `LabelEncoder` to encode these categories into numerical values. The additional code in the REVISED FUNCTION is for testing purposes and does not alter the functionality of the `age_feat_eng` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `age_feat_eng` function modifies the input DataFrame by adding a new column, `Age_band`, and returns the modified DataFrame. This satisfies the condition as it either returns values or modifies input arguments.\n- CONDITION 2: The test cases use assertions to check the equality of DataFrames returned by `age_feat_eng` and `age_feat_eng_new_implementation`, which means they check return values or variable states, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `age_feat_eng` and `age_feat_eng_new_implementation` for various inputs, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- CONDITION 4: The test cases use `assert result_old.equals(result_new)` to compare DataFrames, which is appropriate for checking DataFrame equality. They do not use inappropriate assertions like comparing function calls directly.\n- CONDITION 5: The test cases cover a wide range of scenarios, including typical cases, edge cases, negative and high values, empty DataFrames, single age groups, missing values, non-integer ages, large datasets, and non-numeric data. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "97fffa4144046f9b8d4b75e2ba646b1b085bcdfe"
    },
    {
        "func_name": "preprocess_feat",
        "idx": "490",
        "repo_name": "15jkw205___titanic_machine_learning",
        "func_path": "models/features.py",
        "orig_func": "def preprocess_feat(X, fit=False):\n    \"\"\"Apply the preprocessor to the dataset.\n    Parameters:\n    - X: Feature dataset to process\n    - fit: If True, fit and transform (use for training data)\n            If False, just transform (use for test/validation data)\n    \"\"\"\n    if fit:\n        X_transformed = preprocessor.fit_transform(X)\n    else:\n        X_transformed = preprocessor.transform(X)\n    return X_transformed",
        "orig_context": "```python\n## models/features.py\nfrom sklearn.impute import SimpleImputer\n\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n\nfrom sklearn.compose import ColumnTransformer\n\nfrom sklearn.pipeline import Pipeline\n\npreprocessor = ColumnTransformer(\n    transformers=[\n        # Pipeline for numerical features\n        (\n            \"num\",\n            Pipeline(\n                steps=[\n                    (\n                        \"imputer\",\n                        SimpleImputer(strategy=\"median\"),\n                    ),  # Impute missing values for numerical features\n                    (\"scaler\", StandardScaler()),\n                ]\n            ),\n            [\n                \"Fare\",\n            ],\n        ),\n        # Pipeline for categorical features\n        (\n            \"cat\",\n            Pipeline(\n                steps=[\n                    (\n                        \"imputer\",\n                        SimpleImputer(strategy=\"most_frequent\"),\n                    ),  # Impute missing values for categorical features\n                    (\n                        \"onehot\",\n                        OneHotEncoder(sparse_output=False, handle_unknown=\"ignore\"),\n                    ),\n                ]\n            ),\n            [\"Pclass\", \"Age_band\", \"Family_category\", \"Sex\", \"Embarked\"],\n        ),\n    ],\n    remainder=\"passthrough\",\n)\n\ndef preprocess_feat(X, fit=False):\n    \"\"\"Apply the preprocessor to the dataset.\n    Parameters:\n    - X: Feature dataset to process\n    - fit: If True, fit and transform (use for training data)\n            If False, just transform (use for test/validation data)\n    \"\"\"\n\n    if fit:\n        X_transformed = preprocessor.fit_transform(X)\n    else:\n        X_transformed = preprocessor.transform(X)\n\n    return X_transformed\n\n```\n\n\n",
        "eval_script": "## models/features.py\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nimport pandas as pd\nimport numpy as np\n\n# Define the preprocessor\npreprocessor = ColumnTransformer(\n    transformers=[\n        # Pipeline for numerical features\n        (\n            \"num\",\n            Pipeline(\n                steps=[\n                    (\n                        \"imputer\",\n                        SimpleImputer(strategy=\"median\"),\n                    ),  # Impute missing values for numerical features\n                    (\"scaler\", StandardScaler()),\n                ]\n            ),\n            [\n                \"Fare\",\n            ],\n        ),\n        # Pipeline for categorical features\n        (\n            \"cat\",\n            Pipeline(\n                steps=[\n                    (\n                        \"imputer\",\n                        SimpleImputer(strategy=\"most_frequent\"),\n                    ),  # Impute missing values for categorical features\n                    (\n                        \"onehot\",\n                        OneHotEncoder(sparse_output=False, handle_unknown=\"ignore\"),\n                    ),\n                ]\n            ),\n            [\"Pclass\", \"Age_band\", \"Family_category\", \"Sex\", \"Embarked\"],\n        ),\n    ],\n    remainder=\"passthrough\",\n)\n\ndef preprocess_feat(X, fit=False):\n    \"\"\"Apply the preprocessor to the dataset.\n    Parameters:\n    - X: Feature dataset to process\n    - fit: If True, fit and transform (use for training data)\n            If False, just transform (use for test/validation data)\n    \"\"\"\n\n    if fit:\n        X_transformed = preprocessor.fit_transform(X)\n    else:\n        X_transformed = preprocessor.transform(X)\n\n    return X_transformed\n\n\ndef test_preprocess_feat():\n    # Test case 1: Fit and transform on a sample dataset\n    data = pd.DataFrame({\n        \"Fare\": [7.25, 71.83, np.nan],\n        \"Pclass\": [3, 1, 3],\n        \"Age_band\": [\"Adult\", \"Senior\", \"Adult\"],\n        \"Family_category\": [\"Single\", \"Small\", \"Large\"],\n        \"Sex\": [\"male\", \"female\", \"female\"],\n        \"Embarked\": [\"S\", \"C\", \"Q\"]\n    })\n    result_old = preprocess_feat(data, fit=True)\n    result_new = preprocess_feat_new_implementation(data, fit=True)\n    assert np.allclose(result_old, result_new), \"Test case 1 failed\"\n\n    # Test case 2: Transform on a sample dataset without fitting\n    data2 = pd.DataFrame({\n        \"Fare\": [8.05, 53.10],\n        \"Pclass\": [3, 1],\n        \"Age_band\": [\"Adult\", \"Senior\"],\n        \"Family_category\": [\"Single\", \"Small\"],\n        \"Sex\": [\"female\", \"male\"],\n        \"Embarked\": [\"Q\", \"S\"]\n    })\n    result_old2 = preprocess_feat(data2, fit=False)\n    result_new2 = preprocess_feat_new_implementation(data2, fit=False)\n    assert np.allclose(result_old2, result_new2), \"Test case 2 failed\"\n\n    # Test case 3: Check handling of missing values\n    data3 = pd.DataFrame({\n        \"Fare\": [np.nan, 8.05],\n        \"Pclass\": [3, np.nan],\n        \"Age_band\": [np.nan, \"Senior\"],\n        \"Family_category\": [\"Single\", np.nan],\n        \"Sex\": [\"female\", \"male\"],\n        \"Embarked\": [\"Q\", np.nan]\n    })\n    result_old3 = preprocess_feat(data3, fit=True)\n    result_new3 = preprocess_feat_new_implementation(data3, fit=True)\n    assert np.allclose(result_old3, result_new3), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_preprocess_feat()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `preprocess_feat` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions take a dataset `X` and a boolean `fit` as parameters. If `fit` is `True`, they apply `fit_transform` using a preprocessor; otherwise, they apply `transform`. The additional code in the revised version, such as the definition of the `preprocessor` and the test cases, does not alter the functionality of the `preprocess_feat` function itself. The test cases are used to verify that the function behaves as expected, but they do not change the function's logic or behavior.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `preprocess_feat` function returns a transformed dataset (`X_transformed`), satisfying this condition.\n- [CONDITION 2] The test cases use assertions to compare the return values of `preprocess_feat` and `preprocess_feat_new_implementation`, not printed or logged contents, satisfying this condition.\n- [CONDITION 3] The test cases check both fitting and transforming, as well as handling of missing values, which are core functionalities of the `preprocess_feat` function. If `preprocess_feat_new_implementation` passes these tests, it would have the same functionality as `preprocess_feat`, satisfying this condition.\n- [CONDITION 4] The test cases use `np.allclose` to compare the results, which is appropriate for numerical data and ensures that the results are reasonably close. The assertions are reasonable given the function's purpose, satisfying this condition.\n- [CONDITION 5] The test cases cover fitting and transforming, transforming without fitting, and handling missing values, which are non-trivial aspects of the preprocessing pipeline, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "97fffa4144046f9b8d4b75e2ba646b1b085bcdfe"
    },
    {
        "func_name": "load_config",
        "idx": "492",
        "repo_name": "William-Gardner-Biotech___SRA_Dispatch",
        "func_path": "sra_dispatch/read_config.py",
        "orig_func": "def load_config(json_file) -> dict:\n    \"\"\"\n    Reads a JSON config file and returns a dictionary object.\n    \"\"\"\n    with open(json_file) as file:\n        config = json.load(file)\n    return config",
        "orig_context": "```python\n## sra_dispatch/read_config.py\nimport json\n\ndef load_config(json_file)-> dict:\n    \"\"\"\n    Reads a JSON config file and returns a dictionary object.\n    \"\"\"\n\n    with open(json_file) as file:\n        config = json.load(file)\n    return config\n\n```\n\n\n",
        "eval_script": "import json\nimport os\n\ndef load_config(json_file) -> dict:\n    \"\"\"\n    Reads a JSON config file and returns a dictionary object.\n    \"\"\"\n    with open(json_file) as file:\n        config = json.load(file)\n    return config\n\n\ndef test_load_config():\n    # Create a mock JSON file in the specified directory\n    mock_json_content = {\n        \"key1\": \"value1\",\n        \"key2\": \"value2\",\n        \"key3\": \"value3\"\n    }\n\n    # Ensure the directory exists\n    os.makedirs('/home/user/tmp', exist_ok=True)\n\n    # Define the path for the mock JSON file\n    mock_json_file_path = '/home/user/tmp/mock_config.json'\n\n    # Write the mock JSON content to the file\n    with open(mock_json_file_path, 'w') as mock_file:\n        json.dump(mock_json_content, mock_file)\n\n    # Test both implementations\n    config_old = load_config(mock_json_file_path)\n    config_new = load_config_new_implementation(mock_json_file_path)\n\n    # Assert that both configurations are equal\n    assert config_old == config_new, \"Configurations do not match\"\n\n    # Additional assertions to cover major branches\n    assert config_old[\"key1\"] == \"value1\", \"Key1 does not match expected value\"\n    assert config_old[\"key2\"] == \"value2\", \"Key2 does not match expected value\"\n    assert config_old[\"key3\"] == \"value3\", \"Key3 does not match expected value\"\n\n    # Test with an empty JSON file\n    empty_json_file_path = '/home/user/tmp/empty_config.json'\n    with open(empty_json_file_path, 'w') as empty_file:\n        empty_file.write('{}')\n    config_empty_old = load_config(empty_json_file_path)\n    config_empty_new = load_config_new_implementation(empty_json_file_path)\n    assert config_empty_old == config_empty_new == {}, \"Empty JSON did not return an empty dictionary\"\n\n    # Test with a malformed JSON file\n    malformed_json_file_path = '/home/user/tmp/malformed_config.json'\n    with open(malformed_json_file_path, 'w') as malformed_file:\n        malformed_file.write('{key1: value1, key2: value2}')\n    try:\n        load_config(malformed_json_file_path)\n        assert False, \"Malformed JSON did not raise an error\"\n    except json.JSONDecodeError:\n        pass\n    try:\n        load_config_new_implementation(malformed_json_file_path)\n        assert False, \"Malformed JSON did not raise an error\"\n    except json.JSONDecodeError:\n        pass\n\n    # Test with a non-existent file\n    non_existent_file_path = '/home/user/tmp/non_existent_config.json'\n    try:\n        load_config(non_existent_file_path)\n        assert False, \"Non-existent file did not raise an error\"\n    except FileNotFoundError:\n        pass\n    try:\n        load_config_new_implementation(non_existent_file_path)\n        assert False, \"Non-existent file did not raise an error\"\n    except FileNotFoundError:\n        pass\n\n    # Test with nested JSON objects\n    nested_json_content = {\n        \"key1\": {\n            \"subkey1\": \"subvalue1\"\n        },\n        \"key2\": [1, 2, 3],\n        \"key3\": True\n    }\n    nested_json_file_path = '/home/user/tmp/nested_config.json'\n    with open(nested_json_file_path, 'w') as nested_file:\n        json.dump(nested_json_content, nested_file)\n    config_nested_old = load_config(nested_json_file_path)\n    config_nested_new = load_config_new_implementation(nested_json_file_path)\n    assert config_nested_old == config_nested_new, \"Nested JSON configurations do not match\"\n    assert config_nested_old[\"key1\"][\"subkey1\"] == \"subvalue1\", \"Nested key1.subkey1 does not match expected value\"\n    assert config_nested_old[\"key2\"] == [1, 2, 3], \"Nested key2 does not match expected value\"\n    assert config_nested_old[\"key3\"] is True, \"Nested key3 does not match expected value\"\n\nif __name__ == \"__main__\":\n    test_load_config()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `load_config` is identical to the ORIGINAL FUNCTION in terms of its implementation. Both functions read a JSON file and return its contents as a dictionary. The additional code provided is a test suite that tests the functionality of `load_config` and another function `load_config_new_implementation`, which is not defined in the provided code. The test suite does not alter the functionality of the `load_config` function itself. Since the `load_config` function in the revised code is exactly the same as the original, the functionality remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `load_config` function returns a dictionary object, satisfying the requirement of having return values or modifying global variables/input arguments.\n\n2. **CONDITION 2**: The test cases check the return values of the `load_config` function and do not rely on printed or logged content. Assertions are made on the returned dictionary and exceptions, not on any printed output.\n\n3. **CONDITION 3**: The test cases compare the outputs of `load_config` and `load_config_new_implementation` directly, ensuring that both implementations must have exactly the same functionality to pass. The test cases cover various scenarios, including normal JSON, empty JSON, malformed JSON, non-existent files, and nested JSON objects.\n\n4. **CONDITION 4**: The test cases and assert statements are reasonable. They check for equality of the returned dictionaries and handle exceptions appropriately. The assertions are based on the expected behavior of the function, such as returning a dictionary or raising specific exceptions.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover a range of scenarios, including normal, empty, malformed, non-existent, and nested JSON files. This ensures comprehensive testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "8aa4d67d931a188a1fc6756c40f8997173d4c7e7"
    },
    {
        "func_name": "get_nt_indels",
        "idx": "496",
        "repo_name": "William-Gardner-Biotech___SRA_Dispatch",
        "func_path": "assets/static_files/SAM_Refiner.py",
        "orig_func": "def get_nt_indels(col_reads):\n    \"\"\"\n    Called to collect indels if amino acid reporting is disabled\n    Parameters:\n    col_reads - dict of unique variant sequences\n    Functionality: goes through unique sequences looking for indels and adds them to a new dict\n    Returns the new indel dict\n    \"\"\"\n    indel_dict = {}\n    for SNP_sequence in col_reads:\n        if 'del' in SNP_sequence or 'insert' in SNP_sequence:\n            for mut in SNP_sequence.split(' '):\n                if 'del' in mut or 'ins' in mut:\n                    try:\n                        indel_dict[mut] += sum(col_reads[SNP_sequence].values())\n                    except:\n                        indel_dict[mut] = sum(col_reads[SNP_sequence].values())\n    return indel_dict",
        "orig_context": "```python\n## assets/static_files/SAM_Refiner.py\ndef get_nt_indels(col_reads):\n    \"\"\"\n    Called to collect indels if amino acid reporting is disabled\n    Parameters:\n    col_reads - dict of unique variant sequences\n    Functionality: goes through unique sequences looking for indels and adds them to a new dict\n    Returns the new indel dict\n    \"\"\"\n    indel_dict = {}\n    for SNP_sequence in col_reads:\n        if \"del\" in SNP_sequence or \"insert\" in SNP_sequence:\n            for mut in SNP_sequence.split(\" \"):\n                if \"del\" in mut or \"ins\" in mut:\n                    try:\n                        indel_dict[mut] += sum(col_reads[SNP_sequence].values())\n                    except:\n                        indel_dict[mut] = sum(col_reads[SNP_sequence].values())\n    return(indel_dict)\n\n```\n\n\n",
        "eval_script": "## assets/static_files/SAM_Refiner.py\ndef get_nt_indels(col_reads):\n    \"\"\"\n    Called to collect indels if amino acid reporting is disabled\n    Parameters:\n    col_reads - dict of unique variant sequences\n    Functionality: goes through unique sequences looking for indels and adds them to a new dict\n    Returns the new indel dict\n    \"\"\"\n    indel_dict = {}\n    for SNP_sequence in col_reads:\n        if \"del\" in SNP_sequence or \"insert\" in SNP_sequence:\n            for mut in SNP_sequence.split(\" \"):\n                if \"del\" in mut or \"ins\" in mut:\n                    try:\n                        indel_dict[mut] += sum(col_reads[SNP_sequence].values())\n                    except:\n                        indel_dict[mut] = sum(col_reads[SNP_sequence].values())\n    return(indel_dict)\n\n\ndef test_get_nt_indels():\n    # Test case 1: Simple case with one deletion\n    col_reads_1 = {\n        \"A del1\": {\"read1\": 1, \"read2\": 2},\n        \"B\": {\"read3\": 3}\n    }\n    assert get_nt_indels(col_reads_1) == get_nt_indels_new_implementation(col_reads_1)\n\n    # Test case 2: Case with insertion and deletion\n    col_reads_2 = {\n        \"A del1 ins2\": {\"read1\": 1},\n        \"B insert3\": {\"read2\": 2}\n    }\n    assert get_nt_indels(col_reads_2) == get_nt_indels_new_implementation(col_reads_2)\n\n    # Test case 3: No indels present\n    col_reads_3 = {\n        \"A\": {\"read1\": 1},\n        \"B\": {\"read2\": 2}\n    }\n    assert get_nt_indels(col_reads_3) == get_nt_indels_new_implementation(col_reads_3)\n\n    # Test case 4: Empty input\n    col_reads_4 = {}\n    assert get_nt_indels(col_reads_4) == get_nt_indels_new_implementation(col_reads_4)\n\n    # Test case 5: Multiple indels in one sequence\n    col_reads_5 = {\n        \"A del1 del2\": {\"read1\": 1, \"read2\": 2},\n        \"B ins3 ins4\": {\"read3\": 3}\n    }\n    assert get_nt_indels(col_reads_5) == get_nt_indels_new_implementation(col_reads_5)\n\n    # Test case 6: Non-standard indel descriptions\n    col_reads_6 = {\n        \"A deletion1\": {\"read1\": 1},\n        \"B insertion2\": {\"read2\": 2}\n    }\n    assert get_nt_indels(col_reads_6) == get_nt_indels_new_implementation(col_reads_6)\n\n    # Test case 7: Large numbers of reads\n    col_reads_7 = {\n        \"A del1\": {\"read1\": 1000, \"read2\": 2000},\n        \"B ins2\": {\"read3\": 3000}\n    }\n    assert get_nt_indels(col_reads_7) == get_nt_indels_new_implementation(col_reads_7)\n\n    # Test case 8: Mixed case indel descriptions\n    col_reads_8 = {\n        \"A Del1\": {\"read1\": 1},\n        \"B INS2\": {\"read2\": 2}\n    }\n    assert get_nt_indels(col_reads_8) == get_nt_indels_new_implementation(col_reads_8)\n\n    # Test case 9: Indels at different positions\n    col_reads_9 = {\n        \"del1 A\": {\"read1\": 1},\n        \"B ins2\": {\"read2\": 2}\n    }\n    assert get_nt_indels(col_reads_9) == get_nt_indels_new_implementation(col_reads_9)\n\nif __name__ == \"__main__\":\n    test_get_nt_indels()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      8      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      8      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is essentially the same as the ORIGINAL FUNCTION with only minor differences in formatting and syntax that do not affect functionality. The changes include the use of double quotes instead of single quotes for string literals and the addition of parentheses around the return statement, which is optional in Python and does not change the behavior of the function. The logic and flow of the function remain unchanged, as it still iterates over the sequences, checks for 'del' or 'insert', splits the sequences, and sums the values in the dictionary. Therefore, the functionality of both functions is the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `get_nt_indels` returns a dictionary (`indel_dict`), which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `get_nt_indels` and `get_nt_indels_new_implementation`, which means they are checking return values or variable states, not printed or logged contents.\n\n3. **CONDITION 3**: The test cases are designed to compare the outputs of `get_nt_indels` and `get_nt_indels_new_implementation` for the same inputs. This ensures that `get_nt_indels_new_implementation` can pass all the test cases if and only if it has the exact same functionality as `get_nt_indels`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the outputs of the two functions. Since `get_nt_indels` returns a dictionary, using assertions to compare the return values is reasonable.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including simple cases, cases with multiple indels, cases with no indels, empty input, non-standard indel descriptions, large numbers of reads, mixed case descriptions, and indels at different positions. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "8aa4d67d931a188a1fc6756c40f8997173d4c7e7"
    },
    {
        "func_name": "MyKNNReg.fit",
        "idx": "499",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "knnreg.py",
        "orig_func": "def fit(self, X, y):\n    \"\"\"\n        \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438.\n\n        Args:\n            X (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n            y (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n        \"\"\"\n    self.X_train = X.copy()\n    self.y_train = y.copy()",
        "orig_context": "```python\n## knnreg.py\nimport numpy as np\n\nimport pandas as pd\n\nX = pd.DataFrame(X)\n\ny = pd.Series(y)\n\nX.columns = [f'col_{col}' for col in X.columns]\n\nclass MyKNNReg:\n    \"\"\"\n    \u041a\u043b\u0430\u0441\u0441 \u0434\u043b\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 k-\u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439 \u0434\u043b\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438.\n\n    Attributes:\n        k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n        metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n        weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        X_train (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n        y_train (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n    \"\"\"\n\n    def __init__(self, k=3, metric='euclidean', weight='uniform'):\n        \"\"\"\n        \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043a\u043b\u0430\u0441\u0441\u0430.\n\n        Args:\n            k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n            metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n            weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        self.k = k\n        self.metric = metric\n        self.weight = weight\n        self.X_train = None\n        self.y_train = None\n\n    def __repr__(self):\n        return f'MyKNNReg class: k={self.k}'\n\n    def fit(self, X, y):\n        \"\"\"\n        \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438.\n\n        Args:\n            X (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n            y (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n        \"\"\"\n        self.X_train = X.copy()\n        self.y_train = y.copy()\n\n    def predict(self, X_test):\n        \"\"\"\n        \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n\n        Args:\n            X_test (pd.DataFrame): \u0422\u0435\u0441\u0442\u043e\u0432\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n\n        Returns:\n            np.array: \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n        \"\"\"\n        predictions = []\n\n        for index, test_point in X_test.iterrows():\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0434\u043e \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u0430 \u0438\u0437 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438\n            distances = self._calculate_distance(self.X_train, test_point)\n\n            # \u041d\u0430\u0445\u043e\u0434\u0438\u043c \u0438\u043d\u0434\u0435\u043a\u0441\u044b k \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439\n            k_indices = np.argsort(distances)[:self.k]\n\n            # \u0423\u0441\u0440\u0435\u0434\u043d\u044f\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0430\u0440\u0433\u0435\u0442\u0430 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 k \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n            k_nearest_targets = self.y_train.iloc[k_indices]\n            k_nearest_distances = distances[k_indices]\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0432\u0435\u0441\u0430\n            weight = self._calculate_weight(k_nearest_distances)\n\n            # \u041d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u0435\u043c \u0432\u0435\u0441\u0430\n            weight /= weight.sum()\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u0432\u0435\u0441\u043e\u0432\n            prediction = np.dot(weight, k_nearest_targets)\n            predictions.append(prediction)\n\n        return np.array(predictions)\n\n    def _calculate_distance(self, x1, x2):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n\n        Args:\n            x1 (pd.DataFrame): \u041f\u0435\u0440\u0432\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n            x2 (pd.Series): \u0412\u0442\u043e\u0440\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n\n        Returns:\n            np.array: \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n        \"\"\"\n        metric = {\n            'euclidean': lambda x1, x2: np.sqrt(((x1 - x2) ** 2).sum(axis=1)),\n            'chebyshev': lambda x1, x2: np.max(np.abs(x1 - x2), axis=1),\n            'manhattan': lambda x1, x2: np.sum(np.abs(x1 - x2), axis=1),\n            'cosine': lambda x1, x2: 1 - np.dot(x1, x2) / (np.linalg.norm(x1, axis=1) * np.linalg.norm(x2)),\n        }\n        return metric[self.metric](x1, x2)\n\n    def _calculate_weight(self, distances):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n\n        Args:\n            distances (np.array): \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0434\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n\n        Returns:\n            np.array: \u0412\u0435\u0441\u0430 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        weight = {\n            'distance': 1 / (distances + 1e-12),\n            'rank': 1 / (np.arange(1, self.k + 1)),\n            'uniform': np.ones(self.k)\n        }[self.weight]\n        return weight\n\n```\n\n\n",
        "eval_script": "## knnreg.py\nimport numpy as np\nimport pandas as pd\n\nclass MyKNNReg:\n    \"\"\"\n    \u041a\u043b\u0430\u0441\u0441 \u0434\u043b\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 k-\u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439 \u0434\u043b\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438.\n\n    Attributes:\n        k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n        metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n        weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        X_train (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n        y_train (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n    \"\"\"\n\n    def __init__(self, k=3, metric='euclidean', weight='uniform'):\n        \"\"\"\n        \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043a\u043b\u0430\u0441\u0441\u0430.\n\n        Args:\n            k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n            metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n            weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        self.k = k\n        self.metric = metric\n        self.weight = weight\n        self.X_train = None\n        self.y_train = None\n\n    def __repr__(self):\n        return f'MyKNNReg class: k={self.k}'\n\n    def fit(self, X, y):\n        \"\"\"\n        \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438.\n\n        Args:\n            X (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n            y (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n        \"\"\"\n        self.X_train = X.copy()\n        self.y_train = y.copy()\n\n\n    def predict(self, X_test):\n        \"\"\"\n        \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n\n        Args:\n            X_test (pd.DataFrame): \u0422\u0435\u0441\u0442\u043e\u0432\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n\n        Returns:\n            np.array: \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n        \"\"\"\n        predictions = []\n\n        for index, test_point in X_test.iterrows():\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0434\u043e \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u0430 \u0438\u0437 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438\n            distances = self._calculate_distance(self.X_train, test_point)\n\n            # \u041d\u0430\u0445\u043e\u0434\u0438\u043c \u0438\u043d\u0434\u0435\u043a\u0441\u044b k \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439\n            k_indices = np.argsort(distances)[:self.k]\n\n            # \u0423\u0441\u0440\u0435\u0434\u043d\u044f\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0430\u0440\u0433\u0435\u0442\u0430 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 k \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n            k_nearest_targets = self.y_train.iloc[k_indices]\n            k_nearest_distances = distances[k_indices]\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0432\u0435\u0441\u0430\n            weight = self._calculate_weight(k_nearest_distances)\n\n            # \u041d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u0435\u043c \u0432\u0435\u0441\u0430\n            weight /= weight.sum()\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u0432\u0435\u0441\u043e\u0432\n            prediction = np.dot(weight, k_nearest_targets)\n            predictions.append(prediction)\n\n        return np.array(predictions)\n\n    def _calculate_distance(self, x1, x2):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n\n        Args:\n            x1 (pd.DataFrame): \u041f\u0435\u0440\u0432\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n            x2 (pd.Series): \u0412\u0442\u043e\u0440\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n\n        Returns:\n            np.array: \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n        \"\"\"\n        metric = {\n            'euclidean': lambda x1, x2: np.sqrt(((x1 - x2) ** 2).sum(axis=1)),\n            'chebyshev': lambda x1, x2: np.max(np.abs(x1 - x2), axis=1),\n            'manhattan': lambda x1, x2: np.sum(np.abs(x1 - x2), axis=1),\n            'cosine': lambda x1, x2: 1 - np.dot(x1, x2) / (np.linalg.norm(x1, axis=1) * np.linalg.norm(x2)),\n        }\n        return metric[self.metric](x1, x2)\n\n    def _calculate_weight(self, distances):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n\n        Args:\n            distances (np.array): \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0434\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n\n        Returns:\n            np.array: \u0412\u0435\u0441\u0430 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        weight = {\n            'distance': 1 / (distances + 1e-12),\n            'rank': 1 / (np.arange(1, self.k + 1)),\n            'uniform': np.ones(self.k)\n        }[self.weight]\n        return weight\n\ndef test_fit():\n    # Create sample data\n    X = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [4, 5, 6]})\n    y = pd.Series([7, 8, 9])\n\n    # Initialize two instances of MyKNNReg\n    knn1 = MyKNNReg()\n    knn2 = MyKNNReg()\n\n    # Fit using the original implementation\n    knn1.fit(X, y)\n\n    # Fit using the new implementation\n    knn2.fit_new_implementation(X, y)\n\n    # Assert that both implementations produce the same X_train\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch\"\n\n    # Assert that both implementations produce the same y_train\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch\"\n\n    # Assert that both implementations have the same k value\n    assert knn1.k == knn2.k, \"k value mismatch\"\n\n    # Test with different data types\n    X_float = pd.DataFrame({'feature1': [1.0, 2.0, 3.0], 'feature2': [4.0, 5.0, 6.0]})\n    y_float = pd.Series([7.0, 8.0, 9.0])\n    knn1.fit(X_float, y_float)\n    knn2.fit_new_implementation(X_float, y_float)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with float data\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with float data\"\n\n    # Test with empty data\n    X_empty = pd.DataFrame({'feature1': [], 'feature2': []})\n    y_empty = pd.Series([])\n    knn1.fit(X_empty, y_empty)\n    knn2.fit_new_implementation(X_empty, y_empty)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with empty data\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with empty data\"\n\n    # Test with larger data\n    X_large = pd.DataFrame({'feature1': range(100), 'feature2': range(100, 200)})\n    y_large = pd.Series(range(200, 300))\n    knn1.fit(X_large, y_large)\n    knn2.fit_new_implementation(X_large, y_large)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with large data\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with large data\"\n\n    # Test with edge cases\n    knn1 = MyKNNReg(k=1)\n    knn2 = MyKNNReg(k=1)\n    knn1.fit(X, y)\n    knn2.fit_new_implementation(X, y)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with k=1\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with k=1\"\n\n    knn1 = MyKNNReg(k=5)\n    knn2 = MyKNNReg(k=5)\n    knn1.fit(X, y)\n    knn2.fit_new_implementation(X, y)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with k=5\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with k=5\"\n\n    knn1 = MyKNNReg(metric='manhattan')\n    knn2 = MyKNNReg(metric='manhattan')\n    knn1.fit(X, y)\n    knn2.fit_new_implementation(X, y)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with manhattan metric\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with manhattan metric\"\n\n    knn1 = MyKNNReg(weight='distance')\n    knn2 = MyKNNReg(weight='distance')\n    knn1.fit(X, y)\n    knn2.fit_new_implementation(X, y)\n    assert knn1.X_train.equals(knn2.X_train), \"X_train mismatch with distance weight\"\n    assert knn1.y_train.equals(knn2.y_train), \"y_train mismatch with distance weight\"\n\nif __name__ == \"__main__\":\n    test_fit()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function both perform the same task: they copy the input data `X` and `y` into the instance variables `self.X_train` and `self.y_train`. The revised function is part of a class `MyKNNReg`, but the functionality of the `fit` method itself remains unchanged. The additional code in the revised version, such as the class definition and other methods, does not affect the functionality of the `fit` method. Therefore, the functionality of the revised `fit` method is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `fit` function modifies the instance variables `X_train` and `y_train` of the `MyKNNReg` class. These are global to the instance and thus satisfy the condition of modifying global variables or input arguments.\n\n2. **CONDITION 2**: The test cases in `test_fit` check the state of the `X_train` and `y_train` attributes of the `MyKNNReg` instances. They do not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the `X_train` and `y_train` attributes of two instances of `MyKNNReg` (one using `fit` and the other using `fit_new_implementation`). If `fit_new_implementation` has the same functionality as `fit`, these attributes will be identical, thus passing the tests. This satisfies the condition.\n\n4. **CONDITION 4**: The test cases use `assert` statements to compare the `X_train` and `y_train` attributes of the two instances, which is reasonable given that `fit` modifies these attributes. The test cases do not use inappropriate assertions, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover various scenarios, including different data types, empty data, larger datasets, and different configurations of `k`, `metric`, and `weight`. This variety ensures that the tests are non-trivial and comprehensive.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyKNNReg._calculate_distance",
        "idx": "501",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "knnreg.py",
        "orig_func": "def _calculate_distance(self, x1, x2):\n    \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n\n        Args:\n            x1 (pd.DataFrame): \u041f\u0435\u0440\u0432\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n            x2 (pd.Series): \u0412\u0442\u043e\u0440\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n\n        Returns:\n            np.array: \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n        \"\"\"\n    metric = {'euclidean': lambda x1, x2: np.sqrt(((x1 - x2) ** 2).sum(axis=1)), 'chebyshev': lambda x1, x2: np.max(np.abs(x1 - x2), axis=1), 'manhattan': lambda x1, x2: np.sum(np.abs(x1 - x2), axis=1), 'cosine': lambda x1, x2: 1 - np.dot(x1, x2) / (np.linalg.norm(x1, axis=1) * np.linalg.norm(x2))}\n    return metric[self.metric](x1, x2)",
        "orig_context": "```python\n## knnreg.py\nimport numpy as np\n\nimport pandas as pd\n\nX = pd.DataFrame(X)\n\ny = pd.Series(y)\n\nX.columns = [f'col_{col}' for col in X.columns]\n\nclass MyKNNReg:\n    \"\"\"\n    \u041a\u043b\u0430\u0441\u0441 \u0434\u043b\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 k-\u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439 \u0434\u043b\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438.\n\n    Attributes:\n        k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n        metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n        weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        X_train (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n        y_train (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n    \"\"\"\n\n    def __init__(self, k=3, metric='euclidean', weight='uniform'):\n        \"\"\"\n        \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043a\u043b\u0430\u0441\u0441\u0430.\n\n        Args:\n            k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n            metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n            weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        self.k = k\n        self.metric = metric\n        self.weight = weight\n        self.X_train = None\n        self.y_train = None\n\n    def __repr__(self):\n        return f'MyKNNReg class: k={self.k}'\n\n    def fit(self, X, y):\n        \"\"\"\n        \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438.\n\n        Args:\n            X (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n            y (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n        \"\"\"\n        self.X_train = X.copy()\n        self.y_train = y.copy()\n\n    def predict(self, X_test):\n        \"\"\"\n        \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n\n        Args:\n            X_test (pd.DataFrame): \u0422\u0435\u0441\u0442\u043e\u0432\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n\n        Returns:\n            np.array: \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n        \"\"\"\n        predictions = []\n\n        for index, test_point in X_test.iterrows():\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0434\u043e \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u0430 \u0438\u0437 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438\n            distances = self._calculate_distance(self.X_train, test_point)\n\n            # \u041d\u0430\u0445\u043e\u0434\u0438\u043c \u0438\u043d\u0434\u0435\u043a\u0441\u044b k \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439\n            k_indices = np.argsort(distances)[:self.k]\n\n            # \u0423\u0441\u0440\u0435\u0434\u043d\u044f\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0430\u0440\u0433\u0435\u0442\u0430 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 k \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n            k_nearest_targets = self.y_train.iloc[k_indices]\n            k_nearest_distances = distances[k_indices]\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0432\u0435\u0441\u0430\n            weight = self._calculate_weight(k_nearest_distances)\n\n            # \u041d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u0435\u043c \u0432\u0435\u0441\u0430\n            weight /= weight.sum()\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u0432\u0435\u0441\u043e\u0432\n            prediction = np.dot(weight, k_nearest_targets)\n            predictions.append(prediction)\n\n        return np.array(predictions)\n\n    def _calculate_distance(self, x1, x2):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n\n        Args:\n            x1 (pd.DataFrame): \u041f\u0435\u0440\u0432\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n            x2 (pd.Series): \u0412\u0442\u043e\u0440\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n\n        Returns:\n            np.array: \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n        \"\"\"\n        metric = {\n            'euclidean': lambda x1, x2: np.sqrt(((x1 - x2) ** 2).sum(axis=1)),\n            'chebyshev': lambda x1, x2: np.max(np.abs(x1 - x2), axis=1),\n            'manhattan': lambda x1, x2: np.sum(np.abs(x1 - x2), axis=1),\n            'cosine': lambda x1, x2: 1 - np.dot(x1, x2) / (np.linalg.norm(x1, axis=1) * np.linalg.norm(x2)),\n        }\n        return metric[self.metric](x1, x2)\n\n    def _calculate_weight(self, distances):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n\n        Args:\n            distances (np.array): \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0434\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n\n        Returns:\n            np.array: \u0412\u0435\u0441\u0430 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        weight = {\n            'distance': 1 / (distances + 1e-12),\n            'rank': 1 / (np.arange(1, self.k + 1)),\n            'uniform': np.ones(self.k)\n        }[self.weight]\n        return weight\n\n```\n\n\n",
        "eval_script": "## knnreg.py\nimport numpy as np\nimport pandas as pd\n\n# Mock data for demonstration\nX = pd.DataFrame({\n    'feature1': [1, 2, 3],\n    'feature2': [4, 5, 6]\n})\n\ny = pd.Series([1, 2, 3])\n\nX.columns = [f'col_{col}' for col in X.columns]\n\nclass MyKNNReg:\n    \"\"\"\n    \u041a\u043b\u0430\u0441\u0441 \u0434\u043b\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 k-\u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439 \u0434\u043b\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438.\n\n    Attributes:\n        k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n        metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n        weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        X_train (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n        y_train (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n    \"\"\"\n\n    def __init__(self, k=3, metric='euclidean', weight='uniform'):\n        \"\"\"\n        \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043a\u043b\u0430\u0441\u0441\u0430.\n\n        Args:\n            k (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n            metric (str): \u041c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f.\n            weight (str): \u0422\u0438\u043f \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        self.k = k\n        self.metric = metric\n        self.weight = weight\n        self.X_train = None\n        self.y_train = None\n\n    def __repr__(self):\n        return f'MyKNNReg class: k={self.k}'\n\n    def fit(self, X, y):\n        \"\"\"\n        \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438.\n\n        Args:\n            X (pd.DataFrame): \u041e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n            y (pd.Series): \u0426\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438.\n        \"\"\"\n        self.X_train = X.copy()\n        self.y_train = y.copy()\n\n    def predict(self, X_test):\n        \"\"\"\n        \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n\n        Args:\n            X_test (pd.DataFrame): \u0422\u0435\u0441\u0442\u043e\u0432\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430.\n\n        Returns:\n            np.array: \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439.\n        \"\"\"\n        predictions = []\n\n        for index, test_point in X_test.iterrows():\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0434\u043e \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u0430 \u0438\u0437 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438\n            distances = self._calculate_distance(self.X_train, test_point)\n\n            # \u041d\u0430\u0445\u043e\u0434\u0438\u043c \u0438\u043d\u0434\u0435\u043a\u0441\u044b k \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439\n            k_indices = np.argsort(distances)[:self.k]\n\n            # \u0423\u0441\u0440\u0435\u0434\u043d\u044f\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0430\u0440\u0433\u0435\u0442\u0430 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 k \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n            k_nearest_targets = self.y_train.iloc[k_indices]\n            k_nearest_distances = distances[k_indices]\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0432\u0435\u0441\u0430\n            weight = self._calculate_weight(k_nearest_distances)\n\n            # \u041d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u0435\u043c \u0432\u0435\u0441\u0430\n            weight /= weight.sum()\n\n            # \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0441 \u0443\u0447\u0435\u0442\u043e\u043c \u0432\u0435\u0441\u043e\u0432\n            prediction = np.dot(weight, k_nearest_targets)\n            predictions.append(prediction)\n\n        return np.array(predictions)\n\n    def _calculate_distance(self, x1, x2):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n\n        Args:\n            x1 (pd.DataFrame): \u041f\u0435\u0440\u0432\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n            x2 (pd.Series): \u0412\u0442\u043e\u0440\u0430\u044f \u0442\u043e\u0447\u043a\u0430.\n\n        Returns:\n            np.array: \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0442\u043e\u0447\u043a\u0430\u043c\u0438.\n        \"\"\"\n        metric = {\n            'euclidean': lambda x1, x2: np.sqrt(((x1 - x2) ** 2).sum(axis=1)),\n            'chebyshev': lambda x1, x2: np.max(np.abs(x1 - x2), axis=1),\n            'manhattan': lambda x1, x2: np.sum(np.abs(x1 - x2), axis=1),\n            'cosine': lambda x1, x2: 1 - np.dot(x1, x2) / (np.linalg.norm(x1, axis=1) * np.linalg.norm(x2)),\n        }\n        return metric[self.metric](x1, x2)\n\n\n    def _calculate_weight(self, distances):\n        \"\"\"\n        \u0420\u0430\u0441\u0447\u0435\u0442 \u0432\u0435\u0441\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n\n        Args:\n            distances (np.array): \u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0434\u043e \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0445 \u0441\u043e\u0441\u0435\u0434\u0435\u0439.\n\n        Returns:\n            np.array: \u0412\u0435\u0441\u0430 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.\n        \"\"\"\n        weight = {\n            'distance': 1 / (distances + 1e-12),\n            'rank': 1 / (np.arange(1, self.k + 1)),\n            'uniform': np.ones(self.k)\n        }[self.weight]\n        return weight\n\ndef test__calculate_distance():\n    knn = MyKNNReg(metric='euclidean')\n    original_distances = knn._calculate_distance(X, X.iloc[0])\n    new_distances = knn._calculate_distance_new_implementation(X, X.iloc[0])\n    assert np.allclose(original_distances, new_distances), \"Euclidean distance mismatch\"\n\n    knn.metric = 'chebyshev'\n    original_distances = knn._calculate_distance(X, X.iloc[0])\n    new_distances = knn._calculate_distance_new_implementation(X, X.iloc[0])\n    assert np.allclose(original_distances, new_distances), \"Chebyshev distance mismatch\"\n\n    knn.metric = 'manhattan'\n    original_distances = knn._calculate_distance(X, X.iloc[0])\n    new_distances = knn._calculate_distance_new_implementation(X, X.iloc[0])\n    assert np.allclose(original_distances, new_distances), \"Manhattan distance mismatch\"\n\n    knn.metric = 'cosine'\n    original_distances = knn._calculate_distance(X, X.iloc[0])\n    new_distances = knn._calculate_distance_new_implementation(X, X.iloc[0])\n    assert np.allclose(original_distances, new_distances), \"Cosine distance mismatch\"\n\nif __name__ == \"__main__\":\n    test__calculate_distance()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_calculate_distance` in the `MyKNNReg` class is identical to the ORIGINAL FUNCTION. Both functions define a dictionary `metric` that maps metric names ('euclidean', 'chebyshev', 'manhattan', 'cosine') to their respective lambda functions for calculating distances. The function then returns the result of the lambda function corresponding to `self.metric` applied to `x1` and `x2`. There are no changes in the logic or implementation between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `_calculate_distance` returns a numpy array representing the distances between points, satisfying the requirement of having return values.\n\n- CONDITION 2: The test function `test__calculate_distance` uses `assert np.allclose(...)` to compare the return values of `_calculate_distance` and `_calculate_distance_new_implementation`, which checks the return values and not printed or logged contents.\n\n- CONDITION 3: The test function checks the distances calculated using different metrics ('euclidean', 'chebyshev', 'manhattan', 'cosine'). If `_calculate_distance_new_implementation` passes all these tests, it implies that it has the same functionality as `_calculate_distance` for these metrics.\n\n- CONDITION 4: The test cases use `np.allclose` to compare the results of the two implementations, which is appropriate for comparing floating-point arrays. The test cases do not use inappropriate assertions.\n\n- CONDITION 5: The test cases cover multiple distance metrics, which are non-trivial and ensure that different aspects of the functionality are tested.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyTreeClf.entropy",
        "idx": "502",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "tree_clas.py",
        "orig_func": "def entropy(self, y):\n    value_counts = y.value_counts(normalize=True)\n    return -np.sum(value_counts * np.log2(value_counts))",
        "orig_context": "```python\n## tree_clas.py\nimport numpy as np\n\nfrom graphviz import Digraph\n\nclass MyTreeClf:\n    \n    def __init__(self, max_depth=15, min_samples_split=2, max_leafs=20, bins=None, criterion='entropy'):\n        self.max_depth = max_depth\n        self.min_samples_split = min_samples_split\n        self.max_leafs = max_leafs\n        self.bins = bins\n        self.leafs_cnt = 0\n        self.tree = None\n        self.histograms = {}\n        self.criterion = criterion\n        self.fi = {col: 0 for col in X.columns}\n        # self.reserved_leaves = 0\n        \n\n    def entropy(self, y):\n        value_counts = y.value_counts(normalize=True)\n        return -np.sum(value_counts * np.log2(value_counts))\n    \n    def gini(self, y):\n        value_counts = y.value_counts(normalize=True)\n        return 1 - np.sum(value_counts**2)\n    \n    def impurity(self, y):\n        criterion = {'entropy': self.entropy, 'gini': self.gini}\n        return criterion[self.criterion](y)\n\n    def get_best_split(self, X, y):\n        best_ig = -np.inf\n        best_col_name = None\n        best_split_value = None\n        parent_impurity = self.impurity(y)\n\n        for col_name in X.columns:\n            unique_values = X[col_name].unique()\n            unique_values.sort()\n\n            if self.bins is not None:\n                if col_name not in self.histograms:\n                    hist, bin_edges = np.histogram(X[col_name], bins=self.bins)\n                    self.histograms[col_name] = bin_edges[1:-1]\n                split_values = self.histograms[col_name]\n            else:\n                split_values = unique_values[:-1] + np.diff(unique_values) / 2 \n\n            for split_value in split_values:\n                left_mask = X[col_name] <= split_value\n                right_mask = X[col_name] > split_value\n\n                left_y = y[left_mask]\n                right_y = y[right_mask]\n\n                if len(left_y) == 0 or len(right_y) == 0:\n                    continue\n\n                left_impurity = self.impurity(left_y)\n                right_impurity = self.impurity(right_y)\n\n                left_impurity *= len(left_y) / len(y)\n                right_impurity *= len(right_y) / len(y)\n                ig = parent_impurity - left_impurity - right_impurity\n\n                Fi = len(y) / self.N * ig\n\n                if ig > best_ig:\n                    best_ig = ig\n                    best_col_name = col_name\n                    best_split_value = split_value\n                    best_fi = Fi\n\n        # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u043d\u0430 \u0441\u043b\u0443\u0447\u0430\u0439, \u0435\u0441\u043b\u0438 \u043d\u0435 \u043d\u0430\u0439\u0434\u0435\u043d\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u044f\u0449\u0435\u0435 \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u0435\n        if best_col_name is None:\n            return None, None, None\n\n        self.fi[best_col_name] += best_fi\n\n        return best_col_name, best_split_value, best_ig\n    \n    def fit(self, X, y, depth=0, node=None):\n        if node is None:\n            self.tree = {}\n            node = self.tree\n            is_root = True\n            self.N = len(y)\n        else:\n            is_root = False\n\n        if (depth >= self.max_depth or len(y) < self.min_samples_split or len(set(y)) == 1 or \n            # self.max_leafs - self.leafs_cnt - self.reserved_leaves <= 0) and not is_root:\n            self.max_leafs - self.leafs_cnt <= 1) and not is_root:\n            node['leaf'] = y.value_counts(normalize=True).to_dict()\n            self.leafs_cnt += 1\n            return\n\n        col_name, split_value, ig = self.get_best_split(X, y)\n\n        if col_name is None:\n            node['leaf'] = y.value_counts(normalize=True).to_dict()\n            self.leafs_cnt += 1\n            return\n\n        node[col_name] = {}\n        node[col_name][f'<= {split_value}'] = {}\n        node[col_name][f'> {split_value}'] = {}\n\n        # self.reserved_leaves += 1\n\n        left_mask = X[col_name] <= split_value\n        right_mask = X[col_name] > split_value\n\n        self.fit(X[left_mask], y[left_mask], depth + 1, node[col_name][f'<= {split_value}'])\n        self.fit(X[right_mask], y[right_mask], depth + 1, node[col_name][f'> {split_value}'])\n        \n        # self.reserved_leaves -= 1\n\n\n    def _class(self, row):\n        node = self.tree\n        while True:\n            if 'leaf' in node:\n                return node['leaf'].get(1, 0) \n            if not node or not node.keys():\n                return 0 \n            col_name = list(node.keys())[0]\n            if col_name not in node:\n                return 0 \n            split_value = list(node[col_name].keys())[0].split()[1]\n            if row[col_name] <= float(split_value):\n                node = node[col_name][f'<= {split_value}']\n            else:\n                node = node[col_name][f'> {split_value}']\n\n    def predict_proba(self, X):\n        return X.apply(self._class, axis=1)\n\n    def predict(self, X):\n        return self.predict_proba(X).apply(lambda x: 1 if x > 0.5 else 0)\n    \n    def sum_leaf_probabilities(self):\n        return self._sum_leaf_probabilities(self.tree)\n\n    def _sum_leaf_probabilities(self, node):\n        if 'leaf' in node:\n            return node['leaf'].get(1, 0)  # \u0412\u0435\u0440\u043d\u0443\u0442\u044c \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u043a\u043b\u0430\u0441\u0441\u0430 1 \u0432 \u043b\u0438\u0441\u0442\u0435\n        else:\n            total_probability = 0\n            for key, value in node.items():\n                total_probability += self._sum_leaf_probabilities(value)\n            return total_probability\n\n    def get_feature_importance(self):\n        return self.fi  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0430\u0436\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432\n    \n    def visualize_tree(self, node=None, graph=None, parent=None, edge_label=''):\n        if graph is None:\n            graph = Digraph()\n\n        if node is None:\n            node = self.tree\n\n        for key, value in node.items():\n            if key == 'leaf':\n                leaf_str = ', '.join([f'{k}: {v:.2f}' for k, v in value.items()])\n                graph.node(str(id(node)), f'Leaf: {leaf_str}', shape='box', style='filled', fillcolor='lightgreen')  # \u041b\u0438\u0441\u0442 \u0437\u0435\u043b\u0435\u043d\u044b\u0439\n                if parent is not None:\n                    # \u0426\u0432\u0435\u0442 \u0441\u0442\u0440\u0435\u043b\u043a\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u043e edge_label\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(node)), label=edge_label, color=edge_color)  # \u0421\u0442\u0440\u0435\u043b\u043a\u0430 \u0441 \u0446\u0432\u0435\u0442\u043e\u043c\n            else:\n                split_value = list(value.keys())[0].split()[1]\n                left_node = value[f'<= {split_value}']\n                right_node = value[f'> {split_value}']\n\n                # \u0423\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u043c \u0446\u0432\u0435\u0442 \u0443\u0437\u043b\u0430 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439\n                left_leaf_probability = left_node.get('leaf', {}).get(1, 0)\n                right_leaf_probability = right_node.get('leaf', {}).get(1, 0)\n\n                # \u0423\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u043c \u0446\u0432\u0435\u0442 \u0443\u0437\u043b\u0430 (\u0432\u0435\u0442\u0432\u0438) \u0438 \u0441\u0442\u0440\u0435\u043b\u043a\u0438\n                if parent is None:\n                    fill_color = '#FFA500'  # \u041e\u0440\u0430\u043d\u0436\u0435\u0432\u044b\u0439 \u0434\u043b\u044f \u043a\u043e\u0440\u043d\u044f\n                else:\n                    fill_color = '#FFCCCB' if left_leaf_probability > right_leaf_probability else '#ADD8E6'  # \u041a\u0440\u0430\u0441\u043d\u044b\u0439 \u0438\u043b\u0438 \u0433\u043e\u043b\u0443\u0431\u043e\u0439 \u0434\u043b\u044f \u0432\u0435\u0442\u0432\u0435\u0439\n\n                graph.node(str(id(value)), f'{key} <= {split_value}', style='filled', fillcolor=fill_color)\n\n                if parent is not None:\n                    # \u0426\u0432\u0435\u0442 \u0441\u0442\u0440\u0435\u043b\u043a\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 edge_label\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(value)), label=edge_label, color=edge_color)\n\n                self.visualize_tree(left_node, graph, str(id(value)), '<=' + split_value)\n                self.visualize_tree(right_node, graph, str(id(value)), '>' + split_value)\n\n        return graph\n\n```\n\n\n",
        "eval_script": "## tree_clas.py\nimport numpy as np\nfrom graphviz import Digraph\n\nclass MyTreeClf:\n    \n    def __init__(self, max_depth=15, min_samples_split=2, max_leafs=20, bins=None, criterion='entropy'):\n        self.max_depth = max_depth\n        self.min_samples_split = min_samples_split\n        self.max_leafs = max_leafs\n        self.bins = bins\n        self.leafs_cnt = 0\n        self.tree = None\n        self.histograms = {}\n        self.criterion = criterion\n        self.fi = {}  # Initialize as an empty dictionary\n\n    def entropy(self, y):\n        value_counts = y.value_counts(normalize=True)\n        return -np.sum(value_counts * np.log2(value_counts))\n    \n\n\n    \n    def gini(self, y):\n        value_counts = y.value_counts(normalize=True)\n        return 1 - np.sum(value_counts**2)\n    \n    def impurity(self, y):\n        criterion = {'entropy': self.entropy, 'gini': self.gini}\n        return criterion[self.criterion](y)\n\n    def get_best_split(self, X, y):\n        best_ig = -np.inf\n        best_col_name = None\n        best_split_value = None\n        parent_impurity = self.impurity(y)\n\n        for col_name in X.columns:\n            unique_values = X[col_name].unique()\n            unique_values.sort()\n\n            if self.bins is not None:\n                if col_name not in self.histograms:\n                    hist, bin_edges = np.histogram(X[col_name], bins=self.bins)\n                    self.histograms[col_name] = bin_edges[1:-1]\n                split_values = self.histograms[col_name]\n            else:\n                split_values = unique_values[:-1] + np.diff(unique_values) / 2 \n\n            for split_value in split_values:\n                left_mask = X[col_name] <= split_value\n                right_mask = X[col_name] > split_value\n\n                left_y = y[left_mask]\n                right_y = y[right_mask]\n\n                if len(left_y) == 0 or len(right_y) == 0:\n                    continue\n\n                left_impurity = self.impurity(left_y)\n                right_impurity = self.impurity(right_y)\n\n                left_impurity *= len(left_y) / len(y)\n                right_impurity *= len(right_y) / len(y)\n                ig = parent_impurity - left_impurity - right_impurity\n\n                Fi = len(y) / self.N * ig\n\n                if ig > best_ig:\n                    best_ig = ig\n                    best_col_name = col_name\n                    best_split_value = split_value\n                    best_fi = Fi\n\n        if best_col_name is None:\n            return None, None, None\n\n        self.fi[best_col_name] += best_fi\n\n        return best_col_name, best_split_value, best_ig\n    \n    def fit(self, X, y, depth=0, node=None):\n        if node is None:\n            self.tree = {}\n            node = self.tree\n            is_root = True\n            self.N = len(y)\n            self.fi = {col: 0 for col in X.columns}  # Initialize feature importance here\n        else:\n            is_root = False\n\n        if (depth >= self.max_depth or len(y) < self.min_samples_split or len(set(y)) == 1 or \n            self.max_leafs - self.leafs_cnt <= 1) and not is_root:\n            node['leaf'] = y.value_counts(normalize=True).to_dict()\n            self.leafs_cnt += 1\n            return\n\n        col_name, split_value, ig = self.get_best_split(X, y)\n\n        if col_name is None:\n            node['leaf'] = y.value_counts(normalize=True).to_dict()\n            self.leafs_cnt += 1\n            return\n\n        node[col_name] = {}\n        node[col_name][f'<= {split_value}'] = {}\n        node[col_name][f'> {split_value}'] = {}\n\n        left_mask = X[col_name] <= split_value\n        right_mask = X[col_name] > split_value\n\n        self.fit(X[left_mask], y[left_mask], depth + 1, node[col_name][f'<= {split_value}'])\n        self.fit(X[right_mask], y[right_mask], depth + 1, node[col_name][f'> {split_value}'])\n        \n    def _class(self, row):\n        node = self.tree\n        while True:\n            if 'leaf' in node:\n                return node['leaf'].get(1, 0) \n            if not node or not node.keys():\n                return 0 \n            col_name = list(node.keys())[0]\n            if col_name not in node:\n                return 0 \n            split_value = list(node[col_name].keys())[0].split()[1]\n            if row[col_name] <= float(split_value):\n                node = node[col_name][f'<= {split_value}']\n            else:\n                node = node[col_name][f'> {split_value}']\n\n    def predict_proba(self, X):\n        return X.apply(self._class, axis=1)\n\n    def predict(self, X):\n        return self.predict_proba(X).apply(lambda x: 1 if x > 0.5 else 0)\n    \n    def sum_leaf_probabilities(self):\n        return self._sum_leaf_probabilities(self.tree)\n\n    def _sum_leaf_probabilities(self, node):\n        if 'leaf' in node:\n            return node['leaf'].get(1, 0)  \n        else:\n            total_probability = 0\n            for key, value in node.items():\n                total_probability += self._sum_leaf_probabilities(value)\n            return total_probability\n\n    def get_feature_importance(self):\n        return self.fi  \n    \n    def visualize_tree(self, node=None, graph=None, parent=None, edge_label=''):\n        if graph is None:\n            graph = Digraph()\n\n        if node is None:\n            node = self.tree\n\n        for key, value in node.items():\n            if key == 'leaf':\n                leaf_str = ', '.join([f'{k}: {v:.2f}' for k, v in value.items()])\n                graph.node(str(id(node)), f'Leaf: {leaf_str}', shape='box', style='filled', fillcolor='lightgreen')  \n                if parent is not None:\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(node)), label=edge_label, color=edge_color)  \n            else:\n                split_value = list(value.keys())[0].split()[1]\n                left_node = value[f'<= {split_value}']\n                right_node = value[f'> {split_value}']\n\n                left_leaf_probability = left_node.get('leaf', {}).get(1, 0)\n                right_leaf_probability = right_node.get('leaf', {}).get(1, 0)\n\n                if parent is None:\n                    fill_color = '#FFA500'  \n                else:\n                    fill_color = '#FFCCCB' if left_leaf_probability > right_leaf_probability else '#ADD8E6'  \n\n                graph.node(str(id(value)), f'{key} <= {split_value}', style='filled', fillcolor=fill_color)\n\n                if parent is not None:\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(value)), label=edge_label, color=edge_color)\n\n                self.visualize_tree(left_node, graph, str(id(value)), '<=' + split_value)\n                self.visualize_tree(right_node, graph, str(id(value)), '>' + split_value)\n\n        return graph\n\ndef test_entropy():\n    import pandas as pd\n\n    clf = MyTreeClf()\n\n    # Test case 1: Uniform distribution\n    y1 = pd.Series([1, 1, 2, 2])\n    assert np.isclose(clf.entropy(y1), clf.entropy_new_implementation(y1)), \"Test case 1 failed\"\n\n    # Test case 2: Single class\n    y2 = pd.Series([1, 1, 1, 1])\n    assert np.isclose(clf.entropy(y2), clf.entropy_new_implementation(y2)), \"Test case 2 failed\"\n\n    # Test case 3: Varying probabilities\n    y3 = pd.Series([1, 1, 1, 2, 2, 3])\n    assert np.isclose(clf.entropy(y3), clf.entropy_new_implementation(y3)), \"Test case 3 failed\"\n\n    # Test case 4: Empty Series\n    y4 = pd.Series([])\n    assert np.isclose(clf.entropy(y4), clf.entropy_new_implementation(y4)), \"Test case 4 failed\"\n\n    # Test case 5: Large numbers\n    y5 = pd.Series([1000, 1000, 2000, 2000])\n    assert np.isclose(clf.entropy(y5), clf.entropy_new_implementation(y5)), \"Test case 5 failed\"\n\n    # Test case 6: Negative numbers\n    y6 = pd.Series([-1, -1, -2, -2])\n    assert np.isclose(clf.entropy(y6), clf.entropy_new_implementation(y6)), \"Test case 6 failed\"\n\n    # Test case 7: Non-integer values\n    y7 = pd.Series([1.5, 1.5, 2.5, 2.5])\n    assert np.isclose(clf.entropy(y7), clf.entropy_new_implementation(y7)), \"Test case 7 failed\"\n\n    # Test case 8: String values\n    y8 = pd.Series(['a', 'a', 'b', 'b'])\n    assert np.isclose(clf.entropy(y8), clf.entropy_new_implementation(y8)), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test_entropy()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION for entropy is identical to the ORIGINAL FUNCTION. Both functions calculate the entropy of a given series `y` by first obtaining the normalized value counts and then computing the negative sum of the product of these counts and their logarithm base 2. There are no changes in the logic or the operations performed in the REVISED FUNCTION compared to the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `entropy` function returns a value, which is the calculated entropy of the input series `y`. Therefore, it satisfies this condition.\n- CONDITION 2: The test cases use `assert` statements to compare the return values of `entropy` and `entropy_new_implementation`, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the results of `entropy` and `entropy_new_implementation` using `np.isclose`, which checks if the two implementations produce the same results for the same inputs. This ensures that `entropy_new_implementation` must have the same functionality as `entropy` to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use `np.isclose` to compare the return values of `entropy` and `entropy_new_implementation`, which is appropriate given that `entropy` returns a numerical value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including uniform distributions, single class, varying probabilities, empty series, large numbers, negative numbers, non-integer values, and string values. These are non-trivial and comprehensive, ensuring that the function is tested under different conditions. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyTreeReg.fit",
        "idx": "503",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "tree_reg.py",
        "orig_func": "def fit(self, X, y, depth=0, node=None):\n    if node is None:\n        self.tree = {}\n        node = self.tree\n        is_root = True\n    else:\n        is_root = False\n    if (depth >= self.max_depth or len(y) < self.min_samples_split or len(set(y)) == 1 or (self.max_leafs - self.leafs_cnt <= 1)) and (not is_root):\n        node['leaf'] = np.mean(y)\n        self.leafs_cnt += 1\n        return\n    col_name, split_value, ig = self.get_best_split(X, y)\n    node[col_name] = {}\n    node[col_name][f'<= {split_value}'] = {}\n    node[col_name][f'> {split_value}'] = {}\n    left_mask = X[col_name] <= split_value\n    right_mask = X[col_name] > split_value\n    self.fit(X[left_mask], y[left_mask], depth + 1, node[col_name][f'<= {split_value}'])\n    self.fit(X[right_mask], y[right_mask], depth + 1, node[col_name][f'> {split_value}'])",
        "orig_context": "```python\n## tree_reg.py\nimport numpy as np\n\nfrom graphviz import Digraph\n\nclass MyTreeReg:\n    def __init__(self, max_depth=5, min_samples_split=2, max_leafs=20):\n        self.max_depth = max_depth\n        self.min_samples_split = min_samples_split\n        self.max_leafs = max_leafs - 1\n        self.leafs_cnt = 0\n        self.reserved_leaves = 0\n        \n    def __repr__(self):\n        return f'MyTreeReg class: max_depth={self.max_depth}, min_samples_split={self.min_samples_split}, max_leafs={self.max_leafs}'\n    \n    def get_best_split(self, X, y):\n        best_gain = -np.inf\n        best_col_name = None\n        best_split_value = None\n        \n        for col_name in X.columns:\n            unique_values = np.sort(X[col_name].unique())\n            \n            for i in range(len(unique_values) - 1):\n                split_value = (unique_values[i] + unique_values[i + 1]) / 2\n                gain = self._calculate_mse_gain(X, y, col_name, split_value)\n                \n                if gain > best_gain:\n                    best_gain = gain\n                    best_col_name = col_name\n                    best_split_value = split_value\n        \n        return best_col_name, best_split_value, best_gain\n    \n    def _calculate_mse_gain(self, X, y, col_name, split_value):\n        left_mask = X[col_name] <= split_value\n        right_mask = X[col_name] > split_value\n        \n        y_left = y[left_mask]\n        y_right = y[right_mask]\n        \n        if len(y_left) == 0 or len(y_right) == 0:\n            return -np.inf\n        \n        mse_left = np.mean((y_left - np.mean(y_left)) ** 2)\n        mse_right = np.mean((y_right - np.mean(y_right)) ** 2)\n        total_mse = (len(y_left) * mse_left + len(y_right) * mse_right) / (len(y_left) + len(y_right))\n        \n        total_mse_original = np.mean((y - np.mean(y)) ** 2)\n        gain = total_mse_original - total_mse\n        \n        return gain\n    \n    def fit(self, X, y, depth=0, node=None):\n        if node is None:\n            self.tree = {}\n            node = self.tree\n            is_root = True\n        else:\n            is_root = False\n\n        if (depth >= self.max_depth or len(y) < self.min_samples_split or len(set(y)) == 1 or \n            self.max_leafs - self.leafs_cnt <= 1) and not is_root:\n            node['leaf'] = np.mean(y)\n            self.leafs_cnt += 1\n            return\n\n        col_name, split_value, ig = self.get_best_split(X, y)\n\n        node[col_name] = {}\n        node[col_name][f'<= {split_value}'] = {}\n        node[col_name][f'> {split_value}'] = {}\n\n\n        left_mask = X[col_name] <= split_value\n        right_mask = X[col_name] > split_value\n\n        self.fit(X[left_mask], y[left_mask], depth + 1, node[col_name][f'<= {split_value}'])\n        self.fit(X[right_mask], y[right_mask], depth + 1, node[col_name][f'> {split_value}'])\n        \n\n    def visualize_tree(self, node=None, graph=None, parent=None, edge_label=''):\n        if graph is None:\n            graph = Digraph()\n\n        if node is None:\n            node = self.tree\n\n        for key, value in node.items():\n            if key == 'leaf':\n                # \u041e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u043c \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u043b\u0438\u0441\u0442\u044c\u044f\u0445\n                graph.node(str(id(node)), f'Leaf: {value:.2f}', shape='box', style='filled', fillcolor='lightgreen')  # \u041b\u0438\u0441\u0442 \u0437\u0435\u043b\u0435\u043d\u044b\u0439\n                if parent is not None:\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(node)), label=edge_label, color=edge_color)  # \u0421\u0442\u0440\u0435\u043b\u043a\u0430 \u0441 \u0446\u0432\u0435\u0442\u043e\u043c\n            else:\n                split_value = list(value.keys())[0].split()[1]\n                left_node = value[f'<= {split_value}']\n                right_node = value[f'> {split_value}']\n\n                # \u0423\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u043c \u0446\u0432\u0435\u0442 \u0443\u0437\u043b\u0430 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0441\u0440\u0435\u0434\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439\n                left_leaf_value = left_node.get('leaf', 0)\n                right_leaf_value = right_node.get('leaf', 0)\n\n                if parent is None:\n                    fill_color = '#FFA500'  # \u041e\u0440\u0430\u043d\u0436\u0435\u0432\u044b\u0439 \u0434\u043b\u044f \u043a\u043e\u0440\u043d\u044f\n                else:\n                    fill_color = '#FFCCCB' if left_leaf_value > right_leaf_value else '#ADD8E6'  # \u041a\u0440\u0430\u0441\u043d\u044b\u0439 \u0438\u043b\u0438 \u0433\u043e\u043b\u0443\u0431\u043e\u0439 \u0434\u043b\u044f \u0432\u0435\u0442\u0432\u0435\u0439\n\n                graph.node(str(id(value)), f'{key} <= {split_value}', style='filled', fillcolor=fill_color)\n\n                if parent is not None:\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(value)), label=edge_label, color=edge_color)\n\n                self.visualize_tree(left_node, graph, str(id(value)), '<=' + split_value)\n                self.visualize_tree(right_node, graph, str(id(value)), '>' + split_value)\n\n        return graph\n    \n    def sum_leaves(self, node=None):\n        if node is None:\n            node = self.tree\n\n        total_sum = 0\n\n        for key, value in node.items():\n            if key == 'leaf':\n                total_sum += value  # \u0421\u0443\u043c\u043c\u0438\u0440\u0443\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u043b\u0438\u0441\u0442\u0435\n            else:\n                total_sum += self.sum_leaves(value[f'<= {list(value.keys())[0].split()[1]}'])\n                total_sum += self.sum_leaves(value[f'> {list(value.keys())[0].split()[1]}'])\n\n        return total_sum\n\n```\n\n\n",
        "eval_script": "## tree_reg.py\nimport numpy as np\nimport pandas as pd\nfrom graphviz import Digraph\n\nclass MyTreeReg:\n    def __init__(self, max_depth=5, min_samples_split=2, max_leafs=20):\n        self.max_depth = max_depth\n        self.min_samples_split = min_samples_split\n        self.max_leafs = max_leafs - 1\n        self.leafs_cnt = 0\n        self.reserved_leaves = 0\n        \n    def __repr__(self):\n        return f'MyTreeReg class: max_depth={self.max_depth}, min_samples_split={self.min_samples_split}, max_leafs={self.max_leafs}'\n    \n    def get_best_split(self, X, y):\n        best_gain = -np.inf\n        best_col_name = None\n        best_split_value = None\n        \n        for col_name in X.columns:\n            unique_values = np.sort(X[col_name].unique())\n            \n            for i in range(len(unique_values) - 1):\n                split_value = (unique_values[i] + unique_values[i + 1]) / 2\n                gain = self._calculate_mse_gain(X, y, col_name, split_value)\n                \n                if gain > best_gain:\n                    best_gain = gain\n                    best_col_name = col_name\n                    best_split_value = split_value\n        \n        return best_col_name, best_split_value, best_gain\n    \n    def _calculate_mse_gain(self, X, y, col_name, split_value):\n        left_mask = X[col_name] <= split_value\n        right_mask = X[col_name] > split_value\n        \n        y_left = y[left_mask]\n        y_right = y[right_mask]\n        \n        if len(y_left) == 0 or len(y_right) == 0:\n            return -np.inf\n        \n        mse_left = np.mean((y_left - np.mean(y_left)) ** 2)\n        mse_right = np.mean((y_right - np.mean(y_right)) ** 2)\n        total_mse = (len(y_left) * mse_left + len(y_right) * mse_right) / (len(y_left) + len(y_right))\n        \n        total_mse_original = np.mean((y - np.mean(y)) ** 2)\n        gain = total_mse_original - total_mse\n        \n        return gain\n    \n    def fit(self, X, y, depth=0, node=None):\n        if node is None:\n            self.tree = {}\n            node = self.tree\n            is_root = True\n        else:\n            is_root = False\n\n        if (depth >= self.max_depth or len(y) < self.min_samples_split or len(set(y)) == 1 or \n            self.max_leafs - self.leafs_cnt <= 1) and not is_root:\n            node['leaf'] = np.mean(y)\n            self.leafs_cnt += 1\n            return\n\n        col_name, split_value, ig = self.get_best_split(X, y)\n\n        node[col_name] = {}\n        node[col_name][f'<= {split_value}'] = {}\n        node[col_name][f'> {split_value}'] = {}\n\n\n        left_mask = X[col_name] <= split_value\n        right_mask = X[col_name] > split_value\n\n        self.fit(X[left_mask], y[left_mask], depth + 1, node[col_name][f'<= {split_value}'])\n        self.fit(X[right_mask], y[right_mask], depth + 1, node[col_name][f'> {split_value}'])\n        \n\n    def visualize_tree(self, node=None, graph=None, parent=None, edge_label=''):\n        if graph is None:\n            graph = Digraph()\n\n        if node is None:\n            node = self.tree\n\n        for key, value in node.items():\n            if key == 'leaf':\n                # \u041e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u043c \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u043b\u0438\u0441\u0442\u044c\u044f\u0445\n                graph.node(str(id(node)), f'Leaf: {value:.2f}', shape='box', style='filled', fillcolor='lightgreen')  # \u041b\u0438\u0441\u0442 \u0437\u0435\u043b\u0435\u043d\u044b\u0439\n                if parent is not None:\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(node)), label=edge_label, color=edge_color)  # \u0421\u0442\u0440\u0435\u043b\u043a\u0430 \u0441 \u0446\u0432\u0435\u0442\u043e\u043c\n            else:\n                split_value = list(value.keys())[0].split()[1]\n                left_node = value[f'<= {split_value}']\n                right_node = value[f'> {split_value}']\n\n                # \u0423\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u043c \u0446\u0432\u0435\u0442 \u0443\u0437\u043b\u0430 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0441\u0440\u0435\u0434\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439\n                left_leaf_value = left_node.get('leaf', 0)\n                right_leaf_value = right_node.get('leaf', 0)\n\n                if parent is None:\n                    fill_color = '#FFA500'  # \u041e\u0440\u0430\u043d\u0436\u0435\u0432\u044b\u0439 \u0434\u043b\u044f \u043a\u043e\u0440\u043d\u044f\n                else:\n                    fill_color = '#FFCCCB' if left_leaf_value > right_leaf_value else '#ADD8E6'  # \u041a\u0440\u0430\u0441\u043d\u044b\u0439 \u0438\u043b\u0438 \u0433\u043e\u043b\u0443\u0431\u043e\u0439 \u0434\u043b\u044f \u0432\u0435\u0442\u0432\u0435\u0439\n\n                graph.node(str(id(value)), f'{key} <= {split_value}', style='filled', fillcolor=fill_color)\n\n                if parent is not None:\n                    edge_color = 'blue' if '<=' in edge_label else 'red'\n                    graph.edge(parent, str(id(value)), label=edge_label, color=edge_color)\n\n                self.visualize_tree(left_node, graph, str(id(value)), '<=' + split_value)\n                self.visualize_tree(right_node, graph, str(id(value)), '>' + split_value)\n\n        return graph\n    \n    def sum_leaves(self, node=None):\n        if node is None:\n            node = self.tree\n\n        total_sum = 0\n\n        for key, value in node.items():\n            if key == 'leaf':\n                total_sum += value  # \u0421\u0443\u043c\u043c\u0438\u0440\u0443\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u043b\u0438\u0441\u0442\u0435\n            else:\n                total_sum += self.sum_leaves(value[f'<= {list(value.keys())[0].split()[1]}'])\n                total_sum += self.sum_leaves(value[f'> {list(value.keys())[0].split()[1]}'])\n\n        return total_sum\n\ndef test_fit():\n    # Create sample data\n    X = pd.DataFrame({\n        'feature1': [1, 2, 3, 4, 5],\n        'feature2': [5, 4, 3, 2, 1]\n    })\n    y = pd.Series([1, 2, 3, 4, 5])\n\n    # Initialize two instances of MyTreeReg\n    tree1 = MyTreeReg(max_depth=3, min_samples_split=2, max_leafs=5)\n    tree2 = MyTreeReg(max_depth=3, min_samples_split=2, max_leafs=5)\n\n    # Fit both trees using different implementations\n    tree1.fit(X, y)\n    tree2.fit_new_implementation(X, y)\n\n    # Assert that both trees have the same structure\n    assert tree1.tree == tree2.tree, \"Tree structures do not match.\"\n\n    # Test with different data\n    X2 = pd.DataFrame({\n        'feature1': [10, 20, 30, 40, 50],\n        'feature2': [50, 40, 30, 20, 10]\n    })\n    y2 = pd.Series([10, 20, 30, 40, 50])\n\n    tree1.fit(X2, y2)\n    tree2.fit_new_implementation(X2, y2)\n\n    assert tree1.tree == tree2.tree, \"Tree structures do not match for different data.\"\n\n    # Test with different parameters\n    tree1 = MyTreeReg(max_depth=2, min_samples_split=3, max_leafs=4)\n    tree2 = MyTreeReg(max_depth=2, min_samples_split=3, max_leafs=4)\n\n    tree1.fit(X, y)\n    tree2.fit_new_implementation(X, y)\n\n    assert tree1.tree == tree2.tree, \"Tree structures do not match with different parameters.\"\n\nif __name__ == \"__main__\":\n    test_fit()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing the original and revised functions, the logic and flow of the `fit` function remain unchanged. Both functions initialize the tree, check stopping conditions, find the best split, and recursively call themselves to build the tree. The conditions for stopping and splitting are identical, and the recursive structure is preserved. The revised function is embedded within a class with additional methods and attributes, but these do not alter the core functionality of the `fit` method itself. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `fit` function modifies the `tree` attribute of the `MyTreeReg` class, which is a global variable in the context of the class instance. Therefore, this condition is satisfied.\n- [CONDITION 2] The test cases check the `tree` attribute of the `MyTreeReg` instances, which is a variable state, not printed or logged content. This condition is satisfied.\n- [CONDITION 3] The test cases compare the `tree` structures of two instances of `MyTreeReg` after fitting, which ensures that `fit_new_implementation` must have the same functionality as `fit` to pass the tests. This condition is satisfied.\n- [CONDITION 4] The test cases use assertions to compare the `tree` structures, which is reasonable given that `fit` modifies the `tree` attribute. This condition is satisfied.\n- [CONDITION 5] The test cases use different datasets and parameters to test the `fit` function, making them non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyLogReg._binary_labels",
        "idx": "504",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "log_reg.py",
        "orig_func": "def _binary_labels(self, y_true, y_pred):\n    median = np.median(y_true)\n    y_true = (y_true >= median).astype(int)\n    y_pred_binary = (y_pred >= 0.5).astype(int)\n    return (y_true, y_pred_binary)",
        "orig_context": "```python\n## log_reg.py\nimport random\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score\n\nclass MyLogReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter \n        self.learning_rate = learning_rate \n        self.weights = weights\n        self.metric = metric\n        self.best_score = None\n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n\n        \n    def __str__(self):\n        return f\"MyLineReg class: n_iter={self.n_iter}, learning_rate={self.learning_rate}\"\n\n\n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose=False):\n        random.seed(self.random_state)\n        X = np.hstack((np.ones((X.shape[0], 1)), X))  # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0435\u0434\u0438\u043d\u0438\u0446\n        self.weights = np.ones(X.shape[1])  # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c weights \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c\u044e\n        \n        for i in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X, y)\n            y_pred, loss, gradient = self._data_calculations(batch_X, batch_y)\n            learning_rate = self.learning_rate(i) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            metric_value = self._calculate_metric(batch_y, y_pred)\n            \n            if verbose and i % verbose == 0:\n                print(f\"{i} | loss: {loss} | {self.metric}: {metric_value}\")\n                \n        self.best_score = self._calculate_metric(y, self.predict_proba(X))\n        \n\n    def y_predict(self, X: np.ndarray):\n        z = np.dot(X, self.weights)\n        return 1 / (1 + np.exp(-z))\n\n    def get_coef(self):\n        return self.weights[1:]  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0435\u0441\u0430 \u0431\u0435\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0447\u043b\u0435\u043d\u0430\n    \n\n    def _log_loss(self, y_true, y_pred):\n        eps = 1e-15\n        return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))\n    \n    \n    def predict_proba(self, X: np.ndarray):\n        return self.y_predict(X)\n    \n    \n    def predict(self, X: np.ndarray):\n        proba = self.predict_proba(X)\n        return (proba >= 0.5).astype(int)\n    \n    \n    def _data_calculations(self, X, y):\n        regularization, regularization_gradient = self._calculate_regularization()\n        y_pred = self.y_predict(X)\n        loss = self._log_loss(y, y_pred) + regularization\n        gradient = np.dot(X.T, (y_pred - y)) / X.shape[0] + regularization_gradient\n        return y_pred, loss, gradient\n    \n    \n    def _calculate_metric(self, y_true, y_pred):\n        y_true, y_pred_binary = self._binary_labels(y_true, y_pred)\n\n        if self.metric == 'accuracy':\n            return accuracy_score(y_true, y_pred_binary)\n        elif self.metric == 'f1':\n            return f1_score(y_true, y_pred_binary)\n        elif self.metric == 'roc_auc':\n            if len(np.unique(y_true)) > 1:\n                roc_auc = roc_auc_score(y_true, y_pred)\n            else:\n                roc_auc = None\n            return roc_auc\n        elif self.metric == 'precision':\n            return precision_score(y_true, y_pred_binary)\n        elif self.metric == 'recall':\n            return recall_score(y_true, y_pred_binary)\n        else:\n            return None\n\n\n    def _binary_labels(self, y_true, y_pred):\n        median = np.median(y_true)\n        y_true = (y_true >= median).astype(int)\n        y_pred_binary = (y_pred >= 0.5).astype(int)\n        return y_true, y_pred_binary\n\n\n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _calculate_regularization(self):\n        reg_type = self.reg\n        weights = self.weights\n        l1_coef = self.l1_coef\n        l2_coef = self.l2_coef\n        \n        if reg_type == 'l1':\n            regularization = l1_coef * np.sum(np.abs(weights))\n            gradient_reg = l1_coef * np.sign(weights)\n        elif reg_type == 'l2':\n            regularization = l2_coef * np.sum(weights ** 2)\n            gradient_reg = 2 * l2_coef * weights\n        elif reg_type == 'elasticnet':\n            regularization = (l1_coef * np.sum(np.abs(weights)) +\n                             l2_coef * np.sum(weights ** 2))\n            gradient_reg = (l1_coef * np.sign(weights) +\n                           2 * l2_coef * weights)\n        else:\n            regularization = 0\n            gradient_reg = 0\n            \n        return regularization, gradient_reg\n    \n    \n    def _get_mini_batch(self, X, y):\n        \"\"\"Returns a mini-batch of data.\"\"\"\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))  # sample size as fraction of X.shape[0]\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n\n```\n\n\n",
        "eval_script": "## log_reg.py\nimport random\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score\n\nclass MyLogReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter \n        self.learning_rate = learning_rate \n        self.weights = weights\n        self.metric = metric\n        self.best_score = None\n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n\n        \n    def __str__(self):\n        return f\"MyLineReg class: n_iter={self.n_iter}, learning_rate={self.learning_rate}\"\n\n\n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose=False):\n        random.seed(self.random_state)\n        X = np.hstack((np.ones((X.shape[0], 1)), X))  # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0435\u0434\u0438\u043d\u0438\u0446\n        self.weights = np.ones(X.shape[1])  # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c weights \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c\u044e\n        \n        for i in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X, y)\n            y_pred, loss, gradient = self._data_calculations(batch_X, batch_y)\n            learning_rate = self.learning_rate(i) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            metric_value = self._calculate_metric(batch_y, y_pred)\n            \n            if verbose and i % verbose == 0:\n                print(f\"{i} | loss: {loss} | {self.metric}: {metric_value}\")\n                \n        self.best_score = self._calculate_metric(y, self.predict_proba(X))\n        \n\n    def y_predict(self, X: np.ndarray):\n        z = np.dot(X, self.weights)\n        return 1 / (1 + np.exp(-z))\n\n    def get_coef(self):\n        return self.weights[1:]  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0435\u0441\u0430 \u0431\u0435\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0447\u043b\u0435\u043d\u0430\n    \n\n    def _log_loss(self, y_true, y_pred):\n        eps = 1e-15\n        return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))\n    \n    \n    def predict_proba(self, X: np.ndarray):\n        return self.y_predict(X)\n    \n    \n    def predict(self, X: np.ndarray):\n        proba = self.predict_proba(X)\n        return (proba >= 0.5).astype(int)\n    \n    \n    def _data_calculations(self, X, y):\n        regularization, regularization_gradient = self._calculate_regularization()\n        y_pred = self.y_predict(X)\n        loss = self._log_loss(y, y_pred) + regularization\n        gradient = np.dot(X.T, (y_pred - y)) / X.shape[0] + regularization_gradient\n        return y_pred, loss, gradient\n    \n    \n    def _calculate_metric(self, y_true, y_pred):\n        y_true, y_pred_binary = self._binary_labels(y_true, y_pred)\n\n        if self.metric == 'accuracy':\n            return accuracy_score(y_true, y_pred_binary)\n        elif self.metric == 'f1':\n            return f1_score(y_true, y_pred_binary)\n        elif self.metric == 'roc_auc':\n            if len(np.unique(y_true)) > 1:\n                roc_auc = roc_auc_score(y_true, y_pred)\n            else:\n                roc_auc = None\n            return roc_auc\n        elif self.metric == 'precision':\n            return precision_score(y_true, y_pred_binary)\n        elif self.metric == 'recall':\n            return recall_score(y_true, y_pred_binary)\n        else:\n            return None\n\n\n    def _binary_labels(self, y_true, y_pred):\n        median = np.median(y_true)\n        y_true = (y_true >= median).astype(int)\n        y_pred_binary = (y_pred >= 0.5).astype(int)\n        return y_true, y_pred_binary\n\n    # Added public method to access _binary_labels\n    def binary_labels(self, y_true, y_pred):\n        return self._binary_labels(y_true, y_pred)\n\n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _calculate_regularization(self):\n        reg_type = self.reg\n        weights = self.weights\n        l1_coef = self.l1_coef\n        l2_coef = self.l2_coef\n        \n        if reg_type == 'l1':\n            regularization = l1_coef * np.sum(np.abs(weights))\n            gradient_reg = l1_coef * np.sign(weights)\n        elif reg_type == 'l2':\n            regularization = l2_coef * np.sum(weights ** 2)\n            gradient_reg = 2 * l2_coef * weights\n        elif reg_type == 'elasticnet':\n            regularization = (l1_coef * np.sum(np.abs(weights)) +\n                             l2_coef * np.sum(weights ** 2))\n            gradient_reg = (l1_coef * np.sign(weights) +\n                           2 * l2_coef * weights)\n        else:\n            regularization = 0\n            gradient_reg = 0\n            \n        return regularization, gradient_reg\n    \n    \n    def _get_mini_batch(self, X, y):\n        \"\"\"Returns a mini-batch of data.\"\"\"\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))  # sample size as fraction of X.shape[0]\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n\n\ndef test__binary_labels():\n    model = MyLogReg()\n\n    # Test case 1: Simple case\n    y_true = np.array([0, 1, 1, 0])\n    y_pred = np.array([0.3, 0.7, 0.8, 0.2])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 2: All ones\n    y_true = np.array([1, 1, 1, 1])\n    y_pred = np.array([0.6, 0.7, 0.8, 0.9])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 3: All zeros\n    y_true = np.array([0, 0, 0, 0])\n    y_pred = np.array([0.1, 0.2, 0.3, 0.4])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 4: Mixed values\n    y_true = np.array([0, 1, 0, 1, 0])\n    y_pred = np.array([0.4, 0.6, 0.5, 0.7, 0.3])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 5: Single element\n    y_true = np.array([1])\n    y_pred = np.array([0.9])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 6: Large values\n    y_true = np.array([100, 200, 300, 400])\n    y_pred = np.array([0.1, 0.9, 0.8, 0.2])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 7: Negative values\n    y_true = np.array([-1, -2, -3, -4])\n    y_pred = np.array([0.3, 0.7, 0.8, 0.2])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 8: Edge probabilities\n    y_true = np.array([0, 1, 1, 0])\n    y_pred = np.array([0.5, 0.5, 0.5, 0.5])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\n    # Test case 9: Non-binary y_true\n    y_true = np.array([2, 3, 4, 5])\n    y_pred = np.array([0.2, 0.3, 0.4, 0.5])\n    assert np.array_equal(\n        model._binary_labels(y_true, y_pred),\n        model._binary_labels_new_implementation(y_true, y_pred)\n    )\n\nif __name__ == \"__main__\":\n    test__binary_labels()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_binary_labels` in the `MyLogReg` class is identical to the ORIGINAL FUNCTION. Both functions calculate the median of `y_true`, convert `y_true` to binary labels based on whether each element is greater than or equal to the median, and convert `y_pred` to binary labels based on whether each element is greater than or equal to 0.5. The logic and operations performed in both functions are exactly the same, and there are no changes in functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_binary_labels` function returns a tuple of arrays, so it satisfies this condition.\n- CONDITION 2: The test cases use `assert` statements to check the return values of `_binary_labels` and `_binary_labels_new_implementation`, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_binary_labels` and `_binary_labels_new_implementation` using `np.array_equal`, ensuring that both functions must have the same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use `assert` statements to compare the outputs of the two implementations, which is reasonable given that `_binary_labels` returns values. This satisfies the condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including simple cases, all ones, all zeros, mixed values, single elements, large values, negative values, edge probabilities, and non-binary `y_true`. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyLogReg._data_calculations",
        "idx": "506",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "log_reg.py",
        "orig_func": "def _data_calculations(self, X, y):\n    regularization, regularization_gradient = self._calculate_regularization()\n    y_pred = self.y_predict(X)\n    loss = self._log_loss(y, y_pred) + regularization\n    gradient = np.dot(X.T, y_pred - y) / X.shape[0] + regularization_gradient\n    return (y_pred, loss, gradient)",
        "orig_context": "```python\n## log_reg.py\nimport random\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score\n\nclass MyLogReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter \n        self.learning_rate = learning_rate \n        self.weights = weights\n        self.metric = metric\n        self.best_score = None\n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n\n        \n    def __str__(self):\n        return f\"MyLineReg class: n_iter={self.n_iter}, learning_rate={self.learning_rate}\"\n\n\n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose=False):\n        random.seed(self.random_state)\n        X = np.hstack((np.ones((X.shape[0], 1)), X))  # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0435\u0434\u0438\u043d\u0438\u0446\n        self.weights = np.ones(X.shape[1])  # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c weights \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c\u044e\n        \n        for i in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X, y)\n            y_pred, loss, gradient = self._data_calculations(batch_X, batch_y)\n            learning_rate = self.learning_rate(i) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            metric_value = self._calculate_metric(batch_y, y_pred)\n            \n            if verbose and i % verbose == 0:\n                print(f\"{i} | loss: {loss} | {self.metric}: {metric_value}\")\n                \n        self.best_score = self._calculate_metric(y, self.predict_proba(X))\n        \n\n    def y_predict(self, X: np.ndarray):\n        z = np.dot(X, self.weights)\n        return 1 / (1 + np.exp(-z))\n\n    def get_coef(self):\n        return self.weights[1:]  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0435\u0441\u0430 \u0431\u0435\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0447\u043b\u0435\u043d\u0430\n    \n\n    def _log_loss(self, y_true, y_pred):\n        eps = 1e-15\n        return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))\n    \n    \n    def predict_proba(self, X: np.ndarray):\n        return self.y_predict(X)\n    \n    \n    def predict(self, X: np.ndarray):\n        proba = self.predict_proba(X)\n        return (proba >= 0.5).astype(int)\n    \n    \n    def _data_calculations(self, X, y):\n        regularization, regularization_gradient = self._calculate_regularization()\n        y_pred = self.y_predict(X)\n        loss = self._log_loss(y, y_pred) + regularization\n        gradient = np.dot(X.T, (y_pred - y)) / X.shape[0] + regularization_gradient\n        return y_pred, loss, gradient\n    \n    \n    def _calculate_metric(self, y_true, y_pred):\n        y_true, y_pred_binary = self._binary_labels(y_true, y_pred)\n\n        if self.metric == 'accuracy':\n            return accuracy_score(y_true, y_pred_binary)\n        elif self.metric == 'f1':\n            return f1_score(y_true, y_pred_binary)\n        elif self.metric == 'roc_auc':\n            if len(np.unique(y_true)) > 1:\n                roc_auc = roc_auc_score(y_true, y_pred)\n            else:\n                roc_auc = None\n            return roc_auc\n        elif self.metric == 'precision':\n            return precision_score(y_true, y_pred_binary)\n        elif self.metric == 'recall':\n            return recall_score(y_true, y_pred_binary)\n        else:\n            return None\n\n\n    def _binary_labels(self, y_true, y_pred):\n        median = np.median(y_true)\n        y_true = (y_true >= median).astype(int)\n        y_pred_binary = (y_pred >= 0.5).astype(int)\n        return y_true, y_pred_binary\n\n\n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _calculate_regularization(self):\n        reg_type = self.reg\n        weights = self.weights\n        l1_coef = self.l1_coef\n        l2_coef = self.l2_coef\n        \n        if reg_type == 'l1':\n            regularization = l1_coef * np.sum(np.abs(weights))\n            gradient_reg = l1_coef * np.sign(weights)\n        elif reg_type == 'l2':\n            regularization = l2_coef * np.sum(weights ** 2)\n            gradient_reg = 2 * l2_coef * weights\n        elif reg_type == 'elasticnet':\n            regularization = (l1_coef * np.sum(np.abs(weights)) +\n                             l2_coef * np.sum(weights ** 2))\n            gradient_reg = (l1_coef * np.sign(weights) +\n                           2 * l2_coef * weights)\n        else:\n            regularization = 0\n            gradient_reg = 0\n            \n        return regularization, gradient_reg\n    \n    \n    def _get_mini_batch(self, X, y):\n        \"\"\"Returns a mini-batch of data.\"\"\"\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))  # sample size as fraction of X.shape[0]\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n\n```\n\n\n",
        "eval_script": "## log_reg.py\nimport random\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score\n\nclass MyLogReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter \n        self.learning_rate = learning_rate \n        self.weights = weights\n        self.metric = metric\n        self.best_score = None\n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n\n        \n    def __str__(self):\n        return f\"MyLineReg class: n_iter={self.n_iter}, learning_rate={self.learning_rate}\"\n\n\n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose=False):\n        random.seed(self.random_state)\n        X = np.hstack((np.ones((X.shape[0], 1)), X))  # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0435\u0434\u0438\u043d\u0438\u0446\n        self.weights = np.ones(X.shape[1])  # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c weights \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c\u044e\n        \n        for i in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X, y)\n            y_pred, loss, gradient = self._data_calculations(batch_X, batch_y)\n            learning_rate = self.learning_rate(i) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            metric_value = self._calculate_metric(batch_y, y_pred)\n            \n            if verbose and i % verbose == 0:\n                print(f\"{i} | loss: {loss} | {self.metric}: {metric_value}\")\n                \n        self.best_score = self._calculate_metric(y, self.predict_proba(X))\n        \n\n    def y_predict(self, X: np.ndarray):\n        z = np.dot(X, self.weights)\n        return 1 / (1 + np.exp(-z))\n\n    def get_coef(self):\n        return self.weights[1:]  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0435\u0441\u0430 \u0431\u0435\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0447\u043b\u0435\u043d\u0430\n    \n\n    def _log_loss(self, y_true, y_pred):\n        eps = 1e-15\n        return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))\n    \n    \n    def predict_proba(self, X: np.ndarray):\n        return self.y_predict(X)\n    \n    \n    def predict(self, X: np.ndarray):\n        proba = self.predict_proba(X)\n        return (proba >= 0.5).astype(int)\n    \n    \n    def _data_calculations(self, X, y):\n        regularization, regularization_gradient = self._calculate_regularization()\n        y_pred = self.y_predict(X)\n        loss = self._log_loss(y, y_pred) + regularization\n        gradient = np.dot(X.T, (y_pred - y)) / X.shape[0] + regularization_gradient\n        return y_pred, loss, gradient\n    \n    \n    def _calculate_metric(self, y_true, y_pred):\n        y_true, y_pred_binary = self._binary_labels(y_true, y_pred)\n\n        if self.metric == 'accuracy':\n            return accuracy_score(y_true, y_pred_binary)\n        elif self.metric == 'f1':\n            return f1_score(y_true, y_pred_binary)\n        elif self.metric == 'roc_auc':\n            if len(np.unique(y_true)) > 1:\n                roc_auc = roc_auc_score(y_true, y_pred)\n            else:\n                roc_auc = None\n            return roc_auc\n        elif self.metric == 'precision':\n            return precision_score(y_true, y_pred_binary)\n        elif self.metric == 'recall':\n            return recall_score(y_true, y_pred_binary)\n        else:\n            return None\n\n\n    def _binary_labels(self, y_true, y_pred):\n        median = np.median(y_true)\n        y_true = (y_true >= median).astype(int)\n        y_pred_binary = (y_pred >= 0.5).astype(int)\n        return y_true, y_pred_binary\n\n\n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _calculate_regularization(self):\n        reg_type = self.reg\n        weights = self.weights\n        l1_coef = self.l1_coef\n        l2_coef = self.l2_coef\n        \n        if reg_type == 'l1':\n            regularization = l1_coef * np.sum(np.abs(weights))\n            gradient_reg = l1_coef * np.sign(weights)\n        elif reg_type == 'l2':\n            regularization = l2_coef * np.sum(weights ** 2)\n            gradient_reg = 2 * l2_coef * weights\n        elif reg_type == 'elasticnet':\n            regularization = (l1_coef * np.sum(np.abs(weights)) +\n                             l2_coef * np.sum(weights ** 2))\n            gradient_reg = (l1_coef * np.sign(weights) +\n                           2 * l2_coef * weights)\n        else:\n            regularization = 0\n            gradient_reg = 0\n            \n        return regularization, gradient_reg\n    \n    \n    def _get_mini_batch(self, X, y):\n        \"\"\"Returns a mini-batch of data.\"\"\"\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))  # sample size as fraction of X.shape[0]\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n\n\ndef test__data_calculations():\n    # Create an instance of MyLogReg\n    model = MyLogReg()\n\n    # Mock data to test the _data_calculations method\n    X_mock = np.array([[0.5, 1.5], [1.0, 2.0], [1.5, 2.5]])\n    y_mock = np.array([0, 1, 0])\n\n    # Initialize weights for the instance\n    model.weights = np.ones(X_mock.shape[1] + 1)  # Adding 1 for the bias term\n\n    # Prepare input data with bias term\n    X_input = np.hstack((np.ones((X_mock.shape[0], 1)), X_mock))\n\n    # Get results from the original implementation\n    y_pred_orig, loss_orig, gradient_orig = model._data_calculations(X_input, y_mock)\n\n    # Get results from the new implementation\n    y_pred_new, loss_new, gradient_new = model._data_calculations_new_implementation(X_input, y_mock)\n\n    # Assertions to check if both implementations give the same results\n    assert np.allclose(y_pred_orig, y_pred_new), \"Predicted probabilities do not match\"\n    assert np.isclose(loss_orig, loss_new), \"Loss values do not match\"\n    assert np.allclose(gradient_orig, gradient_new), \"Gradient values do not match\"\n\nif __name__ == \"__main__\":\n    test__data_calculations()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of functionality. The revised function `_data_calculations` in the `MyLogReg` class performs the same operations as the original function: it calculates regularization and its gradient, predicts `y` using `y_predict`, computes the loss using `_log_loss` with regularization added, and calculates the gradient. The operations and their order are the same, ensuring that the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `_data_calculations` returns three values: `y_pred`, `loss`, and `gradient`. Therefore, it satisfies the condition of having return values.\n\n- CONDITION 2: The test function `test__data_calculations` checks the return values of `_data_calculations` and `_data_calculations_new_implementation` using assertions. It does not rely on printed or logged content, thus satisfying this condition.\n\n- CONDITION 3: The test function compares the outputs of `_data_calculations` and `_data_calculations_new_implementation` using `np.allclose` and `np.isclose`, which are appropriate for checking numerical equality. This ensures that the new implementation must have exactly the same functionality as the original to pass the tests.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_data_calculations` returns values. The assertions are appropriate for the type of data being compared.\n\n- CONDITION 5: The test cases use a non-trivial set of mock data (`X_mock` and `y_mock`) to test the functionality. The data is varied enough to provide a meaningful test of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyLogReg._log_loss",
        "idx": "507",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "log_reg.py",
        "orig_func": "def _log_loss(self, y_true, y_pred):\n    eps = 1e-15\n    return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))",
        "orig_context": "```python\n## log_reg.py\nimport random\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score\n\nclass MyLogReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter \n        self.learning_rate = learning_rate \n        self.weights = weights\n        self.metric = metric\n        self.best_score = None\n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n\n        \n    def __str__(self):\n        return f\"MyLineReg class: n_iter={self.n_iter}, learning_rate={self.learning_rate}\"\n\n\n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose=False):\n        random.seed(self.random_state)\n        X = np.hstack((np.ones((X.shape[0], 1)), X))  # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0435\u0434\u0438\u043d\u0438\u0446\n        self.weights = np.ones(X.shape[1])  # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c weights \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c\u044e\n        \n        for i in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X, y)\n            y_pred, loss, gradient = self._data_calculations(batch_X, batch_y)\n            learning_rate = self.learning_rate(i) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            metric_value = self._calculate_metric(batch_y, y_pred)\n            \n            if verbose and i % verbose == 0:\n                print(f\"{i} | loss: {loss} | {self.metric}: {metric_value}\")\n                \n        self.best_score = self._calculate_metric(y, self.predict_proba(X))\n        \n\n    def y_predict(self, X: np.ndarray):\n        z = np.dot(X, self.weights)\n        return 1 / (1 + np.exp(-z))\n\n    def get_coef(self):\n        return self.weights[1:]  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0435\u0441\u0430 \u0431\u0435\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0447\u043b\u0435\u043d\u0430\n    \n\n    def _log_loss(self, y_true, y_pred):\n        eps = 1e-15\n        return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))\n    \n    \n    def predict_proba(self, X: np.ndarray):\n        return self.y_predict(X)\n    \n    \n    def predict(self, X: np.ndarray):\n        proba = self.predict_proba(X)\n        return (proba >= 0.5).astype(int)\n    \n    \n    def _data_calculations(self, X, y):\n        regularization, regularization_gradient = self._calculate_regularization()\n        y_pred = self.y_predict(X)\n        loss = self._log_loss(y, y_pred) + regularization\n        gradient = np.dot(X.T, (y_pred - y)) / X.shape[0] + regularization_gradient\n        return y_pred, loss, gradient\n    \n    \n    def _calculate_metric(self, y_true, y_pred):\n        y_true, y_pred_binary = self._binary_labels(y_true, y_pred)\n\n        if self.metric == 'accuracy':\n            return accuracy_score(y_true, y_pred_binary)\n        elif self.metric == 'f1':\n            return f1_score(y_true, y_pred_binary)\n        elif self.metric == 'roc_auc':\n            if len(np.unique(y_true)) > 1:\n                roc_auc = roc_auc_score(y_true, y_pred)\n            else:\n                roc_auc = None\n            return roc_auc\n        elif self.metric == 'precision':\n            return precision_score(y_true, y_pred_binary)\n        elif self.metric == 'recall':\n            return recall_score(y_true, y_pred_binary)\n        else:\n            return None\n\n\n    def _binary_labels(self, y_true, y_pred):\n        median = np.median(y_true)\n        y_true = (y_true >= median).astype(int)\n        y_pred_binary = (y_pred >= 0.5).astype(int)\n        return y_true, y_pred_binary\n\n\n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _calculate_regularization(self):\n        reg_type = self.reg\n        weights = self.weights\n        l1_coef = self.l1_coef\n        l2_coef = self.l2_coef\n        \n        if reg_type == 'l1':\n            regularization = l1_coef * np.sum(np.abs(weights))\n            gradient_reg = l1_coef * np.sign(weights)\n        elif reg_type == 'l2':\n            regularization = l2_coef * np.sum(weights ** 2)\n            gradient_reg = 2 * l2_coef * weights\n        elif reg_type == 'elasticnet':\n            regularization = (l1_coef * np.sum(np.abs(weights)) +\n                             l2_coef * np.sum(weights ** 2))\n            gradient_reg = (l1_coef * np.sign(weights) +\n                           2 * l2_coef * weights)\n        else:\n            regularization = 0\n            gradient_reg = 0\n            \n        return regularization, gradient_reg\n    \n    \n    def _get_mini_batch(self, X, y):\n        \"\"\"Returns a mini-batch of data.\"\"\"\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))  # sample size as fraction of X.shape[0]\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n\n```\n\n\n",
        "eval_script": "## log_reg.py\nimport random\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score\n\nclass MyLogReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter \n        self.learning_rate = learning_rate \n        self.weights = weights\n        self.metric = metric\n        self.best_score = None\n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n\n        \n    def __str__(self):\n        return f\"MyLineReg class: n_iter={self.n_iter}, learning_rate={self.learning_rate}\"\n\n\n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose=False):\n        random.seed(self.random_state)\n        X = np.hstack((np.ones((X.shape[0], 1)), X))  # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0435\u0434\u0438\u043d\u0438\u0446\n        self.weights = np.ones(X.shape[1])  # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c weights \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c\u044e\n        \n        for i in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X, y)\n            y_pred, loss, gradient = self._data_calculations(batch_X, batch_y)\n            learning_rate = self.learning_rate(i) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            metric_value = self._calculate_metric(batch_y, y_pred)\n            \n            if verbose and i % verbose == 0:\n                print(f\"{i} | loss: {loss} | {self.metric}: {metric_value}\")\n                \n        self.best_score = self._calculate_metric(y, self.predict_proba(X))\n        \n\n    def y_predict(self, X: np.ndarray):\n        z = np.dot(X, self.weights)\n        return 1 / (1 + np.exp(-z))\n\n    def get_coef(self):\n        return self.weights[1:]  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432\u0435\u0441\u0430 \u0431\u0435\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0447\u043b\u0435\u043d\u0430\n    \n\n    def _log_loss(self, y_true, y_pred):\n        eps = 1e-15\n        return -np.mean(y_true * np.log(y_pred + eps) + (1 - y_true) * np.log(1 - y_pred + eps))\n    \n    \n    def predict_proba(self, X: np.ndarray):\n        return self.y_predict(X)\n    \n    \n    def predict(self, X: np.ndarray):\n        proba = self.predict_proba(X)\n        return (proba >= 0.5).astype(int)\n    \n    \n    def _data_calculations(self, X, y):\n        regularization, regularization_gradient = self._calculate_regularization()\n        y_pred = self.y_predict(X)\n        loss = self._log_loss(y, y_pred) + regularization\n        gradient = np.dot(X.T, (y_pred - y)) / X.shape[0] + regularization_gradient\n        return y_pred, loss, gradient\n    \n    \n    def _calculate_metric(self, y_true, y_pred):\n        y_true, y_pred_binary = self._binary_labels(y_true, y_pred)\n\n        if self.metric == 'accuracy':\n            return accuracy_score(y_true, y_pred_binary)\n        elif self.metric == 'f1':\n            return f1_score(y_true, y_pred_binary)\n        elif self.metric == 'roc_auc':\n            if len(np.unique(y_true)) > 1:\n                roc_auc = roc_auc_score(y_true, y_pred)\n            else:\n                roc_auc = None\n            return roc_auc\n        elif self.metric == 'precision':\n            return precision_score(y_true, y_pred_binary)\n        elif self.metric == 'recall':\n            return recall_score(y_true, y_pred_binary)\n        else:\n            return None\n\n\n    def _binary_labels(self, y_true, y_pred):\n        median = np.median(y_true)\n        y_true = (y_true >= median).astype(int)\n        y_pred_binary = (y_pred >= 0.5).astype(int)\n        return y_true, y_pred_binary\n\n\n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _calculate_regularization(self):\n        reg_type = self.reg\n        weights = self.weights\n        l1_coef = self.l1_coef\n        l2_coef = self.l2_coef\n        \n        if reg_type == 'l1':\n            regularization = l1_coef * np.sum(np.abs(weights))\n            gradient_reg = l1_coef * np.sign(weights)\n        elif reg_type == 'l2':\n            regularization = l2_coef * np.sum(weights ** 2)\n            gradient_reg = 2 * l2_coef * weights\n        elif reg_type == 'elasticnet':\n            regularization = (l1_coef * np.sum(np.abs(weights)) +\n                             l2_coef * np.sum(weights ** 2))\n            gradient_reg = (l1_coef * np.sign(weights) +\n                           2 * l2_coef * weights)\n        else:\n            regularization = 0\n            gradient_reg = 0\n            \n        return regularization, gradient_reg\n    \n    \n    def _get_mini_batch(self, X, y):\n        \"\"\"Returns a mini-batch of data.\"\"\"\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))  # sample size as fraction of X.shape[0]\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n\n\ndef test__log_loss():\n    model = MyLogReg()\n\n    # Test case 1: Perfect prediction\n    y_true = np.array([1, 0, 1, 0])\n    y_pred = np.array([1, 0, 1, 0])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 2: Completely wrong prediction\n    y_true = np.array([1, 0, 1, 0])\n    y_pred = np.array([0, 1, 0, 1])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 3: Mixed prediction\n    y_true = np.array([1, 0, 1, 0])\n    y_pred = np.array([0.9, 0.1, 0.8, 0.2])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 4: Small values\n    y_true = np.array([1, 0, 1, 0])\n    y_pred = np.array([1e-15, 1-1e-15, 1e-15, 1-1e-15])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 5: Large arrays\n    y_true = np.random.randint(0, 2, size=10000)\n    y_pred = np.random.rand(10000)\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 6: Single element\n    y_true = np.array([1])\n    y_pred = np.array([0.5])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 7: All ones\n    y_true = np.array([1, 1, 1, 1])\n    y_pred = np.array([1, 1, 1, 1])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 8: All zeros\n    y_true = np.array([0, 0, 0, 0])\n    y_pred = np.array([0, 0, 0, 0])\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\n    # Test case 9: Random predictions\n    y_true = np.random.randint(0, 2, size=10)\n    y_pred = np.random.rand(10)\n    assert model._log_loss(y_true, y_pred) == model._log_loss_new_implementation(y_true, y_pred)\n\nif __name__ == \"__main__\":\n    test__log_loss()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_log_loss` in the `MyLogReg` class is identical to the ORIGINAL FUNCTION. Both functions calculate the log loss using the same formula with an epsilon value of `1e-15` to prevent logarithm of zero. The logic and implementation are exactly the same, with no changes in functionality or logic.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_log_loss` function returns a value, specifically the log loss calculation, which satisfies this condition.\n- CONDITION 2: The test cases use assertions to check the return values of `_log_loss` and `_log_loss_new_implementation`, not printed or logged outputs, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_log_loss` and `_log_loss_new_implementation` for various inputs, ensuring that `_log_loss_new_implementation` must have the same functionality to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare return values, which is appropriate since `_log_loss` returns a value. This satisfies the condition.\n- CONDITION 5: The test cases cover a range of scenarios, including perfect predictions, completely wrong predictions, mixed predictions, edge cases with small values, large arrays, single elements, all ones, all zeros, and random predictions. This ensures the test cases are non-trivial.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "MyLineReg.fit",
        "idx": "512",
        "repo_name": "xNightish___mash_obuch",
        "func_path": "line_reg.py",
        "orig_func": "def fit(self, X: pd.DataFrame, y: pd.Series, verbose: int=0) -> None:\n    random.seed(self.random_state)\n    X_with_intercept = np.hstack((np.ones((X.shape[0], 1)), X))\n    self.weights = np.ones(X_with_intercept.shape[1])\n    self.y = y\n    self.X = X_with_intercept\n    for iteration in range(1, self.n_iter + 1):\n        batch_X, batch_y = self._get_mini_batch(X_with_intercept, y)\n        predictions = np.dot(batch_X, self.weights)\n        errors = predictions - batch_y\n        regularization, regularization_gradient = self._calculate_reg_and_grad()\n        loss = np.mean(errors ** 2) + regularization\n        self.loss_history.append(loss)\n        gradient = 2 / batch_X.shape[0] * np.dot(batch_X.T, errors) + regularization_gradient\n        learning_rate = self.learning_rate(iteration) if callable(self.learning_rate) else self.learning_rate\n        self.weights -= learning_rate * gradient\n        if self.metric and iteration % verbose == 0:\n            metric_value = self._calculate_metric(y, np.dot(X_with_intercept, self.weights))\n            print(f'Iteration {iteration} | Loss: {loss:.5f} | {self.metric}: {metric_value:.5f}')\n    self.best_score = self._calculate_metric(y, np.dot(X_with_intercept, self.weights)) if self.metric else None",
        "orig_context": "```python\n## line_reg.py\nimport random\n\nfrom matplotlib import pyplot as plt\n\nimport numpy as np\n\nimport pandas as pd\n\nX = pd.DataFrame(X)\n\ny = pd.Series(y)\n\nX.columns = [f'col_{col}' for col in X.columns]\n\nclass MyLineReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter\n        self.learning_rate = learning_rate\n        self.weights = weights\n        self.metric = metric\n        self.best_score = None \n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n        self.loss_history = []\n        \n        \n    def __str__(self) -> str:\n        return (\n            f\"MyLineReg(n_iter={self.n_iter}, learning_rate={self.learning_rate}, \"\n            f\"metric={self.metric}, reg={self.reg}, l1_coef={self.l1_coef}, \"\n            f\"l2_coef={self.l2_coef}, sgd_sample={self.sgd_sample}, \"\n            f\"random_state={self.random_state})\"\n        )\n\n    \n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose: int = 0) -> None:\n        random.seed(self.random_state)\n        X_with_intercept = np.hstack((np.ones((X.shape[0], 1)), X))\n        self.weights = np.ones(X_with_intercept.shape[1])\n        self.y = y\n        self.X = X_with_intercept\n        \n        for iteration in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X_with_intercept, y)\n            predictions = np.dot(batch_X, self.weights)\n            errors = predictions - batch_y\n            regularization, regularization_gradient = self._calculate_reg_and_grad()\n            loss = np.mean(errors ** 2) + regularization\n            self.loss_history.append(loss)\n            gradient = (2 / batch_X.shape[0] * np.dot(batch_X.T, errors)) + regularization_gradient\n            \n            \n            learning_rate = self.learning_rate(iteration) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            \n            if self.metric and iteration % verbose == 0:\n                metric_value = self._calculate_metric(y, np.dot(X_with_intercept,    self.weights))\n                print(f\"Iteration {iteration} | Loss: {loss:.5f} | {self.metric}: {metric_value:.5f}\")\n        \n        self.best_score = self._calculate_metric(y, np.dot(X_with_intercept, self.weights)) if self.metric else None\n\n\n    def get_coef(self):\n        return np.array(self.weights[1:])\n    \n    \n    def predict(self, features: pd.DataFrame) -> np.ndarray:\n        features_with_intercept = np.hstack((np.ones((features.shape[0], 1)), features))\n        return np.dot(features_with_intercept, self.weights)\n    \n    \n    def _calculate_metric(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:\n        metric = {\n            'mae': lambda y_true, y_pred: np.mean(np.abs(y_true - y_pred)),\n            'mse': lambda y_true, y_pred: np.mean((y_true - y_pred) ** 2),\n            'rmse': lambda y_true, y_pred: np.sqrt(np.mean((y_true - y_pred) ** 2)),\n            'mape': lambda y_true, y_pred: np.mean(np.abs((y_true - y_pred) / y_true)) * 100,\n            'r2': lambda y_true, y_pred: 1 - np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2)\n        }\n\n        return metric.get(self.metric, lambda y_true, y_pred: None)(y_true, y_pred)\n        \n    \n    \n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _get_mini_batch(self, X, y):\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n    \n    \n    def _calculate_reg_and_grad(self):\n        regularization = self._calculate_regularization()\n        gradient_reg = self._calculate_regularization_gradient()\n        return regularization, gradient_reg\n    \n    \n    def _calculate_regularization(self):\n        reg = {\n            'l1': lambda weights: self.l1_coef * np.sum(np.abs(weights)),\n            'l2': lambda weights: self.l2_coef * np.sum(weights ** 2),\n            'elasticnet': lambda weights: (self.l1_coef * np.sum(np.abs(weights)) +\n                                          self.l2_coef * np.sum(weights ** 2))\n        }\n\n        return reg.get(self.reg, lambda weights: 0)(self.weights)\n    \n    \n    def _calculate_regularization_gradient(self):\n        reg = {\n            'l1': lambda weights: (self.l1_coef * np.sign(weights)),\n            'l2': lambda weights: (2 * (self.l2_coef * weights)),\n            'elasticnet': lambda weights: ((self.l1_coef * np.sign(weights) +\n                                          2 * self.l2_coef * weights))\n        }\n\n        return reg.get(self.reg, lambda weights: 0)(self.weights)\n    \n    \n    def plot_loss(self, start=0, step=1):\n        plt.figure(figsize=(10, 6))\n        plt.plot(range(1, self.n_iter + 1)[start::step], self.loss_history[start::step], marker='o', linestyle='-', color='b')\n        plt.title('\u0418\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 Loss \u043f\u043e \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u044f\u043c')\n        plt.xlabel('\u0418\u0442\u0435\u0440\u0430\u0446\u0438\u0438')\n        plt.ylabel('Loss')\n        plt.grid(True)\n        plt.tight_layout()\n        plt.show()\n\n```\n\n\n",
        "eval_script": "## line_reg.py\nimport random\n\nfrom matplotlib import pyplot as plt\n\nimport numpy as np\n\nimport pandas as pd\n\nclass MyLineReg:\n    def __init__(self, n_iter=100, learning_rate=0.1, weights=None, metric=None, reg=None, l1_coef=0, l2_coef=0, sgd_sample=None, random_state=42):\n        self.n_iter = n_iter\n        self.learning_rate = learning_rate\n        self.weights = weights\n        self.metric = metric\n        self.best_score = None \n        self.reg = reg\n        self.l1_coef = l1_coef\n        self.l2_coef = l2_coef\n        self.sgd_sample = sgd_sample\n        self.random_state = random_state\n        self.loss_history = []\n        \n        \n    def __str__(self) -> str:\n        return (\n            f\"MyLineReg(n_iter={self.n_iter}, learning_rate={self.learning_rate}, \"\n            f\"metric={self.metric}, reg={self.reg}, l1_coef={self.l1_coef}, \"\n            f\"l2_coef={self.l2_coef}, sgd_sample={self.sgd_sample}, \"\n            f\"random_state={self.random_state})\"\n        )\n\n    \n    def fit(self, X: pd.DataFrame, y: pd.Series, verbose: int = 0) -> None:\n        random.seed(self.random_state)\n        X_with_intercept = np.hstack((np.ones((X.shape[0], 1)), X))\n        self.weights = np.ones(X_with_intercept.shape[1])\n        self.y = y\n        self.X = X_with_intercept\n        \n        for iteration in range(1, self.n_iter + 1):\n            batch_X, batch_y = self._get_mini_batch(X_with_intercept, y)\n            predictions = np.dot(batch_X, self.weights)\n            errors = predictions - batch_y\n            regularization, regularization_gradient = self._calculate_reg_and_grad()\n            loss = np.mean(errors ** 2) + regularization\n            self.loss_history.append(loss)\n            gradient = (2 / batch_X.shape[0] * np.dot(batch_X.T, errors)) + regularization_gradient\n            \n            \n            learning_rate = self.learning_rate(iteration) if callable(self.learning_rate) else self.learning_rate\n            self.weights -= learning_rate * gradient\n            \n            if self.metric and iteration % verbose == 0:\n                metric_value = self._calculate_metric(y, np.dot(X_with_intercept,    self.weights))\n                print(f\"Iteration {iteration} | Loss: {loss:.5f} | {self.metric}: {metric_value:.5f}\")\n        \n        self.best_score = self._calculate_metric(y, np.dot(X_with_intercept, self.weights)) if self.metric else None\n\n\n    def get_coef(self):\n        return np.array(self.weights[1:])\n    \n    \n    def predict(self, features: pd.DataFrame) -> np.ndarray:\n        features_with_intercept = np.hstack((np.ones((features.shape[0], 1)), features))\n        return np.dot(features_with_intercept, self.weights)\n    \n    \n    def _calculate_metric(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:\n        metric = {\n            'mae': lambda y_true, y_pred: np.mean(np.abs(y_true - y_pred)),\n            'mse': lambda y_true, y_pred: np.mean((y_true - y_pred) ** 2),\n            'rmse': lambda y_true, y_pred: np.sqrt(np.mean((y_true - y_pred) ** 2)),\n            'mape': lambda y_true, y_pred: np.mean(np.abs((y_true - y_pred) / y_true)) * 100,\n            'r2': lambda y_true, y_pred: 1 - np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2)\n        }\n\n        return metric.get(self.metric, lambda y_true, y_pred: None)(y_true, y_pred)\n        \n    \n    \n    def get_best_score(self):\n        return self.best_score\n    \n    \n    def _get_mini_batch(self, X, y):\n        if self.sgd_sample is not None:\n            sample_size = (\n                self.sgd_sample if isinstance(self.sgd_sample, int) else\n                max(1, int(self.sgd_sample * X.shape[0]))\n            )\n            sample_rows_idx = random.sample(range(X.shape[0]), sample_size)\n            X_batch = X[sample_rows_idx]\n            y_batch = y.iloc[sample_rows_idx].values\n        else:\n            X_batch = X\n            y_batch = y.values\n        return X_batch, y_batch\n    \n    \n    def _calculate_reg_and_grad(self):\n        regularization = self._calculate_regularization()\n        gradient_reg = self._calculate_regularization_gradient()\n        return regularization, gradient_reg\n    \n    \n    def _calculate_regularization(self):\n        reg = {\n            'l1': lambda weights: self.l1_coef * np.sum(np.abs(weights)),\n            'l2': lambda weights: self.l2_coef * np.sum(weights ** 2),\n            'elasticnet': lambda weights: (self.l1_coef * np.sum(np.abs(weights)) +\n                                          self.l2_coef * np.sum(weights ** 2))\n        }\n\n        return reg.get(self.reg, lambda weights: 0)(self.weights)\n    \n    \n    def _calculate_regularization_gradient(self):\n        reg = {\n            'l1': lambda weights: (self.l1_coef * np.sign(weights)),\n            'l2': lambda weights: (2 * (self.l2_coef * weights)),\n            'elasticnet': lambda weights: ((self.l1_coef * np.sign(weights) +\n                                          2 * self.l2_coef * weights))\n        }\n\n        return reg.get(self.reg, lambda weights: 0)(self.weights)\n    \n    \n    def plot_loss(self, start=0, step=1):\n        plt.figure(figsize=(10, 6))\n        plt.plot(range(1, self.n_iter + 1)[start::step], self.loss_history[start::step], marker='o', linestyle='-', color='b')\n        plt.title('\u0418\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 Loss \u043f\u043e \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u044f\u043c')\n        plt.xlabel('\u0418\u0442\u0435\u0440\u0430\u0446\u0438\u0438')\n        plt.ylabel('Loss')\n        plt.grid(True)\n        plt.tight_layout()\n        plt.show()\n\ndef test_fit():\n    # Create a simple dataset\n    X = pd.DataFrame({'feature1': [1, 2, 3, 4, 5], 'feature2': [2, 3, 4, 5, 6]})\n    y = pd.Series([1, 2, 3, 4, 5])\n\n    # Initialize two models\n    model_old = MyLineReg(n_iter=10, learning_rate=0.01, random_state=42)\n    model_new = MyLineReg(n_iter=10, learning_rate=0.01, random_state=42)\n\n    # Fit both models\n    model_old.fit(X, y)\n    model_new.fit_new_implementation(X, y)\n\n    # Assert weights are the same\n    assert np.allclose(model_old.weights, model_new.weights), \"Weights do not match\"\n\n    # Assert loss history is the same\n    assert np.allclose(model_old.loss_history, model_new.loss_history), \"Loss history does not match\"\n\n    # Assert best score is the same\n    assert model_old.get_best_score() == model_new.get_best_score(), \"Best score does not match\"\n\nif __name__ == \"__main__\":\n    test_fit()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is essentially the same as the ORIGINAL FUNCTION. Both functions perform the same operations in the same sequence: initializing weights, adding an intercept to the input data, iterating through the specified number of iterations, computing predictions, calculating errors, applying regularization, updating weights, and optionally printing metrics. The logic for calculating the learning rate, gradient, and loss is identical. The additional methods and attributes in the REVISED FUNCTION, such as `get_coef`, `predict`, and regularization methods, do not alter the core functionality of the `fit` method. The test function `test_fit` also suggests that the functionality is intended to be the same, as it compares the results of the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `fit` function modifies the `weights`, `loss_history`, and `best_score` attributes of the `MyLineReg` class, which are instance variables. Therefore, it satisfies the condition of modifying input arguments or global variables.\n  \n- [CONDITION 2] The test function `test_fit` checks the state of the `weights`, `loss_history`, and `best_score` attributes of the model instances. It does not rely on printed or logged content, satisfying this condition.\n\n- [CONDITION 3] The test cases compare the `weights`, `loss_history`, and `best_score` of the two model instances after fitting, which are the key outcomes of the `fit` function. If `fit_new_implementation` passes these tests, it must have the same functionality as `fit`, satisfying this condition.\n\n- [CONDITION 4] The test cases use `assert` statements to compare the relevant attributes of the two model instances. These assertions are reasonable because they check the outcomes that `fit` is expected to modify.\n\n- [CONDITION 5] The test cases are non-trivial as they check multiple aspects of the model's state after fitting, including `weights`, `loss_history`, and `best_score`, which are critical for evaluating the correctness of a linear regression implementation.",
            "answer": "yes"
        },
        "commit_id": "f8f093fefd36327aa50291bbc4a59fdc2117e9c3"
    },
    {
        "func_name": "User.convert_class_user_to_object",
        "idx": "545",
        "repo_name": "AbdallahM19___Note_FastApi",
        "func_path": "api/models/users.py",
        "orig_func": "@classmethod\ndef convert_class_user_to_object(cls, user: UserDb) -> dict:\n    \"\"\"Convert a UserDb object to a User dict\"\"\"\n    return {'id': user.id, 'username': user.username, 'email': user.email, 'hashed_password': user.hashed_password, 'session_id': user.session_id, 'time_created': user.time_created, 'last_opened': user.last_opened, 'date_of_birth': user.date_of_birth, 'description': user.description}",
        "orig_context": "```python\n## api/database.py\nfrom datetime import datetime\n\nfrom sqlalchemy.orm import DeclarativeBase, sessionmaker\n\nfrom sqlalchemy import create_engine, Column, Integer, String, Text, text, DateTime\n\nUSERNAME = \"root\"\n\nPASSWORD = \"Abdallah%402004\"\n\nHOST = \"localhost\"\n\nDATABASE = \"note_db\"\n\nclass Base(DeclarativeBase):\n    \"\"\"Base class for all models\"\"\"\n\nclass UserDb(Base):\n    \"\"\"This class represents the user table in the database.\"\"\"\n    __tablename__ = 'users'\n\n    id = Column(Integer, autoincrement=True, primary_key=True)\n    username = Column(String(50), unique=True, nullable=False)\n    email = Column(String(100), unique=True, nullable=False)\n    hashed_password = Column(String(255), nullable=False)\n    session_id = Column(String(40), default=None)\n    time_created = Column(DateTime, default=datetime.now)\n    last_opened = Column(DateTime, default=datetime.now)\n    date_of_birth = Column(Text, default=None)\n    description = Column(String(500), default=None)\n\ndef create_engine_and_connect():\n    \"\"\"Creates the engine and connects to the database.\"\"\"\n    return create_engine(\n        f'mysql+mysqlconnector://{USERNAME}:{PASSWORD}@{HOST}/{DATABASE}'\n    )\n\ndef get_session():\n    \"\"\"Return a new session.\"\"\"\n    engine = create_engine_and_connect()\n    session = sessionmaker(bind=engine)\n    return session()\n\n```\n\n\n```python\n## api/models/users.py\nfrom typing import Union, Optional\n\nfrom sqlalchemy import or_, and_\n\nfrom sqlalchemy.exc import SQLAlchemyError\n\nfrom api.database import UserDb, get_session\n\nclass User():\n    \"\"\"User Class\"\"\"\n    def __init__(self):\n        self.sess = get_session()\n        # self.users_data = [\n        #     {\"id\": 1, \"username\": \"Ali\"},\n        #     {\"id\": 2, \"username\": \"John\"},\n        #     {\"id\": 3, \"username\": \"Jane\"},\n        #     {\"id\": 4, \"username\": \"Bob\"},\n        #     {\"id\": 5, \"username\": \"Alice\"},\n        #     {\"id\": 6, \"username\": \"Charlie\"},\n        #     {\"id\": 7, \"username\": \"Diana\"},\n        #     {\"id\": 8, \"username\": \"Eva\"},\n        #     {\"id\": 9, \"username\": \"Frank\"},\n        #     {\"id\": 10, \"username\": \"Gabriel\"},\n        #     {\"id\": 11, \"username\": \"Hannah\"},\n        #     {\"id\": 12, \"username\": \"Isaac\"},\n        #     {\"id\": 13, \"username\": \"Julia\"},\n        #     {\"id\": 14, \"username\": \"Kevin\"},\n        #     {\"id\": 15, \"username\": \"Lily\"}\n        # ]\n\n    def get_user_by_id(self, user_id):\n        \"\"\"Get user by id function\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                UserDb.id == user_id\n            ).first()\n            return user\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting user by id: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def get_user_by_session_id(self, session_id):\n        \"\"\"Get user by session id function\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                UserDb.session_id == session_id\n            ).first()\n            return user\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting user by session id: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def get_user_by_username(\n        self, name: str, skip: Optional[int] = 0, limit: Optional[int] = None\n    ) -> Union[list, dict, str]:\n        \"\"\"Get user by username function\"\"\"\n        try:\n            users_data = self.sess.query(UserDb).filter(\n                UserDb.username.like(f\"%{name.lower()}%\")\n            ).offset(skip).limit(limit).all()\n\n            if not users_data:\n                return f\"User with name {name} not found\"\n\n            return users_data\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting user by username: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def get_all_users_data(\n        self,\n        skip: Optional[int],\n        limit: Optional[int]\n    ) -> list:\n        \"\"\"Get all users in list of dict\"\"\"\n        try:\n            users = self.sess.query(UserDb)\n\n            if skip is not None and limit is not None:\n                users = users.offset(skip).limit(limit)\n            elif skip and limit is None:\n                users = users.offset(skip).limit(10)\n            elif skip is None and limit:\n                users = users.offset(0).limit(limit)\n\n            return users.all()\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting all users: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def check_if_user_exists(self, username: str, email: str) -> Optional[UserDb]:\n        \"\"\"Check if user exists in database\"\"\"\n        try:\n            user_existed = self.sess.query(UserDb).filter(\n                or_(\n                    UserDb.username == username,\n                    UserDb.email == email\n                )\n            ).first()\n            if user_existed:\n                return user_existed\n            return None\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error checking user existence: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    # authenticate_user Not Used \u2b07\n    def authenticate_user(self, username: str, password: str) -> Union[dict, str]:\n        \"\"\"Authenticate user by username and password\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                and_(\n                    or_(\n                        UserDb.username == username,\n                        UserDb.email == username\n                    )\n                    # UserDb.hashed_password == password\n                )\n            ).first()\n            if user:\n                if user.hashed_password == password:\n                    return self.convert_class_user_to_object(user)\n                return \"Invalid password. password not correct\"\n            return \"Invalid username. user not exists\"\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error authenticating user: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def insert_new_user(self, **kwargs: dict):\n        \"\"\"Insert new user into database\"\"\"\n        try:\n            new_user = UserDb(**kwargs)\n            self.sess.add(new_user)\n            self.sess.commit()\n            self.sess.refresh(new_user)\n            return new_user\n        except SQLAlchemyError as e:\n            self.sess.rollback()\n            raise SQLAlchemyError(f\"Error inserting new user: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def update_user_account(self, kwargs: dict) -> dict:\n        \"\"\"Update user account information\"\"\"\n        try:\n            user = None\n\n            if kwargs['session_id']:\n                user = self.sess.query(UserDb).filter(\n                    UserDb.session_id == kwargs['session_id']\n                ).first()\n            elif kwargs['id']:\n                user = self.sess.query(UserDb).filter(\n                    UserDb.id == kwargs['id']\n                ).first()\n\n            if user:\n                for key, value in kwargs.items():\n                    if key not in ['id', 'session_id'] and value is not None:\n                        setattr(user, key, value)\n                self.sess.commit()\n                return True\n            return False\n        except SQLAlchemyError as e:\n            self.sess.rollback()\n            raise SQLAlchemyError(f\"Error updating user account: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def delete_user(self, user_id: int) -> bool:\n        \"\"\"Delete user Account permanently from database\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                UserDb.id == user_id\n            ).first()\n\n            if user:\n                self.sess.delete(user)\n                self.sess.commit()\n                return True\n\n            return False\n        except SQLAlchemyError as e:\n            self.sess.rollback()\n            raise SQLAlchemyError(f\"Error deleting user with id ({user_id}): {e}\") from e\n        finally:\n            self.sess.close()\n\n    @classmethod\n    def convert_class_user_to_object(cls, user: UserDb) -> dict:\n        \"\"\"Convert a UserDb object to a User dict\"\"\"\n        return {\n            \"id\": user.id,\n            \"username\": user.username,\n            \"email\": user.email,\n            \"hashed_password\": user.hashed_password,\n            \"session_id\": user.session_id,\n            \"time_created\": user.time_created,\n            \"last_opened\": user.last_opened,\n            \"date_of_birth\": user.date_of_birth,\n            \"description\": user.description,\n        }\n\n```\n\n\n",
        "eval_script": "# Combined code from api/models/users.py and api/database.py\n\nfrom datetime import datetime\nfrom typing import Union, Optional\n\nfrom sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, or_, and_\nfrom sqlalchemy.orm import DeclarativeBase, sessionmaker\nfrom sqlalchemy.exc import SQLAlchemyError\n\n# Mock database credentials and connection\nUSERNAME = \"root\"\nPASSWORD = \"Abdallah%402004\"\nHOST = \"localhost\"\nDATABASE = \"note_db\"\n\nclass Base(DeclarativeBase):\n    \"\"\"Base class for all models\"\"\"\n\nclass UserDb(Base):\n    \"\"\"This class represents the user table in the database.\"\"\"\n    __tablename__ = 'users'\n\n    id = Column(Integer, autoincrement=True, primary_key=True)\n    username = Column(String(50), unique=True, nullable=False)\n    email = Column(String(100), unique=True, nullable=False)\n    hashed_password = Column(String(255), nullable=False)\n    session_id = Column(String(40), default=None)\n    time_created = Column(DateTime, default=datetime.now)\n    last_opened = Column(DateTime, default=datetime.now)\n    date_of_birth = Column(Text, default=None)\n    description = Column(String(500), default=None)\n\ndef create_engine_and_connect():\n    \"\"\"Creates the engine and connects to the database.\"\"\"\n    # Mock connection string\n    return create_engine(\n        f'sqlite:///:memory:'  # Using in-memory SQLite database for testing\n    )\n\ndef get_session():\n    \"\"\"Return a new session.\"\"\"\n    engine = create_engine_and_connect()\n    session = sessionmaker(bind=engine)\n    return session()\n\nclass User():\n    \"\"\"User Class\"\"\"\n    def __init__(self):\n        self.sess = get_session()\n\n    def get_user_by_id(self, user_id):\n        \"\"\"Get user by id function\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                UserDb.id == user_id\n            ).first()\n            return user\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting user by id: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def get_user_by_session_id(self, session_id):\n        \"\"\"Get user by session id function\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                UserDb.session_id == session_id\n            ).first()\n            return user\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting user by session id: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def get_user_by_username(\n        self, name: str, skip: Optional[int] = 0, limit: Optional[int] = None\n    ) -> Union[list, dict, str]:\n        \"\"\"Get user by username function\"\"\"\n        try:\n            users_data = self.sess.query(UserDb).filter(\n                UserDb.username.like(f\"%{name.lower()}%\")\n            ).offset(skip).limit(limit).all()\n\n            if not users_data:\n                return f\"User with name {name} not found\"\n\n            return users_data\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting user by username: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def get_all_users_data(\n        self,\n        skip: Optional[int],\n        limit: Optional[int]\n    ) -> list:\n        \"\"\"Get all users in list of dict\"\"\"\n        try:\n            users = self.sess.query(UserDb)\n\n            if skip is not None and limit is not None:\n                users = users.offset(skip).limit(limit)\n            elif skip and limit is None:\n                users = users.offset(skip).limit(10)\n            elif skip is None and limit:\n                users = users.offset(0).limit(limit)\n\n            return users.all()\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error getting all users: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def check_if_user_exists(self, username: str, email: str) -> Optional[UserDb]:\n        \"\"\"Check if user exists in database\"\"\"\n        try:\n            user_existed = self.sess.query(UserDb).filter(\n                or_(\n                    UserDb.username == username,\n                    UserDb.email == email\n                )\n            ).first()\n            if user_existed:\n                return user_existed\n            return None\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error checking user existence: {str(e)}\") from e\n        finally:\n            self.sess.close()\n\n    def authenticate_user(self, username: str, password: str) -> Union[dict, str]:\n        \"\"\"Authenticate user by username and password\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                and_(\n                    or_(\n                        UserDb.username == username,\n                        UserDb.email == username\n                    )\n                )\n            ).first()\n            if user:\n                if user.hashed_password == password:\n                    return self.convert_class_user_to_object(user)\n                return \"Invalid password. password not correct\"\n            return \"Invalid username. user not exists\"\n        except SQLAlchemyError as e:\n            raise SQLAlchemyError(f\"Error authenticating user: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def insert_new_user(self, **kwargs: dict):\n        \"\"\"Insert new user into database\"\"\"\n        try:\n            new_user = UserDb(**kwargs)\n            self.sess.add(new_user)\n            self.sess.commit()\n            self.sess.refresh(new_user)\n            return new_user\n        except SQLAlchemyError as e:\n            self.sess.rollback()\n            raise SQLAlchemyError(f\"Error inserting new user: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def update_user_account(self, kwargs: dict) -> dict:\n        \"\"\"Update user account information\"\"\"\n        try:\n            user = None\n\n            if kwargs['session_id']:\n                user = self.sess.query(UserDb).filter(\n                    UserDb.session_id == kwargs['session_id']\n                ).first()\n            elif kwargs['id']:\n                user = self.sess.query(UserDb).filter(\n                    UserDb.id == kwargs['id']\n                ).first()\n\n            if user:\n                for key, value in kwargs.items():\n                    if key not in ['id', 'session_id'] and value is not None:\n                        setattr(user, key, value)\n                self.sess.commit()\n                return True\n            return False\n        except SQLAlchemyError as e:\n            self.sess.rollback()\n            raise SQLAlchemyError(f\"Error updating user account: {e}\") from e\n        finally:\n            self.sess.close()\n\n    def delete_user(self, user_id: int) -> bool:\n        \"\"\"Delete user Account permanently from database\"\"\"\n        try:\n            user = self.sess.query(UserDb).filter(\n                UserDb.id == user_id\n            ).first()\n\n            if user:\n                self.sess.delete(user)\n                self.sess.commit()\n                return True\n\n            return False\n        except SQLAlchemyError as e:\n            self.sess.rollback()\n            raise SQLAlchemyError(f\"Error deleting user with id ({user_id}): {e}\") from e\n        finally:\n            self.sess.close()\n\n    @classmethod\n    def convert_class_user_to_object(cls, user: UserDb) -> dict:\n        \"\"\"Convert a UserDb object to a User dict\"\"\"\n        return {\n            \"id\": user.id,\n            \"username\": user.username,\n            \"email\": user.email,\n            \"hashed_password\": user.hashed_password,\n            \"session_id\": user.session_id,\n            \"time_created\": user.time_created,\n            \"last_opened\": user.last_opened,\n            \"date_of_birth\": user.date_of_birth,\n            \"description\": user.description,\n        }\n\n\ndef test_convert_class_user_to_object():\n    \"\"\"Test function to compare the original and new implementations\"\"\"\n    user1 = UserDb(\n        id=1,\n        username=\"testuser\",\n        email=\"test@example.com\",\n        hashed_password=\"hashedpassword\",\n        session_id=\"session123\",\n        time_created=datetime(2023, 1, 1),\n        last_opened=datetime(2023, 1, 2),\n        date_of_birth=\"1990-01-01\",\n        description=\"Test user\"\n    )\n\n    user2 = UserDb(\n        id=2,\n        username=\"anotheruser\",\n        email=\"another@example.com\",\n        hashed_password=\"anotherhashedpassword\",\n        session_id=None,\n        time_created=datetime(2023, 2, 1),\n        last_opened=datetime(2023, 2, 2),\n        date_of_birth=None,\n        description=None\n    )\n\n    user3 = UserDb(\n        id=3,\n        username=\"thirduser\",\n        email=\"third@example.com\",\n        hashed_password=\"thirdhashedpassword\",\n        session_id=\"session456\",\n        time_created=datetime(2023, 3, 1),\n        last_opened=datetime(2023, 3, 2),\n        date_of_birth=\"1985-05-05\",\n        description=\"Third test user\"\n    )\n\n    user4 = UserDb(\n        id=0,\n        username=\"\",\n        email=\"\",\n        hashed_password=\"\",\n        session_id=\"\",\n        time_created=datetime(2023, 4, 1),\n        last_opened=datetime(2023, 4, 2),\n        date_of_birth=\"\",\n        description=\"\"\n    )\n\n    user5 = UserDb(\n        id=999999999,\n        username=\"special!@#$%^&*()\",\n        email=\"special@example.com\",\n        hashed_password=\"specialhashedpassword\",\n        session_id=\"session789\",\n        time_created=datetime(2023, 5, 1),\n        last_opened=datetime(2023, 5, 2),\n        date_of_birth=\"2000-12-31\",\n        description=\"Special characters test\"\n    )\n\n    assert User.convert_class_user_to_object(user1) == User.convert_class_user_to_object_new_implementation(user1)\n    assert User.convert_class_user_to_object(user2) == User.convert_class_user_to_object_new_implementation(user2)\n    assert User.convert_class_user_to_object(user3) == User.convert_class_user_to_object_new_implementation(user3)\n    assert User.convert_class_user_to_object(user4) == User.convert_class_user_to_object_new_implementation(user4)\n    assert User.convert_class_user_to_object(user5) == User.convert_class_user_to_object_new_implementation(user5)\n\nif __name__ == \"__main__\":\n    test_convert_class_user_to_object()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are class methods of the `User` class and take a `UserDb` object as input, returning a dictionary with the same keys and values extracted from the `UserDb` object. The structure and logic of converting the `UserDb` object to a dictionary are unchanged between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `convert_class_user_to_object` returns a dictionary, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `convert_class_user_to_object` and `convert_class_user_to_object_new_implementation`. There is no checking of printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `convert_class_user_to_object` and `convert_class_user_to_object_new_implementation` for various inputs. This ensures that the new implementation must have the exact same functionality as the original to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `convert_class_user_to_object` returns a dictionary. This is reasonable, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including typical user data, empty fields, and special characters. This makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "c2424fb27f625b3db0c2d06baa09a991f7739d01"
    },
    {
        "func_name": "heranca",
        "idx": "548",
        "repo_name": "RicardoMourao-py___mlops-predict-online-or-batch",
        "func_path": "model/simulator/data_simulator.py",
        "orig_func": "def heranca(genotipo_pai, genotipo_mae):\n    genotipo_filho = {}\n    for g in genotipo_pai:\n        selecao_pai = np.random.randint(2)\n        selecao_mae = np.random.randint(2)\n        genotipo_filho[g] = (genotipo_pai[g][selecao_pai], genotipo_mae[g][selecao_mae])\n    return genotipo_filho",
        "orig_context": "```python\n## model/simulator/data_simulator.py\nimport numpy as np\n\ndef heranca(genotipo_pai, genotipo_mae):\n    genotipo_filho = {}\n    for g in genotipo_pai:\n        selecao_pai = np.random.randint(2)\n        selecao_mae = np.random.randint(2)\n        genotipo_filho[g] = (\n            genotipo_pai[g][selecao_pai],\n            genotipo_mae[g][selecao_mae],\n        )\n    return genotipo_filho\n\n```\n\n\n",
        "eval_script": "## model/simulator/data_simulator.py\nimport numpy as np\n\ndef heranca(genotipo_pai, genotipo_mae):\n    genotipo_filho = {}\n    for g in genotipo_pai:\n        selecao_pai = np.random.randint(2)\n        selecao_mae = np.random.randint(2)\n        genotipo_filho[g] = (\n            genotipo_pai[g][selecao_pai],\n            genotipo_mae[g][selecao_mae],\n        )\n    return genotipo_filho\n\n\ndef test_heranca():\n    # Test case 1: Simple case with one gene\n    genotipo_pai = {'gene1': ('A', 'a')}\n    genotipo_mae = {'gene1': ('B', 'b')}\n    np.random.seed(0)\n    result1 = heranca(genotipo_pai, genotipo_mae)\n    np.random.seed(0)\n    result2 = heranca_new_implementation(genotipo_pai, genotipo_mae)\n    assert result1 == result2\n    \n    # Test case 2: Multiple genes\n    genotipo_pai = {'gene1': ('A', 'a'), 'gene2': ('C', 'c')}\n    genotipo_mae = {'gene1': ('B', 'b'), 'gene2': ('D', 'd')}\n    np.random.seed(0)\n    result1 = heranca(genotipo_pai, genotipo_mae)\n    np.random.seed(0)\n    result2 = heranca_new_implementation(genotipo_pai, genotipo_mae)\n    assert result1 == result2\n    \n    # Test case 3: Different alleles\n    genotipo_pai = {'gene1': ('X', 'x'), 'gene2': ('Y', 'y')}\n    genotipo_mae = {'gene1': ('Z', 'z'), 'gene2': ('W', 'w')}\n    np.random.seed(0)\n    result1 = heranca(genotipo_pai, genotipo_mae)\n    np.random.seed(0)\n    result2 = heranca_new_implementation(genotipo_pai, genotipo_mae)\n    assert result1 == result2\n\nif __name__ == \"__main__\":\n    test_heranca()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they iterate over the keys in `genotipo_pai`, randomly select an allele from both `genotipo_pai` and `genotipo_mae` for each gene, and store the selected alleles in `genotipo_filho`. The test cases provided in the code further confirm that the functionality is preserved, as they compare the outputs of the original and revised implementations and assert their equality. The only difference is the addition of a testing function, which does not alter the functionality of the `heranca` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `heranca` function returns a dictionary `genotipo_filho`, which is a new genotype created from the parents' genotypes. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases check the return values of the `heranca` function and compare them with the return values of `heranca_new_implementation`. There is no checking of printed or logged content.\n- CONDITION 3: The test cases use a fixed random seed (`np.random.seed(0)`) before calling both `heranca` and `heranca_new_implementation`, ensuring that the random choices are consistent across both implementations. This means that `heranca_new_implementation` can only pass all test cases if it has the exact same functionality as `heranca`.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `heranca` returns a value. The assertions are correctly structured.\n- CONDITION 5: The test cases cover different scenarios: a simple case with one gene, multiple genes, and different alleles. This provides a non-trivial set of tests to ensure the function's correctness.",
            "answer": "yes"
        },
        "commit_id": "b9b2942ab8bb7dcbc048fbe87ab3eec79899515c"
    },
    {
        "func_name": "simular_heredogramas",
        "idx": "550",
        "repo_name": "RicardoMourao-py___mlops-predict-online-or-batch",
        "func_path": "model/simulator/data_simulator.py",
        "orig_func": "def simular_heredogramas(num_pacientes):\n    pacientes = []\n    for _ in tqdm(range(num_pacientes), desc='Simulando heredogramas'):\n        pacientes.append(heredograma(prob_alelos_dominantes, prob_cancer))\n    return pacientes",
        "orig_context": "```python\n## model/simulator/data_simulator.py\nimport numpy as np\n\nfrom tqdm import tqdm\n\nprob_alelos_dominantes = {\n    1: 0.01,   # Pulm\u00e3o\n    2: 0.005,  # Ov\u00e1rio\n    3: 0.008,  # Est\u00f4mago\n    4: 0.015,  # Pele\n    5: 0.012,  # Leucemia\n    6: 0.009,  # Intestino\n    7: 0.007,  # Cabe\u00e7a e Pesco\u00e7o\n    8: 0.006,  # Mama\n    9: 0.011,  # Pr\u00f3stata\n    10:0.013, # Tire\u00f3ide\n    11:0.014, # Cerebral/sistema nervoso central\n    12:0.018, # Bexiga\n    13:0.016, # Linfoma\n    14:0.02,  # Bra\u00e7os/pernas\n    15:0.017, # Outro\n}\n\nprob_cancer = {\n    'homem': {\n        1: (0.3, 0.02),  # Pulm\u00e3o\n        2: (0.2, 0.03),  # Ov\u00e1rio\n        3: (0.25, 0.015),# Est\u00f4mago\n        4: (0.2, 0.01),  # Pele\n        5: (0.3, 0.025), # Leucemia\n        6: (0.2, 0.015), # Intestino\n        7: (0.25, 0.02), # Cabe\u00e7a e Pesco\u00e7o\n        8: (0.15, 0.01), # Mama\n        9: (0.25, 0.015),# Pr\u00f3stata\n        10: (0.25, 0.02),# Tire\u00f3ide\n        11: (0.2, 0.015),# Cerebral/sistema nervoso central\n        12: (0.2, 0.015),# Bexiga\n        13: (0.3, 0.025),# Linfoma\n        14: (0.3, 0.02), # Bra\u00e7os/pernas\n        15: (0.15, 0.01) # Outro\n    },\n    'mulher': {\n        1: (0.3, 0.02),  # Pulm\u00e3o\n        2: (0.2, 0.03),  # Ov\u00e1rio\n        3: (0.25, 0.015),# Est\u00f4mago\n        4: (0.2, 0.01),  # Pele\n        5: (0.3, 0.025), # Leucemia\n        6: (0.2, 0.015), # Intestino\n        7: (0.25, 0.02), # Cabe\u00e7a e Pesco\u00e7o\n        8: (0.15, 0.01), # Mama\n        9: (0.25, 0.015),# Pr\u00f3stata\n        10: (0.25, 0.02),# Tire\u00f3ide\n        11: (0.2, 0.015),# Cerebral/sistema nervoso central\n        12: (0.2, 0.015),# Bexiga\n        13: (0.3, 0.025),# Linfoma\n        14: (0.3, 0.02), # Bra\u00e7os/pernas\n        15: (0.15, 0.01) # Outro\n    },\n}\n\ndef eh_alelo_dominante(prob_alelo_dominante):\n    return np.random.rand() < prob_alelo_dominante\n\ndef tem_cancer(prob_cancer):\n    return np.random.rand() < prob_cancer\n\ndef gene(prob_alelo_dominante):\n    return tuple(eh_alelo_dominante(prob_alelo_dominante) for _ in range(2))\n\ndef genotipo(prob_alelos_dominantes):\n    return {g: gene(p) for g, p in prob_alelos_dominantes.items()}\n\ndef fenotipo(genotipo_, genero, prob_cancer):\n    f = {}\n    for g, genes_ in genotipo_.items():\n        dominant = any(genes_)\n        pR, pr = prob_cancer[genero][g]\n        p_cancer = pR if dominant else pr\n        f[g] = tem_cancer(p_cancer)\n    return f\n\ndef heranca(genotipo_pai, genotipo_mae):\n    genotipo_filho = {}\n    for g in genotipo_pai:\n        selecao_pai = np.random.randint(2)\n        selecao_mae = np.random.randint(2)\n        genotipo_filho[g] = (\n            genotipo_pai[g][selecao_pai],\n            genotipo_mae[g][selecao_mae],\n        )\n    return genotipo_filho\n\ndef genero():\n    return 'homem' if np.random.randint(2) == 0 else 'mulher'\n\ndef heredograma(prob_alelos_dominantes, prob_cancer):\n\n    genotipo_avo_paterno = genotipo(prob_alelos_dominantes)\n    fenotipo_avo_paterno = fenotipo(genotipo_avo_paterno, 'homem', prob_cancer)\n\n    genotipo_avoh_paterna = genotipo(prob_alelos_dominantes)\n    fenotipo_avoh_paterna = fenotipo(genotipo_avoh_paterna, 'mulher', prob_cancer)\n\n    genotipo_avo_materno = genotipo(prob_alelos_dominantes)\n    fenotipo_avo_materno = fenotipo(genotipo_avo_materno, 'homem', prob_cancer)\n\n    genotipo_avoh_materna = genotipo(prob_alelos_dominantes)\n    fenotipo_avoh_materna = fenotipo(genotipo_avoh_materna, 'mulher', prob_cancer)\n\n    genotipo_pai = heranca(genotipo_avo_paterno, genotipo_avoh_paterna)\n    fenotipo_pai = fenotipo(genotipo_pai, 'homem', prob_cancer)\n\n    genotipo_mae = heranca(genotipo_avo_materno, genotipo_avoh_materna)\n    fenotipo_mae = fenotipo(genotipo_mae, 'mulher', prob_cancer)\n\n    genero_paciente = genero()\n    genotipo_paciente = heranca(genotipo_pai, genotipo_mae)\n    fenotipo_paciente = fenotipo(genotipo_paciente, genero_paciente, prob_cancer)\n\n    genotipo_esposo_esposa = genotipo(prob_alelos_dominantes)\n\n    genero_filho = genero()\n    genotipo_filho = heranca(genotipo_paciente, genotipo_esposo_esposa)\n    fenotipo_filho = fenotipo(genotipo_filho, genero_filho, prob_cancer)\n\n    return {\n        'fenotipo_avo_paterno': fenotipo_avo_paterno,\n        'fenotipo_avoh_paterna': fenotipo_avoh_paterna,\n        'fenotipo_avo_materno': fenotipo_avo_materno,\n        'fenotipo_avoh_materna': fenotipo_avoh_materna,\n        'fenotipo_pai': fenotipo_pai,\n        'fenotipo_mae': fenotipo_mae,\n        'fenotipo_paciente': fenotipo_paciente,\n        'fenotipo_filho': fenotipo_filho,\n        'genotipo_paciente': genotipo_paciente,\n        'genero_paciente': genero_paciente,\n        'genero_filho': genero_filho,\n    }\n\ndef simular_heredogramas(num_pacientes):\n    pacientes = []\n    for _ in tqdm(range(num_pacientes), desc=\"Simulando heredogramas\"):\n        pacientes.append(heredograma(prob_alelos_dominantes, prob_cancer))\n    return pacientes\n\n```\n\n\n",
        "eval_script": "## model/simulator/data_simulator.py\nimport numpy as np\n\nfrom tqdm import tqdm\n\nprob_alelos_dominantes = {\n    1: 0.01,   # Pulm\u00e3o\n    2: 0.005,  # Ov\u00e1rio\n    3: 0.008,  # Est\u00f4mago\n    4: 0.015,  # Pele\n    5: 0.012,  # Leucemia\n    6: 0.009,  # Intestino\n    7: 0.007,  # Cabe\u00e7a e Pesco\u00e7o\n    8: 0.006,  # Mama\n    9: 0.011,  # Pr\u00f3stata\n    10:0.013, # Tire\u00f3ide\n    11:0.014, # Cerebral/sistema nervoso central\n    12:0.018, # Bexiga\n    13:0.016, # Linfoma\n    14:0.02,  # Bra\u00e7os/pernas\n    15:0.017, # Outro\n}\n\nprob_cancer = {\n    'homem': {\n        1: (0.3, 0.02),  # Pulm\u00e3o\n        2: (0.2, 0.03),  # Ov\u00e1rio\n        3: (0.25, 0.015),# Est\u00f4mago\n        4: (0.2, 0.01),  # Pele\n        5: (0.3, 0.025), # Leucemia\n        6: (0.2, 0.015), # Intestino\n        7: (0.25, 0.02), # Cabe\u00e7a e Pesco\u00e7o\n        8: (0.15, 0.01), # Mama\n        9: (0.25, 0.015),# Pr\u00f3stata\n        10: (0.25, 0.02),# Tire\u00f3ide\n        11: (0.2, 0.015),# Cerebral/sistema nervoso central\n        12: (0.2, 0.015),# Bexiga\n        13: (0.3, 0.025),# Linfoma\n        14: (0.3, 0.02), # Bra\u00e7os/pernas\n        15: (0.15, 0.01) # Outro\n    },\n    'mulher': {\n        1: (0.3, 0.02),  # Pulm\u00e3o\n        2: (0.2, 0.03),  # Ov\u00e1rio\n        3: (0.25, 0.015),# Est\u00f4mago\n        4: (0.2, 0.01),  # Pele\n        5: (0.3, 0.025), # Leucemia\n        6: (0.2, 0.015), # Intestino\n        7: (0.25, 0.02), # Cabe\u00e7a e Pesco\u00e7o\n        8: (0.15, 0.01), # Mama\n        9: (0.25, 0.015),# Pr\u00f3stata\n        10: (0.25, 0.02),# Tire\u00f3ide\n        11: (0.2, 0.015),# Cerebral/sistema nervoso central\n        12: (0.2, 0.015),# Bexiga\n        13: (0.3, 0.025),# Linfoma\n        14: (0.3, 0.02), # Bra\u00e7os/pernas\n        15: (0.15, 0.01) # Outro\n    },\n}\n\ndef eh_alelo_dominante(prob_alelo_dominante):\n    return np.random.rand() < prob_alelo_dominante\n\ndef tem_cancer(prob_cancer):\n    return np.random.rand() < prob_cancer\n\ndef gene(prob_alelo_dominante):\n    return tuple(eh_alelo_dominante(prob_alelo_dominante) for _ in range(2))\n\ndef genotipo(prob_alelos_dominantes):\n    return {g: gene(p) for g, p in prob_alelos_dominantes.items()}\n\ndef fenotipo(genotipo_, genero, prob_cancer):\n    f = {}\n    for g, genes_ in genotipo_.items():\n        dominant = any(genes_)\n        pR, pr = prob_cancer[genero][g]\n        p_cancer = pR if dominant else pr\n        f[g] = tem_cancer(p_cancer)\n    return f\n\ndef heranca(genotipo_pai, genotipo_mae):\n    genotipo_filho = {}\n    for g in genotipo_pai:\n        selecao_pai = np.random.randint(2)\n        selecao_mae = np.random.randint(2)\n        genotipo_filho[g] = (\n            genotipo_pai[g][selecao_pai],\n            genotipo_mae[g][selecao_mae],\n        )\n    return genotipo_filho\n\ndef genero():\n    return 'homem' if np.random.randint(2) == 0 else 'mulher'\n\ndef heredograma(prob_alelos_dominantes, prob_cancer):\n\n    genotipo_avo_paterno = genotipo(prob_alelos_dominantes)\n    fenotipo_avo_paterno = fenotipo(genotipo_avo_paterno, 'homem', prob_cancer)\n\n    genotipo_avoh_paterna = genotipo(prob_alelos_dominantes)\n    fenotipo_avoh_paterna = fenotipo(genotipo_avoh_paterna, 'mulher', prob_cancer)\n\n    genotipo_avo_materno = genotipo(prob_alelos_dominantes)\n    fenotipo_avo_materno = fenotipo(genotipo_avo_materno, 'homem', prob_cancer)\n\n    genotipo_avoh_materna = genotipo(prob_alelos_dominantes)\n    fenotipo_avoh_materna = fenotipo(genotipo_avoh_materna, 'mulher', prob_cancer)\n\n    genotipo_pai = heranca(genotipo_avo_paterno, genotipo_avoh_paterna)\n    fenotipo_pai = fenotipo(genotipo_pai, 'homem', prob_cancer)\n\n    genotipo_mae = heranca(genotipo_avo_materno, genotipo_avoh_materna)\n    fenotipo_mae = fenotipo(genotipo_mae, 'mulher', prob_cancer)\n\n    genero_paciente = genero()\n    genotipo_paciente = heranca(genotipo_pai, genotipo_mae)\n    fenotipo_paciente = fenotipo(genotipo_paciente, genero_paciente, prob_cancer)\n\n    genotipo_esposo_esposa = genotipo(prob_alelos_dominantes)\n\n    genero_filho = genero()\n    genotipo_filho = heranca(genotipo_paciente, genotipo_esposo_esposa)\n    fenotipo_filho = fenotipo(genotipo_filho, genero_filho, prob_cancer)\n\n    return {\n        'fenotipo_avo_paterno': fenotipo_avo_paterno,\n        'fenotipo_avoh_paterna': fenotipo_avoh_paterna,\n        'fenotipo_avo_materno': fenotipo_avo_materno,\n        'fenotipo_avoh_materna': fenotipo_avoh_materna,\n        'fenotipo_pai': fenotipo_pai,\n        'fenotipo_mae': fenotipo_mae,\n        'fenotipo_paciente': fenotipo_paciente,\n        'fenotipo_filho': fenotipo_filho,\n        'genotipo_paciente': genotipo_paciente,\n        'genero_paciente': genero_paciente,\n        'genero_filho': genero_filho,\n    }\n\ndef simular_heredogramas(num_pacientes):\n    pacientes = []\n    for _ in tqdm(range(num_pacientes), desc=\"Simulando heredogramas\"):\n        pacientes.append(heredograma(prob_alelos_dominantes, prob_cancer))\n    return pacientes\n\n\ndef test_simular_heredogramas():\n    num_pacientes = 5\n    np.random.seed(0)  # Set seed for reproducibility\n    result_original = simular_heredogramas(num_pacientes)\n    np.random.seed(0)  # Reset seed to ensure same random numbers\n    result_new = simular_heredogramas_new_implementation(num_pacientes)\n    \n    # Check if both results have the same length\n    assert len(result_original) == len(result_new), \"Mismatch in number of patients\"\n    \n    # Check if the structure of the first patient's heredogram is the same\n    assert result_original[0].keys() == result_new[0].keys(), \"Mismatch in heredogram keys\"\n    \n    # Check if the genotypes and phenotypes are the same for the first patient\n    assert result_original[0]['genotipo_paciente'] == result_new[0]['genotipo_paciente'], \"Mismatch in genotipo_paciente\"\n    assert result_original[0]['fenotipo_paciente'] == result_new[0]['fenotipo_paciente'], \"Mismatch in fenotipo_paciente\"\n\nif __name__ == \"__main__\":\n    test_simular_heredogramas()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `simular_heredogramas` is identical to the ORIGINAL FUNCTION. Both functions are defined in the same way, with the same parameters and logic. The REVISED FUNCTION includes additional context, such as the definitions of helper functions and data structures, but the core functionality of `simular_heredogramas` remains unchanged. The test function `test_simular_heredogramas` is also provided to verify the functionality, but it does not alter the function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The function `simular_heredogramas` returns a list of patients, each with a heredogram, so it satisfies this condition.\n- [CONDITION 2] The test function checks the return values (`result_original` and `result_new`) and does not rely on printed or logged content, satisfying this condition.\n- [CONDITION 3] The test function uses a fixed random seed to ensure reproducibility and checks for equality in the structure and specific fields of the heredograms. This ensures that `simular_heredogramas_new_implementation` must have the same functionality as `simular_heredogramas` to pass the test, satisfying this condition.\n- [CONDITION 4] The test cases use assertions to compare the length of the results and specific keys and values within the heredograms, which is reasonable given the return structure of `simular_heredogramas`. Thus, this condition is satisfied.\n- [CONDITION 5] The test cases are non-trivial as they check multiple aspects of the returned data, including the length, keys, and specific genotype and phenotype values, ensuring comprehensive coverage.",
            "answer": "yes"
        },
        "commit_id": "b9b2942ab8bb7dcbc048fbe87ab3eec79899515c"
    },
    {
        "func_name": "CustomCategoricalImputer.fit",
        "idx": "551",
        "repo_name": "RicardoMourao-py___mlops-predict-online-or-batch",
        "func_path": "model/train.py",
        "orig_func": "def fit(self, X, y=None):\n    \"\"\"Ajusta o imputador aos dados de treinamento.\"\"\"\n    return self",
        "orig_context": "```python\n## model/train.py\nimport pandas as pd\n\nimport numpy as np\n\nfrom sklearn.base import TransformerMixin, BaseEstimator\n\nclass CustomCategoricalImputer(BaseEstimator, TransformerMixin):\n    \"\"\"Imputador personalizado para valores ausentes em colunas categ\u00f3ricas.\"\"\"\n    \n    def __init__(self, categorical_features):\n        self.categorical_features = categorical_features\n    \n    def fit(self, X, y=None):\n        \"\"\"Ajusta o imputador aos dados de treinamento.\"\"\"\n        return self\n    \n    def transform(self, X):\n        \"\"\"Transforma os dados substituindo os valores ausentes nas colunas categ\u00f3ricas.\"\"\"\n        imputed_X = X.copy()\n        for col in self.categorical_features:\n            value_counts = imputed_X[col].value_counts(normalize=True)\n            imputed_X[col] = imputed_X[col].apply(lambda x: np.random.choice(value_counts.index, p=value_counts.values) if pd.isnull(x) else x)\n        return imputed_X\n\n```\n\n\n",
        "eval_script": "## model/train.py\nimport pandas as pd\nimport numpy as np\nfrom sklearn.base import TransformerMixin, BaseEstimator\n\nclass CustomCategoricalImputer(BaseEstimator, TransformerMixin):\n    \"\"\"Imputador personalizado para valores ausentes em colunas categ\u00f3ricas.\"\"\"\n    \n    def __init__(self, categorical_features):\n        self.categorical_features = categorical_features\n    \n    def fit(self, X, y=None):\n        \"\"\"Ajusta o imputador aos dados de treinamento.\"\"\"\n        return self\n    \n\n\n    \n    def transform(self, X):\n        \"\"\"Transforma os dados substituindo os valores ausentes nas colunas categ\u00f3ricas.\"\"\"\n        imputed_X = X.copy()\n        for col in self.categorical_features:\n            value_counts = imputed_X[col].value_counts(normalize=True)\n            imputed_X[col] = imputed_X[col].apply(lambda x: np.random.choice(value_counts.index, p=value_counts.values) if pd.isnull(x) else x)\n        return imputed_X\n\ndef test_fit():\n    # Test data\n    categorical_features = ['feature1', 'feature2']\n    imputer = CustomCategoricalImputer(categorical_features)\n\n    # Test 1: Check if both fit methods return the same object\n    assert imputer.fit(None) is imputer.fit_new_implementation(None), \"Fit methods do not return the same object\"\n\n    # Test 2: Check if categorical_features attribute is unchanged\n    imputer.fit(None)\n    assert imputer.categorical_features == categorical_features, \"categorical_features attribute changed after fit\"\n\n    # Test 3: Check if categorical_features attribute is unchanged in new implementation\n    imputer.fit_new_implementation(None)\n    assert imputer.categorical_features == categorical_features, \"categorical_features attribute changed after fit_new_implementation\"\n\n    # Test 4: Check with actual data\n    df = pd.DataFrame({\n        'feature1': ['a', 'b', None, 'a'],\n        'feature2': [None, 'x', 'y', 'x']\n    })\n    imputer.fit(df)\n    imputer.fit_new_implementation(df)\n\n    # Test 5: Check for side effects on input data\n    original_df = df.copy()\n    imputer.fit(df)\n    assert df.equals(original_df), \"Input data was altered by fit\"\n    imputer.fit_new_implementation(df)\n    assert df.equals(original_df), \"Input data was altered by fit_new_implementation\"\n\n    # Test 6: Multiple calls consistency\n    imputer.fit(df)\n    imputer.fit(df)\n    assert imputer.categorical_features == categorical_features, \"categorical_features attribute changed after multiple fit calls\"\n\n    imputer.fit_new_implementation(df)\n    imputer.fit_new_implementation(df)\n    assert imputer.categorical_features == categorical_features, \"categorical_features attribute changed after multiple fit_new_implementation calls\"\n\n    # Test 7: Edge case with empty dataframe\n    empty_df = pd.DataFrame(columns=categorical_features)\n    imputer.fit(empty_df)\n    imputer.fit_new_implementation(empty_df)\n\n    # Test 8: Edge case with no categorical features\n    no_cat_features_imputer = CustomCategoricalImputer([])\n    no_cat_features_imputer.fit(df)\n    no_cat_features_imputer.fit_new_implementation(df)\n\nif __name__ == \"__main__\":\n    test_fit()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `fit` simply returns the instance of the class without performing any operations on the input data `X` or `y`. The revised function `fit` in the `CustomCategoricalImputer` class also returns the instance of the class without performing any operations on the input data `X` or `y`. Both functions have the same behavior and functionality, as they both serve as a placeholder for fitting the model without altering any attributes or the input data. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `fit` function does not have return values, but it is expected to modify the state of the `CustomCategoricalImputer` instance, which is a global variable in the context of the test. The test checks the state of the `categorical_features` attribute, which satisfies this condition.\n- [CONDITION 2] The test cases do not check printed or logged contents; they only check the state of the object and the input data.\n- [CONDITION 3] The test cases check if both `fit` and `fit_new_implementation` return the same object and ensure no side effects on the input data, which implies that `fit_new_implementation` must have the same functionality as `fit` to pass all tests.\n- [CONDITION 4] The test cases and assertions are reasonable. They check the state of the object and ensure no side effects on the input data, which is appropriate given that `fit` does not return values.\n- [CONDITION 5] The test cases are non-trivial as they cover various scenarios, including checking object identity, attribute consistency, side effects on input data, multiple calls, and edge cases with empty dataframes and no categorical features.",
            "answer": "yes"
        },
        "commit_id": "b9b2942ab8bb7dcbc048fbe87ab3eec79899515c"
    },
    {
        "func_name": "MockModel.save",
        "idx": "554",
        "repo_name": "RicardoMourao-py___mlops-predict-online-or-batch",
        "func_path": "model/mock_model.py",
        "orig_func": "def save(self, filename):\n    \"\"\"Saves the model to a file using pickle.\"\"\"\n    with open(filename, 'wb') as file:\n        pickle.dump(self.model, file)\n    print(f'Model saved successfully at {filename}')",
        "orig_context": "```python\n## model/mock_model.py\nimport pickle\n\nfrom abc import ABC, abstractmethod\n\nfrom sklearn.base import BaseEstimator\n\nfrom sklearn.linear_model import LogisticRegression\n\nclass Model(BaseEstimator, ABC):\n    \"\"\"Abstract class defining the behavior of a model.\"\"\"\n\n    @abstractmethod\n    def fit(self, X, y):\n        \"\"\"Abstract method to train the model.\"\"\"\n        raise NotImplementedError(\"The fit method must be implemented in subclasses.\")\n\n    @abstractmethod\n    def predict(self, X):\n        \"\"\"Abstract method to make predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n    \n    @abstractmethod\n    def predict_proba(self, X):\n        \"\"\"Abstract method to make probabilities predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n\nclass MockModel(Model):\n    \"\"\"Class implementing a RandomForestClassifier model.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializer of the class.\"\"\"\n        self.model = LogisticRegression(\n           C=0.01,\n           solver='newton-cg',\n           max_iter=100\n        )\n\n    def fit(self, X, y):\n        \"\"\"Trains the model with input data X and labels y.\"\"\"\n        self.model.fit(X, y)\n\n    def predict(self, X):\n        \"\"\"Makes predictions with the trained model using input data X.\"\"\"\n        return self.model.predict(X)\n    \n    def predict_proba(self, X):\n        \"\"\"Makes probabilities predictions with the trained model using input data X.\"\"\"\n        return self.model.predict_proba(X)\n\n    def save(self, filename):\n        \"\"\"Saves the model to a file using pickle.\"\"\"\n        with open(filename, 'wb') as file:\n            pickle.dump(self.model, file)\n        print(f\"Model saved successfully at {filename}\")\n\n    @classmethod\n    def load(cls, filename):\n        \"\"\"Loads the model from a file using pickle.\"\"\"\n        with open(filename, 'rb') as file:\n            model = pickle.load(file)\n        instance = cls()\n        instance.model = model\n        return instance\n\n```\n\n\n",
        "eval_script": "## model/mock_model.py\nimport pickle\nimport os\n\nfrom abc import ABC, abstractmethod\n\nfrom sklearn.base import BaseEstimator\n\nfrom sklearn.linear_model import LogisticRegression\n\nclass Model(BaseEstimator, ABC):\n    \"\"\"Abstract class defining the behavior of a model.\"\"\"\n\n    @abstractmethod\n    def fit(self, X, y):\n        \"\"\"Abstract method to train the model.\"\"\"\n        raise NotImplementedError(\"The fit method must be implemented in subclasses.\")\n\n    @abstractmethod\n    def predict(self, X):\n        \"\"\"Abstract method to make predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n    \n    @abstractmethod\n    def predict_proba(self, X):\n        \"\"\"Abstract method to make probabilities predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n\nclass MockModel(Model):\n    \"\"\"Class implementing a RandomForestClassifier model.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializer of the class.\"\"\"\n        self.model = LogisticRegression(\n           C=0.01,\n           solver='newton-cg',\n           max_iter=100\n        )\n\n    def fit(self, X, y):\n        \"\"\"Trains the model with input data X and labels y.\"\"\"\n        self.model.fit(X, y)\n\n    def predict(self, X):\n        \"\"\"Makes predictions with the trained model using input data X.\"\"\"\n        return self.model.predict(X)\n    \n    def predict_proba(self, X):\n        \"\"\"Makes probabilities predictions with the trained model using input data X.\"\"\"\n        return self.model.predict_proba(X)\n\n    def save(self, filename):\n        \"\"\"Saves the model to a file using pickle.\"\"\"\n        with open(filename, 'wb') as file:\n            pickle.dump(self.model, file)\n        print(f\"Model saved successfully at {filename}\")\n\n\n    @classmethod\n    def load(cls, filename):\n        \"\"\"Loads the model from a file using pickle.\"\"\"\n        with open(filename, 'rb') as file:\n            model = pickle.load(file)\n        instance = cls()\n        instance.model = model\n        return instance\n\ndef test_save():\n    \"\"\"Test function to compare save and save_new_implementation.\"\"\"\n    model = MockModel()\n    model.fit([[0, 0], [1, 1]], [0, 1])\n    \n    # Save using the original save method\n    original_filename = '/home/user/tmp/original_model.pkl'\n    model.save(original_filename)\n    \n    # Save using the new save implementation\n    new_filename = '/home/user/tmp/new_model.pkl'\n    model.save_new_implementation(new_filename)\n    \n    # Load both models\n    original_model = MockModel.load(original_filename)\n    new_model = MockModel.load(new_filename)\n    \n    # Assertions to ensure both models are identical\n    assert original_model.model.get_params() == new_model.model.get_params(), \"Model parameters differ\"\n    assert (original_model.predict([[0, 0], [1, 1]]) == new_model.predict([[0, 0], [1, 1]])).all(), \"Model predictions differ\"\n    assert (original_model.predict_proba([[0, 0], [1, 1]]) == new_model.predict_proba([[0, 0], [1, 1]])).all(), \"Model probabilities differ\"\n\nif __name__ == \"__main__\":\n    test_save()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function both aim to save a model to a file using the `pickle` module. The original function is a standalone function, while the revised function is a method within the `MockModel` class. However, the core functionality of saving the `self.model` attribute to a file using `pickle.dump` is identical in both cases. The print statement confirming the save operation is also present in both functions, with only a minor difference in the quotation marks used. The context of the revised function being part of a class does not alter its core functionality compared to the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `save` function modifies the state of the file system by writing a file, which is a form of modifying a global state. It does not return a value, but it affects the environment by creating a file.\n  \n- [CONDITION 2] The test function `test_save` does not check printed or logged contents. Instead, it checks the state of the models loaded from the files saved by both the original and new implementations of the `save` function.\n\n- [CONDITION 3] The test cases ensure that both models, when loaded from their respective files, have identical parameters and produce identical predictions and probabilities. This is a strong indication that the two implementations of `save` have the same functionality.\n\n- [CONDITION 4] The test cases and assertions are reasonable. They do not compare the return values of `save` (since it doesn't return anything) but instead compare the effects of the `save` function by loading the models and checking their states and outputs.\n\n- [CONDITION 5] The test cases are non-trivial as they involve training a model, saving it, loading it back, and comparing the loaded models' parameters and outputs. This ensures that the `save` function works correctly in a realistic scenario.",
            "answer": "yes"
        },
        "commit_id": "b9b2942ab8bb7dcbc048fbe87ab3eec79899515c"
    },
    {
        "func_name": "MockModel.load",
        "idx": "555",
        "repo_name": "RicardoMourao-py___mlops-predict-online-or-batch",
        "func_path": "model/mock_model.py",
        "orig_func": "@classmethod\ndef load(cls, filename):\n    \"\"\"Loads the model from a file using pickle.\"\"\"\n    with open(filename, 'rb') as file:\n        model = pickle.load(file)\n    instance = cls()\n    instance.model = model\n    return instance",
        "orig_context": "```python\n## model/mock_model.py\nimport pickle\n\nfrom abc import ABC, abstractmethod\n\nfrom sklearn.base import BaseEstimator\n\nfrom sklearn.linear_model import LogisticRegression\n\nclass Model(BaseEstimator, ABC):\n    \"\"\"Abstract class defining the behavior of a model.\"\"\"\n\n    @abstractmethod\n    def fit(self, X, y):\n        \"\"\"Abstract method to train the model.\"\"\"\n        raise NotImplementedError(\"The fit method must be implemented in subclasses.\")\n\n    @abstractmethod\n    def predict(self, X):\n        \"\"\"Abstract method to make predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n    \n    @abstractmethod\n    def predict_proba(self, X):\n        \"\"\"Abstract method to make probabilities predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n\nclass MockModel(Model):\n    \"\"\"Class implementing a RandomForestClassifier model.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializer of the class.\"\"\"\n        self.model = LogisticRegression(\n           C=0.01,\n           solver='newton-cg',\n           max_iter=100\n        )\n\n    def fit(self, X, y):\n        \"\"\"Trains the model with input data X and labels y.\"\"\"\n        self.model.fit(X, y)\n\n    def predict(self, X):\n        \"\"\"Makes predictions with the trained model using input data X.\"\"\"\n        return self.model.predict(X)\n    \n    def predict_proba(self, X):\n        \"\"\"Makes probabilities predictions with the trained model using input data X.\"\"\"\n        return self.model.predict_proba(X)\n\n    def save(self, filename):\n        \"\"\"Saves the model to a file using pickle.\"\"\"\n        with open(filename, 'wb') as file:\n            pickle.dump(self.model, file)\n        print(f\"Model saved successfully at {filename}\")\n\n    @classmethod\n    def load(cls, filename):\n        \"\"\"Loads the model from a file using pickle.\"\"\"\n        with open(filename, 'rb') as file:\n            model = pickle.load(file)\n        instance = cls()\n        instance.model = model\n        return instance\n\n```\n\n\n",
        "eval_script": "## model/mock_model.py\nimport pickle\nimport os\n\nfrom abc import ABC, abstractmethod\n\nfrom sklearn.base import BaseEstimator\n\nfrom sklearn.linear_model import LogisticRegression\n\nclass Model(BaseEstimator, ABC):\n    \"\"\"Abstract class defining the behavior of a model.\"\"\"\n\n    @abstractmethod\n    def fit(self, X, y):\n        \"\"\"Abstract method to train the model.\"\"\"\n        raise NotImplementedError(\"The fit method must be implemented in subclasses.\")\n\n    @abstractmethod\n    def predict(self, X):\n        \"\"\"Abstract method to make predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n    \n    @abstractmethod\n    def predict_proba(self, X):\n        \"\"\"Abstract method to make probabilities predictions with the trained model.\"\"\"\n        raise NotImplementedError(\"The predict method must be implemented in subclasses.\")\n\nclass MockModel(Model):\n    \"\"\"Class implementing a RandomForestClassifier model.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializer of the class.\"\"\"\n        self.model = LogisticRegression(\n           C=0.01,\n           solver='newton-cg',\n           max_iter=100\n        )\n\n    def fit(self, X, y):\n        \"\"\"Trains the model with input data X and labels y.\"\"\"\n        self.model.fit(X, y)\n\n    def predict(self, X):\n        \"\"\"Makes predictions with the trained model using input data X.\"\"\"\n        return self.model.predict(X)\n    \n    def predict_proba(self, X):\n        \"\"\"Makes probabilities predictions with the trained model using input data X.\"\"\"\n        return self.model.predict_proba(X)\n\n    def save(self, filename):\n        \"\"\"Saves the model to a file using pickle.\"\"\"\n        with open(filename, 'wb') as file:\n            pickle.dump(self.model, file)\n        print(f\"Model saved successfully at {filename}\")\n\n    @classmethod\n    def load(cls, filename):\n        \"\"\"Loads the model from a file using pickle.\"\"\"\n        with open(filename, 'rb') as file:\n            model = pickle.load(file)\n        instance = cls()\n        instance.model = model\n        return instance\n\n\ndef test_load():\n    \"\"\"Test function to compare MockModel.load and MockModel.load_new_implementation.\"\"\"\n    # Create a mock model and save it\n    model = MockModel()\n    model.fit([[0, 0], [1, 1]], [0, 1])\n    filename = '/home/user/tmp/mock_model.pkl'\n    model.save(filename)\n\n    # Load using the original load method\n    loaded_model_original = MockModel.load(filename)\n    # Load using the new implementation\n    loaded_model_new = MockModel.load_new_implementation(filename)\n\n    # Assert that both loaded models are instances of MockModel\n    assert isinstance(loaded_model_original, MockModel), \"Original load did not return a MockModel instance\"\n    assert isinstance(loaded_model_new, MockModel), \"New load did not return a MockModel instance\"\n\n    # Assert that both models have the same parameters\n    assert loaded_model_original.model.get_params() == loaded_model_new.model.get_params(), \"Model parameters differ\"\n\n    # Assert that predictions from both models are the same\n    test_data = [[2, 2]]\n    assert (loaded_model_original.predict(test_data) == loaded_model_new.predict(test_data)).all(), \"Predictions differ\"\n\n    # Test with different data\n    model.fit([[1, 0], [0, 1]], [1, 0])\n    model.save(filename)\n    loaded_model_original = MockModel.load(filename)\n    loaded_model_new = MockModel.load_new_implementation(filename)\n    test_data = [[1, 1]]\n    assert (loaded_model_original.predict(test_data) == loaded_model_new.predict(test_data)).all(), \"Predictions differ with new data\"\n\n    # Test model consistency\n    original_predictions = model.predict(test_data)\n    assert (loaded_model_original.predict(test_data) == original_predictions).all(), \"Original model predictions differ after loading\"\n    assert (loaded_model_new.predict(test_data) == original_predictions).all(), \"New model predictions differ after loading\"\n\n    # Test with invalid file\n    try:\n        MockModel.load('/home/user/tmp/non_existent_file.pkl')\n        assert False, \"Loading from a non-existent file should raise an error\"\n    except FileNotFoundError:\n        pass\n\n    try:\n        MockModel.load_new_implementation('/home/user/tmp/non_existent_file.pkl')\n        assert False, \"Loading from a non-existent file should raise an error\"\n    except FileNotFoundError:\n        pass\n\n    # Test with corrupted file\n    with open(filename, 'wb') as file:\n        file.write(b\"corrupted data\")\n    try:\n        MockModel.load(filename)\n        assert False, \"Loading from a corrupted file should raise an error\"\n    except Exception:\n        pass\n\n    try:\n        MockModel.load_new_implementation(filename)\n        assert False, \"Loading from a corrupted file should raise an error\"\n    except Exception:\n        pass\n\n    # Test with different model configurations\n    model = MockModel()\n    model.model = LogisticRegression(C=0.1, solver='liblinear', max_iter=200)\n    model.fit([[0, 0], [1, 1]], [0, 1])\n    model.save(filename)\n    loaded_model_original = MockModel.load(filename)\n    loaded_model_new = MockModel.load_new_implementation(filename)\n    assert loaded_model_original.model.get_params() == loaded_model_new.model.get_params(), \"Model parameters differ with different configurations\"\n\nif __name__ == \"__main__\":\n    test_load()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are class methods that load a model from a file using the `pickle` module. They open the file in binary read mode, load the model using `pickle.load`, create an instance of the class, assign the loaded model to the instance's `model` attribute, and return the instance. There are no differences in the logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `load` function returns an instance of `MockModel`, which satisfies the condition of having a return value.\n- [CONDITION 2] The test cases check the return values and states of the models loaded by `load` and `load_new_implementation`. They do not rely on printed or logged content.\n- [CONDITION 3] The test cases compare the instances returned by both `load` and `load_new_implementation` for equality in terms of type, parameters, and predictions, ensuring that both implementations must have the same functionality to pass.\n- [CONDITION 4] The test cases use appropriate assertions to check the equality of model parameters and predictions, which are reasonable checks for the functionality of the `load` method.\n- [CONDITION 5] The test cases cover various scenarios, including loading from a valid file, handling non-existent and corrupted files, and different model configurations, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "b9b2942ab8bb7dcbc048fbe87ab3eec79899515c"
    },
    {
        "func_name": "TodoList.add_task",
        "idx": "556",
        "repo_name": "COSC381-2024Fall___Todo-List-CLI",
        "func_path": "TodoList.py",
        "orig_func": "def add_task(self, task, priority='Medium', date=None):\n    \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n    task = task.strip()\n    task_number = self.task_exists(task)\n    if task_number:\n        print(f\"Task '{task}' already exists at position {task_number}.\")\n        return\n    self.tasks.append((task, priority, date, [], None, False))\n    print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")",
        "orig_context": "```python\n## TodoList.py\nfrom datetime import datetime\n\nclass TodoList:\n    \"\"\"A class to store and manage one list of tasks\n    \"\"\"\n    def __init__(self):\n        \"\"\"initialize an empty list\n        \"\"\"\n        self.tasks = []\n\n        \"\"\"Task is a 6-tuple (task, priority, date, tags, delegate, completed)\n        \"\"\"\n\n    def add_task(self, task, priority = \"Medium\", date = None):\n        \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n\n        task = task.strip()  # Remove any leading/trailing whitespace\n        task_number = self.task_exists(task)\n        if task_number:\n            print(f\"Task '{task}' already exists at position {task_number}.\")\n            return\n\n        self.tasks.append((task, priority, date, [], None, False))\n        print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")\n\n    def list_tasks(self):\n        \"\"\"Lists all tasks in the to-do list.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            for idx, task in enumerate(self.tasks, start=1):\n                print(f'{idx}. {task}')\n\n    def list_tasks_numeric(self):\n        \"\"\"Lists all tasks in the to-do list in numerical order, including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            print(\"\\nCurrent To-Do List:\")\n            for idx, task in enumerate(self.tasks, start=1):\n                task_name, priority, due_date, _, _, _ = task\n                if due_date:\n                    print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                else:\n                    print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                    \n    # Good job with implementing this function and listing the list in alphabetical order\n    def list_tasks_alphabetic(self):\n        \"\"\"lists all tasks in the to-do list in alphabetical order, \n           including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            sorted_tasks = [] \n            # Although I would review the for loops to make code smaller and faster\n            for idx, task in enumerate(self.tasks, start=1):\n                item = (idx, task) \n                sorted_tasks.append(item)\n            sorted_tasks = sorted(sorted_tasks, key=lambda x: x[1][0].lower() if isinstance(x[1], tuple) else x[1].lower())\n            for idx, task in sorted_tasks:\n                if isinstance(task, tuple):  # Task with due date\n                    task_name, priority, due_date, _, _, _ = task\n                    if due_date:\n                        print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                    else:\n                        print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                else:  # Task without due date\n                    # all the tasks should now be a tuple\n                    raise Exception(\"Internal error: the task is not a tuple.\")\n\n    def delete_task(self, task_number):\n        \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to delete!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            removed_task = self.tasks.pop(task_number - 1)\n            print(f'Task removed: {removed_task}')\n\n\n    # this method was generated by ChatGPT as well as the subsequent adjustments\n    def add_task_date(self, task_number, due_date):\n        \"\"\"Adds or updates a due date for a specific task.\n\n        Args:\n            task_number (_type_): an integer\n            due_date (_type_): string\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            try:\n                # Validate date format\n                datetime.strptime(due_date, \"%Y-%m-%d\")\n            except ValueError:\n                print(\"Invalid date format! Please enter the date in YYYY-MM-DD format.\")\n                return\n        \n            task_name, priority, _, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, completed)\n            print(f'Task updated with due date: {self.tasks[task_number - 1]}')\n            \n\n    def add_tag(self, task_number, tag):\n        if not self.tasks:\n            print(\"No tasks in the list to add a tag!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task = self.tasks[task_number - 1]\n            if type(task) is tuple:\n                task_name, priority, due_date, tags, delegate, _ = task\n                tags.append(tag)\n                print(f'Task updated: {self.tasks[task_number - 1]}')\n            else:\n                # all the tasks should now be a tuple\n                raise Exception(\"Internal error: the task is not a tuple.\")\n\n    \n    def update_task(self, task_number, updated_message):\n        \"\"\"Change the description of a task\"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n            \n\n    def delete_all_tasks(self):\n        \"\"\"Deletes all tasks in the list.\"\"\"\n        if not self.tasks:\n            print(\"No tasks to delete.\")\n        else:\n            self.tasks = []\n            print(\"All tasks deleted.\")\n\n    # that were made to print_menu and main\n    def get_total_tasks(self):\n        \"\"\"Returns the total number of tasks in the to-do list.\"\"\"\n        return len(self.tasks)\n\n    def remove_due_date(self, task_number):\n        \"\"\"Removes the due date from a task.\"\"\"\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            if due_date is not None:  # Check if the task has a due date\n                # Remove due date by converting it back to a string\n                self.tasks[task_number - 1] = (task_name, priority, None, tags, delegate, completed)\n                print(f'Due date removed from task: {task_name}')\n            else:\n                print(\"This task doesn't have a due date.\")                \n\n    def add_task_delegate(self, task_number, task_delegate):\n        \"\"\"Delegates a task to someone else.\"\"\"\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n            return\n\n        if not task_delegate.isalnum() or not task_delegate:\n            print(\"Invalid delegate\")\n            return\n\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, task_delegate, completed)\n            print(f\"Task delegated to {task_delegate}: {self.tasks[task_number - 1]}\")\n        \n\n    def task_exists(self, task):\n        \"\"\"Checks if a task already exists. Returns the task number if it exists, or None otherwise.\n\n        Args:\n            task (_type_): a string\n\n        Returns:\n            _type_: None if task does not exist. Int if task exists.\n        \"\"\"\n        task = task.strip()\n\n        for idx, t in enumerate(self.tasks):\n            task_name = t[0].strip().lower()\n            if task_name == task.lower():\n                return idx + 1\n\n        return None  # Task does not exist\n\n    def checkoff_task(self,task_number):\n        \"\"\"Mark the task completed\"\"\"\n        # Check if the provided task number is valid\n        if task_number <= 0 or task_number > len(self.tasks): \n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, True)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n\n```\n\n\n",
        "eval_script": "## TodoList.py\nfrom datetime import datetime\n\nclass TodoList:\n    \"\"\"A class to store and manage one list of tasks\n    \"\"\"\n    def __init__(self):\n        \"\"\"initialize an empty list\n        \"\"\"\n        self.tasks = []\n\n        \"\"\"Task is a 6-tuple (task, priority, date, tags, delegate, completed)\n        \"\"\"\n\n    def add_task(self, task, priority = \"Medium\", date = None):\n        \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n\n        task = task.strip()  # Remove any leading/trailing whitespace\n        task_number = self.task_exists(task)\n        if task_number:\n            print(f\"Task '{task}' already exists at position {task_number}.\")\n            return\n\n        self.tasks.append((task, priority, date, [], None, False))\n        print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")\n\n\n    def list_tasks(self):\n        \"\"\"Lists all tasks in the to-do list.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            for idx, task in enumerate(self.tasks, start=1):\n                print(f'{idx}. {task}')\n\n    def list_tasks_numeric(self):\n        \"\"\"Lists all tasks in the to-do list in numerical order, including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            print(\"\\nCurrent To-Do List:\")\n            for idx, task in enumerate(self.tasks, start=1):\n                task_name, priority, due_date, _, _, _ = task\n                if due_date:\n                    print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                else:\n                    print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                    \n    # Good job with implementing this function and listing the list in alphabetical order\n    def list_tasks_alphabetic(self):\n        \"\"\"lists all tasks in the to-do list in alphabetical order, \n           including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            sorted_tasks = [] \n            # Although I would review the for loops to make code smaller and faster\n            for idx, task in enumerate(self.tasks, start=1):\n                item = (idx, task) \n                sorted_tasks.append(item)\n            sorted_tasks = sorted(sorted_tasks, key=lambda x: x[1][0].lower() if isinstance(x[1], tuple) else x[1].lower())\n            for idx, task in sorted_tasks:\n                if isinstance(task, tuple):  # Task with due date\n                    task_name, priority, due_date, _, _, _ = task\n                    if due_date:\n                        print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                    else:\n                        print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                else:  # Task without due date\n                    # all the tasks should now be a tuple\n                    raise Exception(\"Internal error: the task is not a tuple.\")\n\n    def delete_task(self, task_number):\n        \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to delete!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            removed_task = self.tasks.pop(task_number - 1)\n            print(f'Task removed: {removed_task}')\n\n\n    # this method was generated by ChatGPT as well as the subsequent adjustments\n    def add_task_date(self, task_number, due_date):\n        \"\"\"Adds or updates a due date for a specific task.\n\n        Args:\n            task_number (_type_): an integer\n            due_date (_type_): string\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            try:\n                # Validate date format\n                datetime.strptime(due_date, \"%Y-%m-%d\")\n            except ValueError:\n                print(\"Invalid date format! Please enter the date in YYYY-MM-DD format.\")\n                return\n        \n            task_name, priority, _, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, completed)\n            print(f'Task updated with due date: {self.tasks[task_number - 1]}')\n            \n\n    def add_tag(self, task_number, tag):\n        if not self.tasks:\n            print(\"No tasks in the list to add a tag!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task = self.tasks[task_number - 1]\n            if type(task) is tuple:\n                task_name, priority, due_date, tags, delegate, _ = task\n                tags.append(tag)\n                print(f'Task updated: {self.tasks[task_number - 1]}')\n            else:\n                # all the tasks should now be a tuple\n                raise Exception(\"Internal error: the task is not a tuple.\")\n\n    \n    def update_task(self, task_number, updated_message):\n        \"\"\"Change the description of a task\"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n            \n\n    def delete_all_tasks(self):\n        \"\"\"Deletes all tasks in the list.\"\"\"\n        if not self.tasks:\n            print(\"No tasks to delete.\")\n        else:\n            self.tasks = []\n            print(\"All tasks deleted.\")\n\n    # that were made to print_menu and main\n    def get_total_tasks(self):\n        \"\"\"Returns the total number of tasks in the to-do list.\"\"\"\n        return len(self.tasks)\n\n    def remove_due_date(self, task_number):\n        \"\"\"Removes the due date from a task.\"\"\"\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            if due_date is not None:  # Check if the task has a due date\n                # Remove due date by converting it back to a string\n                self.tasks[task_number - 1] = (task_name, priority, None, tags, delegate, completed)\n                print(f'Due date removed from task: {task_name}')\n            else:\n                print(\"This task doesn't have a due date.\")                \n\n    def add_task_delegate(self, task_number, task_delegate):\n        \"\"\"Delegates a task to someone else.\"\"\"\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n            return\n\n        if not task_delegate.isalnum() or not task_delegate:\n            print(\"Invalid delegate\")\n            return\n\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, task_delegate, completed)\n            print(f\"Task delegated to {task_delegate}: {self.tasks[task_number - 1]}\")\n        \n\n    def task_exists(self, task):\n        \"\"\"Checks if a task already exists. Returns the task number if it exists, or None otherwise.\n\n        Args:\n            task (_type_): a string\n\n        Returns:\n            _type_: None if task does not exist. Int if task exists.\n        \"\"\"\n        task = task.strip()\n\n        for idx, t in enumerate(self.tasks):\n            task_name = t[0].strip().lower()\n            if task_name == task.lower():\n                return idx + 1\n\n        return None  # Task does not exist\n\n    def checkoff_task(self,task_number):\n        \"\"\"Mark the task completed\"\"\"\n        # Check if the provided task number is valid\n        if task_number <= 0 or task_number > len(self.tasks): \n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, True)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n\ndef test_add_task():\n    # Test case 1: Adding a new task\n    todo_list1 = TodoList()\n    todo_list2 = TodoList()\n    todo_list1.add_task(\"Task 1\")\n    todo_list2.add_task_new_implementation(\"Task 1\")\n    assert todo_list1.tasks == todo_list2.tasks, \"Test case 1 failed\"\n\n    # Test case 2: Adding a duplicate task\n    todo_list1.add_task(\"Task 1\")\n    todo_list2.add_task_new_implementation(\"Task 1\")\n    assert todo_list1.tasks == todo_list2.tasks, \"Test case 2 failed\"\n\n    # Test case 3: Adding a task with different priority and date\n    todo_list1.add_task(\"Task 2\", priority=\"High\", date=\"2023-10-10\")\n    todo_list2.add_task_new_implementation(\"Task 2\", priority=\"High\", date=\"2023-10-10\")\n    assert todo_list1.tasks == todo_list2.tasks, \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_add_task()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions perform the same operations: they strip whitespace from the task, check if the task already exists using the `task_exists` method, and if it does not exist, they append the task with its priority and date to the `tasks` list. The structure and logic of the function remain unchanged between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `add_task` function modifies the `tasks` attribute of the `TodoList` class, which is a global variable in the context of the class instance. Therefore, this condition is satisfied.\n- CONDITION 2: The test cases use assertions to check the state of the `tasks` attribute, which is a variable state, not printed or logged content. Thus, this condition is satisfied.\n- CONDITION 3: The test cases compare the `tasks` attribute of two instances of `TodoList`, one using `add_task` and the other using `add_task_new_implementation`. If `add_task_new_implementation` has the same functionality as `add_task`, the `tasks` attributes should be identical. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the `tasks` attributes of two `TodoList` instances, which is reasonable given that `add_task` modifies the `tasks` attribute. This condition is satisfied.\n- CONDITION 5: The test cases cover adding a new task, adding a duplicate task, and adding a task with different priority and date, which are non-trivial scenarios. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "d055c41d513049263e024b41d4812576be2b160f"
    },
    {
        "func_name": "TodoList.delete_task",
        "idx": "557",
        "repo_name": "COSC381-2024Fall___Todo-List-CLI",
        "func_path": "TodoList.py",
        "orig_func": "def delete_task(self, task_number):\n    \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n    if not self.tasks:\n        print('No tasks in the list to delete!')\n        return\n    if task_number <= 0 or task_number > len(self.tasks):\n        print('Invalid task number!')\n    else:\n        removed_task = self.tasks.pop(task_number - 1)\n        print(f'Task removed: {removed_task}')",
        "orig_context": "```python\n## TodoList.py\nfrom datetime import datetime\n\nclass TodoList:\n    \"\"\"A class to store and manage one list of tasks\n    \"\"\"\n    def __init__(self):\n        \"\"\"initialize an empty list\n        \"\"\"\n        self.tasks = []\n\n        \"\"\"Task is a 6-tuple (task, priority, date, tags, delegate, completed)\n        \"\"\"\n\n    def add_task(self, task, priority = \"Medium\", date = None):\n        \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n\n        task = task.strip()  # Remove any leading/trailing whitespace\n        task_number = self.task_exists(task)\n        if task_number:\n            print(f\"Task '{task}' already exists at position {task_number}.\")\n            return\n\n        self.tasks.append((task, priority, date, [], None, False))\n        print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")\n\n    def list_tasks(self):\n        \"\"\"Lists all tasks in the to-do list.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            for idx, task in enumerate(self.tasks, start=1):\n                print(f'{idx}. {task}')\n\n    def list_tasks_numeric(self):\n        \"\"\"Lists all tasks in the to-do list in numerical order, including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            print(\"\\nCurrent To-Do List:\")\n            for idx, task in enumerate(self.tasks, start=1):\n                task_name, priority, due_date, _, _, _ = task\n                if due_date:\n                    print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                else:\n                    print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                    \n    # Good job with implementing this function and listing the list in alphabetical order\n    def list_tasks_alphabetic(self):\n        \"\"\"lists all tasks in the to-do list in alphabetical order, \n           including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            sorted_tasks = [] \n            # Although I would review the for loops to make code smaller and faster\n            for idx, task in enumerate(self.tasks, start=1):\n                item = (idx, task) \n                sorted_tasks.append(item)\n            sorted_tasks = sorted(sorted_tasks, key=lambda x: x[1][0].lower() if isinstance(x[1], tuple) else x[1].lower())\n            for idx, task in sorted_tasks:\n                if isinstance(task, tuple):  # Task with due date\n                    task_name, priority, due_date, _, _, _ = task\n                    if due_date:\n                        print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                    else:\n                        print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                else:  # Task without due date\n                    # all the tasks should now be a tuple\n                    raise Exception(\"Internal error: the task is not a tuple.\")\n\n    def delete_task(self, task_number):\n        \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to delete!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            removed_task = self.tasks.pop(task_number - 1)\n            print(f'Task removed: {removed_task}')\n\n\n    # this method was generated by ChatGPT as well as the subsequent adjustments\n    def add_task_date(self, task_number, due_date):\n        \"\"\"Adds or updates a due date for a specific task.\n\n        Args:\n            task_number (_type_): an integer\n            due_date (_type_): string\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            try:\n                # Validate date format\n                datetime.strptime(due_date, \"%Y-%m-%d\")\n            except ValueError:\n                print(\"Invalid date format! Please enter the date in YYYY-MM-DD format.\")\n                return\n        \n            task_name, priority, _, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, completed)\n            print(f'Task updated with due date: {self.tasks[task_number - 1]}')\n            \n\n    def add_tag(self, task_number, tag):\n        if not self.tasks:\n            print(\"No tasks in the list to add a tag!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task = self.tasks[task_number - 1]\n            if type(task) is tuple:\n                task_name, priority, due_date, tags, delegate, _ = task\n                tags.append(tag)\n                print(f'Task updated: {self.tasks[task_number - 1]}')\n            else:\n                # all the tasks should now be a tuple\n                raise Exception(\"Internal error: the task is not a tuple.\")\n\n    \n    def update_task(self, task_number, updated_message):\n        \"\"\"Change the description of a task\"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n            \n\n    def delete_all_tasks(self):\n        \"\"\"Deletes all tasks in the list.\"\"\"\n        if not self.tasks:\n            print(\"No tasks to delete.\")\n        else:\n            self.tasks = []\n            print(\"All tasks deleted.\")\n\n    # that were made to print_menu and main\n    def get_total_tasks(self):\n        \"\"\"Returns the total number of tasks in the to-do list.\"\"\"\n        return len(self.tasks)\n\n    def remove_due_date(self, task_number):\n        \"\"\"Removes the due date from a task.\"\"\"\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            if due_date is not None:  # Check if the task has a due date\n                # Remove due date by converting it back to a string\n                self.tasks[task_number - 1] = (task_name, priority, None, tags, delegate, completed)\n                print(f'Due date removed from task: {task_name}')\n            else:\n                print(\"This task doesn't have a due date.\")                \n\n    def add_task_delegate(self, task_number, task_delegate):\n        \"\"\"Delegates a task to someone else.\"\"\"\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n            return\n\n        if not task_delegate.isalnum() or not task_delegate:\n            print(\"Invalid delegate\")\n            return\n\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, task_delegate, completed)\n            print(f\"Task delegated to {task_delegate}: {self.tasks[task_number - 1]}\")\n        \n\n    def task_exists(self, task):\n        \"\"\"Checks if a task already exists. Returns the task number if it exists, or None otherwise.\n\n        Args:\n            task (_type_): a string\n\n        Returns:\n            _type_: None if task does not exist. Int if task exists.\n        \"\"\"\n        task = task.strip()\n\n        for idx, t in enumerate(self.tasks):\n            task_name = t[0].strip().lower()\n            if task_name == task.lower():\n                return idx + 1\n\n        return None  # Task does not exist\n\n    def checkoff_task(self,task_number):\n        \"\"\"Mark the task completed\"\"\"\n        # Check if the provided task number is valid\n        if task_number <= 0 or task_number > len(self.tasks): \n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, True)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n\n```\n\n\n",
        "eval_script": "## TodoList.py\nfrom datetime import datetime\n\nclass TodoList:\n    \"\"\"A class to store and manage one list of tasks\n    \"\"\"\n    def __init__(self):\n        \"\"\"initialize an empty list\n        \"\"\"\n        self.tasks = []\n\n        \"\"\"Task is a 6-tuple (task, priority, date, tags, delegate, completed)\n        \"\"\"\n\n    def add_task(self, task, priority = \"Medium\", date = None):\n        \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n\n        task = task.strip()  # Remove any leading/trailing whitespace\n        task_number = self.task_exists(task)\n        if task_number:\n            print(f\"Task '{task}' already exists at position {task_number}.\")\n            return\n\n        self.tasks.append((task, priority, date, [], None, False))\n        print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")\n\n    def list_tasks(self):\n        \"\"\"Lists all tasks in the to-do list.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            for idx, task in enumerate(self.tasks, start=1):\n                print(f'{idx}. {task}')\n\n    def list_tasks_numeric(self):\n        \"\"\"Lists all tasks in the to-do list in numerical order, including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            print(\"\\nCurrent To-Do List:\")\n            for idx, task in enumerate(self.tasks, start=1):\n                task_name, priority, due_date, _, _, _ = task\n                if due_date:\n                    print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                else:\n                    print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                    \n    # Good job with implementing this function and listing the list in alphabetical order\n    def list_tasks_alphabetic(self):\n        \"\"\"lists all tasks in the to-do list in alphabetical order, \n           including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            sorted_tasks = [] \n            # Although I would review the for loops to make code smaller and faster\n            for idx, task in enumerate(self.tasks, start=1):\n                item = (idx, task) \n                sorted_tasks.append(item)\n            sorted_tasks = sorted(sorted_tasks, key=lambda x: x[1][0].lower() if isinstance(x[1], tuple) else x[1].lower())\n            for idx, task in sorted_tasks:\n                if isinstance(task, tuple):  # Task with due date\n                    task_name, priority, due_date, _, _, _ = task\n                    if due_date:\n                        print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                    else:\n                        print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                else:  # Task without due date\n                    # all the tasks should now be a tuple\n                    raise Exception(\"Internal error: the task is not a tuple.\")\n\n    def delete_task(self, task_number):\n        \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to delete!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            removed_task = self.tasks.pop(task_number - 1)\n            print(f'Task removed: {removed_task}')\n\n\n    # this method was generated by ChatGPT as well as the subsequent adjustments\n    def add_task_date(self, task_number, due_date):\n        \"\"\"Adds or updates a due date for a specific task.\n\n        Args:\n            task_number (_type_): an integer\n            due_date (_type_): string\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            try:\n                # Validate date format\n                datetime.strptime(due_date, \"%Y-%m-%d\")\n            except ValueError:\n                print(\"Invalid date format! Please enter the date in YYYY-MM-DD format.\")\n                return\n        \n            task_name, priority, _, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, completed)\n            print(f'Task updated with due date: {self.tasks[task_number - 1]}')\n            \n\n    def add_tag(self, task_number, tag):\n        if not self.tasks:\n            print(\"No tasks in the list to add a tag!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task = self.tasks[task_number - 1]\n            if type(task) is tuple:\n                task_name, priority, due_date, tags, delegate, _ = task\n                tags.append(tag)\n                print(f'Task updated: {self.tasks[task_number - 1]}')\n            else:\n                # all the tasks should now be a tuple\n                raise Exception(\"Internal error: the task is not a tuple.\")\n\n    \n    def update_task(self, task_number, updated_message):\n        \"\"\"Change the description of a task\"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n            \n\n    def delete_all_tasks(self):\n        \"\"\"Deletes all tasks in the list.\"\"\"\n        if not self.tasks:\n            print(\"No tasks to delete.\")\n        else:\n            self.tasks = []\n            print(\"All tasks deleted.\")\n\n    # that were made to print_menu and main\n    def get_total_tasks(self):\n        \"\"\"Returns the total number of tasks in the to-do list.\"\"\"\n        return len(self.tasks)\n\n    def remove_due_date(self, task_number):\n        \"\"\"Removes the due date from a task.\"\"\"\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            if due_date is not None:  # Check if the task has a due date\n                # Remove due date by converting it back to a string\n                self.tasks[task_number - 1] = (task_name, priority, None, tags, delegate, completed)\n                print(f'Due date removed from task: {task_name}')\n            else:\n                print(\"This task doesn't have a due date.\")                \n\n    def add_task_delegate(self, task_number, task_delegate):\n        \"\"\"Delegates a task to someone else.\"\"\"\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n            return\n\n        if not task_delegate.isalnum() or not task_delegate:\n            print(\"Invalid delegate\")\n            return\n\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, task_delegate, completed)\n            print(f\"Task delegated to {task_delegate}: {self.tasks[task_number - 1]}\")\n        \n\n    def task_exists(self, task):\n        \"\"\"Checks if a task already exists. Returns the task number if it exists, or None otherwise.\n\n        Args:\n            task (_type_): a string\n\n        Returns:\n            _type_: None if task does not exist. Int if task exists.\n        \"\"\"\n        task = task.strip()\n\n        for idx, t in enumerate(self.tasks):\n            task_name = t[0].strip().lower()\n            if task_name == task.lower():\n                return idx + 1\n\n        return None  # Task does not exist\n\n    def checkoff_task(self,task_number):\n        \"\"\"Mark the task completed\"\"\"\n        # Check if the provided task number is valid\n        if task_number <= 0 or task_number > len(self.tasks): \n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, True)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n\ndef test_delete_task():\n    todo_list_1 = TodoList()\n    todo_list_2 = TodoList()\n\n    # Add tasks to both lists\n    todo_list_1.add_task(\"Task 1\")\n    todo_list_1.add_task(\"Task 2\")\n    todo_list_1.add_task(\"Task 3\")\n\n    todo_list_2.add_task(\"Task 1\")\n    todo_list_2.add_task(\"Task 2\")\n    todo_list_2.add_task(\"Task 3\")\n\n    # Test deleting a valid task\n    todo_list_1.delete_task(2)\n    todo_list_2.delete_task_new_implementation(2)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting a valid task\"\n\n    # Test deleting from an empty list\n    todo_list_1.delete_all_tasks()\n    todo_list_2.delete_all_tasks()\n    todo_list_1.delete_task(1)\n    todo_list_2.delete_task_new_implementation(1)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting from an empty list\"\n\n    # Test deleting with an invalid task number\n    todo_list_1.add_task(\"Task 1\")\n    todo_list_2.add_task(\"Task 1\")\n    todo_list_1.delete_task(5)\n    todo_list_2.delete_task_new_implementation(5)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting with an invalid task number\"\n\n    # Test deleting the first task\n    todo_list_1.add_task(\"Task 2\")\n    todo_list_1.add_task(\"Task 3\")\n    todo_list_2.add_task(\"Task 2\")\n    todo_list_2.add_task(\"Task 3\")\n    todo_list_1.delete_task(1)\n    todo_list_2.delete_task_new_implementation(1)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting the first task\"\n\n    # Test deleting the last task\n    todo_list_1.delete_task(2)\n    todo_list_2.delete_task_new_implementation(2)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting the last task\"\n\n    # Test deleting with a negative task number\n    todo_list_1.delete_task(-1)\n    todo_list_2.delete_task_new_implementation(-1)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting with a negative task number\"\n\n    # Test deleting with zero as task number\n    todo_list_1.delete_task(0)\n    todo_list_2.delete_task_new_implementation(0)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting with zero as task number\"\n\n    # Test deleting after modifying the list\n    todo_list_1.add_task(\"Task 4\")\n    todo_list_1.add_task(\"Task 5\")\n    todo_list_2.add_task(\"Task 4\")\n    todo_list_2.add_task(\"Task 5\")\n    todo_list_1.delete_task(1)\n    todo_list_2.delete_task_new_implementation(1)\n    assert todo_list_1.tasks == todo_list_2.tasks, \"Test failed: Deleting after modifying the list\"\n\nif __name__ == \"__main__\":\n    test_delete_task()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `delete_task` in the `TodoList` class is identical to the ORIGINAL FUNCTION. Both functions check if the task list is empty and print a message if it is. They also validate the task number to ensure it is within the valid range and print an error message if it is not. If the task number is valid, they remove the task from the list and print a confirmation message. The logic and functionality of both functions are the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `delete_task` function modifies the `tasks` list, which is an attribute of the `TodoList` class. This satisfies the condition as it modifies an input argument (the `tasks` list).\n\n2. **CONDITION 2**: The test cases check the state of the `tasks` list after operations, using assertions to compare the `tasks` list of two `TodoList` instances. There are no checks for printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the `tasks` list of two `TodoList` instances after performing the same operations with `delete_task` and `delete_task_new_implementation`. This ensures that `delete_task_new_implementation` must have the same functionality as `delete_task` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the `tasks` list, which is reasonable since `delete_task` modifies this list. The test cases do not use return values, which aligns with the function's behavior, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover various scenarios, including deleting a valid task, deleting from an empty list, using invalid task numbers, and modifying the list before deletion. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "d055c41d513049263e024b41d4812576be2b160f"
    },
    {
        "func_name": "TodoList.update_task",
        "idx": "558",
        "repo_name": "COSC381-2024Fall___Todo-List-CLI",
        "func_path": "TodoList.py",
        "orig_func": "def update_task(self, task_number, updated_message):\n    \"\"\"Change the description of a task\"\"\"\n    if not self.tasks:\n        print('No tasks in the list to update!')\n        return\n    if task_number <= 0 or task_number > len(self.tasks):\n        print('Invalid task number!')\n    else:\n        task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n        self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n        print(f'Task updated: {self.tasks[task_number - 1]}')",
        "orig_context": "```python\n## TodoList.py\nfrom datetime import datetime\n\nclass TodoList:\n    \"\"\"A class to store and manage one list of tasks\n    \"\"\"\n    def __init__(self):\n        \"\"\"initialize an empty list\n        \"\"\"\n        self.tasks = []\n\n        \"\"\"Task is a 6-tuple (task, priority, date, tags, delegate, completed)\n        \"\"\"\n\n    def add_task(self, task, priority = \"Medium\", date = None):\n        \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n\n        task = task.strip()  # Remove any leading/trailing whitespace\n        task_number = self.task_exists(task)\n        if task_number:\n            print(f\"Task '{task}' already exists at position {task_number}.\")\n            return\n\n        self.tasks.append((task, priority, date, [], None, False))\n        print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")\n\n    def list_tasks(self):\n        \"\"\"Lists all tasks in the to-do list.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            for idx, task in enumerate(self.tasks, start=1):\n                print(f'{idx}. {task}')\n\n    def list_tasks_numeric(self):\n        \"\"\"Lists all tasks in the to-do list in numerical order, including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            print(\"\\nCurrent To-Do List:\")\n            for idx, task in enumerate(self.tasks, start=1):\n                task_name, priority, due_date, _, _, _ = task\n                if due_date:\n                    print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                else:\n                    print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                    \n    # Good job with implementing this function and listing the list in alphabetical order\n    def list_tasks_alphabetic(self):\n        \"\"\"lists all tasks in the to-do list in alphabetical order, \n           including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            sorted_tasks = [] \n            # Although I would review the for loops to make code smaller and faster\n            for idx, task in enumerate(self.tasks, start=1):\n                item = (idx, task) \n                sorted_tasks.append(item)\n            sorted_tasks = sorted(sorted_tasks, key=lambda x: x[1][0].lower() if isinstance(x[1], tuple) else x[1].lower())\n            for idx, task in sorted_tasks:\n                if isinstance(task, tuple):  # Task with due date\n                    task_name, priority, due_date, _, _, _ = task\n                    if due_date:\n                        print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                    else:\n                        print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                else:  # Task without due date\n                    # all the tasks should now be a tuple\n                    raise Exception(\"Internal error: the task is not a tuple.\")\n\n    def delete_task(self, task_number):\n        \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to delete!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            removed_task = self.tasks.pop(task_number - 1)\n            print(f'Task removed: {removed_task}')\n\n\n    # this method was generated by ChatGPT as well as the subsequent adjustments\n    def add_task_date(self, task_number, due_date):\n        \"\"\"Adds or updates a due date for a specific task.\n\n        Args:\n            task_number (_type_): an integer\n            due_date (_type_): string\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            try:\n                # Validate date format\n                datetime.strptime(due_date, \"%Y-%m-%d\")\n            except ValueError:\n                print(\"Invalid date format! Please enter the date in YYYY-MM-DD format.\")\n                return\n        \n            task_name, priority, _, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, completed)\n            print(f'Task updated with due date: {self.tasks[task_number - 1]}')\n            \n\n    def add_tag(self, task_number, tag):\n        if not self.tasks:\n            print(\"No tasks in the list to add a tag!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task = self.tasks[task_number - 1]\n            if type(task) is tuple:\n                task_name, priority, due_date, tags, delegate, _ = task\n                tags.append(tag)\n                print(f'Task updated: {self.tasks[task_number - 1]}')\n            else:\n                # all the tasks should now be a tuple\n                raise Exception(\"Internal error: the task is not a tuple.\")\n\n    \n    def update_task(self, task_number, updated_message):\n        \"\"\"Change the description of a task\"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n            \n\n    def delete_all_tasks(self):\n        \"\"\"Deletes all tasks in the list.\"\"\"\n        if not self.tasks:\n            print(\"No tasks to delete.\")\n        else:\n            self.tasks = []\n            print(\"All tasks deleted.\")\n\n    # that were made to print_menu and main\n    def get_total_tasks(self):\n        \"\"\"Returns the total number of tasks in the to-do list.\"\"\"\n        return len(self.tasks)\n\n    def remove_due_date(self, task_number):\n        \"\"\"Removes the due date from a task.\"\"\"\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            if due_date is not None:  # Check if the task has a due date\n                # Remove due date by converting it back to a string\n                self.tasks[task_number - 1] = (task_name, priority, None, tags, delegate, completed)\n                print(f'Due date removed from task: {task_name}')\n            else:\n                print(\"This task doesn't have a due date.\")                \n\n    def add_task_delegate(self, task_number, task_delegate):\n        \"\"\"Delegates a task to someone else.\"\"\"\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n            return\n\n        if not task_delegate.isalnum() or not task_delegate:\n            print(\"Invalid delegate\")\n            return\n\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, task_delegate, completed)\n            print(f\"Task delegated to {task_delegate}: {self.tasks[task_number - 1]}\")\n        \n\n    def task_exists(self, task):\n        \"\"\"Checks if a task already exists. Returns the task number if it exists, or None otherwise.\n\n        Args:\n            task (_type_): a string\n\n        Returns:\n            _type_: None if task does not exist. Int if task exists.\n        \"\"\"\n        task = task.strip()\n\n        for idx, t in enumerate(self.tasks):\n            task_name = t[0].strip().lower()\n            if task_name == task.lower():\n                return idx + 1\n\n        return None  # Task does not exist\n\n    def checkoff_task(self,task_number):\n        \"\"\"Mark the task completed\"\"\"\n        # Check if the provided task number is valid\n        if task_number <= 0 or task_number > len(self.tasks): \n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, True)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n\n```\n\n\n",
        "eval_script": "## TodoList.py\nfrom datetime import datetime\n\nclass TodoList:\n    \"\"\"A class to store and manage one list of tasks\n    \"\"\"\n    def __init__(self):\n        \"\"\"initialize an empty list\n        \"\"\"\n        self.tasks = []\n\n        \"\"\"Task is a 6-tuple (task, priority, date, tags, delegate, completed)\n        \"\"\"\n\n    def add_task(self, task, priority = \"Medium\", date = None):\n        \"\"\"Adds a new task to the list if it doesn't already exist.\n\n        Args:\n            task (_type_): a string. Leading or trailing whitespace will be removed. Cannot add a duplicate task.\n            priority (str, optional): the priority of the task. Defaults to \"Medium\".\n            date (_type_, optional): the due date of the task. Defaults to None.\n        \"\"\"\n\n        task = task.strip()  # Remove any leading/trailing whitespace\n        task_number = self.task_exists(task)\n        if task_number:\n            print(f\"Task '{task}' already exists at position {task_number}.\")\n            return\n\n        self.tasks.append((task, priority, date, [], None, False))\n        print(f\"Task added: {task} with priority '{priority}' and due date: '{date}'\")\n\n    def list_tasks(self):\n        \"\"\"Lists all tasks in the to-do list.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            for idx, task in enumerate(self.tasks, start=1):\n                print(f'{idx}. {task}')\n\n    def list_tasks_numeric(self):\n        \"\"\"Lists all tasks in the to-do list in numerical order, including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            print(\"\\nCurrent To-Do List:\")\n            for idx, task in enumerate(self.tasks, start=1):\n                task_name, priority, due_date, _, _, _ = task\n                if due_date:\n                    print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                else:\n                    print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                    \n    # Good job with implementing this function and listing the list in alphabetical order\n    def list_tasks_alphabetic(self):\n        \"\"\"lists all tasks in the to-do list in alphabetical order, \n           including due dates if available.\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list!\")\n        else:\n            sorted_tasks = [] \n            # Although I would review the for loops to make code smaller and faster\n            for idx, task in enumerate(self.tasks, start=1):\n                item = (idx, task) \n                sorted_tasks.append(item)\n            sorted_tasks = sorted(sorted_tasks, key=lambda x: x[1][0].lower() if isinstance(x[1], tuple) else x[1].lower())\n            for idx, task in sorted_tasks:\n                if isinstance(task, tuple):  # Task with due date\n                    task_name, priority, due_date, _, _, _ = task\n                    if due_date:\n                        print(f'{idx}. {task_name} (Due: {due_date}) [Priority: {priority}]')\n                    else:\n                        print(f'{idx}. {task_name} (Due: None) [Priority: {priority}]')\n                else:  # Task without due date\n                    # all the tasks should now be a tuple\n                    raise Exception(\"Internal error: the task is not a tuple.\")\n\n    def delete_task(self, task_number):\n        \"\"\"Deletes a task by its number in the list.\n\n        Args:\n            task_number (_type_): an integer\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to delete!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            removed_task = self.tasks.pop(task_number - 1)\n            print(f'Task removed: {removed_task}')\n\n\n    # this method was generated by ChatGPT as well as the subsequent adjustments\n    def add_task_date(self, task_number, due_date):\n        \"\"\"Adds or updates a due date for a specific task.\n\n        Args:\n            task_number (_type_): an integer\n            due_date (_type_): string\n        \"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            try:\n                # Validate date format\n                datetime.strptime(due_date, \"%Y-%m-%d\")\n            except ValueError:\n                print(\"Invalid date format! Please enter the date in YYYY-MM-DD format.\")\n                return\n        \n            task_name, priority, _, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, completed)\n            print(f'Task updated with due date: {self.tasks[task_number - 1]}')\n            \n\n    def add_tag(self, task_number, tag):\n        if not self.tasks:\n            print(\"No tasks in the list to add a tag!\")\n            return\n        \n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task = self.tasks[task_number - 1]\n            if type(task) is tuple:\n                task_name, priority, due_date, tags, delegate, _ = task\n                tags.append(tag)\n                print(f'Task updated: {self.tasks[task_number - 1]}')\n            else:\n                # all the tasks should now be a tuple\n                raise Exception(\"Internal error: the task is not a tuple.\")\n\n    \n    def update_task(self, task_number, updated_message):\n        \"\"\"Change the description of a task\"\"\"\n        if not self.tasks:\n            print(\"No tasks in the list to update!\")\n            return\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (updated_message, priority, due_date, tags, delegate, completed)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n            \n\n\n    def delete_all_tasks(self):\n        \"\"\"Deletes all tasks in the list.\"\"\"\n        if not self.tasks:\n            print(\"No tasks to delete.\")\n        else:\n            self.tasks = []\n            print(\"All tasks deleted.\")\n\n    # that were made to print_menu and main\n    def get_total_tasks(self):\n        \"\"\"Returns the total number of tasks in the to-do list.\"\"\"\n        return len(self.tasks)\n\n    def remove_due_date(self, task_number):\n        \"\"\"Removes the due date from a task.\"\"\"\n\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            if due_date is not None:  # Check if the task has a due date\n                # Remove due date by converting it back to a string\n                self.tasks[task_number - 1] = (task_name, priority, None, tags, delegate, completed)\n                print(f'Due date removed from task: {task_name}')\n            else:\n                print(\"This task doesn't have a due date.\")                \n\n    def add_task_delegate(self, task_number, task_delegate):\n        \"\"\"Delegates a task to someone else.\"\"\"\n        if task_number <= 0 or task_number > len(self.tasks):\n            print(\"Invalid task number!\")\n            return\n\n        if not task_delegate.isalnum() or not task_delegate:\n            print(\"Invalid delegate\")\n            return\n\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, task_delegate, completed)\n            print(f\"Task delegated to {task_delegate}: {self.tasks[task_number - 1]}\")\n        \n\n    def task_exists(self, task):\n        \"\"\"Checks if a task already exists. Returns the task number if it exists, or None otherwise.\n\n        Args:\n            task (_type_): a string\n\n        Returns:\n            _type_: None if task does not exist. Int if task exists.\n        \"\"\"\n        task = task.strip()\n\n        for idx, t in enumerate(self.tasks):\n            task_name = t[0].strip().lower()\n            if task_name == task.lower():\n                return idx + 1\n\n        return None  # Task does not exist\n\n    def checkoff_task(self,task_number):\n        \"\"\"Mark the task completed\"\"\"\n        # Check if the provided task number is valid\n        if task_number <= 0 or task_number > len(self.tasks): \n            print(\"Invalid task number!\")\n        else:\n            task_name, priority, due_date, tags, delegate, completed = self.tasks[task_number - 1]\n            self.tasks[task_number - 1] = (task_name, priority, due_date, tags, delegate, True)\n            print(f'Task updated: {self.tasks[task_number - 1]}')\n\ndef test_update_task():\n    todo_list = TodoList()\n\n    # Test 1: Update task when list is empty\n    todo_list.update_task(1, \"New Task\")\n    todo_list.update_task_new_implementation(1, \"New Task\")\n    assert todo_list.tasks == [], \"Test 1 Failed: The task list should remain empty.\"\n\n    # Add a task for further tests\n    todo_list.add_task(\"Task 1\")\n\n    # Test 2: Update task with invalid task number\n    todo_list.update_task(0, \"Updated Task\")\n    todo_list.update_task_new_implementation(0, \"Updated Task\")\n    assert todo_list.tasks[0][0] == \"Task 1\", \"Test 2 Failed: The task should not be updated with an invalid task number.\"\n\n    # Test 3: Successfully update a task\n    todo_list.update_task(1, \"Updated Task\")\n    assert todo_list.tasks[0][0] == \"Updated Task\", \"Test 3 Failed: The task should be updated.\"\n\n    todo_list.update_task_new_implementation(1, \"Updated Task Again\")\n    assert todo_list.tasks[0][0] == \"Updated Task Again\", \"Test 3 Failed: The task should be updated by the new implementation.\"\n\n    # Test 4: Update task with negative task number\n    todo_list.update_task(-1, \"Negative Task\")\n    todo_list.update_task_new_implementation(-1, \"Negative Task\")\n    assert todo_list.tasks[0][0] == \"Updated Task Again\", \"Test 4 Failed: The task should not be updated with a negative task number.\"\n\n    # Test 5: Update task with a task number greater than the number of tasks\n    todo_list.update_task(2, \"Out of Bounds Task\")\n    todo_list.update_task_new_implementation(2, \"Out of Bounds Task\")\n    assert todo_list.tasks[0][0] == \"Updated Task Again\", \"Test 5 Failed: The task should not be updated with an out of bounds task number.\"\n\n    # Test 6: Update task with an empty string\n    todo_list.update_task(1, \"\")\n    assert todo_list.tasks[0][0] == \"\", \"Test 6 Failed: The task should be updated to an empty string.\"\n\n    todo_list.update_task_new_implementation(1, \"Non-empty Task\")\n    assert todo_list.tasks[0][0] == \"Non-empty Task\", \"Test 6 Failed: The task should be updated by the new implementation.\"\n\n    # Test 7: Update task with special characters\n    todo_list.update_task(1, \"@#$%^&*()\")\n    assert todo_list.tasks[0][0] == \"@#$%^&*()\", \"Test 7 Failed: The task should be updated with special characters.\"\n\n    todo_list.update_task_new_implementation(1, \"Special Characters Task\")\n    assert todo_list.tasks[0][0] == \"Special Characters Task\", \"Test 7 Failed: The task should be updated by the new implementation.\"\n\n    # Add another task for further tests\n    todo_list.add_task(\"Task 2\")\n\n    # Test 8: Ensure updating one task doesn't affect others\n    todo_list.update_task(1, \"First Task Updated\")\n    assert todo_list.tasks[0][0] == \"First Task Updated\", \"Test 8 Failed: The first task should be updated.\"\n    assert todo_list.tasks[1][0] == \"Task 2\", \"Test 8 Failed: The second task should remain unchanged.\"\n\n    todo_list.update_task_new_implementation(1, \"First Task Updated Again\")\n    assert todo_list.tasks[0][0] == \"First Task Updated Again\", \"Test 8 Failed: The first task should be updated by the new implementation.\"\n    assert todo_list.tasks[1][0] == \"Task 2\", \"Test 8 Failed: The second task should remain unchanged by the new implementation.\"\n\nif __name__ == \"__main__\":\n    test_update_task()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `update_task` in the `TodoList` class performs the same operations as the ORIGINAL FUNCTION. Both functions check if the task list is empty and print a message if it is. They also verify if the provided task number is valid, printing an error message if it is not. If the task number is valid, both functions update the task description while preserving the other attributes of the task tuple. The print statements in both functions confirm the task update. The functionality and logic of both functions are identical.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `update_task` function modifies the `tasks` list, which is an attribute of the `TodoList` class. This satisfies the condition as it modifies input arguments (in this case, the class instance's state).\n- CONDITION 2: The test cases use assertions to check the state of the `tasks` list after calling `update_task` and `update_task_new_implementation`. They do not rely on printed or logged output, satisfying this condition.\n- CONDITION 3: The test cases compare the state of the `tasks` list after calling both `update_task` and `update_task_new_implementation`. They ensure that both implementations must have the same effect on the `tasks` list, satisfying this condition.\n- CONDITION 4: The test cases use assertions to check the state of the `tasks` list, which is reasonable given that `update_task` modifies this list. The assertions are appropriate and correctly test the functionality.\n- CONDITION 5: The test cases cover various scenarios, including updating tasks with valid and invalid task numbers, updating with special characters, and ensuring that updating one task does not affect others. These are non-trivial tests that adequately cover different edge cases and typical use cases.",
            "answer": "yes"
        },
        "commit_id": "d055c41d513049263e024b41d4812576be2b160f"
    },
    {
        "func_name": "ActiveAuctions.get_page",
        "idx": "559",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/Auctions.py",
        "orig_func": "def get_page(self, page_number: int=0) -> AuctionsPage:\n    \"\"\"\n        Fetch a specific page of auctions, using cache if available.\n\n        Args:\n            page_number (int): The page number to fetch.\n\n        Returns:\n            AuctionsPage: The AuctionsPage object for the requested page.\n        \"\"\"\n    if page_number in self.cache_pages:\n        return self.cache_pages[page_number]\n    params = {'page': page_number}\n    try:\n        response = requests.get(self._api_endpoint, params=params)\n        response.raise_for_status()\n        data = response.json()\n        if data.get('success'):\n            page = AuctionsPage(data)\n            self.cache_pages[page_number] = page\n            return page\n        else:\n            raise ValueError('API response was not successful')\n    except requests.exceptions.RequestException as e:\n        raise ConnectionError(f'An error occurred while fetching page {page_number}: {e}')",
        "orig_context": "```python\n## hypixel_api_lib/utils.py\nfrom datetime import datetime, timezone\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\n```\n\n\n```python\n## hypixel_api_lib/Auctions.py\nfrom datetime import datetime, timezone, tzinfo\n\nimport requests\n\nfrom hypixel_api_lib.utils import get_uuid_from_username, convert_timestamp\n\nACTIVE_AUCTIONS_API_URL = r\"https://api.hypixel.net/skyblock/auctions\"\n\nclass Bid:\n    \"\"\"\n    Represents a single bid in an auction.\n\n    Attributes:\n        auction_id (str): The ID of the auction.\n        bidder (str): The UUID of the bidder.\n        profile_id (str): The profile ID of the bidder.\n        amount (int): The amount of the bid.\n        timestamp (datetime): The timestamp of the bid.\n    \"\"\"\n\n    def __init__(self, bid_data : dict) -> None:\n        self.auction_id: str = bid_data.get('auction_id')\n        self.bidder: str = bid_data.get('bidder')\n        self.profile_id: str = bid_data.get('profile_id')\n        self.amount: int = bid_data.get('amount')\n        self.timestamp: datetime | None = convert_timestamp(bid_data.get('timestamp'))\n\n    def __str__(self) -> str:\n        timestamp_str = self.timestamp.strftime(\"%Y-%m-%d %H:%M:%S %Z\") if self.timestamp else \"N/A\"\n        return f\"Bid of {self.amount} by {self.bidder} at {timestamp_str}\"\n\nclass SkyBlockAuction:\n    \"\"\"\n    Represents a single SkyBlock auction.\n\n    Attributes:\n        _id (str): The unique identifier of the auction.\n        uuid (str): The UUID of the auction.\n        auctioneer (str): The UUID of the auctioneer.\n        profile_id (str): The profile ID of the auctioneer.\n        coop (list of str): List of coop member UUIDs.\n        start (datetime): The start time of the auction.\n        end (datetime): The end time of the auction.\n        item_name (str): The name of the item being auctioned.\n        item_lore (str): The lore of the item.\n        extra (str): Additional information.\n        category (str): The category of the item.\n        tier (str): The tier of the item.\n        starting_bid (int): The starting bid amount.\n        item_bytes (object): Serialized item data.\n        claimed (bool): Whether the auction has been claimed.\n        claimed_bidders (list): List of bidders who have claimed the item.\n        highest_bid_amount (int): The highest bid amount.\n        bids (list[Bid]): List of bids.\n    \"\"\"\n\n    def __init__(self, auction_data: dict) -> None:\n        self._id: str = auction_data.get('_id')\n        self.uuid: str = auction_data.get('uuid')\n        self.auctioneer: str = auction_data.get('auctioneer')\n        self.profile_id: str = auction_data.get('profile_id')\n        self.coop: list[str] = auction_data.get('coop', [])\n        self.start: datetime | None = convert_timestamp(auction_data.get('start'))\n        self.end: datetime | None = convert_timestamp(auction_data.get('end'))\n        self.item_name: str = auction_data.get('item_name')\n        self.item_lore: str = auction_data.get('item_lore')\n        self.extra: str = auction_data.get('extra')\n        self.category: str = auction_data.get('category')\n        self.tier: str = auction_data.get('tier')\n        self.starting_bid: int = auction_data.get('starting_bid')\n        self.item_bytes: object = auction_data.get('item_bytes')\n        self.claimed: bool = auction_data.get('claimed')\n        self.claimed_bidders: list = auction_data.get('claimed_bidders', [])\n        self.highest_bid_amount: int = auction_data.get('highest_bid_amount')\n        self.bids: list[Bid] | None = [Bid(bid) for bid in auction_data.get('bids', [])]\n\n    def get_start_time_in_timezone(self, tz: tzinfo) -> datetime | None:\n        \"\"\"\n        Get the start time converted to the specified time zone.\n\n        Args:\n            tz (timezone): A timezone object.\n\n        Returns:\n            datetime: The start time in the specified time zone.\n        \"\"\"\n        if self.start:\n            return self.start.astimezone(tz)\n        return None\n\n    def get_end_time_in_timezone(self, tz: tzinfo) -> datetime | None:\n        \"\"\"\n        Get the end time converted to the specified time zone.\n\n        Args:\n            tz (timezone): A timezone object.\n\n        Returns:\n            datetime: The end time in the specified time zone.\n        \"\"\"\n        if self.end:\n            return self.end.astimezone(tz)\n        return None\n\n    @property\n    def current_price(self) -> int:\n        \"\"\"\n        Get the current price of the auction.\n\n        For BIN auctions (with no bids), this is the starting_bid.\n        For regular auctions, this is the highest_bid_amount.\n\n        Returns:\n            int: The current price of the auction.\n        \"\"\"\n        if not self.bids:\n            return self.starting_bid\n        else:\n            return max(self.starting_bid, self.highest_bid_amount)\n\n    @property\n    def is_bin(self) -> bool:\n        \"\"\"\n        Estimate whether the auction is a BIN auction.\n\n        Returns:\n            bool: True if the auction is likely a BIN auction, False otherwise.\n        \"\"\"\n        # Since I can't know from the API, I'm assume auctions with no bids are BIN\n        return not self.bids\n\n    def __str__(self) -> str:\n        auction_type = \"BIN\" if self.is_bin else \"Auction\"\n        return f\"{auction_type} '{self.item_name}' by {self.auctioneer}, Price: {self.current_price}\"\n\nclass AuctionsPage:\n    \"\"\"\n    Represents a single page of auctions from the Hypixel SkyBlock Auctions API.\n\n    Attributes:\n        success (bool): Indicates whether the API request was successful.\n        page (int): The current page number.\n        totalPages (int): The total number of pages.\n        totalAuctions (int): The total number of auctions.\n        lastUpdated (datetime): The last updated timestamp.\n        auctions (list[SkyBlockAuction]): The list of auctions on this page.\n    \"\"\"\n\n    def __init__(self, page_data: dict) -> None:\n        self.success: bool = page_data.get('success', False)\n        self.page: int = page_data.get('page', 0)\n        self.totalPages: int = page_data.get('totalPages', 0)\n        self.totalAuctions: int = page_data.get('totalAuctions', 0)\n        self.lastUpdated: datetime | None = convert_timestamp(page_data.get('lastUpdated'))\n        self.auctions: list[SkyBlockAuction] = [SkyBlockAuction(auction) for auction in page_data.get('auctions', [])]\n\n    def get_auction_by_id(self, auction_id: str) -> SkyBlockAuction | None:\n        \"\"\"\n        Retrieve an auction by its ID from the current page.\n\n        Args:\n            auction_id (str): The ID of the auction.\n\n        Returns:\n            SkyBlockAuction or None: The auction object, or None if not found.\n        \"\"\"\n        return next((auction for auction in self.auctions if auction._id == auction_id), None)\n\n    def get_auctions_by_item_name(self, item_name: str) -> SkyBlockAuction:\n        \"\"\"\n        Retrieve auctions by item name from the current page.\n\n        Args:\n            item_name (str): The name of the item.\n\n        Returns:\n            list of SkyBlockAuction: A list of auctions matching the item name.\n        \"\"\"\n        return [auction for auction in self.auctions if auction.item_name.lower() == item_name.lower()]\n\n    def __str__(self) -> str:\n        return f\"Auctions Page {self.page}/{self.totalPages}, Total Auctions: {self.totalAuctions}\"\n\nclass ActiveAuctions:\n    \"\"\"\n    Manages fetching and storing auction data from the Hypixel SkyBlock Auctions API.\n\n    Attributes:\n        api_endpoint (str): The API endpoint URL.\n        all_auctions (list of SkyBlockAuction): Cached list of all auctions.\n        cache_pages (dict): Cached pages of auctions.\n    \"\"\"\n\n    def __init__(self, api_endpoint: str = ACTIVE_AUCTIONS_API_URL, preload_all: bool = False) -> None:\n        self._api_endpoint: str = api_endpoint\n        self.all_auctions: list[SkyBlockAuction] | list = []\n        self.cache_pages: dict[int, AuctionsPage] | dict = {}\n        if preload_all:\n            self.all_auctions = self.get_all_auctions()\n\n    def get_page(self, page_number: int = 0) -> AuctionsPage:\n        \"\"\"\n        Fetch a specific page of auctions, using cache if available.\n\n        Args:\n            page_number (int): The page number to fetch.\n\n        Returns:\n            AuctionsPage: The AuctionsPage object for the requested page.\n        \"\"\"\n        if page_number in self.cache_pages:\n            return self.cache_pages[page_number]\n\n        params = {'page': page_number}\n        try:\n            response = requests.get(self._api_endpoint, params=params)\n            response.raise_for_status()\n            data = response.json()\n\n            if data.get('success'):\n                page = AuctionsPage(data)\n                self.cache_pages[page_number] = page\n                return page\n            else:\n                raise ValueError(\"API response was not successful\")\n        except requests.exceptions.RequestException as e:\n            raise ConnectionError(f\"An error occurred while fetching page {page_number}: {e}\")\n\n    def get_all_auctions(self) -> list[SkyBlockAuction]:\n        \"\"\"\n        Fetch all auctions by iterating through all available pages.\n\n        Returns:\n            list of SkyBlockAuction: A list of all auctions.\n        \"\"\"\n        if self.all_auctions:\n            return self.all_auctions  # Return cached data\n\n        all_auctions = []\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n        all_auctions.extend(first_page.auctions)\n\n        for page_number in range(1, total_pages):\n            page = self.get_page(page_number)\n            all_auctions.extend(page.auctions)\n\n        self.all_auctions = all_auctions  # Cache the results\n        return all_auctions\n\n    def search_auctions(self, item_name: str | None = None, min_price: int | None = None, max_price: int | None = None, sort_by_price: bool = False, descending: bool = False, max_pages: int | None = None) -> list[SkyBlockAuction]:\n        \"\"\"\n        Search for auctions matching the specified criteria.\n\n        Args:\n            item_name (str, optional): The name of the item to search for.\n            min_price (int, optional): The minimum price.\n            max_price (int, optional): The maximum price.\n            sort_by_price (bool, optional): Whether to sort the results by price.\n            descending (bool, optional): Whether to sort in descending order.\n            max_pages (int, optional): Maximum number of pages to search.\n\n        Returns:\n            list of SkyBlockAuction: A list of auctions matching the criteria.\n        \"\"\"\n        matching_auctions = []\n\n        # Use cached data if available\n        if self.all_auctions:\n            auctions_to_search = self.all_auctions\n        else:\n            first_page = self.get_page(0)\n            total_pages = first_page.totalPages\n            if max_pages:\n                total_pages = min(total_pages, max_pages)\n\n            auctions_to_search = []\n            auctions_to_search.extend(first_page.auctions)\n\n            for page_number in range(1, total_pages):\n                page = self.get_page(page_number)\n                auctions_to_search.extend(page.auctions)\n\n        # Function to check if an auction matches the criteria\n        def matches(auction: SkyBlockAuction) -> bool:\n            if item_name and item_name.lower() not in auction.item_name.lower():\n                return False\n            price = auction.current_price\n            if min_price is not None and price < min_price:\n                return False\n            if max_price is not None and price > max_price:\n                return False\n            return True\n\n        for auction in auctions_to_search:\n            if matches(auction):\n                matching_auctions.append(auction)\n\n        if sort_by_price:\n            matching_auctions.sort(key=lambda x: x.current_price, reverse=descending)\n\n        return matching_auctions\n\n    def get_auction_by_id(self, auction_id: str) -> SkyBlockAuction | None:\n        \"\"\"\n        Fetch a specific auction by its ID.\n\n        Args:\n            auction_id (str): The ID of the auction.\n\n        Returns:\n            SkyBlockAuction: The auction with the specified ID, or None if not found.\n        \"\"\"\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n\n        for page_number in range(total_pages):\n            page = self.get_page(page_number)\n            auction = page.get_auction_by_id(auction_id)\n            if auction:\n                return auction\n        return None\n\n    def __str__(self) -> str:\n        return f\"Auctions Manager using endpoint {self._api_endpoint}\"\n\n```\n\n\n",
        "eval_script": "from datetime import datetime, timezone, tzinfo\nimport requests  # Import the requests module\n\n# Mocking requests module\nclass MockResponse:\n    @staticmethod\n    def json():\n        return {\n            \"success\": True,\n            \"page\": 0,\n            \"totalPages\": 1,\n            \"totalAuctions\": 1,\n            \"lastUpdated\": 1638316800000,\n            \"auctions\": [\n                {\n                    \"_id\": \"auction_id_1\",\n                    \"uuid\": \"uuid_1\",\n                    \"auctioneer\": \"auctioneer_uuid\",\n                    \"profile_id\": \"profile_id_1\",\n                    \"coop\": [\"coop_member_1\"],\n                    \"start\": 1638316800000,\n                    \"end\": 1638403200000,\n                    \"item_name\": \"Sword of Testing\",\n                    \"item_lore\": \"A powerful sword for testing.\",\n                    \"extra\": \"Extra info\",\n                    \"category\": \"Weapon\",\n                    \"tier\": \"Legendary\",\n                    \"starting_bid\": 1000,\n                    \"item_bytes\": None,\n                    \"claimed\": False,\n                    \"claimed_bidders\": [],\n                    \"highest_bid_amount\": 1500,\n                    \"bids\": [\n                        {\n                            \"auction_id\": \"auction_id_1\",\n                            \"bidder\": \"bidder_uuid\",\n                            \"profile_id\": \"profile_id_2\",\n                            \"amount\": 1500,\n                            \"timestamp\": 1638320400000\n                        }\n                    ]\n                }\n            ]\n        }\n\n    @staticmethod\n    def raise_for_status():\n        pass\n\ndef mock_requests_get(url, params=None):\n    return MockResponse()\n\n# Replacing requests.get with the mock function\nrequests.get = mock_requests_get\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\nACTIVE_AUCTIONS_API_URL = r\"https://api.hypixel.net/skyblock/auctions\"\n\nclass Bid:\n    def __init__(self, bid_data: dict) -> None:\n        self.auction_id: str = bid_data.get('auction_id')\n        self.bidder: str = bid_data.get('bidder')\n        self.profile_id: str = bid_data.get('profile_id')\n        self.amount: int = bid_data.get('amount')\n        self.timestamp: datetime | None = convert_timestamp(bid_data.get('timestamp'))\n\n    def __str__(self) -> str:\n        timestamp_str = self.timestamp.strftime(\"%Y-%m-%d %H:%M:%S %Z\") if self.timestamp else \"N/A\"\n        return f\"Bid of {self.amount} by {self.bidder} at {timestamp_str}\"\n\nclass SkyBlockAuction:\n    def __init__(self, auction_data: dict) -> None:\n        self._id: str = auction_data.get('_id')\n        self.uuid: str = auction_data.get('uuid')\n        self.auctioneer: str = auction_data.get('auctioneer')\n        self.profile_id: str = auction_data.get('profile_id')\n        self.coop: list[str] = auction_data.get('coop', [])\n        self.start: datetime | None = convert_timestamp(auction_data.get('start'))\n        self.end: datetime | None = convert_timestamp(auction_data.get('end'))\n        self.item_name: str = auction_data.get('item_name')\n        self.item_lore: str = auction_data.get('item_lore')\n        self.extra: str = auction_data.get('extra')\n        self.category: str = auction_data.get('category')\n        self.tier: str = auction_data.get('tier')\n        self.starting_bid: int = auction_data.get('starting_bid')\n        self.item_bytes: object = auction_data.get('item_bytes')\n        self.claimed: bool = auction_data.get('claimed')\n        self.claimed_bidders: list = auction_data.get('claimed_bidders', [])\n        self.highest_bid_amount: int = auction_data.get('highest_bid_amount')\n        self.bids: list[Bid] | None = [Bid(bid) for bid in auction_data.get('bids', [])]\n\n    def get_start_time_in_timezone(self, tz: tzinfo) -> datetime | None:\n        if self.start:\n            return self.start.astimezone(tz)\n        return None\n\n    def get_end_time_in_timezone(self, tz: tzinfo) -> datetime | None:\n        if self.end:\n            return self.end.astimezone(tz)\n        return None\n\n    @property\n    def current_price(self) -> int:\n        if not self.bids:\n            return self.starting_bid\n        else:\n            return max(self.starting_bid, self.highest_bid_amount)\n\n    @property\n    def is_bin(self) -> bool:\n        return not self.bids\n\n    def __str__(self) -> str:\n        auction_type = \"BIN\" if self.is_bin else \"Auction\"\n        return f\"{auction_type} '{self.item_name}' by {self.auctioneer}, Price: {self.current_price}\"\n\nclass AuctionsPage:\n    def __init__(self, page_data: dict) -> None:\n        self.success: bool = page_data.get('success', False)\n        self.page: int = page_data.get('page', 0)\n        self.totalPages: int = page_data.get('totalPages', 0)\n        self.totalAuctions: int = page_data.get('totalAuctions', 0)\n        self.lastUpdated: datetime | None = convert_timestamp(page_data.get('lastUpdated'))\n        self.auctions: list[SkyBlockAuction] = [SkyBlockAuction(auction) for auction in page_data.get('auctions', [])]\n\n    def get_auction_by_id(self, auction_id: str) -> SkyBlockAuction | None:\n        return next((auction for auction in self.auctions if auction._id == auction_id), None)\n\n    def get_auctions_by_item_name(self, item_name: str) -> list[SkyBlockAuction]:\n        return [auction for auction in self.auctions if auction.item_name.lower() == item_name.lower()]\n\n    def __str__(self) -> str:\n        return f\"Auctions Page {self.page}/{self.totalPages}, Total Auctions: {self.totalAuctions}\"\n\nclass ActiveAuctions:\n    def __init__(self, api_endpoint: str = ACTIVE_AUCTIONS_API_URL, preload_all: bool = False) -> None:\n        self._api_endpoint: str = api_endpoint\n        self.all_auctions: list[SkyBlockAuction] | list = []\n        self.cache_pages: dict[int, AuctionsPage] | dict = {}\n        if preload_all:\n            self.all_auctions = self.get_all_auctions()\n\n    def get_page(self, page_number: int = 0) -> AuctionsPage:\n        if page_number in self.cache_pages:\n            return self.cache_pages[page_number]\n\n        params = {'page': page_number}\n        try:\n            response = requests.get(self._api_endpoint, params=params)\n            response.raise_for_status()\n            data = response.json()\n\n            if data.get('success'):\n                page = AuctionsPage(data)\n                self.cache_pages[page_number] = page\n                return page\n            else:\n                raise ValueError(\"API response was not successful\")\n        except requests.exceptions.RequestException as e:\n            raise ConnectionError(f\"An error occurred while fetching page {page_number}: {e}\")\n\n\n    def get_all_auctions(self) -> list[SkyBlockAuction]:\n        if self.all_auctions:\n            return self.all_auctions\n\n        all_auctions = []\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n        all_auctions.extend(first_page.auctions)\n\n        for page_number in range(1, total_pages):\n            page = self.get_page(page_number)\n            all_auctions.extend(page.auctions)\n\n        self.all_auctions = all_auctions\n        return all_auctions\n\n    def search_auctions(self, item_name: str | None = None, min_price: int | None = None, max_price: int | None = None, sort_by_price: bool = False, descending: bool = False, max_pages: int | None = None) -> list[SkyBlockAuction]:\n        matching_auctions = []\n\n        if self.all_auctions:\n            auctions_to_search = self.all_auctions\n        else:\n            first_page = self.get_page(0)\n            total_pages = first_page.totalPages\n            if max_pages:\n                total_pages = min(total_pages, max_pages)\n\n            auctions_to_search = []\n            auctions_to_search.extend(first_page.auctions)\n\n            for page_number in range(1, total_pages):\n                page = self.get_page(page_number)\n                auctions_to_search.extend(page.auctions)\n\n        def matches(auction: SkyBlockAuction) -> bool:\n            if item_name and item_name.lower() not in auction.item_name.lower():\n                return False\n            price = auction.current_price\n            if min_price is not None and price < min_price:\n                return False\n            if max_price is not None and price > max_price:\n                return False\n            return True\n\n        for auction in auctions_to_search:\n            if matches(auction):\n                matching_auctions.append(auction)\n\n        if sort_by_price:\n            matching_auctions.sort(key=lambda x: x.current_price, reverse=descending)\n\n        return matching_auctions\n\n    def get_auction_by_id(self, auction_id: str) -> SkyBlockAuction | None:\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n\n        for page_number in range(total_pages):\n            page = self.get_page(page_number)\n            auction = page.get_auction_by_id(auction_id)\n            if auction:\n                return auction\n        return None\n\n    def __str__(self) -> str:\n        return f\"Auctions Manager using endpoint {self._api_endpoint}\"\n\ndef test_get_page():\n    active_auctions = ActiveAuctions()\n    \n    # Test 1: Compare the first page\n    page1_old = active_auctions.get_page(0)\n    page1_new = active_auctions.get_page_new_implementation(0)\n    assert page1_old.success == page1_new.success\n    assert page1_old.page == page1_new.page\n    assert page1_old.totalPages == page1_new.totalPages\n    assert page1_old.totalAuctions == page1_new.totalAuctions\n    assert page1_old.lastUpdated == page1_new.lastUpdated\n    assert len(page1_old.auctions) == len(page1_new.auctions)\n\n    # Test 2: Compare a cached page\n    page2_old = active_auctions.get_page(0)  # Should be cached\n    page2_new = active_auctions.get_page_new_implementation(0)  # Should be cached\n    assert page2_old is page1_old\n    assert page2_new is page1_new\n\n    # Test 3: Compare a non-cached page\n    page3_old = active_auctions.get_page(1)\n    page3_new = active_auctions.get_page_new_implementation(1)\n    assert page3_old.success == page3_new.success\n    assert page3_old.page == page3_new.page\n    assert page3_old.totalPages == page3_new.totalPages\n    assert page3_old.totalAuctions == page3_new.totalAuctions\n    assert page3_old.lastUpdated == page3_new.lastUpdated\n    assert len(page3_old.auctions) == len(page3_new.auctions)\n\nif __name__ == \"__main__\":\n    test_get_page()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_page` in the provided code is identical in functionality to the ORIGINAL FUNCTION. Both functions check if the requested page is in the cache and return it if available. If not, they make an HTTP GET request to the API endpoint with the page number as a parameter, check for a successful response, parse the JSON data, and store the result in the cache before returning it. Both functions handle exceptions in the same way by raising a `ConnectionError` if a request exception occurs. The only difference is the context in which the REVISED FUNCTION is placed, which includes additional classes and mock setups for testing purposes, but these do not alter the core functionality of the `get_page` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `get_page` function returns an `AuctionsPage` object, which contains various attributes such as `success`, `page`, `totalPages`, `totalAuctions`, `lastUpdated`, and `auctions`. Therefore, it satisfies CONDITION 1 as it has return values.\n\n2. **CONDITION 2**: The test cases in `test_get_page` compare the attributes of the `AuctionsPage` objects returned by `get_page` and `get_page_new_implementation`. They do not check printed or logged contents, thus satisfying CONDITION 2.\n\n3. **CONDITION 3**: The test cases compare all relevant attributes of the `AuctionsPage` objects returned by both implementations, ensuring that `get_page_new_implementation` can only pass if it has the same functionality as `get_page`. Therefore, CONDITION 3 is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the attributes of the returned `AuctionsPage` objects, which is reasonable given that `get_page` returns an object. Thus, CONDITION 4 is satisfied.\n\n5. **CONDITION 5**: The test cases cover different scenarios: fetching the first page, fetching a cached page, and fetching a non-cached page. These scenarios are non-trivial as they test different aspects of the caching mechanism and API response handling. Therefore, CONDITION 5 is satisfied.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "SlayerBoss._extract_tier_data",
        "idx": "560",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/member/Slayer.py",
        "orig_func": "@staticmethod\ndef _extract_tier_data(data: dict, prefix: str) -> dict[int, int]:\n    \"\"\"\n        Extracts boss kills or attempts per tier.\n\n        Args:\n            data (dict): The data dictionary.\n            prefix (str): The prefix to look for in keys.\n\n        Returns:\n            dict[int,int]: A dictionary with tier as key and count as value.\n        \"\"\"\n    result = {}\n    for key, value in data.items():\n        if key.startswith(prefix):\n            tier = int(key[len(prefix):])\n            result[tier] = value\n    return result",
        "orig_context": "```python\n## hypixel_api_lib/member/Slayer.py\nclass SlayerBoss:\n    \"\"\"\n    Represents a generic slayer boss with common attributes.\n\n    Attributes:\n        claimed_levels (dict[str,bool]): Dictionary of claimed levels.\n        boss_kills (dict[int,int]): Dictionary of boss kills per tier.\n        boss_attempts (dict[int,int]): Dictionary of boss attempts per tier.\n        xp (int): Total experience gained from this slayer boss.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.claimed_levels: dict[str, bool] = data.get('claimed_levels', {})\n        self.xp: int = data.get('xp', 0)\n        self.boss_kills: dict[int,int] = self._extract_tier_data(data, 'boss_kills_tier_')\n        self.boss_attempts: dict[int,int] = self._extract_tier_data(data, 'boss_attempts_tier_')\n\n    @staticmethod\n    def _extract_tier_data(data: dict, prefix: str) -> dict[int,int]:\n        \"\"\"\n        Extracts boss kills or attempts per tier.\n\n        Args:\n            data (dict): The data dictionary.\n            prefix (str): The prefix to look for in keys.\n\n        Returns:\n            dict[int,int]: A dictionary with tier as key and count as value.\n        \"\"\"\n        result = {}\n        for key, value in data.items():\n            if key.startswith(prefix):\n                tier = int(key[len(prefix):])\n                result[tier] = value\n        return result\n\n    def total_boss_kills(self) -> int:\n        \"\"\"\n        Calculates the total number of boss kills.\n\n        Returns:\n            int: Total boss kills.\n        \"\"\"\n        return sum(self.boss_kills.values())\n\n    def __str__(self) -> str:\n        return (\n            f\"SlayerBoss(claimed_levels={self.claimed_levels}, xp={self.xp}, \"\n            f\"boss_kills={self.boss_kills}, boss_attempts={self.boss_attempts})\"\n        )\n\n```\n\n\n",
        "eval_script": "## hypixel_api_lib/member/Slayer.py\nclass SlayerBoss:\n    \"\"\"\n    Represents a generic slayer boss with common attributes.\n\n    Attributes:\n        claimed_levels (dict[str,bool]): Dictionary of claimed levels.\n        boss_kills (dict[int,int]): Dictionary of boss kills per tier.\n        boss_attempts (dict[int,int]): Dictionary of boss attempts per tier.\n        xp (int): Total experience gained from this slayer boss.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.claimed_levels: dict[str, bool] = data.get('claimed_levels', {})\n        self.xp: int = data.get('xp', 0)\n        self.boss_kills: dict[int,int] = self._extract_tier_data(data, 'boss_kills_tier_')\n        self.boss_attempts: dict[int,int] = self._extract_tier_data(data, 'boss_attempts_tier_')\n\n    @staticmethod\n    def _extract_tier_data(data: dict, prefix: str) -> dict[int,int]:\n        \"\"\"\n        Extracts boss kills or attempts per tier.\n\n        Args:\n            data (dict): The data dictionary.\n            prefix (str): The prefix to look for in keys.\n\n        Returns:\n            dict[int,int]: A dictionary with tier as key and count as value.\n        \"\"\"\n        result = {}\n        for key, value in data.items():\n            if key.startswith(prefix):\n                tier = int(key[len(prefix):])\n                result[tier] = value\n        return result\n\n\n    def total_boss_kills(self) -> int:\n        \"\"\"\n        Calculates the total number of boss kills.\n\n        Returns:\n            int: Total boss kills.\n        \"\"\"\n        return sum(self.boss_kills.values())\n\n    def __str__(self) -> str:\n        return (\n            f\"SlayerBoss(claimed_levels={self.claimed_levels}, xp={self.xp}, \"\n            f\"boss_kills={self.boss_kills}, boss_attempts={self.boss_attempts})\"\n        )\n\ndef test__extract_tier_data():\n    # Test case 1: Multiple tiers\n    data = {\n        'boss_kills_tier_1': 10,\n        'boss_kills_tier_2': 5,\n        'boss_attempts_tier_1': 15,\n        'boss_attempts_tier_2': 7\n    }\n    assert SlayerBoss._extract_tier_data(data, 'boss_kills_tier_') == SlayerBoss._extract_tier_data_new_implementation(data, 'boss_kills_tier_')\n    assert SlayerBoss._extract_tier_data(data, 'boss_attempts_tier_') == SlayerBoss._extract_tier_data_new_implementation(data, 'boss_attempts_tier_')\n\n    # Test case 2: No relevant keys\n    data = {\n        'some_other_key': 20,\n        'another_key': 30\n    }\n    assert SlayerBoss._extract_tier_data(data, 'boss_kills_tier_') == SlayerBoss._extract_tier_data_new_implementation(data, 'boss_kills_tier_')\n\n    # Test case 3: Single tier\n    data = {\n        'boss_kills_tier_3': 8\n    }\n    assert SlayerBoss._extract_tier_data(data, 'boss_kills_tier_') == SlayerBoss._extract_tier_data_new_implementation(data, 'boss_kills_tier_')\n\nif __name__ == \"__main__\":\n    test__extract_tier_data()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are static methods that take a dictionary and a prefix string as arguments, iterate over the dictionary items, check if the keys start with the given prefix, extract the tier number from the key, and store the value in a result dictionary with the tier as the key. The logic and implementation details are exactly the same in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `_extract_tier_data` returns a dictionary, which satisfies the condition of having return values.\n  \n- CONDITION 2: The test cases use assertions to compare the return values of `_extract_tier_data` and `_extract_tier_data_new_implementation`, which means they check the return values and not printed or logged contents.\n\n- CONDITION 3: The test cases compare the outputs of `_extract_tier_data` and `_extract_tier_data_new_implementation` directly. This ensures that `_extract_tier_data_new_implementation` can only pass if it has the same functionality as `_extract_tier_data`.\n\n- CONDITION 4: The test cases do not use any unreasonable assertions. They compare the return values of the two implementations, which is appropriate given that `_extract_tier_data` returns a dictionary.\n\n- CONDITION 5: The test cases cover multiple scenarios: multiple tiers, no relevant keys, and a single tier. These scenarios are non-trivial and provide a reasonable coverage of possible inputs.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "Bazaar._generate_possible_product_ids",
        "idx": "561",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/Bazaar.py",
        "orig_func": "def _generate_possible_product_ids(self, base_term: str) -> list[str]:\n    \"\"\"Generate possible product IDs by adding common prefixes and suffixes.\"\"\"\n    possible_ids = []\n    for prefix in [''] + self.COMMON_PREFIXES:\n        term_with_prefix = prefix + base_term\n        for suffix in [''] + self.COMMON_SUFFIXES:\n            possible_id = term_with_prefix + suffix\n            possible_ids.append(possible_id)\n    possible_ids = list(set(possible_ids))\n    return possible_ids",
        "orig_context": "```python\n## hypixel_api_lib/utils.py\nfrom datetime import datetime, timezone\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\n```\n\n\n```python\n## hypixel_api_lib/Bazaar.py\nfrom datetime import datetime\n\nimport requests\n\nfrom hypixel_api_lib.utils import convert_timestamp\n\nimport re\n\nfrom difflib import get_close_matches\n\nBAZAAR_API_URL = \"https://api.hypixel.net/skyblock/bazaar\"\n\nclass BazaarOrderSummaryItem:\n    \"\"\"\n    Represents an order summary item in the bazaar.\n\n    Attributes:\n        amount (int): The total amount of items in the order.\n        price_per_unit (float): The price per unit of the item.\n        orders (int): The number of orders at this price.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.amount: int = data.get('amount', 0)\n        self.price_per_unit: float = data.get('pricePerUnit', 0.0)\n        self.orders: int = data.get('orders', 0)\n\n    def __str__(self) -> str:\n        return f\"Amount: {self.amount}, Price per Unit: {self.price_per_unit}, Orders: {self.orders}\"\n\nclass BazaarProductQuickStatus:\n    \"\"\"\n    Represents the quick status of a bazaar product.\n\n    Attributes:\n        product_id (str): The product ID.\n        sell_price (float): The weighted average sell price.\n        sell_volume (int): The total sell volume.\n        sell_moving_week (int): The sell volume over the moving week.\n        sell_orders (int): The number of sell orders.\n        buy_price (float): The weighted average buy price.\n        buy_volume (int): The total buy volume.\n        buy_moving_week (int): The buy volume over the moving week.\n        buy_orders (int): The number of buy orders.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.product_id: str = data.get('productId', '')\n        self.sell_price: float = data.get('sellPrice', 0.0)\n        self.sell_volume: int = data.get('sellVolume', 0)\n        self.sell_moving_week: int = data.get('sellMovingWeek', 0)\n        self.sell_orders: int = data.get('sellOrders', 0)\n        self.buy_price: float = data.get('buyPrice', 0.0)\n        self.buy_volume: int = data.get('buyVolume', 0)\n        self.buy_moving_week: int = data.get('buyMovingWeek', 0)\n        self.buy_orders: int = data.get('buyOrders', 0)\n\n    def __str__(self) -> str:\n        return (f\"Product ID: {self.product_id}, Sell Price: {self.sell_price}, Sell Volume: {self.sell_volume}, \"\n                f\"Sell Moving Week: {self.sell_moving_week}, Sell Orders: {self.sell_orders}, \"\n                f\"Buy Price: {self.buy_price}, Buy Volume: {self.buy_volume}, \"\n                f\"Buy Moving Week: {self.buy_moving_week}, Buy Orders: {self.buy_orders}\")\n\nclass BazaarProduct:\n    \"\"\"\n    Represents a bazaar product.\n\n    Attributes:\n        product_id (str): The product ID.\n        sell_summary (list of BazaarOrderSummaryItem): The sell order summaries.\n        buy_summary (list of BazaarOrderSummaryItem): The buy order summaries.\n        quick_status (BazaarProductQuickStatus): The quick status of the product.\n    \"\"\"\n\n    def __init__(self, product_id: str, data: dict) -> None:\n        self.product_id: str = product_id\n        self.sell_summary: list[BazaarOrderSummaryItem] = [BazaarOrderSummaryItem(item) for item in data.get('sell_summary', [])]\n        self.buy_summary: list[BazaarOrderSummaryItem] = [BazaarOrderSummaryItem(item) for item in data.get('buy_summary', [])]\n        self.quick_status: BazaarProductQuickStatus = BazaarProductQuickStatus(data.get('quick_status', {}))\n\n    def get_top_buy_order(self) -> BazaarOrderSummaryItem | None:\n        \"\"\"\n        Returns the top buy order summary item.\n\n        Returns:\n            BazaarOrderSummaryItem or None: The top buy order summary item, or None if not available.\n        \"\"\"\n        return self.buy_summary[0] if self.buy_summary else None\n\n    def get_top_sell_order(self) -> BazaarOrderSummaryItem | None:\n        \"\"\"\n        Returns the top sell order summary item.\n\n        Returns:\n            BazaarOrderSummaryItem or None: The top sell order summary item, or None if not available.\n        \"\"\"\n        return self.sell_summary[0] if self.sell_summary else None\n\n    def __str__(self) -> str:\n        return f\"Bazaar Product: {self.product_id}\"\n\nclass Bazaar:\n    \"\"\"\n    Manages fetching and storing the bazaar data from the API.\n\n    Attributes:\n        last_updated (datetime): The timestamp of the last update.\n        products (dict of str to BazaarProduct): The bazaar products.\n        normalized_product_ids (dict of str to str): Mapping of normalized product names to actual product IDs.\n    \"\"\"\n\n    COMMON_PREFIXES = [\n        \"ENCHANTMENT_ULTIMATE_\",\n        \"ENCHANTMENT_\",\n        \"DUNGEON_\",\n    ]\n\n    COMMON_SUFFIXES = [\n        \"_ITEM\",\n        \"_SCROLL\",\n        \"_GEM\",\n        \"_ORE\",\n        \"_1\",\n        \"_2\",\n        \"_3\",\n        \"_4\",\n        \"_5\",\n        \"_6\",\n        \"_7\",\n        \"_8\",\n        \"_9\",\n        \"_10\",\n    ]\n\n    def __init__(self, api_endpoint: str = BAZAAR_API_URL) -> None:\n        self.api_endpoint: str = api_endpoint\n        self.last_updated: datetime | None = None\n        self.products: dict[str, BazaarProduct] = {}\n        self.normalized_product_ids: dict[str, str] = {}\n        self._load_bazaar_data()\n\n    def _load_bazaar_data(self) -> None:\n        \"\"\"Fetch the bazaar data from the API.\"\"\"\n        try:\n            response = requests.get(self.api_endpoint)\n            response.raise_for_status()\n            data = response.json()\n\n            if data.get('success'):\n                self.last_updated = convert_timestamp(data.get('lastUpdated'))\n                products_data = data.get('products', {})\n                for product_id, product_data in products_data.items():\n                    self.products[product_id] = BazaarProduct(product_id, product_data)\n                    normalized_id = self._normalize_product_id(product_id)\n                    self.normalized_product_ids[normalized_id] = product_id\n            else:\n                raise ValueError(\"Failed to fetch bazaar data\")\n        except requests.exceptions.RequestException as e:\n            raise ConnectionError(f\"An error occurred: {e}\")\n\n    def _normalize_product_id(self, product_id: str) -> str:\n        \"\"\"Normalize the product ID for easier searching.\"\"\"\n        normalized = product_id.upper()\n        for prefix in self.COMMON_PREFIXES:\n            if normalized.startswith(prefix):\n                normalized = normalized[len(prefix):]\n                break\n        for suffix in self.COMMON_SUFFIXES:\n            if normalized.endswith(suffix):\n                normalized = normalized[:-len(suffix)]\n                break\n        normalized = re.sub(r'[^A-Z0-9]', '_', normalized)\n        return normalized\n\n    def search_product(self, search_term: str) -> BazaarProduct | None:\n        \"\"\"\n        Search for a product using a search term.\n\n        Args:\n            search_term (str): The search term provided by the user.\n\n        Returns:\n            BazaarProduct or None: The matching BazaarProduct object, or None if not found.\n        \"\"\"\n        normalized_search = self._normalize_search_term(search_term)\n        product_id = self.normalized_product_ids.get(normalized_search)\n        if product_id and product_id in self.products:\n            return self.products[product_id]\n        possible_ids = self._generate_possible_product_ids(normalized_search)\n        for pid in possible_ids:\n            if pid in self.products:\n                return self.products[pid]\n        return self._fuzzy_search(normalized_search)\n\n    def _normalize_search_term(self, search_term: str) -> str:\n        \"\"\"Normalize the search term to match normalized product IDs.\"\"\"\n        normalized = search_term.upper().replace(' ', '_')\n        normalized = re.sub(r'[^A-Z0-9]', '_', normalized)\n        return normalized\n\n    def _generate_possible_product_ids(self, base_term: str) -> list[str]:\n        \"\"\"Generate possible product IDs by adding common prefixes and suffixes.\"\"\"\n        possible_ids = []\n        for prefix in [''] + self.COMMON_PREFIXES:\n            term_with_prefix = prefix + base_term\n            for suffix in [''] + self.COMMON_SUFFIXES:\n                possible_id = term_with_prefix + suffix\n                possible_ids.append(possible_id)\n        possible_ids = list(set(possible_ids))\n        return possible_ids\n\n    # TODO: Potentially consider changing some of this class to be able to search and pass back multiple products\n    #   if a search is not clear enough. (e.x. ENCHANTMENT_ULTIMATE_WISDOM_1, ENCHANTMENT_ULTIMATE_WISDOM_2, \n    #   ENCHANTMENT_ULTIMATE_WISDOM_3, etc..)\n    def _fuzzy_search(self, normalized_search: str) -> BazaarProduct | None:\n        \"\"\"Perform a simple fuzzy search to find the closest matching product.\"\"\"\n        possible_matches = get_close_matches(\n            normalized_search,\n            self.normalized_product_ids.keys(),\n            n=1,\n            cutoff=0.6\n        )\n        if possible_matches:\n            matched_id = self.normalized_product_ids[possible_matches[0]]\n            return self.products.get(matched_id)\n        return None\n\n    def get_product_by_id(self, product_id: str) -> BazaarProduct | None:\n        \"\"\"\n        Retrieve a product by its exact ID.\n\n        Args:\n            product_id (str): The exact ID of the product.\n\n        Returns:\n            BazaarProduct or None: The BazaarProduct object, or None if not found.\n        \"\"\"\n        return self.products.get(product_id)\n\n    def __str__(self) -> str:\n        product_ids = ', '.join(self.products.keys())\n        return f\"Bazaar Data (Last Updated: {self.last_updated})\\nProducts: {product_ids}\"\n\n```\n\n\n",
        "eval_script": "from datetime import datetime, timezone\nimport re\nfrom difflib import get_close_matches\n\n# Mock requests to avoid external API calls\nclass MockResponse:\n    @staticmethod\n    def json():\n        return {\n            'success': True,\n            'lastUpdated': 1638316800000,\n            'products': {\n                'ENCHANTMENT_ULTIMATE_WISDOM_1': {\n                    'sell_summary': [{'amount': 100, 'pricePerUnit': 50.0, 'orders': 5}],\n                    'buy_summary': [{'amount': 200, 'pricePerUnit': 45.0, 'orders': 10}],\n                    'quick_status': {\n                        'productId': 'ENCHANTMENT_ULTIMATE_WISDOM_1',\n                        'sellPrice': 50.0,\n                        'sellVolume': 1000,\n                        'sellMovingWeek': 7000,\n                        'sellOrders': 50,\n                        'buyPrice': 45.0,\n                        'buyVolume': 2000,\n                        'buyMovingWeek': 14000,\n                        'buyOrders': 100\n                    }\n                }\n            }\n        }\n\nclass requests:\n    @staticmethod\n    def get(url):\n        return MockResponse()\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\nBAZAAR_API_URL = \"https://api.hypixel.net/skyblock/bazaar\"\n\nclass BazaarOrderSummaryItem:\n    def __init__(self, data: dict) -> None:\n        self.amount: int = data.get('amount', 0)\n        self.price_per_unit: float = data.get('pricePerUnit', 0.0)\n        self.orders: int = data.get('orders', 0)\n\n    def __str__(self) -> str:\n        return f\"Amount: {self.amount}, Price per Unit: {self.price_per_unit}, Orders: {self.orders}\"\n\nclass BazaarProductQuickStatus:\n    def __init__(self, data: dict) -> None:\n        self.product_id: str = data.get('productId', '')\n        self.sell_price: float = data.get('sellPrice', 0.0)\n        self.sell_volume: int = data.get('sellVolume', 0)\n        self.sell_moving_week: int = data.get('sellMovingWeek', 0)\n        self.sell_orders: int = data.get('sellOrders', 0)\n        self.buy_price: float = data.get('buyPrice', 0.0)\n        self.buy_volume: int = data.get('buyVolume', 0)\n        self.buy_moving_week: int = data.get('buyMovingWeek', 0)\n        self.buy_orders: int = data.get('buyOrders', 0)\n\n    def __str__(self) -> str:\n        return (f\"Product ID: {self.product_id}, Sell Price: {self.sell_price}, Sell Volume: {self.sell_volume}, \"\n                f\"Sell Moving Week: {self.sell_moving_week}, Sell Orders: {self.sell_orders}, \"\n                f\"Buy Price: {self.buy_price}, Buy Volume: {self.buy_volume}, \"\n                f\"Buy Moving Week: {self.buy_moving_week}, Buy Orders: {self.buy_orders}\")\n\nclass BazaarProduct:\n    def __init__(self, product_id: str, data: dict) -> None:\n        self.product_id: str = product_id\n        self.sell_summary: list[BazaarOrderSummaryItem] = [BazaarOrderSummaryItem(item) for item in data.get('sell_summary', [])]\n        self.buy_summary: list[BazaarOrderSummaryItem] = [BazaarOrderSummaryItem(item) for item in data.get('buy_summary', [])]\n        self.quick_status: BazaarProductQuickStatus = BazaarProductQuickStatus(data.get('quick_status', {}))\n\n    def get_top_buy_order(self) -> BazaarOrderSummaryItem | None:\n        return self.buy_summary[0] if self.buy_summary else None\n\n    def get_top_sell_order(self) -> BazaarOrderSummaryItem | None:\n        return self.sell_summary[0] if self.sell_summary else None\n\n    def __str__(self) -> str:\n        return f\"Bazaar Product: {self.product_id}\"\n\nclass Bazaar:\n    COMMON_PREFIXES = [\n        \"ENCHANTMENT_ULTIMATE_\",\n        \"ENCHANTMENT_\",\n        \"DUNGEON_\",\n    ]\n\n    COMMON_SUFFIXES = [\n        \"_ITEM\",\n        \"_SCROLL\",\n        \"_GEM\",\n        \"_ORE\",\n        \"_1\",\n        \"_2\",\n        \"_3\",\n        \"_4\",\n        \"_5\",\n        \"_6\",\n        \"_7\",\n        \"_8\",\n        \"_9\",\n        \"_10\",\n    ]\n\n    def __init__(self, api_endpoint: str = BAZAAR_API_URL) -> None:\n        self.api_endpoint: str = api_endpoint\n        self.last_updated: datetime | None = None\n        self.products: dict[str, BazaarProduct] = {}\n        self.normalized_product_ids: dict[str, str] = {}\n        self._load_bazaar_data()\n\n    def _load_bazaar_data(self) -> None:\n        try:\n            response = requests.get(self.api_endpoint)\n            data = response.json()\n\n            if data.get('success'):\n                self.last_updated = convert_timestamp(data.get('lastUpdated'))\n                products_data = data.get('products', {})\n                for product_id, product_data in products_data.items():\n                    self.products[product_id] = BazaarProduct(product_id, product_data)\n                    normalized_id = self._normalize_product_id(product_id)\n                    self.normalized_product_ids[normalized_id] = product_id\n            else:\n                raise ValueError(\"Failed to fetch bazaar data\")\n        except Exception as e:\n            raise ConnectionError(f\"An error occurred: {e}\")\n\n    def _normalize_product_id(self, product_id: str) -> str:\n        normalized = product_id.upper()\n        for prefix in self.COMMON_PREFIXES:\n            if normalized.startswith(prefix):\n                normalized = normalized[len(prefix):]\n                break\n        for suffix in self.COMMON_SUFFIXES:\n            if normalized.endswith(suffix):\n                normalized = normalized[:-len(suffix)]\n                break\n        normalized = re.sub(r'[^A-Z0-9]', '_', normalized)\n        return normalized\n\n    def search_product(self, search_term: str) -> BazaarProduct | None:\n        normalized_search = self._normalize_search_term(search_term)\n        product_id = self.normalized_product_ids.get(normalized_search)\n        if product_id and product_id in self.products:\n            return self.products[product_id]\n        possible_ids = self._generate_possible_product_ids(normalized_search)\n        for pid in possible_ids:\n            if pid in self.products:\n                return self.products[pid]\n        return self._fuzzy_search(normalized_search)\n\n    def _normalize_search_term(self, search_term: str) -> str:\n        normalized = search_term.upper().replace(' ', '_')\n        normalized = re.sub(r'[^A-Z0-9]', '_', normalized)\n        return normalized\n\n    def _generate_possible_product_ids(self, base_term: str) -> list[str]:\n        possible_ids = []\n        for prefix in [''] + self.COMMON_PREFIXES:\n            term_with_prefix = prefix + base_term\n            for suffix in [''] + self.COMMON_SUFFIXES:\n                possible_id = term_with_prefix + suffix\n                possible_ids.append(possible_id)\n        possible_ids = list(set(possible_ids))\n        return possible_ids\n\n\n    def _fuzzy_search(self, normalized_search: str) -> BazaarProduct | None:\n        possible_matches = get_close_matches(\n            normalized_search,\n            self.normalized_product_ids.keys(),\n            n=1,\n            cutoff=0.6\n        )\n        if possible_matches:\n            matched_id = self.normalized_product_ids[possible_matches[0]]\n            return self.products.get(matched_id)\n        return None\n\n    def get_product_by_id(self, product_id: str) -> BazaarProduct | None:\n        return self.products.get(product_id)\n\n    def __str__(self) -> str:\n        product_ids = ', '.join(self.products.keys())\n        return f\"Bazaar Data (Last Updated: {self.last_updated})\\nProducts: {product_ids}\"\n\ndef test__generate_possible_product_ids():\n    bazaar = Bazaar()\n\n    # Test case 1: Basic term\n    base_term = \"WISDOM\"\n    original_ids = bazaar._generate_possible_product_ids(base_term)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term)\n    assert set(original_ids) == set(new_ids), \"Test case 1 failed\"\n\n    # Test case 2: Term with common prefix\n    base_term_with_prefix = \"ENCHANTMENT_WISDOM\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_with_prefix)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_with_prefix)\n    assert set(original_ids) == set(new_ids), \"Test case 2 failed\"\n\n    # Test case 3: Term with common suffix\n    base_term_with_suffix = \"WISDOM_1\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_with_suffix)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_with_suffix)\n    assert set(original_ids) == set(new_ids), \"Test case 3 failed\"\n\n    # Test case 4: Empty base term\n    base_term_empty = \"\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_empty)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_empty)\n    assert set(original_ids) == set(new_ids), \"Test case 4 failed\"\n\n    # Test case 5: Base term with special characters\n    base_term_special_chars = \"WISDOM@#\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_special_chars)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_special_chars)\n    assert set(original_ids) == set(new_ids), \"Test case 5 failed\"\n\n    # Test case 6: Base term with mixed case\n    base_term_mixed_case = \"WiSdOm\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_mixed_case)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_mixed_case)\n    assert set(original_ids) == set(new_ids), \"Test case 6 failed\"\n\n    # Test case 7: Base term with spaces\n    base_term_with_spaces = \"WISDOM OF THE AGES\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_with_spaces)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_with_spaces)\n    assert set(original_ids) == set(new_ids), \"Test case 7 failed\"\n\n    # Test case 8: Base term with no common prefix or suffix\n    base_term_no_common = \"RARE_ITEM\"\n    original_ids = bazaar._generate_possible_product_ids(base_term_no_common)\n    new_ids = bazaar._generate_possible_product_ids_new_implementation(base_term_no_common)\n    assert set(original_ids) == set(new_ids), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test__generate_possible_product_ids()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of logic and implementation. Both functions generate possible product IDs by iterating over common prefixes and suffixes, appending them to the base term, and ensuring uniqueness by converting the list to a set and back to a list. There are no differences in the code structure or logic between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The function `_generate_possible_product_ids` returns a list of strings, which satisfies the condition of having return values.\n- [CONDITION 2] The test cases use assertions to compare the return values of `_generate_possible_product_ids` and `_generate_possible_product_ids_new_implementation`, which means they are checking return values, not printed or logged content.\n- [CONDITION 3] The test cases compare the sets of possible product IDs generated by both implementations, ensuring that the new implementation must have exactly the same functionality to pass.\n- [CONDITION 4] The test cases use assertions to compare the sets of IDs, which is reasonable given that the function returns a list of strings. The test cases do not use inappropriate assertions.\n- [CONDITION 5] The test cases cover a variety of scenarios, including basic terms, terms with common prefixes and suffixes, empty terms, terms with special characters, mixed case, spaces, and terms without common prefixes or suffixes. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "InventoryData._decode_data",
        "idx": "563",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/member/Rift.py",
        "orig_func": "def _decode_data(self, data: str) -> str:\n    \"\"\"Attempt to decode and decompress the inventory data.\"\"\"\n    if not data:\n        return 'No data available'\n    try:\n        decoded_data = base64.b64decode(data)\n        try:\n            decompressed_data = zlib.decompress(decoded_data)\n            return decompressed_data.decode('utf-8', errors='ignore')\n        except zlib.error as e:\n            try:\n                with gzip.open(BytesIO(decoded_data), 'rb') as f:\n                    return f.read().decode('utf-8', errors='ignore')\n            except gzip.BadGzipFile as e:\n                return f'Error decoding inventory: Gzip decompression failed - {e}'\n    except (base64.binascii.Error, zlib.error) as e:\n        return f'Error decoding inventory: {e}'",
        "orig_context": "```python\n## hypixel_api_lib/member/Rift.py\nimport zlib\n\nimport base64\n\nimport gzip\n\nfrom io import BytesIO\n\nclass InventoryData:\n    \"\"\"\n    Represents inventory-related data, handling Minecraft-specific encoding and compression formats.\n\n    Attributes:\n        type (int): Type identifier of the inventory data.\n        raw_data (str): Raw compressed data of the inventory.\n        data (str): Decoded or decompressed data, or an error message if decoding fails.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.type: int = data.get('type')\n        self.raw_data: str = data.get('data', '')\n\n        self.data: str = self._decode_data(self.raw_data)\n\n    def _decode_data(self, data: str) -> str:\n        \"\"\"Attempt to decode and decompress the inventory data.\"\"\"\n        if not data:\n            return \"No data available\"\n        \n        try:\n            decoded_data = base64.b64decode(data)\n            \n            try:\n                decompressed_data = zlib.decompress(decoded_data)\n                return decompressed_data.decode('utf-8', errors='ignore')\n            except zlib.error as e:\n                try:\n                    with gzip.open(BytesIO(decoded_data), 'rb') as f:\n                        return f.read().decode('utf-8', errors='ignore')\n                except gzip.BadGzipFile as e:\n                    return f\"Error decoding inventory: Gzip decompression failed - {e}\"\n\n        except (base64.binascii.Error, zlib.error) as e:\n            return f\"Error decoding inventory: {e}\"\n\n    def __str__(self) -> str:\n        data_preview = self.data[:500] + \"...\" if len(self.data) > 500 else self.data\n        return f\"InventoryData(Type: {self.type}, Data Preview: {data_preview})\"\n\n```\n\n\n",
        "eval_script": "import zlib\nimport base64\nimport gzip\nfrom io import BytesIO\n\nclass InventoryData:\n    \"\"\"\n    Represents inventory-related data, handling Minecraft-specific encoding and compression formats.\n\n    Attributes:\n        type (int): Type identifier of the inventory data.\n        raw_data (str): Raw compressed data of the inventory.\n        data (str): Decoded or decompressed data, or an error message if decoding fails.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.type: int = data.get('type')\n        self.raw_data: str = data.get('data', '')\n\n        self.data: str = self._decode_data(self.raw_data)\n\n    def _decode_data(self, data: str) -> str:\n        \"\"\"Attempt to decode and decompress the inventory data.\"\"\"\n        if not data:\n            return \"No data available\"\n        \n        try:\n            decoded_data = base64.b64decode(data)\n            \n            try:\n                decompressed_data = zlib.decompress(decoded_data)\n                return decompressed_data.decode('utf-8', errors='ignore')\n            except zlib.error as e:\n                try:\n                    with gzip.open(BytesIO(decoded_data), 'rb') as f:\n                        return f.read().decode('utf-8', errors='ignore')\n                except gzip.BadGzipFile as e:\n                    return f\"Error decoding inventory: Gzip decompression failed - {e}\"\n\n        except (base64.binascii.Error, zlib.error) as e:\n            return f\"Error decoding inventory: {e}\"\n\n\n    def __str__(self) -> str:\n        data_preview = self.data[:500] + \"...\" if len(self.data) > 500 else self.data\n        return f\"InventoryData(Type: {self.type}, Data Preview: {data_preview})\"\n\ndef test__decode_data():\n    # Test case 1: Empty data\n    data_empty = ''\n    assert InventoryData({})._decode_data(data_empty) == InventoryData({})._decode_data_new_implementation(data_empty)\n\n    # Test case 2: Valid zlib compressed data\n    data_zlib = base64.b64encode(zlib.compress(b'Test inventory data')).decode('utf-8')\n    assert InventoryData({})._decode_data(data_zlib) == InventoryData({})._decode_data_new_implementation(data_zlib)\n\n    # Test case 3: Invalid base64 data\n    data_invalid_base64 = '!!!invalid_base64!!!'\n    assert InventoryData({})._decode_data(data_invalid_base64) == InventoryData({})._decode_data_new_implementation(data_invalid_base64)\n\n    # Test case 4: Valid gzip compressed data\n    buf = BytesIO()\n    with gzip.GzipFile(fileobj=buf, mode='wb') as f:\n        f.write(b'Test inventory data')\n    data_gzip = base64.b64encode(buf.getvalue()).decode('utf-8')\n    assert InventoryData({})._decode_data(data_gzip) == InventoryData({})._decode_data_new_implementation(data_gzip)\n\n    # Test case 5: Corrupted compressed data\n    data_corrupted = base64.b64encode(b'corrupted data').decode('utf-8')\n    assert InventoryData({})._decode_data(data_corrupted) == InventoryData({})._decode_data_new_implementation(data_corrupted)\n\n    # Test case 6: Non-UTF-8 encoded data\n    data_non_utf8 = base64.b64encode(zlib.compress(b'\\x80\\x81\\x82')).decode('utf-8')\n    assert InventoryData({})._decode_data(data_non_utf8) == InventoryData({})._decode_data_new_implementation(data_non_utf8)\n\n    # Test case 7: Valid base64 but not compressed\n    data_not_compressed = base64.b64encode(b'Not compressed data').decode('utf-8')\n    assert InventoryData({})._decode_data(data_not_compressed) == InventoryData({})._decode_data_new_implementation(data_not_compressed)\n\n    # Additional test cases can be added to cover more branches\n\nif __name__ == \"__main__\":\n    test__decode_data()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `InventoryData` class is identical to the ORIGINAL FUNCTION in terms of logic and functionality. Both functions attempt to decode a base64-encoded string and then decompress it using either zlib or gzip. They handle errors in the same way, returning specific error messages if decoding or decompression fails. The only differences are in the formatting of strings (single vs. double quotes) and the context in which the function is used (as part of a class in the revised code), neither of which affect the functionality of the function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `_decode_data` function returns a string, which is either the decoded data or an error message. This satisfies the condition as it has return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `_decode_data` and `_decode_data_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases cover various scenarios such as empty data, valid and invalid compressed data, and non-UTF-8 encoded data. These tests ensure that `_decode_data_new_implementation` must have the same functionality as `_decode_data` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the outputs of the two implementations. Since `_decode_data` returns a value, using assertions to compare these return values is reasonable, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover a wide range of scenarios, including edge cases and error handling, making them non-trivial. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "ActiveAuctions.get_all_auctions",
        "idx": "567",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/Auctions.py",
        "orig_func": "def get_all_auctions(self) -> list[SkyBlockAuction]:\n    \"\"\"\n        Fetch all auctions by iterating through all available pages.\n\n        Returns:\n            list of SkyBlockAuction: A list of all auctions.\n        \"\"\"\n    if self.all_auctions:\n        return self.all_auctions\n    all_auctions = []\n    first_page = self.get_page(0)\n    total_pages = first_page.totalPages\n    all_auctions.extend(first_page.auctions)\n    for page_number in range(1, total_pages):\n        page = self.get_page(page_number)\n        all_auctions.extend(page.auctions)\n    self.all_auctions = all_auctions\n    return all_auctions",
        "orig_context": "```python\n## hypixel_api_lib/utils.py\nfrom datetime import datetime, timezone\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\n```\n\n\n```python\n## hypixel_api_lib/Auctions.py\nfrom datetime import datetime, timezone, tzinfo\n\nimport requests\n\nfrom hypixel_api_lib.utils import get_uuid_from_username, convert_timestamp\n\nACTIVE_AUCTIONS_API_URL = r\"https://api.hypixel.net/skyblock/auctions\"\n\nclass Bid:\n    \"\"\"\n    Represents a single bid in an auction.\n\n    Attributes:\n        auction_id (str): The ID of the auction.\n        bidder (str): The UUID of the bidder.\n        profile_id (str): The profile ID of the bidder.\n        amount (int): The amount of the bid.\n        timestamp (datetime): The timestamp of the bid.\n    \"\"\"\n\n    def __init__(self, bid_data : dict) -> None:\n        self.auction_id: str = bid_data.get('auction_id')\n        self.bidder: str = bid_data.get('bidder')\n        self.profile_id: str = bid_data.get('profile_id')\n        self.amount: int = bid_data.get('amount')\n        self.timestamp: datetime | None = convert_timestamp(bid_data.get('timestamp'))\n\n    def __str__(self) -> str:\n        timestamp_str = self.timestamp.strftime(\"%Y-%m-%d %H:%M:%S %Z\") if self.timestamp else \"N/A\"\n        return f\"Bid of {self.amount} by {self.bidder} at {timestamp_str}\"\n\nclass SkyBlockAuction:\n    \"\"\"\n    Represents a single SkyBlock auction.\n\n    Attributes:\n        _id (str): The unique identifier of the auction.\n        uuid (str): The UUID of the auction.\n        auctioneer (str): The UUID of the auctioneer.\n        profile_id (str): The profile ID of the auctioneer.\n        coop (list of str): List of coop member UUIDs.\n        start (datetime): The start time of the auction.\n        end (datetime): The end time of the auction.\n        item_name (str): The name of the item being auctioned.\n        item_lore (str): The lore of the item.\n        extra (str): Additional information.\n        category (str): The category of the item.\n        tier (str): The tier of the item.\n        starting_bid (int): The starting bid amount.\n        item_bytes (object): Serialized item data.\n        claimed (bool): Whether the auction has been claimed.\n        claimed_bidders (list): List of bidders who have claimed the item.\n        highest_bid_amount (int): The highest bid amount.\n        bids (list[Bid]): List of bids.\n    \"\"\"\n\n    def __init__(self, auction_data: dict) -> None:\n        self._id: str = auction_data.get('_id')\n        self.uuid: str = auction_data.get('uuid')\n        self.auctioneer: str = auction_data.get('auctioneer')\n        self.profile_id: str = auction_data.get('profile_id')\n        self.coop: list[str] = auction_data.get('coop', [])\n        self.start: datetime | None = convert_timestamp(auction_data.get('start'))\n        self.end: datetime | None = convert_timestamp(auction_data.get('end'))\n        self.item_name: str = auction_data.get('item_name')\n        self.item_lore: str = auction_data.get('item_lore')\n        self.extra: str = auction_data.get('extra')\n        self.category: str = auction_data.get('category')\n        self.tier: str = auction_data.get('tier')\n        self.starting_bid: int = auction_data.get('starting_bid')\n        self.item_bytes: object = auction_data.get('item_bytes')\n        self.claimed: bool = auction_data.get('claimed')\n        self.claimed_bidders: list = auction_data.get('claimed_bidders', [])\n        self.highest_bid_amount: int = auction_data.get('highest_bid_amount')\n        self.bids: list[Bid] | None = [Bid(bid) for bid in auction_data.get('bids', [])]\n\n    def get_start_time_in_timezone(self, tz: tzinfo) -> datetime | None:\n        \"\"\"\n        Get the start time converted to the specified time zone.\n\n        Args:\n            tz (timezone): A timezone object.\n\n        Returns:\n            datetime: The start time in the specified time zone.\n        \"\"\"\n        if self.start:\n            return self.start.astimezone(tz)\n        return None\n\n    def get_end_time_in_timezone(self, tz: tzinfo) -> datetime | None:\n        \"\"\"\n        Get the end time converted to the specified time zone.\n\n        Args:\n            tz (timezone): A timezone object.\n\n        Returns:\n            datetime: The end time in the specified time zone.\n        \"\"\"\n        if self.end:\n            return self.end.astimezone(tz)\n        return None\n\n    @property\n    def current_price(self) -> int:\n        \"\"\"\n        Get the current price of the auction.\n\n        For BIN auctions (with no bids), this is the starting_bid.\n        For regular auctions, this is the highest_bid_amount.\n\n        Returns:\n            int: The current price of the auction.\n        \"\"\"\n        if not self.bids:\n            return self.starting_bid\n        else:\n            return max(self.starting_bid, self.highest_bid_amount)\n\n    @property\n    def is_bin(self) -> bool:\n        \"\"\"\n        Estimate whether the auction is a BIN auction.\n\n        Returns:\n            bool: True if the auction is likely a BIN auction, False otherwise.\n        \"\"\"\n        # Since I can't know from the API, I'm assume auctions with no bids are BIN\n        return not self.bids\n\n    def __str__(self) -> str:\n        auction_type = \"BIN\" if self.is_bin else \"Auction\"\n        return f\"{auction_type} '{self.item_name}' by {self.auctioneer}, Price: {self.current_price}\"\n\nclass AuctionsPage:\n    \"\"\"\n    Represents a single page of auctions from the Hypixel SkyBlock Auctions API.\n\n    Attributes:\n        success (bool): Indicates whether the API request was successful.\n        page (int): The current page number.\n        totalPages (int): The total number of pages.\n        totalAuctions (int): The total number of auctions.\n        lastUpdated (datetime): The last updated timestamp.\n        auctions (list[SkyBlockAuction]): The list of auctions on this page.\n    \"\"\"\n\n    def __init__(self, page_data: dict) -> None:\n        self.success: bool = page_data.get('success', False)\n        self.page: int = page_data.get('page', 0)\n        self.totalPages: int = page_data.get('totalPages', 0)\n        self.totalAuctions: int = page_data.get('totalAuctions', 0)\n        self.lastUpdated: datetime | None = convert_timestamp(page_data.get('lastUpdated'))\n        self.auctions: list[SkyBlockAuction] = [SkyBlockAuction(auction) for auction in page_data.get('auctions', [])]\n\n    def get_auction_by_id(self, auction_id: str) -> SkyBlockAuction | None:\n        \"\"\"\n        Retrieve an auction by its ID from the current page.\n\n        Args:\n            auction_id (str): The ID of the auction.\n\n        Returns:\n            SkyBlockAuction or None: The auction object, or None if not found.\n        \"\"\"\n        return next((auction for auction in self.auctions if auction._id == auction_id), None)\n\n    def get_auctions_by_item_name(self, item_name: str) -> SkyBlockAuction:\n        \"\"\"\n        Retrieve auctions by item name from the current page.\n\n        Args:\n            item_name (str): The name of the item.\n\n        Returns:\n            list of SkyBlockAuction: A list of auctions matching the item name.\n        \"\"\"\n        return [auction for auction in self.auctions if auction.item_name.lower() == item_name.lower()]\n\n    def __str__(self) -> str:\n        return f\"Auctions Page {self.page}/{self.totalPages}, Total Auctions: {self.totalAuctions}\"\n\nclass ActiveAuctions:\n    \"\"\"\n    Manages fetching and storing auction data from the Hypixel SkyBlock Auctions API.\n\n    Attributes:\n        api_endpoint (str): The API endpoint URL.\n        all_auctions (list of SkyBlockAuction): Cached list of all auctions.\n        cache_pages (dict): Cached pages of auctions.\n    \"\"\"\n\n    def __init__(self, api_endpoint: str = ACTIVE_AUCTIONS_API_URL, preload_all: bool = False) -> None:\n        self._api_endpoint: str = api_endpoint\n        self.all_auctions: list[SkyBlockAuction] | list = []\n        self.cache_pages: dict[int, AuctionsPage] | dict = {}\n        if preload_all:\n            self.all_auctions = self.get_all_auctions()\n\n    def get_page(self, page_number: int = 0) -> AuctionsPage:\n        \"\"\"\n        Fetch a specific page of auctions, using cache if available.\n\n        Args:\n            page_number (int): The page number to fetch.\n\n        Returns:\n            AuctionsPage: The AuctionsPage object for the requested page.\n        \"\"\"\n        if page_number in self.cache_pages:\n            return self.cache_pages[page_number]\n\n        params = {'page': page_number}\n        try:\n            response = requests.get(self._api_endpoint, params=params)\n            response.raise_for_status()\n            data = response.json()\n\n            if data.get('success'):\n                page = AuctionsPage(data)\n                self.cache_pages[page_number] = page\n                return page\n            else:\n                raise ValueError(\"API response was not successful\")\n        except requests.exceptions.RequestException as e:\n            raise ConnectionError(f\"An error occurred while fetching page {page_number}: {e}\")\n\n    def get_all_auctions(self) -> list[SkyBlockAuction]:\n        \"\"\"\n        Fetch all auctions by iterating through all available pages.\n\n        Returns:\n            list of SkyBlockAuction: A list of all auctions.\n        \"\"\"\n        if self.all_auctions:\n            return self.all_auctions  # Return cached data\n\n        all_auctions = []\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n        all_auctions.extend(first_page.auctions)\n\n        for page_number in range(1, total_pages):\n            page = self.get_page(page_number)\n            all_auctions.extend(page.auctions)\n\n        self.all_auctions = all_auctions  # Cache the results\n        return all_auctions\n\n    def search_auctions(self, item_name: str | None = None, min_price: int | None = None, max_price: int | None = None, sort_by_price: bool = False, descending: bool = False, max_pages: int | None = None) -> list[SkyBlockAuction]:\n        \"\"\"\n        Search for auctions matching the specified criteria.\n\n        Args:\n            item_name (str, optional): The name of the item to search for.\n            min_price (int, optional): The minimum price.\n            max_price (int, optional): The maximum price.\n            sort_by_price (bool, optional): Whether to sort the results by price.\n            descending (bool, optional): Whether to sort in descending order.\n            max_pages (int, optional): Maximum number of pages to search.\n\n        Returns:\n            list of SkyBlockAuction: A list of auctions matching the criteria.\n        \"\"\"\n        matching_auctions = []\n\n        # Use cached data if available\n        if self.all_auctions:\n            auctions_to_search = self.all_auctions\n        else:\n            first_page = self.get_page(0)\n            total_pages = first_page.totalPages\n            if max_pages:\n                total_pages = min(total_pages, max_pages)\n\n            auctions_to_search = []\n            auctions_to_search.extend(first_page.auctions)\n\n            for page_number in range(1, total_pages):\n                page = self.get_page(page_number)\n                auctions_to_search.extend(page.auctions)\n\n        # Function to check if an auction matches the criteria\n        def matches(auction: SkyBlockAuction) -> bool:\n            if item_name and item_name.lower() not in auction.item_name.lower():\n                return False\n            price = auction.current_price\n            if min_price is not None and price < min_price:\n                return False\n            if max_price is not None and price > max_price:\n                return False\n            return True\n\n        for auction in auctions_to_search:\n            if matches(auction):\n                matching_auctions.append(auction)\n\n        if sort_by_price:\n            matching_auctions.sort(key=lambda x: x.current_price, reverse=descending)\n\n        return matching_auctions\n\n    def get_auction_by_id(self, auction_id: str) -> SkyBlockAuction | None:\n        \"\"\"\n        Fetch a specific auction by its ID.\n\n        Args:\n            auction_id (str): The ID of the auction.\n\n        Returns:\n            SkyBlockAuction: The auction with the specified ID, or None if not found.\n        \"\"\"\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n\n        for page_number in range(total_pages):\n            page = self.get_page(page_number)\n            auction = page.get_auction_by_id(auction_id)\n            if auction:\n                return auction\n        return None\n\n    def __str__(self) -> str:\n        return f\"Auctions Manager using endpoint {self._api_endpoint}\"\n\n```\n\n\n",
        "eval_script": "from datetime import datetime, timezone, tzinfo\nfrom typing import List, Dict, Optional\n\n# Mock function to simulate the API response\ndef mock_get_auctions_api(page_number: int = 0) -> Dict:\n    # Simulate a response structure similar to the Hypixel API\n    return {\n        'success': True,\n        'page': page_number,\n        'totalPages': 1,\n        'totalAuctions': 1,\n        'lastUpdated': int(datetime.now().timestamp() * 1000),\n        'auctions': [\n            {\n                '_id': 'auction1',\n                'uuid': 'uuid1',\n                'auctioneer': 'auctioneer1',\n                'profile_id': 'profile1',\n                'coop': ['coop1'],\n                'start': int(datetime.now().timestamp() * 1000),\n                'end': int(datetime.now().timestamp() * 1000 + 3600000),\n                'item_name': 'Item1',\n                'item_lore': 'Lore1',\n                'extra': 'Extra1',\n                'category': 'Category1',\n                'tier': 'Tier1',\n                'starting_bid': 100,\n                'item_bytes': None,\n                'claimed': False,\n                'claimed_bidders': [],\n                'highest_bid_amount': 150,\n                'bids': [\n                    {\n                        'auction_id': 'auction1',\n                        'bidder': 'bidder1',\n                        'profile_id': 'profile1',\n                        'amount': 150,\n                        'timestamp': int(datetime.now().timestamp() * 1000)\n                    }\n                ]\n            }\n        ]\n    }\n\ndef convert_timestamp(timestamp: Optional[int]) -> Optional[datetime]:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\nACTIVE_AUCTIONS_API_URL = r\"https://api.hypixel.net/skyblock/auctions\"\n\nclass Bid:\n    def __init__(self, bid_data: dict) -> None:\n        self.auction_id: str = bid_data.get('auction_id')\n        self.bidder: str = bid_data.get('bidder')\n        self.profile_id: str = bid_data.get('profile_id')\n        self.amount: int = bid_data.get('amount')\n        self.timestamp: Optional[datetime] = convert_timestamp(bid_data.get('timestamp'))\n\n    def __str__(self) -> str:\n        timestamp_str = self.timestamp.strftime(\"%Y-%m-%d %H:%M:%S %Z\") if self.timestamp else \"N/A\"\n        return f\"Bid of {self.amount} by {self.bidder} at {timestamp_str}\"\n\nclass SkyBlockAuction:\n    def __init__(self, auction_data: dict) -> None:\n        self._id: str = auction_data.get('_id')\n        self.uuid: str = auction_data.get('uuid')\n        self.auctioneer: str = auction_data.get('auctioneer')\n        self.profile_id: str = auction_data.get('profile_id')\n        self.coop: List[str] = auction_data.get('coop', [])\n        self.start: Optional[datetime] = convert_timestamp(auction_data.get('start'))\n        self.end: Optional[datetime] = convert_timestamp(auction_data.get('end'))\n        self.item_name: str = auction_data.get('item_name')\n        self.item_lore: str = auction_data.get('item_lore')\n        self.extra: str = auction_data.get('extra')\n        self.category: str = auction_data.get('category')\n        self.tier: str = auction_data.get('tier')\n        self.starting_bid: int = auction_data.get('starting_bid')\n        self.item_bytes: object = auction_data.get('item_bytes')\n        self.claimed: bool = auction_data.get('claimed')\n        self.claimed_bidders: List = auction_data.get('claimed_bidders', [])\n        self.highest_bid_amount: int = auction_data.get('highest_bid_amount')\n        self.bids: Optional[List[Bid]] = [Bid(bid) for bid in auction_data.get('bids', [])]\n\n    def get_start_time_in_timezone(self, tz: tzinfo) -> Optional[datetime]:\n        if self.start:\n            return self.start.astimezone(tz)\n        return None\n\n    def get_end_time_in_timezone(self, tz: tzinfo) -> Optional[datetime]:\n        if self.end:\n            return self.end.astimezone(tz)\n        return None\n\n    @property\n    def current_price(self) -> int:\n        if not self.bids:\n            return self.starting_bid\n        else:\n            return max(self.starting_bid, self.highest_bid_amount)\n\n    @property\n    def is_bin(self) -> bool:\n        return not self.bids\n\n    def __str__(self) -> str:\n        auction_type = \"BIN\" if self.is_bin else \"Auction\"\n        return f\"{auction_type} '{self.item_name}' by {self.auctioneer}, Price: {self.current_price}\"\n\nclass AuctionsPage:\n    def __init__(self, page_data: dict) -> None:\n        self.success: bool = page_data.get('success', False)\n        self.page: int = page_data.get('page', 0)\n        self.totalPages: int = page_data.get('totalPages', 0)\n        self.totalAuctions: int = page_data.get('totalAuctions', 0)\n        self.lastUpdated: Optional[datetime] = convert_timestamp(page_data.get('lastUpdated'))\n        self.auctions: List[SkyBlockAuction] = [SkyBlockAuction(auction) for auction in page_data.get('auctions', [])]\n\n    def get_auction_by_id(self, auction_id: str) -> Optional[SkyBlockAuction]:\n        return next((auction for auction in self.auctions if auction._id == auction_id), None)\n\n    def get_auctions_by_item_name(self, item_name: str) -> List[SkyBlockAuction]:\n        return [auction for auction in self.auctions if auction.item_name.lower() == item_name.lower()]\n\n    def __str__(self) -> str:\n        return f\"Auctions Page {self.page}/{self.totalPages}, Total Auctions: {self.totalAuctions}\"\n\nclass ActiveAuctions:\n    def __init__(self, api_endpoint: str = ACTIVE_AUCTIONS_API_URL, preload_all: bool = False) -> None:\n        self._api_endpoint: str = api_endpoint\n        self.all_auctions: List[SkyBlockAuction] = []\n        self.cache_pages: Dict[int, AuctionsPage] = {}\n        if preload_all:\n            self.all_auctions = self.get_all_auctions()\n\n    def get_page(self, page_number: int = 0) -> AuctionsPage:\n        if page_number in self.cache_pages:\n            return self.cache_pages[page_number]\n\n        # Use the mock function instead of a real API call\n        data = mock_get_auctions_api(page_number)\n\n        if data.get('success'):\n            page = AuctionsPage(data)\n            self.cache_pages[page_number] = page\n            return page\n        else:\n            raise ValueError(\"API response was not successful\")\n\n    def get_all_auctions(self) -> List[SkyBlockAuction]:\n        if self.all_auctions:\n            return self.all_auctions\n\n        all_auctions = []\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n        all_auctions.extend(first_page.auctions)\n\n        for page_number in range(1, total_pages):\n            page = self.get_page(page_number)\n            all_auctions.extend(page.auctions)\n\n        self.all_auctions = all_auctions\n        return all_auctions\n\n\n    def search_auctions(self, item_name: Optional[str] = None, min_price: Optional[int] = None, max_price: Optional[int] = None, sort_by_price: bool = False, descending: bool = False, max_pages: Optional[int] = None) -> List[SkyBlockAuction]:\n        matching_auctions = []\n\n        if self.all_auctions:\n            auctions_to_search = self.all_auctions\n        else:\n            first_page = self.get_page(0)\n            total_pages = first_page.totalPages\n            if max_pages:\n                total_pages = min(total_pages, max_pages)\n\n            auctions_to_search = []\n            auctions_to_search.extend(first_page.auctions)\n\n            for page_number in range(1, total_pages):\n                page = self.get_page(page_number)\n                auctions_to_search.extend(page.auctions)\n\n        def matches(auction: SkyBlockAuction) -> bool:\n            if item_name and item_name.lower() not in auction.item_name.lower():\n                return False\n            price = auction.current_price\n            if min_price is not None and price < min_price:\n                return False\n            if max_price is not None and price > max_price:\n                return False\n            return True\n\n        for auction in auctions_to_search:\n            if matches(auction):\n                matching_auctions.append(auction)\n\n        if sort_by_price:\n            matching_auctions.sort(key=lambda x: x.current_price, reverse=descending)\n\n        return matching_auctions\n\n    def get_auction_by_id(self, auction_id: str) -> Optional[SkyBlockAuction]:\n        first_page = self.get_page(0)\n        total_pages = first_page.totalPages\n\n        for page_number in range(total_pages):\n            page = self.get_page(page_number)\n            auction = page.get_auction_by_id(auction_id)\n            if auction:\n                return auction\n        return None\n\n    def __str__(self) -> str:\n        return f\"Auctions Manager using endpoint {self._api_endpoint}\"\n\ndef test_get_all_auctions():\n    active_auctions = ActiveAuctions()\n    original_auctions = active_auctions.get_all_auctions()\n    new_auctions = active_auctions.get_all_auctions_new_implementation()\n\n    # Assert that both implementations return the same number of auctions\n    assert len(original_auctions) == len(new_auctions), \"Mismatch in number of auctions\"\n\n    # Assert that each auction in the original is the same as in the new implementation\n    for original, new in zip(original_auctions, new_auctions):\n        assert original._id == new._id, f\"Mismatch in auction ID: {original._id} vs {new._id}\"\n        assert original.uuid == new.uuid, f\"Mismatch in UUID for auction ID: {original._id}\"\n        assert original.auctioneer == new.auctioneer, f\"Mismatch in auctioneer for auction ID: {original._id}\"\n        assert original.profile_id == new.profile_id, f\"Mismatch in profile ID for auction ID: {original._id}\"\n        assert original.coop == new.coop, f\"Mismatch in coop for auction ID: {original._id}\"\n        assert original.start == new.start, f\"Mismatch in start time for auction ID: {original._id}\"\n        assert original.end == new.end, f\"Mismatch in end time for auction ID: {original._id}\"\n        assert original.item_name == new.item_name, f\"Mismatch in item name for auction ID: {original._id}\"\n        assert original.item_lore == new.item_lore, f\"Mismatch in item lore for auction ID: {original._id}\"\n        assert original.extra == new.extra, f\"Mismatch in extra for auction ID: {original._id}\"\n        assert original.category == new.category, f\"Mismatch in category for auction ID: {original._id}\"\n        assert original.tier == new.tier, f\"Mismatch in tier for auction ID: {original._id}\"\n        assert original.starting_bid == new.starting_bid, f\"Mismatch in starting bid for auction ID: {original._id}\"\n        assert original.item_bytes == new.item_bytes, f\"Mismatch in item bytes for auction ID: {original._id}\"\n        assert original.claimed == new.claimed, f\"Mismatch in claimed status for auction ID: {original._id}\"\n        assert original.claimed_bidders == new.claimed_bidders, f\"Mismatch in claimed bidders for auction ID: {original._id}\"\n        assert original.highest_bid_amount == new.highest_bid_amount, f\"Mismatch in highest bid amount for auction ID: {original._id}\"\n        assert len(original.bids) == len(new.bids), f\"Mismatch in number of bids for auction ID: {original._id}\"\n        for original_bid, new_bid in zip(original.bids, new.bids):\n            assert original_bid.auction_id == new_bid.auction_id, f\"Mismatch in bid auction ID for auction ID: {original._id}\"\n            assert original_bid.bidder == new_bid.bidder, f\"Mismatch in bidder for auction ID: {original._id}\"\n            assert original_bid.profile_id == new_bid.profile_id, f\"Mismatch in bid profile ID for auction ID: {original._id}\"\n            assert original_bid.amount == new_bid.amount, f\"Mismatch in bid amount for auction ID: {original._id}\"\n            assert original_bid.timestamp == new_bid.timestamp, f\"Mismatch in bid timestamp for auction ID: {original._id}\"\n\n    # Assert that the cache is populated in the same way\n    assert len(active_auctions.cache_pages) > 0, \"Cache pages should be populated\"\n\n    # Check if all pages are cached correctly\n    for page_number in range(active_auctions.cache_pages[0].totalPages):\n        assert page_number in active_auctions.cache_pages, f\"Page {page_number} should be cached\"\n\nif __name__ == \"__main__\":\n    test_get_all_auctions()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_all_auctions` in the `ActiveAuctions` class is identical in functionality to the ORIGINAL FUNCTION. Both functions check if `self.all_auctions` is already populated and return it if so. If not, they initialize an empty list `all_auctions`, fetch the first page using `get_page(0)`, determine the total number of pages, and iterate through each page to extend `all_auctions` with the auctions from each page. Finally, they assign `all_auctions` to `self.all_auctions` and return it. The logic and flow of both functions are the same, and there are no differences in their implementation.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `get_all_auctions` function returns a list of `SkyBlockAuction` objects, satisfying this condition.\n- CONDITION 2: The test function checks the return values and states of variables (e.g., comparing attributes of auctions), not printed or logged content, satisfying this condition.\n- CONDITION 3: The test function compares all relevant attributes of the auctions returned by both implementations, ensuring that `get_all_auctions_new_implementation` must have the exact same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use appropriate assertions to compare the attributes of the auctions and the cache, which are reasonable given the function's behavior, satisfying this condition.\n- CONDITION 5: The test cases are non-trivial as they check multiple attributes of the auctions and the caching mechanism, ensuring comprehensive testing of the functionality.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "PlayerData._parse_perks",
        "idx": "568",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/member/PlayerData.py",
        "orig_func": "def _parse_perks(self, perks_dict: dict) -> list[Perk]:\n    \"\"\"Convert the perks dictionary into a list of Perk objects.\"\"\"\n    return [Perk(name, level) for name, level in perks_dict.items()]",
        "orig_context": "```python\n## hypixel_api_lib/utils.py\nfrom datetime import datetime, timezone\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\n```\n\n\n```python\n## hypixel_api_lib/member/PlayerData.py\nfrom datetime import datetime\n\nfrom hypixel_api_lib.utils import convert_timestamp\n\nclass Perk:\n    \"\"\"\n    Represents a perk and its level.\n\n    Attributes:\n        name (str): The name of the perk.\n        level (int): The level of the perk.\n    \"\"\"\n\n    def __init__(self, name: str, level: int) -> None:\n        self.name: str = name\n        self.level: int = level\n\n    def __str__(self) -> str:\n        return f\"{self.name}: Level {self.level}\"\n\nclass SkillExperience:\n    \"\"\"\n    Represents experience in a specific skill.\n\n    Attributes:\n        skill_name (str): The name of the skill.\n        experience (float): The amount of experience in the skill.\n    \"\"\"\n\n    def __init__(self, skill_name: str, experience: float) -> None:\n        self.skill_name: str = skill_name\n        self.experience: float = experience\n\n    def __str__(self) -> str:\n        return f\"{self.skill_name}: {self.experience} XP\"\n    \n    def __repr__(self) -> str:\n        return f\"{self.skill_name}: {self.experience} XP\"\n\nclass PlayerData:\n    \"\"\"\n    Represents the player data for a SkyBlock profile member.\n\n    Attributes:\n        visited_zones (list of str): List of zones the player has visited.\n        last_death (datetime): Datetime of the player's last death.\n        perks (dict of str to int): Perks and their levels.\n        active_effects (list): List of active effects.\n        paused_effects (list): List of paused effects.\n        temp_stat_buffs (list): List of temporary stat buffs.\n        death_count (int): Total number of deaths.\n        disabled_potion_effects (list of str): List of disabled potion effects.\n        achievement_spawned_island_types (list of str): List of spawned island types for achievements.\n        visited_modes (list of str): List of game modes the player has visited.\n        unlocked_coll_tiers (list of str): List of unlocked collection tiers.\n        crafted_generators (list of str): List of crafted generators.\n        fastest_target_practice (float): Fastest time in target practice.\n        fishing_treasure_caught (int): Number of fishing treasures caught.\n        experience (dict of str to float): Experience in various skills.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.visited_zones: list = data.get('visited_zones', [])\n        self.last_death: datetime | None = convert_timestamp(data.get('last_death'))\n        self.perks: dict[str,int] = self._parse_perks(data.get('perks', {}))\n        self.active_effects: list = data.get('active_effects', [])\n        self.paused_effects: list = data.get('paused_effects', [])\n        self.temp_stat_buffs: list = data.get('temp_stat_buffs', [])\n        self.death_count: int = data.get('death_count', 0)\n        self.disabled_potion_effects: list[str] = data.get('disabled_potion_effects', [])\n        self.achievement_spawned_island_types: list[str] = data.get('achievement_spawned_island_types', [])\n        self.visited_modes: list[str] = data.get('visited_modes', [])\n        self.unlocked_coll_tiers: list[str] = data.get('unlocked_coll_tiers', [])\n        self.crafted_generators: list[str] = data.get('crafted_generators', [])\n        self.fastest_target_practice: float | None = data.get('fastest_target_practice', None)\n        self.fishing_treasure_caught: int = data.get('fishing_treasure_caught', 0)\n        self.experience: dict[str,float] = self._parse_experience(data.get('experience', {}))\n    \n    def _parse_perks(self, perks_dict:dict) -> list[Perk]:\n        \"\"\"Convert the perks dictionary into a list of Perk objects.\"\"\"\n        return [Perk(name, level) for name, level in perks_dict.items()]\n\n    def _parse_experience(self, experience_dict: dict) -> list[SkillExperience]:\n        \"\"\"Convert the experience dictionary into a list of SkillExperience objects.\"\"\"\n        return [SkillExperience(skill, xp) for skill, xp in experience_dict.items()]\n\n    def __str__(self) -> str:\n        last_death_str = self.last_death.strftime('%Y-%m-%d %H:%M:%S') if self.last_death else 'N/A'\n        return (f\"PlayerData(Deaths: {self.death_count}, Last Death: {last_death_str}, \"\n                f\"Visited Zones: {len(self.visited_zones)}, Experience: {self.experience})\")\n\n```\n\n\n",
        "eval_script": "from datetime import datetime, timezone\n\ndef convert_timestamp(timestamp: int | None) -> datetime | None:\n    \"\"\"Convert a timestamp in milliseconds to a timezone-aware datetime object in UTC.\"\"\"\n    if timestamp:\n        return datetime.fromtimestamp(timestamp / 1000, tz=timezone.utc)\n    return None\n\nclass Perk:\n    \"\"\"\n    Represents a perk and its level.\n\n    Attributes:\n        name (str): The name of the perk.\n        level (int): The level of the perk.\n    \"\"\"\n\n    def __init__(self, name: str, level: int) -> None:\n        self.name: str = name\n        self.level: int = level\n\n    def __str__(self) -> str:\n        return f\"{self.name}: Level {self.level}\"\n\n    def __eq__(self, other) -> bool:\n        if isinstance(other, Perk):\n            return self.name == other.name and self.level == other.level\n        return False\n\nclass SkillExperience:\n    \"\"\"\n    Represents experience in a specific skill.\n\n    Attributes:\n        skill_name (str): The name of the skill.\n        experience (float): The amount of experience in the skill.\n    \"\"\"\n\n    def __init__(self, skill_name: str, experience: float) -> None:\n        self.skill_name: str = skill_name\n        self.experience: float = experience\n\n    def __str__(self) -> str:\n        return f\"{self.skill_name}: {self.experience} XP\"\n    \n    def __repr__(self) -> str:\n        return f\"{self.skill_name}: {self.experience} XP\"\n\nclass PlayerData:\n    \"\"\"\n    Represents the player data for a SkyBlock profile member.\n\n    Attributes:\n        visited_zones (list of str): List of zones the player has visited.\n        last_death (datetime): Datetime of the player's last death.\n        perks (dict of str to int): Perks and their levels.\n        active_effects (list): List of active effects.\n        paused_effects (list): List of paused effects.\n        temp_stat_buffs (list): List of temporary stat buffs.\n        death_count (int): Total number of deaths.\n        disabled_potion_effects (list of str): List of disabled potion effects.\n        achievement_spawned_island_types (list of str): List of spawned island types for achievements.\n        visited_modes (list of str): List of game modes the player has visited.\n        unlocked_coll_tiers (list of str): List of unlocked collection tiers.\n        crafted_generators (list of str): List of crafted generators.\n        fastest_target_practice (float): Fastest time in target practice.\n        fishing_treasure_caught (int): Number of fishing treasures caught.\n        experience (dict of str to float): Experience in various skills.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.visited_zones: list = data.get('visited_zones', [])\n        self.last_death: datetime | None = convert_timestamp(data.get('last_death'))\n        self.perks: dict[str,int] = self._parse_perks(data.get('perks', {}))\n        self.active_effects: list = data.get('active_effects', [])\n        self.paused_effects: list = data.get('paused_effects', [])\n        self.temp_stat_buffs: list = data.get('temp_stat_buffs', [])\n        self.death_count: int = data.get('death_count', 0)\n        self.disabled_potion_effects: list[str] = data.get('disabled_potion_effects', [])\n        self.achievement_spawned_island_types: list[str] = data.get('achievement_spawned_island_types', [])\n        self.visited_modes: list[str] = data.get('visited_modes', [])\n        self.unlocked_coll_tiers: list[str] = data.get('unlocked_coll_tiers', [])\n        self.crafted_generators: list[str] = data.get('crafted_generators', [])\n        self.fastest_target_practice: float | None = data.get('fastest_target_practice', None)\n        self.fishing_treasure_caught: int = data.get('fishing_treasure_caught', 0)\n        self.experience: dict[str,float] = self._parse_experience(data.get('experience', {}))\n    \n    def _parse_perks(self, perks_dict:dict) -> list[Perk]:\n        \"\"\"Convert the perks dictionary into a list of Perk objects.\"\"\"\n        return [Perk(name, level) for name, level in perks_dict.items()]\n\n\n    def _parse_experience(self, experience_dict: dict) -> list[SkillExperience]:\n        \"\"\"Convert the experience dictionary into a list of SkillExperience objects.\"\"\"\n        return [SkillExperience(skill, xp) for skill, xp in experience_dict.items()]\n\n    def __str__(self) -> str:\n        last_death_str = self.last_death.strftime('%Y-%m-%d %H:%M:%S') if self.last_death else 'N/A'\n        return (f\"PlayerData(Deaths: {self.death_count}, Last Death: {last_death_str}, \"\n                f\"Visited Zones: {len(self.visited_zones)}, Experience: {self.experience})\")\n\ndef test__parse_perks():\n    # Test case 1: Empty perks dictionary\n    data_empty = {}\n    player_data_empty = PlayerData(data_empty)\n    assert player_data_empty._parse_perks(data_empty) == player_data_empty._parse_perks_new_implementation(data_empty)\n\n    # Test case 2: Single perk\n    data_single = {'perks': {'Speed Boost': 1}}\n    player_data_single = PlayerData(data_single)\n    assert player_data_single._parse_perks(data_single['perks']) == player_data_single._parse_perks_new_implementation(data_single['perks'])\n\n    # Test case 3: Multiple perks\n    data_multiple = {'perks': {'Speed Boost': 1, 'Strength Boost': 2}}\n    player_data_multiple = PlayerData(data_multiple)\n    assert player_data_multiple._parse_perks(data_multiple['perks']) == player_data_multiple._parse_perks_new_implementation(data_multiple['perks'])\n\nif __name__ == \"__main__\":\n    test__parse_perks()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `_parse_perks` and the revised function `_parse_perks` within the `PlayerData` class both perform the same task: converting a dictionary of perks into a list of `Perk` objects. The logic and implementation of both functions are identical, as they both iterate over the items in the `perks_dict` and create a `Perk` object for each name-level pair. The test cases provided in the revised code also confirm that the functionality remains consistent across different scenarios. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_parse_perks` function returns a list of `Perk` objects, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `_parse_perks` and `_parse_perks_new_implementation`, and do not rely on printed or logged content.\n- CONDITION 3: The test cases check for equality between the outputs of `_parse_perks` and `_parse_perks_new_implementation`, ensuring that the new implementation must have the same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_parse_perks` returns a list of `Perk` objects.\n- CONDITION 5: The test cases cover different scenarios: an empty dictionary, a single perk, and multiple perks, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "TrophyFishStats._parse_fish_data",
        "idx": "569",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/member/TrophyFish.py",
        "orig_func": "def _parse_fish_data(self, data: dict) -> None:\n    \"\"\"\n        Parses the fish data and populates the fish_types dictionary.\n\n        Args:\n            data (dict): The data dictionary.\n        \"\"\"\n    special_keys = {'rewards', 'total_caught', 'last_caught'}\n    for key, value in data.items():\n        if key in special_keys:\n            continue\n        parts = key.split('_')\n        if parts[-1] in {'bronze', 'silver', 'gold', 'diamond'}:\n            tier = parts[-1]\n            fish_name = '_'.join(parts[:-1])\n        else:\n            tier = None\n            fish_name = key\n        if fish_name not in self.fish_types:\n            self.fish_types[fish_name] = TrophyFish(fish_name)\n        self.fish_types[fish_name].add_catch(tier, value)",
        "orig_context": "```python\n## hypixel_api_lib/member/TrophyFish.py\nclass TrophyFish:\n    \"\"\"\n    Represents a single type of trophy fish with counts per tier.\n\n    Attributes:\n        name (str): The name of the fish.\n        total_caught (int): Total number of this fish caught.\n        tiers (dict[str,int]): Counts per tier (bronze, silver, gold, diamond).\n    \"\"\"\n\n    def __init__(self, name: str) -> None:\n        self.name: str = name\n        self.total_caught: int = 0\n        self.tiers: dict[str,int] = {}  # e.g., {'bronze': 10, 'silver': 5}\n\n    def add_catch(self, tier: str | None, count: int) -> None:\n        \"\"\"\n        Adds catch count to the fish.\n\n        Args:\n            tier (Optional[str]): The tier of the catch (e.g., 'bronze', 'silver').\n            count (int): Number of fish caught.\n        \"\"\"\n        if tier:\n            self.tiers[tier] = self.tiers.get(tier, 0) + count\n        else:\n            self.total_caught += count\n\n    def total_tier_catches(self) -> int:\n        \"\"\"\n        Calculates the total catches across all tiers.\n\n        Returns:\n            int: Total catches across all tiers.\n        \"\"\"\n        return sum(self.tiers.values())\n\n    def __str__(self) -> str:\n        return (\n            f\"TrophyFish(name={self.name}, total_caught={self.total_caught}, \"\n            f\"tiers={self.tiers})\"\n        )\n\nclass TrophyFishStats:\n    \"\"\"\n    Represents the player's trophy fish statistics.\n\n    Attributes:\n        rewards (list[int]): List of reward IDs.\n        total_caught (int): Total number of trophy fish caught.\n        fish_types (dict[str,TrophyFish]): Dictionary of trophy fish by name.\n        last_caught (str): The last trophy fish caught with its tier.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.rewards: list[int] = data.get('rewards', [])\n        self.total_caught: int = data.get('total_caught', 0)\n        self.last_caught: str = data.get('last_caught', '')\n        self.fish_types: dict[str,TrophyFish] = {}\n\n        self._parse_fish_data(data)\n\n    def _parse_fish_data(self, data: dict) -> None:\n        \"\"\"\n        Parses the fish data and populates the fish_types dictionary.\n\n        Args:\n            data (dict): The data dictionary.\n        \"\"\"\n        special_keys = {'rewards', 'total_caught', 'last_caught'}\n        for key, value in data.items():\n            if key in special_keys:\n                continue\n            parts = key.split('_')\n            if parts[-1] in {'bronze', 'silver', 'gold', 'diamond'}:\n                tier = parts[-1]\n                fish_name = '_'.join(parts[:-1])\n            else:\n                tier = None\n                fish_name = key\n\n            if fish_name not in self.fish_types:\n                self.fish_types[fish_name] = TrophyFish(fish_name)\n\n            self.fish_types[fish_name].add_catch(tier, value)\n\n    def get_fish(self, fish_name: str) -> TrophyFish | None:\n        \"\"\"\n        Retrieves a TrophyFish instance by name.\n\n        Args:\n            fish_name (str): The name of the fish.\n\n        Returns:\n            TrophyFish | None: The TrophyFish instance or None if not found.\n        \"\"\"\n        return self.fish_types.get(fish_name)\n\n    def __str__(self) -> str:\n        fish_str = ', '.join(str(fish) for fish in self.fish_types.values())\n        return (\n            f\"TrophyFishStats(rewards={self.rewards}, total_caught={self.total_caught}, \"\n            f\"last_caught={self.last_caught}, fish_types=[{fish_str}])\"\n        )\n\n```\n\n\n",
        "eval_script": "## hypixel_api_lib/member/TrophyFish.py\nclass TrophyFish:\n    \"\"\"\n    Represents a single type of trophy fish with counts per tier.\n\n    Attributes:\n        name (str): The name of the fish.\n        total_caught (int): Total number of this fish caught.\n        tiers (dict[str,int]): Counts per tier (bronze, silver, gold, diamond).\n    \"\"\"\n\n    def __init__(self, name: str) -> None:\n        self.name: str = name\n        self.total_caught: int = 0\n        self.tiers: dict[str,int] = {}  # e.g., {'bronze': 10, 'silver': 5}\n\n    def add_catch(self, tier: str | None, count: int) -> None:\n        \"\"\"\n        Adds catch count to the fish.\n\n        Args:\n            tier (Optional[str]): The tier of the catch (e.g., 'bronze', 'silver').\n            count (int): Number of fish caught.\n        \"\"\"\n        if tier:\n            self.tiers[tier] = self.tiers.get(tier, 0) + count\n        else:\n            self.total_caught += count\n\n    def total_tier_catches(self) -> int:\n        \"\"\"\n        Calculates the total catches across all tiers.\n\n        Returns:\n            int: Total catches across all tiers.\n        \"\"\"\n        return sum(self.tiers.values())\n\n    def __str__(self) -> str:\n        return (\n            f\"TrophyFish(name={self.name}, total_caught={self.total_caught}, \"\n            f\"tiers={self.tiers})\"\n        )\n\nclass TrophyFishStats:\n    \"\"\"\n    Represents the player's trophy fish statistics.\n\n    Attributes:\n        rewards (list[int]): List of reward IDs.\n        total_caught (int): Total number of trophy fish caught.\n        fish_types (dict[str,TrophyFish]): Dictionary of trophy fish by name.\n        last_caught (str): The last trophy fish caught with its tier.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.rewards: list[int] = data.get('rewards', [])\n        self.total_caught: int = data.get('total_caught', 0)\n        self.last_caught: str = data.get('last_caught', '')\n        self.fish_types: dict[str,TrophyFish] = {}\n\n        self._parse_fish_data(data)\n\n    def _parse_fish_data(self, data: dict) -> None:\n        \"\"\"\n        Parses the fish data and populates the fish_types dictionary.\n\n        Args:\n            data (dict): The data dictionary.\n        \"\"\"\n        special_keys = {'rewards', 'total_caught', 'last_caught'}\n        for key, value in data.items():\n            if key in special_keys:\n                continue\n            parts = key.split('_')\n            if parts[-1] in {'bronze', 'silver', 'gold', 'diamond'}:\n                tier = parts[-1]\n                fish_name = '_'.join(parts[:-1])\n            else:\n                tier = None\n                fish_name = key\n\n            if fish_name not in self.fish_types:\n                self.fish_types[fish_name] = TrophyFish(fish_name)\n\n            self.fish_types[fish_name].add_catch(tier, value)\n\n\n    def get_fish(self, fish_name: str) -> TrophyFish | None:\n        \"\"\"\n        Retrieves a TrophyFish instance by name.\n\n        Args:\n            fish_name (str): The name of the fish.\n\n        Returns:\n            TrophyFish | None: The TrophyFish instance or None if not found.\n        \"\"\"\n        return self.fish_types.get(fish_name)\n\n    def __str__(self) -> str:\n        fish_str = ', '.join(str(fish) for fish in self.fish_types.values())\n        return (\n            f\"TrophyFishStats(rewards={self.rewards}, total_caught={self.total_caught}, \"\n            f\"last_caught={self.last_caught}, fish_types=[{fish_str}])\"\n        )\n\ndef test__parse_fish_data():\n    # Mock data to test the _parse_fish_data function\n    mock_data = {\n        'rewards': [1, 2, 3],\n        'total_caught': 100,\n        'last_caught': 'shark_gold',\n        'shark_bronze': 10,\n        'shark_silver': 5,\n        'shark_gold': 2,\n        'tuna': 20\n    }\n\n    # Create instances of TrophyFishStats with both implementations\n    original_stats = TrophyFishStats(mock_data)\n    new_stats = TrophyFishStats(mock_data)\n    new_stats.fish_types = {}\n    new_stats._parse_fish_data_new_implementation(mock_data)\n\n    # Assert that both implementations produce the same results\n    assert original_stats.rewards == new_stats.rewards\n    assert original_stats.total_caught == new_stats.total_caught\n    assert original_stats.last_caught == new_stats.last_caught\n    assert original_stats.fish_types.keys() == new_stats.fish_types.keys()\n\n    for fish_name in original_stats.fish_types:\n        original_fish = original_stats.fish_types[fish_name]\n        new_fish = new_stats.fish_types[fish_name]\n        assert original_fish.name == new_fish.name\n        assert original_fish.total_caught == new_fish.total_caught\n        assert original_fish.tiers == new_fish.tiers\n\n    # Test with empty data\n    empty_data = {}\n    original_stats_empty = TrophyFishStats(empty_data)\n    new_stats_empty = TrophyFishStats(empty_data)\n    new_stats_empty.fish_types = {}\n    new_stats_empty._parse_fish_data_new_implementation(empty_data)\n\n    assert original_stats_empty.rewards == new_stats_empty.rewards\n    assert original_stats_empty.total_caught == new_stats_empty.total_caught\n    assert original_stats_empty.last_caught == new_stats_empty.last_caught\n    assert original_stats_empty.fish_types.keys() == new_stats_empty.fish_types.keys()\n\n    # Test with no tiers\n    no_tiers_data = {\n        'rewards': [4, 5],\n        'total_caught': 50,\n        'last_caught': 'salmon',\n        'salmon': 50\n    }\n    original_stats_no_tiers = TrophyFishStats(no_tiers_data)\n    new_stats_no_tiers = TrophyFishStats(no_tiers_data)\n    new_stats_no_tiers.fish_types = {}\n    new_stats_no_tiers._parse_fish_data_new_implementation(no_tiers_data)\n\n    assert original_stats_no_tiers.rewards == new_stats_no_tiers.rewards\n    assert original_stats_no_tiers.total_caught == new_stats_no_tiers.total_caught\n    assert original_stats_no_tiers.last_caught == new_stats_no_tiers.last_caught\n    assert original_stats_no_tiers.fish_types.keys() == new_stats_no_tiers.fish_types.keys()\n\n    # Test with multiple fish types\n    multiple_fish_data = {\n        'rewards': [],\n        'total_caught': 200,\n        'last_caught': 'trout_gold',\n        'trout_bronze': 30,\n        'trout_silver': 20,\n        'trout_gold': 10,\n        'bass_bronze': 15,\n        'bass_silver': 10,\n        'bass_gold': 5\n    }\n    original_stats_multiple_fish = TrophyFishStats(multiple_fish_data)\n    new_stats_multiple_fish = TrophyFishStats(multiple_fish_data)\n    new_stats_multiple_fish.fish_types = {}\n    new_stats_multiple_fish._parse_fish_data_new_implementation(multiple_fish_data)\n\n    assert original_stats_multiple_fish.rewards == new_stats_multiple_fish.rewards\n    assert original_stats_multiple_fish.total_caught == new_stats_multiple_fish.total_caught\n    assert original_stats_multiple_fish.last_caught == new_stats_multiple_fish.last_caught\n    assert original_stats_multiple_fish.fish_types.keys() == new_stats_multiple_fish.fish_types.keys()\n\n    # Test with invalid tier names\n    invalid_tier_data = {\n        'rewards': [6],\n        'total_caught': 30,\n        'last_caught': 'carp_unknown',\n        'carp_unknown': 10,\n        'carp_bronze': 5\n    }\n    original_stats_invalid_tier = TrophyFishStats(invalid_tier_data)\n    new_stats_invalid_tier = TrophyFishStats(invalid_tier_data)\n    new_stats_invalid_tier.fish_types = {}\n    new_stats_invalid_tier._parse_fish_data_new_implementation(invalid_tier_data)\n\n    assert original_stats_invalid_tier.rewards == new_stats_invalid_tier.rewards\n    assert original_stats_invalid_tier.total_caught == new_stats_invalid_tier.total_caught\n    assert original_stats_invalid_tier.last_caught == new_stats_invalid_tier.last_caught\n    assert original_stats_invalid_tier.fish_types.keys() == new_stats_invalid_tier.fish_types.keys()\n\n    # Test with large numbers\n    large_numbers_data = {\n        'rewards': [7, 8],\n        'total_caught': 1000000,\n        'last_caught': 'whale_diamond',\n        'whale_bronze': 100000,\n        'whale_silver': 200000,\n        'whale_gold': 300000,\n        'whale_diamond': 400000\n    }\n    original_stats_large_numbers = TrophyFishStats(large_numbers_data)\n    new_stats_large_numbers = TrophyFishStats(large_numbers_data)\n    new_stats_large_numbers.fish_types = {}\n    new_stats_large_numbers._parse_fish_data_new_implementation(large_numbers_data)\n\n    assert original_stats_large_numbers.rewards == new_stats_large_numbers.rewards\n    assert original_stats_large_numbers.total_caught == new_stats_large_numbers.total_caught\n    assert original_stats_large_numbers.last_caught == new_stats_large_numbers.last_caught\n    assert original_stats_large_numbers.fish_types.keys() == new_stats_large_numbers.fish_types.keys()\n\nif __name__ == \"__main__\":\n    test__parse_fish_data()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_parse_fish_data` in the `TrophyFishStats` class is identical to the ORIGINAL FUNCTION. Both functions iterate over the `data` dictionary, skip the special keys, split the remaining keys to determine the tier and fish name, and then update the `fish_types` dictionary with the appropriate `TrophyFish` instances and their catch counts. The logic and flow of the code are unchanged between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `_parse_fish_data` function modifies the `fish_types` attribute of the `TrophyFishStats` class, which is a dictionary. This satisfies the condition that the function modifies input arguments or global variables.\n\n2. **CONDITION 2**: The test cases check the state of the `TrophyFishStats` objects and their attributes (`rewards`, `total_caught`, `last_caught`, and `fish_types`) rather than any printed or logged output. Therefore, this condition is satisfied.\n\n3. **CONDITION 3**: The test cases compare the state of the `TrophyFishStats` objects created using the original and new implementations. They ensure that both implementations produce the same results for various scenarios, which implies that the new implementation must have the same functionality as the original to pass all tests. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the attributes of the `TrophyFishStats` objects. They do not use inappropriate assertions like comparing the return values of functions that do not return anything. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a range of scenarios, including normal data, empty data, data with no tiers, multiple fish types, invalid tier names, and large numbers. This variety ensures that the test cases are non-trivial and robust. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "Bestiary.search_mobs",
        "idx": "570",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/member/Bestiary.py",
        "orig_func": "def search_mobs(self, query: str) -> list[str]:\n    \"\"\"\n        Search for mobs containing the query string in their names.\n\n        Args:\n            query (str): The search query string.\n\n        Returns:\n            list[str]: A list of mob names that match the query.\n        \"\"\"\n    query_lower = query.lower()\n    matched_mobs = [mob for mob in self.kills.keys() if query_lower in mob.lower()]\n    return matched_mobs",
        "orig_context": "```python\n## hypixel_api_lib/member/Bestiary.py\nclass Bestiary:\n    \"\"\"\n    Represents the player's Bestiary data.\n\n    Attributes:\n        migrated_stats (bool): Whether the stats have been migrated.\n        migration (bool): Migration status.\n        kills (dict[str, int]): Dictionary of mobs and their kill counts.\n        deaths (dict[str, int]): Dictionary of mobs and their death counts.\n        milestone (int): Last claimed milestone.\n        miscellaneous (bool): Whether max kills are visible.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.migrated_stats: bool = data.get(\"migrated_stats\", False)\n        self.migration: bool = data.get(\"migration\", False)\n        self.kills: dict[str, int] = data.get(\"kills\", {}).copy()\n        self.last_killed_mob: str | None = self.kills.pop('last_killed_mob', None) \n        self.deaths: dict[str,int] = data.get(\"deaths\", {})\n        self.milestone: int = data.get(\"milestone\", {}).get(\"last_claimed_milestone\", 0)\n        self.miscellaneous: bool = data.get(\"miscellaneous\", {}).get(\"max_kills_visible\", False)\n\n    def get_kill_count(self, mob_name: str) -> int:\n        \"\"\"\n        Get the kill count for a specific mob.\n\n        Args:\n            mob_name (str): The name of the mob.\n\n        Returns:\n            int: The kill count for the mob, or 0 if not found.\n        \"\"\"\n        return self.kills.get(mob_name, 0)\n\n    def get_death_count(self, mob_name: str) -> int:\n        \"\"\"\n        Get the death count for a specific mob.\n\n        Args:\n            mob_name (str): The name of the mob.\n\n        Returns:\n            int: The death count for the mob, or 0 if not found.\n        \"\"\"\n        return self.deaths.get(mob_name, 0)\n\n    def top_kills(self, n: int = 5) -> list[tuple[str, int]]:\n        \"\"\"\n        Get the top N mobs by kill count.\n\n        Args:\n            n (int): The number of top mobs to return.\n\n        Returns:\n            list[tuple[str, int]]: A list of tuples containing mob names and kill counts.\n        \"\"\"\n        sorted_kills = sorted(self.kills.items(), key=lambda item: item[1], reverse=True)\n        return sorted_kills[:n]\n\n    def top_deaths(self, n: int = 5) -> list[tuple[str, int]]:\n        \"\"\"\n        Get the top N mobs by death count.\n\n        Args:\n            n (int): The number of top mobs to return.\n\n        Returns:\n            list[tuple[str, int]]: A list of tuples containing mob names and death counts.\n        \"\"\"\n        sorted_deaths = sorted(self.deaths.items(), key=lambda item: item[1], reverse=True)\n        return sorted_deaths[:n]\n\n    def total_kills(self) -> int:\n        \"\"\"\n        Get the total number of kills across all mobs.\n\n        Returns:\n            int: Total kills.\n        \"\"\"\n        return sum(self.kills.values())\n\n    def total_deaths(self) -> int:\n        \"\"\"\n        Get the total number of deaths across all mobs.\n\n        Returns:\n            int: Total deaths.\n        \"\"\"\n        return sum(self.deaths.values())\n\n    def search_mobs(self, query: str) -> list[str]:\n        \"\"\"\n        Search for mobs containing the query string in their names.\n\n        Args:\n            query (str): The search query string.\n\n        Returns:\n            list[str]: A list of mob names that match the query.\n        \"\"\"\n        query_lower = query.lower()\n        matched_mobs = [mob for mob in self.kills.keys() if query_lower in mob.lower()]\n        return matched_mobs\n\n    def __str__(self) -> str:\n        return (\n            f\"Bestiary(milestone={self.milestone}, total_kills={self.total_kills()}, \"\n            f\"total_deaths={self.total_deaths()})\"\n        )\n\n```\n\n\n",
        "eval_script": "## hypixel_api_lib/member/Bestiary.py\nclass Bestiary:\n    \"\"\"\n    Represents the player's Bestiary data.\n\n    Attributes:\n        migrated_stats (bool): Whether the stats have been migrated.\n        migration (bool): Migration status.\n        kills (dict[str, int]): Dictionary of mobs and their kill counts.\n        deaths (dict[str, int]): Dictionary of mobs and their death counts.\n        milestone (int): Last claimed milestone.\n        miscellaneous (bool): Whether max kills are visible.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        self.migrated_stats: bool = data.get(\"migrated_stats\", False)\n        self.migration: bool = data.get(\"migration\", False)\n        self.kills: dict[str, int] = data.get(\"kills\", {}).copy()\n        self.last_killed_mob: str | None = self.kills.pop('last_killed_mob', None) \n        self.deaths: dict[str,int] = data.get(\"deaths\", {})\n        self.milestone: int = data.get(\"milestone\", {}).get(\"last_claimed_milestone\", 0)\n        self.miscellaneous: bool = data.get(\"miscellaneous\", {}).get(\"max_kills_visible\", False)\n\n    def get_kill_count(self, mob_name: str) -> int:\n        \"\"\"\n        Get the kill count for a specific mob.\n\n        Args:\n            mob_name (str): The name of the mob.\n\n        Returns:\n            int: The kill count for the mob, or 0 if not found.\n        \"\"\"\n        return self.kills.get(mob_name, 0)\n\n    def get_death_count(self, mob_name: str) -> int:\n        \"\"\"\n        Get the death count for a specific mob.\n\n        Args:\n            mob_name (str): The name of the mob.\n\n        Returns:\n            int: The death count for the mob, or 0 if not found.\n        \"\"\"\n        return self.deaths.get(mob_name, 0)\n\n    def top_kills(self, n: int = 5) -> list[tuple[str, int]]:\n        \"\"\"\n        Get the top N mobs by kill count.\n\n        Args:\n            n (int): The number of top mobs to return.\n\n        Returns:\n            list[tuple[str, int]]: A list of tuples containing mob names and kill counts.\n        \"\"\"\n        sorted_kills = sorted(self.kills.items(), key=lambda item: item[1], reverse=True)\n        return sorted_kills[:n]\n\n    def top_deaths(self, n: int = 5) -> list[tuple[str, int]]:\n        \"\"\"\n        Get the top N mobs by death count.\n\n        Args:\n            n (int): The number of top mobs to return.\n\n        Returns:\n            list[tuple[str, int]]: A list of tuples containing mob names and death counts.\n        \"\"\"\n        sorted_deaths = sorted(self.deaths.items(), key=lambda item: item[1], reverse=True)\n        return sorted_deaths[:n]\n\n    def total_kills(self) -> int:\n        \"\"\"\n        Get the total number of kills across all mobs.\n\n        Returns:\n            int: Total kills.\n        \"\"\"\n        return sum(self.kills.values())\n\n    def total_deaths(self) -> int:\n        \"\"\"\n        Get the total number of deaths across all mobs.\n\n        Returns:\n            int: Total deaths.\n        \"\"\"\n        return sum(self.deaths.values())\n\n    def search_mobs(self, query: str) -> list[str]:\n        \"\"\"\n        Search for mobs containing the query string in their names.\n\n        Args:\n            query (str): The search query string.\n\n        Returns:\n            list[str]: A list of mob names that match the query.\n        \"\"\"\n        query_lower = query.lower()\n        matched_mobs = [mob for mob in self.kills.keys() if query_lower in mob.lower()]\n        return matched_mobs\n\n\n    def __str__(self) -> str:\n        return (\n            f\"Bestiary(milestone={self.milestone}, total_kills={self.total_kills()}, \"\n            f\"total_deaths={self.total_deaths()})\"\n        )\n\n# Mock data for testing\nmock_data = {\n    \"migrated_stats\": True,\n    \"migration\": True,\n    \"kills\": {\n        \"zombie\": 150,\n        \"skeleton\": 100,\n        \"creeper\": 50,\n        \"spider\": 75,\n        \"enderman\": 25\n    },\n    \"deaths\": {\n        \"zombie\": 10,\n        \"skeleton\": 5,\n        \"creeper\": 3,\n        \"spider\": 7,\n        \"enderman\": 2\n    },\n    \"milestone\": {\n        \"last_claimed_milestone\": 3\n    },\n    \"miscellaneous\": {\n        \"max_kills_visible\": True\n    }\n}\n\ndef test_search_mobs():\n    bestiary = Bestiary(mock_data)\n\n    # Test case 1: Search for a mob name that exists\n    query1 = \"zom\"\n    assert bestiary.search_mobs(query1) == bestiary.search_mobs_new_implementation(query1)\n\n    # Test case 2: Search for a substring that matches multiple mob names\n    query2 = \"e\"\n    assert bestiary.search_mobs(query2) == bestiary.search_mobs_new_implementation(query2)\n\n    # Test case 3: Search for a mob name that does not exist\n    query3 = \"dragon\"\n    assert bestiary.search_mobs(query3) == bestiary.search_mobs_new_implementation(query3)\n\n    # Test case 4: Search with different case to check case insensitivity\n    query4 = \"ZOMBIE\"\n    assert bestiary.search_mobs(query4) == bestiary.search_mobs_new_implementation(query4)\n\n    # Test case 5: Search with an empty string\n    query5 = \"\"\n    assert bestiary.search_mobs(query5) == bestiary.search_mobs_new_implementation(query5)\n\n    # Test case 6: Search with special characters\n    query6 = \"@#!\"\n    assert bestiary.search_mobs(query6) == bestiary.search_mobs_new_implementation(query6)\n\n    # Test case 7: Search with exact match\n    query7 = \"skeleton\"\n    assert bestiary.search_mobs(query7) == bestiary.search_mobs_new_implementation(query7)\n\n    # Test case 8: Search with leading and trailing whitespace\n    query8 = \"  creeper  \"\n    assert bestiary.search_mobs(query8.strip()) == bestiary.search_mobs_new_implementation(query8.strip())\n\n    # Test case 9: Search with a non-existent substring\n    query9 = \"xyz\"\n    assert bestiary.search_mobs(query9) == bestiary.search_mobs_new_implementation(query9)\n\nif __name__ == \"__main__\":\n    test_search_mobs()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `search_mobs` in the `Bestiary` class is identical to the ORIGINAL FUNCTION. Both functions take a query string, convert it to lowercase, and then search for mob names in the `self.kills` dictionary whose lowercase versions contain the query string. The functionality remains unchanged as both functions return a list of mob names that match the query. The test cases provided in the code also confirm that the behavior of the function is consistent with the expected outcomes.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `search_mobs` function returns a list of strings, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `search_mobs` and `search_mobs_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `search_mobs` and `search_mobs_new_implementation` for various inputs, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- CONDITION 4: The assertions are reasonable as they compare the outputs of the two implementations directly, which is appropriate given that `search_mobs` returns a list of strings.\n- CONDITION 5: The test cases cover a variety of scenarios, including exact matches, substrings, case insensitivity, empty strings, special characters, and non-existent substrings, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "HarpQuest._extract_song_stats",
        "idx": "571",
        "repo_name": "Feromond___hypixel-api-lib",
        "func_path": "hypixel_api_lib/member/Quests.py",
        "orig_func": "def _extract_song_stats(self, harp_data: dict) -> dict[str, dict[str, float | int]]:\n    \"\"\"\n        Extracts song statistics from the harp quest data.\n\n        Args:\n            harp_data (dict): The harp quest data.\n\n        Returns:\n            Dict[str, Dict[str, float or int]]: A dictionary of song statistics.\n        \"\"\"\n    songs = {}\n    for key, value in harp_data.items():\n        if key.startswith('song_') and (not key.endswith('_completions')) and (not key.endswith('_perfect_completions')) and (not key.endswith('_best_completion')):\n            continue\n        if key.startswith('song_'):\n            parts = key.split('_')\n            song_name = '_'.join(parts[1:-1])\n            stat_type = parts[-1]\n            if song_name not in songs:\n                songs[song_name] = {}\n            songs[song_name][stat_type] = value\n    return songs",
        "orig_context": "```python\n## hypixel_api_lib/member/Quests.py\nclass HarpQuest:\n    \"\"\"\n    Represents the player's Harp Quest data.\n\n    Attributes:\n        selected_song (str): The currently selected song.\n        selected_song_epoch (int): The epoch time when the song was selected.\n        songs (Dict[str, Dict[str, float or int]]): A dictionary containing song statistics.\n        claimed_talisman (bool): Whether the talisman has been claimed.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        harp_data = data.get('harp_quest', {})\n        self.selected_song: str | None = harp_data.get('selected_song')\n        self.selected_song_epoch: int | None = harp_data.get('selected_song_epoch')\n        self.claimed_talisman: bool = harp_data.get('claimed_talisman', False)\n        self.songs: dict[str,dict[str,float|int]] = self._extract_song_stats(harp_data)\n\n    def _extract_song_stats(self, harp_data: dict) -> dict[str,dict[str,float|int]]:\n        \"\"\"\n        Extracts song statistics from the harp quest data.\n\n        Args:\n            harp_data (dict): The harp quest data.\n\n        Returns:\n            Dict[str, Dict[str, float or int]]: A dictionary of song statistics.\n        \"\"\"\n        songs = {}\n        for key, value in harp_data.items():\n            if key.startswith('song_') and not key.endswith('_completions') and not key.endswith('_perfect_completions') and not key.endswith('_best_completion'):\n                continue\n            if key.startswith('song_'):\n                parts = key.split('_')\n                song_name = '_'.join(parts[1:-1])\n                stat_type = parts[-1]\n                if song_name not in songs:\n                    songs[song_name] = {}\n                songs[song_name][stat_type] = value\n        return songs\n\n    def get_song_stats(self, song_name: str) -> dict[str,float|int]:\n        \"\"\"\n        Get statistics for a specific song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            Dict[str, float or int]: A dictionary of statistics for the song.\n        \"\"\"\n        return self.songs.get(song_name, {})\n\n    def get_best_completion(self, song_name: str) -> float | None:\n        \"\"\"\n        Get the best completion percentage for a song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            Optional[float]: The best completion percentage, or None if not found.\n        \"\"\"\n        return self.songs.get(song_name, {}).get('best_completion')\n\n    def get_total_completions(self, song_name: str) -> int:\n        \"\"\"\n        Get the total number of completions for a song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            int: The number of completions.\n        \"\"\"\n        return self.songs.get(song_name, {}).get('completions', 0)\n\n    def get_perfect_completions(self, song_name: str) -> int:\n        \"\"\"\n        Get the number of perfect completions for a song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            int: The number of perfect completions.\n        \"\"\"\n        return self.songs.get(song_name, {}).get('perfect_completions', 0)\n\n    def __str__(self) -> str:\n        return (\n            f\"HarpQuest(selected_song={self.selected_song}, claimed_talisman={self.claimed_talisman}, \"\n            f\"songs_completed={len(self.songs)})\"\n        )\n\n```\n\n\n",
        "eval_script": "## hypixel_api_lib/member/Quests.py\nclass HarpQuest:\n    \"\"\"\n    Represents the player's Harp Quest data.\n\n    Attributes:\n        selected_song (str): The currently selected song.\n        selected_song_epoch (int): The epoch time when the song was selected.\n        songs (Dict[str, Dict[str, float or int]]): A dictionary containing song statistics.\n        claimed_talisman (bool): Whether the talisman has been claimed.\n    \"\"\"\n\n    def __init__(self, data: dict) -> None:\n        harp_data = data.get('harp_quest', {})\n        self.selected_song: str | None = harp_data.get('selected_song')\n        self.selected_song_epoch: int | None = harp_data.get('selected_song_epoch')\n        self.claimed_talisman: bool = harp_data.get('claimed_talisman', False)\n        self.songs: dict[str,dict[str,float|int]] = self._extract_song_stats(harp_data)\n\n    def _extract_song_stats(self, harp_data: dict) -> dict[str,dict[str,float|int]]:\n        \"\"\"\n        Extracts song statistics from the harp quest data.\n\n        Args:\n            harp_data (dict): The harp quest data.\n\n        Returns:\n            Dict[str, Dict[str, float or int]]: A dictionary of song statistics.\n        \"\"\"\n        songs = {}\n        for key, value in harp_data.items():\n            if key.startswith('song_') and not key.endswith('_completions') and not key.endswith('_perfect_completions') and not key.endswith('_best_completion'):\n                continue\n            if key.startswith('song_'):\n                parts = key.split('_')\n                song_name = '_'.join(parts[1:-1])\n                stat_type = parts[-1]\n                if song_name not in songs:\n                    songs[song_name] = {}\n                songs[song_name][stat_type] = value\n        return songs\n\n\n    def extract_song_stats(self, harp_data: dict) -> dict[str,dict[str,float|int]]:\n        \"\"\"\n        Public method to extract song statistics using the private method.\n\n        Args:\n            harp_data (dict): The harp quest data.\n\n        Returns:\n            Dict[str, Dict[str, float or int]]: A dictionary of song statistics.\n        \"\"\"\n        return self._extract_song_stats(harp_data)\n\n    def get_song_stats(self, song_name: str) -> dict[str,float|int]:\n        \"\"\"\n        Get statistics for a specific song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            Dict[str, float or int]: A dictionary of statistics for the song.\n        \"\"\"\n        return self.songs.get(song_name, {})\n\n    def get_best_completion(self, song_name: str) -> float | None:\n        \"\"\"\n        Get the best completion percentage for a song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            Optional[float]: The best completion percentage, or None if not found.\n        \"\"\"\n        return self.songs.get(song_name, {}).get('best_completion')\n\n    def get_total_completions(self, song_name: str) -> int:\n        \"\"\"\n        Get the total number of completions for a song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            int: The number of completions.\n        \"\"\"\n        return self.songs.get(song_name, {}).get('completions', 0)\n\n    def get_perfect_completions(self, song_name: str) -> int:\n        \"\"\"\n        Get the number of perfect completions for a song.\n\n        Args:\n            song_name (str): The name of the song.\n\n        Returns:\n            int: The number of perfect completions.\n        \"\"\"\n        return self.songs.get(song_name, {}).get('perfect_completions', 0)\n\n    def __str__(self) -> str:\n        return (\n            f\"HarpQuest(selected_song={self.selected_song}, claimed_talisman={self.claimed_talisman}, \"\n            f\"songs_completed={len(self.songs)})\"\n        )\n\ndef test__extract_song_stats():\n    # Test case 1: Basic functionality\n    data1 = {\n        'harp_quest': {\n            'song_1_completions': 5,\n            'song_1_perfect_completions': 2,\n            'song_1_best_completion': 95.5\n        }\n    }\n    harp_quest1 = HarpQuest(data1)\n    old_stats1 = harp_quest1._extract_song_stats(data1['harp_quest'])\n    new_stats1 = harp_quest1._extract_song_stats_new_implementation(data1['harp_quest'])\n    assert old_stats1 == new_stats1, \"Test case 1 failed\"\n\n    # Test case 2: Multiple songs\n    data2 = {\n        'harp_quest': {\n            'song_1_completions': 3,\n            'song_2_completions': 7,\n            'song_2_perfect_completions': 3\n        }\n    }\n    harp_quest2 = HarpQuest(data2)\n    old_stats2 = harp_quest2._extract_song_stats(data2['harp_quest'])\n    new_stats2 = harp_quest2._extract_song_stats_new_implementation(data2['harp_quest'])\n    assert old_stats2 == new_stats2, \"Test case 2 failed\"\n\n    # Test case 3: No song data\n    data3 = {'harp_quest': {}}\n    harp_quest3 = HarpQuest(data3)\n    old_stats3 = harp_quest3._extract_song_stats(data3['harp_quest'])\n    new_stats3 = harp_quest3._extract_song_stats_new_implementation(data3['harp_quest'])\n    assert old_stats3 == new_stats3, \"Test case 3 failed\"\n\n    # Test case 4: Invalid keys\n    data4 = {\n        'harp_quest': {\n            'invalid_key': 10,\n            'song_1_completions': 4\n        }\n    }\n    harp_quest4 = HarpQuest(data4)\n    old_stats4 = harp_quest4._extract_song_stats(data4['harp_quest'])\n    new_stats4 = harp_quest4._extract_song_stats_new_implementation(data4['harp_quest'])\n    assert old_stats4 == new_stats4, \"Test case 4 failed\"\n\n    # Test case 5: Mixed valid and invalid keys\n    data5 = {\n        'harp_quest': {\n            'song_1_completions': 2,\n            'invalid_song_2_completions': 5,\n            'song_2_best_completion': 88.0\n        }\n    }\n    harp_quest5 = HarpQuest(data5)\n    old_stats5 = harp_quest5._extract_song_stats(data5['harp_quest'])\n    new_stats5 = harp_quest5._extract_song_stats_new_implementation(data5['harp_quest'])\n    assert old_stats5 == new_stats5, \"Test case 5 failed\"\n\n    # Test case 6: Empty strings and zero values\n    data6 = {\n        'harp_quest': {\n            'song_1_completions': 0,\n            'song_1_perfect_completions': 0,\n            'song_1_best_completion': 0.0\n        }\n    }\n    harp_quest6 = HarpQuest(data6)\n    old_stats6 = harp_quest6._extract_song_stats(data6['harp_quest'])\n    new_stats6 = harp_quest6._extract_song_stats_new_implementation(data6['harp_quest'])\n    assert old_stats6 == new_stats6, \"Test case 6 failed\"\n\n    # Test case 7: Duplicate song names\n    data7 = {\n        'harp_quest': {\n            'song_1_completions': 5,\n            'song_1_completions': 3,\n            'song_1_perfect_completions': 1\n        }\n    }\n    harp_quest7 = HarpQuest(data7)\n    old_stats7 = harp_quest7._extract_song_stats(data7['harp_quest'])\n    new_stats7 = harp_quest7._extract_song_stats_new_implementation(data7['harp_quest'])\n    assert old_stats7 == new_stats7, \"Test case 7 failed\"\n\n    # Test case 8: Non-standard characters in song names\n    data8 = {\n        'harp_quest': {\n            'song_@_completions': 4,\n            'song_@_perfect_completions': 2\n        }\n    }\n    harp_quest8 = HarpQuest(data8)\n    old_stats8 = harp_quest8._extract_song_stats(data8['harp_quest'])\n    new_stats8 = harp_quest8._extract_song_stats_new_implementation(data8['harp_quest'])\n    assert old_stats8 == new_stats8, \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test__extract_song_stats()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions iterate over the `harp_data` dictionary, filter keys that start with 'song_' and end with specific suffixes, and then construct a nested dictionary structure to store song statistics. The logic for splitting the key and organizing the data into the `songs` dictionary is the same in both functions. Additionally, the test cases provided in the code verify that the functionality of the REVISED FUNCTION matches the expected behavior of the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `_extract_song_stats` function returns a dictionary of song statistics, satisfying the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases check the return values of `_extract_song_stats` and `_extract_song_stats_new_implementation` by comparing them directly using assertions. There are no checks for printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `_extract_song_stats` and `_extract_song_stats_new_implementation` directly. This ensures that the new implementation must have exactly the same functionality to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the outputs of the two implementations. Since `_extract_song_stats` has return values, using assertions to compare these return values is reasonable, satisfying this condition.\n\n5. **CONDITION 5**: The test cases cover various scenarios, including basic functionality, multiple songs, no song data, invalid keys, mixed valid and invalid keys, empty strings and zero values, duplicate song names, and non-standard characters in song names. These scenarios are non-trivial and ensure comprehensive testing of the function's behavior, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "5a3e123dbaf41c8be2735bc06313373f13c3bc8a"
    },
    {
        "func_name": "Temperature.configure",
        "idx": "576",
        "repo_name": "jheilman-resoundant___LabjackT7",
        "func_path": "labjackt7/temperature.py",
        "orig_func": "def configure(self, pos_ch: int, neg_ch: int, thermocouple_type: str, arange=0.05):\n    \"\"\" Enables temperature sensing on a given channel pair for \n            thermocouple_type = 'J' or 'K'.  \n        \"\"\"\n    kind = {'J': 21, 'K': 22}[thermocouple_type]\n    self.labjack._command(f'AIN{pos_ch}_EF_INDEX', kind)\n    self.labjack._write_dict({f'AIN{pos_ch}_EF_INDEX': kind, f'AIN{pos_ch}_EF_CONFIG_B': 60052, f'AIN{pos_ch}_EF_CONFIG_D': 1, f'AIN{pos_ch}_EF_CONFIG_E': 0, f'AIN{pos_ch}_NEGATIVE_CH': neg_ch, f'AIN{pos_ch}_RANGE': arange})",
        "orig_context": "```python\n## labjackt7/temperature.py\nclass Temperature:\n    def __init__(self, labjack):\n        self.labjack = labjack\n\n    def configure(self, pos_ch:int, neg_ch:int, thermocouple_type:str, arange=0.05):\n        ''' Enables temperature sensing on a given channel pair for \n            thermocouple_type = 'J' or 'K'.  \n        '''\n        kind = {'J': 21, 'K': 22}[thermocouple_type]\n        self.labjack._command(f'AIN{pos_ch}_EF_INDEX', kind)\n        self.labjack._write_dict({f'AIN{pos_ch}_EF_INDEX': kind,\n                                  f'AIN{pos_ch}_EF_CONFIG_B': 60052,\n                                  f'AIN{pos_ch}_EF_CONFIG_D': 1,\n                                  f'AIN{pos_ch}_EF_CONFIG_E': 0,\n                                  f'AIN{pos_ch}_NEGATIVE_CH': neg_ch,\n                                  f'AIN{pos_ch}_RANGE': arange\n                                  })\n\n    def temp_in(self, ch):\n        ''' Returns the temperature of a given channel in degC. '''\n        return self.labjack._query(f'AIN{ch}_EF_READ_A') - 273.15\n\n```\n\n\n",
        "eval_script": "# Mock class to simulate the labjack object\nclass MockLabJack:\n    def __init__(self):\n        self.commands = []\n        self.configs = []\n\n    def _command(self, command, value):\n        self.commands.append((command, value))\n\n    def _write_dict(self, config_dict):\n        self.configs.append(config_dict)\n\n    def _query(self, query):\n        # Return a mock temperature value in Kelvin\n        return 300.15  # Equivalent to 27\u00b0C\n\n# Original Temperature class\nclass Temperature:\n    def __init__(self, labjack):\n        self.labjack = labjack\n\n    def configure(self, pos_ch:int, neg_ch:int, thermocouple_type:str, arange=0.05):\n        ''' Enables temperature sensing on a given channel pair for \n            thermocouple_type = 'J' or 'K'.  \n        '''\n        kind = {'J': 21, 'K': 22}[thermocouple_type]\n        self.labjack._command(f'AIN{pos_ch}_EF_INDEX', kind)\n        self.labjack._write_dict({f'AIN{pos_ch}_EF_INDEX': kind,\n                                  f'AIN{pos_ch}_EF_CONFIG_B': 60052,\n                                  f'AIN{pos_ch}_EF_CONFIG_D': 1,\n                                  f'AIN{pos_ch}_EF_CONFIG_E': 0,\n                                  f'AIN{pos_ch}_NEGATIVE_CH': neg_ch,\n                                  f'AIN{pos_ch}_RANGE': arange\n                                  })\n\n    def temp_in(self, ch):\n        ''' Returns the temperature of a given channel in degC. '''\n        return self.labjack._query(f'AIN{ch}_EF_READ_A') - 273.15\n\n\ndef test_configure():\n    # Test case 1\n    labjack1 = MockLabJack()\n    labjack2 = MockLabJack()\n    temp1 = Temperature(labjack1)\n    temp2 = Temperature(labjack2)\n\n    temp1.configure(0, 1, 'J')\n    temp2.configure_new_implementation(0, 1, 'J')\n\n    assert labjack1.commands == labjack2.commands, \"Commands do not match for test case 1\"\n    assert labjack1.configs == labjack2.configs, \"Configurations do not match for test case 1\"\n\n    # Test case 2\n    labjack1 = MockLabJack()\n    labjack2 = MockLabJack()\n    temp1 = Temperature(labjack1)\n    temp2 = Temperature(labjack2)\n\n    temp1.configure(2, 3, 'K', 0.1)\n    temp2.configure_new_implementation(2, 3, 'K', 0.1)\n\n    assert labjack1.commands == labjack2.commands, \"Commands do not match for test case 2\"\n    assert labjack1.configs == labjack2.configs, \"Configurations do not match for test case 2\"\n\n    # Test case 3\n    labjack1 = MockLabJack()\n    labjack2 = MockLabJack()\n    temp1 = Temperature(labjack1)\n    temp2 = Temperature(labjack2)\n\n    temp1.configure(4, 5, 'J', 0.2)\n    temp2.configure_new_implementation(4, 5, 'J', 0.2)\n\n    assert labjack1.commands == labjack2.commands, \"Commands do not match for test case 3\"\n    assert labjack1.configs == labjack2.configs, \"Configurations do not match for test case 3\"\n\n    # Test case 4: Different thermocouple type with default range\n    labjack1 = MockLabJack()\n    labjack2 = MockLabJack()\n    temp1 = Temperature(labjack1)\n    temp2 = Temperature(labjack2)\n\n    temp1.configure(6, 7, 'K')\n    temp2.configure_new_implementation(6, 7, 'K')\n\n    assert labjack1.commands == labjack2.commands, \"Commands do not match for test case 4\"\n    assert labjack1.configs == labjack2.configs, \"Configurations do not match for test case 4\"\n\n    # Test case 5: Edge case with maximum channel numbers\n    labjack1 = MockLabJack()\n    labjack2 = MockLabJack()\n    temp1 = Temperature(labjack1)\n    temp2 = Temperature(labjack2)\n\n    temp1.configure(8, 9, 'J', 0.01)\n    temp2.configure_new_implementation(8, 9, 'J', 0.01)\n\n    assert labjack1.commands == labjack2.commands, \"Commands do not match for test case 5\"\n    assert labjack1.configs == labjack2.configs, \"Configurations do not match for test case 5\"\n\n    # Test case 6: Different range value\n    labjack1 = MockLabJack()\n    labjack2 = MockLabJack()\n    temp1 = Temperature(labjack1)\n    temp2 = Temperature(labjack2)\n\n    temp1.configure(10, 11, 'K', 0.5)\n    temp2.configure_new_implementation(10, 11, 'K', 0.5)\n\n    assert labjack1.commands == labjack2.commands, \"Commands do not match for test case 6\"\n    assert labjack1.configs == labjack2.configs, \"Configurations do not match for test case 6\"\n\nif __name__ == \"__main__\":\n    test_configure()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is the same as the ORIGINAL FUNCTION. Both functions are named `configure` and have the same parameters and default values. They perform the same operations: determining the `kind` based on the `thermocouple_type`, sending a command to the `labjack` object, and writing a configuration dictionary to the `labjack` object. The test cases provided in the CODE are designed to compare the behavior of the `configure` function with a hypothetical `configure_new_implementation`, but since the `configure_new_implementation` is not defined in the provided CODE, the test cases are irrelevant to the comparison of the `configure` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `configure` function modifies the state of the `MockLabJack` object by appending commands and configurations to its `commands` and `configs` lists. This satisfies the condition as it modifies input arguments (the `MockLabJack` instance).\n\n- CONDITION 2: The test cases check the state of the `commands` and `configs` lists of the `MockLabJack` instances, which are modified by the `configure` function. There is no checking of printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the `commands` and `configs` lists of two `MockLabJack` instances, one modified by `configure` and the other by `configure_new_implementation`. This ensures that `configure_new_implementation` must have the exact same functionality as `configure` to pass all tests, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the states of the `commands` and `configs` lists, which are reasonable since `configure` modifies these lists. There are no assertions comparing return values, which is appropriate since `configure` does not return any values. This condition is satisfied.\n\n- CONDITION 5: The test cases cover various scenarios, including different thermocouple types, channel numbers, and range values. This variety ensures that the tests are non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "1c07be5cbaaba5ea111eeebb9bab3b25a59b1d4a"
    },
    {
        "func_name": "Digital.state",
        "idx": "584",
        "repo_name": "jheilman-resoundant___LabjackT7",
        "func_path": "labjackt7/digital.py",
        "orig_func": "@staticmethod\ndef state(channels, states):\n    \"\"\" Returns a bitmask representation of the passed channels and states. \"\"\"\n    bitmask = 0\n    for j in range(len(channels)):\n        bitmask = bitmask | int(states[j]) << channels[j]\n    return bitmask",
        "orig_context": "```python\n## labjackt7/digital.py\nimport numpy as np\n\nclass Digital():\n    ''' Handles digital subsystems of the LabJack T-series device family, covering\n        digital in/out, simultaneous DIO_STATE updates, and streaming.\n    '''\n    def __init__(self, labjack):\n        self.labjack = labjack\n\n    def din(self, channel) -> int:\n        ''' Read a digital signal.\n\n            Args:\n                channel (str or int): a digital channel on the LabJack, e.g. 'FIO4'.\n            Return:\n                state (int): 1 or 0\n        '''\n        channel = self._chan_to_str(channel)\n        return int(self.labjack._query(channel))\n\n    def dout(self, channel, state:int):\n        ''' Output a digital signal.\n\n            Args:\n                channel (str or int): a digital channel on the LabJack, e.g. 'FIO4'.\n                state (int): 1 or 0\n        '''\n        channel = self._chan_to_str(channel)\n        self.labjack._command(channel, state)\n\n    def dout_multi(self, channels:list, states:list):\n        ''' Set multiple digital channels simultaneously. \n        \n            Args:\n                channels (list of numbers): numerical channels (str NOT supported)\n                states (list of 1/0): 1's or 0's\n        '''\n        bitstate = 0\n        for i in range(len(channels)):\n            bitstate = bitstate | (states[i] << channels[i])\n\n        bitmask = self.bitmask(channels)\n        self.labjack._write_dict({'DIO_INHIBIT':   0x7FFFFF-bitmask,\n                                  'DIO_DIRECTION': bitmask,\n                                  'DIO_STATE':     bitstate})\n\n    @staticmethod\n    def inhibit_string(channels):\n        inhibit = ''\n        for i in range(23):\n            if 23-i-1 in channels:\n                inhibit += '0'\n            else:\n                inhibit += '1'\n        return inhibit\n\n    @staticmethod\n    def array_to_bitmask(arr, channels):\n        ''' Convert multidimensional array with one column for each channel to an array of bitmasks. '''\n        y = np.zeros(len(arr))\n\n        inhibit = 0x7FFFFF-Digital.bitmask(channels)\n        inhibit_string = Digital.inhibit_string(channels)\n        for i in range(len(arr)):\n            states = arr[i,:]\n            bitmask = Digital.state(channels, states)\n            lower_bits = format(bitmask, '#010b')\n            y[i] = int(inhibit_string[-8:]+lower_bits[2:], 2)\n        return y\n\n    @staticmethod\n    def bitmask(channels):\n        bm = 0\n        for ch in channels:\n            bm |= 1 << ch\n        return bm\n\n    @staticmethod\n    def state(channels, states):\n        ''' Returns a bitmask representation of the passed channels and states. '''\n        bitmask = 0\n        for j in range(len(channels)):\n            bitmask = bitmask | (int(states[j]) << channels[j])\n        return bitmask\n\n    def _chan_to_dio(self, channel):\n        if type(channel) is int:\n            channel = f'DIO{channel}'\n        return channel\n\n```\n\n\n",
        "eval_script": "## labjackt7/digital.py\nimport numpy as np\n\nclass MockLabJack:\n    def _query(self, channel):\n        # Mock response for digital input query\n        return 1  # Always return 1 for simplicity\n\n    def _command(self, channel, state):\n        # Mock command execution for digital output\n        print(f\"Command executed on {channel} with state {state}\")\n\n    def _write_dict(self, data):\n        # Mock write dictionary command\n        print(f\"Write dictionary with data: {data}\")\n\nclass Digital():\n    ''' Handles digital subsystems of the LabJack T-series device family, covering\n        digital in/out, simultaneous DIO_STATE updates, and streaming.\n    '''\n    def __init__(self, labjack):\n        self.labjack = labjack\n\n    def din(self, channel) -> int:\n        ''' Read a digital signal.\n\n            Args:\n                channel (str or int): a digital channel on the LabJack, e.g. 'FIO4'.\n            Return:\n                state (int): 1 or 0\n        '''\n        channel = self._chan_to_str(channel)\n        return int(self.labjack._query(channel))\n\n    def dout(self, channel, state:int):\n        ''' Output a digital signal.\n\n            Args:\n                channel (str or int): a digital channel on the LabJack, e.g. 'FIO4'.\n                state (int): 1 or 0\n        '''\n        channel = self._chan_to_str(channel)\n        self.labjack._command(channel, state)\n\n    def dout_multi(self, channels:list, states:list):\n        ''' Set multiple digital channels simultaneously. \n        \n            Args:\n                channels (list of numbers): numerical channels (str NOT supported)\n                states (list of 1/0): 1's or 0's\n        '''\n        bitstate = 0\n        for i in range(len(channels)):\n            bitstate = bitstate | (states[i] << channels[i])\n\n        bitmask = self.bitmask(channels)\n        self.labjack._write_dict({'DIO_INHIBIT':   0x7FFFFF-bitmask,\n                                  'DIO_DIRECTION': bitmask,\n                                  'DIO_STATE':     bitstate})\n\n    @staticmethod\n    def inhibit_string(channels):\n        inhibit = ''\n        for i in range(23):\n            if 23-i-1 in channels:\n                inhibit += '0'\n            else:\n                inhibit += '1'\n        return inhibit\n\n    @staticmethod\n    def array_to_bitmask(arr, channels):\n        ''' Convert multidimensional array with one column for each channel to an array of bitmasks. '''\n        y = np.zeros(len(arr))\n\n        inhibit = 0x7FFFFF-Digital.bitmask(channels)\n        inhibit_string = Digital.inhibit_string(channels)\n        for i in range(len(arr)):\n            states = arr[i,:]\n            bitmask = Digital.state(channels, states)\n            lower_bits = format(bitmask, '#010b')\n            y[i] = int(inhibit_string[-8:]+lower_bits[2:], 2)\n        return y\n\n    @staticmethod\n    def bitmask(channels):\n        bm = 0\n        for ch in channels:\n            bm |= 1 << ch\n        return bm\n\n    @staticmethod\n    def state(channels, states):\n        ''' Returns a bitmask representation of the passed channels and states. '''\n        bitmask = 0\n        for j in range(len(channels)):\n            bitmask = bitmask | (int(states[j]) << channels[j])\n        return bitmask\n\n\n    def _chan_to_str(self, channel):\n        if type(channel) is int:\n            channel = f'DIO{channel}'\n        return channel\n\ndef test_state():\n    # Test case 1: Simple case\n    assert Digital.state([0, 1, 2], [1, 0, 1]) == Digital.state_new_implementation([0, 1, 2], [1, 0, 1])\n    # Test case 2: All channels set to 1\n    assert Digital.state([0, 1, 2], [1, 1, 1]) == Digital.state_new_implementation([0, 1, 2], [1, 1, 1])\n    # Test case 3: Alternating states\n    assert Digital.state([0, 1, 2, 3], [1, 0, 1, 0]) == Digital.state_new_implementation([0, 1, 2, 3], [1, 0, 1, 0])\n    # Test case 4: Empty input\n    assert Digital.state([], []) == Digital.state_new_implementation([], [])\n    # Test case 5: Single channel\n    assert Digital.state([0], [1]) == Digital.state_new_implementation([0], [1])\n    # Test case 6: Maximum channel\n    assert Digital.state([22], [1]) == Digital.state_new_implementation([22], [1])\n    # Test case 7: All channels off\n    assert Digital.state([0, 1, 2], [0, 0, 0]) == Digital.state_new_implementation([0, 1, 2], [0, 0, 0])\n    # Test case 8: Non-sequential channels\n    assert Digital.state([2, 0, 3], [1, 0, 1]) == Digital.state_new_implementation([2, 0, 3], [1, 0, 1])\n    # Test case 9: Large number of channels\n    channels = list(range(10))\n    states = [1] * 10\n    assert Digital.state(channels, states) == Digital.state_new_implementation(channels, states)\n\nif __name__ == \"__main__\":\n    test_state()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `state` method within the `Digital` class. Upon comparing the REVISED FUNCTION with the ORIGINAL FUNCTION, we can see that both functions perform the same operation: they iterate over the `channels` list and use the corresponding `states` values to construct a bitmask. The bitmask is constructed by shifting the state value left by the channel number and combining it with the existing bitmask using a bitwise OR operation. The only difference is the addition of parentheses around the bitwise shift operation in the REVISED FUNCTION, which does not affect the functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `state` function returns a bitmask representation of the passed channels and states, which means it has return values. This satisfies CONDITION 1.\n\n2. **CONDITION 2**: The test cases in `test_state` use assertions to compare the return values of `state` and `state_new_implementation`. There are no checks for printed or logged contents, satisfying CONDITION 2.\n\n3. **CONDITION 3**: The test cases cover various scenarios such as simple cases, all channels set to 1, alternating states, empty input, single channel, maximum channel, all channels off, non-sequential channels, and a large number of channels. These tests ensure that `state_new_implementation` can only pass if it has the exact same functionality as `state`, satisfying CONDITION 3.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of `state` and `state_new_implementation`, which is appropriate given that `state` has return values. This satisfies CONDITION 4.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover a wide range of input scenarios, including edge cases like empty input and maximum channel. This satisfies CONDITION 5.",
            "answer": "yes"
        },
        "commit_id": "1c07be5cbaaba5ea111eeebb9bab3b25a59b1d4a"
    },
    {
        "func_name": "Stream.stream_start",
        "idx": "585",
        "repo_name": "jheilman-resoundant___LabjackT7",
        "func_path": "labjackt7/stream.py",
        "orig_func": "def stream_start(self, channels: list, scan_rate):\n    self.stop()\n    scan_list = ljm.namesToAddresses(len(channels), channels)[0]\n    scans_per_read = int(scan_rate / 2)\n    ljm.eStreamStart(self.labjack.handle, scans_per_read, len(channels), scan_list, scan_rate)",
        "orig_context": "```python\n## labjackt7/stream.py\nfrom labjack import ljm\n\nimport numpy as np\n\nfrom datetime import datetime\n\nclass Stream():\n    def __init__(self, labjack):\n        self.labjack = labjack\n        self.configure()\n\n    def configure(self, settling_time=0, resolution_index=0, clock_source=0):\n        self.stop()\n        self.labjack._write_dict({\n            'STREAM_SETTLING_US': settling_time,\n            'STREAM_RESOLUTION_INDEX': resolution_index,\n            'STREAM_CLOCK_SOURCE': clock_source\n        })\n        #STREAM_TRIGGER_INDEX\n\n    def configure_from_dict(self, stream_options: dict):\n        self.labjack._write_dict(stream_options)\n\n    def set_inhibit(self, channels):\n        bitmask = self.labjack.digital.bitmask(channels)\n        inhibit = 0x7FFFFF-bitmask\n\n        self.labjack._write_dict({'DIO_INHIBIT': inhibit,\n                                  'DIO_DIRECTION': bitmask\n                                  })\n\n    def stop(self):\n        ''' Stop streaming if currently running '''\n        try:\n            ljm.eStreamStop(self.labjack.handle)\n        except:\n            pass\n\n    #disabled because removed scipy.signal.resample()\n    # def resample(self, array, period, max_samples = 8191):\n    #     ''' Compute optimum scan rate and number of samples '''\n    #     max_speed = self._device_scanRate()\n    #     cutoff = max_samples / max_speed\n    #     if period >= cutoff:\n    #         samples = max_samples\n    #         scanRate = int(samples/period)\n    #     else:\n    #         scanRate = max_speed\n    #         samples = int(period*scanRate)\n    #     # stream = resample(array, samples)\n    #     stream = []\n    #     scanRate /= array.shape[1]    ## divide by number of channels being streamed\n    #     return stream, scanRate\n\n    def stream_burst(self, aScanListNames:list, scanRate:int=0, scanTime_s:float=1) -> list:\n        ''' \n            Args:\n                scanListNames: [\"AIN0\", \"AIN1\"] etc\n                scan_rate: 0 for max rate\n                scan_time_s: number of seconds to sample (default 1)\n        '''\n        aScanList = ljm.namesToAddresses(len(aScanListNames), aScanListNames)[0]  # Names to addresses for streamBurst\n        if (scanRate <= 0) or (scanRate > self._device_scanRate()):\n            scanRate = self._device_scanRate()\n        num_scans = int(scanTime_s*scanRate)  # Number of scans to perform\n        num_scans = num_scans - (num_scans % len(aScanList)) # ensure all channels have equal samples\n        start = datetime.now()\n        scanRate, aData = ljm.streamBurst(self.labjack.handle, len(aScanList), aScanList, scanRate, num_scans)\n        end = datetime.now()\n        if False:\n            print(f\"Channels: {len(aScanListNames)},  Samples per Ch: {len(aData)/len(aScanListNames)}, ScanRate: {scanRate}, Elapsed Time = {(end - start).seconds + float((end - start).microseconds) / 1000000}s\" )\n        if aData.count(-9999.0) > 0:\n            print(f\"WARNING: some samples were skipped! Total skips, all channels) = f{aData.count(-9999.0)}\")\n        return scanRate, self._reshape_data(aData, len(aScanList))\n\n    def stream_start(self, channels:list, scan_rate):\n        self.stop()\n        scan_list = ljm.namesToAddresses(len(channels), channels)[0]\n\n        scans_per_read = int(scan_rate/2)\n        ljm.eStreamStart(self.labjack.handle, scans_per_read, len(channels), scan_list, scan_rate)\n\n    def stream_read(self):\n        return ljm.eStreamRead(self.labjack.handle)\n\n    def aout(self, channels, data, scanRate, loop=0):\n        self._start([1000+2*ch for ch in channels], data, scanRate, loop=loop, dtype='F32')\n\n    def dout(self, data, scanRate, loop=0):\n        self._start([2500], data, scanRate, loop=loop, dtype='U16')\n\n    def _start(self, channels, data, scanRate, loop = 0, dtype='F32'):\n        self.stop()\n        n = np.ceil(np.log10(2*(1+len(data)))/np.log10(2))\n        buffer_size = 2**n\n        i = 0\n        scan_list = []\n        for ch in channels:\n            self.labjack._write_dict({\n                f'STREAM_OUT{i}_TARGET': ch,\n                f'STREAM_OUT{i}_BUFFER_SIZE': buffer_size,\n                f'STREAM_OUT{i}_ENABLE': 1\n            })\n\n            target = ['STREAM_OUT%i_BUFFER_%s'%(i, dtype)] * len(data)\n            self.labjack._write_array(target, list(data[:, i]))\n\n            self.labjack._write_dict({\n                f'STREAM_OUT{i}_LOOP_SIZE': loop*len(data),\n                f'STREAM_OUT{i}_SET_LOOP': 1\n            })\n            scan_list.append(4800+i)\n            i += 1\n        scanRate = ljm.eStreamStart(self.labjack.handle, 1, len(scan_list), scan_list, scanRate)\n\n    def set_trigger(self, ch):\n        if ch is None:\n            self.labjack._command(\"STREAM_TRIGGER_INDEX\", 0) # disable triggered stream\n        else:\n            self.labjack._write_dict({f\"DIO{ch}_EF_ENABLE\": 0\n                              })\n            self.labjack._write_dict({\n                f\"DIO{ch}_EF_INDEX\": 3,\n                f\"DIO{ch}_EF_OPTIONS\": 12,   ## current value: 0 (PWM Out)\n                # f\"DIO{ch}_EF_VALUE_A\": 2,\n                f\"DIO{ch}_EF_CONFIG_A\": 1,\n                f\"DIO{ch}_EF_CONFIG_B\": 1,\n                f\"DIO{ch}_EF_ENABLE\": 1,\n                \"STREAM_TRIGGER_INDEX\": 2000+ch\n                })\n            ljm.writeLibraryConfigS('LJM_STREAM_RECEIVE_TIMEOUT_MS',0)  #disable timeout\n\n    def _device_scanRate(self):\n        if self.labjack.device_type == ljm.constants.dtT7:\n            return 100000\n        elif self.labjack.device_type == ljm.constants.dtT4:\n            return 40000\n\n    def _reshape_data(self, aData:list, num_channels:int):\n        ''' splits scan data into list of lists'''\n        channels = []\n        ch = []\n        for i in range(num_channels):\n            ch = aData[i:][::num_channels]\n            channels.append(ch)\n        return channels\n\n```\n\n\n",
        "eval_script": "## labjackt7/stream.py\nimport numpy as np\nfrom datetime import datetime\n\n# Mock ljm module\nclass MockLJM:\n    def eStreamStop(self, handle):\n        print(\"Mock eStreamStop called\")\n\n    def eStreamStart(self, handle, scans_per_read, num_channels, scan_list, scan_rate):\n        print(\"Mock eStreamStart called\")\n        return scan_rate\n\n    def eStreamRead(self, handle):\n        print(\"Mock eStreamRead called\")\n        return []\n\n    def namesToAddresses(self, num_addresses, names):\n        print(\"Mock namesToAddresses called\")\n        return list(range(num_addresses)), []\n\n    def streamBurst(self, handle, num_channels, aScanList, scanRate, num_scans):\n        print(\"Mock streamBurst called\")\n        return scanRate, [-9999.0] * num_scans\n\n    def writeLibraryConfigS(self, config_name, value):\n        print(f\"Mock writeLibraryConfigS called with {config_name} = {value}\")\n\n    class constants:\n        dtT7 = 7\n        dtT4 = 4\n\nljm = MockLJM()\n\n# Mock LabJack class\nclass MockLabJack:\n    def __init__(self):\n        self.handle = 1\n        self.device_type = ljm.constants.dtT7\n        self.digital = self\n\n    def _write_dict(self, config):\n        print(f\"Mock _write_dict called with {config}\")\n\n    def bitmask(self, channels):\n        print(f\"Mock bitmask called with {channels}\")\n        return 0x7FFFFF\n\n# Stream class as provided\nclass Stream():\n    def __init__(self, labjack):\n        self.labjack = labjack\n        self.configure()\n\n    def configure(self, settling_time=0, resolution_index=0, clock_source=0):\n        self.stop()\n        self.labjack._write_dict({\n            'STREAM_SETTLING_US': settling_time,\n            'STREAM_RESOLUTION_INDEX': resolution_index,\n            'STREAM_CLOCK_SOURCE': clock_source\n        })\n\n    def configure_from_dict(self, stream_options: dict):\n        self.labjack._write_dict(stream_options)\n\n    def set_inhibit(self, channels):\n        bitmask = self.labjack.digital.bitmask(channels)\n        inhibit = 0x7FFFFF-bitmask\n\n        self.labjack._write_dict({'DIO_INHIBIT': inhibit,\n                                  'DIO_DIRECTION': bitmask\n                                  })\n\n    def stop(self):\n        ''' Stop streaming if currently running '''\n        try:\n            ljm.eStreamStop(self.labjack.handle)\n        except:\n            pass\n\n    def stream_burst(self, aScanListNames:list, scanRate:int=0, scanTime_s:float=1) -> list:\n        aScanList = ljm.namesToAddresses(len(aScanListNames), aScanListNames)[0]\n        if (scanRate <= 0) or (scanRate > self._device_scanRate()):\n            scanRate = self._device_scanRate()\n        num_scans = int(scanTime_s*scanRate)\n        num_scans = num_scans - (num_scans % len(aScanList))\n        start = datetime.now()\n        scanRate, aData = ljm.streamBurst(self.labjack.handle, len(aScanList), aScanList, scanRate, num_scans)\n        end = datetime.now()\n        if False:\n            print(f\"Channels: {len(aScanListNames)},  Samples per Ch: {len(aData)/len(aScanListNames)}, ScanRate: {scanRate}, Elapsed Time = {(end - start).seconds + float((end - start).microseconds) / 1000000}s\" )\n        if aData.count(-9999.0) > 0:\n            print(f\"WARNING: some samples were skipped! Total skips, all channels) = f{aData.count(-9999.0)}\")\n        return scanRate, self._reshape_data(aData, len(aScanList))\n\n    def stream_start(self, channels:list, scan_rate):\n        self.stop()\n        scan_list = ljm.namesToAddresses(len(channels), channels)[0]\n        scans_per_read = int(scan_rate/2)\n        ljm.eStreamStart(self.labjack.handle, scans_per_read, len(channels), scan_list, scan_rate)\n\n\n    def stream_read(self):\n        return ljm.eStreamRead(self.labjack.handle)\n\n    def aout(self, channels, data, scanRate, loop=0):\n        self._start([1000+2*ch for ch in channels], data, scanRate, loop=loop, dtype='F32')\n\n    def dout(self, data, scanRate, loop=0):\n        self._start([2500], data, scanRate, loop=0, dtype='U16')\n\n    def _start(self, channels, data, scanRate, loop = 0, dtype='F32'):\n        self.stop()\n        n = np.ceil(np.log10(2*(1+len(data)))/np.log10(2))\n        buffer_size = 2**n\n        i = 0\n        scan_list = []\n        for ch in channels:\n            self.labjack._write_dict({\n                f'STREAM_OUT{i}_TARGET': ch,\n                f'STREAM_OUT{i}_BUFFER_SIZE': buffer_size,\n                f'STREAM_OUT{i}_ENABLE': 1\n            })\n\n            target = ['STREAM_OUT%i_BUFFER_%s'%(i, dtype)] * len(data)\n            self.labjack._write_array(target, list(data[:, i]))\n\n            self.labjack._write_dict({\n                f'STREAM_OUT{i}_LOOP_SIZE': loop*len(data),\n                f'STREAM_OUT{i}_SET_LOOP': 1\n            })\n            scan_list.append(4800+i)\n            i += 1\n        scanRate = ljm.eStreamStart(self.labjack.handle, 1, len(scan_list), scan_list, scanRate)\n\n    def set_trigger(self, ch):\n        if ch is None:\n            self.labjack._command(\"STREAM_TRIGGER_INDEX\", 0)\n        else:\n            self.labjack._write_dict({f\"DIO{ch}_EF_ENABLE\": 0\n                              })\n            self.labjack._write_dict({\n                f\"DIO{ch}_EF_INDEX\": 3,\n                f\"DIO{ch}_EF_OPTIONS\": 12,\n                f\"DIO{ch}_EF_CONFIG_A\": 1,\n                f\"DIO{ch}_EF_CONFIG_B\": 1,\n                f\"DIO{ch}_EF_ENABLE\": 1,\n                \"STREAM_TRIGGER_INDEX\": 2000+ch\n                })\n            ljm.writeLibraryConfigS('LJM_STREAM_RECEIVE_TIMEOUT_MS',0)\n\n    def _device_scanRate(self):\n        if self.labjack.device_type == ljm.constants.dtT7:\n            return 100000\n        elif self.labjack.device_type == ljm.constants.dtT4:\n            return 40000\n\n    def _reshape_data(self, aData:list, num_channels:int):\n        channels = []\n        ch = []\n        for i in range(num_channels):\n            ch = aData[i:][::num_channels]\n            channels.append(ch)\n        return channels\n\ndef test_stream_start():\n    labjack = MockLabJack()\n    stream = Stream(labjack)\n\n    # Test case 1: Single channel, standard scan rate\n    channels = [\"AIN0\"]\n    scan_rate = 1000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 1 failed\"\n\n    # Test case 2: Multiple channels, standard scan rate\n    channels = [\"AIN0\", \"AIN1\"]\n    scan_rate = 2000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 2 failed\"\n\n    # Test case 3: Multiple channels, high scan rate\n    channels = [\"AIN0\", \"AIN1\", \"AIN2\"]\n    scan_rate = 50000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 3 failed\"\n\n    # Test case 4: Zero channels\n    channels = []\n    scan_rate = 1000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 4 failed\"\n\n    # Test case 5: Zero scan rate\n    channels = [\"AIN0\"]\n    scan_rate = 0\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 5 failed\"\n\n    # Test case 6: Negative scan rate\n    channels = [\"AIN0\"]\n    scan_rate = -1000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 6 failed\"\n\n    # Test case 7: Maximum channels\n    channels = [f\"AIN{i}\" for i in range(20)]\n    scan_rate = 1000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 7 failed\"\n\n    # Test case 8: Non-standard channel names\n    channels = [\"AINX\", \"AINY\"]\n    scan_rate = 1000\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 8 failed\"\n\n    # Test case 9: Boundary scan rate\n    channels = [\"AIN0\"]\n    scan_rate = stream._device_scanRate()\n    stream.stream_start(channels, scan_rate)\n    result_old = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    stream.stream_start_new_implementation(channels, scan_rate)\n    result_new = ljm.eStreamStart(labjack.handle, int(scan_rate/2), len(channels), ljm.namesToAddresses(len(channels), channels)[0], scan_rate)\n    assert result_old == result_new, \"Test case 9 failed\"\n\nif __name__ == \"__main__\":\n    test_stream_start()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of functionality. Both functions perform the same operations in the same sequence: they first call the `stop()` method, convert channel names to addresses using `ljm.namesToAddresses`, calculate `scans_per_read` as half of `scan_rate`, and finally call `ljm.eStreamStart` with the same parameters. There are no differences in logic or implementation between the two versions of the `stream_start` function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `stream_start` function does not have a return value but interacts with the `ljm` mock object, which is a global variable. This satisfies the condition as it modifies the state of the `ljm` object.\n- [CONDITION 2] The test cases check the results of `ljm.eStreamStart`, which is the function called within `stream_start`. The test does not rely on printed or logged content, satisfying this condition.\n- [CONDITION 3] The test cases compare the results of `ljm.eStreamStart` after calling both `stream_start` and `stream_start_new_implementation`. This ensures that both implementations must have the same effect on the `ljm` object to pass the tests, satisfying this condition.\n- [CONDITION 4] The test cases use the results of `ljm.eStreamStart` for assertions, which is reasonable given that `stream_start` does not return a value. The assertions are based on the state of the `ljm` object, which is appropriate.\n- [CONDITION 5] The test cases cover a variety of scenarios, including different numbers of channels, different scan rates, and edge cases like zero or negative scan rates. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "1c07be5cbaaba5ea111eeebb9bab3b25a59b1d4a"
    },
    {
        "func_name": "Analog._chan_to_ain",
        "idx": "586",
        "repo_name": "jheilman-resoundant___LabjackT7",
        "func_path": "labjackt7/analog.py",
        "orig_func": "def _chan_to_ain(self, channel):\n    if type(channel) is int:\n        channel = f'AIN{channel}'\n    return channel",
        "orig_context": "```python\n## labjackt7/analog.py\nfrom labjack import ljm\n\nclass Analog():\n    def __init__(self, labjack):\n        self.labjack = labjack\n        self.configure()\n\n    def configure(self, range=10):\n        self.labjack._write_dict({\n            \"AIN_ALL_NEGATIVE_CH\" : ljm.constants.GND,\n            \"AIN_ALL_RANGE\" : range\n        })\n\n    def ain(self, channel):\n        ''' Read a channel and return the voltage.\n         \n            Args:\n                channel (int or str): number of the target DAC channel.\n        '''\n        channel = self._chan_to_ain(channel)\n        return self.labjack._query(channel)\n\n    def aout(self, channel, value):\n        ''' Output an analog voltage.\n\n            Args:\n                channel (int or str): number of the target DAC channel.\n                value (float): Voltage in volts.\n        '''\n        channel = self._chan_to_dac(channel)\n        self.labjack._command(channel, value)\n\n    # def TDAC(self, channel, value):\n    #     ''' Output an analog voltage.\n\n    #         Args:\n    #             channel (int): number of the target TDAC channel.\n    #             value (float): Voltage in volts.\n    #             TDAC (bool): If False, use a DAC channel (0-5 V); if True, use a TDAC channel with the LJTick-DAC accessory (+/-10 V).\n    #     '''\n    #     self.labjack._command(\"TDAC%i\"%channel, value)\n\n\n    def _chan_to_ain(self, channel):\n        if type(channel) is int:\n            channel = f'AIN{channel}'\n        return channel\n    \n    def _chan_to_dac(self, channel):\n        if type(channel) is int:\n            channel = f'DAC{channel}'\n        return channel\n\n```\n\n\n",
        "eval_script": "# The debugged PYTHON CODE in one piece.\nclass MockLJM:\n    GND = 0\n\nclass MockLabjack:\n    def _write_dict(self, config):\n        pass\n\n    def _query(self, channel):\n        return 0.0\n\n    def _command(self, channel, value):\n        pass\n\n# Mocking the labjack module\nlabjack = type('labjack', (object,), {'ljm': MockLJM()})\n\nclass Analog():\n    def __init__(self, labjack):\n        self.labjack = labjack\n        self.configure()\n\n    def configure(self, range=10):\n        self.labjack._write_dict({\n            \"AIN_ALL_NEGATIVE_CH\" : labjack.ljm.GND,\n            \"AIN_ALL_RANGE\" : range\n        })\n\n    def ain(self, channel):\n        ''' Read a channel and return the voltage.\n         \n            Args:\n                channel (int or str): number of the target DAC channel.\n        '''\n        channel = self._chan_to_ain(channel)\n        return self.labjack._query(channel)\n\n    def aout(self, channel, value):\n        ''' Output an analog voltage.\n\n            Args:\n                channel (int or str): number of the target DAC channel.\n                value (float): Voltage in volts.\n        '''\n        channel = self._chan_to_dac(channel)\n        self.labjack._command(channel, value)\n\n    @staticmethod\n    def _chan_to_ain(channel):\n        if type(channel) is int:\n            channel = f'AIN{channel}'\n        return channel\n\n\n    def _chan_to_dac(self, channel):\n        if type(channel) is int:\n            channel = f'DAC{channel}'\n        return channel\n\n\ndef test__chan_to_ain():\n    assert Analog._chan_to_ain(0) == Analog._chan_to_ain_new_implementation(0)\n    assert Analog._chan_to_ain('AIN1') == Analog._chan_to_ain_new_implementation('AIN1')\n    assert Analog._chan_to_ain(5) == Analog._chan_to_ain_new_implementation(5)\n\nif __name__ == \"__main__\":\n    test__chan_to_ain()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `_chan_to_ain` is a method that takes a `channel` argument and checks if its type is `int`. If it is, it converts the integer to a string in the format `AIN{channel}`. If the `channel` is already a string, it returns it unchanged. The revised function is a static method with the same logic: it checks if `channel` is an `int`, converts it to the `AIN{channel}` format if true, and returns the `channel` unchanged if it is already a string. Both functions perform the same operations and return the same results for the same inputs.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `_chan_to_ain` has a return value, as it returns a string based on the input channel. This condition is satisfied.\n- CONDITION 2: The test cases use assertions to check the return values of `_chan_to_ain` and `_chan_to_ain_new_implementation`, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `_chan_to_ain` and `_chan_to_ain_new_implementation` for the same inputs, ensuring that the new implementation must have the exact same functionality to pass. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable since `_chan_to_ain` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases check both integer and string inputs, covering different types of input that the function might handle, making the test cases non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "1c07be5cbaaba5ea111eeebb9bab3b25a59b1d4a"
    },
    {
        "func_name": "NightscoutSensor._parse_icon",
        "idx": "599",
        "repo_name": "mrspouse___nsplus",
        "func_path": "custom_components/nsplus/sensor.py",
        "orig_func": "def _parse_icon(self, direction: str) -> str:\n    \"\"\"Update the icon based on the direction attribute.\"\"\"\n    switcher = {'Flat': 'mdi:arrow-right', 'SingleDown': 'mdi:arrow-down', 'FortyFiveDown': 'mdi:arrow-bottom-right', 'DoubleDown': 'mdi:chevron-triple-down', 'SingleUp': 'mdi:arrow-up', 'FortyFiveUp': 'mdi:arrow-top-right', 'DoubleUp': 'mdi:chevron-triple-up'}\n    return switcher.get(direction, 'mdi:cloud-question')",
        "orig_context": "```python\n## custom_components/nsplus/const.py\nATTR_DEVICE = \"device\"\n\nATTR_DELTA = \"delta\"\n\nATTR_DIRECTION = \"direction\"\n\n```\n\n\n```python\n## custom_components/nsplus/sensor.py\nimport logging\n\nfrom typing import Any\n\nfrom aiohttp import ClientError\n\nfrom py_nightscout import Api as NightscoutAPI\n\nfrom homeassistant.components.sensor import SensorEntity\n\nfrom homeassistant.const import ATTR_DATE\n\nfrom .const import ATTR_DELTA, ATTR_DEVICE, ATTR_DIRECTION, DOMAIN\n\n_LOGGER = logging.getLogger(__name__)\n\nclass NightscoutSensor(SensorEntity):\n    \"\"\"Implementation of a Nightscout sensor.\"\"\"\n\n    _attr_native_unit_of_measurement = \"mg/dL\"\n    _attr_icon = \"mdi:cloud-question\"\n\n    def __init__(self, api: NightscoutAPI, name: str, unique_id: str | None) -> None:\n        \"\"\"Initialize the Nightscout sensor.\"\"\"\n        self.api = api\n        self._attr_unique_id = unique_id\n        self._attr_name = name\n        self._attr_extra_state_attributes: dict[str, Any] = {}\n\n    async def async_update(self) -> None:\n        \"\"\"Fetch the latest data from Nightscout REST API and update the state.\"\"\"\n        try:\n            values = await self.api.get_sgvs()\n        except (ClientError, TimeoutError, OSError) as error:\n            _LOGGER.error(\"Error fetching data. Failed with %s\", error)\n            self._attr_available = False\n            return\n\n        self._attr_available = True\n        self._attr_extra_state_attributes = {}\n        self._attr_native_value = None\n        if values:\n            value = values[0]\n            self._attr_extra_state_attributes = {\n                ATTR_DEVICE: value.device,\n                ATTR_DATE: value.date,\n                ATTR_DELTA: value.delta,\n                ATTR_DIRECTION: value.direction,\n            }\n            self._attr_native_value = value.sgv\n            self._attr_icon = self._parse_icon(value.direction)\n        else:\n            self._attr_available = False\n            _LOGGER.warning(\"Empty reply found when expecting JSON data\")\n\n    def _parse_icon(self, direction: str) -> str:\n        \"\"\"Update the icon based on the direction attribute.\"\"\"\n        switcher = {\n            \"Flat\": \"mdi:arrow-right\",\n            \"SingleDown\": \"mdi:arrow-down\",\n            \"FortyFiveDown\": \"mdi:arrow-bottom-right\",\n            \"DoubleDown\": \"mdi:chevron-triple-down\",\n            \"SingleUp\": \"mdi:arrow-up\",\n            \"FortyFiveUp\": \"mdi:arrow-top-right\",\n            \"DoubleUp\": \"mdi:chevron-triple-up\",\n        }\n        return switcher.get(direction, \"mdi:cloud-question\")\n\n```\n\n\n",
        "eval_script": "import logging\nfrom typing import Any\n\n# Mocking aiohttp.ClientError\nclass ClientError(Exception):\n    pass\n\n# Mocking the NightscoutAPI class\nclass NightscoutAPI:\n    async def get_sgvs(self):\n        # Mock response\n        return [{\n            'device': 'mock_device',\n            'date': '2023-10-01T00:00:00Z',\n            'delta': 5,\n            'direction': 'Flat',\n            'sgv': 100\n        }]\n\n# Mocking the SensorEntity class from Home Assistant\nclass SensorEntity:\n    _attr_native_unit_of_measurement: str\n    _attr_icon: str\n    _attr_unique_id: str | None\n    _attr_name: str\n    _attr_extra_state_attributes: dict[str, Any]\n    _attr_available: bool\n    _attr_native_value: Any\n\n# Constants from const.py\nATTR_DEVICE = \"device\"\nATTR_DELTA = \"delta\"\nATTR_DIRECTION = \"direction\"\n\n_LOGGER = logging.getLogger(__name__)\n\nclass NightscoutSensor(SensorEntity):\n    \"\"\"Implementation of a Nightscout sensor.\"\"\"\n\n    _attr_native_unit_of_measurement = \"mg/dL\"\n    _attr_icon = \"mdi:cloud-question\"\n\n    def __init__(self, api: NightscoutAPI, name: str, unique_id: str | None) -> None:\n        \"\"\"Initialize the Nightscout sensor.\"\"\"\n        self.api = api\n        self._attr_unique_id = unique_id\n        self._attr_name = name\n        self._attr_extra_state_attributes: dict[str, Any] = {}\n\n    async def async_update(self) -> None:\n        \"\"\"Fetch the latest data from Nightscout REST API and update the state.\"\"\"\n        try:\n            values = await self.api.get_sgvs()\n        except (ClientError, TimeoutError, OSError) as error:\n            _LOGGER.error(\"Error fetching data. Failed with %s\", error)\n            self._attr_available = False\n            return\n\n        self._attr_available = True\n        self._attr_extra_state_attributes = {}\n        self._attr_native_value = None\n        if values:\n            value = values[0]\n            self._attr_extra_state_attributes = {\n                ATTR_DEVICE: value['device'],\n                ATTR_DATE: value['date'],\n                ATTR_DELTA: value['delta'],\n                ATTR_DIRECTION: value['direction'],\n            }\n            self._attr_native_value = value['sgv']\n            self._attr_icon = self._parse_icon(value['direction'])\n        else:\n            self._attr_available = False\n            _LOGGER.warning(\"Empty reply found when expecting JSON data\")\n\n    def _parse_icon(self, direction: str) -> str:\n        \"\"\"Update the icon based on the direction attribute.\"\"\"\n        switcher = {\n            \"Flat\": \"mdi:arrow-right\",\n            \"SingleDown\": \"mdi:arrow-down\",\n            \"FortyFiveDown\": \"mdi:arrow-bottom-right\",\n            \"DoubleDown\": \"mdi:chevron-triple-down\",\n            \"SingleUp\": \"mdi:arrow-up\",\n            \"FortyFiveUp\": \"mdi:arrow-top-right\",\n            \"DoubleUp\": \"mdi:chevron-triple-up\",\n        }\n        return switcher.get(direction, \"mdi:cloud-question\")\n\n\ndef test__parse_icon():\n    sensor = NightscoutSensor(NightscoutAPI(), \"Test Sensor\", \"unique_id_123\")\n    # Test Flat direction\n    assert sensor._parse_icon(\"Flat\") == sensor._parse_icon_new_implementation(\"Flat\")\n    # Test SingleDown direction\n    assert sensor._parse_icon(\"SingleDown\") == sensor._parse_icon_new_implementation(\"SingleDown\")\n    # Test FortyFiveDown direction\n    assert sensor._parse_icon(\"FortyFiveDown\") == sensor._parse_icon_new_implementation(\"FortyFiveDown\")\n    # Test DoubleDown direction\n    assert sensor._parse_icon(\"DoubleDown\") == sensor._parse_icon_new_implementation(\"DoubleDown\")\n    # Test SingleUp direction\n    assert sensor._parse_icon(\"SingleUp\") == sensor._parse_icon_new_implementation(\"SingleUp\")\n    # Test FortyFiveUp direction\n    assert sensor._parse_icon(\"FortyFiveUp\") == sensor._parse_icon_new_implementation(\"FortyFiveUp\")\n    # Test DoubleUp direction\n    assert sensor._parse_icon(\"DoubleUp\") == sensor._parse_icon_new_implementation(\"DoubleUp\")\n    # Test unknown direction\n    assert sensor._parse_icon(\"Unknown\") == sensor._parse_icon_new_implementation(\"Unknown\")\n    # Test empty string direction\n    assert sensor._parse_icon(\"\") == sensor._parse_icon_new_implementation(\"\")\n    # Test None as direction\n    assert sensor._parse_icon(None) == sensor._parse_icon_new_implementation(None)\n\nif __name__ == \"__main__\":\n    test__parse_icon()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_parse_icon` is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions use a dictionary `switcher` to map specific direction strings to corresponding Material Design Icons (MDI) strings. If the direction is not found in the dictionary, both functions return the default icon `'mdi:cloud-question'`. The test cases provided in the code also confirm that the behavior of the function is consistent across various inputs, including known directions, unknown directions, empty strings, and `None`. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_parse_icon` function returns a string based on the input `direction`, so it satisfies this condition as it has return values.\n- CONDITION 2: The test cases use assertions to check the return values of `_parse_icon` against `_parse_icon_new_implementation`, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_parse_icon` and `_parse_icon_new_implementation` for various inputs. If both functions produce the same output for all inputs, they have the same functionality. Therefore, this condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_parse_icon` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of inputs, including known directions, unknown directions, an empty string, and `None`. This ensures that the test cases are non-trivial and cover edge cases, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "b9f890c88672f042ef2d952093e73e5cc03bda84"
    },
    {
        "func_name": "BasePlant._datetime_to_utc",
        "idx": "607",
        "repo_name": "rundef___async_rithmic",
        "func_path": "async_rithmic/plants/base.py",
        "orig_func": "def _datetime_to_utc(self, dt: datetime):\n    if dt.tzinfo is None:\n        system_timezone = pytz.timezone(str(get_localzone()))\n        dt = system_timezone.localize(dt)\n    if dt.tzinfo != pytz.utc:\n        dt = dt.astimezone(pytz.utc)\n    return dt",
        "orig_context": "```python\n## async_rithmic/enums.py\nfrom . import protocol_buffers as pb\n\n```\n\n\n```python\n## async_rithmic/__init__.py\nfrom .enums import *\n\n```\n\n\n```python\n## async_rithmic/logger.py\nimport logging\n\nimport sys\n\nclass Logger:\n    def __init__(self, level: int = logging.INFO):\n        self.logger = logging.getLogger(\"rithmic\")\n        self.logger.setLevel(level)\n\n        formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n\n        console_handler = logging.StreamHandler(sys.stdout)\n        console_handler.setFormatter(formatter)\n\n        if not self.logger.hasHandlers():\n            self.logger.addHandler(console_handler)\n\n    def get_logger(self):\n        return self.logger\n\nlogger = Logger(level=logging.DEBUG).get_logger()\n\n```\n\n\n```python\n## async_rithmic/plants/base.py\nimport websockets\n\nfrom websockets import ConnectionClosedError, ConnectionClosedOK\n\nfrom websockets.protocol import OPEN\n\nimport asyncio\n\nimport time\n\nimport traceback\n\nfrom datetime import datetime\n\nimport pytz\n\nfrom tzlocal import get_localzone\n\nfrom google.protobuf.descriptor import FieldDescriptor\n\nfrom google.protobuf.json_format import MessageToDict\n\nfrom .. import protocol_buffers as pb\n\nfrom ..logger import logger\n\nTEMPLATES_MAP = {\n    # Shared\n    10: pb.request_login_pb2.RequestLogin,\n    11: pb.response_login_pb2.ResponseLogin,\n    12: pb.request_logout_pb2.RequestLogout,\n    13: pb.response_logout_pb2.ResponseLogout,\n    14: pb.request_reference_data_pb2.RequestReferenceData,\n    15: pb.response_reference_data_pb2.ResponseReferenceData,\n    16: pb.request_rithmic_system_info_pb2.RequestRithmicSystemInfo,\n    17: pb.response_rithmic_system_info_pb2.ResponseRithmicSystemInfo,\n    18: pb.request_heartbeat_pb2.RequestHeartbeat,\n    19: pb.response_heartbeat_pb2.ResponseHeartbeat,\n\n    # Market Data Infrastructure\n    100: pb.request_market_data_update_pb2.RequestMarketDataUpdate,\n    101: pb.response_market_data_update_pb2.ResponseMarketDataUpdate,\n    109: pb.request_search_symbols_pb2.RequestSearchSymbols,\n    110: pb.response_search_symbols_pb2.ResponseSearchSymbols,\n    113: pb.request_front_month_contract_pb2.RequestFrontMonthContract,\n    114: pb.response_front_month_contract_pb2.ResponseFrontMonthContract,\n\n    #115: pb.request_depth_by_order_snapshot_pb2.RequestDepthByOrderSnapshot,\n    #116: pb.response_depth_by_order_snapshot_pb2.ResponseDepthByOrderSnapshot,\n    #117: pb.request_depth_by_order_updates_pb2.RequestDepthByOrderUpdates,\n    #118: pb.response_depth_by_order_updates_pb2.ResponseDepthByOrderUpdates,\n\n    150: pb.last_trade_pb2.LastTrade,\n    151: pb.best_bid_offer_pb2.BestBidOffer,\n    #156: pb.order_book_pb2.OrderBook,\n    #160: pb.depth_by_order.DepthByOrder,\n    #161: pb.depth_by_order_end_event.DepthByOrderEndEvent,\n\n    # Order Plant Infrastructure\n    300: pb.request_login_info_pb2.RequestLoginInfo,\n    301: pb.response_login_info_pb2.ResponseLoginInfo,\n    302: pb.request_account_list_pb2.RequestAccountList,\n    303: pb.response_account_list_pb2.ResponseAccountList,\n    304: pb.request_account_rms_info_pb2.RequestAccountRmsInfo,\n    305: pb.response_account_rms_info_pb2.ResponseAccountRmsInfo,\n    306: pb.request_product_rms_info_pb2.RequestProductRmsInfo,\n    307: pb.response_product_rms_info_pb2.ResponseProductRmsInfo,\n    308: pb.request_subscribe_for_order_updates_pb2.RequestSubscribeForOrderUpdates,\n    309: pb.response_subscribe_for_order_updates_pb2.ResponseSubscribeForOrderUpdates,\n    310: pb.request_trade_routes_pb2.RequestTradeRoutes,\n    311: pb.response_trade_routes_pb2.ResponseTradeRoutes,\n    312: pb.request_new_order_pb2.RequestNewOrder,\n    313: pb.response_new_order_pb2.ResponseNewOrder,\n    314: pb.request_modify_order_pb2.RequestModifyOrder,\n    315: pb.response_modify_order_pb2.ResponseModifyOrder,\n    316: pb.request_cancel_order_pb2.RequestCancelOrder,\n    317: pb.response_cancel_order_pb2.ResponseCancelOrder,\n    320: pb.request_show_orders_pb2.RequestShowOrders,\n    321: pb.response_show_orders_pb2.ResponseShowOrders,\n    330: pb.request_bracket_order_pb2.RequestBracketOrder,\n    331: pb.response_bracket_order_pb2.ResponseBracketOrder,\n    332: pb.request_update_target_bracket_level_pb2.RequestUpdateTargetBracketLevel,\n    333: pb.response_update_target_bracket_level_pb2.ResponseUpdateTargetBracketLevel,\n    334: pb.request_update_stop_bracket_level_pb2.RequestUpdateStopBracketLevel,\n    335: pb.response_update_stop_bracket_level_pb2.ResponseUpdateStopBracketLevel,\n    336: pb.request_subscribe_to_bracket_updates_pb2.RequestSubscribeToBracketUpdates,\n    337: pb.response_subscribe_to_bracket_updates_pb2.ResponseSubscribeToBracketUpdates,\n\n    350: pb.trade_route_pb2.TradeRoute,\n    351: pb.rithmic_order_notification_pb2.RithmicOrderNotification,\n    352: pb.exchange_order_notification_pb2.ExchangeOrderNotification,\n    353: pb.bracket_updates_pb2.BracketUpdates,\n\n    # History Plant Infrastructure\n    200: pb.request_time_bar_update_pb2.RequestTimeBarUpdate,\n    201: pb.response_time_bar_update_pb2.ResponseTimeBarUpdate,\n    202: pb.request_time_bar_replay_pb2.RequestTimeBarReplay,\n    203: pb.response_time_bar_replay_pb2.ResponseTimeBarReplay,\n    204: pb.request_tick_bar_update_pb2.RequestTickBarUpdate,\n    205: pb.response_tick_bar_update_pb2.ResponseTickBarUpdate,\n    206: pb.request_tick_bar_replay_pb2.RequestTickBarReplay,\n    207: pb.response_tick_bar_replay_pb2.ResponseTickBarReplay,\n    250: pb.time_bar_pb2.TimeBar,\n    251: pb.tick_bar_pb2.TickBar,\n\n    # PnL Plant Infrastructure\n    400: pb.request_pnl_position_updates_pb2.RequestPnLPositionUpdates,\n    401: pb.response_pnl_position_updates_pb2.ResponsePnLPositionUpdates,\n    402: pb.request_pnl_position_snapshot_pb2.RequestPnLPositionSnapshot,\n    403: pb.response_pnl_position_snapshot_pb2.ResponsePnLPositionSnapshot,\n    450: pb.instrument_pnl_position_update_pb2.InstrumentPnLPositionUpdate,\n    451: pb.account_pnl_position_update_pb2.AccountPnLPositionUpdate,\n}\n\nclass BasePlant:\n    infra_type = None\n\n    def __init__(self, client, listen_interval=0.1):\n        self.ws = None\n        self.client = client\n        self.lock = asyncio.Lock()\n\n        # Heartbeats has to be sent every {interval} seconds, unless an update was received\n        self.heartbeat_interval = None\n        self.listen_interval = listen_interval\n        self.last_message_time = None\n\n    @property\n    def is_connected(self) -> bool:\n        return self.ws is not None and self.ws.state == OPEN\n\n    @property\n    def credentials(self):\n        return self.client.credentials\n\n    @property\n    def ssl_context(self):\n        return self.client.ssl_context\n\n    @property\n    def plant_type(self):\n        return {\n            pb.request_login_pb2.RequestLogin.SysInfraType.HISTORY_PLANT: \"history\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.PNL_PLANT: \"pnl\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.TICKER_PLANT: \"ticker\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.ORDER_PLANT: \"order\",\n        }[self.infra_type]\n\n    async def _connect(self):\n        \"\"\"\n        Clients should follow the below sequence for communicating with protocol server,\n        1. Open a websocket, upon connecting send 'RequestRithmicSystemInfo' message.\n           Parse the response and record list of 'system names' available. Close this connection\n\n        2. Open a new websocket, and login using the desired 'system_name'.\n        \"\"\"\n        self.ws = await websockets.connect(\n            self.credentials[\"gateway\"],\n            ssl=self.ssl_context,\n            ping_interval=10\n        )\n\n        if self.plant_type == \"ticker\":\n            info = await self.get_system_info()\n            await self._disconnect()\n\n            if self.credentials[\"system_name\"] not in info.system_name:\n                raise Exception(f\"You must specify valid SYSTEM_NAME in the credentials file: {info.system_name}\")\n\n            self.ws = await websockets.connect(\n                self.credentials[\"gateway\"],\n                ssl=self.ssl_context,\n                ping_interval=10\n            )\n\n    async def _disconnect(self):\n        if self.is_connected:\n            await self.ws.close(1000, \"Closing Connection\")\n\n    async def _login(self):\n        response = await self._send_and_recv(\n            template_id=10,\n            template_version=\"3.9\",\n            user=self.credentials[\"user\"],\n            password=self.credentials[\"password\"],\n            system_name=self.credentials[\"system_name\"],\n            app_name=self.credentials[\"app_name\"],\n            app_version=self.credentials[\"app_version\"],\n            infra_type=self.infra_type,\n        )\n\n        self.heartbeat_interval = response.heartbeat_interval\n\n        # Upon making a successful login, clients are expected to send at least a heartbeat request to the server\n        await self._send_heartbeat()\n\n        return response\n\n    async def _logout(self):\n        try:\n            return await self._send_and_recv(template_id=12)\n        except ConnectionClosedOK:\n            pass\n\n    async def get_system_info(self):\n        return await self._send_and_recv(template_id=16)\n\n    async def get_reference_data(self, symbol: str, exchange: str):\n        return await self._send_and_recv(\n            template_id=14,\n            symbol=symbol,\n            exchange=exchange\n        )\n\n    async def _send(self, message: bytes):\n        await self.ws.send(message)\n\n    async def _recv(self):\n        buffer = await self.ws.recv()\n        self.last_message_time = time.time()\n        return buffer\n\n    async def _send_request(self, **kwargs):\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        buffer = self._convert_request_to_bytes(request)\n        await self._send(buffer)\n\n        return template_id\n\n    async def _send_and_recv(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and decode the response\n        \"\"\"\n\n        async with self.lock:\n            template_id = await self._send_request(**kwargs)\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if not hasattr(response, \"rp_code\") or response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                break\n\n        if len(response.rp_code) and response.rp_code[0] != '0':\n            raise Exception(f\"Rithmic returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n        return response\n\n    async def _send_and_recv_many(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and expect 1...n responses back\n        \"\"\"\n\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        results = []\n        async with self.lock:\n            await self._send(self._convert_request_to_bytes(request))\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                if len(response.rp_code) > 0:\n                    if response.rp_code[0] != '0':\n                        raise Exception(f\"Server returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n                    break\n                else:\n                    results.append(response)\n\n        return results\n\n    def _convert_request_to_bytes(self, request):\n        serialized = request.SerializeToString()\n        length = len(serialized)\n        buffer = length.to_bytes(4, byteorder='big', signed=True)\n        buffer += serialized\n        return buffer\n\n    def _convert_bytes_to_response(self, buffer):\n        b = pb.base_pb2.Base()\n        b.ParseFromString(buffer[4:])\n        if b.template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown response template id: {b.template_id}\")\n\n        response = TEMPLATES_MAP[b.template_id]()\n        response.ParseFromString(buffer[4:])\n        return response\n\n    def _set_pb_field(self, obj, field_name, value):\n        field_descriptor = obj.DESCRIPTOR.fields_by_name[field_name]\n\n        if field_descriptor.label == FieldDescriptor.LABEL_REPEATED:\n            # Handle repeated fields (lists in protobuf)\n            field = getattr(obj, field_name)\n            if isinstance(value, list):\n                field.extend(value)\n            else:\n                field.append(value)\n        elif field_descriptor.type == FieldDescriptor.TYPE_MESSAGE:\n            # Handle nested message fields\n            nested_message = getattr(obj, field_name)\n            for sub_key, sub_value in value.items():\n                self._set_pb_field(nested_message, sub_key, sub_value)\n        else:\n            # Handle normal fields\n            try:\n                setattr(obj, field_name, value)\n            except:\n                logger.error(f\"Error when trying to set {field_name}\")\n                raise\n\n    async def _send_heartbeat(self):\n        return await self._send_and_recv(template_id=18)\n\n    async def _listen(self, max_iterations=None):\n        iteration_count = 0\n\n        try:\n            while True:\n                if max_iterations and iteration_count >= max_iterations:\n                    break\n\n                try:\n                    async with self.lock:\n                        buffer = await asyncio.wait_for(self._recv(), timeout=self.listen_interval)\n\n                    response = self._convert_bytes_to_response(buffer)\n                    await self._process_response(response)\n                    iteration_count += 1\n\n                except asyncio.TimeoutError:\n                    current_time = time.time()\n\n                    # Send regular heartbeats\n                    if current_time - self.last_message_time > self.heartbeat_interval-2:\n                        await self._send_heartbeat()\n\n                except ConnectionClosedError:\n                    logger.exception(\"WebSocket connection closed with error\")\n                    if not await self._handle_reconnection():\n                        break\n\n                except ConnectionClosedOK:\n                    logger.info(f\"WebSocket connection closed normally\")\n                    break\n\n        except Exception as e:\n            logger.error(f\"Exception in listener: {e}\")\n            traceback.print_exc()\n\n    async def _handle_reconnection(self, attempt=1):\n        max_retries = 5\n        wait_time = min(2 ** attempt, 120)\n\n        logger.info(f\"{self.plant_type} plant reconnection attempt {attempt} in {wait_time} seconds...\")\n        await asyncio.sleep(wait_time)\n\n        try:\n            # Attempt to reconnect this specific plant\n            await self._connect()\n            await self._login()\n\n            logger.info(f\"{self.plant_type} plant reconnection successful.\")\n            return True\n\n        except Exception as e:\n            if attempt < max_retries:\n                logger.warning(f\"{self.plant_type} plant reconnection failed: {e}. Retrying...\")\n                return await self._handle_reconnection(attempt + 1)\n            else:\n                logger.error(f\"{self.plant_type} plant max reconnection attempts reached. Could not reconnect: {e}\")\n\n        return False\n\n\n    def _response_to_dict(self, response):\n        data = MessageToDict(response, preserving_proto_field_name=True, use_integers_for_enums=True)\n\n        data.pop(\"template_id\", None)\n        data.pop(\"request_key\", None)\n        data.pop(\"user_msg\", None)\n        data.pop(\"rq_handler_rp_code\", None)\n        data.pop(\"rp_code\", None)\n\n        return data\n\n    async def _process_response(self, response):\n        raise NotImplementedError\n\n    def _datetime_to_utc(self, dt: datetime):\n        if dt.tzinfo is None:\n            # Use system timezone\n            system_timezone = pytz.timezone(str(get_localzone()))\n            dt = system_timezone.localize(dt)\n\n        if dt.tzinfo != pytz.utc:\n            # Convert to utc\n            dt = dt.astimezone(pytz.utc)\n\n        return dt\n\n    def _ssboe_usecs_to_datetime(self, ssboe: int, usecs: int):\n        ts = '{0}.{1}'.format(ssboe, usecs)\n        return datetime.fromtimestamp(float(ts), tz=pytz.utc)\n\n    def _datetime_to_ssboe_usecs(self, dt: datetime):\n        \"\"\"\n        Split the timestamp into integer seconds (ssboe) and rounded microseconds (usecs)\n        \"\"\"\n        timestamp = dt.timestamp()\n\n        ssboe = int(timestamp)\n        usecs = round((timestamp - ssboe) * 1_000_000)\n\n        return ssboe, usecs\n\n```\n\n\n",
        "eval_script": "# Mocking protocol_buffers module as its implementation is not provided.\nclass MockProtocolBuffers:\n    class request_login_pb2:\n        class RequestLogin:\n            class SysInfraType:\n                HISTORY_PLANT = 0\n                PNL_PLANT = 1\n                TICKER_PLANT = 2\n                ORDER_PLANT = 3\n\n    class response_login_pb2:\n        class ResponseLogin:\n            pass\n\n    class request_logout_pb2:\n        class RequestLogout:\n            pass\n\n    class response_logout_pb2:\n        class ResponseLogout:\n            pass\n\n    class request_reference_data_pb2:\n        class RequestReferenceData:\n            pass\n\n    class response_reference_data_pb2:\n        class ResponseReferenceData:\n            pass\n\n    class request_rithmic_system_info_pb2:\n        class RequestRithmicSystemInfo:\n            pass\n\n    class response_rithmic_system_info_pb2:\n        class ResponseRithmicSystemInfo:\n            pass\n\n    class request_heartbeat_pb2:\n        class RequestHeartbeat:\n            pass\n\n    class response_heartbeat_pb2:\n        class ResponseHeartbeat:\n            pass\n\n    class request_market_data_update_pb2:\n        class RequestMarketDataUpdate:\n            pass\n\n    class response_market_data_update_pb2:\n        class ResponseMarketDataUpdate:\n            pass\n\n    class request_search_symbols_pb2:\n        class RequestSearchSymbols:\n            pass\n\n    class response_search_symbols_pb2:\n        class ResponseSearchSymbols:\n            pass\n\n    class request_front_month_contract_pb2:\n        class RequestFrontMonthContract:\n            pass\n\n    class response_front_month_contract_pb2:\n        class ResponseFrontMonthContract:\n            pass\n\n    class last_trade_pb2:\n        class LastTrade:\n            pass\n\n    class best_bid_offer_pb2:\n        class BestBidOffer:\n            pass\n\n    class request_login_info_pb2:\n        class RequestLoginInfo:\n            pass\n\n    class response_login_info_pb2:\n        class ResponseLoginInfo:\n            pass\n\n    class request_account_list_pb2:\n        class RequestAccountList:\n            pass\n\n    class response_account_list_pb2:\n        class ResponseAccountList:\n            pass\n\n    class request_account_rms_info_pb2:\n        class RequestAccountRmsInfo:\n            pass\n\n    class response_account_rms_info_pb2:\n        class ResponseAccountRmsInfo:\n            pass\n\n    class request_product_rms_info_pb2:\n        class RequestProductRmsInfo:\n            pass\n\n    class response_product_rms_info_pb2:\n        class ResponseProductRmsInfo:\n            pass\n\n    class request_subscribe_for_order_updates_pb2:\n        class RequestSubscribeForOrderUpdates:\n            pass\n\n    class response_subscribe_for_order_updates_pb2:\n        class ResponseSubscribeForOrderUpdates:\n            pass\n\n    class request_trade_routes_pb2:\n        class RequestTradeRoutes:\n            pass\n\n    class response_trade_routes_pb2:\n        class ResponseTradeRoutes:\n            pass\n\n    class request_new_order_pb2:\n        class RequestNewOrder:\n            pass\n\n    class response_new_order_pb2:\n        class ResponseNewOrder:\n            pass\n\n    class request_modify_order_pb2:\n        class RequestModifyOrder:\n            pass\n\n    class response_modify_order_pb2:\n        class ResponseModifyOrder:\n            pass\n\n    class request_cancel_order_pb2:\n        class RequestCancelOrder:\n            pass\n\n    class response_cancel_order_pb2:\n        class ResponseCancelOrder:\n            pass\n\n    class request_show_orders_pb2:\n        class RequestShowOrders:\n            pass\n\n    class response_show_orders_pb2:\n        class ResponseShowOrders:\n            pass\n\n    class request_bracket_order_pb2:\n        class RequestBracketOrder:\n            pass\n\n    class response_bracket_order_pb2:\n        class ResponseBracketOrder:\n            pass\n\n    class request_update_target_bracket_level_pb2:\n        class RequestUpdateTargetBracketLevel:\n            pass\n\n    class response_update_target_bracket_level_pb2:\n        class ResponseUpdateTargetBracketLevel:\n            pass\n\n    class request_update_stop_bracket_level_pb2:\n        class RequestUpdateStopBracketLevel:\n            pass\n\n    class response_update_stop_bracket_level_pb2:\n        class ResponseUpdateStopBracketLevel:\n            pass\n\n    class request_subscribe_to_bracket_updates_pb2:\n        class RequestSubscribeToBracketUpdates:\n            pass\n\n    class response_subscribe_to_bracket_updates_pb2:\n        class ResponseSubscribeToBracketUpdates:\n            pass\n\n    class trade_route_pb2:\n        class TradeRoute:\n            pass\n\n    class rithmic_order_notification_pb2:\n        class RithmicOrderNotification:\n            pass\n\n    class exchange_order_notification_pb2:\n        class ExchangeOrderNotification:\n            pass\n\n    class bracket_updates_pb2:\n        class BracketUpdates:\n            pass\n\n    class request_time_bar_update_pb2:\n        class RequestTimeBarUpdate:\n            pass\n\n    class response_time_bar_update_pb2:\n        class ResponseTimeBarUpdate:\n            pass\n\n    class request_time_bar_replay_pb2:\n        class RequestTimeBarReplay:\n            pass\n\n    class response_time_bar_replay_pb2:\n        class ResponseTimeBarReplay:\n            pass\n\n    class request_tick_bar_update_pb2:\n        class RequestTickBarUpdate:\n            pass\n\n    class response_tick_bar_update_pb2:\n        class ResponseTickBarUpdate:\n            pass\n\n    class request_tick_bar_replay_pb2:\n        class RequestTickBarReplay:\n            pass\n\n    class response_tick_bar_replay_pb2:\n        class ResponseTickBarReplay:\n            pass\n\n    class time_bar_pb2:\n        class TimeBar:\n            pass\n\n    class tick_bar_pb2:\n        class TickBar:\n            pass\n\n    class request_pnl_position_updates_pb2:\n        class RequestPnLPositionUpdates:\n            pass\n\n    class response_pnl_position_updates_pb2:\n        class ResponsePnLPositionUpdates:\n            pass\n\n    class request_pnl_position_snapshot_pb2:\n        class RequestPnLPositionSnapshot:\n            pass\n\n    class response_pnl_position_snapshot_pb2:\n        class ResponsePnLPositionSnapshot:\n            pass\n\n    class instrument_pnl_position_update_pb2:\n        class InstrumentPnLPositionUpdate:\n            pass\n\n    class account_pnl_position_update_pb2:\n        class AccountPnLPositionUpdate:\n            pass\n\n    class base_pb2:\n        class Base:\n            def ParseFromString(self, data):\n                pass\n\npb = MockProtocolBuffers()\n\n# Logger setup from context\nimport logging\nimport sys\n\nclass Logger:\n    def __init__(self, level: int = logging.INFO):\n        self.logger = logging.getLogger(\"rithmic\")\n        self.logger.setLevel(level)\n\n        formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n\n        console_handler = logging.StreamHandler(sys.stdout)\n        console_handler.setFormatter(formatter)\n\n        if not self.logger.hasHandlers():\n            self.logger.addHandler(console_handler)\n\n    def get_logger(self):\n        return self.logger\n\nlogger = Logger(level=logging.DEBUG).get_logger()\n\n# Main code from the provided Python code\nimport websockets\nfrom websockets import ConnectionClosedError, ConnectionClosedOK\nfrom websockets.protocol import OPEN\nimport asyncio\nimport time\nimport traceback\nfrom datetime import datetime\nimport pytz\nfrom tzlocal import get_localzone\nfrom google.protobuf.descriptor import FieldDescriptor\nfrom google.protobuf.json_format import MessageToDict\n\nTEMPLATES_MAP = {\n    # Shared\n    10: pb.request_login_pb2.RequestLogin,\n    11: pb.response_login_pb2.ResponseLogin,\n    12: pb.request_logout_pb2.RequestLogout,\n    13: pb.response_logout_pb2.ResponseLogout,\n    14: pb.request_reference_data_pb2.RequestReferenceData,\n    15: pb.response_reference_data_pb2.ResponseReferenceData,\n    16: pb.request_rithmic_system_info_pb2.RequestRithmicSystemInfo,\n    17: pb.response_rithmic_system_info_pb2.ResponseRithmicSystemInfo,\n    18: pb.request_heartbeat_pb2.RequestHeartbeat,\n    19: pb.response_heartbeat_pb2.ResponseHeartbeat,\n\n    # Market Data Infrastructure\n    100: pb.request_market_data_update_pb2.RequestMarketDataUpdate,\n    101: pb.response_market_data_update_pb2.ResponseMarketDataUpdate,\n    109: pb.request_search_symbols_pb2.RequestSearchSymbols,\n    110: pb.response_search_symbols_pb2.ResponseSearchSymbols,\n    113: pb.request_front_month_contract_pb2.RequestFrontMonthContract,\n    114: pb.response_front_month_contract_pb2.ResponseFrontMonthContract,\n\n    #115: pb.request_depth_by_order_snapshot_pb2.RequestDepthByOrderSnapshot,\n    #116: pb.response_depth_by_order_snapshot_pb2.ResponseDepthByOrderSnapshot,\n    #117: pb.request_depth_by_order_updates_pb2.RequestDepthByOrderUpdates,\n    #118: pb.response_depth_by_order_updates_pb2.ResponseDepthByOrderUpdates,\n\n    150: pb.last_trade_pb2.LastTrade,\n    151: pb.best_bid_offer_pb2.BestBidOffer,\n    #156: pb.order_book_pb2.OrderBook,\n    #160: pb.depth_by_order.DepthByOrder,\n    #161: pb.depth_by_order_end_event.DepthByOrderEndEvent,\n\n    # Order Plant Infrastructure\n    300: pb.request_login_info_pb2.RequestLoginInfo,\n    301: pb.response_login_info_pb2.ResponseLoginInfo,\n    302: pb.request_account_list_pb2.RequestAccountList,\n    303: pb.response_account_list_pb2.ResponseAccountList,\n    304: pb.request_account_rms_info_pb2.RequestAccountRmsInfo,\n    305: pb.response_account_rms_info_pb2.ResponseAccountRmsInfo,\n    306: pb.request_product_rms_info_pb2.RequestProductRmsInfo,\n    307: pb.response_product_rms_info_pb2.ResponseProductRmsInfo,\n    308: pb.request_subscribe_for_order_updates_pb2.RequestSubscribeForOrderUpdates,\n    309: pb.response_subscribe_for_order_updates_pb2.ResponseSubscribeForOrderUpdates,\n    310: pb.request_trade_routes_pb2.RequestTradeRoutes,\n    311: pb.response_trade_routes_pb2.ResponseTradeRoutes,\n    312: pb.request_new_order_pb2.RequestNewOrder,\n    313: pb.response_new_order_pb2.ResponseNewOrder,\n    314: pb.request_modify_order_pb2.RequestModifyOrder,\n    315: pb.response_modify_order_pb2.ResponseModifyOrder,\n    316: pb.request_cancel_order_pb2.RequestCancelOrder,\n    317: pb.response_cancel_order_pb2.ResponseCancelOrder,\n    320: pb.request_show_orders_pb2.RequestShowOrders,\n    321: pb.response_show_orders_pb2.ResponseShowOrders,\n    330: pb.request_bracket_order_pb2.RequestBracketOrder,\n    331: pb.response_bracket_order_pb2.ResponseBracketOrder,\n    332: pb.request_update_target_bracket_level_pb2.RequestUpdateTargetBracketLevel,\n    333: pb.response_update_target_bracket_level_pb2.ResponseUpdateTargetBracketLevel,\n    334: pb.request_update_stop_bracket_level_pb2.RequestUpdateStopBracketLevel,\n    335: pb.response_update_stop_bracket_level_pb2.ResponseUpdateStopBracketLevel,\n    336: pb.request_subscribe_to_bracket_updates_pb2.RequestSubscribeToBracketUpdates,\n    337: pb.response_subscribe_to_bracket_updates_pb2.ResponseSubscribeToBracketUpdates,\n\n    350: pb.trade_route_pb2.TradeRoute,\n    351: pb.rithmic_order_notification_pb2.RithmicOrderNotification,\n    352: pb.exchange_order_notification_pb2.ExchangeOrderNotification,\n    353: pb.bracket_updates_pb2.BracketUpdates,\n\n    # History Plant Infrastructure\n    200: pb.request_time_bar_update_pb2.RequestTimeBarUpdate,\n    201: pb.response_time_bar_update_pb2.ResponseTimeBarUpdate,\n    202: pb.request_time_bar_replay_pb2.RequestTimeBarReplay,\n    203: pb.response_time_bar_replay_pb2.ResponseTimeBarReplay,\n    204: pb.request_tick_bar_update_pb2.RequestTickBarUpdate,\n    205: pb.response_tick_bar_update_pb2.ResponseTickBarUpdate,\n    206: pb.request_tick_bar_replay_pb2.RequestTickBarReplay,\n    207: pb.response_tick_bar_replay_pb2.ResponseTickBarReplay,\n    250: pb.time_bar_pb2.TimeBar,\n    251: pb.tick_bar_pb2.TickBar,\n\n    # PnL Plant Infrastructure\n    400: pb.request_pnl_position_updates_pb2.RequestPnLPositionUpdates,\n    401: pb.response_pnl_position_updates_pb2.ResponsePnLPositionUpdates,\n    402: pb.request_pnl_position_snapshot_pb2.RequestPnLPositionSnapshot,\n    403: pb.response_pnl_position_snapshot_pb2.ResponsePnLPositionSnapshot,\n    450: pb.instrument_pnl_position_update_pb2.InstrumentPnLPositionUpdate,\n    451: pb.account_pnl_position_update_pb2.AccountPnLPositionUpdate,\n}\n\nclass BasePlant:\n    infra_type = None\n\n    def __init__(self, client, listen_interval=0.1):\n        self.ws = None\n        self.client = client\n        self.lock = asyncio.Lock()\n\n        # Heartbeats has to be sent every {interval} seconds, unless an update was received\n        self.heartbeat_interval = None\n        self.listen_interval = listen_interval\n        self.last_message_time = None\n\n    @property\n    def is_connected(self) -> bool:\n        return self.ws is not None and self.ws.state == OPEN\n\n    @property\n    def credentials(self):\n        return self.client.credentials\n\n    @property\n    def ssl_context(self):\n        return self.client.ssl_context\n\n    @property\n    def plant_type(self):\n        return {\n            pb.request_login_pb2.RequestLogin.SysInfraType.HISTORY_PLANT: \"history\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.PNL_PLANT: \"pnl\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.TICKER_PLANT: \"ticker\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.ORDER_PLANT: \"order\",\n        }[self.infra_type]\n\n    async def _connect(self):\n        \"\"\"\n        Clients should follow the below sequence for communicating with protocol server,\n        1. Open a websocket, upon connecting send 'RequestRithmicSystemInfo' message.\n           Parse the response and record list of 'system names' available. Close this connection\n\n        2. Open a new websocket, and login using the desired 'system_name'.\n        \"\"\"\n        self.ws = await websockets.connect(\n            self.credentials[\"gateway\"],\n            ssl=self.ssl_context,\n            ping_interval=10\n        )\n\n        if self.plant_type == \"ticker\":\n            info = await self.get_system_info()\n            await self._disconnect()\n\n            if self.credentials[\"system_name\"] not in info.system_name:\n                raise Exception(f\"You must specify valid SYSTEM_NAME in the credentials file: {info.system_name}\")\n\n            self.ws = await websockets.connect(\n                self.credentials[\"gateway\"],\n                ssl=self.ssl_context,\n                ping_interval=10\n            )\n\n    async def _disconnect(self):\n        if self.is_connected:\n            await self.ws.close(1000, \"Closing Connection\")\n\n    async def _login(self):\n        response = await self._send_and_recv(\n            template_id=10,\n            template_version=\"3.9\",\n            user=self.credentials[\"user\"],\n            password=self.credentials[\"password\"],\n            system_name=self.credentials[\"system_name\"],\n            app_name=self.credentials[\"app_name\"],\n            app_version=self.credentials[\"app_version\"],\n            infra_type=self.infra_type,\n        )\n\n        self.heartbeat_interval = response.heartbeat_interval\n\n        # Upon making a successful login, clients are expected to send at least a heartbeat request to the server\n        await self._send_heartbeat()\n\n        return response\n\n    async def _logout(self):\n        try:\n            return await self._send_and_recv(template_id=12)\n        except ConnectionClosedOK:\n            pass\n\n    async def get_system_info(self):\n        return await self._send_and_recv(template_id=16)\n\n    async def get_reference_data(self, symbol: str, exchange: str):\n        return await self._send_and_recv(\n            template_id=14,\n            symbol=symbol,\n            exchange=exchange\n        )\n\n    async def _send(self, message: bytes):\n        await self.ws.send(message)\n\n    async def _recv(self):\n        buffer = await self.ws.recv()\n        self.last_message_time = time.time()\n        return buffer\n\n    async def _send_request(self, **kwargs):\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        buffer = self._convert_request_to_bytes(request)\n        await self._send(buffer)\n\n        return template_id\n\n    async def _send_and_recv(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and decode the response\n        \"\"\"\n\n        async with self.lock:\n            template_id = await self._send_request(**kwargs)\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if not hasattr(response, \"rp_code\") or response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                break\n\n        if len(response.rp_code) and response.rp_code[0] != '0':\n            raise Exception(f\"Rithmic returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n        return response\n\n    async def _send_and_recv_many(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and expect 1...n responses back\n        \"\"\"\n\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        results = []\n        async with self.lock:\n            await self._send(self._convert_request_to_bytes(request))\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                if len(response.rp_code) > 0:\n                    if response.rp_code[0] != '0':\n                        raise Exception(f\"Server returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n                    break\n                else:\n                    results.append(response)\n\n        return results\n\n    def _convert_request_to_bytes(self, request):\n        serialized = request.SerializeToString()\n        length = len(serialized)\n        buffer = length.to_bytes(4, byteorder='big', signed=True)\n        buffer += serialized\n        return buffer\n\n    def _convert_bytes_to_response(self, buffer):\n        b = pb.base_pb2.Base()\n        b.ParseFromString(buffer[4:])\n        if b.template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown response template id: {b.template_id}\")\n\n        response = TEMPLATES_MAP[b.template_id]()\n        response.ParseFromString(buffer[4:])\n        return response\n\n    def _set_pb_field(self, obj, field_name, value):\n        field_descriptor = obj.DESCRIPTOR.fields_by_name[field_name]\n\n        if field_descriptor.label == FieldDescriptor.LABEL_REPEATED:\n            # Handle repeated fields (lists in protobuf)\n            field = getattr(obj, field_name)\n            if isinstance(value, list):\n                field.extend(value)\n            else:\n                field.append(value)\n        elif field_descriptor.type == FieldDescriptor.TYPE_MESSAGE:\n            # Handle nested message fields\n            nested_message = getattr(obj, field_name)\n            for sub_key, sub_value in value.items():\n                self._set_pb_field(nested_message, sub_key, sub_value)\n        else:\n            # Handle normal fields\n            try:\n                setattr(obj, field_name, value)\n            except:\n                logger.error(f\"Error when trying to set {field_name}\")\n                raise\n\n    async def _send_heartbeat(self):\n        return await self._send_and_recv(template_id=18)\n\n    async def _listen(self, max_iterations=None):\n        iteration_count = 0\n\n        try:\n            while True:\n                if max_iterations and iteration_count >= max_iterations:\n                    break\n\n                try:\n                    async with self.lock:\n                        buffer = await asyncio.wait_for(self._recv(), timeout=self.listen_interval)\n\n                    response = self._convert_bytes_to_response(buffer)\n                    await self._process_response(response)\n                    iteration_count += 1\n\n                except asyncio.TimeoutError:\n                    current_time = time.time()\n\n                    # Send regular heartbeats\n                    if current_time - self.last_message_time > self.heartbeat_interval-2:\n                        await self._send_heartbeat()\n\n                except ConnectionClosedError:\n                    logger.exception(\"WebSocket connection closed with error\")\n                    if not await self._handle_reconnection():\n                        break\n\n                except ConnectionClosedOK:\n                    logger.info(f\"WebSocket connection closed normally\")\n                    break\n\n        except Exception as e:\n            logger.error(f\"Exception in listener: {e}\")\n            traceback.print_exc()\n\n    async def _handle_reconnection(self, attempt=1):\n        max_retries = 5\n        wait_time = min(2 ** attempt, 120)\n\n        logger.info(f\"{self.plant_type} plant reconnection attempt {attempt} in {wait_time} seconds...\")\n        await asyncio.sleep(wait_time)\n\n        try:\n            # Attempt to reconnect this specific plant\n            await self._connect()\n            await self._login()\n\n            logger.info(f\"{self.plant_type} plant reconnection successful.\")\n            return True\n\n        except Exception as e:\n            if attempt < max_retries:\n                logger.warning(f\"{self.plant_type} plant reconnection failed: {e}. Retrying...\")\n                return await self._handle_reconnection(attempt + 1)\n            else:\n                logger.error(f\"{self.plant_type} plant max reconnection attempts reached. Could not reconnect: {e}\")\n\n        return False\n\n    def _response_to_dict(self, response):\n        data = MessageToDict(response, preserving_proto_field_name=True, use_integers_for_enums=True)\n\n        data.pop(\"template_id\", None)\n        data.pop(\"request_key\", None)\n        data.pop(\"user_msg\", None)\n        data.pop(\"rq_handler_rp_code\", None)\n        data.pop(\"rp_code\", None)\n\n        return data\n\n    async def _process_response(self, response):\n        raise NotImplementedError\n\n    def _datetime_to_utc(self, dt: datetime):\n        if dt.tzinfo is None:\n            # Use system timezone\n            system_timezone = pytz.timezone(str(get_localzone()))\n            dt = system_timezone.localize(dt)\n\n        if dt.tzinfo != pytz.utc:\n            # Convert to utc\n            dt = dt.astimezone(pytz.utc)\n\n        return dt\n\n\n    def _ssboe_usecs_to_datetime(self, ssboe: int, usecs: int):\n        ts = '{0}.{1}'.format(ssboe, usecs)\n        return datetime.fromtimestamp(float(ts), tz=pytz.utc)\n\n    def _datetime_to_ssboe_usecs(self, dt: datetime):\n        \"\"\"\n        Split the timestamp into integer seconds (ssboe) and rounded microseconds (usecs)\n        \"\"\"\n        timestamp = dt.timestamp()\n\n        ssboe = int(timestamp)\n        usecs = round((timestamp - ssboe) * 1_000_000)\n\n        return ssboe, usecs\n\ndef test__datetime_to_utc():\n    base_plant = BasePlant(None)\n\n    # Test case 1: Naive datetime\n    naive_dt = datetime(2023, 10, 1, 12, 0, 0)\n    assert base_plant._datetime_to_utc(naive_dt) == base_plant._datetime_to_utc_new_implementation(naive_dt)\n\n    # Test case 2: Non-UTC timezone\n    non_utc_dt = datetime(2023, 10, 1, 12, 0, 0, tzinfo=pytz.timezone('America/New_York'))\n    assert base_plant._datetime_to_utc(non_utc_dt) == base_plant._datetime_to_utc_new_implementation(non_utc_dt)\n\n    # Test case 3: UTC timezone\n    utc_dt = datetime(2023, 10, 1, 12, 0, 0, tzinfo=pytz.utc)\n    assert base_plant._datetime_to_utc(utc_dt) == base_plant._datetime_to_utc_new_implementation(utc_dt)\n\n    # Test case 4: Different non-UTC timezone\n    tokyo_dt = datetime(2023, 10, 1, 12, 0, 0, tzinfo=pytz.timezone('Asia/Tokyo'))\n    assert base_plant._datetime_to_utc(tokyo_dt) == base_plant._datetime_to_utc_new_implementation(tokyo_dt)\n\n    # Test case 5: Daylight saving time transition\n    dst_dt = datetime(2023, 3, 12, 2, 30, 0, tzinfo=pytz.timezone('America/New_York'))\n    assert base_plant._datetime_to_utc(dst_dt) == base_plant._datetime_to_utc_new_implementation(dst_dt)\n\n    # Test case 6: Minimum datetime\n    min_dt = datetime.min.replace(tzinfo=pytz.utc)\n    assert base_plant._datetime_to_utc(min_dt) == base_plant._datetime_to_utc_new_implementation(min_dt)\n\n    # Test case 7: Maximum datetime\n    max_dt = datetime.max.replace(tzinfo=pytz.utc)\n    assert base_plant._datetime_to_utc(max_dt) == base_plant._datetime_to_utc_new_implementation(max_dt)\n\n    # Test case 8: Datetime with microseconds\n    microsecond_dt = datetime(2023, 10, 1, 12, 0, 0, 123456, tzinfo=pytz.utc)\n    assert base_plant._datetime_to_utc(microsecond_dt) == base_plant._datetime_to_utc_new_implementation(microsecond_dt)\n\nif __name__ == \"__main__\":\n    test__datetime_to_utc()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_datetime_to_utc` is identical to the ORIGINAL FUNCTION. Both functions check if the input datetime object `dt` has a timezone. If it doesn't, they localize it to the system's timezone. Then, they convert the datetime to UTC if it is not already in UTC. The logic and steps in both functions are exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `_datetime_to_utc` returns a datetime object, which satisfies this condition as it has a return value.\n- CONDITION 2: The test cases use assert statements to compare return values of `_datetime_to_utc` and `_datetime_to_utc_new_implementation`, without checking printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_datetime_to_utc` and `_datetime_to_utc_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assert statements to compare return values, which is appropriate given that `_datetime_to_utc` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including naive datetime, different timezones, daylight saving time transition, minimum and maximum datetime, and datetime with microseconds. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "6cbc0f98bac31e1d99ff63f77fb9bd30bc04c281"
    },
    {
        "func_name": "BasePlant._ssboe_usecs_to_datetime",
        "idx": "608",
        "repo_name": "rundef___async_rithmic",
        "func_path": "async_rithmic/plants/base.py",
        "orig_func": "def _ssboe_usecs_to_datetime(self, ssboe: int, usecs: int):\n    ts = '{0}.{1}'.format(ssboe, usecs)\n    return datetime.fromtimestamp(float(ts), tz=pytz.utc)",
        "orig_context": "```python\n## async_rithmic/enums.py\nfrom . import protocol_buffers as pb\n\n```\n\n\n```python\n## async_rithmic/__init__.py\nfrom .enums import *\n\n```\n\n\n```python\n## async_rithmic/logger.py\nimport logging\n\nimport sys\n\nclass Logger:\n    def __init__(self, level: int = logging.INFO):\n        self.logger = logging.getLogger(\"rithmic\")\n        self.logger.setLevel(level)\n\n        formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n\n        console_handler = logging.StreamHandler(sys.stdout)\n        console_handler.setFormatter(formatter)\n\n        if not self.logger.hasHandlers():\n            self.logger.addHandler(console_handler)\n\n    def get_logger(self):\n        return self.logger\n\nlogger = Logger(level=logging.DEBUG).get_logger()\n\n```\n\n\n```python\n## async_rithmic/plants/base.py\nimport websockets\n\nfrom websockets import ConnectionClosedError, ConnectionClosedOK\n\nfrom websockets.protocol import OPEN\n\nimport asyncio\n\nimport time\n\nimport traceback\n\nfrom datetime import datetime\n\nimport pytz\n\nfrom tzlocal import get_localzone\n\nfrom google.protobuf.descriptor import FieldDescriptor\n\nfrom google.protobuf.json_format import MessageToDict\n\nfrom .. import protocol_buffers as pb\n\nfrom ..logger import logger\n\nTEMPLATES_MAP = {\n    # Shared\n    10: pb.request_login_pb2.RequestLogin,\n    11: pb.response_login_pb2.ResponseLogin,\n    12: pb.request_logout_pb2.RequestLogout,\n    13: pb.response_logout_pb2.ResponseLogout,\n    14: pb.request_reference_data_pb2.RequestReferenceData,\n    15: pb.response_reference_data_pb2.ResponseReferenceData,\n    16: pb.request_rithmic_system_info_pb2.RequestRithmicSystemInfo,\n    17: pb.response_rithmic_system_info_pb2.ResponseRithmicSystemInfo,\n    18: pb.request_heartbeat_pb2.RequestHeartbeat,\n    19: pb.response_heartbeat_pb2.ResponseHeartbeat,\n\n    # Market Data Infrastructure\n    100: pb.request_market_data_update_pb2.RequestMarketDataUpdate,\n    101: pb.response_market_data_update_pb2.ResponseMarketDataUpdate,\n    109: pb.request_search_symbols_pb2.RequestSearchSymbols,\n    110: pb.response_search_symbols_pb2.ResponseSearchSymbols,\n    113: pb.request_front_month_contract_pb2.RequestFrontMonthContract,\n    114: pb.response_front_month_contract_pb2.ResponseFrontMonthContract,\n\n    #115: pb.request_depth_by_order_snapshot_pb2.RequestDepthByOrderSnapshot,\n    #116: pb.response_depth_by_order_snapshot_pb2.ResponseDepthByOrderSnapshot,\n    #117: pb.request_depth_by_order_updates_pb2.RequestDepthByOrderUpdates,\n    #118: pb.response_depth_by_order_updates_pb2.ResponseDepthByOrderUpdates,\n\n    150: pb.last_trade_pb2.LastTrade,\n    151: pb.best_bid_offer_pb2.BestBidOffer,\n    #156: pb.order_book_pb2.OrderBook,\n    #160: pb.depth_by_order.DepthByOrder,\n    #161: pb.depth_by_order_end_event.DepthByOrderEndEvent,\n\n    # Order Plant Infrastructure\n    300: pb.request_login_info_pb2.RequestLoginInfo,\n    301: pb.response_login_info_pb2.ResponseLoginInfo,\n    302: pb.request_account_list_pb2.RequestAccountList,\n    303: pb.response_account_list_pb2.ResponseAccountList,\n    304: pb.request_account_rms_info_pb2.RequestAccountRmsInfo,\n    305: pb.response_account_rms_info_pb2.ResponseAccountRmsInfo,\n    306: pb.request_product_rms_info_pb2.RequestProductRmsInfo,\n    307: pb.response_product_rms_info_pb2.ResponseProductRmsInfo,\n    308: pb.request_subscribe_for_order_updates_pb2.RequestSubscribeForOrderUpdates,\n    309: pb.response_subscribe_for_order_updates_pb2.ResponseSubscribeForOrderUpdates,\n    310: pb.request_trade_routes_pb2.RequestTradeRoutes,\n    311: pb.response_trade_routes_pb2.ResponseTradeRoutes,\n    312: pb.request_new_order_pb2.RequestNewOrder,\n    313: pb.response_new_order_pb2.ResponseNewOrder,\n    314: pb.request_modify_order_pb2.RequestModifyOrder,\n    315: pb.response_modify_order_pb2.ResponseModifyOrder,\n    316: pb.request_cancel_order_pb2.RequestCancelOrder,\n    317: pb.response_cancel_order_pb2.ResponseCancelOrder,\n    320: pb.request_show_orders_pb2.RequestShowOrders,\n    321: pb.response_show_orders_pb2.ResponseShowOrders,\n    330: pb.request_bracket_order_pb2.RequestBracketOrder,\n    331: pb.response_bracket_order_pb2.ResponseBracketOrder,\n    332: pb.request_update_target_bracket_level_pb2.RequestUpdateTargetBracketLevel,\n    333: pb.response_update_target_bracket_level_pb2.ResponseUpdateTargetBracketLevel,\n    334: pb.request_update_stop_bracket_level_pb2.RequestUpdateStopBracketLevel,\n    335: pb.response_update_stop_bracket_level_pb2.ResponseUpdateStopBracketLevel,\n    336: pb.request_subscribe_to_bracket_updates_pb2.RequestSubscribeToBracketUpdates,\n    337: pb.response_subscribe_to_bracket_updates_pb2.ResponseSubscribeToBracketUpdates,\n\n    350: pb.trade_route_pb2.TradeRoute,\n    351: pb.rithmic_order_notification_pb2.RithmicOrderNotification,\n    352: pb.exchange_order_notification_pb2.ExchangeOrderNotification,\n    353: pb.bracket_updates_pb2.BracketUpdates,\n\n    # History Plant Infrastructure\n    200: pb.request_time_bar_update_pb2.RequestTimeBarUpdate,\n    201: pb.response_time_bar_update_pb2.ResponseTimeBarUpdate,\n    202: pb.request_time_bar_replay_pb2.RequestTimeBarReplay,\n    203: pb.response_time_bar_replay_pb2.ResponseTimeBarReplay,\n    204: pb.request_tick_bar_update_pb2.RequestTickBarUpdate,\n    205: pb.response_tick_bar_update_pb2.ResponseTickBarUpdate,\n    206: pb.request_tick_bar_replay_pb2.RequestTickBarReplay,\n    207: pb.response_tick_bar_replay_pb2.ResponseTickBarReplay,\n    250: pb.time_bar_pb2.TimeBar,\n    251: pb.tick_bar_pb2.TickBar,\n\n    # PnL Plant Infrastructure\n    400: pb.request_pnl_position_updates_pb2.RequestPnLPositionUpdates,\n    401: pb.response_pnl_position_updates_pb2.ResponsePnLPositionUpdates,\n    402: pb.request_pnl_position_snapshot_pb2.RequestPnLPositionSnapshot,\n    403: pb.response_pnl_position_snapshot_pb2.ResponsePnLPositionSnapshot,\n    450: pb.instrument_pnl_position_update_pb2.InstrumentPnLPositionUpdate,\n    451: pb.account_pnl_position_update_pb2.AccountPnLPositionUpdate,\n}\n\nclass BasePlant:\n    infra_type = None\n\n    def __init__(self, client, listen_interval=0.1):\n        self.ws = None\n        self.client = client\n        self.lock = asyncio.Lock()\n\n        # Heartbeats has to be sent every {interval} seconds, unless an update was received\n        self.heartbeat_interval = None\n        self.listen_interval = listen_interval\n        self.last_message_time = None\n\n    @property\n    def is_connected(self) -> bool:\n        return self.ws is not None and self.ws.state == OPEN\n\n    @property\n    def credentials(self):\n        return self.client.credentials\n\n    @property\n    def ssl_context(self):\n        return self.client.ssl_context\n\n    @property\n    def plant_type(self):\n        return {\n            pb.request_login_pb2.RequestLogin.SysInfraType.HISTORY_PLANT: \"history\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.PNL_PLANT: \"pnl\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.TICKER_PLANT: \"ticker\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.ORDER_PLANT: \"order\",\n        }[self.infra_type]\n\n    async def _connect(self):\n        \"\"\"\n        Clients should follow the below sequence for communicating with protocol server,\n        1. Open a websocket, upon connecting send 'RequestRithmicSystemInfo' message.\n           Parse the response and record list of 'system names' available. Close this connection\n\n        2. Open a new websocket, and login using the desired 'system_name'.\n        \"\"\"\n        self.ws = await websockets.connect(\n            self.credentials[\"gateway\"],\n            ssl=self.ssl_context,\n            ping_interval=10\n        )\n\n        if self.plant_type == \"ticker\":\n            info = await self.get_system_info()\n            await self._disconnect()\n\n            if self.credentials[\"system_name\"] not in info.system_name:\n                raise Exception(f\"You must specify valid SYSTEM_NAME in the credentials file: {info.system_name}\")\n\n            self.ws = await websockets.connect(\n                self.credentials[\"gateway\"],\n                ssl=self.ssl_context,\n                ping_interval=10\n            )\n\n    async def _disconnect(self):\n        if self.is_connected:\n            await self.ws.close(1000, \"Closing Connection\")\n\n    async def _login(self):\n        response = await self._send_and_recv(\n            template_id=10,\n            template_version=\"3.9\",\n            user=self.credentials[\"user\"],\n            password=self.credentials[\"password\"],\n            system_name=self.credentials[\"system_name\"],\n            app_name=self.credentials[\"app_name\"],\n            app_version=self.credentials[\"app_version\"],\n            infra_type=self.infra_type,\n        )\n\n        self.heartbeat_interval = response.heartbeat_interval\n\n        # Upon making a successful login, clients are expected to send at least a heartbeat request to the server\n        await self._send_heartbeat()\n\n        return response\n\n    async def _logout(self):\n        try:\n            return await self._send_and_recv(template_id=12)\n        except ConnectionClosedOK:\n            pass\n\n    async def get_system_info(self):\n        return await self._send_and_recv(template_id=16)\n\n    async def get_reference_data(self, symbol: str, exchange: str):\n        return await self._send_and_recv(\n            template_id=14,\n            symbol=symbol,\n            exchange=exchange\n        )\n\n    async def _send(self, message: bytes):\n        await self.ws.send(message)\n\n    async def _recv(self):\n        buffer = await self.ws.recv()\n        self.last_message_time = time.time()\n        return buffer\n\n    async def _send_request(self, **kwargs):\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        buffer = self._convert_request_to_bytes(request)\n        await self._send(buffer)\n\n        return template_id\n\n    async def _send_and_recv(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and decode the response\n        \"\"\"\n\n        async with self.lock:\n            template_id = await self._send_request(**kwargs)\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if not hasattr(response, \"rp_code\") or response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                break\n\n        if len(response.rp_code) and response.rp_code[0] != '0':\n            raise Exception(f\"Rithmic returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n        return response\n\n    async def _send_and_recv_many(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and expect 1...n responses back\n        \"\"\"\n\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        results = []\n        async with self.lock:\n            await self._send(self._convert_request_to_bytes(request))\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                if len(response.rp_code) > 0:\n                    if response.rp_code[0] != '0':\n                        raise Exception(f\"Server returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n                    break\n                else:\n                    results.append(response)\n\n        return results\n\n    def _convert_request_to_bytes(self, request):\n        serialized = request.SerializeToString()\n        length = len(serialized)\n        buffer = length.to_bytes(4, byteorder='big', signed=True)\n        buffer += serialized\n        return buffer\n\n    def _convert_bytes_to_response(self, buffer):\n        b = pb.base_pb2.Base()\n        b.ParseFromString(buffer[4:])\n        if b.template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown response template id: {b.template_id}\")\n\n        response = TEMPLATES_MAP[b.template_id]()\n        response.ParseFromString(buffer[4:])\n        return response\n\n    def _set_pb_field(self, obj, field_name, value):\n        field_descriptor = obj.DESCRIPTOR.fields_by_name[field_name]\n\n        if field_descriptor.label == FieldDescriptor.LABEL_REPEATED:\n            # Handle repeated fields (lists in protobuf)\n            field = getattr(obj, field_name)\n            if isinstance(value, list):\n                field.extend(value)\n            else:\n                field.append(value)\n        elif field_descriptor.type == FieldDescriptor.TYPE_MESSAGE:\n            # Handle nested message fields\n            nested_message = getattr(obj, field_name)\n            for sub_key, sub_value in value.items():\n                self._set_pb_field(nested_message, sub_key, sub_value)\n        else:\n            # Handle normal fields\n            try:\n                setattr(obj, field_name, value)\n            except:\n                logger.error(f\"Error when trying to set {field_name}\")\n                raise\n\n    async def _send_heartbeat(self):\n        return await self._send_and_recv(template_id=18)\n\n    async def _listen(self, max_iterations=None):\n        iteration_count = 0\n\n        try:\n            while True:\n                if max_iterations and iteration_count >= max_iterations:\n                    break\n\n                try:\n                    async with self.lock:\n                        buffer = await asyncio.wait_for(self._recv(), timeout=self.listen_interval)\n\n                    response = self._convert_bytes_to_response(buffer)\n                    await self._process_response(response)\n                    iteration_count += 1\n\n                except asyncio.TimeoutError:\n                    current_time = time.time()\n\n                    # Send regular heartbeats\n                    if current_time - self.last_message_time > self.heartbeat_interval-2:\n                        await self._send_heartbeat()\n\n                except ConnectionClosedError:\n                    logger.exception(\"WebSocket connection closed with error\")\n                    if not await self._handle_reconnection():\n                        break\n\n                except ConnectionClosedOK:\n                    logger.info(f\"WebSocket connection closed normally\")\n                    break\n\n        except Exception as e:\n            logger.error(f\"Exception in listener: {e}\")\n            traceback.print_exc()\n\n    async def _handle_reconnection(self, attempt=1):\n        max_retries = 5\n        wait_time = min(2 ** attempt, 120)\n\n        logger.info(f\"{self.plant_type} plant reconnection attempt {attempt} in {wait_time} seconds...\")\n        await asyncio.sleep(wait_time)\n\n        try:\n            # Attempt to reconnect this specific plant\n            await self._connect()\n            await self._login()\n\n            logger.info(f\"{self.plant_type} plant reconnection successful.\")\n            return True\n\n        except Exception as e:\n            if attempt < max_retries:\n                logger.warning(f\"{self.plant_type} plant reconnection failed: {e}. Retrying...\")\n                return await self._handle_reconnection(attempt + 1)\n            else:\n                logger.error(f\"{self.plant_type} plant max reconnection attempts reached. Could not reconnect: {e}\")\n\n        return False\n\n\n    def _response_to_dict(self, response):\n        data = MessageToDict(response, preserving_proto_field_name=True, use_integers_for_enums=True)\n\n        data.pop(\"template_id\", None)\n        data.pop(\"request_key\", None)\n        data.pop(\"user_msg\", None)\n        data.pop(\"rq_handler_rp_code\", None)\n        data.pop(\"rp_code\", None)\n\n        return data\n\n    async def _process_response(self, response):\n        raise NotImplementedError\n\n    def _datetime_to_utc(self, dt: datetime):\n        if dt.tzinfo is None:\n            # Use system timezone\n            system_timezone = pytz.timezone(str(get_localzone()))\n            dt = system_timezone.localize(dt)\n\n        if dt.tzinfo != pytz.utc:\n            # Convert to utc\n            dt = dt.astimezone(pytz.utc)\n\n        return dt\n\n    def _ssboe_usecs_to_datetime(self, ssboe: int, usecs: int):\n        ts = '{0}.{1}'.format(ssboe, usecs)\n        return datetime.fromtimestamp(float(ts), tz=pytz.utc)\n\n    def _datetime_to_ssboe_usecs(self, dt: datetime):\n        \"\"\"\n        Split the timestamp into integer seconds (ssboe) and rounded microseconds (usecs)\n        \"\"\"\n        timestamp = dt.timestamp()\n\n        ssboe = int(timestamp)\n        usecs = round((timestamp - ssboe) * 1_000_000)\n\n        return ssboe, usecs\n\n```\n\n\n",
        "eval_script": "# Mocking the protocol_buffers module\nclass MockProtocolBuffers:\n    class request_login_pb2:\n        class RequestLogin:\n            class SysInfraType:\n                HISTORY_PLANT = 0\n                PNL_PLANT = 1\n                TICKER_PLANT = 2\n                ORDER_PLANT = 3\n\n    class response_login_pb2:\n        class ResponseLogin:\n            pass\n\n    class request_logout_pb2:\n        class RequestLogout:\n            pass\n\n    class response_logout_pb2:\n        class ResponseLogout:\n            pass\n\n    class request_reference_data_pb2:\n        class RequestReferenceData:\n            pass\n\n    class response_reference_data_pb2:\n        class ResponseReferenceData:\n            pass\n\n    class request_rithmic_system_info_pb2:\n        class RequestRithmicSystemInfo:\n            pass\n\n    class response_rithmic_system_info_pb2:\n        class ResponseRithmicSystemInfo:\n            pass\n\n    class request_heartbeat_pb2:\n        class RequestHeartbeat:\n            pass\n\n    class response_heartbeat_pb2:\n        class ResponseHeartbeat:\n            pass\n\n    class request_market_data_update_pb2:\n        class RequestMarketDataUpdate:\n            pass\n\n    class response_market_data_update_pb2:\n        class ResponseMarketDataUpdate:\n            pass\n\n    class request_search_symbols_pb2:\n        class RequestSearchSymbols:\n            pass\n\n    class response_search_symbols_pb2:\n        class ResponseSearchSymbols:\n            pass\n\n    class request_front_month_contract_pb2:\n        class RequestFrontMonthContract:\n            pass\n\n    class response_front_month_contract_pb2:\n        class ResponseFrontMonthContract:\n            pass\n\n    class last_trade_pb2:\n        class LastTrade:\n            pass\n\n    class best_bid_offer_pb2:\n        class BestBidOffer:\n            pass\n\n    class request_login_info_pb2:\n        class RequestLoginInfo:\n            pass\n\n    class response_login_info_pb2:\n        class ResponseLoginInfo:\n            pass\n\n    class request_account_list_pb2:\n        class RequestAccountList:\n            pass\n\n    class response_account_list_pb2:\n        class ResponseAccountList:\n            pass\n\n    class request_account_rms_info_pb2:\n        class RequestAccountRmsInfo:\n            pass\n\n    class response_account_rms_info_pb2:\n        class ResponseAccountRmsInfo:\n            pass\n\n    class request_product_rms_info_pb2:\n        class RequestProductRmsInfo:\n            pass\n\n    class response_product_rms_info_pb2:\n        class ResponseProductRmsInfo:\n            pass\n\n    class request_subscribe_for_order_updates_pb2:\n        class RequestSubscribeForOrderUpdates:\n            pass\n\n    class response_subscribe_for_order_updates_pb2:\n        class ResponseSubscribeForOrderUpdates:\n            pass\n\n    class request_trade_routes_pb2:\n        class RequestTradeRoutes:\n            pass\n\n    class response_trade_routes_pb2:\n        class ResponseTradeRoutes:\n            pass\n\n    class request_new_order_pb2:\n        class RequestNewOrder:\n            pass\n\n    class response_new_order_pb2:\n        class ResponseNewOrder:\n            pass\n\n    class request_modify_order_pb2:\n        class RequestModifyOrder:\n            pass\n\n    class response_modify_order_pb2:\n        class ResponseModifyOrder:\n            pass\n\n    class request_cancel_order_pb2:\n        class RequestCancelOrder:\n            pass\n\n    class response_cancel_order_pb2:\n        class ResponseCancelOrder:\n            pass\n\n    class request_show_orders_pb2:\n        class RequestShowOrders:\n            pass\n\n    class response_show_orders_pb2:\n        class ResponseShowOrders:\n            pass\n\n    class request_bracket_order_pb2:\n        class RequestBracketOrder:\n            pass\n\n    class response_bracket_order_pb2:\n        class ResponseBracketOrder:\n            pass\n\n    class request_update_target_bracket_level_pb2:\n        class RequestUpdateTargetBracketLevel:\n            pass\n\n    class response_update_target_bracket_level_pb2:\n        class ResponseUpdateTargetBracketLevel:\n            pass\n\n    class request_update_stop_bracket_level_pb2:\n        class RequestUpdateStopBracketLevel:\n            pass\n\n    class response_update_stop_bracket_level_pb2:\n        class ResponseUpdateStopBracketLevel:\n            pass\n\n    class request_subscribe_to_bracket_updates_pb2:\n        class RequestSubscribeToBracketUpdates:\n            pass\n\n    class response_subscribe_to_bracket_updates_pb2:\n        class ResponseSubscribeToBracketUpdates:\n            pass\n\n    class trade_route_pb2:\n        class TradeRoute:\n            pass\n\n    class rithmic_order_notification_pb2:\n        class RithmicOrderNotification:\n            pass\n\n    class exchange_order_notification_pb2:\n        class ExchangeOrderNotification:\n            pass\n\n    class bracket_updates_pb2:\n        class BracketUpdates:\n            pass\n\n    class request_time_bar_update_pb2:\n        class RequestTimeBarUpdate:\n            pass\n\n    class response_time_bar_update_pb2:\n        class ResponseTimeBarUpdate:\n            pass\n\n    class request_time_bar_replay_pb2:\n        class RequestTimeBarReplay:\n            pass\n\n    class response_time_bar_replay_pb2:\n        class ResponseTimeBarReplay:\n            pass\n\n    class request_tick_bar_update_pb2:\n        class RequestTickBarUpdate:\n            pass\n\n    class response_tick_bar_update_pb2:\n        class ResponseTickBarUpdate:\n            pass\n\n    class request_tick_bar_replay_pb2:\n        class RequestTickBarReplay:\n            pass\n\n    class response_tick_bar_replay_pb2:\n        class ResponseTickBarReplay:\n            pass\n\n    class time_bar_pb2:\n        class TimeBar:\n            pass\n\n    class tick_bar_pb2:\n        class TickBar:\n            pass\n\n    class request_pnl_position_updates_pb2:\n        class RequestPnLPositionUpdates:\n            pass\n\n    class response_pnl_position_updates_pb2:\n        class ResponsePnLPositionUpdates:\n            pass\n\n    class request_pnl_position_snapshot_pb2:\n        class RequestPnLPositionSnapshot:\n            pass\n\n    class response_pnl_position_snapshot_pb2:\n        class ResponsePnLPositionSnapshot:\n            pass\n\n    class instrument_pnl_position_update_pb2:\n        class InstrumentPnLPositionUpdate:\n            pass\n\n    class account_pnl_position_update_pb2:\n        class AccountPnLPositionUpdate:\n            pass\n\n    class base_pb2:\n        class Base:\n            def ParseFromString(self, data):\n                pass\n\npb = MockProtocolBuffers()\n\n# Logger setup\nimport logging\nimport sys\n\nclass Logger:\n    def __init__(self, level: int = logging.INFO):\n        self.logger = logging.getLogger(\"rithmic\")\n        self.logger.setLevel(level)\n\n        formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n\n        console_handler = logging.StreamHandler(sys.stdout)\n        console_handler.setFormatter(formatter)\n\n        if not self.logger.hasHandlers():\n            self.logger.addHandler(console_handler)\n\n    def get_logger(self):\n        return self.logger\n\nlogger = Logger(level=logging.DEBUG).get_logger()\n\n# BasePlant class\nimport websockets\nfrom websockets import ConnectionClosedError, ConnectionClosedOK\nfrom websockets.protocol import OPEN\nimport asyncio\nimport time\nimport traceback\nfrom datetime import datetime\nimport pytz\nfrom tzlocal import get_localzone\nfrom google.protobuf.descriptor import FieldDescriptor\nfrom google.protobuf.json_format import MessageToDict\n\nTEMPLATES_MAP = {\n    # Shared\n    10: pb.request_login_pb2.RequestLogin,\n    11: pb.response_login_pb2.ResponseLogin,\n    12: pb.request_logout_pb2.RequestLogout,\n    13: pb.response_logout_pb2.ResponseLogout,\n    14: pb.request_reference_data_pb2.RequestReferenceData,\n    15: pb.response_reference_data_pb2.ResponseReferenceData,\n    16: pb.request_rithmic_system_info_pb2.RequestRithmicSystemInfo,\n    17: pb.response_rithmic_system_info_pb2.ResponseRithmicSystemInfo,\n    18: pb.request_heartbeat_pb2.RequestHeartbeat,\n    19: pb.response_heartbeat_pb2.ResponseHeartbeat,\n\n    # Market Data Infrastructure\n    100: pb.request_market_data_update_pb2.RequestMarketDataUpdate,\n    101: pb.response_market_data_update_pb2.ResponseMarketDataUpdate,\n    109: pb.request_search_symbols_pb2.RequestSearchSymbols,\n    110: pb.response_search_symbols_pb2.ResponseSearchSymbols,\n    113: pb.request_front_month_contract_pb2.RequestFrontMonthContract,\n    114: pb.response_front_month_contract_pb2.ResponseFrontMonthContract,\n\n    150: pb.last_trade_pb2.LastTrade,\n    151: pb.best_bid_offer_pb2.BestBidOffer,\n\n    # Order Plant Infrastructure\n    300: pb.request_login_info_pb2.RequestLoginInfo,\n    301: pb.response_login_info_pb2.ResponseLoginInfo,\n    302: pb.request_account_list_pb2.RequestAccountList,\n    303: pb.response_account_list_pb2.ResponseAccountList,\n    304: pb.request_account_rms_info_pb2.RequestAccountRmsInfo,\n    305: pb.response_account_rms_info_pb2.ResponseAccountRmsInfo,\n    306: pb.request_product_rms_info_pb2.RequestProductRmsInfo,\n    307: pb.response_product_rms_info_pb2.ResponseProductRmsInfo,\n    308: pb.request_subscribe_for_order_updates_pb2.RequestSubscribeForOrderUpdates,\n    309: pb.response_subscribe_for_order_updates_pb2.ResponseSubscribeForOrderUpdates,\n    310: pb.request_trade_routes_pb2.RequestTradeRoutes,\n    311: pb.response_trade_routes_pb2.ResponseTradeRoutes,\n    312: pb.request_new_order_pb2.RequestNewOrder,\n    313: pb.response_new_order_pb2.ResponseNewOrder,\n    314: pb.request_modify_order_pb2.RequestModifyOrder,\n    315: pb.response_modify_order_pb2.ResponseModifyOrder,\n    316: pb.request_cancel_order_pb2.RequestCancelOrder,\n    317: pb.response_cancel_order_pb2.ResponseCancelOrder,\n    320: pb.request_show_orders_pb2.RequestShowOrders,\n    321: pb.response_show_orders_pb2.ResponseShowOrders,\n    330: pb.request_bracket_order_pb2.RequestBracketOrder,\n    331: pb.response_bracket_order_pb2.ResponseBracketOrder,\n    332: pb.request_update_target_bracket_level_pb2.RequestUpdateTargetBracketLevel,\n    333: pb.response_update_target_bracket_level_pb2.ResponseUpdateTargetBracketLevel,\n    334: pb.request_update_stop_bracket_level_pb2.RequestUpdateStopBracketLevel,\n    335: pb.response_update_stop_bracket_level_pb2.ResponseUpdateStopBracketLevel,\n    336: pb.request_subscribe_to_bracket_updates_pb2.RequestSubscribeToBracketUpdates,\n    337: pb.response_subscribe_to_bracket_updates_pb2.ResponseSubscribeToBracketUpdates,\n\n    350: pb.trade_route_pb2.TradeRoute,\n    351: pb.rithmic_order_notification_pb2.RithmicOrderNotification,\n    352: pb.exchange_order_notification_pb2.ExchangeOrderNotification,\n    353: pb.bracket_updates_pb2.BracketUpdates,\n\n    # History Plant Infrastructure\n    200: pb.request_time_bar_update_pb2.RequestTimeBarUpdate,\n    201: pb.response_time_bar_update_pb2.ResponseTimeBarUpdate,\n    202: pb.request_time_bar_replay_pb2.RequestTimeBarReplay,\n    203: pb.response_time_bar_replay_pb2.ResponseTimeBarReplay,\n    204: pb.request_tick_bar_update_pb2.RequestTickBarUpdate,\n    205: pb.response_tick_bar_update_pb2.ResponseTickBarUpdate,\n    206: pb.request_tick_bar_replay_pb2.RequestTickBarReplay,\n    207: pb.response_tick_bar_replay_pb2.ResponseTickBarReplay,\n    250: pb.time_bar_pb2.TimeBar,\n    251: pb.tick_bar_pb2.TickBar,\n\n    # PnL Plant Infrastructure\n    400: pb.request_pnl_position_updates_pb2.RequestPnLPositionUpdates,\n    401: pb.response_pnl_position_updates_pb2.ResponsePnLPositionUpdates,\n    402: pb.request_pnl_position_snapshot_pb2.RequestPnLPositionSnapshot,\n    403: pb.response_pnl_position_snapshot_pb2.ResponsePnLPositionSnapshot,\n    450: pb.instrument_pnl_position_update_pb2.InstrumentPnLPositionUpdate,\n    451: pb.account_pnl_position_update_pb2.AccountPnLPositionUpdate,\n}\n\nclass BasePlant:\n    infra_type = None\n\n    def __init__(self, client, listen_interval=0.1):\n        self.ws = None\n        self.client = client\n        self.lock = asyncio.Lock()\n\n        # Heartbeats has to be sent every {interval} seconds, unless an update was received\n        self.heartbeat_interval = None\n        self.listen_interval = listen_interval\n        self.last_message_time = None\n\n    @property\n    def is_connected(self) -> bool:\n        return self.ws is not None and self.ws.state == OPEN\n\n    @property\n    def credentials(self):\n        return self.client.credentials\n\n    @property\n    def ssl_context(self):\n        return self.client.ssl_context\n\n    @property\n    def plant_type(self):\n        return {\n            pb.request_login_pb2.RequestLogin.SysInfraType.HISTORY_PLANT: \"history\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.PNL_PLANT: \"pnl\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.TICKER_PLANT: \"ticker\",\n            pb.request_login_pb2.RequestLogin.SysInfraType.ORDER_PLANT: \"order\",\n        }[self.infra_type]\n\n    async def _connect(self):\n        \"\"\"\n        Clients should follow the below sequence for communicating with protocol server,\n        1. Open a websocket, upon connecting send 'RequestRithmicSystemInfo' message.\n           Parse the response and record list of 'system names' available. Close this connection\n\n        2. Open a new websocket, and login using the desired 'system_name'.\n        \"\"\"\n        self.ws = await websockets.connect(\n            self.credentials[\"gateway\"],\n            ssl=self.ssl_context,\n            ping_interval=10\n        )\n\n        if self.plant_type == \"ticker\":\n            info = await self.get_system_info()\n            await self._disconnect()\n\n            if self.credentials[\"system_name\"] not in info.system_name:\n                raise Exception(f\"You must specify valid SYSTEM_NAME in the credentials file: {info.system_name}\")\n\n            self.ws = await websockets.connect(\n                self.credentials[\"gateway\"],\n                ssl=self.ssl_context,\n                ping_interval=10\n            )\n\n    async def _disconnect(self):\n        if self.is_connected:\n            await self.ws.close(1000, \"Closing Connection\")\n\n    async def _login(self):\n        response = await self._send_and_recv(\n            template_id=10,\n            template_version=\"3.9\",\n            user=self.credentials[\"user\"],\n            password=self.credentials[\"password\"],\n            system_name=self.credentials[\"system_name\"],\n            app_name=self.credentials[\"app_name\"],\n            app_version=self.credentials[\"app_version\"],\n            infra_type=self.infra_type,\n        )\n\n        self.heartbeat_interval = response.heartbeat_interval\n\n        # Upon making a successful login, clients are expected to send at least a heartbeat request to the server\n        await self._send_heartbeat()\n\n        return response\n\n    async def _logout(self):\n        try:\n            return await self._send_and_recv(template_id=12)\n        except ConnectionClosedOK:\n            pass\n\n    async def get_system_info(self):\n        return await self._send_and_recv(template_id=16)\n\n    async def get_reference_data(self, symbol: str, exchange: str):\n        return await self._send_and_recv(\n            template_id=14,\n            symbol=symbol,\n            exchange=exchange\n        )\n\n    async def _send(self, message: bytes):\n        await self.ws.send(message)\n\n    async def _recv(self):\n        buffer = await self.ws.recv()\n        self.last_message_time = time.time()\n        return buffer\n\n    async def _send_request(self, **kwargs):\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        buffer = self._convert_request_to_bytes(request)\n        await self._send(buffer)\n\n        return template_id\n\n    async def _send_and_recv(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and decode the response\n        \"\"\"\n\n        async with self.lock:\n            template_id = await self._send_request(**kwargs)\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if not hasattr(response, \"rp_code\") or response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                break\n\n        if len(response.rp_code) and response.rp_code[0] != '0':\n            raise Exception(f\"Rithmic returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n        return response\n\n    async def _send_and_recv_many(self, **kwargs):\n        \"\"\"\n        Sends a request to the API and expect 1...n responses back\n        \"\"\"\n\n        template_id = kwargs[\"template_id\"]\n\n        if template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown request template id: {template_id}\")\n\n        request = TEMPLATES_MAP[template_id]()\n        for k, v in kwargs.items():\n            self._set_pb_field(request, k, v)\n\n        results = []\n        async with self.lock:\n            await self._send(self._convert_request_to_bytes(request))\n\n            while True:\n                buffer = await self._recv()\n                response = self._convert_bytes_to_response(buffer)\n\n                if response.template_id != template_id + 1:\n                    await self._process_response(response)\n                    continue\n\n                if len(response.rp_code) > 0:\n                    if response.rp_code[0] != '0':\n                        raise Exception(f\"Server returned an error after request {template_id}: {', '.join(response.rp_code)}\")\n\n                    break\n                else:\n                    results.append(response)\n\n        return results\n\n    def _convert_request_to_bytes(self, request):\n        serialized = request.SerializeToString()\n        length = len(serialized)\n        buffer = length.to_bytes(4, byteorder='big', signed=True)\n        buffer += serialized\n        return buffer\n\n    def _convert_bytes_to_response(self, buffer):\n        b = pb.base_pb2.Base()\n        b.ParseFromString(buffer[4:])\n        if b.template_id not in TEMPLATES_MAP:\n            raise Exception(f\"Unknown response template id: {b.template_id}\")\n\n        response = TEMPLATES_MAP[b.template_id]()\n        response.ParseFromString(buffer[4:])\n        return response\n\n    def _set_pb_field(self, obj, field_name, value):\n        field_descriptor = obj.DESCRIPTOR.fields_by_name[field_name]\n\n        if field_descriptor.label == FieldDescriptor.LABEL_REPEATED:\n            # Handle repeated fields (lists in protobuf)\n            field = getattr(obj, field_name)\n            if isinstance(value, list):\n                field.extend(value)\n            else:\n                field.append(value)\n        elif field_descriptor.type == FieldDescriptor.TYPE_MESSAGE:\n            # Handle nested message fields\n            nested_message = getattr(obj, field_name)\n            for sub_key, sub_value in value.items():\n                self._set_pb_field(nested_message, sub_key, sub_value)\n        else:\n            # Handle normal fields\n            try:\n                setattr(obj, field_name, value)\n            except:\n                logger.error(f\"Error when trying to set {field_name}\")\n                raise\n\n    async def _send_heartbeat(self):\n        return await self._send_and_recv(template_id=18)\n\n    async def _listen(self, max_iterations=None):\n        iteration_count = 0\n\n        try:\n            while True:\n                if max_iterations and iteration_count >= max_iterations:\n                    break\n\n                try:\n                    async with self.lock:\n                        buffer = await asyncio.wait_for(self._recv(), timeout=self.listen_interval)\n\n                    response = self._convert_bytes_to_response(buffer)\n                    await self._process_response(response)\n                    iteration_count += 1\n\n                except asyncio.TimeoutError:\n                    current_time = time.time()\n\n                    # Send regular heartbeats\n                    if current_time - self.last_message_time > self.heartbeat_interval-2:\n                        await self._send_heartbeat()\n\n                except ConnectionClosedError:\n                    logger.exception(\"WebSocket connection closed with error\")\n                    if not await self._handle_reconnection():\n                        break\n\n                except ConnectionClosedOK:\n                    logger.info(f\"WebSocket connection closed normally\")\n                    break\n\n        except Exception as e:\n            logger.error(f\"Exception in listener: {e}\")\n            traceback.print_exc()\n\n    async def _handle_reconnection(self, attempt=1):\n        max_retries = 5\n        wait_time = min(2 ** attempt, 120)\n\n        logger.info(f\"{self.plant_type} plant reconnection attempt {attempt} in {wait_time} seconds...\")\n        await asyncio.sleep(wait_time)\n\n        try:\n            # Attempt to reconnect this specific plant\n            await self._connect()\n            await self._login()\n\n            logger.info(f\"{self.plant_type} plant reconnection successful.\")\n            return True\n\n        except Exception as e:\n            if attempt < max_retries:\n                logger.warning(f\"{self.plant_type} plant reconnection failed: {e}. Retrying...\")\n                return await self._handle_reconnection(attempt + 1)\n            else:\n                logger.error(f\"{self.plant_type} plant max reconnection attempts reached. Could not reconnect: {e}\")\n\n        return False\n\n    def _response_to_dict(self, response):\n        data = MessageToDict(response, preserving_proto_field_name=True, use_integers_for_enums=True)\n\n        data.pop(\"template_id\", None)\n        data.pop(\"request_key\", None)\n        data.pop(\"user_msg\", None)\n        data.pop(\"rq_handler_rp_code\", None)\n        data.pop(\"rp_code\", None)\n\n        return data\n\n    async def _process_response(self, response):\n        raise NotImplementedError\n\n    def _datetime_to_utc(self, dt: datetime):\n        if dt.tzinfo is None:\n            # Use system timezone\n            system_timezone = pytz.timezone(str(get_localzone()))\n            dt = system_timezone.localize(dt)\n\n        if dt.tzinfo != pytz.utc:\n            # Convert to utc\n            dt = dt.astimezone(pytz.utc)\n\n        return dt\n\n    def _ssboe_usecs_to_datetime(self, ssboe: int, usecs: int):\n        ts = '{0}.{1}'.format(ssboe, usecs)\n        return datetime.fromtimestamp(float(ts), tz=pytz.utc)\n\n\n    def _datetime_to_ssboe_usecs(self, dt: datetime):\n        \"\"\"\n        Split the timestamp into integer seconds (ssboe) and rounded microseconds (usecs)\n        \"\"\"\n        timestamp = dt.timestamp()\n\n        ssboe = int(timestamp)\n        usecs = round((timestamp - ssboe) * 1_000_000)\n\n        return ssboe, usecs\n\ndef test__ssboe_usecs_to_datetime():\n    plant = BasePlant(None)\n\n    # Test case 1: Normal timestamp\n    ssboe, usecs = 1633072800, 123456\n    assert plant._ssboe_usecs_to_datetime(ssboe, usecs) == plant._ssboe_usecs_to_datetime_new_implementation(ssboe, usecs)\n\n    # Test case 2: Timestamp with zero microseconds\n    ssboe, usecs = 1633072800, 0\n    assert plant._ssboe_usecs_to_datetime(ssboe, usecs) == plant._ssboe_usecs_to_datetime_new_implementation(ssboe, usecs)\n\n    # Test case 3: Timestamp with maximum microseconds\n    ssboe, usecs = 1633072800, 999999\n    assert plant._ssboe_usecs_to_datetime(ssboe, usecs) == plant._ssboe_usecs_to_datetime_new_implementation(ssboe, usecs)\n\nif __name__ == \"__main__\":\n    test__ssboe_usecs_to_datetime()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `_ssboe_usecs_to_datetime` takes two arguments, `ssboe` and `usecs`, formats them into a string representing a timestamp with microseconds, and then converts this string into a `datetime` object in UTC timezone using `datetime.fromtimestamp`. The revised function in the provided code is identical in its implementation. It performs the same operations: formatting the input integers into a timestamp string and converting it to a `datetime` object in UTC. There are no changes in logic or functionality between the original and revised function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `_ssboe_usecs_to_datetime` returns a `datetime` object, which is a return value. Thus, this condition is satisfied.\n\n2. **CONDITION 2**: The test cases use assertions to check the return values of `_ssboe_usecs_to_datetime` and `_ssboe_usecs_to_datetime_new_implementation`. There is no checking of printed or logged content. Therefore, this condition is satisfied.\n\n3. **CONDITION 3**: The test cases compare the outputs of `_ssboe_usecs_to_datetime` and `_ssboe_usecs_to_datetime_new_implementation` for the same inputs. This ensures that the new implementation can pass all test cases only if it has the exact same functionality as the original. Thus, this condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations. Since `_ssboe_usecs_to_datetime` has return values, the use of assertions is reasonable. Therefore, this condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover different scenarios: normal timestamp, timestamp with zero microseconds, and timestamp with maximum microseconds. These cases are non-trivial as they test the function's ability to handle typical and edge-case inputs. Thus, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "6cbc0f98bac31e1d99ff63f77fb9bd30bc04c281"
    },
    {
        "func_name": "RGB_LED.wheel",
        "idx": "613",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/RGB_LED.py",
        "orig_func": "@staticmethod\ndef wheel(position=0):\n    \"\"\"Generate rainbow colors across 0-255 positions.\"\"\"\n    if position < 85:\n        return (position * 3, 255 - position * 3, 0)\n    elif position < 170:\n        position -= 85\n        return (255 - position * 3, 0, position * 3)\n    else:\n        position -= 170\n        return (0, position * 3, 255 - position * 3)",
        "orig_context": "```python\n## src/rpi_gpio/RGB_LED.py\nimport RPi.GPIO as GPIO\n\nimport time as t\n\nimport threading\n\nclass RGB_LED:\n    def __init__(self, red_pin, green_pin, blue_pin):\n        self.red_pin = red_pin\n        self.green_pin = green_pin\n        self.blue_pin = blue_pin\n\n        GPIO.setup(self.red_pin, GPIO.OUT)\n        GPIO.setup(self.green_pin, GPIO.OUT)\n        GPIO.setup(self.blue_pin, GPIO.OUT)\n\n        self.red_pwm = GPIO.PWM(self.red_pin, 100)\n        self.green_pwm = GPIO.PWM(self.green_pin, 100)\n        self.blue_pwm = GPIO.PWM(self.blue_pin, 100)\n\n        self.red_pwm.start(0)\n        self.green_pwm.start(0)\n        self.blue_pwm.start(0)\n\n        self.running = False\n        self.rainbow_thread = None\n\n    def set_color(self, hex_color):\n        \"\"\"Set the RGB LED to a specific hex color.\"\"\"\n        try:\n            hex_color = hex_color.lstrip('#')\n            red = int(hex_color[0:2], 16)\n            green = int(hex_color[2:4], 16)\n            blue = int(hex_color[4:6], 16)\n\n            # Convert RGB values (0-255) to PWM duty cycle (0-100)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n        except ValueError:\n            print(\"Invalid hex color format. Please use a hex string (e.g., '#FF00FF').\")\n\n    def rainbow_cycle_sequence(self, wait=0.05):\n        \"\"\"Cycle through colors in a rainbow effect until stopped.\"\"\"\n        self.running = True\n        position = 0  # Start at the beginning of the color wheel\n        while self.running:\n            red, green, blue = self.wheel(position % 256)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n            t.sleep(wait)\n            position += 1\n\n    def rainbow_cycle(self, wait=0.05):\n        \"\"\"Start the rainbow cycle in a separate thread if not already running.\"\"\"\n        if self.rainbow_thread is None or not self.rainbow_thread.is_alive():\n            self.rainbow_thread = threading.Thread(target=self.rainbow_cycle_sequence, args=(wait,))\n            self.rainbow_thread.start()\n\n    def stop_rainbow_cycle(self):\n        \"\"\"Stop the rainbow cycle.\"\"\"\n        self.running = False\n        self.off()\n\n    @staticmethod\n    def wheel(position=0):\n        \"\"\"Generate rainbow colors across 0-255 positions.\"\"\"\n        if position < 85:\n            return (position * 3, 255 - position * 3, 0)\n        elif position < 170:\n            position -= 85\n            return (255 - position * 3, 0, position * 3)\n        else:\n            position -= 170\n            return (0, position * 3, 255 - position * 3)\n\n    def off(self):\n        \"\"\"Turn off the RGB LED.\"\"\"\n        self.red_pwm.ChangeDutyCycle(0)\n        self.green_pwm.ChangeDutyCycle(0)\n        self.blue_pwm.ChangeDutyCycle(0)\n\n    def __del__(self):\n        \"\"\"Cleanup GPIO pins when the object is deleted.\"\"\"\n        self.off()\n        self.red_pwm.stop()\n        self.green_pwm.stop()\n        self.blue_pwm.stop()\n        if self.rainbow_thread and self.rainbow_thread.is_alive():\n            self.rainbow_thread.join()\n        GPIO.cleanup(self.red_pin, self.green_pin, self.blue_pin)\n\n```\n\n\n",
        "eval_script": "# Mock RPi.GPIO library\nclass MockGPIO:\n    BOARD = 'BOARD'\n    OUT = 'OUT'\n\n    @staticmethod\n    def setup(pin, mode):\n        print(f\"Mock setup: pin {pin}, mode {mode}\")\n\n    @staticmethod\n    def PWM(pin, frequency):\n        print(f\"Mock PWM: pin {pin}, frequency {frequency}\")\n        return MockPWM(pin, frequency)\n\n    @staticmethod\n    def cleanup(*args):\n        print(f\"Mock cleanup: pins {args}\")\n\nclass MockPWM:\n    def __init__(self, pin, frequency):\n        self.pin = pin\n        self.frequency = frequency\n\n    def start(self, duty_cycle):\n        print(f\"Mock PWM start: pin {self.pin}, duty_cycle {duty_cycle}\")\n\n    def ChangeDutyCycle(self, duty_cycle):\n        print(f\"Mock PWM ChangeDutyCycle: pin {self.pin}, duty_cycle {duty_cycle}\")\n\n    def stop(self):\n        print(f\"Mock PWM stop: pin {self.pin}\")\n\n# Use the mock GPIO library\nGPIO = MockGPIO\n\n# Original RGB_LED class code\nimport time as t\nimport threading\n\nclass RGB_LED:\n    def __init__(self, red_pin, green_pin, blue_pin):\n        self.red_pin = red_pin\n        self.green_pin = green_pin\n        self.blue_pin = blue_pin\n\n        GPIO.setup(self.red_pin, GPIO.OUT)\n        GPIO.setup(self.green_pin, GPIO.OUT)\n        GPIO.setup(self.blue_pin, GPIO.OUT)\n\n        self.red_pwm = GPIO.PWM(self.red_pin, 100)\n        self.green_pwm = GPIO.PWM(self.green_pin, 100)\n        self.blue_pwm = GPIO.PWM(self.blue_pin, 100)\n\n        self.red_pwm.start(0)\n        self.green_pwm.start(0)\n        self.blue_pwm.start(0)\n\n        self.running = False\n        self.rainbow_thread = None\n\n    def set_color(self, hex_color):\n        \"\"\"Set the RGB LED to a specific hex color.\"\"\"\n        try:\n            hex_color = hex_color.lstrip('#')\n            red = int(hex_color[0:2], 16)\n            green = int(hex_color[2:4], 16)\n            blue = int(hex_color[4:6], 16)\n\n            # Convert RGB values (0-255) to PWM duty cycle (0-100)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n        except ValueError:\n            print(\"Invalid hex color format. Please use a hex string (e.g., '#FF00FF').\")\n\n    def rainbow_cycle_sequence(self, wait=0.05):\n        \"\"\"Cycle through colors in a rainbow effect until stopped.\"\"\"\n        self.running = True\n        position = 0  # Start at the beginning of the color wheel\n        while self.running:\n            red, green, blue = self.wheel(position % 256)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n            t.sleep(wait)\n            position += 1\n\n    def rainbow_cycle(self, wait=0.05):\n        \"\"\"Start the rainbow cycle in a separate thread if not already running.\"\"\"\n        if self.rainbow_thread is None or not self.rainbow_thread.is_alive():\n            self.rainbow_thread = threading.Thread(target=self.rainbow_cycle_sequence, args=(wait,))\n            self.rainbow_thread.start()\n\n    def stop_rainbow_cycle(self):\n        \"\"\"Stop the rainbow cycle.\"\"\"\n        self.running = False\n        self.off()\n\n    @staticmethod\n    def wheel(position=0):\n        \"\"\"Generate rainbow colors across 0-255 positions.\"\"\"\n        if position < 85:\n            return (position * 3, 255 - position * 3, 0)\n        elif position < 170:\n            position -= 85\n            return (255 - position * 3, 0, position * 3)\n        else:\n            position -= 170\n            return (0, position * 3, 255 - position * 3)\n\n    def off(self):\n        \"\"\"Turn off the RGB LED.\"\"\"\n        self.red_pwm.ChangeDutyCycle(0)\n        self.green_pwm.ChangeDutyCycle(0)\n        self.blue_pwm.ChangeDutyCycle(0)\n\n    def __del__(self):\n        \"\"\"Cleanup GPIO pins when the object is deleted.\"\"\"\n        self.off()\n        self.red_pwm.stop()\n        self.green_pwm.stop()\n        self.blue_pwm.stop()\n        if self.rainbow_thread and self.rainbow_thread.is_alive():\n            self.rainbow_thread.join()\n        GPIO.cleanup(self.red_pin, self.green_pin, self.blue_pin)\n\n\ndef test_wheel():\n    # Test for position < 85\n    assert RGB_LED.wheel(0) == RGB_LED.wheel_new_implementation(0)\n    assert RGB_LED.wheel(42) == RGB_LED.wheel_new_implementation(42)\n    assert RGB_LED.wheel(84) == RGB_LED.wheel_new_implementation(84)\n\n    # Test for 85 <= position < 170\n    assert RGB_LED.wheel(85) == RGB_LED.wheel_new_implementation(85)\n    assert RGB_LED.wheel(127) == RGB_LED.wheel_new_implementation(127)\n    assert RGB_LED.wheel(169) == RGB_LED.wheel_new_implementation(169)\n\n    # Test for position >= 170\n    assert RGB_LED.wheel(170) == RGB_LED.wheel_new_implementation(170)\n    assert RGB_LED.wheel(212) == RGB_LED.wheel_new_implementation(212)\n    assert RGB_LED.wheel(255) == RGB_LED.wheel_new_implementation(255)\n\nif __name__ == \"__main__\":\n    test_wheel()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the same as the ORIGINAL FUNCTION. Both functions are static methods named `wheel` and have the same logic for generating rainbow colors across 0-255 positions. The logic involves checking the position and calculating RGB values based on the position range: less than 85, between 85 and 170, and greater than or equal to 170. The RGB values are calculated using the same formulas in both functions. There is no indication of a `wheel_new_implementation` function in the provided code, suggesting that the test function is incorrectly named or incomplete. However, based on the provided code, the `wheel` function itself remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `wheel` function returns a tuple of RGB values based on the input position, satisfying the condition of having return values.\n- CONDITION 2: The test cases in `test_wheel` only use assert statements to compare the return values of `wheel` and `wheel_new_implementation`, without checking printed or logged contents.\n- CONDITION 3: The test cases cover all three logical branches of the `wheel` function (position < 85, 85 <= position < 170, and position >= 170), ensuring that `wheel_new_implementation` must have the same functionality to pass all tests.\n- CONDITION 4: The assert statements are reasonable as they compare the return values of `wheel` and `wheel_new_implementation` for various positions, which is appropriate given that `wheel` has return values.\n- CONDITION 5: The test cases are non-trivial as they cover different ranges of the input space, ensuring that the functionality of the `wheel` function is thoroughly tested.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "RGB_LED.off",
        "idx": "614",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/RGB_LED.py",
        "orig_func": "def off(self):\n    \"\"\"Turn off the RGB LED.\"\"\"\n    self.red_pwm.ChangeDutyCycle(0)\n    self.green_pwm.ChangeDutyCycle(0)\n    self.blue_pwm.ChangeDutyCycle(0)",
        "orig_context": "```python\n## src/rpi_gpio/RGB_LED.py\nimport RPi.GPIO as GPIO\n\nimport time as t\n\nimport threading\n\nclass RGB_LED:\n    def __init__(self, red_pin, green_pin, blue_pin):\n        self.red_pin = red_pin\n        self.green_pin = green_pin\n        self.blue_pin = blue_pin\n\n        GPIO.setup(self.red_pin, GPIO.OUT)\n        GPIO.setup(self.green_pin, GPIO.OUT)\n        GPIO.setup(self.blue_pin, GPIO.OUT)\n\n        self.red_pwm = GPIO.PWM(self.red_pin, 100)\n        self.green_pwm = GPIO.PWM(self.green_pin, 100)\n        self.blue_pwm = GPIO.PWM(self.blue_pin, 100)\n\n        self.red_pwm.start(0)\n        self.green_pwm.start(0)\n        self.blue_pwm.start(0)\n\n        self.running = False\n        self.rainbow_thread = None\n\n    def set_color(self, hex_color):\n        \"\"\"Set the RGB LED to a specific hex color.\"\"\"\n        try:\n            hex_color = hex_color.lstrip('#')\n            red = int(hex_color[0:2], 16)\n            green = int(hex_color[2:4], 16)\n            blue = int(hex_color[4:6], 16)\n\n            # Convert RGB values (0-255) to PWM duty cycle (0-100)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n        except ValueError:\n            print(\"Invalid hex color format. Please use a hex string (e.g., '#FF00FF').\")\n\n    def rainbow_cycle_sequence(self, wait=0.05):\n        \"\"\"Cycle through colors in a rainbow effect until stopped.\"\"\"\n        self.running = True\n        position = 0  # Start at the beginning of the color wheel\n        while self.running:\n            red, green, blue = self.wheel(position % 256)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n            t.sleep(wait)\n            position += 1\n\n    def rainbow_cycle(self, wait=0.05):\n        \"\"\"Start the rainbow cycle in a separate thread if not already running.\"\"\"\n        if self.rainbow_thread is None or not self.rainbow_thread.is_alive():\n            self.rainbow_thread = threading.Thread(target=self.rainbow_cycle_sequence, args=(wait,))\n            self.rainbow_thread.start()\n\n    def stop_rainbow_cycle(self):\n        \"\"\"Stop the rainbow cycle.\"\"\"\n        self.running = False\n        self.off()\n\n    @staticmethod\n    def wheel(position=0):\n        \"\"\"Generate rainbow colors across 0-255 positions.\"\"\"\n        if position < 85:\n            return (position * 3, 255 - position * 3, 0)\n        elif position < 170:\n            position -= 85\n            return (255 - position * 3, 0, position * 3)\n        else:\n            position -= 170\n            return (0, position * 3, 255 - position * 3)\n\n    def off(self):\n        \"\"\"Turn off the RGB LED.\"\"\"\n        self.red_pwm.ChangeDutyCycle(0)\n        self.green_pwm.ChangeDutyCycle(0)\n        self.blue_pwm.ChangeDutyCycle(0)\n\n    def __del__(self):\n        \"\"\"Cleanup GPIO pins when the object is deleted.\"\"\"\n        self.off()\n        self.red_pwm.stop()\n        self.green_pwm.stop()\n        self.blue_pwm.stop()\n        if self.rainbow_thread and self.rainbow_thread.is_alive():\n            self.rainbow_thread.join()\n        GPIO.cleanup(self.red_pin, self.green_pin, self.blue_pin)\n\n```\n\n\n",
        "eval_script": "# Mock RPi.GPIO module\nclass MockGPIO:\n    OUT = 'out'\n\n    @staticmethod\n    def setup(pin, mode):\n        print(f\"Mock setup: pin {pin}, mode {mode}\")\n\n    class PWM:\n        def __init__(self, pin, frequency):\n            self.pin = pin\n            self.frequency = frequency\n            self.duty_cycle = 0\n            print(f\"Mock PWM init: pin {pin}, frequency {frequency}\")\n\n        def start(self, duty_cycle):\n            self.duty_cycle = duty_cycle\n            print(f\"Mock PWM start: pin {self.pin}, duty_cycle {duty_cycle}\")\n\n        def ChangeDutyCycle(self, duty_cycle):\n            self.duty_cycle = duty_cycle\n            print(f\"Mock PWM ChangeDutyCycle: pin {self.pin}, duty_cycle {duty_cycle}\")\n\n        def stop(self):\n            print(f\"Mock PWM stop: pin {self.pin}\")\n\n    @staticmethod\n    def cleanup(*pins):\n        print(f\"Mock cleanup: pins {pins}\")\n\n# Replace RPi.GPIO with the mock\nGPIO = MockGPIO\n\n# Original code with the mock in place\nimport time as t\nimport threading\n\nclass RGB_LED:\n    def __init__(self, red_pin, green_pin, blue_pin):\n        self.red_pin = red_pin\n        self.green_pin = green_pin\n        self.blue_pin = blue_pin\n\n        GPIO.setup(self.red_pin, GPIO.OUT)\n        GPIO.setup(self.green_pin, GPIO.OUT)\n        GPIO.setup(self.blue_pin, GPIO.OUT)\n\n        self.red_pwm = GPIO.PWM(self.red_pin, 100)\n        self.green_pwm = GPIO.PWM(self.green_pin, 100)\n        self.blue_pwm = GPIO.PWM(self.blue_pin, 100)\n\n        self.red_pwm.start(0)\n        self.green_pwm.start(0)\n        self.blue_pwm.start(0)\n\n        self.running = False\n        self.rainbow_thread = None\n\n    def set_color(self, hex_color):\n        \"\"\"Set the RGB LED to a specific hex color.\"\"\"\n        try:\n            hex_color = hex_color.lstrip('#')\n            red = int(hex_color[0:2], 16)\n            green = int(hex_color[2:4], 16)\n            blue = int(hex_color[4:6], 16)\n\n            # Convert RGB values (0-255) to PWM duty cycle (0-100)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n        except ValueError:\n            print(\"Invalid hex color format. Please use a hex string (e.g., '#FF00FF').\")\n\n    def rainbow_cycle_sequence(self, wait=0.05):\n        \"\"\"Cycle through colors in a rainbow effect until stopped.\"\"\"\n        self.running = True\n        position = 0  # Start at the beginning of the color wheel\n        while self.running:\n            red, green, blue = self.wheel(position % 256)\n            self.red_pwm.ChangeDutyCycle(red / 255 * 100)\n            self.green_pwm.ChangeDutyCycle(green / 255 * 100)\n            self.blue_pwm.ChangeDutyCycle(blue / 255 * 100)\n            t.sleep(wait)\n            position += 1\n\n    def rainbow_cycle(self, wait=0.05):\n        \"\"\"Start the rainbow cycle in a separate thread if not already running.\"\"\"\n        if self.rainbow_thread is None or not self.rainbow_thread.is_alive():\n            self.rainbow_thread = threading.Thread(target=self.rainbow_cycle_sequence, args=(wait,))\n            self.rainbow_thread.start()\n\n    def stop_rainbow_cycle(self):\n        \"\"\"Stop the rainbow cycle.\"\"\"\n        self.running = False\n        self.off()\n\n    @staticmethod\n    def wheel(position=0):\n        \"\"\"Generate rainbow colors across 0-255 positions.\"\"\"\n        if position < 85:\n            return (position * 3, 255 - position * 3, 0)\n        elif position < 170:\n            position -= 85\n            return (255 - position * 3, 0, position * 3)\n        else:\n            position -= 170\n            return (0, position * 3, 255 - position * 3)\n\n    def off(self):\n        \"\"\"Turn off the RGB LED.\"\"\"\n        self.red_pwm.ChangeDutyCycle(0)\n        self.green_pwm.ChangeDutyCycle(0)\n        self.blue_pwm.ChangeDutyCycle(0)\n\n\n    def __del__(self):\n        \"\"\"Cleanup GPIO pins when the object is deleted.\"\"\"\n        self.off()\n        self.red_pwm.stop()\n        self.green_pwm.stop()\n        self.blue_pwm.stop()\n        if self.rainbow_thread and self.rainbow_thread.is_alive():\n            self.rainbow_thread.join()\n        GPIO.cleanup(self.red_pin, self.green_pin, self.blue_pin)\n\ndef test_off():\n    led = RGB_LED(17, 27, 22)\n    # Test original off method\n    led.off()\n    assert led.red_pwm.duty_cycle == 0, \"Red PWM not off\"\n    assert led.green_pwm.duty_cycle == 0, \"Green PWM not off\"\n    assert led.blue_pwm.duty_cycle == 0, \"Blue PWM not off\"\n    \n    # Test new off implementation\n    led.off_new_implementation()\n    assert led.red_pwm.duty_cycle == 0, \"Red PWM not off in new implementation\"\n    assert led.green_pwm.duty_cycle == 0, \"Green PWM not off in new implementation\"\n    assert led.blue_pwm.duty_cycle == 0, \"Blue PWM not off in new implementation\"\n\nif __name__ == \"__main__\":\n    test_off()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `off` in the `RGB_LED` class is identical to the ORIGINAL FUNCTION. Both functions set the duty cycle of the red, green, and blue PWM to 0, effectively turning off the RGB LED. There is no change in functionality between the two versions of the `off` method. The test function `test_off` also confirms that the functionality remains the same by asserting that the duty cycles are set to 0 in both the original and new implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `off` function modifies the state of the `RGB_LED` object by setting the duty cycle of the PWM objects to 0. This satisfies the condition as it modifies the input arguments (in this case, the PWM objects within the `RGB_LED` class).\n\n- **CONDITION 2**: The test cases in `test_off` check the state of the PWM objects' duty cycles after calling the `off` and `off_new_implementation` methods. They do not rely on printed or logged output, satisfying this condition.\n\n- **CONDITION 3**: The test cases check that the duty cycles are set to 0 for both the original and new implementations of `off`. This ensures that `off_new_implementation` must have the exact same functionality as `off` to pass the tests, satisfying this condition.\n\n- **CONDITION 4**: The test cases use assertions to verify the state of the PWM objects, which is reasonable given that `off` does not return a value but modifies the state of the object. This satisfies the condition.\n\n- **CONDITION 5**: The test cases are non-trivial as they verify the state of three different PWM objects, ensuring that the `off` method correctly turns off all components of the RGB LED. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "buzzer.beep",
        "idx": "615",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/buzzer.py",
        "orig_func": "def beep(self, duration):\n    \"\"\"Uses threads to make Beep() a non-blocking function\"\"\"\n    if self.beep_thread and self.beep_thread.is_alive():\n        self.beep_thread.join()\n    self.beep_thread = threading.Thread(target=self.beep_sequence, args=(duration,))\n    self.beep_thread.start()",
        "orig_context": "```python\n## src/rpi_gpio/buzzer.py\nimport RPi.GPIO as GPIO\n\nimport time as t\n\nimport threading\n\nclass buzzer:\n    \"\"\"This uses GPIO pin-number on the pi.\\n\n    This class is for controlling basic Piezo Buzzers.\"\"\"\n    def __init__(self, pin):\n        self.pin = pin\n\n        self.beep_thread = None\n\n        GPIO.setup(pin, GPIO.OUT)\n\n    def on(self):\n        if GPIO.input(self.pin) == 0:\n            GPIO.output(self.pin, GPIO.HIGH)\n\n    def off(self):\n        if GPIO.input(self.pin) == 1:\n            GPIO.output(self.pin, GPIO.LOW)\n\n    def toggle(self):\n        if GPIO.input(self.pin) == 0:\n            self.on()\n        else:\n            self.off()\n\n    def beep_sequence(self, duration):\n        GPIO.output(self.pin, GPIO.LOW)\n        for i in range(int(duration)):\n            self.on()\n            t.sleep(0.5)\n            self.off()\n            t.sleep(0.5)\n\n    def beep(self, duration):\n        \"\"\"Uses threads to make Beep() a non-blocking function\"\"\"\n        if self.beep_thread and self.beep_thread.is_alive():\n            self.beep_thread.join()\n\n        self.beep_thread = threading.Thread(target=self.beep_sequence, args=(duration,))\n        self.beep_thread.start()\n\n    def __del__(self):\n        GPIO.output(self.pin, GPIO.LOW)\n        if self.beep_thread and self.beep_thread.is_alive():\n            self.beep_thread.join()\n        GPIO.cleanup(self.pin)\n\n```\n\n\n",
        "eval_script": "## src/rpi_gpio/buzzer.py\n# Mock RPi.GPIO library\nclass MockGPIO:\n    BOARD = 'BOARD'\n    OUT = 'OUT'\n    HIGH = 'HIGH'\n    LOW = 'LOW'\n\n    def __init__(self):\n        self.pin_states = {}\n\n    def setup(self, pin, mode):\n        self.pin_states[pin] = self.LOW\n\n    def output(self, pin, state):\n        self.pin_states[pin] = state\n        print(f\"Pin {pin} set to {state}\")\n\n    def input(self, pin):\n        return self.pin_states.get(pin, self.LOW)\n\n    def cleanup(self, pin=None):\n        if pin:\n            del self.pin_states[pin]\n        else:\n            self.pin_states.clear()\n        print(f\"Cleaned up pin {pin}\")\n\n# Use the mock GPIO for testing\nGPIO = MockGPIO()\n\nimport time as t\nimport threading\n\nclass buzzer:\n    \"\"\"This uses GPIO pin-number on the pi.\\n\n    This class is for controlling basic Piezo Buzzers.\"\"\"\n    def __init__(self, pin):\n        self.pin = pin\n\n        self.beep_thread = None\n\n        GPIO.setup(pin, GPIO.OUT)\n\n    def on(self):\n        if GPIO.input(self.pin) == 0:\n            GPIO.output(self.pin, GPIO.HIGH)\n\n    def off(self):\n        if GPIO.input(self.pin) == 1:\n            GPIO.output(self.pin, GPIO.LOW)\n\n    def toggle(self):\n        if GPIO.input(self.pin) == 0:\n            self.on()\n        else:\n            self.off()\n\n    def beep_sequence(self, duration):\n        GPIO.output(self.pin, GPIO.LOW)\n        for i in range(int(duration)):\n            self.on()\n            t.sleep(0.5)\n            self.off()\n            t.sleep(0.5)\n\n    def beep(self, duration):\n        \"\"\"Uses threads to make Beep() a non-blocking function\"\"\"\n        if self.beep_thread and self.beep_thread.is_alive():\n            self.beep_thread.join()\n\n        self.beep_thread = threading.Thread(target=self.beep_sequence, args=(duration,))\n        self.beep_thread.start()\n\n\n    def __del__(self):\n        GPIO.output(self.pin, GPIO.LOW)\n        if self.beep_thread and self.beep_thread.is_alive():\n            self.beep_thread.join()\n        GPIO.cleanup(self.pin)\n\ndef test_beep():\n    b1 = buzzer(17)\n    b2 = buzzer(17)\n\n    # Test 1: Check if both implementations start the thread\n    b1.beep(1)\n    b2.beep_new_implementation(1)\n    assert b1.beep_thread.is_alive() == b2.beep_thread.is_alive()\n\n    # Test 2: Check if both implementations set the pin state correctly\n    b1.beep_thread.join()\n    b2.beep_thread.join()\n    assert GPIO.input(b1.pin) == GPIO.input(b2.pin)\n\n    # Test 3: Check if both implementations handle consecutive beeps correctly\n    b1.beep(2)\n    b2.beep_new_implementation(2)\n    b1.beep_thread.join()\n    b2.beep_thread.join()\n    assert GPIO.input(b1.pin) == GPIO.input(b2.pin)\n\n    # Test 4: Check simultaneous beeps on different instances\n    b3 = buzzer(18)\n    b4 = buzzer(18)\n    b3.beep(1)\n    b4.beep_new_implementation(1)\n    b3.beep_thread.join()\n    b4.beep_thread.join()\n    assert GPIO.input(b3.pin) == GPIO.input(b4.pin)\n\n    # Test 5: Edge Case - Zero Duration\n    b1.beep(0)\n    b2.beep_new_implementation(0)\n    b1.beep_thread.join()\n    b2.beep_thread.join()\n    assert GPIO.input(b1.pin) == GPIO.input(b2.pin)\n\n    # Test 6: Edge Case - Negative Duration\n    b1.beep(-1)\n    b2.beep_new_implementation(-1)\n    b1.beep_thread.join()\n    b2.beep_thread.join()\n    assert GPIO.input(b1.pin) == GPIO.input(b2.pin)\n\n    # Test 7: Multiple Consecutive Beeps\n    b1.beep(1)\n    b1.beep(1)\n    b2.beep_new_implementation(1)\n    b2.beep_new_implementation(1)\n    b1.beep_thread.join()\n    b2.beep_thread.join()\n    assert GPIO.input(b1.pin) == GPIO.input(b2.pin)\n\nif __name__ == \"__main__\":\n    test_beep()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the same as the ORIGINAL FUNCTION. Both functions are named `beep`, and they perform the same operations: they check if a `beep_thread` is alive and join it if it is, then create a new thread to execute the `beep_sequence` method with the given duration, and start this thread. The functionality of both functions is identical, as they both aim to make the beep operation non-blocking by using threads. The additional code in the revised version, such as the `buzzer` class and the `test_beep` function, does not alter the functionality of the `beep` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `beep` function does not return any values, but it modifies the state of the GPIO pins and uses threading, which affects the state of the `beep_thread` attribute. Therefore, it satisfies CONDITION 1 as it modifies global variables or input arguments.\n\n- CONDITION 2: The test cases check the state of the GPIO pins and the `beep_thread` attribute, not printed or logged contents. Thus, CONDITION 2 is satisfied.\n\n- CONDITION 3: The test cases compare the states of the GPIO pins and the `beep_thread` attribute between `beep` and `beep_new_implementation`. If `beep_new_implementation` has the same functionality as `beep`, it will pass all the test cases. Therefore, CONDITION 3 is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the states of the GPIO pins and the `beep_thread` attribute, which are reasonable checks given that `beep` does not return any values. Thus, CONDITION 4 is satisfied.\n\n- CONDITION 5: The test cases cover various scenarios, including different durations, simultaneous beeps on different instances, and edge cases like zero and negative durations. These are non-trivial test cases, satisfying CONDITION 5.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "Motor_Driver.turn_right",
        "idx": "618",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/Motor_Driver.py",
        "orig_func": "def turn_right(self, speed=90):\n    \"\"\"Turns IN2 and IN3 on\"\"\"\n    GPIO.output(self.in1, GPIO.LOW)\n    GPIO.output(self.in2, GPIO.HIGH)\n    self.en_a.ChangeDutyCycle(speed)\n    GPIO.output(self.in3, GPIO.HIGH)\n    GPIO.output(self.in4, GPIO.LOW)\n    self.en_b.ChangeDutyCycle(speed)",
        "orig_context": "```python\n## src/rpi_gpio/Motor_Driver.py\nimport RPi.GPIO as GPIO\n\nclass Motor_Driver:\n    def __init__(self, in1=0, in2=0, ena=0, in3=0, in4=0, enb=0):\n        \"\"\"This uses GPIO pin-number on the pi.\\n\n        This class is for the L298N Motor Driver\"\"\"\n        self.in1 = int(in1)\n        self.in2 = int(in2)\n        self.ena = int(ena)\n\n        self.in3 = int(in3)\n        self.in4 = int(in4)\n        self.enb = int(enb)\n\n        GPIO.setup(self.in1, GPIO.OUT)\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.setup(self.in2, GPIO.OUT)\n        GPIO.output(self.in2, GPIO.LOW)\n\n        GPIO.setup(self.ena, GPIO.OUT)\n\n        GPIO.setup(self.in3, GPIO.OUT)\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.setup(self.in4, GPIO.OUT)\n        GPIO.output(self.in4, GPIO.LOW)\n\n        GPIO.setup(self.enb, GPIO.OUT)\n\n        self.en_a = GPIO.PWM(self.ena, 100)\n        self.en_a.start(0)\n\n        self.en_b = GPIO.PWM(self.enb, 100)\n        self.en_b.start(0)\n\n    def forward(self, speed=90):\n        \"\"\"Turns IN1 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def backward(self, speed=75):\n        \"\"\"Turns IN2 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_right(self, speed=90):\n        \"\"\"Turns IN2 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_left(self, speed=90):\n        \"\"\"Turns IN1 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def stop(self):\n        \"\"\"Turns all IN# off and turns off ena and enb PWM\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.stop()\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.stop()\n\n    def __del__(self):\n        self.stop()\n        GPIO.cleanup([self.in1, self.in2, self.ena, self.in3, self.in4, self.enb])\n\n```\n\n\n",
        "eval_script": "# Mocking RPi.GPIO for non-Raspberry Pi environments\nclass MockGPIO:\n    BOARD = 'BOARD'\n    BCM = 'BCM'\n    OUT = 'OUT'\n    IN = 'IN'\n    LOW = 0\n    HIGH = 1\n\n    @staticmethod\n    def setup(pin, mode):\n        print(f\"Mock setup: pin {pin}, mode {mode}\")\n\n    @staticmethod\n    def output(pin, state):\n        print(f\"Mock output: pin {pin}, state {state}\")\n\n    @staticmethod\n    def PWM(pin, frequency):\n        print(f\"Mock PWM: pin {pin}, frequency {frequency}\")\n        return MockPWM(pin, frequency)\n\n    @staticmethod\n    def cleanup(pins=None):\n        print(f\"Mock cleanup: pins {pins}\")\n\nclass MockPWM:\n    def __init__(self, pin, frequency):\n        self.pin = pin\n        self.frequency = frequency\n        self.duty_cycle = 0\n\n    def start(self, duty_cycle):\n        self.duty_cycle = duty_cycle\n        print(f\"Mock PWM start: pin {self.pin}, duty cycle {duty_cycle}\")\n\n    def ChangeDutyCycle(self, duty_cycle):\n        self.duty_cycle = duty_cycle\n        print(f\"Mock PWM ChangeDutyCycle: pin {self.pin}, duty cycle {duty_cycle}\")\n\n    def stop(self):\n        print(f\"Mock PWM stop: pin {self.pin}\")\n\n# Use the mock GPIO if RPi.GPIO is not available\ntry:\n    import RPi.GPIO as GPIO\nexcept ImportError:\n    GPIO = MockGPIO\n\n# The original Motor_Driver class code remains unchanged\nclass Motor_Driver:\n    def __init__(self, in1=0, in2=0, ena=0, in3=0, in4=0, enb=0):\n        \"\"\"This uses GPIO pin-number on the pi.\\n\n        This class is for the L298N Motor Driver\"\"\"\n        self.in1 = int(in1)\n        self.in2 = int(in2)\n        self.ena = int(ena)\n\n        self.in3 = int(in3)\n        self.in4 = int(in4)\n        self.enb = int(enb)\n\n        GPIO.setup(self.in1, GPIO.OUT)\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.setup(self.in2, GPIO.OUT)\n        GPIO.output(self.in2, GPIO.LOW)\n\n        GPIO.setup(self.ena, GPIO.OUT)\n\n        GPIO.setup(self.in3, GPIO.OUT)\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.setup(self.in4, GPIO.OUT)\n        GPIO.output(self.in4, GPIO.LOW)\n\n        GPIO.setup(self.enb, GPIO.OUT)\n\n        self.en_a = GPIO.PWM(self.ena, 100)\n        self.en_a.start(0)\n\n        self.en_b = GPIO.PWM(self.enb, 100)\n        self.en_b.start(0)\n\n    def forward(self, speed=90):\n        \"\"\"Turns IN1 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def backward(self, speed=75):\n        \"\"\"Turns IN2 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_right(self, speed=90):\n        \"\"\"Turns IN2 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_left(self, speed=90):\n        \"\"\"Turns IN1 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def stop(self):\n        \"\"\"Turns all IN# off and turns off ena and enb PWM\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.stop()\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.stop()\n\n    def __del__(self):\n        self.stop()\n        GPIO.cleanup([self.in1, self.in2, self.ena, self.in3, self.in4, self.enb])\n\n# New implementation of turn_right for testing purposes\ndef turn_right_new_implementation(self, speed=90):\n    \"\"\"Turns IN2 and IN3 on\"\"\"\n    GPIO.output(self.in1, GPIO.LOW)\n    GPIO.output(self.in2, GPIO.HIGH)\n    self.en_a.ChangeDutyCycle(speed)\n\n    GPIO.output(self.in3, GPIO.HIGH)\n    GPIO.output(self.in4, GPIO.LOW)\n    self.en_b.ChangeDutyCycle(speed)\n\nMotor_Driver.turn_right_new_implementation = turn_right_new_implementation\n\ndef test_turn_right():\n    motor = Motor_Driver(in1=1, in2=2, ena=3, in3=4, in4=5, enb=6)\n\n    # Test original implementation with speed 90\n    motor.turn_right(90)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 90\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 90\n\n    # Test new implementation with speed 90\n    motor.turn_right_new_implementation(90)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 90\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 90\n\n    # Test original implementation with speed 0 (edge case)\n    motor.turn_right(0)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 0\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 0\n\n    # Test new implementation with speed 0 (edge case)\n    motor.turn_right_new_implementation(0)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 0\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 0\n\n    # Test original implementation with speed 100 (edge case)\n    motor.turn_right(100)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 100\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 100\n\n    # Test new implementation with speed 100 (edge case)\n    motor.turn_right_new_implementation(100)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 100\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 100\n\n    # Test original implementation with a mid-range speed\n    motor.turn_right(50)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 50\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 50\n\n    # Test new implementation with a mid-range speed\n    motor.turn_right_new_implementation(50)\n    assert GPIO.output(motor.in1, GPIO.LOW) is None\n    assert GPIO.output(motor.in2, GPIO.HIGH) is None\n    assert motor.en_a.duty_cycle == 50\n    assert GPIO.output(motor.in3, GPIO.HIGH) is None\n    assert GPIO.output(motor.in4, GPIO.LOW) is None\n    assert motor.en_b.duty_cycle == 50\n\nif __name__ == \"__main__\":\n    test_turn_right()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `turn_right` and the revised function `turn_right_new_implementation` are identical in terms of their logic and functionality. Both functions perform the same operations: they set the GPIO outputs for `in1`, `in2`, `in3`, and `in4` to the same states and adjust the duty cycle of `en_a` and `en_b` to the specified speed. The testing code provided also confirms that both implementations behave identically under various conditions, including edge cases and mid-range speeds. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `turn_right` function modifies the state of the `Motor_Driver` instance by changing the duty cycle of the PWM and setting GPIO outputs. These changes are made to the instance's attributes and are not returned directly, but they do modify the state of the object, which is checked in the test cases.\n\n2. **CONDITION 2**: The test cases check the state of the `Motor_Driver` instance after calling `turn_right` and `turn_right_new_implementation`. They verify the duty cycle and GPIO output states, which are not printed or logged contents. Therefore, this condition is satisfied.\n\n3. **CONDITION 3**: The test cases compare the state of the `Motor_Driver` instance after executing both `turn_right` and `turn_right_new_implementation` for various speed values. If both functions modify the state in the same way, they will pass all test cases, indicating they have the same functionality. Thus, this condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to check the state of the GPIO outputs and PWM duty cycles, which are reasonable checks for the functionality of the `turn_right` method. The assertions do not compare return values, as the function does not return anything, which is appropriate.\n\n5. **CONDITION 5**: The test cases cover multiple scenarios, including edge cases (speed 0 and 100) and a mid-range speed (50), making them non-trivial and comprehensive.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "Motor_Driver.turn_left",
        "idx": "619",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/Motor_Driver.py",
        "orig_func": "def turn_left(self, speed=90):\n    \"\"\"Turns IN1 and IN4 on\"\"\"\n    GPIO.output(self.in1, GPIO.HIGH)\n    GPIO.output(self.in2, GPIO.LOW)\n    self.en_a.ChangeDutyCycle(speed)\n    GPIO.output(self.in3, GPIO.LOW)\n    GPIO.output(self.in4, GPIO.HIGH)\n    self.en_b.ChangeDutyCycle(speed)",
        "orig_context": "```python\n## src/rpi_gpio/Motor_Driver.py\nimport RPi.GPIO as GPIO\n\nclass Motor_Driver:\n    def __init__(self, in1=0, in2=0, ena=0, in3=0, in4=0, enb=0):\n        \"\"\"This uses GPIO pin-number on the pi.\\n\n        This class is for the L298N Motor Driver\"\"\"\n        self.in1 = int(in1)\n        self.in2 = int(in2)\n        self.ena = int(ena)\n\n        self.in3 = int(in3)\n        self.in4 = int(in4)\n        self.enb = int(enb)\n\n        GPIO.setup(self.in1, GPIO.OUT)\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.setup(self.in2, GPIO.OUT)\n        GPIO.output(self.in2, GPIO.LOW)\n\n        GPIO.setup(self.ena, GPIO.OUT)\n\n        GPIO.setup(self.in3, GPIO.OUT)\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.setup(self.in4, GPIO.OUT)\n        GPIO.output(self.in4, GPIO.LOW)\n\n        GPIO.setup(self.enb, GPIO.OUT)\n\n        self.en_a = GPIO.PWM(self.ena, 100)\n        self.en_a.start(0)\n\n        self.en_b = GPIO.PWM(self.enb, 100)\n        self.en_b.start(0)\n\n    def forward(self, speed=90):\n        \"\"\"Turns IN1 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def backward(self, speed=75):\n        \"\"\"Turns IN2 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_right(self, speed=90):\n        \"\"\"Turns IN2 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_left(self, speed=90):\n        \"\"\"Turns IN1 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def stop(self):\n        \"\"\"Turns all IN# off and turns off ena and enb PWM\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.stop()\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.stop()\n\n    def __del__(self):\n        self.stop()\n        GPIO.cleanup([self.in1, self.in2, self.ena, self.in3, self.in4, self.enb])\n\n```\n\n\n",
        "eval_script": "# Mock RPi.GPIO module\nclass MockGPIO:\n    BOARD = 'BOARD'\n    BCM = 'BCM'\n    OUT = 'OUT'\n    IN = 'IN'\n    LOW = 0\n    HIGH = 1\n\n    @staticmethod\n    def setup(pin, mode):\n        print(f\"Mock setup: pin {pin}, mode {mode}\")\n\n    @staticmethod\n    def output(pin, state):\n        print(f\"Mock output: pin {pin}, state {state}\")\n\n    @staticmethod\n    def PWM(pin, frequency):\n        print(f\"Mock PWM: pin {pin}, frequency {frequency}\")\n        return MockPWM(pin, frequency)\n\n    @staticmethod\n    def cleanup(pins=None):\n        print(f\"Mock cleanup: pins {pins}\")\n\nclass MockPWM:\n    def __init__(self, pin, frequency):\n        self.pin = pin\n        self.frequency = frequency\n        self.duty_cycle = 0\n\n    def start(self, duty_cycle):\n        self.duty_cycle = duty_cycle\n        print(f\"Mock PWM start: pin {self.pin}, duty_cycle {duty_cycle}\")\n\n    def ChangeDutyCycle(self, duty_cycle):\n        self.duty_cycle = duty_cycle\n        print(f\"Mock PWM ChangeDutyCycle: pin {self.pin}, duty_cycle {duty_cycle}\")\n\n    def stop(self):\n        print(f\"Mock PWM stop: pin {self.pin}\")\n\n# Use the mock GPIO in place of RPi.GPIO\nGPIO = MockGPIO\n\n# Original Motor_Driver class code\nclass Motor_Driver:\n    def __init__(self, in1=0, in2=0, ena=0, in3=0, in4=0, enb=0):\n        \"\"\"This uses GPIO pin-number on the pi.\\n\n        This class is for the L298N Motor Driver\"\"\"\n        self.in1 = int(in1)\n        self.in2 = int(in2)\n        self.ena = int(ena)\n\n        self.in3 = int(in3)\n        self.in4 = int(in4)\n        self.enb = int(enb)\n\n        GPIO.setup(self.in1, GPIO.OUT)\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.setup(self.in2, GPIO.OUT)\n        GPIO.output(self.in2, GPIO.LOW)\n\n        GPIO.setup(self.ena, GPIO.OUT)\n\n        GPIO.setup(self.in3, GPIO.OUT)\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.setup(self.in4, GPIO.OUT)\n        GPIO.output(self.in4, GPIO.LOW)\n\n        GPIO.setup(self.enb, GPIO.OUT)\n\n        self.en_a = GPIO.PWM(self.ena, 100)\n        self.en_a.start(0)\n\n        self.en_b = GPIO.PWM(self.enb, 100)\n        self.en_b.start(0)\n\n    def forward(self, speed=90):\n        \"\"\"Turns IN1 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def backward(self, speed=75):\n        \"\"\"Turns IN2 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_right(self, speed=90):\n        \"\"\"Turns IN2 and IN3 on\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.HIGH)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.HIGH)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.ChangeDutyCycle(speed)\n\n    def turn_left(self, speed=90):\n        \"\"\"Turns IN1 and IN4 on\"\"\"\n        GPIO.output(self.in1, GPIO.HIGH)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.ChangeDutyCycle(speed)\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.HIGH)\n        self.en_b.ChangeDutyCycle(speed)\n\n\n    def stop(self):\n        \"\"\"Turns all IN# off and turns off ena and enb PWM\"\"\"\n        GPIO.output(self.in1, GPIO.LOW)\n        GPIO.output(self.in2, GPIO.LOW)\n        self.en_a.stop()\n\n        GPIO.output(self.in3, GPIO.LOW)\n        GPIO.output(self.in4, GPIO.LOW)\n        self.en_b.stop()\n\n    def __del__(self):\n        self.stop()\n        GPIO.cleanup([self.in1, self.in2, self.ena, self.in3, self.in4, self.enb])\n\ndef test_turn_left():\n    motor = Motor_Driver(in1=1, in2=2, ena=3, in3=4, in4=5, enb=6)\n\n    # Test case 1: Default speed\n    motor.turn_left()\n    original_duty_cycle_a = motor.en_a.duty_cycle\n    original_duty_cycle_b = motor.en_b.duty_cycle\n\n    motor.turn_left_new_implementation()\n    new_duty_cycle_a = motor.en_a.duty_cycle\n    new_duty_cycle_b = motor.en_b.duty_cycle\n\n    assert original_duty_cycle_a == new_duty_cycle_a, \"Duty cycle A mismatch\"\n    assert original_duty_cycle_b == new_duty_cycle_b, \"Duty cycle B mismatch\"\n\n    # Test case 2: Speed 50\n    motor.turn_left(50)\n    original_duty_cycle_a = motor.en_a.duty_cycle\n    original_duty_cycle_b = motor.en_b.duty_cycle\n\n    motor.turn_left_new_implementation(50)\n    new_duty_cycle_a = motor.en_a.duty_cycle\n    new_duty_cycle_b = motor.en_b.duty_cycle\n\n    assert original_duty_cycle_a == new_duty_cycle_a, \"Duty cycle A mismatch at speed 50\"\n    assert original_duty_cycle_b == new_duty_cycle_b, \"Duty cycle B mismatch at speed 50\"\n\n    # Test case 3: Speed 100\n    motor.turn_left(100)\n    original_duty_cycle_a = motor.en_a.duty_cycle\n    original_duty_cycle_b = motor.en_b.duty_cycle\n\n    motor.turn_left_new_implementation(100)\n    new_duty_cycle_a = motor.en_a.duty_cycle\n    new_duty_cycle_b = motor.en_b.duty_cycle\n\n    assert original_duty_cycle_a == new_duty_cycle_a, \"Duty cycle A mismatch at speed 100\"\n    assert original_duty_cycle_b == new_duty_cycle_b, \"Duty cycle B mismatch at speed 100\"\n\n    # Test case 4: Minimum speed (0)\n    motor.turn_left(0)\n    original_duty_cycle_a = motor.en_a.duty_cycle\n    original_duty_cycle_b = motor.en_b.duty_cycle\n\n    motor.turn_left_new_implementation(0)\n    new_duty_cycle_a = motor.en_a.duty_cycle\n    new_duty_cycle_b = motor.en_b.duty_cycle\n\n    assert original_duty_cycle_a == new_duty_cycle_a, \"Duty cycle A mismatch at speed 0\"\n    assert original_duty_cycle_b == new_duty_cycle_b, \"Duty cycle B mismatch at speed 0\"\n\n    # Test case 5: Maximum speed (assumed 100 for this motor driver)\n    motor.turn_left(100)\n    original_duty_cycle_a = motor.en_a.duty_cycle\n    original_duty_cycle_b = motor.en_b.duty_cycle\n\n    motor.turn_left_new_implementation(100)\n    new_duty_cycle_a = motor.en_a.duty_cycle\n    new_duty_cycle_b = motor.en_b.duty_cycle\n\n    assert original_duty_cycle_a == new_duty_cycle_a, \"Duty cycle A mismatch at max speed\"\n    assert original_duty_cycle_b == new_duty_cycle_b, \"Duty cycle B mismatch at max speed\"\n\n    # Test case 6: Repeated calls\n    motor.turn_left(75)\n    motor.turn_left(75)\n    original_duty_cycle_a = motor.en_a.duty_cycle\n    original_duty_cycle_b = motor.en_b.duty_cycle\n\n    motor.turn_left_new_implementation(75)\n    motor.turn_left_new_implementation(75)\n    new_duty_cycle_a = motor.en_a.duty_cycle\n    new_duty_cycle_b = motor.en_b.duty_cycle\n\n    assert original_duty_cycle_a == new_duty_cycle_a, \"Duty cycle A mismatch after repeated calls\"\n    assert original_duty_cycle_b == new_duty_cycle_b, \"Duty cycle B mismatch after repeated calls\"\n\nif __name__ == \"__main__\":\n    test_turn_left()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `turn_left` in the `Motor_Driver` class is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: setting GPIO pins `in1` and `in4` to HIGH, `in2` and `in3` to LOW, and adjusting the duty cycle of `en_a` and `en_b` to the specified speed. The test cases provided in the code are designed to compare the behavior of the original and revised implementations of `turn_left`, but the actual revised implementation is not shown. Assuming the revised implementation is the same as the original, the functionality remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `turn_left` function modifies the state of the `Motor_Driver` instance by changing the duty cycle of the PWM objects (`en_a` and `en_b`). This satisfies the condition as it modifies the state of the object.\n- CONDITION 2: The test cases check the state of the PWM duty cycles after calling `turn_left` and `turn_left_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the duty cycles after calling both implementations of `turn_left`. If `turn_left_new_implementation` has the same effect on the duty cycles as `turn_left`, it will pass the tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the duty cycles, which are reasonable checks for the state changes made by the `turn_left` function. This satisfies the condition.\n- CONDITION 5: The test cases cover various scenarios, including default speed, specific speeds (50, 100, 0), maximum speed, and repeated calls. These are non-trivial tests that cover a range of possible inputs and states, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "LED.blink",
        "idx": "621",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/LED.py",
        "orig_func": "def blink(self, duration=6):\n    if self.led_thread and self.led_thread.is_alive():\n        self.led_thread.join()\n    self.led_thread = threading.Thread(target=self.blink_sequence, args=(duration,))\n    self.led_thread.start()",
        "orig_context": "```python\n## src/rpi_gpio/LED.py\nimport RPi.GPIO as GPIO\n\nimport time as t\n\nimport threading\n\nclass LED:\n    def __init__(self, pin):\n        \"\"\"This uses GPIO pin-number on the pi.\\n\n        This class is for controlling basic LED's.\"\"\"\n        self.pin = int(pin)\n\n        self.led_thread = None\n\n        GPIO.setup(self.pin, GPIO.OUT)\n\n    def on(self):\n        if GPIO.input(self.pin) == 0:\n            GPIO.output(self.pin, GPIO.HIGH)\n\n    def off(self):\n        if GPIO.input(self.pin) == 1:\n            GPIO.output(self.pin, GPIO.LOW)\n\n    def toggle(self):\n        if GPIO.input(self.pin) == 0:\n            self.on()\n        else:\n            self.off()\n\n    def blink_sequence(self, duration=6):\n        GPIO.output(self.pin, GPIO.LOW)\n        for i in range(int(duration)):\n            self.on()\n            t.sleep(0.5)\n            self.off()\n            t.sleep(0.5)\n\n    def blink(self, duration=6):\n        if self.led_thread and self.led_thread.is_alive():\n            self.led_thread.join()\n\n        self.led_thread = threading.Thread(target=self.blink_sequence, args=(duration,))\n        self.led_thread.start()\n\n    def __del__(self):\n        GPIO.output(self.pin, GPIO.LOW)\n        if self.led_thread and self.led_thread.is_alive():\n            self.led_thread.join()\n        GPIO.cleanup([self.pin])\n\n```\n\n\n",
        "eval_script": "# Mock RPi.GPIO library\nclass MockGPIO:\n    BCM = 'BCM'\n    OUT = 'OUT'\n    HIGH = 1\n    LOW = 0\n    \n    def __init__(self):\n        self.pins = {}\n    \n    def setup(self, pin, mode):\n        self.pins[pin] = self.LOW\n    \n    def output(self, pin, state):\n        self.pins[pin] = state\n        print(f\"Pin {pin} set to {'HIGH' if state == self.HIGH else 'LOW'}\")\n    \n    def input(self, pin):\n        return self.pins.get(pin, self.LOW)\n    \n    def cleanup(self, pins=None):\n        if pins is None:\n            self.pins.clear()\n        else:\n            for pin in pins:\n                if pin in self.pins:\n                    del self.pins[pin]\n\n# Replace RPi.GPIO with MockGPIO\nGPIO = MockGPIO()\n\nimport time as t\nimport threading\n\nclass LED:\n    def __init__(self, pin):\n        \"\"\"This uses GPIO pin-number on the pi.\\n\n        This class is for controlling basic LED's.\"\"\"\n        self.pin = int(pin)\n\n        self.led_thread = None\n\n        GPIO.setup(self.pin, GPIO.OUT)\n\n    def on(self):\n        if GPIO.input(self.pin) == 0:\n            GPIO.output(self.pin, GPIO.HIGH)\n\n    def off(self):\n        if GPIO.input(self.pin) == 1:\n            GPIO.output(self.pin, GPIO.LOW)\n\n    def toggle(self):\n        if GPIO.input(self.pin) == 0:\n            self.on()\n        else:\n            self.off()\n\n    def blink_sequence(self, duration=6):\n        GPIO.output(self.pin, GPIO.LOW)\n        for i in range(int(duration)):\n            self.on()\n            t.sleep(0.5)\n            self.off()\n            t.sleep(0.5)\n\n    def blink(self, duration=6):\n        if self.led_thread and self.led_thread.is_alive():\n            self.led_thread.join()\n\n        self.led_thread = threading.Thread(target=self.blink_sequence, args=(duration,))\n        self.led_thread.start()\n\n\n    def __del__(self):\n        GPIO.output(self.pin, GPIO.LOW)\n        if self.led_thread and self.led_thread.is_alive():\n            self.led_thread.join()\n        GPIO.cleanup([self.pin])\n\ndef test_blink():\n    led = LED(17)\n    \n    # Test 1: Check if both methods start with the pin LOW\n    led.blink(1)\n    led.led_thread.join()\n    initial_state_blink = GPIO.input(17)\n    \n    led.blink_new_implementation(1)\n    led.led_thread.join()\n    initial_state_blink_new = GPIO.input(17)\n    \n    assert initial_state_blink == initial_state_blink_new == GPIO.LOW, \"Initial state mismatch\"\n\n    # Test 2: Check if both methods end with the pin LOW after blinking\n    led.blink(1)\n    led.led_thread.join()\n    final_state_blink = GPIO.input(17)\n    \n    led.blink_new_implementation(1)\n    led.led_thread.join()\n    final_state_blink_new = GPIO.input(17)\n    \n    assert final_state_blink == final_state_blink_new == GPIO.LOW, \"Final state mismatch\"\n\n    # Test 3: Check if both methods handle threading correctly\n    led.blink(2)\n    led.led_thread.join()  # Ensure the thread has finished\n    led.blink_new_implementation(2)\n    led.led_thread.join()  # Ensure the thread has finished\n    assert not led.led_thread.is_alive(), \"Thread handling mismatch\"\n\nif __name__ == \"__main__\":\n    test_blink()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions check if the `led_thread` is alive and join it if it is, then create a new thread to run the `blink_sequence` method with the specified duration, and start the thread. There are no changes in the logic or functionality between the two versions of the `blink` function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `blink` function modifies the state of the GPIO pins, which are stored in the `MockGPIO` class instance. This satisfies the condition as it modifies global variables or input arguments.\n- CONDITION 2: The test cases check the state of the GPIO pins using `GPIO.input(pin)` and do not rely on printed or logged output. This satisfies the condition.\n- CONDITION 3: The test cases compare the initial and final states of the GPIO pins for both `blink` and `blink_new_implementation`, ensuring that they have the same functionality. This satisfies the condition.\n- CONDITION 4: The test cases use `assert` statements to compare the states of the GPIO pins and ensure the thread handling is correct. These assertions are reasonable given the functionality of `blink`. This satisfies the condition.\n- CONDITION 5: The test cases are non-trivial as they check the initial and final states of the GPIO pins and ensure proper thread handling, which are essential aspects of the `blink` functionality. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "Temperature_Sensor.humidity",
        "idx": "622",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/Temperature_Sensor.py",
        "orig_func": "def humidity(self):\n    humidity, _ = DHT.read_retry(self.sensor_type, self.out)\n    if humidity is not None:\n        return humidity\n    else:\n        return None",
        "orig_context": "```python\n## src/rpi_gpio/Temperature_Sensor.py\nimport RPi.GPIO as GPIO\n\nimport Adafruit_DHT as DHT\n\nclass Temperature_Sensor:\n    def __init__(self, out, sensor_type):\n        \"\"\"This uses GPIO pin-numbers on the pi.\"\"\"\n        self.out = out\n        self.sensor_type = sensor_type\n\n        GPIO.setup(self.out, GPIO.IN)\n\n    def temperature(self, measure_mode=\"C\"):\n        \"\"\"measure_mode is for:/n\n        'F', 'Fahrenheit', 'C', 'Celsius', 'Centigrade'\"\"\"\n        humidity, temp = DHT.read_retry(self.sensor_type, self.out)\n\n        if temp is not None:\n            if measure_mode in ['F', 'Fahrenheit']:\n                return temp * 9.0 / 5.0 + 32.0\n            elif measure_mode in ['C', 'Celsius', 'Centigrade']:\n                return temp\n        else:\n            return None\n\n    def humidity(self):\n        humidity, _ = DHT.read_retry(self.sensor_type, self.out)\n\n        if humidity is not None:\n            return humidity\n        else:\n            return None\n\n```\n\n\n",
        "eval_script": "# Mocking RPi.GPIO and Adafruit_DHT for testing purposes\nimport sys\nfrom unittest.mock import Mock\n\n# Mock RPi.GPIO\nsys.modules['RPi.GPIO'] = Mock()\nGPIO = sys.modules['RPi.GPIO']\nGPIO.IN = Mock()\n\n# Mock Adafruit_DHT\nsys.modules['Adafruit_DHT'] = Mock()\nDHT = sys.modules['Adafruit_DHT']\nDHT.read_retry = Mock(return_value=(50.0, 25.0))  # Mocked to return 50% humidity and 25\u00b0C temperature\n\n# The Temperature_Sensor class as provided\nclass Temperature_Sensor:\n    def __init__(self, out, sensor_type):\n        \"\"\"This uses GPIO pin-numbers on the pi.\"\"\"\n        self.out = out\n        self.sensor_type = sensor_type\n\n        GPIO.setup(self.out, GPIO.IN)\n\n    def temperature(self, measure_mode=\"C\"):\n        \"\"\"measure_mode is for:/n\n        'F', 'Fahrenheit', 'C', 'Celsius', 'Centigrade'\"\"\"\n        humidity, temp = DHT.read_retry(self.sensor_type, self.out)\n\n        if temp is not None:\n            if measure_mode in ['F', 'Fahrenheit']:\n                return temp * 9.0 / 5.0 + 32.0\n            elif measure_mode in ['C', 'Celsius', 'Centigrade']:\n                return temp\n        else:\n            return None\n\n    def humidity(self):\n        humidity, _ = DHT.read_retry(self.sensor_type, self.out)\n\n        if humidity is not None:\n            return humidity\n        else:\n            return None\n\n\ndef test_humidity():\n    sensor = Temperature_Sensor(out=4, sensor_type='DHT22')\n\n    # Test case 1: Normal condition\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 1 failed\"\n\n    # Test case 2: Mock different return value\n    DHT.read_retry.return_value = (60.0, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 2 failed\"\n\n    # Test case 3: Mock None return value\n    DHT.read_retry.return_value = (None, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 3 failed\"\n\n    # Test case 4: Mock 0% humidity\n    DHT.read_retry.return_value = (0.0, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 4 failed\"\n\n    # Test case 5: Mock 100% humidity\n    DHT.read_retry.return_value = (100.0, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 5 failed\"\n\n    # Test case 6: Invalid sensor type\n    sensor_invalid = Temperature_Sensor(out=4, sensor_type='INVALID')\n    DHT.read_retry.return_value = (50.0, 25.0)\n    assert sensor_invalid.humidity() == sensor_invalid.humidity_new_implementation(), \"Test case 6 failed\"\n\n    # Test case 7: Multiple consecutive reads\n    DHT.read_retry.return_value = (70.0, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 7 failed - first read\"\n    DHT.read_retry.return_value = (80.0, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 7 failed - second read\"\n    DHT.read_retry.return_value = (90.0, 25.0)\n    assert sensor.humidity() == sensor.humidity_new_implementation(), \"Test case 7 failed - third read\"\n\nif __name__ == \"__main__\":\n    test_humidity()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION within the `Temperature_Sensor` class is identical to the ORIGINAL FUNCTION. Both functions perform the same operation: they call `DHT.read_retry` with `self.sensor_type` and `self.out`, and they return the humidity value if it is not `None`. If the humidity value is `None`, they return `None`. The test cases in the revised code are designed to compare the output of the `humidity` function with a non-existent `humidity_new_implementation` function, which suggests that the test cases are not correctly implemented. However, the functionality of the `humidity` function itself remains unchanged between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `humidity` function returns a value, which is the humidity reading from the sensor. This satisfies the condition as it has return values.\n- [CONDITION 2] The test cases use assertions to check the return values of the `humidity` function and its new implementation. They do not check printed or logged contents, satisfying this condition.\n- [CONDITION 3] The test cases compare the return values of `humidity` and `humidity_new_implementation` under various mocked conditions. If `humidity_new_implementation` passes all these tests, it must have the same functionality as `humidity`, satisfying this condition.\n- [CONDITION 4] The test cases use assertions to compare the return values of the two implementations, which is reasonable since `humidity` has return values. This condition is satisfied.\n- [CONDITION 5] The test cases cover a range of scenarios, including normal conditions, edge cases (0% and 100% humidity), invalid sensor types, and multiple consecutive reads. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "IR_LED._send_pulse",
        "idx": "623",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/IR.py",
        "orig_func": "def _send_pulse(self, high_time, low_time):\n    self.pi.gpio_trigger(self.pin, high_time, 1)\n    t.sleep(low_time / 1000000.0)",
        "orig_context": "```python\n## src/rpi_gpio/IR.py\nimport RPi.GPIO as GPIO\n\nimport pigpio\n\nimport time as t\n\nclass IR_LED:\n    def __init__(self, pin):\n        self.pin = pin\n        self.pi = pigpio.pi()\n\n        if not self.pi.connected:\n            raise Exception('could not connect to pigpio daemon')\n\n        self.pi.set_mode(self.pin, pigpio.OUTPUT)\n\n    def send_signal(self, hex_code, protocol=\"NEC\", frequency=38000):\n        if protocol == \"NEC\":\n            data = self._hex_to_bin(hex_code)\n            self._send_nec(data, frequency)\n        else:\n            raise Exception('unsupported protocol')\n\n    def _send_nec(self, data, frequency=38000):\n        self.pi.set_PWM_frequency(self.pin, frequency)\n        self.pi.set_PWM_dutycycle(self.pin, 128)\n\n        self._send_pulse(9000, 4500)\n\n        for i in range(32):\n            bit = (data >> (31 - i)) & 1\n            if bit == 1:\n                self._send_pulse(560, 1690)\n            else:\n                self._send_pulse(560, 560)\n\n        self.pi.set_PWM_dutycycle(self.pin, 0)\n\n    def _send_pulse(self, high_time, low_time):\n        self.pi.gpio_trigger(self.pin, high_time, 1)\n        t.sleep(low_time / 1000000.0)\n\n    def _hex_to_bin(self, hex_code):\n        hex_code = hex_code.lstrip(\"0x\").zfill(8)\n        bin_code = bin(int(hex_code, 16))[2:].zfill(32)\n        return bin_code\n\n    def __del__(self):\n        self.pi.stop()\n        GPIO.cleanup([self.pin])\n\n```\n\n\n",
        "eval_script": "# Mock classes to simulate pigpio and RPi.GPIO behavior for testing purposes\n\nclass MockPigpio:\n    OUTPUT = \"output\"\n\n    def __init__(self):\n        self.connected = True\n        self.triggered_pins = []\n\n    def pi(self):\n        return self\n\n    def set_mode(self, pin, mode):\n        print(f\"Set mode for pin {pin} to {mode}\")\n\n    def set_PWM_frequency(self, pin, frequency):\n        print(f\"Set PWM frequency for pin {pin} to {frequency}\")\n\n    def set_PWM_dutycycle(self, pin, dutycycle):\n        print(f\"Set PWM dutycycle for pin {pin} to {dutycycle}\")\n\n    def gpio_trigger(self, pin, high_time, level):\n        print(f\"Triggered GPIO on pin {pin} with high_time {high_time} and level {level}\")\n        self.triggered_pins.append((pin, high_time, level))\n\n    def stop(self):\n        print(\"Stopped pigpio\")\n\nclass MockGPIO:\n    @staticmethod\n    def cleanup(pins):\n        print(f\"Cleaned up GPIO pins: {pins}\")\n\n# Replace the real pigpio and RPi.GPIO with mock classes\npigpio = MockPigpio()\nGPIO = MockGPIO()\n\n# Original IR_LED class with no changes\nimport time as t\n\nclass IR_LED:\n    def __init__(self, pin):\n        self.pin = pin\n        self.pi = pigpio.pi()\n\n        if not self.pi.connected:\n            raise Exception('could not connect to pigpio daemon')\n\n        self.pi.set_mode(self.pin, pigpio.OUTPUT)\n\n    def send_signal(self, hex_code, protocol=\"NEC\", frequency=38000):\n        if protocol == \"NEC\":\n            data = self._hex_to_bin(hex_code)\n            self._send_nec(data, frequency)\n        else:\n            raise Exception('unsupported protocol')\n\n    def _send_nec(self, data, frequency=38000):\n        self.pi.set_PWM_frequency(self.pin, frequency)\n        self.pi.set_PWM_dutycycle(self.pin, 128)\n\n        self._send_pulse(9000, 4500)\n\n        for i in range(32):\n            bit = (data >> (31 - i)) & 1\n            if bit == 1:\n                self._send_pulse(560, 1690)\n            else:\n                self._send_pulse(560, 560)\n\n        self.pi.set_PWM_dutycycle(self.pin, 0)\n\n    def _send_pulse(self, high_time, low_time):\n        self.pi.gpio_trigger(self.pin, high_time, 1)\n        t.sleep(low_time / 1000000.0)\n\n    def _hex_to_bin(self, hex_code):\n        hex_code = hex_code.lstrip(\"0x\").zfill(8)\n        bin_code = bin(int(hex_code, 16))[2:].zfill(32)\n        return bin_code\n\n    def __del__(self):\n        self.pi.stop()\n        GPIO.cleanup([self.pin])\n\n# New implementation of _send_pulse\ndef _send_pulse_new_implementation(self, high_time, low_time):\n    self.pi.gpio_trigger(self.pin, high_time, 1)\n    t.sleep(low_time / 1000000.0)\n\ndef test__send_pulse():\n    ir_led = IR_LED(17)\n\n    # Test case 1: Standard pulse\n    ir_led._send_pulse(9000, 4500)\n    expected = ir_led.pi.triggered_pins[-1]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(9000, 4500)\n    assert ir_led.pi.triggered_pins[-1] == expected, \"Test case 1 failed\"\n\n    # Test case 2: Short pulse\n    ir_led._send_pulse(560, 560)\n    expected = ir_led.pi.triggered_pins[-1]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(560, 560)\n    assert ir_led.pi.triggered_pins[-1] == expected, \"Test case 2 failed\"\n\n    # Test case 3: Long pulse\n    ir_led._send_pulse(560, 1690)\n    expected = ir_led.pi.triggered_pins[-1]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(560, 1690)\n    assert ir_led.pi.triggered_pins[-1] == expected, \"Test case 3 failed\"\n\n    # Test case 4: Edge case with zero pulse\n    ir_led._send_pulse(0, 0)\n    expected = ir_led.pi.triggered_pins[-1]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(0, 0)\n    assert ir_led.pi.triggered_pins[-1] == expected, \"Test case 4 failed\"\n\n    # Test case 5: Boundary value with very high pulse\n    ir_led._send_pulse(100000, 100000)\n    expected = ir_led.pi.triggered_pins[-1]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(100000, 100000)\n    assert ir_led.pi.triggered_pins[-1] == expected, \"Test case 5 failed\"\n\n    # Test case 6: Random pulse\n    ir_led._send_pulse(1234, 5678)\n    expected = ir_led.pi.triggered_pins[-1]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(1234, 5678)\n    assert ir_led.pi.triggered_pins[-1] == expected, \"Test case 6 failed\"\n\n    # Test case 7: Multiple consecutive pulses\n    ir_led._send_pulse(9000, 4500)\n    ir_led._send_pulse(560, 1690)\n    expected = ir_led.pi.triggered_pins[-2:]\n    ir_led.pi.triggered_pins.clear()\n    ir_led._send_pulse_new_implementation(9000, 4500)\n    ir_led._send_pulse_new_implementation(560, 1690)\n    assert ir_led.pi.triggered_pins[-2:] == expected, \"Test case 7 failed\"\n\n# Main function\nif __name__ == \"__main__\":\n    test__send_pulse()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `_send_pulse` and the revised function `_send_pulse_new_implementation` are identical in their implementation. Both functions call `self.pi.gpio_trigger(self.pin, high_time, 1)` and then pause for `low_time / 1000000.0` seconds using `t.sleep()`. The test cases provided in the code verify that the behavior of both functions is the same under various conditions. Since the implementations are exactly the same, the functionality is also the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_send_pulse` function modifies the `triggered_pins` attribute of the `MockPigpio` class, which acts as a global variable within the context of the `IR_LED` class. This satisfies the condition that the function should either return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases check the state of the `triggered_pins` list, which is a variable state, rather than checking printed or logged contents. This satisfies the condition.\n\n- CONDITION 3: The test cases compare the state of `triggered_pins` after calling both `_send_pulse` and `_send_pulse_new_implementation`. This ensures that the new implementation must have the exact same functionality as the original to pass all tests, satisfying the condition.\n\n- CONDITION 4: The test cases use assertions to compare the state of `triggered_pins` after calling both implementations. This is reasonable because `_send_pulse` does not return a value but modifies a global state. The assertions are appropriate and correctly structured.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including standard, short, long, zero, boundary, random, and multiple consecutive pulses. This provides a comprehensive test suite, making the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "IR_LED._hex_to_bin",
        "idx": "624",
        "repo_name": "TrynaThinkOf1___RpiL",
        "func_path": "src/rpi_gpio/IR.py",
        "orig_func": "def _hex_to_bin(self, hex_code):\n    hex_code = hex_code.lstrip('0x').zfill(8)\n    bin_code = bin(int(hex_code, 16))[2:].zfill(32)\n    return bin_code",
        "orig_context": "```python\n## src/rpi_gpio/IR.py\nimport RPi.GPIO as GPIO\n\nimport pigpio\n\nimport time as t\n\nclass IR_LED:\n    def __init__(self, pin):\n        self.pin = pin\n        self.pi = pigpio.pi()\n\n        if not self.pi.connected:\n            raise Exception('could not connect to pigpio daemon')\n\n        self.pi.set_mode(self.pin, pigpio.OUTPUT)\n\n    def send_signal(self, hex_code, protocol=\"NEC\", frequency=38000):\n        if protocol == \"NEC\":\n            data = self._hex_to_bin(hex_code)\n            self._send_nec(data, frequency)\n        else:\n            raise Exception('unsupported protocol')\n\n    def _send_nec(self, data, frequency=38000):\n        self.pi.set_PWM_frequency(self.pin, frequency)\n        self.pi.set_PWM_dutycycle(self.pin, 128)\n\n        self._send_pulse(9000, 4500)\n\n        for i in range(32):\n            bit = (data >> (31 - i)) & 1\n            if bit == 1:\n                self._send_pulse(560, 1690)\n            else:\n                self._send_pulse(560, 560)\n\n        self.pi.set_PWM_dutycycle(self.pin, 0)\n\n    def _send_pulse(self, high_time, low_time):\n        self.pi.gpio_trigger(self.pin, high_time, 1)\n        t.sleep(low_time / 1000000.0)\n\n    def _hex_to_bin(self, hex_code):\n        hex_code = hex_code.lstrip(\"0x\").zfill(8)\n        bin_code = bin(int(hex_code, 16))[2:].zfill(32)\n        return bin_code\n\n    def __del__(self):\n        self.pi.stop()\n        GPIO.cleanup([self.pin])\n\n```\n\n\n",
        "eval_script": "# Mock setup for pigpio\nclass MockPigpio:\n    OUTPUT = 0\n\n    def __init__(self):\n        self.connected = True\n\n    def pi(self):\n        return self\n\n    def set_mode(self, pin, mode):\n        pass\n\n    def set_PWM_frequency(self, pin, frequency):\n        pass\n\n    def set_PWM_dutycycle(self, pin, dutycycle):\n        pass\n\n    def gpio_trigger(self, pin, pulse_len, level):\n        pass\n\n    def stop(self):\n        pass\n\n# Mock RPi.GPIO\nclass MockGPIO:\n    @staticmethod\n    def cleanup(pins):\n        pass\n\n# Replace pigpio and RPi.GPIO with mocks\npigpio = MockPigpio()\nGPIO = MockGPIO()\n\n# Original code with minimal changes\nimport time as t\n\nclass IR_LED:\n    def __init__(self, pin):\n        self.pin = pin\n        self.pi = pigpio.pi()\n\n        if not self.pi.connected:\n            raise Exception('could not connect to pigpio daemon')\n\n        self.pi.set_mode(self.pin, pigpio.OUTPUT)\n\n    def send_signal(self, hex_code, protocol=\"NEC\", frequency=38000):\n        if protocol == \"NEC\":\n            data = self._hex_to_bin(hex_code)\n            self._send_nec(data, frequency)\n        else:\n            raise Exception('unsupported protocol')\n\n    def _send_nec(self, data, frequency=38000):\n        self.pi.set_PWM_frequency(self.pin, frequency)\n        self.pi.set_PWM_dutycycle(self.pin, 128)\n\n        self._send_pulse(9000, 4500)\n\n        for i in range(32):\n            bit = (data >> (31 - i)) & 1\n            if bit == 1:\n                self._send_pulse(560, 1690)\n            else:\n                self._send_pulse(560, 560)\n\n        self.pi.set_PWM_dutycycle(self.pin, 0)\n\n    def _send_pulse(self, high_time, low_time):\n        self.pi.gpio_trigger(self.pin, high_time, 1)\n        t.sleep(low_time / 1000000.0)\n\n    def _hex_to_bin(self, hex_code):\n        hex_code = hex_code.lstrip(\"0x\").zfill(8)\n        bin_code = bin(int(hex_code, 16))[2:].zfill(32)\n        return bin_code\n\n\n    def __del__(self):\n        self.pi.stop()\n        GPIO.cleanup([self.pin])\n\ndef test__hex_to_bin():\n    ir_led = IR_LED(pin=17)\n    # Test case 1: Typical hexadecimal input\n    assert ir_led._hex_to_bin(\"0x1A2B3C4D\") == ir_led._hex_to_bin_new_implementation(\"0x1A2B3C4D\")\n    # Test case 2: Edge case with all zeros\n    assert ir_led._hex_to_bin(\"0x00000000\") == ir_led._hex_to_bin_new_implementation(\"0x00000000\")\n    # Test case 3: Input with leading zeros\n    assert ir_led._hex_to_bin(\"0x0001\") == ir_led._hex_to_bin_new_implementation(\"0x0001\")\n    # Test case 4: Maximum hexadecimal value\n    assert ir_led._hex_to_bin(\"0xFFFFFFFF\") == ir_led._hex_to_bin_new_implementation(\"0xFFFFFFFF\")\n    # Test case 5: Minimum hexadecimal value\n    assert ir_led._hex_to_bin(\"0x0\") == ir_led._hex_to_bin_new_implementation(\"0x0\")\n    # Test case 6: Mixed-case hexadecimal input\n    assert ir_led._hex_to_bin(\"0x1a2b3c4d\") == ir_led._hex_to_bin_new_implementation(\"0x1a2b3c4d\")\n    # Test case 7: No '0x' prefix\n    assert ir_led._hex_to_bin(\"1A2B3C4D\") == ir_led._hex_to_bin_new_implementation(\"1A2B3C4D\")\n    # Test case 8: Invalid hexadecimal input (should raise ValueError)\n    try:\n        ir_led._hex_to_bin(\"0xGHIJKL\")\n    except ValueError:\n        pass\n    else:\n        assert False, \"Expected ValueError for invalid hexadecimal input\"\n    try:\n        ir_led._hex_to_bin_new_implementation(\"0xGHIJKL\")\n    except ValueError:\n        pass\n    else:\n        assert False, \"Expected ValueError for invalid hexadecimal input\"\n\nif __name__ == \"__main__\":\n    test__hex_to_bin()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_hex_to_bin` in the `IR_LED` class is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they strip the '0x' prefix from the hexadecimal string, pad it to ensure it is 8 characters long, convert it to an integer with base 16, convert that integer to a binary string, and then pad the binary string to ensure it is 32 characters long. The functionality and implementation are exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_hex_to_bin` function returns a value, specifically a binary string representation of the hexadecimal input. This satisfies the condition as it has a return value.\n  \n- CONDITION 2: The test cases check the return values of `_hex_to_bin` and `_hex_to_bin_new_implementation` by using assertions to compare their outputs. There is no checking of printed or logged contents, satisfying this condition.\n  \n- CONDITION 3: The test cases are designed to compare the outputs of `_hex_to_bin` and `_hex_to_bin_new_implementation` for various inputs. If both implementations produce the same outputs for all test cases, they can be considered to have the same functionality. This condition is satisfied.\n  \n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_hex_to_bin` has a return value. The test cases also handle exceptions for invalid inputs appropriately. This condition is satisfied.\n  \n- CONDITION 5: The test cases cover a range of scenarios, including typical inputs, edge cases, inputs with leading zeros, maximum and minimum values, mixed-case inputs, inputs without the '0x' prefix, and invalid inputs. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84566e198624b0177f010e36e21ee5a84b7a2bde"
    },
    {
        "func_name": "Evaluation.initalize_evaluation",
        "idx": "625",
        "repo_name": "AllenNeuralDynamics___aind-ophys-fov-summary-qc",
        "func_path": "code/fov_summary/session_evaluation.py",
        "orig_func": "def initalize_evaluation(self):\n    \"\"\"Initialize the evaluation.\"\"\"\n    self.directories = self._get_directories()\n    self.output_directory = self._make_directory(self.settings.output_directory)",
        "orig_context": "```python\n## code/fov_summary/session_evaluation.py\nimport json\n\nimport math\n\nimport operator\n\nimport sys\n\nfrom pathlib import Path\n\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nfrom aind_data_schema.core.quality_control import (QCEvaluation, QCMetric,\n                                                   QCStatus, Stage)\n\nfrom aind_data_schema_models.modalities import Modality\n\nfrom PIL import Image, ImageDraw, ImageFont\n\nfrom pydantic import BaseModel, Field\n\nclass EvaluationSettings(BaseModel):\n    \"\"\"Settings for the evaluation of the registration.\"\"\"\n\n    # Path to the reference image\n    input_directory: Path = Field(..., description=\"Input directory containing the data\")\n    output_directory: Path = Field(\n        ..., description=\"Output directory to store the results\"\n    )\n    pattern: Optional[List] = Field(\n        default=[], description=\"Pattern to match in file search\"\n    )\n    folder_name: str = Field(..., description=\"Name of the folder to search for files\")\n    metric_name: str = Field(..., description=\"Name of the metric\")\n    metric_status_history: list[QCStatus] = Field(\n        default=[], description=\"Status history for the metric\"\n    )\n    stage: Stage = Field(..., description=\"Stage of the evaluation\")\n    modality: Modality.ONE_OF = Field(..., description=\"Modality of the data\")\n    evaluations_name: str = Field(..., description=\"Name of the evaluation\")\n    allow_failed_metrics: bool = Field(..., description=\"Allow failed metrics\")\n\nclass Evaluation:\n    \"\"\"Build evaluation from provided settings.\"\"\"\n\n    OPERATORS = {\n        \"==\": operator.eq,\n        \"!=\": operator.ne,\n        \"<\": operator.lt,\n        \"<=\": operator.le,\n        \">\": operator.gt,\n        \">=\": operator.ge,\n    }\n\n    def __init__(self, settings: EvaluationSettings):\n        self.settings = settings\n        self.initalize_evaluation()\n\n    def initalize_evaluation(self):\n        \"\"\"Initialize the evaluation.\"\"\"\n        self.directories = self._get_directories()\n        self.output_directory = self._make_directory(self.settings.output_directory)\n\n    def _get_directories(self) -> list:\n        \"\"\"Get directories containing the data.\n\n        Returns\n        -------\n        list\n            List of directories containing the data\n        \"\"\"\n        input_dir = self.settings.input_directory\n        if len(list(input_dir.glob(\"*\"))) == 1:\n            return [plane for plane in input_dir.glob(\"*/*\")]\n        return [plane for plane in input_dir.rglob(\"*\")]\n\n    def _make_directory(self, directory: Path) -> Path:\n        \"\"\"\n        Make a directory if it does not exist.\n\n        Parameters\n        ----------\n        directory : Path\n            Directory path\n\n        Returns\n        -------\n        Path\n            Directory path\n        \"\"\"\n        directory.mkdir(exist_ok=True)\n        return directory\n\n    def collect_pattern_files(self) -> tuple[List[str], List[Path]]:\n        \"\"\"Collect files matching the pattern in the directories.\n\n        Parameters\n        ----------\n        pattern1 : str\n            Pattern to match in file search\n        pattern2 : Optional[str], optional\n            Second pattern to match in file search, by default None\n\n        Returns\n        -------\n        tuple[List[str], List[Path]]\n            List of file paths matching the pattern\n        \"\"\"\n\n        if not self.directories:\n            raise ValueError(\"No directories provided.\")\n        if not self.settings.pattern:\n            raise ValueError(\"No pattern provided.\")\n\n        row_labels: List[str] = []\n        matched_files: List[Path] = []\n\n        for directory in self.directories:\n            for pattern in self.settings.pattern:\n                pattern_matches = [\n                    i for i in directory.rglob(\"*\") if pattern in str(i) and i.is_file()\n                ]\n                if pattern_matches:\n                    matched_files.extend(pattern_matches)\n            row_labels.append(directory.parent.name)\n\n        return row_labels, matched_files\n\n    def combine_images(\n        self,\n        image_paths,\n        image_output_name,\n        num_columns=2,\n        spacing=10,\n        row_labels=None,\n        label_width=200,\n    ) -> Path:\n        \"\"\"\n        Combine multiple PNG images into a matrix layout with row labels.\n\n        Parameters\n        ----------\n        image_paths : List[str]\n            List of paths to PNG images\n        output_path : str\n            Path where the combined image will be saved\n        num_columns : int, optional\n            Number of columns in the matrix, by default 2\n        spacing : int, optional\n            Pixels of spacing between images, by default 10\n        row_labels : List[str], optional\n            List of labels for each row. If None, no labels are added, by default None\n        label_width : int, optional\n            Width in pixels reserved for labels, by default 200\n\n        Returns\n        -------\n        None\n            Saves the combined image to output_path\n        \"\"\"\n        images = [Image.open(path).convert(\"RGBA\") for path in image_paths]\n\n        num_images = len(images)\n        num_rows = math.ceil(num_images / num_columns)\n\n        widths, heights = zip(*(i.size for i in images))\n        max_width = max(widths)\n        max_height = max(heights)\n\n        total_width = (max_width * num_columns) + (spacing * (num_columns - 1))\n        if row_labels:\n            total_width += label_width + spacing  # Add space for labels\n        total_height = (max_height * num_rows) + (spacing * (num_rows - 1))\n\n        new_image = Image.new(\"RGBA\", (total_width, total_height), (255, 255, 255, 0))\n        draw = ImageDraw.Draw(new_image)\n\n        try:\n            if sys.platform == \"win32\":\n                font_path = \"C:/Windows/Fonts/arial.ttf\"\n            elif sys.platform == \"darwin\":  # macOS\n                font_path = \"/System/Library/Fonts/Helvetica.ttc\"\n            else:  # Linux\n                font_path = \"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\"\n\n            font = ImageFont.truetype(font_path, size=100)\n        except Exception:\n            font = ImageFont.load_default()\n\n        label_offset = label_width + spacing if row_labels else 0\n\n        for idx, img in enumerate(images):\n            row = idx // num_columns\n            col = idx % num_columns\n\n            x = label_offset + col * (max_width + spacing)\n            y = row * (max_height + spacing)\n\n            x_center = x + (max_width - img.size[0]) // 2\n            y_center = y + (max_height - img.size[1]) // 2\n\n            new_image.paste(img, (x_center, y_center))\n\n            if col == 0 and row_labels and row < len(row_labels):\n                # Calculate vertical center of the current row\n                text_y = y + (max_height // 2)\n\n                # Get the size of the text\n                text = str(row_labels[row])\n                try:\n                    bbox = draw.textbbox((0, 0), text, font=font)\n                    text_height = bbox[3] - bbox[1]\n                except AttributeError:  # For older Pillow versions\n                    text_height = font.getsize(text)[1]\n\n                # Draw the label vertically centered with the row\n                draw.text(\n                    (spacing, text_y - text_height // 2),\n                    text,\n                    fill=(0, 0, 0, 255),  # Black text\n                    font=font,\n                )\n\n        # Save combined image\n        new_image.save(self.output_directory / image_output_name, \"PNG\")\n\n        # Close all images\n        for img in images:\n            img.close()\n        return self.output_directory / image_output_name\n\n    def build_qc_metric(self, value: Any, reference: List[Path] = None) -> QCMetric:\n        \"\"\"Build a quality control metric from the provided settings.\n\n        Parameters\n        ----------\n        value : Any\n            Value of the metric\n        \"\"\"\n        return QCMetric(\n            name=self.settings.metric_name,\n            value=value,\n            reference=reference,\n            status_history=self.settings.metric_status_history,\n        )\n\n    def build_qc_evaluation(self, metrics: List[QCMetric]) -> QCEvaluation:\n        \"\"\"Build a quality control evaluation from the provided settings.\n\n        Parameters\n        ----------\n        metrics : List[QCMetric]\n            List of metrics\n        \"\"\"\n        return QCEvaluation(\n            name=self.settings.evaluations_name,\n            stage=self.settings.stage,\n            modality=self.settings.modality,\n            metrics=metrics,\n        )\n\n    def write_evaluation_to_json(self, evaluation: QCEvaluation):\n        \"\"\"Write the evaluation to a JSON file.\n\n        Parameters\n        ----------\n        evaluation : QCEvaluation\n            Evaluation object\n        \"\"\"\n        with open(self.output_directory / \"quality_evaluation.json\", \"w\") as f:\n            json.dump(json.loads(evaluation.model_dump_json()), f, indent=4)\n\n    def evaluate_metrics(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Flexible threshold-based evaluation of metrics\n\n        Parameters\n        ----------\n        metrics : dict\n            Dictionary of metrics where values are numeric\n        threshold : float\n            Threshold value for comparison\n        operation : str or Callable\n            Comparison operation to use. Can be one of '==', '!=', '<', '<=', '>', '>='\n            or a custom comparison function that takes two arguments\n\n        Returns\n        -------\n        bool\n            True if any value in metrics satisfies the comparison with threshold\n\n        Raises\n        ------\n        ValueError\n            If operation string is not recognized\n        TypeError\n            If operation is neither a string nor a callable\n        \"\"\"\n        if isinstance(operation, str):\n            if operation not in Evaluation.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(Evaluation.OPERATORS.keys())}\"\n                )\n            compare_func = Evaluation.OPERATORS[operation]\n\n        elif callable(operation):\n            compare_func = operation\n\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return any(compare_func(value, threshold) for value in metrics.values())\n\n    def evaluate_metrics_all(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Similar to evaluate_metrics but requires all values to satisfy the condition\"\"\"\n        if isinstance(operation, str):\n            if operation not in cls.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(cls.OPERATORS.keys())}\"\n                )\n            compare_func = cls.OPERATORS[operation]\n        elif callable(operation):\n            compare_func = operation\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return all(compare_func(value, threshold) for value in metrics.values())\n\n```\n\n\n",
        "eval_script": "# Mock implementations for missing imports\nfrom pathlib import Path\nfrom typing import List, Optional\n\n# Mock classes for missing imports\nclass QCStatus:\n    pass\n\nclass Stage:\n    pass\n\nclass Modality:\n    ONE_OF = None\n\nclass QCEvaluation:\n    def __init__(self, name, stage, modality, metrics):\n        pass\n    \n    def model_dump_json(self):\n        return \"{}\"\n\nclass QCMetric:\n    def __init__(self, name, value, reference, status_history):\n        pass\n\n# Revised code\nimport json\nimport math\nimport operator\nimport sys\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, List, Optional, Union\nfrom PIL import Image, ImageDraw, ImageFont\nfrom pydantic import BaseModel, Field\n\nclass EvaluationSettings(BaseModel):\n    \"\"\"Settings for the evaluation of the registration.\"\"\"\n    input_directory: Path = Field(..., description=\"Input directory containing the data\")\n    output_directory: Path = Field(\n        ..., description=\"Output directory to store the results\"\n    )\n    pattern: Optional[List] = Field(\n        default=[], description=\"Pattern to match in file search\"\n    )\n    folder_name: str = Field(..., description=\"Name of the folder to search for files\")\n    metric_name: str = Field(..., description=\"Name of the metric\")\n    metric_status_history: list[QCStatus] = Field(\n        default=[], description=\"Status history for the metric\"\n    )\n    stage: Stage = Field(..., description=\"Stage of the evaluation\")\n    modality: Modality.ONE_OF = Field(..., description=\"Modality of the data\")\n    evaluations_name: str = Field(..., description=\"Name of the evaluation\")\n    allow_failed_metrics: bool = Field(..., description=\"Allow failed metrics\")\n\n    class Config:\n        arbitrary_types_allowed = True\n\nclass Evaluation:\n    \"\"\"Build evaluation from provided settings.\"\"\"\n\n    OPERATORS = {\n        \"==\": operator.eq,\n        \"!=\": operator.ne,\n        \"<\": operator.lt,\n        \"<=\": operator.le,\n        \">\": operator.gt,\n        \">=\": operator.ge,\n    }\n\n    def __init__(self, settings: EvaluationSettings):\n        self.settings = settings\n        self.initalize_evaluation()\n\n    def initalize_evaluation(self):\n        \"\"\"Initialize the evaluation.\"\"\"\n        self.directories = self._get_directories()\n        self.output_directory = self._make_directory(self.settings.output_directory)\n\n\n    def _get_directories(self) -> list:\n        \"\"\"Get directories containing the data.\n\n        Returns\n        -------\n        list\n            List of directories containing the data\n        \"\"\"\n        input_dir = self.settings.input_directory\n        if len(list(input_dir.glob(\"*\"))) == 1:\n            return [plane for plane in input_dir.glob(\"*/*\")]\n        return [plane for plane in input_dir.rglob(\"*\")]\n\n    def _make_directory(self, directory: Path) -> Path:\n        \"\"\"\n        Make a directory if it does not exist.\n\n        Parameters\n        ----------\n        directory : Path\n            Directory path\n\n        Returns\n        -------\n        Path\n            Directory path\n        \"\"\"\n        directory.mkdir(exist_ok=True)\n        return directory\n\n    def collect_pattern_files(self) -> tuple[List[str], List[Path]]:\n        \"\"\"Collect files matching the pattern in the directories.\n\n        Parameters\n        ----------\n        pattern1 : str\n            Pattern to match in file search\n        pattern2 : Optional[str], optional\n            Second pattern to match in file search, by default None\n\n        Returns\n        -------\n        tuple[List[str], List[Path]]\n            List of file paths matching the pattern\n        \"\"\"\n\n        if not self.directories:\n            raise ValueError(\"No directories provided.\")\n        if not self.settings.pattern:\n            raise ValueError(\"No pattern provided.\")\n\n        row_labels: List[str] = []\n        matched_files: List[Path] = []\n\n        for directory in self.directories:\n            for pattern in self.settings.pattern:\n                pattern_matches = [\n                    i for i in directory.rglob(\"*\") if pattern in str(i) and i.is_file()\n                ]\n                if pattern_matches:\n                    matched_files.extend(pattern_matches)\n            row_labels.append(directory.parent.name)\n\n        return row_labels, matched_files\n\n    def combine_images(\n        self,\n        image_paths,\n        image_output_name,\n        num_columns=2,\n        spacing=10,\n        row_labels=None,\n        label_width=200,\n    ) -> Path:\n        \"\"\"\n        Combine multiple PNG images into a matrix layout with row labels.\n\n        Parameters\n        ----------\n        image_paths : List[str]\n            List of paths to PNG images\n        output_path : str\n            Path where the combined image will be saved\n        num_columns : int, optional\n            Number of columns in the matrix, by default 2\n        spacing : int, optional\n            Pixels of spacing between images, by default 10\n        row_labels : List[str], optional\n            List of labels for each row. If None, no labels are added, by default None\n        label_width : int, optional\n            Width in pixels reserved for labels, by default 200\n\n        Returns\n        -------\n        None\n            Saves the combined image to output_path\n        \"\"\"\n        images = [Image.open(path).convert(\"RGBA\") for path in image_paths]\n\n        num_images = len(images)\n        num_rows = math.ceil(num_images / num_columns)\n\n        widths, heights = zip(*(i.size for i in images))\n        max_width = max(widths)\n        max_height = max(heights)\n\n        total_width = (max_width * num_columns) + (spacing * (num_columns - 1))\n        if row_labels:\n            total_width += label_width + spacing  # Add space for labels\n        total_height = (max_height * num_rows) + (spacing * (num_rows - 1))\n\n        new_image = Image.new(\"RGBA\", (total_width, total_height), (255, 255, 255, 0))\n        draw = ImageDraw.Draw(new_image)\n\n        try:\n            if sys.platform == \"win32\":\n                font_path = \"C:/Windows/Fonts/arial.ttf\"\n            elif sys.platform == \"darwin\":  # macOS\n                font_path = \"/System/Library/Fonts/Helvetica.ttc\"\n            else:  # Linux\n                font_path = \"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\"\n\n            font = ImageFont.truetype(font_path, size=100)\n        except Exception:\n            font = ImageFont.load_default()\n\n        label_offset = label_width + spacing if row_labels else 0\n\n        for idx, img in enumerate(images):\n            row = idx // num_columns\n            col = idx % num_columns\n\n            x = label_offset + col * (max_width + spacing)\n            y = row * (max_height + spacing)\n\n            x_center = x + (max_width - img.size[0]) // 2\n            y_center = y + (max_height - img.size[1]) // 2\n\n            new_image.paste(img, (x_center, y_center))\n\n            if col == 0 and row_labels and row < len(row_labels):\n                # Calculate vertical center of the current row\n                text_y = y + (max_height // 2)\n\n                # Get the size of the text\n                text = str(row_labels[row])\n                try:\n                    bbox = draw.textbbox((0, 0), text, font=font)\n                    text_height = bbox[3] - bbox[1]\n                except AttributeError:  # For older Pillow versions\n                    text_height = font.getsize(text)[1]\n\n                # Draw the label vertically centered with the row\n                draw.text(\n                    (spacing, text_y - text_height // 2),\n                    text,\n                    fill=(0, 0, 0, 255),  # Black text\n                    font=font,\n                )\n\n        # Save combined image\n        new_image.save(self.output_directory / image_output_name, \"PNG\")\n\n        # Close all images\n        for img in images:\n            img.close()\n        return self.output_directory / image_output_name\n\n    def build_qc_metric(self, value: Any, reference: List[Path] = None) -> QCMetric:\n        \"\"\"Build a quality control metric from the provided settings.\n\n        Parameters\n        ----------\n        value : Any\n            Value of the metric\n        \"\"\"\n        return QCMetric(\n            name=self.settings.metric_name,\n            value=value,\n            reference=reference,\n            status_history=self.settings.metric_status_history,\n        )\n\n    def build_qc_evaluation(self, metrics: List[QCMetric]) -> QCEvaluation:\n        \"\"\"Build a quality control evaluation from the provided settings.\n\n        Parameters\n        ----------\n        metrics : List[QCMetric]\n            List of metrics\n        \"\"\"\n        return QCEvaluation(\n            name=self.settings.evaluations_name,\n            stage=self.settings.stage,\n            modality=self.settings.modality,\n            metrics=metrics,\n        )\n\n    def write_evaluation_to_json(self, evaluation: QCEvaluation):\n        \"\"\"Write the evaluation to a JSON file.\n\n        Parameters\n        ----------\n        evaluation : QCEvaluation\n            Evaluation object\n        \"\"\"\n        with open(self.output_directory / \"quality_evaluation.json\", \"w\") as f:\n            json.dump(json.loads(evaluation.model_dump_json()), f, indent=4)\n\n    def evaluate_metrics(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Flexible threshold-based evaluation of metrics\n\n        Parameters\n        ----------\n        metrics : dict\n            Dictionary of metrics where values are numeric\n        threshold : float\n            Threshold value for comparison\n        operation : str or Callable\n            Comparison operation to use. Can be one of '==', '!=', '<', '<=', '>', '>='\n            or a custom comparison function that takes two arguments\n\n        Returns\n        -------\n        bool\n            True if any value in metrics satisfies the comparison with threshold\n\n        Raises\n        ------\n        ValueError\n            If operation string is not recognized\n        TypeError\n            If operation is neither a string nor a callable\n        \"\"\"\n        if isinstance(operation, str):\n            if operation not in Evaluation.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(Evaluation.OPERATORS.keys())}\"\n                )\n            compare_func = Evaluation.OPERATORS[operation]\n\n        elif callable(operation):\n            compare_func = operation\n\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return any(compare_func(value, threshold) for value in metrics.values())\n\n    def evaluate_metrics_all(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Similar to evaluate_metrics but requires all values to satisfy the condition\"\"\"\n        if isinstance(operation, str):\n            if operation not in Evaluation.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(Evaluation.OPERATORS.keys())}\"\n                )\n            compare_func = Evaluation.OPERATORS[operation]\n        elif callable(operation):\n            compare_func = operation\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return all(compare_func(value, threshold) for value in metrics.values())\n\ndef test_initalize_evaluation():\n    \"\"\"Test to ensure new implementation matches the original.\"\"\"\n    settings = EvaluationSettings(\n        input_directory=Path(\"/home/user/tmp/input\"),\n        output_directory=Path(\"/home/user/tmp/output\"),\n        folder_name=\"test_folder\",\n        metric_name=\"test_metric\",\n        stage=Stage(),\n        modality=Modality.ONE_OF,\n        evaluations_name=\"test_evaluation\",\n        allow_failed_metrics=True\n    )\n\n    # Create directories for testing\n    settings.input_directory.mkdir(parents=True, exist_ok=True)\n    settings.output_directory.mkdir(parents=True, exist_ok=True)\n\n    eval_original = Evaluation(settings)\n    eval_original.initalize_evaluation()\n\n    eval_new = Evaluation(settings)\n    eval_new.initalize_evaluation_new_implementation()\n\n    # Assert statements to compare original and new implementation\n    assert eval_original.directories == eval_new.directories, \"Directories mismatch\"\n    assert eval_original.output_directory == eval_new.output_directory, \"Output directory mismatch\"\n    \n    # Additional test case with different input\n    settings.input_directory = Path(\"/home/user/tmp/input2\")\n    settings.input_directory.mkdir(parents=True, exist_ok=True)\n    \n    eval_original = Evaluation(settings)\n    eval_original.initalize_evaluation()\n\n    eval_new = Evaluation(settings)\n    eval_new.initalize_evaluation_new_implementation()\n\n    assert eval_original.directories == eval_new.directories, \"Directories mismatch with different input\"\n    assert eval_original.output_directory == eval_new.output_directory, \"Output directory mismatch with different input\"\n\nif __name__ == \"__main__\":\n    test_initalize_evaluation()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the code is identical to the ORIGINAL FUNCTION. Both functions are named `initalize_evaluation` and perform the same operations: they initialize the `directories` attribute by calling the `_get_directories` method and set the `output_directory` attribute by calling the `_make_directory` method with `self.settings.output_directory` as the argument. There are no differences in the logic or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `initalize_evaluation` function modifies the instance variables `self.directories` and `self.output_directory`. These are not global variables but are part of the class instance, which satisfies the condition as it modifies the state of the object.\n\n2. **CONDITION 2**: The test function checks the state of the variables `directories` and `output_directory` of the `Evaluation` instances. It does not rely on printed or logged content, thus satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the state of the `directories` and `output_directory` attributes between the original and new implementations. If the new implementation has the same functionality, these attributes should match. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the state of the object attributes, which is reasonable given that `initalize_evaluation` modifies these attributes. The test does not use inappropriate assertions like comparing return values when there are none. This condition is satisfied.\n\n5. **CONDITION 5**: The test includes an additional test case with a different input directory to ensure that the function behaves correctly with different inputs. This makes the test non-trivial as it checks the function's behavior under different conditions.",
            "answer": "yes"
        },
        "commit_id": "1a6a5c8ffc0be597be1dc5e1399303a1dd010dac"
    },
    {
        "func_name": "Evaluation._make_directory",
        "idx": "627",
        "repo_name": "AllenNeuralDynamics___aind-ophys-fov-summary-qc",
        "func_path": "code/fov_summary/session_evaluation.py",
        "orig_func": "def _make_directory(self, directory: Path) -> Path:\n    \"\"\"\n        Make a directory if it does not exist.\n\n        Parameters\n        ----------\n        directory : Path\n            Directory path\n\n        Returns\n        -------\n        Path\n            Directory path\n        \"\"\"\n    directory.mkdir(exist_ok=True)\n    return directory",
        "orig_context": "```python\n## code/fov_summary/session_evaluation.py\nimport json\n\nimport math\n\nimport operator\n\nimport sys\n\nfrom pathlib import Path\n\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nfrom aind_data_schema.core.quality_control import (QCEvaluation, QCMetric,\n                                                   QCStatus, Stage)\n\nfrom aind_data_schema_models.modalities import Modality\n\nfrom PIL import Image, ImageDraw, ImageFont\n\nfrom pydantic import BaseModel, Field\n\nclass EvaluationSettings(BaseModel):\n    \"\"\"Settings for the evaluation of the registration.\"\"\"\n\n    # Path to the reference image\n    input_directory: Path = Field(..., description=\"Input directory containing the data\")\n    output_directory: Path = Field(\n        ..., description=\"Output directory to store the results\"\n    )\n    pattern: Optional[List] = Field(\n        default=[], description=\"Pattern to match in file search\"\n    )\n    folder_name: str = Field(..., description=\"Name of the folder to search for files\")\n    metric_name: str = Field(..., description=\"Name of the metric\")\n    metric_status_history: list[QCStatus] = Field(\n        default=[], description=\"Status history for the metric\"\n    )\n    stage: Stage = Field(..., description=\"Stage of the evaluation\")\n    modality: Modality.ONE_OF = Field(..., description=\"Modality of the data\")\n    evaluations_name: str = Field(..., description=\"Name of the evaluation\")\n    allow_failed_metrics: bool = Field(..., description=\"Allow failed metrics\")\n\nclass Evaluation:\n    \"\"\"Build evaluation from provided settings.\"\"\"\n\n    OPERATORS = {\n        \"==\": operator.eq,\n        \"!=\": operator.ne,\n        \"<\": operator.lt,\n        \"<=\": operator.le,\n        \">\": operator.gt,\n        \">=\": operator.ge,\n    }\n\n    def __init__(self, settings: EvaluationSettings):\n        self.settings = settings\n        self.initalize_evaluation()\n\n    def initalize_evaluation(self):\n        \"\"\"Initialize the evaluation.\"\"\"\n        self.directories = self._get_directories()\n        self.output_directory = self._make_directory(self.settings.output_directory)\n\n    def _get_directories(self) -> list:\n        \"\"\"Get directories containing the data.\n\n        Returns\n        -------\n        list\n            List of directories containing the data\n        \"\"\"\n        input_dir = self.settings.input_directory\n        if len(list(input_dir.glob(\"*\"))) == 1:\n            return [plane for plane in input_dir.glob(\"*/*\")]\n        return [plane for plane in input_dir.rglob(\"*\")]\n\n    def _make_directory(self, directory: Path) -> Path:\n        \"\"\"\n        Make a directory if it does not exist.\n\n        Parameters\n        ----------\n        directory : Path\n            Directory path\n\n        Returns\n        -------\n        Path\n            Directory path\n        \"\"\"\n        directory.mkdir(exist_ok=True)\n        return directory\n\n    def collect_pattern_files(self) -> tuple[List[str], List[Path]]:\n        \"\"\"Collect files matching the pattern in the directories.\n\n        Parameters\n        ----------\n        pattern1 : str\n            Pattern to match in file search\n        pattern2 : Optional[str], optional\n            Second pattern to match in file search, by default None\n\n        Returns\n        -------\n        tuple[List[str], List[Path]]\n            List of file paths matching the pattern\n        \"\"\"\n\n        if not self.directories:\n            raise ValueError(\"No directories provided.\")\n        if not self.settings.pattern:\n            raise ValueError(\"No pattern provided.\")\n\n        row_labels: List[str] = []\n        matched_files: List[Path] = []\n\n        for directory in self.directories:\n            for pattern in self.settings.pattern:\n                pattern_matches = [\n                    i for i in directory.rglob(\"*\") if pattern in str(i) and i.is_file()\n                ]\n                if pattern_matches:\n                    matched_files.extend(pattern_matches)\n            row_labels.append(directory.parent.name)\n\n        return row_labels, matched_files\n\n    def combine_images(\n        self,\n        image_paths,\n        image_output_name,\n        num_columns=2,\n        spacing=10,\n        row_labels=None,\n        label_width=200,\n    ) -> Path:\n        \"\"\"\n        Combine multiple PNG images into a matrix layout with row labels.\n\n        Parameters\n        ----------\n        image_paths : List[str]\n            List of paths to PNG images\n        output_path : str\n            Path where the combined image will be saved\n        num_columns : int, optional\n            Number of columns in the matrix, by default 2\n        spacing : int, optional\n            Pixels of spacing between images, by default 10\n        row_labels : List[str], optional\n            List of labels for each row. If None, no labels are added, by default None\n        label_width : int, optional\n            Width in pixels reserved for labels, by default 200\n\n        Returns\n        -------\n        None\n            Saves the combined image to output_path\n        \"\"\"\n        images = [Image.open(path).convert(\"RGBA\") for path in image_paths]\n\n        num_images = len(images)\n        num_rows = math.ceil(num_images / num_columns)\n\n        widths, heights = zip(*(i.size for i in images))\n        max_width = max(widths)\n        max_height = max(heights)\n\n        total_width = (max_width * num_columns) + (spacing * (num_columns - 1))\n        if row_labels:\n            total_width += label_width + spacing  # Add space for labels\n        total_height = (max_height * num_rows) + (spacing * (num_rows - 1))\n\n        new_image = Image.new(\"RGBA\", (total_width, total_height), (255, 255, 255, 0))\n        draw = ImageDraw.Draw(new_image)\n\n        try:\n            if sys.platform == \"win32\":\n                font_path = \"C:/Windows/Fonts/arial.ttf\"\n            elif sys.platform == \"darwin\":  # macOS\n                font_path = \"/System/Library/Fonts/Helvetica.ttc\"\n            else:  # Linux\n                font_path = \"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\"\n\n            font = ImageFont.truetype(font_path, size=100)\n        except Exception:\n            font = ImageFont.load_default()\n\n        label_offset = label_width + spacing if row_labels else 0\n\n        for idx, img in enumerate(images):\n            row = idx // num_columns\n            col = idx % num_columns\n\n            x = label_offset + col * (max_width + spacing)\n            y = row * (max_height + spacing)\n\n            x_center = x + (max_width - img.size[0]) // 2\n            y_center = y + (max_height - img.size[1]) // 2\n\n            new_image.paste(img, (x_center, y_center))\n\n            if col == 0 and row_labels and row < len(row_labels):\n                # Calculate vertical center of the current row\n                text_y = y + (max_height // 2)\n\n                # Get the size of the text\n                text = str(row_labels[row])\n                try:\n                    bbox = draw.textbbox((0, 0), text, font=font)\n                    text_height = bbox[3] - bbox[1]\n                except AttributeError:  # For older Pillow versions\n                    text_height = font.getsize(text)[1]\n\n                # Draw the label vertically centered with the row\n                draw.text(\n                    (spacing, text_y - text_height // 2),\n                    text,\n                    fill=(0, 0, 0, 255),  # Black text\n                    font=font,\n                )\n\n        # Save combined image\n        new_image.save(self.output_directory / image_output_name, \"PNG\")\n\n        # Close all images\n        for img in images:\n            img.close()\n        return self.output_directory / image_output_name\n\n    def build_qc_metric(self, value: Any, reference: List[Path] = None) -> QCMetric:\n        \"\"\"Build a quality control metric from the provided settings.\n\n        Parameters\n        ----------\n        value : Any\n            Value of the metric\n        \"\"\"\n        return QCMetric(\n            name=self.settings.metric_name,\n            value=value,\n            reference=reference,\n            status_history=self.settings.metric_status_history,\n        )\n\n    def build_qc_evaluation(self, metrics: List[QCMetric]) -> QCEvaluation:\n        \"\"\"Build a quality control evaluation from the provided settings.\n\n        Parameters\n        ----------\n        metrics : List[QCMetric]\n            List of metrics\n        \"\"\"\n        return QCEvaluation(\n            name=self.settings.evaluations_name,\n            stage=self.settings.stage,\n            modality=self.settings.modality,\n            metrics=metrics,\n        )\n\n    def write_evaluation_to_json(self, evaluation: QCEvaluation):\n        \"\"\"Write the evaluation to a JSON file.\n\n        Parameters\n        ----------\n        evaluation : QCEvaluation\n            Evaluation object\n        \"\"\"\n        with open(self.output_directory / \"quality_evaluation.json\", \"w\") as f:\n            json.dump(json.loads(evaluation.model_dump_json()), f, indent=4)\n\n    def evaluate_metrics(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Flexible threshold-based evaluation of metrics\n\n        Parameters\n        ----------\n        metrics : dict\n            Dictionary of metrics where values are numeric\n        threshold : float\n            Threshold value for comparison\n        operation : str or Callable\n            Comparison operation to use. Can be one of '==', '!=', '<', '<=', '>', '>='\n            or a custom comparison function that takes two arguments\n\n        Returns\n        -------\n        bool\n            True if any value in metrics satisfies the comparison with threshold\n\n        Raises\n        ------\n        ValueError\n            If operation string is not recognized\n        TypeError\n            If operation is neither a string nor a callable\n        \"\"\"\n        if isinstance(operation, str):\n            if operation not in Evaluation.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(Evaluation.OPERATORS.keys())}\"\n                )\n            compare_func = Evaluation.OPERATORS[operation]\n\n        elif callable(operation):\n            compare_func = operation\n\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return any(compare_func(value, threshold) for value in metrics.values())\n\n    def evaluate_metrics_all(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Similar to evaluate_metrics but requires all values to satisfy the condition\"\"\"\n        if isinstance(operation, str):\n            if operation not in cls.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(cls.OPERATORS.keys())}\"\n                )\n            compare_func = cls.OPERATORS[operation]\n        elif callable(operation):\n            compare_func = operation\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return all(compare_func(value, threshold) for value in metrics.values())\n\n```\n\n\n",
        "eval_script": "## code/fov_summary/session_evaluation.py\nimport json\n\nimport math\n\nimport operator\n\nimport sys\n\nfrom pathlib import Path\n\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\n# Mock imports for demonstration purposes\nclass QCEvaluation:\n    def __init__(self, name, stage, modality, metrics):\n        pass\n\n    def model_dump_json(self):\n        return json.dumps({})\n\nclass QCMetric:\n    def __init__(self, name, value, reference, status_history):\n        pass\n\nclass QCStatus:\n    pass\n\nclass Stage:\n    pass\n\nclass Modality:\n    ONE_OF = None\n\nfrom PIL import Image, ImageDraw, ImageFont\n\nfrom pydantic import BaseModel, Field\n\nclass EvaluationSettings(BaseModel):\n    \"\"\"Settings for the evaluation of the registration.\"\"\"\n\n    # Path to the reference image\n    input_directory: Path = Field(..., description=\"Input directory containing the data\")\n    output_directory: Path = Field(\n        ..., description=\"Output directory to store the results\"\n    )\n    pattern: Optional[List] = Field(\n        default=[], description=\"Pattern to match in file search\"\n    )\n    folder_name: str = Field(..., description=\"Name of the folder to search for files\")\n    metric_name: str = Field(..., description=\"Name of the metric\")\n    metric_status_history: list[QCStatus] = Field(\n        default=[], description=\"Status history for the metric\"\n    )\n    stage: Stage = Field(..., description=\"Stage of the evaluation\")\n    modality: Modality.ONE_OF = Field(..., description=\"Modality of the data\")\n    evaluations_name: str = Field(..., description=\"Name of the evaluation\")\n    allow_failed_metrics: bool = Field(..., description=\"Allow failed metrics\")\n\n    class Config:\n        arbitrary_types_allowed = True\n\nclass Evaluation:\n    \"\"\"Build evaluation from provided settings.\"\"\"\n\n    OPERATORS = {\n        \"==\": operator.eq,\n        \"!=\": operator.ne,\n        \"<\": operator.lt,\n        \"<=\": operator.le,\n        \">\": operator.gt,\n        \">=\": operator.ge,\n    }\n\n    def __init__(self, settings: EvaluationSettings):\n        self.settings = settings\n        self.initalize_evaluation()\n\n    def initalize_evaluation(self):\n        \"\"\"Initialize the evaluation.\"\"\"\n        self.directories = self._get_directories()\n        self.output_directory = self._make_directory(self.settings.output_directory)\n\n    def _get_directories(self) -> list:\n        \"\"\"Get directories containing the data.\n\n        Returns\n        -------\n        list\n            List of directories containing the data\n        \"\"\"\n        input_dir = self.settings.input_directory\n        if len(list(input_dir.glob(\"*\"))) == 1:\n            return [plane for plane in input_dir.glob(\"*/*\")]\n        return [plane for plane in input_dir.rglob(\"*\")]\n\n    @staticmethod\n    def _make_directory(directory: Path) -> Path:\n        \"\"\"\n        Make a directory if it does not exist.\n\n        Parameters\n        ----------\n        directory : Path\n            Directory path\n\n        Returns\n        -------\n        Path\n            Directory path\n        \"\"\"\n        directory.mkdir(exist_ok=True)\n        return directory\n\n\n    def collect_pattern_files(self) -> tuple[List[str], List[Path]]:\n        \"\"\"Collect files matching the pattern in the directories.\n\n        Parameters\n        ----------\n        pattern1 : str\n            Pattern to match in file search\n        pattern2 : Optional[str], optional\n            Second pattern to match in file search, by default None\n\n        Returns\n        -------\n        tuple[List[str], List[Path]]\n            List of file paths matching the pattern\n        \"\"\"\n\n        if not self.directories:\n            raise ValueError(\"No directories provided.\")\n        if not self.settings.pattern:\n            raise ValueError(\"No pattern provided.\")\n\n        row_labels: List[str] = []\n        matched_files: List[Path] = []\n\n        for directory in self.directories:\n            for pattern in self.settings.pattern:\n                pattern_matches = [\n                    i for i in directory.rglob(\"*\") if pattern in str(i) and i.is_file()\n                ]\n                if pattern_matches:\n                    matched_files.extend(pattern_matches)\n            row_labels.append(directory.parent.name)\n\n        return row_labels, matched_files\n\n    def combine_images(\n        self,\n        image_paths,\n        image_output_name,\n        num_columns=2,\n        spacing=10,\n        row_labels=None,\n        label_width=200,\n    ) -> Path:\n        \"\"\"\n        Combine multiple PNG images into a matrix layout with row labels.\n\n        Parameters\n        ----------\n        image_paths : List[str]\n            List of paths to PNG images\n        output_path : str\n            Path where the combined image will be saved\n        num_columns : int, optional\n            Number of columns in the matrix, by default 2\n        spacing : int, optional\n            Pixels of spacing between images, by default 10\n        row_labels : List[str], optional\n            List of labels for each row. If None, no labels are added, by default None\n        label_width : int, optional\n            Width in pixels reserved for labels, by default 200\n\n        Returns\n        -------\n        None\n            Saves the combined image to output_path\n        \"\"\"\n        images = [Image.open(path).convert(\"RGBA\") for path in image_paths]\n\n        num_images = len(images)\n        num_rows = math.ceil(num_images / num_columns)\n\n        widths, heights = zip(*(i.size for i in images))\n        max_width = max(widths)\n        max_height = max(heights)\n\n        total_width = (max_width * num_columns) + (spacing * (num_columns - 1))\n        if row_labels:\n            total_width += label_width + spacing  # Add space for labels\n        total_height = (max_height * num_rows) + (spacing * (num_rows - 1))\n\n        new_image = Image.new(\"RGBA\", (total_width, total_height), (255, 255, 255, 0))\n        draw = ImageDraw.Draw(new_image)\n\n        try:\n            if sys.platform == \"win32\":\n                font_path = \"C:/Windows/Fonts/arial.ttf\"\n            elif sys.platform == \"darwin\":  # macOS\n                font_path = \"/System/Library/Fonts/Helvetica.ttc\"\n            else:  # Linux\n                font_path = \"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\"\n\n            font = ImageFont.truetype(font_path, size=100)\n        except Exception:\n            font = ImageFont.load_default()\n\n        label_offset = label_width + spacing if row_labels else 0\n\n        for idx, img in enumerate(images):\n            row = idx // num_columns\n            col = idx % num_columns\n\n            x = label_offset + col * (max_width + spacing)\n            y = row * (max_height + spacing)\n\n            x_center = x + (max_width - img.size[0]) // 2\n            y_center = y + (max_height - img.size[1]) // 2\n\n            new_image.paste(img, (x_center, y_center))\n\n            if col == 0 and row_labels and row < len(row_labels):\n                # Calculate vertical center of the current row\n                text_y = y + (max_height // 2)\n\n                # Get the size of the text\n                text = str(row_labels[row])\n                try:\n                    bbox = draw.textbbox((0, 0), text, font=font)\n                    text_height = bbox[3] - bbox[1]\n                except AttributeError:  # For older Pillow versions\n                    text_height = font.getsize(text)[1]\n\n                # Draw the label vertically centered with the row\n                draw.text(\n                    (spacing, text_y - text_height // 2),\n                    text,\n                    fill=(0, 0, 0, 255),  # Black text\n                    font=font,\n                )\n\n        # Save combined image\n        new_image.save(self.output_directory / image_output_name, \"PNG\")\n\n        # Close all images\n        for img in images:\n            img.close()\n        return self.output_directory / image_output_name\n\n    def build_qc_metric(self, value: Any, reference: List[Path] = None) -> QCMetric:\n        \"\"\"Build a quality control metric from the provided settings.\n\n        Parameters\n        ----------\n        value : Any\n            Value of the metric\n        \"\"\"\n        return QCMetric(\n            name=self.settings.metric_name,\n            value=value,\n            reference=reference,\n            status_history=self.settings.metric_status_history,\n        )\n\n    def build_qc_evaluation(self, metrics: List[QCMetric]) -> QCEvaluation:\n        \"\"\"Build a quality control evaluation from the provided settings.\n\n        Parameters\n        ----------\n        metrics : List[QCMetric]\n            List of metrics\n        \"\"\"\n        return QCEvaluation(\n            name=self.settings.evaluations_name,\n            stage=self.settings.stage,\n            modality=self.settings.modality,\n            metrics=metrics,\n        )\n\n    def write_evaluation_to_json(self, evaluation: QCEvaluation):\n        \"\"\"Write the evaluation to a JSON file.\n\n        Parameters\n        ----------\n        evaluation : QCEvaluation\n            Evaluation object\n        \"\"\"\n        with open(self.output_directory / \"quality_evaluation.json\", \"w\") as f:\n            json.dump(json.loads(evaluation.model_dump_json()), f, indent=4)\n\n    def evaluate_metrics(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Flexible threshold-based evaluation of metrics\n\n        Parameters\n        ----------\n        metrics : dict\n            Dictionary of metrics where values are numeric\n        threshold : float\n            Threshold value for comparison\n        operation : str or Callable\n            Comparison operation to use. Can be one of '==', '!=', '<', '<=', '>', '>='\n            or a custom comparison function that takes two arguments\n\n        Returns\n        -------\n        bool\n            True if any value in metrics satisfies the comparison with threshold\n\n        Raises\n        ------\n        ValueError\n            If operation string is not recognized\n        TypeError\n            If operation is neither a string nor a callable\n        \"\"\"\n        if isinstance(operation, str):\n            if operation not in Evaluation.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(Evaluation.OPERATORS.keys())}\"\n                )\n            compare_func = Evaluation.OPERATORS[operation]\n\n        elif callable(operation):\n            compare_func = operation\n\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return any(compare_func(value, threshold) for value in metrics.values())\n\n    def evaluate_metrics_all(\n        self,\n        metrics: Dict[str, float],\n        threshold: float,\n        operation: Union[str, Callable] = \">\",\n    ) -> bool:\n        \"\"\"Similar to evaluate_metrics but requires all values to satisfy the condition\"\"\"\n        if isinstance(operation, str):\n            if operation not in cls.OPERATORS:\n                raise ValueError(\n                    f\"Unknown operation '{operation}'. \"\n                    f\"Must be one of {list(cls.OPERATORS.keys())}\"\n                )\n            compare_func = cls.OPERATORS[operation]\n        elif callable(operation):\n            compare_func = operation\n        else:\n            raise TypeError(\n                \"Operation must be either a string or a callable, \"\n                f\"got {type(operation)}\"\n            )\n\n        return all(compare_func(value, threshold) for value in metrics.values())\n\ndef test__make_directory():\n    \"\"\"Test to ensure new implementation matches the original.\"\"\"\n    test_dir = Path(\"/home/user/tmp/test_directory\")\n\n    # Test 1: Directory does not exist initially\n    if test_dir.exists():\n        test_dir.rmdir()  # Ensure the directory does not exist\n\n    result_original = Evaluation._make_directory(test_dir)\n    result_new = Evaluation._make_directory_new_implementation(test_dir)\n\n    assert result_original == result_new, \"Paths do not match for non-existent directory\"\n    assert test_dir.exists(), \"Directory was not created\"\n\n    # Test 2: Directory already exists\n    result_original = Evaluation._make_directory(test_dir)\n    result_new = Evaluation._make_directory_new_implementation(test_dir)\n\n    assert result_original == result_new, \"Paths do not match for existing directory\"\n\n    # Clean up\n    test_dir.rmdir()\n\nif __name__ == \"__main__\":\n    test__make_directory()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_make_directory` is a static method that takes a `Path` object as an argument, checks if the directory exists, and creates it if it does not. It then returns the `Path` object. This functionality is identical to the ORIGINAL FUNCTION, which also creates a directory if it does not exist and returns the `Path` object. Both functions use the `mkdir` method with `exist_ok=True`, ensuring that no error is raised if the directory already exists. The functionality and behavior of both functions are the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `_make_directory` function returns a `Path` object, which is the directory path. This satisfies the condition as it has a return value.\n  \n- **CONDITION 2**: The test function checks the return values of `_make_directory` and `_make_directory_new_implementation` and verifies the existence of the directory using `assert` statements. It does not rely on printed or logged outputs, satisfying this condition.\n\n- **CONDITION 3**: The test cases compare the results of `_make_directory` and `_make_directory_new_implementation` by asserting that their return values are equal. This ensures that the new implementation must have the same functionality as the original to pass the tests, satisfying this condition.\n\n- **CONDITION 4**: The test cases use `assert` statements to compare return values and check the state of the directory (existence). These are reasonable checks given the functionality of `_make_directory`, satisfying this condition.\n\n- **CONDITION 5**: The test cases cover two scenarios: when the directory does not exist and when it already exists. These are non-trivial and relevant scenarios for testing directory creation functionality, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "1a6a5c8ffc0be597be1dc5e1399303a1dd010dac"
    },
    {
        "func_name": "orientation",
        "idx": "632",
        "repo_name": "kong-deyu___LLM_SIM",
        "func_path": "geometry.py",
        "orig_func": "def orientation(p: Point, q: Point, r: Point) -> int:\n    val = (q.y - p.y) * (r.x - q.x) - (q.x - p.x) * (r.y - q.y)\n    if val == 0:\n        return 0\n    return 1 if val > 0 else 2",
        "orig_context": "```python\n## geometry.py\nimport numpy as np\n\nfrom typing import Union\n\nclass Point:\n    def __init__(self, x: float, y: float):\n        self.x = float(x)\n        self.y = float(y)\n        \n    def __str__(self):\n        return 'Point(' + str(self.x) + ', ' + str(self.y) + ')'\n        \n    def __add__(self, other: 'Point') -> 'Point':\n        return Point(self.x + other.x, self.y + other.y)\n        \n    def __sub__(self, other: 'Point') -> 'Point':\n        return Point(self.x - other.x, self.y - other.y)\n    \n    def norm(self, p: int = 2) -> float:\n        return (self.x ** p + self.y ** p)**(1./p)\n        \n    def dot(self, other: 'Point') -> float:\n        return self.x * other.x + self.y * other.y\n        \n    def __mul__(self, other: float) -> 'Point':\n        return Point(other * self.x, other * self.y)\n    \n    def __rmul__(self, other: float) -> 'Point':\n        return self.__mul__(other)\n        \n    def __truediv__(self, other: float) -> 'Point':\n        return self.__mul__(1./other)\n        \n        \n    def isInside(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']) -> bool:\n        if isinstance(other, Line):\n            AM = Line(other.p1, self)\n            MB = Line(self, other.p2)\n            return np.close(np.abs(AM.dot(BM)), AM.length * MB.length)\n        \n        elif isinstance(other, Rectangle):\n            # Based on https://stackoverflow.com/a/2763387\n            AB = Line(other.c1, other.c2)\n            AM = Line(other.c1, self)\n            BC = Line(other.c2, other.c3)\n            BM = Line(other.c2, self)\n        \n            return 0 <= AB.dot(AM) <= AB.dot(AB) and 0 <= BC.dot(BM) <= BC.dot(BC)\n            \n        elif isinstance(other, Circle):\n            return self.distanceTo(other.m) <= other.r\n            \n        elif isinstance(other, Ring):\n            return other.r_inner <= self.distanceTo(other.m) <= other.r_outer\n            \n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: 'Point') -> bool:\n        if isinstance(other, Point):\n            p = other\n        elif isinstance(other, Line):\n            p = (other.p1 + other.p2) / 2.\n        elif isinstance(other, Rectangle):\n            p = (other.c1 + other.c2 + other.c3 + other.c4) / 4.\n        elif isinstance(other, Circle):\n            p = other.m\n        elif isinstance(other, Ring):\n            p = other.m\n        else:\n            raise NotImplementedError\n        return direction.dot(p - self) <= 0\n                    \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point):\n            return (self - other).norm(p = 2)\n    \n        elif isinstance(other, Line):\n            # Based on https://math.stackexchange.com/a/330329\n            s2_minus_s1 = other.p2 - other.p1\n            that = (self - other.p1).dot(s2_minus_s1) / s2_minus_s1.dot(s2_minus_s1)\n            tstar = np.minimum(1, np.maximum(0, that))\n            return (other.p1 + tstar * s2_minus_s1 - self).norm(p = 2)\n        \n        elif isinstance(other, Rectangle):\n            if self.isInside(other): return 0\n            E = other.edges\n            return np.min([self.distanceTo(e) for e in E])\n        \n        elif isinstance(other, Circle):\n            return np.maximum(0, self.distanceTo(other.m) - other.r)\n            \n        elif isinstance(other, Ring):\n            d = self.distanceTo(other.m)\n            return np.max([r_inner - d, d - r_outer, 0])\n            \n        else:\n            try:\n                return other.distanceTo(self) # do we really need to try this? Does it ever succeed?\n            except NameError:\n                raise NotImplementedError\n            print('Something went wrong!')\n            raise\n\ndef onSegment(p: Point, q: Point, r: Point) -> bool:\n    return (q.x <= np.maximum(p.x, r.x) and q.x >= np.minimum(p.x, r.x) and \n        q.y <= np.maximum(p.y, r.y) and q.y >= np.minimum(p.y, r.y))\n\nclass Line:\n    def __init__(self, p1: Point, p2: Point):\n        self.p1 = p1\n        self.p2 = p2\n        \n    def __str__(self):\n        return 'Line(' + str(self.p1) +  ', ' + str(self.p2) + ')'\n        \n    def intersectsWith(self, other: Union['Line','Rectangle','Circle','Ring']):\n        if isinstance(other, Line):\n            p1 = self.p1\n            q1 = self.p2\n            p2 = other.p1\n            q2 = other.p2\n        \n            # Based on https://www.geeksforgeeks.org/check-if-two-given-line-segments-intersect/\n            # Find the four orientations needed for general and special cases \n            o1 = orientation(p1, q1, p2) \n            o2 = orientation(p1, q1, q2) \n            o3 = orientation(p2, q2, p1) \n            o4 = orientation(p2, q2, q1)\n      \n            # General case \n            if o1 != o2 and o3 != o4:\n                return True\n\n            # Special Cases \n            # p1, q1 and p2 are colinear and p2 lies on segment p1q1 \n            if o1 == 0 and onSegment(p1, p2, q1): return True\n      \n            # p1, q1 and q2 are colinear and q2 lies on segment p1q1 \n            if o2 == 0 and onSegment(p1, q2, q1): return True\n      \n            # p2, q2 and p1 are colinear and p1 lies on segment p2q2\n            if o3 == 0 and onSegment(p2, p1, q2): return True\n      \n            # p2, q2 and q1 are colinear and q1 lies on segment p2q2\n            if o4 == 0 and onSegment(p2, q1, q2): return True\n      \n            return False # Doesn't fall in any of the above cases \n            \n        elif isinstance(other, Rectangle):\n            if self.p1.isInside(other) or self.p2.isInside(other): return True\n            E = other.edges\n            for edge in E:\n                if self.intersectsWith(edge): return True\n            return False\n            \n        elif isinstance(other, Circle):\n            return other.m.distanceTo(self) <= other.r\n            \n        elif isinstance(other, Ring):\n            return (other.m.distanceTo(self.p1) >= other.r_inner or other.m.distanceTo(self.p2) >= other.r_inner) and other.m.distanceTo(self) < other.r_outer\n            \n        raise NotImplementedError\n        \n    @property\n    def length(self):\n        return self.p1.distanceTo(self.p2)\n        \n    def dot(self, other: 'Line') -> float: # assumes Line is a vector from p1 to p2\n        v1 = (self.p2 - self.p1)\n        v2 = (other.p2 - other.p1)\n        return v1.dot(v2)\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        p = (self.p1 + self.p2) / 2.\n        return p.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point):\n            return other.distanceTo(self)\n            \n        elif isinstance(other, Line):\n            if self.intersectsWith(other): return 0.\n            return np.min([self.p1.distanceTo(other.p1), self.p1.distanceTo(other.p2), self.p2.distanceTo(other.p1), self.p2.distanceTo(other.p2)])\n            \n        elif isinstance(other, Rectangle):\n            if self.intersectsWith(other): return 0.\n            other_edges = other.edges\n            return np.min([self.distanceTo(e) for e in other_edges])\n            \n        elif isinstance(other, Circle):\n            return np.maximum(0, other.m.distanceTo(self) - other.r)\n            \n        elif isinstance(other, Ring):\n            if self.intersectsWith(other): return 0.\n            p1m = self.p1.distanceTo(other.m)\n            if p1m < other.r_inner: # the line is inside the ring\n                p2m = self.p2.distanceTo(other.m)\n                return other.r_inner - np.maximum(p1m, p2m)\n            else: # the line is completely outside\n                return np.maximum(0, other.m.distanceTo(self) - other.r_outer)   \n                \n        raise NotImplementedError\n\nclass Rectangle:\n    def __init__(self, c1: Point, c2: Point, c3: Point): # 3 points are enough to represent a rectangle\n        self.c1 = c1\n        self.c2 = c2\n        self.c3 = c3\n        self.c4 = c3 + c1 - c2\n        \n    def __str__(self):\n        return 'Rectangle(' + str(self.c1) +  ', ' + str(self.c2) +  ', ' + str(self.c3) +  ', ' + str(self.c4) + ')'\n        \n    @property\n    def edges(self):\n        e1 = Line(self.c1, self.c2)\n        e2 = Line(self.c2, self.c3)\n        e3 = Line(self.c3, self.c4)\n        e4 = Line(self.c4, self.c1)\n        return [e1, e2, e3, e4]\n\n    @property\n    def corners(self):\n        return [self.c1, self.c2, self.c3, self.c4]\n        \n    def intersectsWith(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']) -> bool:\n        if isinstance(other, Line):\n            return other.intersectsWith(self)\n            \n        elif isinstance(other, Rectangle) or isinstance(other, Circle) or isinstance(other, Ring):\n            E = self.edges\n            for e in E:\n                if e.intersectsWith(other): return True\n            return False\n\n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        p = (self.c1 + self.c2 + self.c3 + self.c4) / 4.\n        return p.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point) or isinstance(other, Line):\n            return other.distanceTo(self)\n\n        elif isinstance(other, Rectangle) or isinstance(other, Circle) or isinstance(other, Ring):\n            if self.intersectsWith(other): return 0.\n            E = self.edges\n            return np.min([e.distanceTo(other) for e in E])\n\n        raise NotImplementedError\n\nclass Circle:\n    def __init__(self, m: Point, r: float):\n        self.m = m\n        self.r = r\n        \n    def __str__(self):\n        return 'Circle(' + str(self.m) +  ', radius = ' + str(self.r) + ')'\n        \n    def intersectsWith(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']):\n        if isinstance(other, Line) or isinstance(other, Rectangle):\n            return other.intersectsWith(self)\n            \n        elif isinstance(other, Circle):\n            return self.m.distanceTo(other.m) <= self.r + other.r\n            \n        elif isinstance(other, Ring):\n            return other.r_inner - self.r <= self.m.distanceTo(other.m) <= self.r + other.r_outer\n            \n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        return self.m.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point) or isinstance(other, Line) or isinstance(other, Rectangle):\n            return other.distanceTo(self)\n            \n        elif isinstance(other, Circle):\n            return np.maximum(0, self.m.distanceTo(other.m) - self.r - other.r)\n            \n        elif isinstance(other, Ring):\n            if self.intersectsWith(other): return 0.\n            d = self.m.distanceTo(other.m)\n            return np.maximum(other.r_inner - d, d - other.r_outer) - self.r\n            \n        raise NotImplementedError\n\nclass Ring:\n    def __init__(self, m: Point, r_inner: float, r_outer: float):\n        self.m = m\n        assert r_inner < r_outer\n        self.r_inner = r_inner\n        self.r_outer = r_outer\n        \n    def __str__(self):\n        return 'Ring(' + str(self.m) +  ', inner radius = ' + str(self.r_inner) +  ', outer radius = ' + str(self.r_outer) + ')'\n        \n    def intersectsWith(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']):\n        if isinstance(other, Line) or isinstance(other, Rectangle) or isinstance(other, Circle):\n            return other.intersectsWith(self)\n            \n        elif isinstance(other, Ring):\n            d = self.m.distanceTo(other.m)\n            if d > self.r_outer + other.r_outer: return False # rings are far away\n            if d + self.r_outer < other.r_inner: return False # self is completely inside other\n            if d + other.r_outer < self.r_inner: return False # other is completely inside self\n            return True\n            \n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        return self.m.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point) or isinstance(other, Line) or isinstance(other, Rectangle) or isinstance(other, Circle):\n            return other.distanceTo(self)\n            \n        if isinstance(other, Ring):\n            if d > self.r_outer + other.r_outer: return d - self.r_outer - other.r_outer # rings are far away\n            if d + self.r_outer < other.r_inner: return other.r_inner - d - self.r_outer # self is completely inside other\n            if d + other.r_outer < self.r_inner: return self.r_inner - d - other.r_outer # other is completely inside self\n            return 0\n            \n        raise NotImplementedError\n\ndef orientation(p: Point, q: Point, r: Point) -> int:\n    # See https://www.geeksforgeeks.org/orientation-3-ordered-points/ for details of below formula. \n    val = (q.y - p.y) * (r.x - q.x) - (q.x - p.x) * (r.y - q.y)\n    if val == 0: return 0 # colinear \n    return 1 if val > 0 else 2\n\n```\n\n\n",
        "eval_script": "## geometry.py\nimport numpy as np\n\nfrom typing import Union\n\nclass Point:\n    def __init__(self, x: float, y: float):\n        self.x = float(x)\n        self.y = float(y)\n        \n    def __str__(self):\n        return 'Point(' + str(self.x) + ', ' + str(self.y) + ')'\n        \n    def __add__(self, other: 'Point') -> 'Point':\n        return Point(self.x + other.x, self.y + other.y)\n        \n    def __sub__(self, other: 'Point') -> 'Point':\n        return Point(self.x - other.x, self.y - other.y)\n    \n    def norm(self, p: int = 2) -> float:\n        return (self.x ** p + self.y ** p)**(1./p)\n        \n    def dot(self, other: 'Point') -> float:\n        return self.x * other.x + self.y * other.y\n        \n    def __mul__(self, other: float) -> 'Point':\n        return Point(other * self.x, other * self.y)\n    \n    def __rmul__(self, other: float) -> 'Point':\n        return self.__mul__(other)\n        \n    def __truediv__(self, other: float) -> 'Point':\n        return self.__mul__(1./other)\n        \n        \n    def isInside(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']) -> bool:\n        if isinstance(other, Line):\n            AM = Line(other.p1, self)\n            MB = Line(self, other.p2)\n            return np.close(np.abs(AM.dot(BM)), AM.length * MB.length)\n        \n        elif isinstance(other, Rectangle):\n            # Based on https://stackoverflow.com/a/2763387\n            AB = Line(other.c1, other.c2)\n            AM = Line(other.c1, self)\n            BC = Line(other.c2, other.c3)\n            BM = Line(other.c2, self)\n        \n            return 0 <= AB.dot(AM) <= AB.dot(AB) and 0 <= BC.dot(BM) <= BC.dot(BC)\n            \n        elif isinstance(other, Circle):\n            return self.distanceTo(other.m) <= other.r\n            \n        elif isinstance(other, Ring):\n            return other.r_inner <= self.distanceTo(other.m) <= other.r_outer\n            \n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: 'Point') -> bool:\n        if isinstance(other, Point):\n            p = other\n        elif isinstance(other, Line):\n            p = (other.p1 + other.p2) / 2.\n        elif isinstance(other, Rectangle):\n            p = (other.c1 + other.c2 + other.c3 + other.c4) / 4.\n        elif isinstance(other, Circle):\n            p = other.m\n        elif isinstance(other, Ring):\n            p = other.m\n        else:\n            raise NotImplementedError\n        return direction.dot(p - self) <= 0\n                    \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point):\n            return (self - other).norm(p = 2)\n    \n        elif isinstance(other, Line):\n            # Based on https://math.stackexchange.com/a/330329\n            s2_minus_s1 = other.p2 - other.p1\n            that = (self - other.p1).dot(s2_minus_s1) / s2_minus_s1.dot(s2_minus_s1)\n            tstar = np.minimum(1, np.maximum(0, that))\n            return (other.p1 + tstar * s2_minus_s1 - self).norm(p = 2)\n        \n        elif isinstance(other, Rectangle):\n            if self.isInside(other): return 0\n            E = other.edges\n            return np.min([self.distanceTo(e) for e in E])\n        \n        elif isinstance(other, Circle):\n            return np.maximum(0, self.distanceTo(other.m) - other.r)\n            \n        elif isinstance(other, Ring):\n            d = self.distanceTo(other.m)\n            return np.max([r_inner - d, d - r_outer, 0])\n            \n        else:\n            try:\n                return other.distanceTo(self) # do we really need to try this? Does it ever succeed?\n            except NameError:\n                raise NotImplementedError\n            print('Something went wrong!')\n            raise\n\ndef onSegment(p: Point, q: Point, r: Point) -> bool:\n    return (q.x <= np.maximum(p.x, r.x) and q.x >= np.minimum(p.x, r.x) and \n        q.y <= np.maximum(p.y, r.y) and q.y >= np.minimum(p.y, r.y))\n\nclass Line:\n    def __init__(self, p1: Point, p2: Point):\n        self.p1 = p1\n        self.p2 = p2\n        \n    def __str__(self):\n        return 'Line(' + str(self.p1) +  ', ' + str(self.p2) + ')'\n        \n    def intersectsWith(self, other: Union['Line','Rectangle','Circle','Ring']):\n        if isinstance(other, Line):\n            p1 = self.p1\n            q1 = self.p2\n            p2 = other.p1\n            q2 = other.p2\n        \n            # Based on https://www.geeksforgeeks.org/check-if-two-given-line-segments-intersect/\n            # Find the four orientations needed for general and special cases \n            o1 = orientation(p1, q1, p2) \n            o2 = orientation(p1, q1, q2) \n            o3 = orientation(p2, q2, p1) \n            o4 = orientation(p2, q2, q1)\n      \n            # General case \n            if o1 != o2 and o3 != o4:\n                return True\n\n            # Special Cases \n            # p1, q1 and p2 are colinear and p2 lies on segment p1q1 \n            if o1 == 0 and onSegment(p1, p2, q1): return True\n      \n            # p1, q1 and q2 are colinear and q2 lies on segment p1q1 \n            if o2 == 0 and onSegment(p1, q2, q1): return True\n      \n            # p2, q2 and p1 are colinear and p1 lies on segment p2q2\n            if o3 == 0 and onSegment(p2, p1, q2): return True\n      \n            # p2, q2 and q1 are colinear and q1 lies on segment p2q2\n            if o4 == 0 and onSegment(p2, q1, q2): return True\n      \n            return False # Doesn't fall in any of the above cases \n            \n        elif isinstance(other, Rectangle):\n            if self.p1.isInside(other) or self.p2.isInside(other): return True\n            E = other.edges\n            for edge in E:\n                if self.intersectsWith(edge): return True\n            return False\n            \n        elif isinstance(other, Circle):\n            return other.m.distanceTo(self) <= other.r\n            \n        elif isinstance(other, Ring):\n            return (other.m.distanceTo(self.p1) >= other.r_inner or other.m.distanceTo(self.p2) >= other.r_inner) and other.m.distanceTo(self) < other.r_outer\n            \n        raise NotImplementedError\n        \n    @property\n    def length(self):\n        return self.p1.distanceTo(self.p2)\n        \n    def dot(self, other: 'Line') -> float: # assumes Line is a vector from p1 to p2\n        v1 = (self.p2 - self.p1)\n        v2 = (other.p2 - other.p1)\n        return v1.dot(v2)\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        p = (self.p1 + self.p2) / 2.\n        return p.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point):\n            return other.distanceTo(self)\n            \n        elif isinstance(other, Line):\n            if self.intersectsWith(other): return 0.\n            return np.min([self.p1.distanceTo(other.p1), self.p1.distanceTo(other.p2), self.p2.distanceTo(other.p1), self.p2.distanceTo(other.p2)])\n            \n        elif isinstance(other, Rectangle):\n            if self.intersectsWith(other): return 0.\n            other_edges = other.edges\n            return np.min([self.distanceTo(e) for e in other_edges])\n            \n        elif isinstance(other, Circle):\n            return np.maximum(0, other.m.distanceTo(self) - other.r)\n            \n        elif isinstance(other, Ring):\n            if self.intersectsWith(other): return 0.\n            p1m = self.p1.distanceTo(other.m)\n            if p1m < other.r_inner: # the line is inside the ring\n                p2m = self.p2.distanceTo(other.m)\n                return other.r_inner - np.maximum(p1m, p2m)\n            else: # the line is completely outside\n                return np.maximum(0, other.m.distanceTo(self) - other.r_outer)   \n                \n        raise NotImplementedError\n\nclass Rectangle:\n    def __init__(self, c1: Point, c2: Point, c3: Point): # 3 points are enough to represent a rectangle\n        self.c1 = c1\n        self.c2 = c2\n        self.c3 = c3\n        self.c4 = c3 + c1 - c2\n        \n    def __str__(self):\n        return 'Rectangle(' + str(self.c1) +  ', ' + str(self.c2) +  ', ' + str(self.c3) +  ', ' + str(self.c4) + ')'\n        \n    @property\n    def edges(self):\n        e1 = Line(self.c1, self.c2)\n        e2 = Line(self.c2, self.c3)\n        e3 = Line(self.c3, self.c4)\n        e4 = Line(self.c4, self.c1)\n        return [e1, e2, e3, e4]\n\n    @property\n    def corners(self):\n        return [self.c1, self.c2, self.c3, self.c4]\n        \n    def intersectsWith(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']) -> bool:\n        if isinstance(other, Line):\n            return other.intersectsWith(self)\n            \n        elif isinstance(other, Rectangle) or isinstance(other, Circle) or isinstance(other, Ring):\n            E = self.edges\n            for e in E:\n                if e.intersectsWith(other): return True\n            return False\n\n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        p = (self.c1 + self.c2 + self.c3 + self.c4) / 4.\n        return p.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point) or isinstance(other, Line):\n            return other.distanceTo(self)\n\n        elif isinstance(other, Rectangle) or isinstance(other, Circle) or isinstance(other, Ring):\n            if self.intersectsWith(other): return 0.\n            E = self.edges\n            return np.min([e.distanceTo(other) for e in E])\n\n        raise NotImplementedError\n\nclass Circle:\n    def __init__(self, m: Point, r: float):\n        self.m = m\n        self.r = r\n        \n    def __str__(self):\n        return 'Circle(' + str(self.m) +  ', radius = ' + str(self.r) + ')'\n        \n    def intersectsWith(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']):\n        if isinstance(other, Line) or isinstance(other, Rectangle):\n            return other.intersectsWith(self)\n            \n        elif isinstance(other, Circle):\n            return self.m.distanceTo(other.m) <= self.r + other.r\n            \n        elif isinstance(other, Ring):\n            return other.r_inner - self.r <= self.m.distanceTo(other.m) <= self.r + other.r_outer\n            \n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        return self.m.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point) or isinstance(other, Line) or isinstance(other, Rectangle):\n            return other.distanceTo(self)\n            \n        elif isinstance(other, Circle):\n            return np.maximum(0, self.m.distanceTo(other.m) - self.r - other.r)\n            \n        elif isinstance(other, Ring):\n            if self.intersectsWith(other): return 0.\n            d = self.m.distanceTo(other.m)\n            return np.maximum(other.r_inner - d, d - other.r_outer) - self.r\n            \n        raise NotImplementedError\n\nclass Ring:\n    def __init__(self, m: Point, r_inner: float, r_outer: float):\n        self.m = m\n        assert r_inner < r_outer\n        self.r_inner = r_inner\n        self.r_outer = r_outer\n        \n    def __str__(self):\n        return 'Ring(' + str(self.m) +  ', inner radius = ' + str(self.r_inner) +  ', outer radius = ' + str(self.r_outer) + ')'\n        \n    def intersectsWith(self, other: Union['Line', 'Rectangle', 'Circle', 'Ring']):\n        if isinstance(other, Line) or isinstance(other, Rectangle) or isinstance(other, Circle):\n            return other.intersectsWith(self)\n            \n        elif isinstance(other, Ring):\n            d = self.m.distanceTo(other.m)\n            if d > self.r_outer + other.r_outer: return False # rings are far away\n            if d + self.r_outer < other.r_inner: return False # self is completely inside other\n            if d + other.r_outer < self.r_inner: return False # other is completely inside self\n            return True\n            \n        raise NotImplementedError\n        \n    def hasPassed(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring'], direction: Point) -> bool:\n        return self.m.hasPassed(other, direction)\n        \n    def distanceTo(self, other: Union['Point', 'Line', 'Rectangle', 'Circle', 'Ring']) -> float:\n        if isinstance(other, Point) or isinstance(other, Line) or isinstance(other, Rectangle) or isinstance(other, Circle):\n            return other.distanceTo(self)\n            \n        if isinstance(other, Ring):\n            if d > self.r_outer + other.r_outer: return d - self.r_outer - other.r_outer # rings are far away\n            if d + self.r_outer < other.r_inner: return other.r_inner - d - self.r_outer # self is completely inside other\n            if d + other.r_outer < self.r_inner: return self.r_inner - d - other.r_outer # other is completely inside self\n            return 0\n            \n        raise NotImplementedError\n\ndef orientation(p: Point, q: Point, r: Point) -> int:\n    # See https://www.geeksforgeeks.org/orientation-3-ordered-points/ for details of below formula. \n    val = (q.y - p.y) * (r.x - q.x) - (q.x - p.x) * (r.y - q.y)\n    if val == 0: return 0 # colinear \n    return 1 if val > 0 else 2\n\n# Test case for the orientation function\n\n\ndef test_orientation():\n    # Test case 1: Collinear points\n    p1 = Point(0, 0)\n    q1 = Point(1, 1)\n    r1 = Point(2, 2)\n    assert orientation(p1, q1, r1) == orientation_new_implementation(p1, q1, r1)\n\n    # Test case 2: Clockwise orientation\n    p2 = Point(0, 0)\n    q2 = Point(1, 1)\n    r2 = Point(1, 0)\n    assert orientation(p2, q2, r2) == orientation_new_implementation(p2, q2, r2)\n\n    # Test case 3: Counterclockwise orientation\n    p3 = Point(0, 0)\n    q3 = Point(0, 1)\n    r3 = Point(1, 1)\n    assert orientation(p3, q3, r3) == orientation_new_implementation(p3, q3, r3)\n\n    # Test case 4: Negative coordinates, collinear\n    p4 = Point(-1, -1)\n    q4 = Point(-2, -2)\n    r4 = Point(-3, -3)\n    assert orientation(p4, q4, r4) == orientation_new_implementation(p4, q4, r4)\n\n    # Test case 5: Vertical line, clockwise\n    p5 = Point(0, 0)\n    q5 = Point(0, 1)\n    r5 = Point(-1, 2)\n    assert orientation(p5, q5, r5) == orientation_new_implementation(p5, q5, r5)\n\n    # Test case 6: Horizontal line, counterclockwise\n    p6 = Point(0, 0)\n    q6 = Point(1, 0)\n    r6 = Point(1, 1)\n    assert orientation(p6, q6, r6) == orientation_new_implementation(p6, q6, r6)\n\n    # Test case 7: Points very close to each other, collinear\n    p7 = Point(0.000001, 0.000001)\n    q7 = Point(0.000002, 0.000002)\n    r7 = Point(0.000003, 0.000003)\n    assert orientation(p7, q7, r7) == orientation_new_implementation(p7, q7, r7)\n\n    # Test case 8: Large coordinate values, clockwise\n    p8 = Point(1e9, 1e9)\n    q8 = Point(1e9 + 1, 1e9 + 1)\n    r8 = Point(1e9 + 1, 1e9)\n    assert orientation(p8, q8, r8) == orientation_new_implementation(p8, q8, r8)\n\n    # Test case 9: Large coordinate values, counterclockwise\n    p9 = Point(1e9, 1e9)\n    q9 = Point(1e9, 1e9 + 1)\n    r9 = Point(1e9 + 1, 1e9 + 1)\n    assert orientation(p9, q9, r9) == orientation_new_implementation(p9, q9, r9)\n\nif __name__ == \"__main__\":\n    test_orientation()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       4      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  4      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION for `orientation` is identical to the ORIGINAL FUNCTION. Both functions calculate the orientation of three points using the same formula and return the same values based on the result of the calculation. The logic and implementation are exactly the same, with no changes in functionality or structure.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `orientation` function returns an integer value (0, 1, or 2) indicating the orientation of the points. Therefore, it satisfies the condition of having return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `orientation` and `orientation_new_implementation`. They do not rely on printed or logged outputs, satisfying this condition.\n\n3. **CONDITION 3**: The test cases cover various scenarios including collinear, clockwise, and counterclockwise orientations, as well as edge cases with negative coordinates, vertical and horizontal lines, very close points, and large coordinate values. These tests ensure that `orientation_new_implementation` must have the same functionality as `orientation` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `orientation` returns a value. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover a wide range of scenarios, including edge cases and large values, ensuring comprehensive testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "58748a5b1a692f831acf9a01735a87e73c39d5ac"
    },
    {
        "func_name": "ACCController.target_follow_control",
        "idx": "642",
        "repo_name": "kong-deyu___LLM_SIM",
        "func_path": "ACC_algo.py",
        "orig_func": "def target_follow_control(self, ego_car, target_car):\n    \"\"\"Calculate acceleration command to follow target vehicle\"\"\"\n    current_distance = ego_car.distanceTo(target_car)\n    desired_distance = ego_car.velocity.x * DESIRED_TIME_GAP + MIN_FOLLOWING_DISTANCE\n    distance_error = current_distance - desired_distance\n    acceleration = distance_error / DESIRED_TIME_GAP\n    if acceleration < MAX_DECEL:\n        return MAX_DECEL\n    elif acceleration > MAX_ACCEL:\n        return MAX_ACCEL\n    return acceleration",
        "orig_context": "```python\n## ACC_algo.py\nDESIRED_TIME_GAP = 1.8\n\nMAX_ACCEL = 2.5\n\nMAX_DECEL = -3.0\n\nMIN_FOLLOWING_DISTANCE = 2.0\n\nDETECTION_RANGE = 100\n\nclass ACCController:\n    def __init__(self):\n        self.desired_cruise_speed = 30  # Default cruise speed in km/h\n        \n    def set_desired_cruise_speed(self, speed_kph):\n        \"\"\"Set the desired cruise speed in km/h\"\"\"\n        self.desired_cruise_speed = speed_kph\n        \n    def target_follow_control(self, ego_car, target_car):\n        \"\"\"Calculate acceleration command to follow target vehicle\"\"\"\n        # Calculate current distance and desired following distance\n        current_distance = ego_car.distanceTo(target_car)\n        desired_distance = ego_car.velocity.x * DESIRED_TIME_GAP + MIN_FOLLOWING_DISTANCE\n\n        # Determine acceleration based on distance error\n        distance_error = current_distance - desired_distance\n        acceleration = distance_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration between max deceleration and acceleration\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n    def cruise_control(self, ego_car):\n        \"\"\"Calculate acceleration command to maintain cruise speed\"\"\"\n        # Convert desired cruise speed from km/h to m/s\n        target_speed_ms = self.desired_cruise_speed / 3.6\n            \n        # Determine acceleration based on speed error\n        speed_error = target_speed_ms - ego_car.velocity.x\n        acceleration = speed_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration to vehicle constraints\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n    def acc_control_loop(self, ego_car, target_car=None):\n        \"\"\"Main ACC control loop that switches between following and cruising\"\"\"\n        if target_car and ego_car.distanceTo(target_car) < DETECTION_RANGE:\n            return self.target_follow_control(ego_car, target_car)\n        else:\n            return self.cruise_control(ego_car)\n\n```\n\n\n",
        "eval_script": "## ACC_algo.py\nDESIRED_TIME_GAP = 1.8\n\nMAX_ACCEL = 2.5\n\nMAX_DECEL = -3.0\n\nMIN_FOLLOWING_DISTANCE = 2.0\n\nDETECTION_RANGE = 100\n\nclass ACCController:\n    def __init__(self):\n        self.desired_cruise_speed = 30  # Default cruise speed in km/h\n        \n    def set_desired_cruise_speed(self, speed_kph):\n        \"\"\"Set the desired cruise speed in km/h\"\"\"\n        self.desired_cruise_speed = speed_kph\n        \n    def target_follow_control(self, ego_car, target_car):\n        \"\"\"Calculate acceleration command to follow target vehicle\"\"\"\n        # Calculate current distance and desired following distance\n        current_distance = ego_car.distanceTo(target_car)\n        desired_distance = ego_car.velocity.x * DESIRED_TIME_GAP + MIN_FOLLOWING_DISTANCE\n\n        # Determine acceleration based on distance error\n        distance_error = current_distance - desired_distance\n        acceleration = distance_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration between max deceleration and acceleration\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n\n\n    \n    def cruise_control(self, ego_car):\n        \"\"\"Calculate acceleration command to maintain cruise speed\"\"\"\n        # Convert desired cruise speed from km/h to m/s\n        target_speed_ms = self.desired_cruise_speed / 3.6\n            \n        # Determine acceleration based on speed error\n        speed_error = target_speed_ms - ego_car.velocity.x\n        acceleration = speed_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration to vehicle constraints\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n    def acc_control_loop(self, ego_car, target_car=None):\n        \"\"\"Main ACC control loop that switches between following and cruising\"\"\"\n        if target_car and ego_car.distanceTo(target_car) < DETECTION_RANGE:\n            return self.target_follow_control(ego_car, target_car)\n        else:\n            return self.cruise_control(ego_car)\n\n# Mock classes for ego_car and target_car\nclass MockCar:\n    def __init__(self, velocity_x, distance_to_target):\n        self.velocity = MockVelocity(velocity_x)\n        self._distance_to_target = distance_to_target\n    \n    def distanceTo(self, target_car):\n        return self._distance_to_target\n\nclass MockVelocity:\n    def __init__(self, x):\n        self.x = x\n\ndef test_target_follow_control():\n    controller = ACCController()\n\n    # Test case 1: Acceleration less than MAX_DECEL\n    ego_car = MockCar(velocity_x=20, distance_to_target=10)\n    target_car = MockCar(velocity_x=15, distance_to_target=10)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 2: Acceleration greater than MAX_ACCEL\n    ego_car = MockCar(velocity_x=20, distance_to_target=100)\n    target_car = MockCar(velocity_x=15, distance_to_target=100)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 3: Acceleration within range\n    ego_car = MockCar(velocity_x=20, distance_to_target=50)\n    target_car = MockCar(velocity_x=15, distance_to_target=50)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 4: Acceleration exactly MAX_DECEL\n    ego_car = MockCar(velocity_x=0, distance_to_target=0)\n    target_car = MockCar(velocity_x=0, distance_to_target=0)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 5: Acceleration exactly MAX_ACCEL\n    ego_car = MockCar(velocity_x=100, distance_to_target=200)\n    target_car = MockCar(velocity_x=100, distance_to_target=200)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 6: Zero velocity\n    ego_car = MockCar(velocity_x=0, distance_to_target=30)\n    target_car = MockCar(velocity_x=0, distance_to_target=30)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 7: Zero distance\n    ego_car = MockCar(velocity_x=20, distance_to_target=0)\n    target_car = MockCar(velocity_x=15, distance_to_target=0)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 8: Negative distance\n    ego_car = MockCar(velocity_x=20, distance_to_target=-10)\n    target_car = MockCar(velocity_x=15, distance_to_target=-10)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\n    # Test case 9: High-speed scenario\n    ego_car = MockCar(velocity_x=200, distance_to_target=500)\n    target_car = MockCar(velocity_x=180, distance_to_target=500)\n    assert controller.target_follow_control(ego_car, target_car) == controller.target_follow_control_new_implementation(ego_car, target_car)\n\nif __name__ == \"__main__\":\n    test_target_follow_control()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `target_follow_control` in the `ACCController` class is functionally identical to the ORIGINAL FUNCTION. Both functions calculate the acceleration needed for an ego car to follow a target car by computing the current distance, desired distance, and distance error. They then determine the acceleration based on the distance error and limit it between `MAX_DECEL` and `MAX_ACCEL`. The logic and calculations are the same, and the constants used are defined in the revised code. The additional code in the revised version, such as the `cruise_control` and `acc_control_loop` methods, does not affect the functionality of the `target_follow_control` method. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `target_follow_control` function returns an acceleration value, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `target_follow_control` and `target_follow_control_new_implementation`. There is no checking of printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `target_follow_control` and `target_follow_control_new_implementation` directly. If the new implementation passes all these tests, it would imply that it has the same functionality as the original, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare return values, which is reasonable given that `target_follow_control` returns a value. There is no use of inappropriate assertions like comparing the function calls directly without considering their return values.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including edge cases like maximum and minimum acceleration, zero velocity, zero distance, negative distance, and high-speed scenarios. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "58748a5b1a692f831acf9a01735a87e73c39d5ac"
    },
    {
        "func_name": "ACCController.cruise_control",
        "idx": "646",
        "repo_name": "kong-deyu___LLM_SIM",
        "func_path": "ACC_algo.py",
        "orig_func": "def cruise_control(self, ego_car):\n    \"\"\"Calculate acceleration command to maintain cruise speed\"\"\"\n    target_speed_ms = self.desired_cruise_speed / 3.6\n    speed_error = target_speed_ms - ego_car.velocity.x\n    acceleration = speed_error / DESIRED_TIME_GAP\n    if acceleration < MAX_DECEL:\n        return MAX_DECEL\n    elif acceleration > MAX_ACCEL:\n        return MAX_ACCEL\n    return acceleration",
        "orig_context": "```python\n## ACC_algo.py\nDESIRED_TIME_GAP = 1.8\n\nMAX_ACCEL = 2.5\n\nMAX_DECEL = -3.0\n\nMIN_FOLLOWING_DISTANCE = 2.0\n\nDETECTION_RANGE = 100\n\nclass ACCController:\n    def __init__(self):\n        self.desired_cruise_speed = 30  # Default cruise speed in km/h\n        \n    def set_desired_cruise_speed(self, speed_kph):\n        \"\"\"Set the desired cruise speed in km/h\"\"\"\n        self.desired_cruise_speed = speed_kph\n        \n    def target_follow_control(self, ego_car, target_car):\n        \"\"\"Calculate acceleration command to follow target vehicle\"\"\"\n        # Calculate current distance and desired following distance\n        current_distance = ego_car.distanceTo(target_car)\n        desired_distance = ego_car.velocity.x * DESIRED_TIME_GAP + MIN_FOLLOWING_DISTANCE\n\n        # Determine acceleration based on distance error\n        distance_error = current_distance - desired_distance\n        acceleration = distance_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration between max deceleration and acceleration\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n    def cruise_control(self, ego_car):\n        \"\"\"Calculate acceleration command to maintain cruise speed\"\"\"\n        # Convert desired cruise speed from km/h to m/s\n        target_speed_ms = self.desired_cruise_speed / 3.6\n            \n        # Determine acceleration based on speed error\n        speed_error = target_speed_ms - ego_car.velocity.x\n        acceleration = speed_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration to vehicle constraints\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n    def acc_control_loop(self, ego_car, target_car=None):\n        \"\"\"Main ACC control loop that switches between following and cruising\"\"\"\n        if target_car and ego_car.distanceTo(target_car) < DETECTION_RANGE:\n            return self.target_follow_control(ego_car, target_car)\n        else:\n            return self.cruise_control(ego_car)\n\n```\n\n\n",
        "eval_script": "## ACC_algo.py\nDESIRED_TIME_GAP = 1.8\n\nMAX_ACCEL = 2.5\n\nMAX_DECEL = -3.0\n\nMIN_FOLLOWING_DISTANCE = 2.0\n\nDETECTION_RANGE = 100\n\nclass ACCController:\n    def __init__(self):\n        self.desired_cruise_speed = 30  # Default cruise speed in km/h\n        \n    def set_desired_cruise_speed(self, speed_kph):\n        \"\"\"Set the desired cruise speed in km/h\"\"\"\n        self.desired_cruise_speed = speed_kph\n        \n    def target_follow_control(self, ego_car, target_car):\n        \"\"\"Calculate acceleration command to follow target vehicle\"\"\"\n        # Calculate current distance and desired following distance\n        current_distance = ego_car.distanceTo(target_car)\n        desired_distance = ego_car.velocity.x * DESIRED_TIME_GAP + MIN_FOLLOWING_DISTANCE\n\n        # Determine acceleration based on distance error\n        distance_error = current_distance - desired_distance\n        acceleration = distance_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration between max deceleration and acceleration\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n    def cruise_control(self, ego_car):\n        \"\"\"Calculate acceleration command to maintain cruise speed\"\"\"\n        # Convert desired cruise speed from km/h to m/s\n        target_speed_ms = self.desired_cruise_speed / 3.6\n            \n        # Determine acceleration based on speed error\n        speed_error = target_speed_ms - ego_car.velocity.x\n        acceleration = speed_error / DESIRED_TIME_GAP\n        \n        # Limit acceleration to vehicle constraints\n        if acceleration < MAX_DECEL:\n            return MAX_DECEL\n        elif acceleration > MAX_ACCEL:\n            return MAX_ACCEL\n        return acceleration\n    \n\n\n    \n    def acc_control_loop(self, ego_car, target_car=None):\n        \"\"\"Main ACC control loop that switches between following and cruising\"\"\"\n        if target_car and ego_car.distanceTo(target_car) < DETECTION_RANGE:\n            return self.target_follow_control(ego_car, target_car)\n        else:\n            return self.cruise_control(ego_car)\n\n# Mock classes to simulate ego_car and target_car\nclass MockCar:\n    def __init__(self, velocity_x):\n        self.velocity = MockVelocity(velocity_x)\n    \n    def distanceTo(self, other_car):\n        # Mock distance calculation, assuming a fixed distance for simplicity\n        return 50  # Example fixed distance in meters\n\nclass MockVelocity:\n    def __init__(self, x):\n        self.x = x\n\ndef test_cruise_control():\n    controller = ACCController()\n\n    # Test case 1: Acceleration less than MAX_DECEL\n    ego_car = MockCar(velocity_x=50)  # High speed, should decelerate\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 2: Acceleration more than MAX_ACCEL\n    ego_car = MockCar(velocity_x=5)  # Low speed, should accelerate\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 3: Acceleration within range\n    ego_car = MockCar(velocity_x=27)  # Near desired speed, minimal adjustment\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 4: Exact match with desired speed\n    ego_car = MockCar(velocity_x=30 * 1000 / 3600)  # Exactly at desired speed\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 5: Acceleration exactly at MAX_ACCEL\n    ego_car = MockCar(velocity_x=30 - MAX_ACCEL * DESIRED_TIME_GAP * 3.6)  # Should hit MAX_ACCEL\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 6: Acceleration exactly at MAX_DECEL\n    ego_car = MockCar(velocity_x=30 - MAX_DECEL * DESIRED_TIME_GAP * 3.6)  # Should hit MAX_DECEL\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 7: Ego car is stationary\n    ego_car = MockCar(velocity_x=0)  # Stationary car\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\n    # Test case 8: Negative speed (unrealistic, but for robustness)\n    ego_car = MockCar(velocity_x=-10)  # Negative speed\n    assert controller.cruise_control(ego_car) == controller.cruise_control_new_implementation(ego_car)\n\nif __name__ == \"__main__\":\n    test_cruise_control()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `cruise_control` in the `ACCController` class is functionally identical to the ORIGINAL FUNCTION. Both functions calculate the acceleration needed to maintain a desired cruise speed by converting the desired speed from km/h to m/s, calculating the speed error, and then determining the acceleration based on this error. They both limit the acceleration to be within the bounds of `MAX_DECEL` and `MAX_ACCEL`. The logic and calculations are the same in both functions, ensuring that the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `cruise_control` function returns an acceleration value based on the speed error, which satisfies the condition of having return values.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `cruise_control` and `cruise_control_new_implementation`, ensuring that they do not rely on printed or logged contents.\n\n3. **CONDITION 3**: The test cases are designed to compare the outputs of `cruise_control` and `cruise_control_new_implementation` directly. This ensures that `cruise_control_new_implementation` can only pass all tests if it has the same functionality as `cruise_control`.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `cruise_control` returns values. There are no inappropriate assertions.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including edge cases such as maximum acceleration, maximum deceleration, exact desired speed, stationary ego car, and even negative speed. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "58748a5b1a692f831acf9a01735a87e73c39d5ac"
    },
    {
        "func_name": "check_auth_files",
        "idx": "649",
        "repo_name": "sugartechllc___PowerwallToChords",
        "func_path": "powerwallToChords.py",
        "orig_func": "def check_auth_files(pw_auth_path: str) -> bool:\n    ok = True\n    auth_files = [f'{pw_auth_path}/.pypowerwall.auth', f'{pw_auth_path}/.pypowerwall.site']\n    for f in auth_files:\n        if not (os.path.isfile(f) and os.access(f, os.R_OK)):\n            print(f'Unable to access tesla credentials file {f}')\n            ok = False\n    return ok",
        "orig_context": "```python\n## powerwallToChords.py\nimport os\n\ndef check_auth_files(pw_auth_path:str)->bool:\n    # If the Tesla authorization files can't be found, return false\n    ok = True\n    auth_files = [f'{pw_auth_path}/.pypowerwall.auth', f'{pw_auth_path}/.pypowerwall.site']\n    for f in auth_files:\n        if not (os.path.isfile(f) and os.access(f, os.R_OK)):\n            print(f'Unable to access tesla credentials file {f}')\n            ok = False\n    return ok\n\n```\n\n\n",
        "eval_script": "import os\n\ndef check_auth_files(pw_auth_path: str) -> bool:\n    # If the Tesla authorization files can't be found, return false\n    ok = True\n    auth_files = [f'{pw_auth_path}/.pypowerwall.auth', f'{pw_auth_path}/.pypowerwall.site']\n    for f in auth_files:\n        if not (os.path.isfile(f) and os.access(f, os.R_OK)):\n            print(f'Unable to access tesla credentials file {f}')\n            ok = False\n    return ok\n\n\ndef test_check_auth_files():\n    mock_dir = '/home/user/tmp'\n    os.makedirs(mock_dir, exist_ok=True)\n\n    # Test case 1: Both files exist\n    auth_file_1 = os.path.join(mock_dir, '.pypowerwall.auth')\n    auth_file_2 = os.path.join(mock_dir, '.pypowerwall.site')\n    with open(auth_file_1, 'w') as f:\n        f.write('')\n    with open(auth_file_2, 'w') as f:\n        f.write('')\n    assert check_auth_files(mock_dir) == check_auth_files_new_implementation(mock_dir)\n\n    # Test case 2: One file missing\n    os.remove(auth_file_1)\n    assert check_auth_files(mock_dir) == check_auth_files_new_implementation(mock_dir)\n\n    # Test case 3: Both files missing\n    os.remove(auth_file_2)\n    assert check_auth_files(mock_dir) == check_auth_files_new_implementation(mock_dir)\n\n    # Test case 4: Files exist but are not readable\n    with open(auth_file_1, 'w') as f:\n        f.write('')\n    with open(auth_file_2, 'w') as f:\n        f.write('')\n    os.chmod(auth_file_1, 0o000)\n    os.chmod(auth_file_2, 0o000)\n    assert check_auth_files(mock_dir) == check_auth_files_new_implementation(mock_dir)\n    os.chmod(auth_file_1, 0o644)\n    os.chmod(auth_file_2, 0o644)\n\n    # Test case 5: Directory does not exist\n    non_existent_dir = '/home/user/tmp_non_existent'\n    assert check_auth_files(non_existent_dir) == check_auth_files_new_implementation(non_existent_dir)\n\n    # Test case 6: Directory path is a file\n    file_instead_of_dir = os.path.join(mock_dir, 'not_a_directory')\n    with open(file_instead_of_dir, 'w') as f:\n        f.write('')\n    assert check_auth_files(file_instead_of_dir) == check_auth_files_new_implementation(file_instead_of_dir)\n\nif __name__ == \"__main__\":\n    test_check_auth_files()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       8      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  8      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions perform the same operations: they check if two specific files exist and are readable within a given directory path. The test cases in the CODE are designed to verify the behavior of the function under various conditions, but they do not alter the functionality of the function itself. Since the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `check_auth_files` function returns a boolean value indicating whether the required files are present and readable. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `check_auth_files` and `check_auth_files_new_implementation`, not printed or logged contents. This satisfies the condition.\n- CONDITION 3: The test cases compare the return values of `check_auth_files` and `check_auth_files_new_implementation` for various scenarios, ensuring that the new implementation must have the same functionality to pass all tests. This satisfies the condition.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable given that `check_auth_files` returns a boolean. This satisfies the condition.\n- CONDITION 5: The test cases cover various scenarios, including both files existing, one file missing, both files missing, files not being readable, a non-existent directory, and a directory path being a file. These are non-trivial and comprehensive. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "c97d744aa332d3490451558321c1efbf82ff9065"
    },
    {
        "func_name": "PW_Aggregator.avg",
        "idx": "650",
        "repo_name": "sugartechllc___PowerwallToChords",
        "func_path": "powerwallToChords.py",
        "orig_func": "def avg(self):\n    \"\"\"Return a dictionary of TimeAndValue averages.\n        \n        The aggregators are restarted when their .avg() functions are called.\n        \"\"\"\n    ret_val = {}\n    ret_val['time'] = self.time_aggregator.avg().average\n    ret_val['grid'] = self.grid_aggregator.avg().average\n    ret_val['solar'] = self.solar_aggregator.avg().average\n    ret_val['battery'] = self.battery_aggregator.avg().average\n    ret_val['load'] = self.load_aggregator.avg().average\n    ret_val['level'] = self.level_aggregator.avg().average\n    return ret_val",
        "orig_context": "```python\n## powerwallToChords.py\nimport pypowerwall\n\nimport time\n\nimport statistics\n\nimport datetime\n\nclass TimeAndValue:\n    '''Just a structure to hold a time and value pair.'''\n    def __init__(self, time, value):\n        self.time = time\n        self.average = value\n\nclass Aggregator: \n    '''Collect and average a queue of values\n    \n    When an average is called for, the average over all values \n    is calculated, and then all values except for the last one in the\n    queue is removed.\n    '''\n    def __init__(self, name:str)->None:\n        self.name = name\n        self.times = []\n        self.values = []\n\n    def add(self, value:float, time:str)->None:\n        self.values.append(value)\n        self.times.append(time)\n\n    def avg(self)->list:\n        avg = statistics.mean(self.values)\n        time = statistics.mean(self.times)\n        #print(f'>>> {self.name}')\n        #print(self.values)\n        #print(avg)\n        self.values = [self.values[-1]]\n        self.times = [self.times[-1]]\n        return TimeAndValue(time=time, value=avg)\n\nclass PW_Aggregator:\n    '''Poll Tesla every time poll_pw() is called, and aggregate selected data.\n    \n    avg() returns the averaged values, which causes the Aggregator()s\n    to restart the averaging.\n    '''\n    def __init__(self, pw:pypowerwall.Powerwall):\n        self.pw = pw\n        \n        self.time_aggregator = Aggregator('time')\n        self.grid_aggregator = Aggregator('grid')\n        self.solar_aggregator = Aggregator('solar')\n        self.battery_aggregator = Aggregator('battery')\n        self.load_aggregator = Aggregator('load')\n        self.level_aggregator = Aggregator('level')\n\n    def poll_pw(self):\n        '''Poll Tesla and add data to the set of Aggregators'''\n        pw_success = False\n        while not pw_success:\n            try:\n                # pw.grid() will make a request to Tesla\n                grid = self.pw.grid(verbose=True)\n                if grid:\n                    data_time = datetime.datetime.fromisoformat(grid['last_communication_time']).timestamp()\n                    # pw.power() will make a request to Tesla\n                    power = self.pw.power()\n                    if power:\n                        pw_success = True\n            except Exception as e:\n                print('Exception during Tesla API access')\n                print(e)\n            if not pw_success:\n                time.sleep(6)\n                print('Retrying Tesla access')\n\n        self.time_aggregator.add(time=data_time, value=data_time)\n        self.grid_aggregator.add(time=data_time, value=power['site'])\n        self.solar_aggregator.add(time=data_time, value=power['solar'])\n        self.battery_aggregator.add(time=data_time, value=power['battery'])\n        self.load_aggregator.add(time=data_time, value=power['load'])\n        self.level_aggregator.add(time=data_time, value=self.pw.level())\n\n    def avg(self):\n        '''Return a dictionary of TimeAndValue averages.\n        \n        The aggregators are restarted when their .avg() functions are called.\n        '''\n        ret_val = {}\n        ret_val['time'] = self.time_aggregator.avg().average\n        ret_val['grid'] = self.grid_aggregator.avg().average\n        ret_val['solar'] = self.solar_aggregator.avg().average\n        ret_val['battery'] = self.battery_aggregator.avg().average\n        ret_val['load'] = self.load_aggregator.avg().average\n        ret_val['level'] = self.level_aggregator.avg().average\n        return ret_val\n\n```\n\n\n",
        "eval_script": "## powerwallToChords.py\nimport time\nimport statistics\nimport datetime\n\nclass TimeAndValue:\n    '''Just a structure to hold a time and value pair.'''\n    def __init__(self, time, value):\n        self.time = time\n        self.average = value\n\nclass Aggregator: \n    '''Collect and average a queue of values\n    \n    When an average is called for, the average over all values \n    is calculated, and then all values except for the last one in the\n    queue is removed.\n    '''\n    def __init__(self, name:str)->None:\n        self.name = name\n        self.times = []\n        self.values = []\n\n    def add(self, value:float, time:str)->None:\n        self.values.append(value)\n        self.times.append(time)\n\n    def avg(self)->list:\n        avg = statistics.mean(self.values)\n        time = statistics.mean(self.times)\n        #print(f'>>> {self.name}')\n        #print(self.values)\n        #print(avg)\n        self.values = [self.values[-1]]\n        self.times = [self.times[-1]]\n        return TimeAndValue(time=time, value=avg)\n\nclass MockPowerwall:\n    '''Mock class to simulate pypowerwall.Powerwall behavior'''\n    def grid(self, verbose=False):\n        return {'last_communication_time': datetime.datetime.now().isoformat()}\n\n    def power(self):\n        return {'site': 5.0, 'solar': 10.0, 'battery': 3.0, 'load': 8.0}\n\n    def level(self):\n        return 50.0\n\nclass PW_Aggregator:\n    '''Poll Tesla every time poll_pw() is called, and aggregate selected data.\n    \n    avg() returns the averaged values, which causes the Aggregator()s\n    to restart the averaging.\n    '''\n    def __init__(self, pw):\n        self.pw = pw\n        \n        self.time_aggregator = Aggregator('time')\n        self.grid_aggregator = Aggregator('grid')\n        self.solar_aggregator = Aggregator('solar')\n        self.battery_aggregator = Aggregator('battery')\n        self.load_aggregator = Aggregator('load')\n        self.level_aggregator = Aggregator('level')\n\n    def poll_pw(self):\n        '''Poll Tesla and add data to the set of Aggregators'''\n        pw_success = False\n        while not pw_success:\n            try:\n                # pw.grid() will make a request to Tesla\n                grid = self.pw.grid(verbose=True)\n                if grid:\n                    data_time = datetime.datetime.fromisoformat(grid['last_communication_time']).timestamp()\n                    # pw.power() will make a request to Tesla\n                    power = self.pw.power()\n                    if power:\n                        pw_success = True\n            except Exception as e:\n                print('Exception during Tesla API access')\n                print(e)\n            if not pw_success:\n                time.sleep(6)\n                print('Retrying Tesla access')\n\n        self.time_aggregator.add(time=data_time, value=data_time)\n        self.grid_aggregator.add(time=data_time, value=power['site'])\n        self.solar_aggregator.add(time=data_time, value=power['solar'])\n        self.battery_aggregator.add(time=data_time, value=power['battery'])\n        self.load_aggregator.add(time=data_time, value=power['load'])\n        self.level_aggregator.add(time=data_time, value=self.pw.level())\n\n    def avg(self):\n        '''Return a dictionary of TimeAndValue averages.\n        \n        The aggregators are restarted when their .avg() functions are called.\n        '''\n        ret_val = {}\n        ret_val['time'] = self.time_aggregator.avg().average\n        ret_val['grid'] = self.grid_aggregator.avg().average\n        ret_val['solar'] = self.solar_aggregator.avg().average\n        ret_val['battery'] = self.battery_aggregator.avg().average\n        ret_val['load'] = self.load_aggregator.avg().average\n        ret_val['level'] = self.level_aggregator.avg().average\n        return ret_val\n\n\ndef test_avg():\n    pw = MockPowerwall()\n    aggregator = PW_Aggregator(pw)\n    aggregator.poll_pw()\n    \n    original_avg = aggregator.avg()\n    new_avg = aggregator.avg_new_implementation()\n    \n    assert original_avg['time'] == new_avg['time'], \"Time averages do not match\"\n    assert original_avg['grid'] == new_avg['grid'], \"Grid averages do not match\"\n    assert original_avg['solar'] == new_avg['solar'], \"Solar averages do not match\"\n    assert original_avg['battery'] == new_avg['battery'], \"Battery averages do not match\"\n    assert original_avg['load'] == new_avg['load'], \"Load averages do not match\"\n    assert original_avg['level'] == new_avg['level'], \"Level averages do not match\"\n\nif __name__ == \"__main__\":\n    test_avg()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `avg` method within the `PW_Aggregator` class. Upon examining both the ORIGINAL FUNCTION and the REVISED FUNCTION, they are identical in terms of functionality. Both functions return a dictionary containing the averages of various aggregators, and both reset the aggregators after calculating the averages. The REVISED FUNCTION does not introduce any changes in logic or output compared to the ORIGINAL FUNCTION. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `avg` function in the `PW_Aggregator` class returns a dictionary of averaged values, satisfying the condition of having return values.\n- CONDITION 2: The test cases in `test_avg` check the return values of the `avg` function and do not rely on printed or logged content.\n- CONDITION 3: The test cases compare the return values of `avg` and `avg_new_implementation`. If `avg_new_implementation` has the same functionality as `avg`, it will pass all the test cases. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of `avg` and `avg_new_implementation`, which is reasonable given that `avg` returns a dictionary of values.\n- CONDITION 5: The test cases are non-trivial as they check multiple keys in the returned dictionary, ensuring comprehensive coverage of the functionality.",
            "answer": "yes"
        },
        "commit_id": "c97d744aa332d3490451558321c1efbf82ff9065"
    },
    {
        "func_name": "OpenAIBlogGenerator._prepare_content",
        "idx": "654",
        "repo_name": "Victor-Evogor___ai-blog-writer",
        "func_path": "blog_generator.py",
        "orig_func": "def _prepare_content(self, content: List[Dict]) -> str:\n    \"\"\"Prepare content for the AI prompt.\"\"\"\n    combined = ''\n    for item in content:\n        combined += f\"Title: {item.get('title', '')}\\n\"\n        combined += f\"Content: {item.get('text', '')}\\n\"\n        if 'comments' in item:\n            combined += f\"Comments: {' '.join(item['comments'])}\\n\"\n    return combined",
        "orig_context": "```python\n## blog_generator.py\nfrom typing import Dict, List\n\nfrom openai import OpenAI\n\nimport os\n\nclass OpenAIBlogGenerator:                                                                                                 \n    def __init__(self):\n        self.client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\n    def generate(self, content: List[Dict]) -> str:                                                                        \n        \"\"\"Generate blog content using OpenAI.\"\"\"                                                                          \n        try:\n            # Prepare the content for the prompt                                                                           \n            combined_content = self._prepare_content(content)                                                              \n            \n            # Generate blog using OpenAI                                                                                   \n            response = self.client.chat.completions.create(                                                                       \n                model=\"gpt-4\",                                                                                             \n                messages=[                                                                                                 \n                    {\"role\": \"system\", \"content\": \"You are a professional blog writer. Create a well-structured, engaging  blog post from the provided content.\"},                                                                                    \n                    {\"role\": \"user\", \"content\": f\"Create a blog post from this content: {combined_content}\\n\\nInclude images with alt text accurately describing what the image is all about. The blog should be formatted in markdown syntax\"}\n                ]                                                                                                          \n            )                                                                                                              \n\n            return response.choices[0].message.content                                                                     \n\n        except Exception as e:                                                                                             \n            raise Exception(f\"Failed to generate blog with OpenAI: {str(e)}\")                                              \n\n    def _prepare_content(self, content: List[Dict]) -> str:                                                                \n        \"\"\"Prepare content for the AI prompt.\"\"\"                                                                           \n        combined = \"\"                                                                                                      \n        for item in content:                                                                                               \n            combined += f\"Title: {item.get('title', '')}\\n\"                                                                \n            combined += f\"Content: {item.get('text', '')}\\n\"                                                               \n            if 'comments' in item:                                                                                         \n                combined += f\"Comments: {' '.join(item['comments'])}\\n\"                                                    \n        return combined\n\n```\n\n\n",
        "eval_script": "## blog_generator.py\nfrom typing import Dict, List\n\n# Mocking the OpenAI class since we cannot use the real API\nclass OpenAI:\n    def __init__(self, api_key: str):\n        self.api_key = api_key\n\n    class chat:\n        class completions:\n            @staticmethod\n            def create(model: str, messages: List[Dict]) -> Dict:\n                return {\n                    \"choices\": [\n                        {\n                            \"message\": {\n                                \"content\": \"Mocked blog content based on the provided input.\"\n                            }\n                        }\n                    ]\n                }\n\nimport os\n\nclass OpenAIBlogGenerator:                                                                                                 \n    def __init__(self):\n        self.client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n\n    def generate(self, content: List[Dict]) -> str:                                                                        \n        \"\"\"Generate blog content using OpenAI.\"\"\"                                                                          \n        try:\n            # Prepare the content for the prompt                                                                           \n            combined_content = self._prepare_content(content)                                                              \n            \n            # Generate blog using OpenAI                                                                                   \n            response = self.client.chat.completions.create(                                                                       \n                model=\"gpt-4\",                                                                                             \n                messages=[                                                                                                 \n                    {\"role\": \"system\", \"content\": \"You are a professional blog writer. Create a well-structured, engaging  blog post from the provided content.\"},                                                                                    \n                    {\"role\": \"user\", \"content\": f\"Create a blog post from this content: {combined_content}\\n\\nInclude images with alt text accurately describing what the image is all about. The blog should be formatted in markdown syntax\"}\n                ]                                                                                                          \n            )                                                                                                              \n\n            return response.choices[0].message.content                                                                     \n\n        except Exception as e:                                                                                             \n            raise Exception(f\"Failed to generate blog with OpenAI: {str(e)}\")                                              \n\n    def _prepare_content(self, content: List[Dict]) -> str:                                                                \n        \"\"\"Prepare content for the AI prompt.\"\"\"                                                                           \n        combined = \"\"                                                                                                      \n        for item in content:                                                                                               \n            combined += f\"Title: {item.get('title', '')}\\n\"                                                                \n            combined += f\"Content: {item.get('text', '')}\\n\"                                                               \n            if 'comments' in item:                                                                                         \n                combined += f\"Comments: {' '.join(item['comments'])}\\n\"                                                    \n        return combined\n\n\ndef test__prepare_content():\n    generator = OpenAIBlogGenerator()\n\n    # Test case 1: Basic content with title and text\n    content1 = [{\"title\": \"Test Title\", \"text\": \"Test Content\"}]\n    assert generator._prepare_content(content1) == generator._prepare_content_new_implementation(content1)\n\n    # Test case 2: Content with title, text, and comments\n    content2 = [{\"title\": \"Test Title\", \"text\": \"Test Content\", \"comments\": [\"Great post!\", \"Very informative.\"]}]\n    assert generator._prepare_content(content2) == generator._prepare_content_new_implementation(content2)\n\n    # Test case 3: Content with missing title and text\n    content3 = [{\"comments\": [\"No title or text.\"]}]\n    assert generator._prepare_content(content3) == generator._prepare_content_new_implementation(content3)\n\n    # Test case 4: Empty content list\n    content4 = []\n    assert generator._prepare_content(content4) == generator._prepare_content_new_implementation(content4)\n\n    # Test case 5: Content with missing fields\n    content5 = [{\"title\": \"Only Title\"}, {\"text\": \"Only Text\"}, {\"comments\": [\"Only comments.\"]}]\n    assert generator._prepare_content(content5) == generator._prepare_content_new_implementation(content5)\n\n    # Test case 6: Content with empty strings\n    content6 = [{\"title\": \"\", \"text\": \"\", \"comments\": [\"\"]}]\n    assert generator._prepare_content(content6) == generator._prepare_content_new_implementation(content6)\n\n    # Test case 7: Multiple content items\n    content7 = [\n        {\"title\": \"First Title\", \"text\": \"First Content\", \"comments\": [\"First comment.\"]},\n        {\"title\": \"Second Title\", \"text\": \"Second Content\", \"comments\": [\"Second comment.\"]}\n    ]\n    assert generator._prepare_content(content7) == generator._prepare_content_new_implementation(content7)\n\n    # Test case 8: Special characters in content\n    content8 = [{\"title\": \"Title with special characters !@#$%^&*()\", \"text\": \"Text with special characters <>?/:;\\\"'[]{}|\"}]\n    assert generator._prepare_content(content8) == generator._prepare_content_new_implementation(content8)\n\n    # Test case 9: Large content\n    large_text = \"Lorem ipsum \" * 1000  # Repeat 'Lorem ipsum ' 1000 times\n    content9 = [{\"title\": \"Large Content\", \"text\": large_text}]\n    assert generator._prepare_content(content9) == generator._prepare_content_new_implementation(content9)\n\nif __name__ == \"__main__\":\n    test__prepare_content()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_prepare_content` in the `OpenAIBlogGenerator` class is identical to the ORIGINAL FUNCTION. Both functions iterate over a list of dictionaries, concatenate the 'title', 'text', and 'comments' fields into a single string, and return this combined string. The logic and implementation details, such as using `item.get()` for safely accessing dictionary keys and joining comments with a space, are exactly the same in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_prepare_content` function returns a string, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `_prepare_content` and `_prepare_content_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `_prepare_content` and `_prepare_content_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `_prepare_content` has return values.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic content, content with comments, missing fields, empty content, special characters, and large content, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "50a1811a108dbe2236926146d1b631870e2cc8ab"
    },
    {
        "func_name": "ClaudeBlogGenerator._prepare_content",
        "idx": "655",
        "repo_name": "Victor-Evogor___ai-blog-writer",
        "func_path": "blog_generator.py",
        "orig_func": "def _prepare_content(self, content: List[Dict]) -> str:\n    \"\"\"Prepare content for the AI prompt.\"\"\"\n    combined = ''\n    for item in content:\n        combined += f\"Title: {item.get('title', '')}\\n\"\n        combined += f\"Content: {item.get('text', '')}\\n\"\n        if 'comments' in item:\n            combined += f\"Comments: {' '.join(item['comments'])}\\n\"\n    return combined",
        "orig_context": "```python\n## blog_generator.py\nfrom typing import Dict, List\n\nfrom anthropic import Anthropic\n\nimport os\n\nclass ClaudeBlogGenerator:                                                                                                 \n    def __init__(self):                                                                                                    \n        self.client = Anthropic(api_key=os.getenv('CLAUDE_API_KEY'))                                                       \n                                                                                                                            \n    def generate(self, content: List[Dict]) -> str:                                                                        \n        \"\"\"Generate blog content using Claude.\"\"\"                                                                          \n        try:                                                                                                               \n            # Prepare the content for the prompt                                                                           \n            combined_content = self._prepare_content(content)                                                              \n                                                                                                                            \n            # Generate blog using Claude                                                                                   \n            response = self.client.messages.create(                                                                        \n                model=\"claude-2\",                                                                                          \n                max_tokens=1000,                                                                                           \n                messages=[{                                                                                                \n                    \"role\": \"user\",                                                                                        \n                    \"content\": f\"Create a well-structured, engaging blog post from this content: {combined_content}\\n\\nInclude images with alt text accurately describing what the image is all about\"       \n                }]                                                                                                         \n            )                                                                                                              \n                                                                                                                            \n            return response.content[0].text                                                                                \n                                                                                                                            \n        except Exception as e:                                                                                             \n            raise Exception(f\"Failed to generate blog with Claude: {str(e)}\")                                              \n                                                                                                                            \n    def _prepare_content(self, content: List[Dict]) -> str:                                                                \n        \"\"\"Prepare content for the AI prompt.\"\"\"                                                                           \n        combined = \"\"                                                                                                      \n        for item in content:                                                                                               \n            combined += f\"Title: {item.get('title', '')}\\n\"                                                                \n            combined += f\"Content: {item.get('text', '')}\\n\"                                                               \n            if 'comments' in item:                                                                                         \n                combined += f\"Comments: {' '.join(item['comments'])}\\n\"                                                    \n        return combined\n\n```\n\n\n",
        "eval_script": "## blog_generator.py\nfrom typing import Dict, List\n\n# Mocking the Anthropic class since we don't need it for _prepare_content\nclass Anthropic:\n    def __init__(self, api_key: str):\n        pass\n\nimport os\n\nclass ClaudeBlogGenerator:                                                                                                 \n    def __init__(self):                                                                                                    \n        self.client = Anthropic(api_key=os.getenv('CLAUDE_API_KEY'))                                                       \n                                                                                                                            \n    def generate(self, content: List[Dict]) -> str:                                                                        \n        \"\"\"Generate blog content using Claude.\"\"\"                                                                          \n        try:                                                                                                               \n            # Prepare the content for the prompt                                                                           \n            combined_content = self._prepare_content(content)                                                              \n                                                                                                                            \n            # Generate blog using Claude                                                                                   \n            response = self.client.messages.create(                                                                        \n                model=\"claude-2\",                                                                                          \n                max_tokens=1000,                                                                                           \n                messages=[{                                                                                                \n                    \"role\": \"user\",                                                                                        \n                    \"content\": f\"Create a well-structured, engaging blog post from this content: {combined_content}\\n\\nInclude images with alt text accurately describing what the image is all about\"       \n                }]                                                                                                         \n            )                                                                                                              \n                                                                                                                            \n            return response.content[0].text                                                                                \n                                                                                                                            \n        except Exception as e:                                                                                             \n            raise Exception(f\"Failed to generate blog with Claude: {str(e)}\")                                              \n                                                                                                                            \n    def _prepare_content(self, content: List[Dict]) -> str:                                                                \n        \"\"\"Prepare content for the AI prompt.\"\"\"                                                                           \n        combined = \"\"                                                                                                      \n        for item in content:                                                                                               \n            combined += f\"Title: {item.get('title', '')}\\n\"                                                                \n            combined += f\"Content: {item.get('text', '')}\\n\"                                                               \n            if 'comments' in item:                                                                                         \n                combined += f\"Comments: {' '.join(item['comments'])}\\n\"                                                    \n        return combined\n\n\ndef test__prepare_content():\n    generator = ClaudeBlogGenerator()\n\n    # Test case 1: Basic content with title and text\n    content1 = [{\"title\": \"Test Title\", \"text\": \"Test content.\"}]\n    assert generator._prepare_content(content1) == generator._prepare_content_new_implementation(content1)\n\n    # Test case 2: Content with title, text, and comments\n    content2 = [{\"title\": \"Another Title\", \"text\": \"More content.\", \"comments\": [\"Great post!\", \"Very informative.\"]}]\n    assert generator._prepare_content(content2) == generator._prepare_content_new_implementation(content2)\n\n    # Test case 3: Content with missing fields\n    content3 = [{\"text\": \"Content without a title.\"}]\n    assert generator._prepare_content(content3) == generator._prepare_content_new_implementation(content3)\n\n    # Test case 4: Empty content\n    content4 = []\n    assert generator._prepare_content(content4) == generator._prepare_content_new_implementation(content4)\n\n    # Test case 5: Multiple items in content\n    content5 = [\n        {\"title\": \"First Title\", \"text\": \"First content.\"},\n        {\"title\": \"Second Title\", \"text\": \"Second content.\", \"comments\": [\"Nice!\", \"Well done.\"]}\n    ]\n    assert generator._prepare_content(content5) == generator._prepare_content_new_implementation(content5)\n\n    # Test case 6: Complex comments\n    content6 = [{\"title\": \"Complex Title\", \"text\": \"Complex content.\", \"comments\": [\"Great post!\\nKeep it up.\", \"Very informative.\"]}]\n    assert generator._prepare_content(content6) == generator._prepare_content_new_implementation(content6)\n\n    # Test case 7: No comments key\n    content7 = [{\"title\": \"No Comments Title\", \"text\": \"No comments content.\"}]\n    assert generator._prepare_content(content7) == generator._prepare_content_new_implementation(content7)\n\n    # Test case 8: Empty strings for title, text, and comments\n    content8 = [{\"title\": \"\", \"text\": \"\", \"comments\": [\"\"]}]\n    assert generator._prepare_content(content8) == generator._prepare_content_new_implementation(content8)\n\n    # Test case 9: Nested structures\n    content9 = [{\"title\": \"Nested Title\", \"text\": {\"subtext\": \"Nested content.\"}, \"comments\": [\"Nested comment.\"]}]\n    assert generator._prepare_content(content9) == generator._prepare_content_new_implementation(content9)\n\nif __name__ == \"__main__\":\n    test__prepare_content()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_prepare_content` in the `ClaudeBlogGenerator` class is identical to the ORIGINAL FUNCTION. Both functions iterate over a list of dictionaries, extracting the 'title', 'text', and 'comments' fields, and concatenate them into a single string with the same format. The logic and structure of the code are the same, ensuring that the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. CONDITION 1: The `_prepare_content` function returns a string, which is a return value. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. CONDITION 2: The test cases use assertions to check the return values of `_prepare_content` and `_prepare_content_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n3. CONDITION 3: The test cases compare the outputs of `_prepare_content` and `_prepare_content_new_implementation` directly. This means that `_prepare_content_new_implementation` can only pass all the test cases if it has the exact same functionality as `_prepare_content`, satisfying this condition.\n\n4. CONDITION 4: The test cases use assertions to compare the return values of the two implementations. This is reasonable given that `_prepare_content` returns a string. The test cases do not use inappropriate assertions, satisfying this condition.\n\n5. CONDITION 5: The test cases cover a variety of scenarios, including basic content, content with comments, missing fields, empty content, multiple items, complex comments, no comments key, empty strings, and nested structures. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "50a1811a108dbe2236926146d1b631870e2cc8ab"
    },
    {
        "func_name": "Metrics._request_canceled",
        "idx": "666",
        "repo_name": "zenpalio___vastai-pyworker",
        "func_path": "lib/metrics.py",
        "orig_func": "def _request_canceled(self, workload: float, reqnum: int) -> None:\n    \"\"\"\n        this function is called if client drops connection before model API has responded\n        \"\"\"\n    self.model_metrics.workload_pending -= workload\n    self.model_metrics.workload_cancelled += workload\n    self.model_metrics.requests_working.discard(reqnum)",
        "orig_context": "```python\n## lib/data_types.py\nimport time\n\nfrom dataclasses import dataclass, field\n\nfrom typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type\n\nimport psutil\n\nclass SystemMetrics:\n    \"\"\"General system metrics\"\"\"\n\n    model_loading_start: float\n    model_loading_time: Union[float, None]\n    last_disk_usage: float\n    additional_disk_usage: float\n    model_is_loaded: bool\n\n    @staticmethod\n    def get_disk_usage_GB():\n        return psutil.disk_usage(\"/\").used / (2**30)  # want units of GB\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            model_loading_start=time.time(),\n            model_loading_time=None,\n            last_disk_usage=SystemMetrics.get_disk_usage_GB(),\n            additional_disk_usage=0.0,\n            model_is_loaded=False,\n        )\n\n    def update_disk_usage(self):\n        disk_usage = SystemMetrics.get_disk_usage_GB()\n        self.additional_disk_usage = disk_usage - self.last_disk_usage\n        self.last_disk_usage = disk_usage\n\n    def reset(self):\n        # autoscaler excepts model_loading_time to be populated only once, when the instance has\n        # finished benchmarking and is ready to receive requests. This applies to restarted instances\n        # as well: they should send model_loading_time once when they are done loading\n        self.model_loading_time = None\n\nclass ModelMetrics:\n    \"\"\"Model specific metrics\"\"\"\n\n    # these are reset after being sent to autoscaler\n    workload_served: float\n    workload_received: float\n    workload_cancelled: float\n    workload_errored: float\n    workload_pending: float\n    # these are not\n    cur_perf: float\n    error_msg: Optional[str]\n    max_throughput: float\n    requests_recieved: Set[int] = field(default_factory=set)\n    requests_working: Set[int] = field(default_factory=set)\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            workload_pending=0.0,\n            workload_served=0.0,\n            workload_cancelled=0.0,\n            workload_errored=0.0,\n            cur_perf=0.0,\n            workload_received=0.0,\n            error_msg=None,\n            max_throughput=0.0,\n        )\n\n    @property\n    def workload_processing(self) -> float:\n        return max(self.workload_received - self.workload_cancelled, 0.0)\n\n    def set_errored(self, error_msg):\n        self.reset()\n        self.error_msg = error_msg\n\n    def reset(self):\n        self.workload_served = 0\n        self.workload_received = 0\n        self.workload_cancelled = 0\n        self.workload_errored = 0\n\nclass AutoScalaerData:\n    \"\"\"Data that is reported to autoscaler\"\"\"\n\n    id: int\n    loadtime: float\n    cur_load: float\n    error_msg: str\n    max_perf: float\n    cur_perf: float\n    cur_capacity: float\n    max_capacity: float\n    num_requests_working: int\n    num_requests_recieved: int\n    additional_disk_usage: float\n    url: str\n    type: str\n    provider: str\n\n```\n\n\n```python\n## lib/metrics.py\nfrom enum import Enum\n\nimport os\n\nimport time\n\nimport logging\n\nfrom asyncio import sleep\n\nfrom dataclasses import dataclass, asdict, field\n\nfrom functools import cache\n\nfrom urllib.parse import urljoin\n\nimport requests\n\nfrom lib.data_types import AutoScalaerData, SystemMetrics, ModelMetrics\n\nfrom typing import Awaitable, NoReturn, List\n\nclass InstanceProvider(Enum):\n    RUNPOD = \"runpod\"\n    VASTAI = \"vastai\"\n\nMETRICS_UPDATE_INTERVAL = 1\n\nlog = logging.getLogger(__file__)\n\ndef get_container_id() -> str:\n    return os.environ.get(\"CONTAINER_ID\", None) or os.environ.get(\n        \"RUNPOD_POD_ID\", \"unknown\"\n    )\n\ndef is_runpod_provider() -> bool:\n    return os.environ.get(\"PROVIDER\", None) == \"runpod\"\n\ndef get_provider() -> str:\n    if is_runpod_provider():\n        return InstanceProvider.RUNPOD.value\n    return InstanceProvider.VASTAI.value\n\ndef get_url() -> str:\n    internal_worker_port = os.environ[\"WORKER_PORT\"]\n    if is_runpod_provider():\n        runpod_id = os.environ[\"RUNPOD_POD_ID\"]\n        return f\"https://{runpod_id}-{internal_worker_port}.proxy.runpod.net\"\n    use_ssl = os.environ.get(\"USE_SSL\", \"false\") == \"true\"\n    worker_port = os.environ[f\"VAST_TCP_PORT_{internal_worker_port}\"]\n    public_ip = os.environ[\"PUBLIC_IPADDR\"]\n    return f\"http{'s' if use_ssl else ''}://{public_ip}:{worker_port}\"\n\nclass Metrics:\n    last_metric_update: float = 0.0\n    update_pending: bool = False\n    id: str = field(default_factory=lambda: get_container_id())\n    report_addr: List[str] = field(\n        default_factory=lambda: os.environ[\"REPORT_ADDR\"].split(\",\")\n    )\n    url: str = field(default_factory=get_url)\n    system_metrics: SystemMetrics = field(default_factory=SystemMetrics.empty)\n    model_metrics: ModelMetrics = field(default_factory=ModelMetrics.empty)\n\n    def _request_start(self, workload: float, reqnum: int) -> None:\n        \"\"\"\n        this function is called prior to forwarding a request to a model API.\n        \"\"\"\n        log.debug(\"request start\")\n        self.model_metrics.workload_pending += workload\n        self.model_metrics.workload_received += workload\n        self.model_metrics.requests_recieved.add(reqnum)\n        self.model_metrics.requests_working.add(reqnum)\n\n    def _request_end(\n        self, workload: float, req_response_time: float, reqnum: int\n    ) -> None:\n        \"\"\"\n        this function is called after a response from model API is received.\n        \"\"\"\n        self.model_metrics.workload_served += workload\n        self.model_metrics.workload_pending -= workload\n        self.model_metrics.requests_working.discard(reqnum)\n        self.model_metrics.cur_perf = workload / req_response_time\n        self.update_pending = True\n\n    def _request_errored(self, workload: float, reqnum: int) -> None:\n        \"\"\"\n        this function is called if model API returns an error\n        \"\"\"\n        self.model_metrics.workload_pending -= workload\n        self.model_metrics.workload_errored += workload\n        self.model_metrics.requests_working.discard(reqnum)\n\n    def _request_canceled(self, workload: float, reqnum: int) -> None:\n        \"\"\"\n        this function is called if client drops connection before model API has responded\n        \"\"\"\n        self.model_metrics.workload_pending -= workload\n        self.model_metrics.workload_cancelled += workload\n        self.model_metrics.requests_working.discard(reqnum)\n\n    async def _send_metrics_loop(self) -> Awaitable[NoReturn]:\n        while True:\n            await sleep(METRICS_UPDATE_INTERVAL)\n            elapsed = time.time() - self.last_metric_update\n            if self.system_metrics.model_is_loaded is False and elapsed >= 10:\n                log.debug(f\"sending loading model metrics after {int(elapsed)}s wait\")\n                self.__send_metrics_and_reset(elapsed)\n            elif self.update_pending or elapsed > 10:\n                log.debug(f\"sending loaded model metrics after {int(elapsed)}s wait\")\n                self.__send_metrics_and_reset(elapsed)\n\n    def _model_loaded(self, max_throughput: float) -> None:\n        self.system_metrics.model_loading_time = (\n            time.time() - self.system_metrics.model_loading_start\n        )\n        self.system_metrics.model_is_loaded = True\n        self.model_metrics.max_throughput = max_throughput\n\n    def _model_errored(self, error_msg: str) -> None:\n        self.model_metrics.set_errored(error_msg)\n        self.system_metrics.model_is_loaded = True\n\n    #######################################Private#######################################\n\n    def __send_metrics_and_reset(self, elapsed):\n\n        def compute_autoscaler_data() -> AutoScalaerData:\n            return AutoScalaerData(\n                id=self.id,\n                loadtime=(self.system_metrics.model_loading_time or 0.0),\n                cur_load=(self.model_metrics.workload_processing / elapsed),\n                max_perf=self.model_metrics.max_throughput,\n                cur_perf=self.model_metrics.cur_perf,\n                error_msg=self.model_metrics.error_msg or \"\",\n                num_requests_working=len(self.model_metrics.requests_working),\n                num_requests_recieved=len(self.model_metrics.requests_recieved),\n                additional_disk_usage=self.system_metrics.additional_disk_usage,\n                cur_capacity=0,\n                max_capacity=0,\n                url=self.url,\n                type=os.environ[\"BACKEND\"],\n                provider=get_provider(),\n            )\n\n        def send_data(report_addr: str) -> None:\n            data = compute_autoscaler_data()\n            full_path = urljoin(report_addr, \"/public/v1/webhook/instance/status\")\n            for attempt in range(1, 4):\n                try:\n                    requests.post(full_path, json=asdict(data), timeout=1)\n                    break\n                except requests.Timeout:\n                    log.debug(f\"autoscaler status update timed out\")\n                except Exception as e:\n                    log.debug(f\"autoscaler status update failed with error: {e}\")\n                time.sleep(2)\n                log.debug(f\"retrying autoscaler status update, attempt: {attempt}\")\n\n        ###########\n\n        self.system_metrics.update_disk_usage()\n\n        for report_addr in self.report_addr:\n            send_data(report_addr)\n        self.update_pending = False\n        self.model_metrics.reset()\n        self.system_metrics.reset()\n        self.last_metric_update = time.time()\n\n```\n\n\n",
        "eval_script": "# Mock implementations and imports\nimport os\nimport time\nimport logging\nfrom enum import Enum\nfrom asyncio import sleep\nfrom dataclasses import dataclass, asdict, field\nfrom functools import cache\nfrom urllib.parse import urljoin\nfrom typing import Awaitable, NoReturn, List, Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type\n\n# Mocking psutil for disk usage\nclass psutil:\n    @staticmethod\n    def disk_usage(path):\n        return type('disk_usage', (object,), {'used': 1024 * 1024 * 1024})()  # 1 GB used\n\n# Mock environment variables\nos.environ[\"WORKER_PORT\"] = \"8080\"\nos.environ[\"PUBLIC_IPADDR\"] = \"127.0.0.1\"\nos.environ[\"VAST_TCP_PORT_8080\"] = \"8080\"\nos.environ[\"REPORT_ADDR\"] = \"http://localhost\"\nos.environ[\"BACKEND\"] = \"test_backend\"\n\n# Context code from lib/data_types.py\nclass SystemMetrics:\n    \"\"\"General system metrics\"\"\"\n\n    model_loading_start: float\n    model_loading_time: Union[float, None]\n    last_disk_usage: float\n    additional_disk_usage: float\n    model_is_loaded: bool\n\n    @staticmethod\n    def get_disk_usage_GB():\n        return psutil.disk_usage(\"/\").used / (2**30)  # want units of GB\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            model_loading_start=time.time(),\n            model_loading_time=None,\n            last_disk_usage=SystemMetrics.get_disk_usage_GB(),\n            additional_disk_usage=0.0,\n            model_is_loaded=False,\n        )\n\n    def update_disk_usage(self):\n        disk_usage = SystemMetrics.get_disk_usage_GB()\n        self.additional_disk_usage = disk_usage - self.last_disk_usage\n        self.last_disk_usage = disk_usage\n\n    def reset(self):\n        self.model_loading_time = None\n\nclass ModelMetrics:\n    \"\"\"Model specific metrics\"\"\"\n\n    def __init__(self, workload_pending: float, workload_served: float, workload_cancelled: float,\n                 workload_errored: float, cur_perf: float, workload_received: float,\n                 error_msg: Optional[str], max_throughput: float):\n        self.workload_served = workload_served\n        self.workload_received = workload_received\n        self.workload_cancelled = workload_cancelled\n        self.workload_errored = workload_errored\n        self.workload_pending = workload_pending\n        self.cur_perf = cur_perf\n        self.error_msg = error_msg\n        self.max_throughput = max_throughput\n        self.requests_recieved = set()\n        self.requests_working = set()\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            workload_pending=0.0,\n            workload_served=0.0,\n            workload_cancelled=0.0,\n            workload_errored=0.0,\n            cur_perf=0.0,\n            workload_received=0.0,\n            error_msg=None,\n            max_throughput=0.0,\n        )\n\n    @property\n    def workload_processing(self) -> float:\n        return max(self.workload_received - self.workload_cancelled, 0.0)\n\n    def set_errored(self, error_msg):\n        self.reset()\n        self.error_msg = error_msg\n\n    def reset(self):\n        self.workload_served = 0\n        self.workload_received = 0\n        self.workload_cancelled = 0\n        self.workload_errored = 0\n\nclass AutoScalaerData:\n    \"\"\"Data that is reported to autoscaler\"\"\"\n\n    id: int\n    loadtime: float\n    cur_load: float\n    error_msg: str\n    max_perf: float\n    cur_perf: float\n    cur_capacity: float\n    max_capacity: float\n    num_requests_working: int\n    num_requests_recieved: int\n    additional_disk_usage: float\n    url: str\n    type: str\n    provider: str\n\n# Main code from lib/metrics.py\nclass InstanceProvider(Enum):\n    RUNPOD = \"runpod\"\n    VASTAI = \"vastai\"\n\nMETRICS_UPDATE_INTERVAL = 1\n\nlog = logging.getLogger(__file__)\n\ndef get_container_id() -> str:\n    return os.environ.get(\"CONTAINER_ID\", None) or os.environ.get(\n        \"RUNPOD_POD_ID\", \"unknown\"\n    )\n\ndef is_runpod_provider() -> bool:\n    return os.environ.get(\"PROVIDER\", None) == \"runpod\"\n\ndef get_provider() -> str:\n    if is_runpod_provider():\n        return InstanceProvider.RUNPOD.value\n    return InstanceProvider.VASTAI.value\n\ndef get_url() -> str:\n    internal_worker_port = os.environ[\"WORKER_PORT\"]\n    if is_runpod_provider():\n        runpod_id = os.environ[\"RUNPOD_POD_ID\"]\n        return f\"https://{runpod_id}-{internal_worker_port}.proxy.runpod.net\"\n    use_ssl = os.environ.get(\"USE_SSL\", \"false\") == \"true\"\n    worker_port = os.environ[f\"VAST_TCP_PORT_{internal_worker_port}\"]\n    public_ip = os.environ[\"PUBLIC_IPADDR\"]\n    return f\"http{'s' if use_ssl else ''}://{public_ip}:{worker_port}\"\n\nclass Metrics:\n    last_metric_update: float = 0.0\n    update_pending: bool = False\n    id: str = field(default_factory=lambda: get_container_id())\n    report_addr: List[str] = field(\n        default_factory=lambda: os.environ[\"REPORT_ADDR\"].split(\",\")\n    )\n    url: str = field(default_factory=get_url)\n    system_metrics: SystemMetrics = field(default_factory=SystemMetrics.empty)\n    model_metrics: ModelMetrics = field(default_factory=ModelMetrics.empty)\n\n    def _request_start(self, workload: float, reqnum: int) -> None:\n        log.debug(\"request start\")\n        self.model_metrics.workload_pending += workload\n        self.model_metrics.workload_received += workload\n        self.model_metrics.requests_recieved.add(reqnum)\n        self.model_metrics.requests_working.add(reqnum)\n\n    def _request_end(\n        self, workload: float, req_response_time: float, reqnum: int\n    ) -> None:\n        self.model_metrics.workload_served += workload\n        self.model_metrics.workload_pending -= workload\n        self.model_metrics.requests_working.discard(reqnum)\n        self.model_metrics.cur_perf = workload / req_response_time\n        self.update_pending = True\n\n    def _request_errored(self, workload: float, reqnum: int) -> None:\n        self.model_metrics.workload_pending -= workload\n        self.model_metrics.workload_errored += workload\n        self.model_metrics.requests_working.discard(reqnum)\n\n    def _request_canceled(self, workload: float, reqnum: int) -> None:\n        self.model_metrics.workload_pending -= workload\n        self.model_metrics.workload_cancelled += workload\n        self.model_metrics.requests_working.discard(reqnum)\n\n\n    async def _send_metrics_loop(self) -> Awaitable[NoReturn]:\n        while True:\n            await sleep(METRICS_UPDATE_INTERVAL)\n            elapsed = time.time() - self.last_metric_update\n            if self.system_metrics.model_is_loaded is False and elapsed >= 10:\n                log.debug(f\"sending loading model metrics after {int(elapsed)}s wait\")\n                self.__send_metrics_and_reset(elapsed)\n            elif self.update_pending or elapsed > 10:\n                log.debug(f\"sending loaded model metrics after {int(elapsed)}s wait\")\n                self.__send_metrics_and_reset(elapsed)\n\n    def _model_loaded(self, max_throughput: float) -> None:\n        self.system_metrics.model_loading_time = (\n            time.time() - self.system_metrics.model_loading_start\n        )\n        self.system_metrics.model_is_loaded = True\n        self.model_metrics.max_throughput = max_throughput\n\n    def _model_errored(self, error_msg: str) -> None:\n        self.model_metrics.set_errored(error_msg)\n        self.system_metrics.model_is_loaded = True\n\n    def __send_metrics_and_reset(self, elapsed):\n\n        def compute_autoscaler_data() -> AutoScalaerData:\n            return AutoScalaerData(\n                id=self.id,\n                loadtime=(self.system_metrics.model_loading_time or 0.0),\n                cur_load=(self.model_metrics.workload_processing / elapsed),\n                max_perf=self.model_metrics.max_throughput,\n                cur_perf=self.model_metrics.cur_perf,\n                error_msg=self.model_metrics.error_msg or \"\",\n                num_requests_working=len(self.model_metrics.requests_working),\n                num_requests_recieved=len(self.model_metrics.requests_recieved),\n                additional_disk_usage=self.system_metrics.additional_disk_usage,\n                cur_capacity=0,\n                max_capacity=0,\n                url=self.url,\n                type=os.environ[\"BACKEND\"],\n                provider=get_provider(),\n            )\n\n        def send_data(report_addr: str) -> None:\n            data = compute_autoscaler_data()\n            full_path = urljoin(report_addr, \"/public/v1/webhook/instance/status\")\n            for attempt in range(1, 4):\n                try:\n                    requests.post(full_path, json=asdict(data), timeout=1)\n                    break\n                except requests.Timeout:\n                    log.debug(f\"autoscaler status update timed out\")\n                except Exception as e:\n                    log.debug(f\"autoscaler status update failed with error: {e}\")\n                time.sleep(2)\n                log.debug(f\"retrying autoscaler status update, attempt: {attempt}\")\n\n        self.system_metrics.update_disk_usage()\n\n        for report_addr in self.report_addr:\n            send_data(report_addr)\n        self.update_pending = False\n        self.model_metrics.reset()\n        self.system_metrics.reset()\n        self.last_metric_update = time.time()\n\n# Test function to compare both implementations\ndef test__request_canceled():\n    metrics = Metrics()\n    metrics.model_metrics = ModelMetrics.empty()\n\n    # Test case 1: Basic cancellation\n    metrics._request_start(10.0, 1)\n    metrics._request_canceled(10.0, 1)\n    canceled_metrics = metrics.model_metrics\n\n    metrics.model_metrics = ModelMetrics.empty()\n    metrics._request_start(10.0, 1)\n    metrics._request_canceled_new_implementation(10.0, 1)\n    new_canceled_metrics = metrics.model_metrics\n\n    assert canceled_metrics.workload_pending == new_canceled_metrics.workload_pending\n    assert canceled_metrics.workload_cancelled == new_canceled_metrics.workload_cancelled\n    assert canceled_metrics.requests_working == new_canceled_metrics.requests_working\n\n    # Test case 2: Cancel with different workload\n    metrics.model_metrics = ModelMetrics.empty()\n    metrics._request_start(5.0, 2)\n    metrics._request_canceled(5.0, 2)\n    canceled_metrics = metrics.model_metrics\n\n    metrics.model_metrics = ModelMetrics.empty()\n    metrics._request_start(5.0, 2)\n    metrics._request_canceled_new_implementation(5.0, 2)\n    new_canceled_metrics = metrics.model_metrics\n\n    assert canceled_metrics.workload_pending == new_canceled_metrics.workload_pending\n    assert canceled_metrics.workload_cancelled == new_canceled_metrics.workload_cancelled\n    assert canceled_metrics.requests_working == new_canceled_metrics.requests_working\n\n    # Test case 3: Multiple requests\n    metrics.model_metrics = ModelMetrics.empty()\n    metrics._request_start(3.0, 3)\n    metrics._request_start(7.0, 4)\n    metrics._request_canceled(3.0, 3)\n    canceled_metrics = metrics.model_metrics\n\n    metrics.model_metrics = ModelMetrics.empty()\n    metrics._request_start(3.0, 3)\n    metrics._request_start(7.0, 4)\n    metrics._request_canceled_new_implementation(3.0, 3)\n    new_canceled_metrics = metrics.model_metrics\n\n    assert canceled_metrics.workload_pending == new_canceled_metrics.workload_pending\n    assert canceled_metrics.workload_cancelled == new_canceled_metrics.workload_cancelled\n    assert canceled_metrics.requests_working == new_canceled_metrics.requests_working\n\nif __name__ == \"__main__\":\n    test__request_canceled()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_request_canceled` in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they decrement `workload_pending` by `workload`, increment `workload_cancelled` by `workload`, and remove `reqnum` from `requests_working`. There are no changes in logic or functionality between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `_request_canceled` function modifies the `model_metrics` attribute of the `Metrics` class, which is a global state in the context of the `Metrics` instance. Therefore, this condition is satisfied.\n\n- **CONDITION 2**: The test cases check the state of the `model_metrics` attribute after calling the `_request_canceled` function and its new implementation. They do not rely on printed or logged content. Thus, this condition is satisfied.\n\n- **CONDITION 3**: The test cases compare the state of `model_metrics` after executing both the original and new implementations of `_request_canceled`. If the new implementation has the exact same functionality, the states will match, and the test cases will pass. Therefore, this condition is satisfied.\n\n- **CONDITION 4**: The test cases use assertions to compare the relevant attributes of `model_metrics` after calling both implementations. Since `_request_canceled` modifies the state of `model_metrics`, the use of assertions to compare these states is reasonable. Thus, this condition is satisfied.\n\n- **CONDITION 5**: The test cases cover different scenarios: basic cancellation, cancellation with different workloads, and multiple requests. These scenarios ensure that the test cases are non-trivial and cover a range of possible use cases. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "a10baa05ac593506faae7d7d529a180dd1352bb3"
    },
    {
        "func_name": "SystemMetrics.update_disk_usage",
        "idx": "671",
        "repo_name": "zenpalio___vastai-pyworker",
        "func_path": "lib/data_types.py",
        "orig_func": "def update_disk_usage(self):\n    disk_usage = SystemMetrics.get_disk_usage_GB()\n    self.additional_disk_usage = disk_usage - self.last_disk_usage\n    self.last_disk_usage = disk_usage",
        "orig_context": "```python\n## lib/data_types.py\nimport time\n\nfrom dataclasses import dataclass, field\n\nfrom typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type\n\nimport psutil\n\nclass SystemMetrics:\n    \"\"\"General system metrics\"\"\"\n\n    model_loading_start: float\n    model_loading_time: Union[float, None]\n    last_disk_usage: float\n    additional_disk_usage: float\n    model_is_loaded: bool\n\n    @staticmethod\n    def get_disk_usage_GB():\n        return psutil.disk_usage(\"/\").used / (2**30)  # want units of GB\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            model_loading_start=time.time(),\n            model_loading_time=None,\n            last_disk_usage=SystemMetrics.get_disk_usage_GB(),\n            additional_disk_usage=0.0,\n            model_is_loaded=False,\n        )\n\n    def update_disk_usage(self):\n        disk_usage = SystemMetrics.get_disk_usage_GB()\n        self.additional_disk_usage = disk_usage - self.last_disk_usage\n        self.last_disk_usage = disk_usage\n\n    def reset(self):\n        # autoscaler excepts model_loading_time to be populated only once, when the instance has\n        # finished benchmarking and is ready to receive requests. This applies to restarted instances\n        # as well: they should send model_loading_time once when they are done loading\n        self.model_loading_time = None\n\n```\n\n\n",
        "eval_script": "## lib/data_types.py\nimport time\n\nfrom dataclasses import dataclass, field\n\nfrom typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type\n\nimport psutil\n\nclass SystemMetrics:\n    \"\"\"General system metrics\"\"\"\n\n    def __init__(self, model_loading_start: float, model_loading_time: Union[float, None],\n                 last_disk_usage: float, additional_disk_usage: float, model_is_loaded: bool):\n        self.model_loading_start = model_loading_start\n        self.model_loading_time = model_loading_time\n        self.last_disk_usage = last_disk_usage\n        self.additional_disk_usage = additional_disk_usage\n        self.model_is_loaded = model_is_loaded\n\n    @staticmethod\n    def get_disk_usage_GB():\n        return psutil.disk_usage(\"/\").used / (2**30)  # want units of GB\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            model_loading_start=time.time(),\n            model_loading_time=None,\n            last_disk_usage=SystemMetrics.get_disk_usage_GB(),\n            additional_disk_usage=0.0,\n            model_is_loaded=False,\n        )\n\n    def update_disk_usage(self):\n        disk_usage = SystemMetrics.get_disk_usage_GB()\n        self.additional_disk_usage = disk_usage - self.last_disk_usage\n        self.last_disk_usage = disk_usage\n\n\n    def reset(self):\n        # autoscaler excepts model_loading_time to be populated only once, when the instance has\n        # finished benchmarking and is ready to receive requests. This applies to restarted instances\n        # as well: they should send model_loading_time once when they are done loading\n        self.model_loading_time = None\n\ndef test_update_disk_usage():\n    # Test 1: No change in disk usage\n    metrics1 = SystemMetrics.empty()\n    metrics2 = SystemMetrics.empty()\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert metrics1.last_disk_usage == metrics2.last_disk_usage\n    assert metrics1.additional_disk_usage == metrics2.additional_disk_usage\n\n    # Test 2: Simulate increase in disk usage\n    metrics1.last_disk_usage -= 1.0  # Simulate 1 GB increase\n    metrics2.last_disk_usage -= 1.0\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert metrics1.last_disk_usage == metrics2.last_disk_usage\n    assert metrics1.additional_disk_usage == metrics2.additional_disk_usage\n\n    # Test 3: Simulate decrease in disk usage\n    metrics1.last_disk_usage += 1.0  # Simulate 1 GB decrease\n    metrics2.last_disk_usage += 1.0\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert metrics1.last_disk_usage == metrics2.last_disk_usage\n    assert metrics1.additional_disk_usage == metrics2.additional_disk_usage\n\n    # Test 4: Zero disk usage\n    metrics1.last_disk_usage = 0.0\n    metrics2.last_disk_usage = 0.0\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert metrics1.last_disk_usage == metrics2.last_disk_usage\n    assert metrics1.additional_disk_usage == metrics2.additional_disk_usage\n\n    # Test 5: Large disk usage\n    metrics1.last_disk_usage = 1e6  # Simulate large disk usage\n    metrics2.last_disk_usage = 1e6\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert metrics1.last_disk_usage == metrics2.last_disk_usage\n    assert metrics1.additional_disk_usage == metrics2.additional_disk_usage\n\n    # Test 6: Negative disk usage\n    metrics1.last_disk_usage = -1.0\n    metrics2.last_disk_usage = -1.0\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert metrics1.last_disk_usage == metrics2.last_disk_usage\n    assert metrics1.additional_disk_usage == metrics2.additional_disk_usage\n\n    # Test 7: Floating point precision\n    metrics1.last_disk_usage += 0.000001  # Simulate small change\n    metrics2.last_disk_usage += 0.000001\n    metrics1.update_disk_usage()\n    metrics2.update_disk_usage_new_implementation()\n    assert abs(metrics1.last_disk_usage - metrics2.last_disk_usage) < 1e-9\n    assert abs(metrics1.additional_disk_usage - metrics2.additional_disk_usage) < 1e-9\n\nif __name__ == \"__main__\":\n    test_update_disk_usage()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they retrieve the current disk usage in gigabytes, calculate the additional disk usage since the last recorded value, and update the last recorded disk usage with the current value. There are no changes in logic or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `update_disk_usage` function modifies the instance variables `last_disk_usage` and `additional_disk_usage` of the `SystemMetrics` class, which are input arguments in the sense that they are part of the object's state. Therefore, this condition is satisfied.\n\n- CONDITION 2: The test cases check the state of the `last_disk_usage` and `additional_disk_usage` variables after calling the `update_disk_usage` method. They do not rely on printed or logged content. Thus, this condition is satisfied.\n\n- CONDITION 3: The test cases compare the state of the `SystemMetrics` instances after calling both `update_disk_usage` and `update_disk_usage_new_implementation`. If the new implementation has the same functionality, the states will match. Therefore, this condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the states of the variables, which is reasonable given that `update_disk_usage` does not return a value but modifies the object's state. Thus, this condition is satisfied.\n\n- CONDITION 5: The test cases cover various scenarios, including no change, increase, decrease, zero, large, negative, and small floating-point changes in disk usage. These scenarios are non-trivial and provide a comprehensive test suite. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "a10baa05ac593506faae7d7d529a180dd1352bb3"
    },
    {
        "func_name": "ModelMetrics.reset",
        "idx": "672",
        "repo_name": "zenpalio___vastai-pyworker",
        "func_path": "lib/data_types.py",
        "orig_func": "def reset(self):\n    self.workload_served = 0\n    self.workload_received = 0\n    self.workload_cancelled = 0\n    self.workload_errored = 0",
        "orig_context": "```python\n## lib/data_types.py\nfrom dataclasses import dataclass, field\n\nfrom typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type\n\nclass ModelMetrics:\n    \"\"\"Model specific metrics\"\"\"\n\n    # these are reset after being sent to autoscaler\n    workload_served: float\n    workload_received: float\n    workload_cancelled: float\n    workload_errored: float\n    workload_pending: float\n    # these are not\n    cur_perf: float\n    error_msg: Optional[str]\n    max_throughput: float\n    requests_recieved: Set[int] = field(default_factory=set)\n    requests_working: Set[int] = field(default_factory=set)\n\n    @classmethod\n    def empty(cls):\n        return cls(\n            workload_pending=0.0,\n            workload_served=0.0,\n            workload_cancelled=0.0,\n            workload_errored=0.0,\n            cur_perf=0.0,\n            workload_received=0.0,\n            error_msg=None,\n            max_throughput=0.0,\n        )\n\n    @property\n    def workload_processing(self) -> float:\n        return max(self.workload_received - self.workload_cancelled, 0.0)\n\n    def set_errored(self, error_msg):\n        self.reset()\n        self.error_msg = error_msg\n\n    def reset(self):\n        self.workload_served = 0\n        self.workload_received = 0\n        self.workload_cancelled = 0\n        self.workload_errored = 0\n\n```\n\n\n",
        "eval_script": "## lib/data_types.py\nfrom dataclasses import dataclass, field\n\nfrom typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type\n\nclass ModelMetrics:\n    \"\"\"Model specific metrics\"\"\"\n\n    def __init__(self):\n        # Initialize all attributes\n        self.workload_served = 0.0\n        self.workload_received = 0.0\n        self.workload_cancelled = 0.0\n        self.workload_errored = 0.0\n        self.workload_pending = 0.0\n        self.cur_perf = 0.0\n        self.error_msg = None\n        self.max_throughput = 0.0\n        self.requests_recieved = set()\n        self.requests_working = set()\n\n    @classmethod\n    def empty(cls):\n        return cls()\n\n    @property\n    def workload_processing(self) -> float:\n        return max(self.workload_received - self.workload_cancelled, 0.0)\n\n    def set_errored(self, error_msg):\n        self.reset()\n        self.error_msg = error_msg\n\n    def reset(self):\n        self.workload_served = 0\n        self.workload_received = 0\n        self.workload_cancelled = 0\n        self.workload_errored = 0\n\n\ndef test_reset():\n    metrics = ModelMetrics()\n    # Modify attributes\n    metrics.workload_served = 10\n    metrics.workload_received = 20\n    metrics.workload_cancelled = 5\n    metrics.workload_errored = 2\n    metrics.cur_perf = 1.5\n    metrics.error_msg = \"Error\"\n    metrics.max_throughput = 100.0\n    metrics.requests_recieved = {1, 2, 3}\n    metrics.requests_working = {4, 5}\n\n    # Use the original reset\n    metrics.reset()\n    original_state = (metrics.workload_served, metrics.workload_received, metrics.workload_cancelled, metrics.workload_errored)\n    original_other_attributes = (metrics.cur_perf, metrics.error_msg, metrics.max_throughput, metrics.requests_recieved, metrics.requests_working)\n\n    # Modify attributes again\n    metrics.workload_served = 10\n    metrics.workload_received = 20\n    metrics.workload_cancelled = 5\n    metrics.workload_errored = 2\n    metrics.cur_perf = 1.5\n    metrics.error_msg = \"Error\"\n    metrics.max_throughput = 100.0\n    metrics.requests_recieved = {1, 2, 3}\n    metrics.requests_working = {4, 5}\n\n    # Use the new reset implementation\n    metrics.reset_new_implementation()\n    new_state = (metrics.workload_served, metrics.workload_received, metrics.workload_cancelled, metrics.workload_errored)\n    new_other_attributes = (metrics.cur_perf, metrics.error_msg, metrics.max_throughput, metrics.requests_recieved, metrics.requests_working)\n\n    # Assertions\n    assert original_state == new_state, \"The new implementation does not match the original reset functionality.\"\n    assert metrics.workload_served == 0, \"workload_served was not reset to 0.\"\n    assert metrics.workload_received == 0, \"workload_received was not reset to 0.\"\n    assert original_other_attributes == new_other_attributes, \"Other attributes were unexpectedly modified.\"\n\nif __name__ == \"__main__\":\n    test_reset()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `reset` in the `ModelMetrics` class sets the attributes `workload_served`, `workload_received`, `workload_cancelled`, and `workload_errored` to 0, which is exactly what the ORIGINAL FUNCTION does. The test function `test_reset` compares the state of these attributes before and after calling the `reset` method and confirms that they are reset to 0. The test also checks that other attributes are not modified, ensuring that the reset functionality is isolated to the intended attributes. Since the functionality of resetting these specific attributes is preserved and identical to the original, the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `reset` function modifies the attributes of the `ModelMetrics` class, which are instance variables. Therefore, it satisfies this condition.\n- CONDITION 2: The test function checks the state of the instance variables after calling the `reset` function and does not rely on printed or logged content. This condition is satisfied.\n- CONDITION 3: The test function compares the state of the object after calling both the original `reset` and the `reset_new_implementation`. It ensures that both implementations result in the same state, thus satisfying this condition.\n- CONDITION 4: The test function uses assertions to compare the states of the object, which is reasonable given that `reset` modifies instance variables. It does not use inappropriate assertions like comparing return values when there are none. This condition is satisfied.\n- CONDITION 5: The test function modifies a variety of attributes before calling `reset`, ensuring that the test is non-trivial by checking multiple attributes. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "a10baa05ac593506faae7d7d529a180dd1352bb3"
    },
    {
        "func_name": "InputParameters.from_json_msg",
        "idx": "674",
        "repo_name": "zenpalio___vastai-pyworker",
        "func_path": "workers/vision/data_types.py",
        "orig_func": "@classmethod\ndef from_json_msg(cls, json_msg: Dict[str, Any]) -> 'InputParameters':\n    errors = {}\n    for param in inspect.signature(cls).parameters:\n        if param not in json_msg:\n            errors[param] = 'missing parameter'\n    if errors:\n        raise JsonDataException(errors)\n    return cls(**{k: v for k, v in json_msg.items() if k in inspect.signature(cls).parameters})",
        "orig_context": "```python\n## workers/vision/data_types.py\nimport dataclasses\n\nimport inspect\n\nfrom typing import Dict, Any\n\nfrom lib.data_types import ApiPayload, JsonDataException\n\nclass InputParameters:\n    max_new_tokens: int = 256\n\n    @classmethod\n    def from_json_msg(cls, json_msg: Dict[str, Any]) -> \"InputParameters\":\n        errors = {}\n        for param in inspect.signature(cls).parameters:\n            if param not in json_msg:\n                errors[param] = \"missing parameter\"\n        if errors:\n            raise JsonDataException(errors)\n        return cls(\n            **{\n                k: v\n                for k, v in json_msg.items()\n                if k in inspect.signature(cls).parameters\n            }\n        )\n\n```\n\n\n",
        "eval_script": "## workers/vision/data_types.py\nimport dataclasses\n\nimport inspect\n\nfrom typing import Dict, Any\n\n# Removed the import of ApiPayload as it is not used.\n# Define JsonDataException locally for testing purposes.\nclass JsonDataException(Exception):\n    def __init__(self, errors):\n        self.errors = errors\n    def __str__(self):\n        return str(self.errors)\n\nclass InputParameters:\n    max_new_tokens: int = 256\n\n    def __init__(self, max_new_tokens: int = 256):\n        self.max_new_tokens = max_new_tokens\n\n    @classmethod\n    def from_json_msg(cls, json_msg: Dict[str, Any]) -> \"InputParameters\":\n        errors = {}\n        for param in inspect.signature(cls).parameters:\n            if param not in json_msg:\n                errors[param] = \"missing parameter\"\n        if errors:\n            raise JsonDataException(errors)\n        return cls(\n            **{\n                k: v\n                for k, v in json_msg.items()\n                if k in inspect.signature(cls).parameters\n            }\n        )\n\n\ndef test_from_json_msg():\n    # Test case 1: All parameters provided\n    json_msg = {\"max_new_tokens\": 128}\n    original = InputParameters.from_json_msg(json_msg)\n    new_impl = InputParameters.from_json_msg_new_implementation(json_msg)\n    assert original.max_new_tokens == new_impl.max_new_tokens\n\n    # Test case 2: Missing parameters\n    json_msg = {}\n    try:\n        InputParameters.from_json_msg(json_msg)\n    except JsonDataException as e:\n        original_error = e\n    try:\n        InputParameters.from_json_msg_new_implementation(json_msg)\n    except JsonDataException as e:\n        new_impl_error = e\n    assert str(original_error) == str(new_impl_error)\n\n    # Test case 3: Additional parameters\n    json_msg = {\"max_new_tokens\": 128, \"extra_param\": 42}\n    original = InputParameters.from_json_msg(json_msg)\n    new_impl = InputParameters.from_json_msg_new_implementation(json_msg)\n    assert original.max_new_tokens == new_impl.max_new_tokens\n\nif __name__ == \"__main__\":\n    test_from_json_msg()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are class methods of `InputParameters` and take a dictionary `json_msg` as input. They both check for missing parameters by comparing the keys in `json_msg` with the parameters of the class constructor using `inspect.signature(cls).parameters`. If any parameters are missing, they raise a `JsonDataException` with the missing parameters. Finally, they both return an instance of the class using the parameters present in `json_msg`. The logic and implementation are the same in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `from_json_msg` returns an instance of `InputParameters`, which satisfies the condition of having return values.\n\n- CONDITION 2: The test cases use assertions to check the return values and exceptions raised, not printed or logged content, which satisfies this condition.\n\n- CONDITION 3: The test cases compare the results of `from_json_msg` and `from_json_msg_new_implementation` by checking the attributes of the returned objects and the exceptions raised. This ensures that the new implementation must have the same functionality as the original to pass the tests.\n\n- CONDITION 4: The test cases use assertions to compare the attributes of the returned objects and the exceptions raised, which is reasonable given that `from_json_msg` returns an object and can raise exceptions.\n\n- CONDITION 5: The test cases cover different scenarios: all parameters provided, missing parameters, and additional parameters. These are non-trivial cases that test the function's behavior under different conditions.",
            "answer": "yes"
        },
        "commit_id": "a10baa05ac593506faae7d7d529a180dd1352bb3"
    },
    {
        "func_name": "_generate_base_unit",
        "idx": "676",
        "repo_name": "unexcellent___quantio",
        "func_path": "generate/generate_boilerplate.py",
        "orig_func": "def _generate_base_unit(current_class: str, units: dict[str, str]) -> list[str]:\n    base_unit = None\n    for unit, factor in units.items():\n        if float(eval(factor)) == 1:\n            base_unit = unit\n            break\n    if base_unit is None:\n        raise ValueError(f'{current_class} needs a unit with factor equal to 1.')\n    return [f'    BASE_UNIT = \"{base_unit}\"', '']",
        "orig_context": "```python\n## generate/generate_boilerplate.py\ndef _generate_base_unit(current_class: str, units: dict[str, str]) -> list[str]:\n    base_unit = None\n    for unit, factor in units.items():\n        if float(eval(factor)) == 1:\n            base_unit = unit\n            break\n\n    if base_unit is None:\n        raise ValueError(f\"{current_class} needs a unit with factor equal to 1.\")\n\n    return [f'    BASE_UNIT = \"{base_unit}\"', \"\"]\n\n```\n\n\n",
        "eval_script": "## generate/generate_boilerplate.py\ndef _generate_base_unit(current_class: str, units: dict[str, str]) -> list[str]:\n    base_unit = None\n    for unit, factor in units.items():\n        if float(eval(factor)) == 1:\n            base_unit = unit\n            break\n\n    if base_unit is None:\n        raise ValueError(f\"{current_class} needs a unit with factor equal to 1.\")\n\n    return [f'    BASE_UNIT = \"{base_unit}\"', \"\"]\n\n\ndef test__generate_base_unit():\n    # Test case 1: Single base unit\n    units1 = {\"meter\": \"1\", \"kilometer\": \"1000\"}\n    assert _generate_base_unit(\"Length\", units1) == _generate_base_unit_new_implementation(\"Length\", units1)\n\n    # Test case 2: Multiple base units, should pick the first one\n    units2 = {\"second\": \"1\", \"minute\": \"60\", \"hour\": \"3600\"}\n    assert _generate_base_unit(\"Time\", units2) == _generate_base_unit_new_implementation(\"Time\", units2)\n\n    # Test case 3: No base unit, should raise ValueError\n    units3 = {\"gram\": \"0.001\", \"kilogram\": \"1000.0\"}  # Changed \"kilogram\" factor to ensure no base unit\n    try:\n        _generate_base_unit(\"Mass\", units3)\n    except ValueError as e:\n        assert str(e) == \"Mass needs a unit with factor equal to 1.\"\n\n    try:\n        _generate_base_unit_new_implementation(\"Mass\", units3)\n    except ValueError as e:\n        assert str(e) == \"Mass needs a unit with factor equal to 1.\"\n\nif __name__ == \"__main__\":\n    test__generate_base_unit()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       9      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                  9      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions iterate over the `units` dictionary, evaluate the `factor` for each unit, and check if it equals 1. If a unit with a factor of 1 is found, it is set as the `base_unit`. If no such unit is found, a `ValueError` is raised. The return statement and the error message are also identical. The additional test function provided in the revised code does not alter the functionality of the `_generate_base_unit` function itself. Therefore, the functionality and implementation of the REVISED FUNCTION are exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `_generate_base_unit` returns a list of strings, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases check the return values of `_generate_base_unit` and `_generate_base_unit_new_implementation`, and also handle exceptions. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `_generate_base_unit` and `_generate_base_unit_new_implementation` for the same inputs, ensuring that the new implementation must have the exact same functionality to pass the tests. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations and handle exceptions appropriately. The assertions are reasonable given the function's behavior, satisfying this condition.\n\n- CONDITION 5: The test cases cover different scenarios: a single base unit, multiple base units, and no base unit, which are non-trivial and provide a comprehensive check of the function's behavior. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "_generate_properties",
        "idx": "677",
        "repo_name": "unexcellent___quantio",
        "func_path": "generate/generate_boilerplate.py",
        "orig_func": "def _generate_properties(current_class: str, units: dict[str, str]) -> list[str]:\n    code = []\n    for unit, factor in units.items():\n        code.append(_indent('@property', 1))\n        code.append(_indent(f'def {unit}(self) -> float:', 1))\n        code.append(_indent(f'''\"\"\"The {current_class.lower()} in {unit.replace('_', ' ')}.\"\"\"''', 2))\n        code.append(_indent(f'return self._base_value / {factor}', 2))\n        code.append('')\n    return code",
        "orig_context": "```python\n## generate/generate_boilerplate.py\ndef _indent(text: str, number_of_indents: int) -> str:\n    return \" \" * 4 * number_of_indents + text\n\ndef _generate_properties(current_class: str, units: dict[str, str]) -> list[str]:\n    code = []\n\n    for unit, factor in units.items():\n        code.append(_indent(\"@property\", 1))\n        code.append(_indent(f\"def {unit}(self) -> float:\", 1))\n        code.append(_indent(f'\"\"\"The {current_class.lower()} in {unit.replace(\"_\", \" \")}.\"\"\"', 2))\n        code.append(_indent(f\"return self._base_value / {factor}\", 2))\n        code.append(\"\")\n\n    return code\n\n```\n\n\n",
        "eval_script": "## generate/generate_boilerplate.py\ndef _indent(text: str, number_of_indents: int) -> str:\n    return \" \" * 4 * number_of_indents + text\n\ndef _generate_properties(current_class: str, units: dict[str, str]) -> list[str]:\n    code = []\n\n    for unit, factor in units.items():\n        code.append(_indent(\"@property\", 1))\n        code.append(_indent(f\"def {unit}(self) -> float:\", 1))\n        code.append(_indent(f'\"\"\"The {current_class.lower()} in {unit.replace(\"_\", \" \")}.\"\"\"', 2))\n        code.append(_indent(f\"return self._base_value / {factor}\", 2))\n        code.append(\"\")\n\n    return code\n\n\ndef test__generate_properties():\n    # Test case 1: Single unit\n    units1 = {\"meters\": \"1.0\"}\n    assert _generate_properties(\"Length\", units1) == _generate_properties_new_implementation(\"Length\", units1)\n\n    # Test case 2: Multiple units\n    units2 = {\"meters\": \"1.0\", \"kilometers\": \"1000.0\"}\n    assert _generate_properties(\"Length\", units2) == _generate_properties_new_implementation(\"Length\", units2)\n\n    # Test case 3: Units with underscores\n    units3 = {\"square_meters\": \"1.0\", \"square_kilometers\": \"1000000.0\"}\n    assert _generate_properties(\"Area\", units3) == _generate_properties_new_implementation(\"Area\", units3)\n\n    # Test case 4: Empty units dictionary\n    units4 = {}\n    assert _generate_properties(\"Volume\", units4) == _generate_properties_new_implementation(\"Volume\", units4)\n\n    # Test case 5: Non-standard characters in unit names\n    units5 = {\"m^3\": \"1.0\", \"cm^3\": \"1000000.0\"}\n    assert _generate_properties(\"Volume\", units5) == _generate_properties_new_implementation(\"Volume\", units5)\n\n    # Test case 6: Zero conversion factor\n    units6 = {\"zero_factor\": \"0.0\"}\n    assert _generate_properties(\"Test\", units6) == _generate_properties_new_implementation(\"Test\", units6)\n\n    # Test case 7: Negative conversion factor\n    units7 = {\"negative_factor\": \"-1.0\"}\n    assert _generate_properties(\"Test\", units7) == _generate_properties_new_implementation(\"Test\", units7)\n\n    # Test case 8: Very large conversion factor\n    units8 = {\"large_factor\": \"1e30\"}\n    assert _generate_properties(\"Test\", units8) == _generate_properties_new_implementation(\"Test\", units8)\n\n    # Test case 9: Very small conversion factor\n    units9 = {\"small_factor\": \"1e-30\"}\n    assert _generate_properties(\"Test\", units9) == _generate_properties_new_implementation(\"Test\", units9)\n\n    # Test case 10: Mixed types in conversion factors\n    units10 = {\"integer_factor\": \"2\", \"float_factor\": \"2.5\"}\n    assert _generate_properties(\"Test\", units10) == _generate_properties_new_implementation(\"Test\", units10)\n\nif __name__ == \"__main__\":\n    test__generate_properties()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       9      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  9      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is essentially the same as the ORIGINAL FUNCTION. The only difference is the addition of the `_indent` function definition within the same code block, which was presumably defined elsewhere in the original context. The functionality of `_generate_properties` remains unchanged, as it still iterates over the `units` dictionary, constructs a list of strings representing property methods, and returns this list. The test cases provided further confirm that the functionality is consistent across various scenarios.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_generate_properties` function returns a list of strings, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `_generate_properties` and `_generate_properties_new_implementation`, not relying on printed or logged content.\n- CONDITION 3: The test cases compare the outputs of `_generate_properties` and `_generate_properties_new_implementation` directly, ensuring that only implementations with exactly the same functionality will pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable given that `_generate_properties` returns a list of strings.\n- CONDITION 5: The test cases cover a variety of scenarios, including single and multiple units, units with underscores, empty dictionaries, non-standard characters, zero and negative conversion factors, very large and small conversion factors, and mixed types in conversion factors. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "_generate_init",
        "idx": "678",
        "repo_name": "unexcellent___quantio",
        "func_path": "generate/generate_boilerplate.py",
        "orig_func": "def _generate_init(units: dict[str, str]) -> list[str]:\n    code = [_indent('def __init__(', 1), _indent('self,', 2), _indent('_base_value: float = 0.0,', 2)]\n    for unit in units:\n        code.append(_indent(f'{unit}: float | None = None,', 2))\n    code.append(_indent(') -> None:', 1))\n    code.append(_indent('self._base_value = _base_value', 2))\n    for unit, factor in units.items():\n        code.append(_indent(f'if {unit} is not None:', 2))\n        code.append(_indent(f'self._base_value += {unit} * {factor}', 3))\n    code.append('')\n    return code",
        "orig_context": "```python\n## generate/generate_boilerplate.py\ndef _indent(text: str, number_of_indents: int) -> str:\n    return \" \" * 4 * number_of_indents + text\n\ndef _generate_init(units: dict[str, str]) -> list[str]:\n    code = [\n        _indent(\"def __init__(\", 1),\n        _indent(\"self,\", 2),\n        _indent(\"_base_value: float = 0.0,\", 2),\n    ]\n\n    for unit in units:\n        code.append(_indent(f\"{unit}: float | None = None,\", 2))\n\n    code.append(_indent(\") -> None:\", 1))\n    code.append(_indent(\"self._base_value = _base_value\", 2))\n\n    for unit, factor in units.items():\n        code.append(_indent(f\"if {unit} is not None:\", 2))\n        code.append(_indent(f\"self._base_value += {unit} * {factor}\", 3))\n\n    code.append(\"\")\n    return code\n\n```\n\n\n",
        "eval_script": "## generate/generate_boilerplate.py\ndef _indent(text: str, number_of_indents: int) -> str:\n    return \" \" * 4 * number_of_indents + text\n\ndef _generate_init(units: dict[str, str]) -> list[str]:\n    code = [\n        _indent(\"def __init__(\", 1),\n        _indent(\"self,\", 2),\n        _indent(\"_base_value: float = 0.0,\", 2),\n    ]\n\n    for unit in units:\n        code.append(_indent(f\"{unit}: float | None = None,\", 2))\n\n    code.append(_indent(\") -> None:\", 1))\n    code.append(_indent(\"self._base_value = _base_value\", 2))\n\n    for unit, factor in units.items():\n        code.append(_indent(f\"if {unit} is not None:\", 2))\n        code.append(_indent(f\"self._base_value += {unit} * {factor}\", 3))\n\n    code.append(\"\")\n    return code\n\n\ndef test__generate_init():\n    # Test case 1: Empty dictionary\n    units1 = {}\n    assert _generate_init(units1) == _generate_init_new_implementation(units1), \"Test case 1 failed\"\n\n    # Test case 2: Single unit\n    units2 = {\"unit1\": \"1.0\"}\n    assert _generate_init(units2) == _generate_init_new_implementation(units2), \"Test case 2 failed\"\n\n    # Test case 3: Multiple units\n    units3 = {\"unit1\": \"1.0\", \"unit2\": \"2.0\"}\n    assert _generate_init(units3) == _generate_init_new_implementation(units3), \"Test case 3 failed\"\n\n    # Test case 4: Numeric factors\n    units4 = {\"unit1\": 1.0, \"unit2\": 2.0}\n    assert _generate_init(units4) == _generate_init_new_implementation(units4), \"Test case 4 failed\"\n\n    # Test case 5: Negative and zero factors\n    units5 = {\"unit1\": \"-1.0\", \"unit2\": \"0.0\"}\n    assert _generate_init(units5) == _generate_init_new_implementation(units5), \"Test case 5 failed\"\n\n    # Test case 6: Special characters in unit names\n    units6 = {\"unit@1\": \"1.0\", \"unit#2\": \"2.0\"}\n    assert _generate_init(units6) == _generate_init_new_implementation(units6), \"Test case 6 failed\"\n\n    # Test case 7: Very large numbers\n    units7 = {\"unit1\": \"1e308\", \"unit2\": \"2e308\"}\n    assert _generate_init(units7) == _generate_init_new_implementation(units7), \"Test case 7 failed\"\n\n    # Test case 8: Similar unit names\n    units8 = {\"unit1\": \"1.0\", \"unit1_\": \"2.0\"}\n    assert _generate_init(units8) == _generate_init_new_implementation(units8), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test__generate_init()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions generate a list of strings representing the initialization code for a class, taking into account the units provided in the dictionary. The structure, logic, and order of operations are the same in both versions. The only difference is the formatting of the code (e.g., single vs. double quotes), which does not affect functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `_generate_init` returns a list of strings, so it satisfies this condition.\n- CONDITION 2: The test cases use assertions to compare the return values of `_generate_init` and `_generate_init_new_implementation`, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_generate_init` and `_generate_init_new_implementation` for various inputs, ensuring that the new implementation must have the same functionality to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable since `_generate_init` returns a list of strings. This condition is satisfied.\n- CONDITION 5: The test cases cover a range of scenarios, including empty dictionaries, single and multiple units, numeric and string factors, negative and zero factors, special characters, very large numbers, and similar unit names. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Area.zero",
        "idx": "679",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Area:\n    \"\"\"Create a Area with a value of zero.\"\"\"\n    return Area()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Area(Quantity):\n    \"\"\"The two-dimensional extent of an object.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"square_meters\"\n\n    @property\n    def square_miles(self) -> float:\n        \"\"\"The area in square miles.\"\"\"\n        return self._base_value / 1609.34**2\n\n    @property\n    def square_kilometers(self) -> float:\n        \"\"\"The area in square kilometers.\"\"\"\n        return self._base_value / 10 ** (3 * 2)\n\n    @property\n    def square_meters(self) -> float:\n        \"\"\"The area in square meters.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def square_feet(self) -> float:\n        \"\"\"The area in square feet.\"\"\"\n        return self._base_value / 0.3048**2\n\n    @property\n    def square_inches(self) -> float:\n        \"\"\"The area in square inches.\"\"\"\n        return self._base_value / 0.0254**2\n\n    @property\n    def square_centimeters(self) -> float:\n        \"\"\"The area in square centimeters.\"\"\"\n        return self._base_value / 10 ** (-2 * 2)\n\n    @property\n    def square_millimeters(self) -> float:\n        \"\"\"The area in square millimeters.\"\"\"\n        return self._base_value / 10 ** (-3 * 2)\n\n    @property\n    def square_micrometers(self) -> float:\n        \"\"\"The area in square micrometers.\"\"\"\n        return self._base_value / 10 ** (-6 * 2)\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        square_miles: float | None = None,\n        square_kilometers: float | None = None,\n        square_meters: float | None = None,\n        square_feet: float | None = None,\n        square_inches: float | None = None,\n        square_centimeters: float | None = None,\n        square_millimeters: float | None = None,\n        square_micrometers: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if square_miles is not None:\n            self._base_value += square_miles * 1609.34**2\n        if square_kilometers is not None:\n            self._base_value += square_kilometers * 10 ** (3 * 2)\n        if square_meters is not None:\n            self._base_value += square_meters * 1\n        if square_feet is not None:\n            self._base_value += square_feet * 0.3048**2\n        if square_inches is not None:\n            self._base_value += square_inches * 0.0254**2\n        if square_centimeters is not None:\n            self._base_value += square_centimeters * 10 ** (-2 * 2)\n        if square_millimeters is not None:\n            self._base_value += square_millimeters * 10 ** (-3 * 2)\n        if square_micrometers is not None:\n            self._base_value += square_micrometers * 10 ** (-6 * 2)\n\n    @classmethod\n    def zero(cls) -> Area:\n        \"\"\"Create a Area with a value of zero.\"\"\"\n        return Area()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Area(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# Custom exception classes\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# Abstract Base Class for quantities\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Area(Quantity):\n    \"\"\"The two-dimensional extent of an object.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"square_meters\"\n\n    @property\n    def square_miles(self) -> float:\n        \"\"\"The area in square miles.\"\"\"\n        return self._base_value / 1609.34**2\n\n    @property\n    def square_kilometers(self) -> float:\n        \"\"\"The area in square kilometers.\"\"\"\n        return self._base_value / 10 ** (3 * 2)\n\n    @property\n    def square_meters(self) -> float:\n        \"\"\"The area in square meters.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def square_feet(self) -> float:\n        \"\"\"The area in square feet.\"\"\"\n        return self._base_value / 0.3048**2\n\n    @property\n    def square_inches(self) -> float:\n        \"\"\"The area in square inches.\"\"\"\n        return self._base_value / 0.0254**2\n\n    @property\n    def square_centimeters(self) -> float:\n        \"\"\"The area in square centimeters.\"\"\"\n        return self._base_value / 10 ** (-2 * 2)\n\n    @property\n    def square_millimeters(self) -> float:\n        \"\"\"The area in square millimeters.\"\"\"\n        return self._base_value / 10 ** (-3 * 2)\n\n    @property\n    def square_micrometers(self) -> float:\n        \"\"\"The area in square micrometers.\"\"\"\n        return self._base_value / 10 ** (-6 * 2)\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        square_miles: float | None = None,\n        square_kilometers: float | None = None,\n        square_meters: float | None = None,\n        square_feet: float | None = None,\n        square_inches: float | None = None,\n        square_centimeters: float | None = None,\n        square_millimeters: float | None = None,\n        square_micrometers: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if square_miles is not None:\n            self._base_value += square_miles * 1609.34**2\n        if square_kilometers is not None:\n            self._base_value += square_kilometers * 10 ** (3 * 2)\n        if square_meters is not None:\n            self._base_value += square_meters * 1\n        if square_feet is not None:\n            self._base_value += square_feet * 0.3048**2\n        if square_inches is not None:\n            self._base_value += square_inches * 0.0254**2\n        if square_centimeters is not None:\n            self._base_value += square_centimeters * 10 ** (-2 * 2)\n        if square_millimeters is not None:\n            self._base_value += square_millimeters * 10 ** (-3 * 2)\n        if square_micrometers is not None:\n            self._base_value += square_micrometers * 10 ** (-6 * 2)\n\n    @classmethod\n    def zero(cls) -> 'Area':\n        \"\"\"Create a Area with a value of zero.\"\"\"\n        return Area()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Area(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero and zero_new_implementation have the same functionality.\"\"\"\n    area_zero = Area.zero()\n    area_zero_new = Area.zero_new_implementation()\n\n    # Assert that both methods return an Area object\n    assert isinstance(area_zero, Area), \"zero() did not return an Area instance\"\n    assert isinstance(area_zero_new, Area), \"zero_new_implementation() did not return an Area instance\"\n\n    # Assert that both methods return an Area with a base value of zero\n    assert area_zero._base_value == 0.0, \"zero() did not return an Area with base value of zero\"\n    assert area_zero_new._base_value == 0.0, \"zero_new_implementation() did not return an Area with base value of zero\"\n\n    # Assert that both methods return equivalent Area objects\n    assert area_zero == area_zero_new, \"zero() and zero_new_implementation() did not return equivalent Area objects\"\n\n    # Assert that all unit properties return zero for both methods\n    assert area_zero.square_miles == 0.0, \"zero() did not return an Area with square miles of zero\"\n    assert area_zero_new.square_miles == 0.0, \"zero_new_implementation() did not return an Area with square miles of zero\"\n\n    assert area_zero.square_kilometers == 0.0, \"zero() did not return an Area with square kilometers of zero\"\n    assert area_zero_new.square_kilometers == 0.0, \"zero_new_implementation() did not return an Area with square kilometers of zero\"\n\n    assert area_zero.square_meters == 0.0, \"zero() did not return an Area with square meters of zero\"\n    assert area_zero_new.square_meters == 0.0, \"zero_new_implementation() did not return an Area with square meters of zero\"\n\n    assert area_zero.square_feet == 0.0, \"zero() did not return an Area with square feet of zero\"\n    assert area_zero_new.square_feet == 0.0, \"zero_new_implementation() did not return an Area with square feet of zero\"\n\n    assert area_zero.square_inches == 0.0, \"zero() did not return an Area with square inches of zero\"\n    assert area_zero_new.square_inches == 0.0, \"zero_new_implementation() did not return an Area with square inches of zero\"\n\n    assert area_zero.square_centimeters == 0.0, \"zero() did not return an Area with square centimeters of zero\"\n    assert area_zero_new.square_centimeters == 0.0, \"zero_new_implementation() did not return an Area with square centimeters of zero\"\n\n    assert area_zero.square_millimeters == 0.0, \"zero() did not return an Area with square millimeters of zero\"\n    assert area_zero_new.square_millimeters == 0.0, \"zero_new_implementation() did not return an Area with square millimeters of zero\"\n\n    assert area_zero.square_micrometers == 0.0, \"zero() did not return an Area with square micrometers of zero\"\n    assert area_zero_new.square_micrometers == 0.0, \"zero_new_implementation() did not return an Area with square micrometers of zero\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns an instance of the `Area` class with a default value of zero. The revised function `zero` in the provided code also returns an instance of the `Area` class with a default value of zero. Both functions are class methods and perform the same task of creating an `Area` object with a base value of zero. The functionality of both functions is identical, as they both initialize an `Area` object with zero value and return it.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `zero` function returns an `Area` object, which satisfies the condition of having return values.\n- [CONDITION 2] The test cases only check the return values and properties of the `Area` object, not printed or logged contents.\n- [CONDITION 3] The test cases check for the type of the returned object, the base value, and all unit properties, ensuring that `zero_new_implementation` must have the exact same functionality as `zero` to pass.\n- [CONDITION 4] The test cases use appropriate assertions to check the return values and properties of the `Area` object, which is reasonable given the functionality of `zero`.\n- [CONDITION 5] The test cases are non-trivial as they check multiple aspects of the `Area` object, including its type, base value, and all unit properties.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "ElectricCurrent.zero",
        "idx": "680",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> ElectricCurrent:\n    \"\"\"Create a ElectricCurrent with a value of zero.\"\"\"\n    return ElectricCurrent()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass ElectricCurrent(Quantity):\n    \"\"\"The flow of charged particles moving through an electrical conductor or space.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"amperes\"\n\n    @property\n    def gigaamperes(self) -> float:\n        \"\"\"The electriccurrent in gigaamperes.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megaamperes(self) -> float:\n        \"\"\"The electriccurrent in megaamperes.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kiloamperes(self) -> float:\n        \"\"\"The electriccurrent in kiloamperes.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def amperes(self) -> float:\n        \"\"\"The electriccurrent in amperes.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def milliamperes(self) -> float:\n        \"\"\"The electriccurrent in milliamperes.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigaamperes: float | None = None,\n        megaamperes: float | None = None,\n        kiloamperes: float | None = None,\n        amperes: float | None = None,\n        milliamperes: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigaamperes is not None:\n            self._base_value += gigaamperes * 10**9\n        if megaamperes is not None:\n            self._base_value += megaamperes * 10**6\n        if kiloamperes is not None:\n            self._base_value += kiloamperes * 10**3\n        if amperes is not None:\n            self._base_value += amperes * 1\n        if milliamperes is not None:\n            self._base_value += milliamperes * 10**-3\n\n    @classmethod\n    def zero(cls) -> ElectricCurrent:\n        \"\"\"Create a ElectricCurrent with a value of zero.\"\"\"\n        return ElectricCurrent()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"ElectricCurrent(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "from abc import ABC\n\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass ElectricCurrent(Quantity):\n    \"\"\"The flow of charged particles moving through an electrical conductor or space.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"amperes\"\n\n    @property\n    def gigaamperes(self) -> float:\n        \"\"\"The electriccurrent in gigaamperes.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megaamperes(self) -> float:\n        \"\"\"The electriccurrent in megaamperes.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kiloamperes(self) -> float:\n        \"\"\"The electriccurrent in kiloamperes.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def amperes(self) -> float:\n        \"\"\"The electriccurrent in amperes.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def milliamperes(self) -> float:\n        \"\"\"The electriccurrent in milliamperes.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigaamperes: float | None = None,\n        megaamperes: float | None = None,\n        kiloamperes: float | None = None,\n        amperes: float | None = None,\n        milliamperes: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigaamperes is not None:\n            self._base_value += gigaamperes * 10**9\n        if megaamperes is not None:\n            self._base_value += megaamperes * 10**6\n        if kiloamperes is not None:\n            self._base_value += kiloamperes * 10**3\n        if amperes is not None:\n            self._base_value += amperes * 1\n        if milliamperes is not None:\n            self._base_value += milliamperes * 10**-3\n\n    @classmethod\n    def zero(cls) -> 'ElectricCurrent':\n        \"\"\"Create a ElectricCurrent with a value of zero.\"\"\"\n        return ElectricCurrent()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"ElectricCurrent(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    # Test the original zero implementation\n    original_zero = ElectricCurrent.zero()\n    assert isinstance(original_zero, ElectricCurrent), \"Original zero is not an instance of ElectricCurrent\"\n    assert original_zero._base_value == 0.0, \"Original zero does not have a base value of 0.0\"\n    assert original_zero.gigaamperes == 0.0, \"Original zero gigaamperes is not 0.0\"\n    assert original_zero.megaamperes == 0.0, \"Original zero megaamperes is not 0.0\"\n    assert original_zero.kiloamperes == 0.0, \"Original zero kiloamperes is not 0.0\"\n    assert original_zero.amperes == 0.0, \"Original zero amperes is not 0.0\"\n    assert original_zero.milliamperes == 0.0, \"Original zero milliamperes is not 0.0\"\n    assert str(original_zero) == \"ElectricCurrent(amperes=0.0)\", \"Original zero string representation is incorrect\"\n\n    # Test the new zero implementation\n    new_zero = ElectricCurrent.zero_new_implementation()\n    assert isinstance(new_zero, ElectricCurrent), \"New zero is not an instance of ElectricCurrent\"\n    assert new_zero._base_value == 0.0, \"New zero does not have a base value of 0.0\"\n    assert new_zero.gigaamperes == 0.0, \"New zero gigaamperes is not 0.0\"\n    assert new_zero.megaamperes == 0.0, \"New zero megaamperes is not 0.0\"\n    assert new_zero.kiloamperes == 0.0, \"New zero kiloamperes is not 0.0\"\n    assert new_zero.amperes == 0.0, \"New zero amperes is not 0.0\"\n    assert new_zero.milliamperes == 0.0, \"New zero milliamperes is not 0.0\"\n    assert str(new_zero) == \"ElectricCurrent(amperes=0.0)\", \"New zero string representation is incorrect\"\n\n    # Ensure both implementations are equal\n    assert original_zero == new_zero, \"Original zero and new zero are not equal\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns an instance of `ElectricCurrent` with a default value of zero. The revised function `zero` in the provided code is identical in functionality; it is also a class method that returns an instance of `ElectricCurrent` initialized with a value of zero. The test function `test_zero` verifies that both the original and revised implementations create an `ElectricCurrent` object with a base value of zero and that all unit conversions (gigaamperes, megaamperes, kiloamperes, amperes, milliamperes) return zero, confirming the functionality is preserved. The test also checks that the string representation is correct and that both implementations are equal.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `zero` function is a class method that returns an instance of `ElectricCurrent` with a base value of zero. It satisfies this condition because it returns a value.\n\n2. **CONDITION 2**: The test function `test_zero` checks the return values and the state of the object returned by `zero`. It uses assertions to verify the properties of the `ElectricCurrent` instance, such as `_base_value`, `gigaamperes`, `megaamperes`, `kiloamperes`, `amperes`, and `milliamperes`. It does not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the original and new implementations of `zero` by checking if they return instances of `ElectricCurrent` with the same properties and values. This ensures that `zero_new_implementation` must have the exact same functionality as `zero` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to check the properties of the `ElectricCurrent` instances returned by both implementations. These assertions are reasonable given that `zero` returns an instance with a base value of zero. The test does not use inappropriate assertions like comparing the return values directly if they were not supposed to be compared, satisfying this condition.\n\n5. **CONDITION 5**: The test cases are non-trivial as they check multiple properties of the `ElectricCurrent` instance, ensuring that all aspects of the zero value are correctly implemented. This includes checking the base value and various unit conversions, making the tests comprehensive and non-trivial.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Angle.zero",
        "idx": "681",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Angle:\n    \"\"\"Create a Angle with a value of zero.\"\"\"\n    return Angle()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Angle(Quantity):\n    \"\"\"The figure formed by two rays.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"radians\"\n\n    @property\n    def degrees(self) -> float:\n        \"\"\"The angle in degrees.\"\"\"\n        return self._base_value / (3.141592653589793 / 180)\n\n    @property\n    def radians(self) -> float:\n        \"\"\"The angle in radians.\"\"\"\n        return self._base_value / 1\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        degrees: float | None = None,\n        radians: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if degrees is not None:\n            self._base_value += degrees * (3.141592653589793 / 180)\n        if radians is not None:\n            self._base_value += radians * 1\n\n    @classmethod\n    def zero(cls) -> Angle:\n        \"\"\"Create a Angle with a value of zero.\"\"\"\n        return Angle()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Angle(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# quantio/quantities.py\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Angle(Quantity):\n    \"\"\"The figure formed by two rays.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"radians\"\n\n    @property\n    def degrees(self) -> float:\n        \"\"\"The angle in degrees.\"\"\"\n        return self._base_value / (3.141592653589793 / 180)\n\n    @property\n    def radians(self) -> float:\n        \"\"\"The angle in radians.\"\"\"\n        return self._base_value / 1\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        degrees: float | None = None,\n        radians: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if degrees is not None:\n            self._base_value += degrees * (3.141592653589793 / 180)\n        if radians is not None:\n            self._base_value += radians * 1\n\n    @classmethod\n    def zero(cls) -> 'Angle':\n        \"\"\"Create a Angle with a value of zero.\"\"\"\n        return Angle()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Angle(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    angle_zero = Angle.zero()\n    angle_zero_new = Angle.zero_new_implementation()\n\n    # Assert that both implementations create an angle with a base value of zero\n    assert angle_zero._base_value == 0.0, \"Angle.zero does not create a zero angle\"\n    assert angle_zero_new._base_value == 0.0, \"Angle.zero_new_implementation does not create a zero angle\"\n\n    # Assert that both implementations create equivalent Angle objects\n    assert angle_zero == angle_zero_new, \"Angle.zero and Angle.zero_new_implementation do not create equivalent angles\"\n\n    # Assert that the degrees and radians properties are zero\n    assert angle_zero.degrees == 0.0, \"Angle.zero does not have zero degrees\"\n    assert angle_zero.radians == 0.0, \"Angle.zero does not have zero radians\"\n    assert angle_zero_new.degrees == 0.0, \"Angle.zero_new_implementation does not have zero degrees\"\n    assert angle_zero_new.radians == 0.0, \"Angle.zero_new_implementation does not have zero radians\"\n\n    # Assert that the string representation is consistent with zero angle\n    assert str(angle_zero) == \"Angle(radians=0.0)\", \"Angle.zero string representation is incorrect\"\n    assert str(angle_zero_new) == \"Angle(radians=0.0)\", \"Angle.zero_new_implementation string representation is incorrect\"\n\n    # Assert that adding zero angle to another angle does not change the other angle\n    another_angle = Angle(radians=1.0)\n    assert (another_angle + angle_zero).radians == 1.0, \"Adding Angle.zero changes the angle\"\n    assert (another_angle + angle_zero_new).radians == 1.0, \"Adding Angle.zero_new_implementation changes the angle\"\n\n    # Assert that subtracting zero angle from another angle does not change the other angle\n    assert (another_angle - angle_zero).radians == 1.0, \"Subtracting Angle.zero changes the angle\"\n    assert (another_angle - angle_zero_new).radians == 1.0, \"Subtracting Angle.zero_new_implementation changes the angle\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `zero` in the `Angle` class is implemented as a class method that returns an instance of `Angle` with a default value of zero. This matches the behavior of the ORIGINAL FUNCTION, which also returns an `Angle` instance with a zero value. The test function `test_zero` verifies that the `zero` method creates an `Angle` with a base value of zero and that the properties `degrees` and `radians` are zero, confirming that the functionality is the same. There is no indication of a `zero_new_implementation` method in the provided code, suggesting that the test function might be outdated or incorrect in that aspect. However, the functionality of the `zero` method itself remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `zero` function is a class method that returns an `Angle` object with a base value of zero. It does not modify global variables or input arguments, but it does have a return value, which is an `Angle` object. Therefore, this condition is satisfied.\n\n2. **CONDITION 2**: The test cases in `test_zero` check the return values and states of the `Angle` objects created by `zero` and `zero_new_implementation`. They do not rely on printed or logged contents. This condition is satisfied.\n\n3. **CONDITION 3**: The test cases check various properties and behaviors of the `Angle` objects created by both implementations, ensuring that they have the same functionality. If `zero_new_implementation` passes all these tests, it must have the same functionality as `zero`. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to check the properties and behaviors of the `Angle` objects, which is reasonable given that `zero` returns an object. The assertions are appropriate for testing the functionality of the `zero` method. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the `Angle` objects, including their base value, equality, properties (degrees and radians), string representation, and behavior when added to or subtracted from another angle. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Length.zero",
        "idx": "682",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Length:\n    \"\"\"Create a Length with a value of zero.\"\"\"\n    return Length()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Length(Quantity):\n    \"\"\"The one-dimensional extent of an object or the distance between two points.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"meters\"\n\n    @property\n    def miles(self) -> float:\n        \"\"\"The length in miles.\"\"\"\n        return self._base_value / 1609.34\n\n    @property\n    def kilometers(self) -> float:\n        \"\"\"The length in kilometers.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def meters(self) -> float:\n        \"\"\"The length in meters.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def feet(self) -> float:\n        \"\"\"The length in feet.\"\"\"\n        return self._base_value / 0.3048\n\n    @property\n    def inches(self) -> float:\n        \"\"\"The length in inches.\"\"\"\n        return self._base_value / 0.0254\n\n    @property\n    def centimeters(self) -> float:\n        \"\"\"The length in centimeters.\"\"\"\n        return self._base_value / 10**-2\n\n    @property\n    def millimeters(self) -> float:\n        \"\"\"The length in millimeters.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def micrometers(self) -> float:\n        \"\"\"The length in micrometers.\"\"\"\n        return self._base_value / 10**-6\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        miles: float | None = None,\n        kilometers: float | None = None,\n        meters: float | None = None,\n        feet: float | None = None,\n        inches: float | None = None,\n        centimeters: float | None = None,\n        millimeters: float | None = None,\n        micrometers: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if miles is not None:\n            self._base_value += miles * 1609.34\n        if kilometers is not None:\n            self._base_value += kilometers * 10**3\n        if meters is not None:\n            self._base_value += meters * 1\n        if feet is not None:\n            self._base_value += feet * 0.3048\n        if inches is not None:\n            self._base_value += inches * 0.0254\n        if centimeters is not None:\n            self._base_value += centimeters * 10**-2\n        if millimeters is not None:\n            self._base_value += millimeters * 10**-3\n        if micrometers is not None:\n            self._base_value += micrometers * 10**-6\n\n    @classmethod\n    def zero(cls) -> Length:\n        \"\"\"Create a Length with a value of zero.\"\"\"\n        return Length()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Length(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "from abc import ABC\n\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Length(Quantity):\n    \"\"\"The one-dimensional extent of an object or the distance between two points.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"meters\"\n\n    @property\n    def miles(self) -> float:\n        \"\"\"The length in miles.\"\"\"\n        return self._base_value / 1609.34\n\n    @property\n    def kilometers(self) -> float:\n        \"\"\"The length in kilometers.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def meters(self) -> float:\n        \"\"\"The length in meters.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def feet(self) -> float:\n        \"\"\"The length in feet.\"\"\"\n        return self._base_value / 0.3048\n\n    @property\n    def inches(self) -> float:\n        \"\"\"The length in inches.\"\"\"\n        return self._base_value / 0.0254\n\n    @property\n    def centimeters(self) -> float:\n        \"\"\"The length in centimeters.\"\"\"\n        return self._base_value / 10**-2\n\n    @property\n    def millimeters(self) -> float:\n        \"\"\"The length in millimeters.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def micrometers(self) -> float:\n        \"\"\"The length in micrometers.\"\"\"\n        return self._base_value / 10**-6\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        miles: float | None = None,\n        kilometers: float | None = None,\n        meters: float | None = None,\n        feet: float | None = None,\n        inches: float | None = None,\n        centimeters: float | None = None,\n        millimeters: float | None = None,\n        micrometers: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if miles is not None:\n            self._base_value += miles * 1609.34\n        if kilometers is not None:\n            self._base_value += kilometers * 10**3\n        if meters is not None:\n            self._base_value += meters * 1\n        if feet is not None:\n            self._base_value += feet * 0.3048\n        if inches is not None:\n            self._base_value += inches * 0.0254\n        if centimeters is not None:\n            self._base_value += centimeters * 10**-2\n        if millimeters is not None:\n            self._base_value += millimeters * 10**-3\n        if micrometers is not None:\n            self._base_value += micrometers * 10**-6\n\n    @classmethod\n    def zero(cls) -> 'Length':\n        \"\"\"Create a Length with a value of zero.\"\"\"\n        return Length()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Length(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero_new_implementation matches zero.\"\"\"\n    zero_old = Length.zero()\n    zero_new = Length.zero_new_implementation()\n\n    # Assert that both return a Length object\n    assert isinstance(zero_old, Length), \"zero() did not return a Length instance\"\n    assert isinstance(zero_new, Length), \"zero_new_implementation() did not return a Length instance\"\n\n    # Assert that both have a base value of zero\n    assert zero_old._base_value == 0.0, \"zero() did not return a Length with base value 0\"\n    assert zero_new._base_value == 0.0, \"zero_new_implementation() did not return a Length with base value 0\"\n\n    # Assert that both are equal\n    assert zero_old == zero_new, \"zero() and zero_new_implementation() did not return equal Length instances\"\n\n    # Assert that all unit properties are zero\n    assert zero_old.miles == 0.0, \"zero() did not return a Length with miles value 0\"\n    assert zero_new.miles == 0.0, \"zero_new_implementation() did not return a Length with miles value 0\"\n\n    assert zero_old.kilometers == 0.0, \"zero() did not return a Length with kilometers value 0\"\n    assert zero_new.kilometers == 0.0, \"zero_new_implementation() did not return a Length with kilometers value 0\"\n\n    assert zero_old.meters == 0.0, \"zero() did not return a Length with meters value 0\"\n    assert zero_new.meters == 0.0, \"zero_new_implementation() did not return a Length with meters value 0\"\n\n    assert zero_old.feet == 0.0, \"zero() did not return a Length with feet value 0\"\n    assert zero_new.feet == 0.0, \"zero_new_implementation() did not return a Length with feet value 0\"\n\n    assert zero_old.inches == 0.0, \"zero() did not return a Length with inches value 0\"\n    assert zero_new.inches == 0.0, \"zero_new_implementation() did not return a Length with inches value 0\"\n\n    assert zero_old.centimeters == 0.0, \"zero() did not return a Length with centimeters value 0\"\n    assert zero_new.centimeters == 0.0, \"zero_new_implementation() did not return a Length with centimeters value 0\"\n\n    assert zero_old.millimeters == 0.0, \"zero() did not return a Length with millimeters value 0\"\n    assert zero_new.millimeters == 0.0, \"zero_new_implementation() did not return a Length with millimeters value 0\"\n\n    assert zero_old.micrometers == 0.0, \"zero() did not return a Length with micrometers value 0\"\n    assert zero_new.micrometers == 0.0, \"zero_new_implementation() did not return a Length with micrometers value 0\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns a new instance of the `Length` class with a default base value of zero. The revised function `zero` in the provided code also returns a new instance of the `Length` class with a base value of zero. The functionality of both functions is the same because they both create and return a `Length` object initialized to zero. The test function `test_zero` further confirms that the behavior of the revised `zero` function matches the expected behavior of the original function, as it checks that the returned `Length` object has a base value of zero and all unit properties are zero. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `zero` function returns a `Length` object, which is a return value. This satisfies the condition.\n- CONDITION 2: The test cases in `test_zero` check the return values and the state of the `Length` object (e.g., `_base_value`, unit properties). They do not check printed or logged contents. This satisfies the condition.\n- CONDITION 3: The test cases ensure that `zero_new_implementation` returns a `Length` object with the same properties as `zero`. This means `zero_new_implementation` must have the same functionality as `zero` to pass all tests. This satisfies the condition.\n- CONDITION 4: The test cases and assert statements are reasonable. They check the type of the returned object, its base value, and all unit properties. The tests do not use inappropriate assertions like comparing the functions directly. This satisfies the condition.\n- CONDITION 5: The test cases are non-trivial as they check multiple aspects of the `Length` object returned by `zero` and `zero_new_implementation`, including type, base value, and unit properties. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Frequency.zero",
        "idx": "683",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Frequency:\n    \"\"\"Create a Frequency with a value of zero.\"\"\"\n    return Frequency()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Frequency(Quantity):\n    \"\"\"The number of occurrences of a repeating event per unit of time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"hertz\"\n\n    @property\n    def gigahertz(self) -> float:\n        \"\"\"The frequency in gigahertz.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megahertz(self) -> float:\n        \"\"\"The frequency in megahertz.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kilohertz(self) -> float:\n        \"\"\"The frequency in kilohertz.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def hertz(self) -> float:\n        \"\"\"The frequency in hertz.\"\"\"\n        return self._base_value / 1\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigahertz: float | None = None,\n        megahertz: float | None = None,\n        kilohertz: float | None = None,\n        hertz: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigahertz is not None:\n            self._base_value += gigahertz * 10**9\n        if megahertz is not None:\n            self._base_value += megahertz * 10**6\n        if kilohertz is not None:\n            self._base_value += kilohertz * 10**3\n        if hertz is not None:\n            self._base_value += hertz * 1\n\n    @classmethod\n    def zero(cls) -> Frequency:\n        \"\"\"Create a Frequency with a value of zero.\"\"\"\n        return Frequency()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Frequency(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "from abc import ABC\n\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Frequency(Quantity):\n    \"\"\"The number of occurrences of a repeating event per unit of time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"hertz\"\n\n    @property\n    def gigahertz(self) -> float:\n        \"\"\"The frequency in gigahertz.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megahertz(self) -> float:\n        \"\"\"The frequency in megahertz.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kilohertz(self) -> float:\n        \"\"\"The frequency in kilohertz.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def hertz(self) -> float:\n        \"\"\"The frequency in hertz.\"\"\"\n        return self._base_value / 1\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigahertz: float | None = None,\n        megahertz: float | None = None,\n        kilohertz: float | None = None,\n        hertz: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigahertz is not None:\n            self._base_value += gigahertz * 10**9\n        if megahertz is not None:\n            self._base_value += megahertz * 10**6\n        if kilohertz is not None:\n            self._base_value += kilohertz * 10**3\n        if hertz is not None:\n            self._base_value += hertz * 1\n\n    @classmethod\n    def zero(cls) -> 'Frequency':\n        \"\"\"Create a Frequency with a value of zero.\"\"\"\n        return Frequency()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Frequency(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    freq_zero = Frequency.zero()\n    freq_zero_new = Frequency.zero_new_implementation()\n\n    # Check base value\n    assert freq_zero._base_value == 0.0, \"Frequency.zero() does not return zero base value\"\n    assert freq_zero_new._base_value == 0.0, \"Frequency.zero_new_implementation() does not return zero base value\"\n\n    # Check equality\n    assert freq_zero == freq_zero_new, \"Frequency.zero() and Frequency.zero_new_implementation() do not return equal objects\"\n\n    # Check type\n    assert isinstance(freq_zero, Frequency), \"Frequency.zero() does not return a Frequency instance\"\n    assert isinstance(freq_zero_new, Frequency), \"Frequency.zero_new_implementation() does not return a Frequency instance\"\n\n    # Check string representation\n    assert str(freq_zero) == \"Frequency(hertz=0.0)\", \"Frequency.zero() does not return correct string representation\"\n    assert str(freq_zero_new) == \"Frequency(hertz=0.0)\", \"Frequency.zero_new_implementation() does not return correct string representation\"\n\n    # Check property values\n    assert freq_zero.gigahertz == 0.0, \"Frequency.zero() gigahertz property is not zero\"\n    assert freq_zero.megahertz == 0.0, \"Frequency.zero() megahertz property is not zero\"\n    assert freq_zero.kilohertz == 0.0, \"Frequency.zero() kilohertz property is not zero\"\n    assert freq_zero.hertz == 0.0, \"Frequency.zero() hertz property is not zero\"\n\n    assert freq_zero_new.gigahertz == 0.0, \"Frequency.zero_new_implementation() gigahertz property is not zero\"\n    assert freq_zero_new.megahertz == 0.0, \"Frequency.zero_new_implementation() megahertz property is not zero\"\n    assert freq_zero_new.kilohertz == 0.0, \"Frequency.zero_new_implementation() kilohertz property is not zero\"\n    assert freq_zero_new.hertz == 0.0, \"Frequency.zero_new_implementation() hertz property is not zero\"\n\n    # Check immutability\n    freq_zero._base_value = 10.0\n    assert freq_zero_new._base_value == 0.0, \"Modifying Frequency.zero() affects Frequency.zero_new_implementation()\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method of the `Frequency` class that returns a new instance of `Frequency` with a base value of zero. The revised function in the provided code is also a class method of the `Frequency` class and performs the same operation: it returns a new `Frequency` instance with a base value of zero. The implementation details are identical, as both functions call the `Frequency` constructor without any arguments, which defaults the `_base_value` to zero. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `zero` function returns a `Frequency` object with a base value of zero. This satisfies the condition as it returns a value.\n\n2. **CONDITION 2**: The test cases check the return values and the state of the `Frequency` objects created by `zero` and `zero_new_implementation`. They do not rely on printed or logged content.\n\n3. **CONDITION 3**: The test cases compare the base value, equality, type, string representation, and property values of the objects returned by `zero` and `zero_new_implementation`. These checks ensure that `zero_new_implementation` must have the exact same functionality as `zero` to pass all tests.\n\n4. **CONDITION 4**: The test cases use appropriate assertions to compare the return values and states of the objects. They do not make unreasonable assumptions or use inappropriate assertions.\n\n5. **CONDITION 5**: The test cases are non-trivial as they cover multiple aspects of the `Frequency` object, including its base value, equality, type, string representation, and property values. They also check immutability by modifying the base value of one object and ensuring it does not affect the other.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Energy.zero",
        "idx": "684",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Energy:\n    \"\"\"Create a Energy with a value of zero.\"\"\"\n    return Energy()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Energy(Quantity):\n    \"\"\"Energy describes the ability of an object to perform work.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"joules\"\n\n    @property\n    def gigajoules(self) -> float:\n        \"\"\"The energy in gigajoules.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megajoules(self) -> float:\n        \"\"\"The energy in megajoules.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kilojoules(self) -> float:\n        \"\"\"The energy in kilojoules.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def joules(self) -> float:\n        \"\"\"The energy in joules.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def millijoules(self) -> float:\n        \"\"\"The energy in millijoules.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigajoules: float | None = None,\n        megajoules: float | None = None,\n        kilojoules: float | None = None,\n        joules: float | None = None,\n        millijoules: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigajoules is not None:\n            self._base_value += gigajoules * 10**9\n        if megajoules is not None:\n            self._base_value += megajoules * 10**6\n        if kilojoules is not None:\n            self._base_value += kilojoules * 10**3\n        if joules is not None:\n            self._base_value += joules * 1\n        if millijoules is not None:\n            self._base_value += millijoules * 10**-3\n\n    @classmethod\n    def zero(cls) -> Energy:\n        \"\"\"Create a Energy with a value of zero.\"\"\"\n        return Energy()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Energy(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# quantio/quantities.py\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Energy(Quantity):\n    \"\"\"Energy describes the ability of an object to perform work.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"joules\"\n\n    @property\n    def gigajoules(self) -> float:\n        \"\"\"The energy in gigajoules.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megajoules(self) -> float:\n        \"\"\"The energy in megajoules.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kilojoules(self) -> float:\n        \"\"\"The energy in kilojoules.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def joules(self) -> float:\n        \"\"\"The energy in joules.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def millijoules(self) -> float:\n        \"\"\"The energy in millijoules.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigajoules: float | None = None,\n        megajoules: float | None = None,\n        kilojoules: float | None = None,\n        joules: float | None = None,\n        millijoules: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigajoules is not None:\n            self._base_value += gigajoules * 10**9\n        if megajoules is not None:\n            self._base_value += megajoules * 10**6\n        if kilojoules is not None:\n            self._base_value += kilojoules * 10**3\n        if joules is not None:\n            self._base_value += joules * 1\n        if millijoules is not None:\n            self._base_value += millijoules * 10**-3\n\n    @classmethod\n    def zero(cls) -> 'Energy':\n        \"\"\"Create a Energy with a value of zero.\"\"\"\n        return Energy()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Energy(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure Energy.zero_new_implementation matches Energy.zero functionality.\"\"\"\n    original_zero = Energy.zero()\n    new_zero = Energy.zero_new_implementation()\n\n    # Assert that both have the same base value\n    assert original_zero._base_value == new_zero._base_value, \"Base values do not match\"\n\n    # Assert that the objects are equal\n    assert original_zero == new_zero, \"Energy objects are not equal\"\n\n    # Assert that their string representations are the same\n    assert str(original_zero) == str(new_zero), \"String representations do not match\"\n\n    # Assert that both are instances of Energy\n    assert isinstance(original_zero, Energy), \"original_zero is not an instance of Energy\"\n    assert isinstance(new_zero, Energy), \"new_zero is not an instance of Energy\"\n\n    # Assert that all unit properties return zero\n    assert original_zero.gigajoules == 0, \"original_zero gigajoules is not zero\"\n    assert new_zero.gigajoules == 0, \"new_zero gigajoules is not zero\"\n    assert original_zero.megajoules == 0, \"original_zero megajoules is not zero\"\n    assert new_zero.megajoules == 0, \"new_zero megajoules is not zero\"\n    assert original_zero.kilojoules == 0, \"original_zero kilojoules is not zero\"\n    assert new_zero.kilojoules == 0, \"new_zero kilojoules is not zero\"\n    assert original_zero.joules == 0, \"original_zero joules is not zero\"\n    assert new_zero.joules == 0, \"new_zero joules is not zero\"\n    assert original_zero.millijoules == 0, \"original_zero millijoules is not zero\"\n    assert new_zero.millijoules == 0, \"new_zero millijoules is not zero\"\n\n    # Immutability check: modifying one should not affect the other\n    original_zero._base_value = 10\n    assert new_zero._base_value == 0, \"new_zero base value changed after modifying original_zero\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method of the `Energy` class that returns a new instance of `Energy` with a default `_base_value` of 0. The revised function in the provided code is identical to the original function in terms of functionality. It is a class method of the `Energy` class and returns a new instance of `Energy` with a `_base_value` of 0. The test function `test_zero` is designed to compare the original and revised implementations, ensuring they produce equivalent results. Since the revised function behaves the same as the original function, the functionality is the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `zero` function returns an instance of the `Energy` class, which is a return value. Thus, this condition is satisfied.\n- [CONDITION 2] The test cases check the return values and properties of the `Energy` instances, not printed or logged content. This condition is satisfied.\n- [CONDITION 3] The test cases compare the base values, equality, string representations, instance types, and unit properties of the `Energy` instances created by `zero` and `zero_new_implementation`. These checks ensure that `zero_new_implementation` must have the same functionality as `zero` to pass all tests. This condition is satisfied.\n- [CONDITION 4] The test cases use appropriate assertions to compare the return values and properties of the `Energy` instances. The immutability check ensures that modifying one instance does not affect the other. This condition is satisfied.\n- [CONDITION 5] The test cases are non-trivial as they cover multiple aspects of the `Energy` instances, including base value, equality, string representation, instance type, unit properties, and immutability. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Power.zero",
        "idx": "687",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Power:\n    \"\"\"Create a Power with a value of zero.\"\"\"\n    return Power()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Power(Quantity):\n    \"\"\"The amount of energy transferred or converted per unit time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"watts\"\n\n    @property\n    def gigawatts(self) -> float:\n        \"\"\"The power in gigawatts.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megawatts(self) -> float:\n        \"\"\"The power in megawatts.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kilowatts(self) -> float:\n        \"\"\"The power in kilowatts.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def watts(self) -> float:\n        \"\"\"The power in watts.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def milliwatts(self) -> float:\n        \"\"\"The power in milliwatts.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigawatts: float | None = None,\n        megawatts: float | None = None,\n        kilowatts: float | None = None,\n        watts: float | None = None,\n        milliwatts: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigawatts is not None:\n            self._base_value += gigawatts * 10**9\n        if megawatts is not None:\n            self._base_value += megawatts * 10**6\n        if kilowatts is not None:\n            self._base_value += kilowatts * 10**3\n        if watts is not None:\n            self._base_value += watts * 1\n        if milliwatts is not None:\n            self._base_value += milliwatts * 10**-3\n\n    @classmethod\n    def zero(cls) -> Power:\n        \"\"\"Create a Power with a value of zero.\"\"\"\n        return Power()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Power(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "from abc import ABC\n\n# Exception classes from quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two incompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two incompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# Main functionality from quantio/quantities.py\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Power(Quantity):\n    \"\"\"The amount of energy transferred or converted per unit time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"watts\"\n\n    @property\n    def gigawatts(self) -> float:\n        \"\"\"The power in gigawatts.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def megawatts(self) -> float:\n        \"\"\"The power in megawatts.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def kilowatts(self) -> float:\n        \"\"\"The power in kilowatts.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def watts(self) -> float:\n        \"\"\"The power in watts.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def milliwatts(self) -> float:\n        \"\"\"The power in milliwatts.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        gigawatts: float | None = None,\n        megawatts: float | None = None,\n        kilowatts: float | None = None,\n        watts: float | None = None,\n        milliwatts: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if gigawatts is not None:\n            self._base_value += gigawatts * 10**9\n        if megawatts is not None:\n            self._base_value += megawatts * 10**6\n        if kilowatts is not None:\n            self._base_value += kilowatts * 10**3\n        if watts is not None:\n            self._base_value += watts * 1\n        if milliwatts is not None:\n            self._base_value += milliwatts * 10**-3\n\n    @classmethod\n    def zero(cls) -> 'Power':\n        \"\"\"Create a Power with a value of zero.\"\"\"\n        return Power()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Power(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero_new_implementation matches zero.\"\"\"\n    original_zero = Power.zero()\n    new_zero = Power.zero_new_implementation()\n\n    # Assert that both implementations return a Power object\n    assert isinstance(original_zero, Power)\n    assert isinstance(new_zero, Power)\n\n    # Assert that both implementations have a base value of zero\n    assert original_zero._base_value == 0.0\n    assert new_zero._base_value == 0.0\n\n    # Assert that both implementations are equal\n    assert original_zero == new_zero\n\n    # Assert that the string representations are identical\n    assert str(original_zero) == str(new_zero)\n\n    # Assert that all unit properties return zero\n    assert original_zero.gigawatts == 0.0\n    assert new_zero.gigawatts == 0.0\n    assert original_zero.megawatts == 0.0\n    assert new_zero.megawatts == 0.0\n    assert original_zero.kilowatts == 0.0\n    assert new_zero.kilowatts == 0.0\n    assert original_zero.watts == 0.0\n    assert new_zero.watts == 0.0\n    assert original_zero.milliwatts == 0.0\n    assert new_zero.milliwatts == 0.0\n\n    # Assert that modifying one does not affect the other\n    original_zero._base_value = 10.0\n    assert new_zero._base_value == 0.0\n    assert original_zero._base_value == 10.0\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns an instance of the `Power` class with a default `_base_value` of 0.0. The revised function `zero` in the provided code is also a class method that returns an instance of the `Power` class with the same default `_base_value` of 0.0. Both functions create a `Power` object with a value of zero. The test function `test_zero` confirms that the behavior of the revised function matches the expected behavior of the original function, as it checks that both implementations return a `Power` object with a base value of zero and that they are equal in terms of their properties and string representations. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `zero` function returns a `Power` object, which is a class instance. This satisfies the condition as it returns a value.\n  \n- **CONDITION 2**: The test cases check the return values and the state of the `Power` object, such as `_base_value` and unit properties. They do not rely on printed or logged content, satisfying this condition.\n\n- **CONDITION 3**: The test cases ensure that both `zero` and `zero_new_implementation` return a `Power` object with a `_base_value` of zero and that they are equal. They also check that modifying one does not affect the other. This ensures that `zero_new_implementation` must have the same functionality as `zero` to pass the tests, satisfying this condition.\n\n- **CONDITION 4**: The test cases use appropriate assertions to check the return values and object states. They do not use inappropriate assertions like comparing the return values directly if the function does not return a value. This condition is satisfied.\n\n- **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the `Power` object, including type, base value, equality, string representation, unit properties, and independence of instances. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "AngularVelocity.zero",
        "idx": "688",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> AngularVelocity:\n    \"\"\"Create a AngularVelocity with a value of zero.\"\"\"\n    return AngularVelocity()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass AngularVelocity(Quantity):\n    \"\"\"The change in angle per time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"radians_per_second\"\n\n    @property\n    def degrees_per_second(self) -> float:\n        \"\"\"The angularvelocity in degrees per second.\"\"\"\n        return self._base_value / (3.141592653589793 / 180)\n\n    @property\n    def radians_per_second(self) -> float:\n        \"\"\"The angularvelocity in radians per second.\"\"\"\n        return self._base_value / 1\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        degrees_per_second: float | None = None,\n        radians_per_second: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if degrees_per_second is not None:\n            self._base_value += degrees_per_second * (3.141592653589793 / 180)\n        if radians_per_second is not None:\n            self._base_value += radians_per_second * 1\n\n    @classmethod\n    def zero(cls) -> AngularVelocity:\n        \"\"\"Create a AngularVelocity with a value of zero.\"\"\"\n        return AngularVelocity()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"AngularVelocity(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# Combined code from quantio/quantities.py and quantio/exceptions.py\n\nfrom abc import ABC\n\n# Exception definitions from quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two incompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two incompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# Main code from quantio/quantities.py\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass AngularVelocity(Quantity):\n    \"\"\"The change in angle per time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"radians_per_second\"\n\n    @property\n    def degrees_per_second(self) -> float:\n        \"\"\"The angularvelocity in degrees per second.\"\"\"\n        return self._base_value / (3.141592653589793 / 180)\n\n    @property\n    def radians_per_second(self) -> float:\n        \"\"\"The angularvelocity in radians per second.\"\"\"\n        return self._base_value / 1\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        degrees_per_second: float | None = None,\n        radians_per_second: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if degrees_per_second is not None:\n            self._base_value += degrees_per_second * (3.141592653589793 / 180)\n        if radians_per_second is not None:\n            self._base_value += radians_per_second * 1\n\n    @classmethod\n    def zero(cls) -> 'AngularVelocity':\n        \"\"\"Create a AngularVelocity with a value of zero.\"\"\"\n        return AngularVelocity()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"AngularVelocity(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero_new_implementation matches zero.\"\"\"\n    original_zero = AngularVelocity.zero()\n    new_zero = AngularVelocity.zero_new_implementation()\n\n    # Assert that both methods return an instance of AngularVelocity\n    assert isinstance(original_zero, AngularVelocity), \"Original zero is not an instance of AngularVelocity\"\n    assert isinstance(new_zero, AngularVelocity), \"New zero is not an instance of AngularVelocity\"\n\n    # Assert that both methods return an object with a base value of zero\n    assert original_zero._base_value == 0.0, \"Original zero does not have a base value of zero\"\n    assert new_zero._base_value == 0.0, \"New zero does not have a base value of zero\"\n\n    # Assert that both methods return equivalent objects\n    assert original_zero == new_zero, \"Original zero and new zero are not equivalent\"\n\n    # Assert that the string representations are identical\n    assert str(original_zero) == str(new_zero), \"String representations of original and new zero are not identical\"\n\n    # Assert that the properties return expected values\n    assert original_zero.degrees_per_second == 0.0, \"Original zero degrees_per_second is not zero\"\n    assert new_zero.degrees_per_second == 0.0, \"New zero degrees_per_second is not zero\"\n    assert original_zero.radians_per_second == 0.0, \"Original zero radians_per_second is not zero\"\n    assert new_zero.radians_per_second == 0.0, \"New zero radians_per_second is not zero\"\n\n    # Assert that modifying one does not affect the other\n    original_zero._base_value = 1.0\n    assert new_zero._base_value == 0.0, \"Modifying original zero affected new zero\"\n\n    # Assert that the BASE_UNIT is consistent\n    assert original_zero.BASE_UNIT == new_zero.BASE_UNIT, \"BASE_UNIT is not consistent between original and new zero\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns an instance of `AngularVelocity` with a default value of zero. The revised function in the provided code is the same as the original function. It is a class method of the `AngularVelocity` class and returns an instance of `AngularVelocity` initialized with a base value of zero. The test function `test_zero` confirms that the revised function behaves as expected, creating an `AngularVelocity` object with a base value of zero, and that it is equivalent to the original implementation. Since the functionality and implementation of the revised function are identical to the original function, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n1. **CONDITION 1**: The `zero` function returns an instance of `AngularVelocity`, which is a class. This satisfies the condition as it either returns a value or modifies global variables or input arguments.\n2. **CONDITION 2**: The test cases in `test_zero` check the return values and states of the objects created by `zero` and `zero_new_implementation`. They do not rely on printed or logged content.\n3. **CONDITION 3**: The test cases compare the instances returned by `zero` and `zero_new_implementation` for equivalence in terms of type, base value, properties, and string representation. This ensures that `zero_new_implementation` must have the same functionality as `zero` to pass all tests.\n4. **CONDITION 4**: The test cases use assertions that are appropriate for the functionality being tested. They do not make unreasonable assumptions about the return values or states.\n5. **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the returned objects, including type, base value, properties, string representation, and independence of instances.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Density.zero",
        "idx": "689",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Density:\n    \"\"\"Create a Density with a value of zero.\"\"\"\n    return Density()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Density(Quantity):\n    \"\"\"A substance's mass per unit of volume.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"kilograms_per_cubic_meter\"\n\n    @property\n    def grams_per_cubic_meter(self) -> float:\n        \"\"\"The density in grams per cubic meter.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def kilograms_per_cubic_meter(self) -> float:\n        \"\"\"The density in kilograms per cubic meter.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def kilograms_per_liter(self) -> float:\n        \"\"\"The density in kilograms per liter.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def grams_per_milliliter(self) -> float:\n        \"\"\"The density in grams per milliliter.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        grams_per_cubic_meter: float | None = None,\n        kilograms_per_cubic_meter: float | None = None,\n        kilograms_per_liter: float | None = None,\n        grams_per_milliliter: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if grams_per_cubic_meter is not None:\n            self._base_value += grams_per_cubic_meter * 10**3\n        if kilograms_per_cubic_meter is not None:\n            self._base_value += kilograms_per_cubic_meter * 1\n        if kilograms_per_liter is not None:\n            self._base_value += kilograms_per_liter * 10**-3\n        if grams_per_milliliter is not None:\n            self._base_value += grams_per_milliliter * 10**-3\n\n    @classmethod\n    def zero(cls) -> Density:\n        \"\"\"Create a Density with a value of zero.\"\"\"\n        return Density()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Density(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# quantio/quantities.py\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Density(Quantity):\n    \"\"\"A substance's mass per unit of volume.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"kilograms_per_cubic_meter\"\n\n    @property\n    def grams_per_cubic_meter(self) -> float:\n        \"\"\"The density in grams per cubic meter.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def kilograms_per_cubic_meter(self) -> float:\n        \"\"\"The density in kilograms per cubic meter.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def kilograms_per_liter(self) -> float:\n        \"\"\"The density in kilograms per liter.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def grams_per_milliliter(self) -> float:\n        \"\"\"The density in grams per milliliter.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        grams_per_cubic_meter: float | None = None,\n        kilograms_per_cubic_meter: float | None = None,\n        kilograms_per_liter: float | None = None,\n        grams_per_milliliter: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if grams_per_cubic_meter is not None:\n            self._base_value += grams_per_cubic_meter * 10**3\n        if kilograms_per_cubic_meter is not None:\n            self._base_value += kilograms_per_cubic_meter * 1\n        if kilograms_per_liter is not None:\n            self._base_value += kilograms_per_liter * 10**-3\n        if grams_per_milliliter is not None:\n            self._base_value += grams_per_milliliter * 10**-3\n\n    @classmethod\n    def zero(cls) -> 'Density':\n        \"\"\"Create a Density with a value of zero.\"\"\"\n        return Density()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Density(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero and zero_new_implementation have the same functionality.\"\"\"\n    zero_density = Density.zero()\n    zero_new_density = Density.zero_new_implementation()\n\n    # Assert that both methods return a Density object with a base value of zero\n    assert zero_density._base_value == 0.0\n    assert zero_new_density._base_value == 0.0\n\n    # Assert that the properties are consistent with a zero density\n    assert zero_density.grams_per_cubic_meter == zero_new_density.grams_per_cubic_meter == 0.0\n    assert zero_density.kilograms_per_cubic_meter == zero_new_density.kilograms_per_cubic_meter == 0.0\n    assert zero_density.kilograms_per_liter == zero_new_density.kilograms_per_liter == 0.0\n    assert zero_density.grams_per_milliliter == zero_new_density.grams_per_milliliter == 0.0\n\n    # Assert that both objects are instances of Density\n    assert isinstance(zero_density, Density)\n    assert isinstance(zero_new_density, Density)\n\n    # Assert that both objects are equal\n    assert zero_density == zero_new_density\n\n    # Assert that the string representation is consistent\n    assert str(zero_density) == str(zero_new_density) == \"Density(kilograms_per_cubic_meter=0.0)\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns a new instance of the `Density` class with a default base value of zero. The revised function in the provided code is identical to the original function. It is a class method of the `Density` class and returns a new `Density` object initialized with a base value of zero. There are no changes in the logic or functionality between the original and revised functions. The test function `test_zero` also confirms that the functionality remains consistent, as it checks that the `Density` object returned by the `zero` method has a base value of zero and that its properties reflect this zero value.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `zero` function returns a `Density` object with a base value of zero. This satisfies the condition as it has a return value.\n\n2. **CONDITION 2**: The test cases in `test_zero` check the return values and the state of the `Density` objects. They do not rely on printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the `Density` objects returned by `zero` and `zero_new_implementation` in terms of their base values, properties, instance types, equality, and string representation. These checks ensure that `zero_new_implementation` must have the exact same functionality as `zero` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use appropriate assertions to check the properties and equality of the `Density` objects. They do not use inappropriate assertions like comparing the return values directly if they were not supposed to be compared. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the `Density` objects, including base value, properties, instance type, equality, and string representation. This ensures comprehensive testing of the functionality, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Time.zero",
        "idx": "690",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Time:\n    \"\"\"Create a Time with a value of zero.\"\"\"\n    return Time()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Time(Quantity):\n    \"\"\"The duration of an event.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"seconds\"\n\n    @property\n    def hours(self) -> float:\n        \"\"\"The time in hours.\"\"\"\n        return self._base_value / 60 * 60\n\n    @property\n    def minutes(self) -> float:\n        \"\"\"The time in minutes.\"\"\"\n        return self._base_value / 60\n\n    @property\n    def seconds(self) -> float:\n        \"\"\"The time in seconds.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def milliseconds(self) -> float:\n        \"\"\"The time in milliseconds.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def microseconds(self) -> float:\n        \"\"\"The time in microseconds.\"\"\"\n        return self._base_value / 10**-6\n\n    @property\n    def nanoseconds(self) -> float:\n        \"\"\"The time in nanoseconds.\"\"\"\n        return self._base_value / 10**-9\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        hours: float | None = None,\n        minutes: float | None = None,\n        seconds: float | None = None,\n        milliseconds: float | None = None,\n        microseconds: float | None = None,\n        nanoseconds: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if hours is not None:\n            self._base_value += hours * 60 * 60\n        if minutes is not None:\n            self._base_value += minutes * 60\n        if seconds is not None:\n            self._base_value += seconds * 1\n        if milliseconds is not None:\n            self._base_value += milliseconds * 10**-3\n        if microseconds is not None:\n            self._base_value += microseconds * 10**-6\n        if nanoseconds is not None:\n            self._base_value += nanoseconds * 10**-9\n\n    @classmethod\n    def zero(cls) -> Time:\n        \"\"\"Create a Time with a value of zero.\"\"\"\n        return Time()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Time(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# quantio/quantities.py\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Time(Quantity):\n    \"\"\"The duration of an event.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"seconds\"\n\n    @property\n    def hours(self) -> float:\n        \"\"\"The time in hours.\"\"\"\n        return self._base_value / 60 * 60\n\n    @property\n    def minutes(self) -> float:\n        \"\"\"The time in minutes.\"\"\"\n        return self._base_value / 60\n\n    @property\n    def seconds(self) -> float:\n        \"\"\"The time in seconds.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def milliseconds(self) -> float:\n        \"\"\"The time in milliseconds.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def microseconds(self) -> float:\n        \"\"\"The time in microseconds.\"\"\"\n        return self._base_value / 10**-6\n\n    @property\n    def nanoseconds(self) -> float:\n        \"\"\"The time in nanoseconds.\"\"\"\n        return self._base_value / 10**-9\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        hours: float | None = None,\n        minutes: float | None = None,\n        seconds: float | None = None,\n        milliseconds: float | None = None,\n        microseconds: float | None = None,\n        nanoseconds: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if hours is not None:\n            self._base_value += hours * 60 * 60\n        if minutes is not None:\n            self._base_value += minutes * 60\n        if seconds is not None:\n            self._base_value += seconds * 1\n        if milliseconds is not None:\n            self._base_value += milliseconds * 10**-3\n        if microseconds is not None:\n            self._base_value += microseconds * 10**-6\n        if nanoseconds is not None:\n            self._base_value += nanoseconds * 10**-9\n\n    @classmethod\n    def zero(cls) -> 'Time':\n        \"\"\"Create a Time with a value of zero.\"\"\"\n        return Time()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Time(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure Time.zero and Time.zero_new_implementation are equivalent.\"\"\"\n    zero_old = Time.zero()\n    zero_new = Time.zero_new_implementation()\n\n    # Assert that both return a Time object\n    assert isinstance(zero_old, Time), \"Time.zero did not return a Time instance\"\n    assert isinstance(zero_new, Time), \"Time.zero_new_implementation did not return a Time instance\"\n\n    # Assert that both have a base value of zero\n    assert zero_old._base_value == 0.0, \"Time.zero did not return a zero base value\"\n    assert zero_new._base_value == 0.0, \"Time.zero_new_implementation did not return a zero base value\"\n\n    # Assert that both are equal\n    assert zero_old == zero_new, \"Time.zero and Time.zero_new_implementation are not equal\"\n\n    # Assert that the string representations are the same\n    assert str(zero_old) == str(zero_new), \"String representations of Time.zero and Time.zero_new_implementation are not equal\"\n    assert str(zero_old) == \"Time(seconds=0.0)\", \"String representation of Time.zero is incorrect\"\n\n    # Assert that all properties return zero\n    assert zero_old.hours == 0.0, \"Time.zero hours property is not zero\"\n    assert zero_old.minutes == 0.0, \"Time.zero minutes property is not zero\"\n    assert zero_old.seconds == 0.0, \"Time.zero seconds property is not zero\"\n    assert zero_old.milliseconds == 0.0, \"Time.zero milliseconds property is not zero\"\n    assert zero_old.microseconds == 0.0, \"Time.zero microseconds property is not zero\"\n    assert zero_old.nanoseconds == 0.0, \"Time.zero nanoseconds property is not zero\"\n\n    assert zero_new.hours == 0.0, \"Time.zero_new_implementation hours property is not zero\"\n    assert zero_new.minutes == 0.0, \"Time.zero_new_implementation minutes property is not zero\"\n    assert zero_new.seconds == 0.0, \"Time.zero_new_implementation seconds property is not zero\"\n    assert zero_new.milliseconds == 0.0, \"Time.zero_new_implementation milliseconds property is not zero\"\n    assert zero_new.microseconds == 0.0, \"Time.zero_new_implementation microseconds property is not zero\"\n    assert zero_new.nanoseconds == 0.0, \"Time.zero_new_implementation nanoseconds property is not zero\"\n\n    # Assert immutability: modifying one should not affect the other\n    zero_old._base_value = 10.0\n    assert zero_new._base_value == 0.0, \"Modifying zero_old affected zero_new\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns a new instance of the `Time` class with a default value of zero. The revised function in the provided code is also a class method named `zero` within the `Time` class, which returns a new instance of the `Time` class with a default value of zero. Both functions are intended to create a `Time` object with a base value of zero, and the implementation of the `Time` class constructor ensures that when no arguments are provided, the `_base_value` is set to zero. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `zero` function returns a `Time` object, which satisfies the condition as it returns a value.\n- CONDITION 2: The test cases check the return values and properties of the `Time` object, not printed or logged contents.\n- CONDITION 3: The test cases ensure that both `zero` and `zero_new_implementation` return a `Time` object with a base value of zero and that they are equal, which implies they must have the same functionality.\n- CONDITION 4: The test cases use appropriate assertions to check the properties and equality of the `Time` objects returned by both implementations. They do not use inappropriate assertions like comparing the return values directly if they were not supposed to be compared.\n- CONDITION 5: The test cases are non-trivial as they check multiple aspects of the `Time` object, including type, base value, equality, string representation, and immutability.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Velocity.zero",
        "idx": "691",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Velocity:\n    \"\"\"Create a Velocity with a value of zero.\"\"\"\n    return Velocity()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Velocity(Quantity):\n    \"\"\"Distance per time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"meters_per_second\"\n\n    @property\n    def meters_per_second(self) -> float:\n        \"\"\"The velocity in meters per second.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def kilometers_per_hour(self) -> float:\n        \"\"\"The velocity in kilometers per hour.\"\"\"\n        return self._base_value / (1 / 3.6)\n\n    @property\n    def miles_per_hour(self) -> float:\n        \"\"\"The velocity in miles per hour.\"\"\"\n        return self._base_value / (1 / 2.23694)\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        meters_per_second: float | None = None,\n        kilometers_per_hour: float | None = None,\n        miles_per_hour: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if meters_per_second is not None:\n            self._base_value += meters_per_second * 1\n        if kilometers_per_hour is not None:\n            self._base_value += kilometers_per_hour * (1 / 3.6)\n        if miles_per_hour is not None:\n            self._base_value += miles_per_hour * (1 / 2.23694)\n\n    @classmethod\n    def zero(cls) -> Velocity:\n        \"\"\"Create a Velocity with a value of zero.\"\"\"\n        return Velocity()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Velocity(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# Exception classes from quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# Quantity and Velocity classes from quantio/quantities.py\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Velocity(Quantity):\n    \"\"\"Distance per time.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"meters_per_second\"\n\n    @property\n    def meters_per_second(self) -> float:\n        \"\"\"The velocity in meters per second.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def kilometers_per_hour(self) -> float:\n        \"\"\"The velocity in kilometers per hour.\"\"\"\n        return self._base_value / (1 / 3.6)\n\n    @property\n    def miles_per_hour(self) -> float:\n        \"\"\"The velocity in miles per hour.\"\"\"\n        return self._base_value / (1 / 2.23694)\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        meters_per_second: float | None = None,\n        kilometers_per_hour: float | None = None,\n        miles_per_hour: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if meters_per_second is not None:\n            self._base_value += meters_per_second * 1\n        if kilometers_per_hour is not None:\n            self._base_value += kilometers_per_hour * (1 / 3.6)\n        if miles_per_hour is not None:\n            self._base_value += miles_per_hour * (1 / 2.23694)\n\n    @classmethod\n    def zero(cls) -> 'Velocity':\n        \"\"\"Create a Velocity with a value of zero.\"\"\"\n        return Velocity()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Velocity(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero and zero_new_implementation are equivalent.\"\"\"\n    v1 = Velocity.zero()\n    v2 = Velocity.zero_new_implementation()\n\n    # Check if both are instances of Velocity\n    assert isinstance(v1, Velocity), \"v1 is not an instance of Velocity\"\n    assert isinstance(v2, Velocity), \"v2 is not an instance of Velocity\"\n\n    # Check if both have a base value of zero\n    assert v1._base_value == 0.0, \"v1 base value is not zero\"\n    assert v2._base_value == 0.0, \"v2 base value is not zero\"\n\n    # Check if their unit conversions are equivalent\n    assert v1.meters_per_second == v2.meters_per_second, \"Meters per second mismatch\"\n    assert v1.kilometers_per_hour == v2.kilometers_per_hour, \"Kilometers per hour mismatch\"\n    assert v1.miles_per_hour == v2.miles_per_hour, \"Miles per hour mismatch\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that creates and returns a new instance of the `Velocity` class with a default value of zero. The revised function `zero` in the provided code does exactly the same thing: it is a class method that returns a new instance of the `Velocity` class with a default `_base_value` of zero. The test function `test_zero` confirms that both the original and revised implementations of `zero` produce instances of `Velocity` with a base value of zero and equivalent unit conversions. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `zero` function is a class method that returns a new instance of the `Velocity` class with a base value of zero. It does not modify global variables or input arguments, but it does have a return value, which satisfies this condition.\n\n- **CONDITION 2**: The test function `test_zero` checks the return values and the state of the objects created by `zero` and `zero_new_implementation`. It does not rely on printed or logged content, satisfying this condition.\n\n- **CONDITION 3**: The test cases ensure that both `zero` and `zero_new_implementation` return instances of `Velocity` with a base value of zero and that their unit conversions are equivalent. This implies that `zero_new_implementation` must have the same functionality as `zero` to pass all tests, satisfying this condition.\n\n- **CONDITION 4**: The test cases use assertions to check the type and state of the objects returned by `zero` and `zero_new_implementation`. These assertions are appropriate given that `zero` returns a `Velocity` object with specific properties, satisfying this condition.\n\n- **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the `Velocity` object, including type, base value, and unit conversions. This ensures comprehensive testing of the `zero` method's functionality, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Acceleration.zero",
        "idx": "692",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Acceleration:\n    \"\"\"Create a Acceleration with a value of zero.\"\"\"\n    return Acceleration()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Acceleration(Quantity):\n    \"\"\"Rate of change of velocity.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"meters_per_square_second\"\n\n    @property\n    def meters_per_square_second(self) -> float:\n        \"\"\"The acceleration in meters per square second.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def g_force(self) -> float:\n        \"\"\"The acceleration in g force.\"\"\"\n        return self._base_value / (1 / 9.8)\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        meters_per_square_second: float | None = None,\n        g_force: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if meters_per_square_second is not None:\n            self._base_value += meters_per_square_second * 1\n        if g_force is not None:\n            self._base_value += g_force * (1 / 9.8)\n\n    @classmethod\n    def zero(cls) -> Acceleration:\n        \"\"\"Create a Acceleration with a value of zero.\"\"\"\n        return Acceleration()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Acceleration(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "# Define exceptions directly in the script\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two incompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two incompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# Define the Quantity class\nfrom abc import ABC\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\n# Define the Acceleration class\nclass Acceleration(Quantity):\n    \"\"\"Rate of change of velocity.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"meters_per_square_second\"\n\n    @property\n    def meters_per_square_second(self) -> float:\n        \"\"\"The acceleration in meters per square second.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def g_force(self) -> float:\n        \"\"\"The acceleration in g force.\"\"\"\n        return self._base_value / (1 / 9.8)\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        meters_per_square_second: float | None = None,\n        g_force: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if meters_per_square_second is not None:\n            self._base_value += meters_per_square_second * 1\n        if g_force is not None:\n            self._base_value += g_force * (1 / 9.8)\n\n    @classmethod\n    def zero(cls) -> 'Acceleration':\n        \"\"\"Create a Acceleration with a value of zero.\"\"\"\n        return Acceleration()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Acceleration(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n\ndef test_zero():\n    \"\"\"Test to ensure zero and zero_new_implementation are equivalent.\"\"\"\n    zero_old = Acceleration.zero()\n    zero_new = Acceleration.zero_new_implementation()\n\n    # Assert that both implementations return an Acceleration with base value zero\n    assert zero_old._base_value == 0.0\n    assert zero_new._base_value == 0.0\n\n    # Assert that both implementations return equivalent Acceleration objects\n    assert zero_old == zero_new\n\n    # Assert that the properties return zero\n    assert zero_old.meters_per_square_second == 0.0\n    assert zero_new.meters_per_square_second == 0.0\n    assert zero_old.g_force == 0.0\n    assert zero_new.g_force == 0.0\n\n    # Assert that both are instances of Acceleration\n    assert isinstance(zero_old, Acceleration)\n    assert isinstance(zero_new, Acceleration)\n\n    # Assert that the string representation is correct\n    assert str(zero_old) == \"Acceleration(meters_per_square_second=0.0)\"\n    assert str(zero_new) == \"Acceleration(meters_per_square_second=0.0)\"\n\n    # Assert equality with a new zero Acceleration instance\n    another_zero = Acceleration.zero()\n    assert zero_old == another_zero\n    assert zero_new == another_zero\n\n    # Assert inequality with a non-zero Acceleration instance\n    non_zero = Acceleration(meters_per_square_second=1.0)\n    assert zero_old != non_zero\n    assert zero_new != non_zero\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method of the `Acceleration` class that returns an instance of `Acceleration` with a default base value of zero. The revised function `zero` in the provided code is identical to the original function. It is a class method of the `Acceleration` class and returns an instance of `Acceleration` with a base value of zero. The test function `test_zero` checks the functionality of the `zero` method and confirms that it behaves as expected, returning an `Acceleration` object with a base value of zero. Since the revised function is exactly the same as the original function, the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `zero` function is a class method that returns an instance of the `Acceleration` class with a base value of zero. It does not modify global variables or input arguments, but it does return a value, satisfying this condition.\n\n2. **CONDITION 2**: The test cases in `test_zero` check the return values and states of the `Acceleration` instances created by the `zero` and `zero_new_implementation` methods. They do not check printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the return values and properties of the `zero` and `zero_new_implementation` methods to ensure they are equivalent. This ensures that `zero_new_implementation` can only pass if it has the same functionality as `zero`, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use appropriate assertions to compare the properties and states of the `Acceleration` instances. They do not use inappropriate assertions like comparing return values directly when there are none, satisfying this condition.\n\n5. **CONDITION 5**: The test cases are non-trivial as they check various aspects of the `Acceleration` instances, including base value, properties, instance type, string representation, and equality/inequality with other instances. This ensures comprehensive testing, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "Pressure.zero",
        "idx": "693",
        "repo_name": "unexcellent___quantio",
        "func_path": "quantio/quantities.py",
        "orig_func": "@classmethod\ndef zero(cls) -> Pressure:\n    \"\"\"Create a Pressure with a value of zero.\"\"\"\n    return Pressure()",
        "orig_context": "```python\n## quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n```\n\n\n```python\n## quantio/quantities.py\nfrom abc import ABC\n\nfrom .exceptions import CanNotAddTypesError, CanNotSubtractTypesError\n\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: Quantity) -> Quantity:\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: Quantity) -> Quantity:\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Pressure(Quantity):\n    \"\"\"The amount of force per unit area.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"kilopascal\"\n\n    @property\n    def terapascal(self) -> float:\n        \"\"\"The pressure in terapascal.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def gigapascal(self) -> float:\n        \"\"\"The pressure in gigapascal.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def megapascal(self) -> float:\n        \"\"\"The pressure in megapascal.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def kilopascal(self) -> float:\n        \"\"\"The pressure in kilopascal.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def bar(self) -> float:\n        \"\"\"The pressure in bar.\"\"\"\n        return self._base_value / 10**-2\n\n    @property\n    def pascal(self) -> float:\n        \"\"\"The pressure in pascal.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def millipascal(self) -> float:\n        \"\"\"The pressure in millipascal.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        terapascal: float | None = None,\n        gigapascal: float | None = None,\n        megapascal: float | None = None,\n        kilopascal: float | None = None,\n        bar: float | None = None,\n        pascal: float | None = None,\n        millipascal: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if terapascal is not None:\n            self._base_value += terapascal * 10**9\n        if gigapascal is not None:\n            self._base_value += gigapascal * 10**6\n        if megapascal is not None:\n            self._base_value += megapascal * 10**3\n        if kilopascal is not None:\n            self._base_value += kilopascal * 1\n        if bar is not None:\n            self._base_value += bar * 10**-2\n        if pascal is not None:\n            self._base_value += pascal * 10**-3\n        if millipascal is not None:\n            self._base_value += millipascal * 10**-3\n\n    @classmethod\n    def zero(cls) -> Pressure:\n        \"\"\"Create a Pressure with a value of zero.\"\"\"\n        return Pressure()\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Pressure(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\n```\n\n\n",
        "eval_script": "from abc import ABC\n\n# Exception classes from quantio/exceptions.py\nclass CanNotAddTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are added to one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not add {other_type_descriptor} to {self_type_descriptor}\")\n\nclass CanNotSubtractTypesError(TypeError):\n    \"\"\"Raised when two uncompatible quantities are subtracted from one another.\"\"\"\n\n    def __init__(self, self_type_descriptor: str, other_type_descriptor: str) -> None:\n        super().__init__(f\"Can not subtract {other_type_descriptor} from {self_type_descriptor}\")\n\n# Quantity and Pressure classes from quantio/quantities.py\nclass Quantity(ABC):\n    \"\"\"Parent class to all quantities.\"\"\"\n\n    _base_value: float\n    \"The base unit of the quantity.\"\n\n    BASE_UNIT: str\n    \"Name of the unit with a factor of 1.\"\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Assess if this object is the same as another.\"\"\"\n        if isinstance(other, type(self)):\n            return self._base_value == other._base_value\n\n        return False\n\n    def __add__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Add two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotAddTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value + other._base_value\n        return result\n\n    def __sub__(self, other: 'Quantity') -> 'Quantity':\n        \"\"\"Subtract two quantities of the same type.\"\"\"\n        if type(self) is not type(other):\n            raise CanNotSubtractTypesError(self.__class__.__name__, other.__class__.__name__)\n\n        result = type(self)()\n        result._base_value = self._base_value - other._base_value\n        return result\n\n    def __repr__(self) -> str:\n        \"\"\"Return an unambiguous representation of this quantity.\"\"\"\n        return self.__str__()\n\nclass Pressure(Quantity):\n    \"\"\"The amount of force per unit area.\"\"\"\n\n    # --- This part is auto generated. Do not change manually. ---\n\n    BASE_UNIT = \"kilopascal\"\n\n    @property\n    def terapascal(self) -> float:\n        \"\"\"The pressure in terapascal.\"\"\"\n        return self._base_value / 10**9\n\n    @property\n    def gigapascal(self) -> float:\n        \"\"\"The pressure in gigapascal.\"\"\"\n        return self._base_value / 10**6\n\n    @property\n    def megapascal(self) -> float:\n        \"\"\"The pressure in megapascal.\"\"\"\n        return self._base_value / 10**3\n\n    @property\n    def kilopascal(self) -> float:\n        \"\"\"The pressure in kilopascal.\"\"\"\n        return self._base_value / 1\n\n    @property\n    def bar(self) -> float:\n        \"\"\"The pressure in bar.\"\"\"\n        return self._base_value / 10**-2\n\n    @property\n    def pascal(self) -> float:\n        \"\"\"The pressure in pascal.\"\"\"\n        return self._base_value / 10**-3\n\n    @property\n    def millipascal(self) -> float:\n        \"\"\"The pressure in millipascal.\"\"\"\n        return self._base_value / 10**-3\n\n    def __init__(\n        self,\n        _base_value: float = 0.0,\n        terapascal: float | None = None,\n        gigapascal: float | None = None,\n        megapascal: float | None = None,\n        kilopascal: float | None = None,\n        bar: float | None = None,\n        pascal: float | None = None,\n        millipascal: float | None = None,\n    ) -> None:\n        self._base_value = _base_value\n        if terapascal is not None:\n            self._base_value += terapascal * 10**9\n        if gigapascal is not None:\n            self._base_value += gigapascal * 10**6\n        if megapascal is not None:\n            self._base_value += megapascal * 10**3\n        if kilopascal is not None:\n            self._base_value += kilopascal * 1\n        if bar is not None:\n            self._base_value += bar * 10**-2\n        if pascal is not None:\n            self._base_value += pascal * 10**-3\n        if millipascal is not None:\n            self._base_value += millipascal * 10**-3\n\n    @classmethod\n    def zero(cls) -> 'Pressure':\n        \"\"\"Create a Pressure with a value of zero.\"\"\"\n        return Pressure()\n\n\n    def __str__(self) -> str:\n        \"\"\"Display this quantity as a string for printing.\"\"\"\n        return \"Pressure(\" + self.BASE_UNIT + \"=\" + str(self._base_value) + \")\"\n\ndef test_zero():\n    \"\"\"Test to ensure zero_new_implementation matches zero.\"\"\"\n    zero_old = Pressure.zero()\n    zero_new = Pressure.zero_new_implementation()\n\n    # Assert that both methods return a Pressure object\n    assert isinstance(zero_old, Pressure), \"zero() did not return a Pressure instance\"\n    assert isinstance(zero_new, Pressure), \"zero_new_implementation() did not return a Pressure instance\"\n\n    # Assert that both methods return a Pressure object with a base value of zero\n    assert zero_old._base_value == 0.0, \"zero() did not return a Pressure with base value of zero\"\n    assert zero_new._base_value == 0.0, \"zero_new_implementation() did not return a Pressure with base value of zero\"\n\n    # Assert that both Pressure objects are equal\n    assert zero_old == zero_new, \"zero() and zero_new_implementation() did not return equivalent Pressure objects\"\n\n    # Assert that the string representations are the same\n    assert str(zero_old) == str(zero_new), \"String representations of zero() and zero_new_implementation() do not match\"\n\n    # Assert that all properties return zero\n    assert zero_old.terapascal == 0.0, \"zero() terapascal property is not zero\"\n    assert zero_new.terapascal == 0.0, \"zero_new_implementation() terapascal property is not zero\"\n\n    assert zero_old.gigapascal == 0.0, \"zero() gigapascal property is not zero\"\n    assert zero_new.gigapascal == 0.0, \"zero_new_implementation() gigapascal property is not zero\"\n\n    assert zero_old.megapascal == 0.0, \"zero() megapascal property is not zero\"\n    assert zero_new.megapascal == 0.0, \"zero_new_implementation() megapascal property is not zero\"\n\n    assert zero_old.kilopascal == 0.0, \"zero() kilopascal property is not zero\"\n    assert zero_new.kilopascal == 0.0, \"zero_new_implementation() kilopascal property is not zero\"\n\n    assert zero_old.bar == 0.0, \"zero() bar property is not zero\"\n    assert zero_new.bar == 0.0, \"zero_new_implementation() bar property is not zero\"\n\n    assert zero_old.pascal == 0.0, \"zero() pascal property is not zero\"\n    assert zero_new.pascal == 0.0, \"zero_new_implementation() pascal property is not zero\"\n\n    assert zero_old.millipascal == 0.0, \"zero() millipascal property is not zero\"\n    assert zero_new.millipascal == 0.0, \"zero_new_implementation() millipascal property is not zero\"\n\n    # Assert that modifying one does not affect the other\n    zero_old._base_value = 10.0\n    assert zero_new._base_value == 0.0, \"Modifying zero() affected zero_new_implementation()\"\n\nif __name__ == \"__main__\":\n    test_zero()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `zero` is a class method that returns a new instance of the `Pressure` class with a default base value of zero. The revised function `zero` in the provided code also returns a new instance of the `Pressure` class with a default base value of zero. The functionality of both functions is to create a `Pressure` object with a value of zero, and they achieve this in the same way by calling the `Pressure` constructor with default arguments. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `zero` function returns a `Pressure` object, which is a return value, satisfying this condition.\n- CONDITION 2: The test cases check the return values and properties of the `Pressure` object, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases ensure that `zero_new_implementation` returns a `Pressure` object with the same properties and behavior as `zero`, ensuring they have the same functionality, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values and properties of the `Pressure` objects, which is reasonable given that `zero` returns a `Pressure` object. The tests do not use inappropriate assertions, satisfying this condition.\n- CONDITION 5: The test cases are non-trivial as they check multiple properties and behaviors of the `Pressure` object, including type, value, equality, string representation, and independence of instances, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "84f013d638aeba39c8cfd03e832de28d481e916f"
    },
    {
        "func_name": "VNetwork.forward",
        "idx": "708",
        "repo_name": "seakers___sacTransformerEOS",
        "func_path": "scripts/sac.py",
        "orig_func": "def forward(self, states, actions):\n    if states.shape[-2] != actions.shape[-2]:\n        raise ValueError('The states and actions sequences must have the same length!')\n    if states.shape[-2] < self.max_len:\n        states = torch.cat([states, torch.zeros(states.shape[0], self.max_len - states.shape[-2], states.shape[-1], device=self.gpu_device)], dim=-2)\n        actions = torch.cat([actions, torch.zeros(actions.shape[0], self.max_len - actions.shape[-2], actions.shape[-1], device=self.gpu_device)], dim=-2)\n    if self.aug_state_contains_actions:\n        aug_state_1D = torch.cat([states, actions], dim=2).view(-1)\n    else:\n        aug_state_1D = states.view(-1)\n    x = aug_state_1D\n    x = super(VNetwork, self).forward(x)\n    return x",
        "orig_context": "```python\n## scripts/sac.py\nimport torch\n\nimport torch.nn as nn\n\nclass Critic(nn.Module):\n    \"\"\"\n    Class to represent a Critic in the context of the SAC algorithm. Children class of nn.Module.\n    \"\"\"\n    def __init__(self, in_dim: int, out_dim: int, n_hidden: tuple[int], lr: float=1e-3):\n        super(Critic, self).__init__()\n        self.role_type = \"Critic\"\n        self.in_dim = in_dim\n        self.out_dim = out_dim\n        self.n_hidden = n_hidden\n        self.lr = lr\n\n        layers = []\n        \n        for i in range(len(n_hidden)):\n            if i == 0:\n                layers.append(nn.Linear(in_dim, n_hidden[i]))\n            else:\n                layers.append(nn.Linear(n_hidden[i-1], n_hidden[i]))\n            layers.append(nn.ReLU())\n\n        layers.append(nn.Linear(n_hidden[-1], out_dim))\n\n        self.sequential = nn.Sequential(*layers)\n\n        self.init_weights()\n\n    def init_weights(self):\n        initrange = 0.1\n        for module in self.sequential:\n            if type(module) == torch.nn.Linear:\n                    nn.init.zeros_(module.bias)\n                    nn.init.uniform_(module.weight, -initrange, initrange)\n\n    def forward(self, x):\n        return self.sequential(x)\n\nclass VNetwork(Critic):\n\n    \"\"\"\n    Class to represent a V-network. Children class of Critic.\n    \"\"\"\n    def __init__(self, state_dim: int, action_dim: int, max_len: int, out_dim: int, n_hidden: tuple[int], lr: float=1e-3, aug_state_contains_actions: bool=False):\n        # Adjust the size of the augmented state based on the architecture\n        if aug_state_contains_actions:\n            aug_state_size = (state_dim + action_dim) * max_len\n        else:\n            aug_state_size = state_dim * max_len\n\n        super(VNetwork, self).__init__(in_dim=aug_state_size, out_dim=out_dim, n_hidden=n_hidden, lr=lr)\n        self.critic_type = \"V-network\"\n        self.state_dim = state_dim\n        self.action_dim = action_dim\n        self.max_len = max_len\n        self.aug_state_contains_actions = aug_state_contains_actions\n        self.gpu_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n    def forward(self, states, actions):\n        if states.shape[-2] != actions.shape[-2]:\n            raise ValueError(\"The states and actions sequences must have the same length!\")\n\n        # Fill the state and actions tensors so that there are max_len elements\n        if states.shape[-2] < self.max_len:\n            states = torch.cat([states, torch.zeros(states.shape[0], self.max_len - states.shape[-2], states.shape[-1], device=self.gpu_device)], dim=-2)\n            actions = torch.cat([actions, torch.zeros(actions.shape[0], self.max_len - actions.shape[-2], actions.shape[-1], device=self.gpu_device)], dim=-2)\n\n        if self.aug_state_contains_actions:\n            aug_state_1D = torch.cat([states, actions], dim=2).view(-1)\n        else:\n            aug_state_1D = states.view(-1)\n\n        x = aug_state_1D\n        x = super(VNetwork, self).forward(x)\n        return x\n\n```\n\n\n",
        "eval_script": "## scripts/sac.py\nimport torch\n\nimport torch.nn as nn\n\nclass Critic(nn.Module):\n    \"\"\"\n    Class to represent a Critic in the context of the SAC algorithm. Children class of nn.Module.\n    \"\"\"\n    def __init__(self, in_dim: int, out_dim: int, n_hidden: tuple[int], lr: float=1e-3):\n        super(Critic, self).__init__()\n        self.role_type = \"Critic\"\n        self.in_dim = in_dim\n        self.out_dim = out_dim\n        self.n_hidden = n_hidden\n        self.lr = lr\n\n        layers = []\n        \n        for i in range(len(n_hidden)):\n            if i == 0:\n                layers.append(nn.Linear(in_dim, n_hidden[i]))\n            else:\n                layers.append(nn.Linear(n_hidden[i-1], n_hidden[i]))\n            layers.append(nn.ReLU())\n\n        layers.append(nn.Linear(n_hidden[-1], out_dim))\n\n        self.sequential = nn.Sequential(*layers)\n\n        self.init_weights()\n\n    def init_weights(self):\n        initrange = 0.1\n        for module in self.sequential:\n            if type(module) == torch.nn.Linear:\n                    nn.init.zeros_(module.bias)\n                    nn.init.uniform_(module.weight, -initrange, initrange)\n\n    def forward(self, x):\n        return self.sequential(x)\n\nclass VNetwork(Critic):\n\n    \"\"\"\n    Class to represent a V-network. Children class of Critic.\n    \"\"\"\n    def __init__(self, state_dim: int, action_dim: int, max_len: int, out_dim: int, n_hidden: tuple[int], lr: float=1e-3, aug_state_contains_actions: bool=False):\n        # Adjust the size of the augmented state based on the architecture\n        if aug_state_contains_actions:\n            aug_state_size = (state_dim + action_dim) * max_len\n        else:\n            aug_state_size = state_dim * max_len\n\n        super(VNetwork, self).__init__(in_dim=aug_state_size, out_dim=out_dim, n_hidden=n_hidden, lr=lr)\n        self.critic_type = \"V-network\"\n        self.state_dim = state_dim\n        self.action_dim = action_dim\n        self.max_len = max_len\n        self.aug_state_contains_actions = aug_state_contains_actions\n        self.gpu_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n    def forward(self, states, actions):\n        if states.shape[-2] != actions.shape[-2]:\n            raise ValueError(\"The states and actions sequences must have the same length!\")\n\n        # Fill the state and actions tensors so that there are max_len elements\n        if states.shape[-2] < self.max_len:\n            states = torch.cat([states, torch.zeros(states.shape[0], self.max_len - states.shape[-2], states.shape[-1], device=self.gpu_device)], dim=-2)\n            actions = torch.cat([actions, torch.zeros(actions.shape[0], self.max_len - actions.shape[-2], actions.shape[-1], device=self.gpu_device)], dim=-2)\n\n        if self.aug_state_contains_actions:\n            aug_state_1D = torch.cat([states, actions], dim=2).view(-1)\n        else:\n            aug_state_1D = states.view(-1)\n\n        x = aug_state_1D\n        x = super(VNetwork, self).forward(x)\n        return x\n\n\ndef test_forward():\n    state_dim = 3\n    action_dim = 2\n    max_len = 5\n    out_dim = 1\n    n_hidden = (10, 10)\n    lr = 1e-3\n\n    # Test case 1: aug_state_contains_actions = False\n    vnetwork = VNetwork(state_dim, action_dim, max_len, out_dim, n_hidden, lr, aug_state_contains_actions=False)\n    states = torch.rand(1, 3, state_dim)\n    actions = torch.rand(1, 3, action_dim)\n    output_old = vnetwork.forward(states, actions)\n    output_new = vnetwork.forward_new_implementation(states, actions)\n    assert torch.allclose(output_old, output_new), \"Test case 1 failed\"\n\n    # Test case 2: aug_state_contains_actions = True\n    vnetwork = VNetwork(state_dim, action_dim, max_len, out_dim, n_hidden, lr, aug_state_contains_actions=True)\n    states = torch.rand(1, 4, state_dim)\n    actions = torch.rand(1, 4, action_dim)\n    output_old = vnetwork.forward(states, actions)\n    output_new = vnetwork.forward_new_implementation(states, actions)\n    assert torch.allclose(output_old, output_new), \"Test case 2 failed\"\n\n    # Test case 3: max_len longer than input sequence\n    states = torch.rand(1, 2, state_dim)\n    actions = torch.rand(1, 2, action_dim)\n    output_old = vnetwork.forward(states, actions)\n    output_new = vnetwork.forward_new_implementation(states, actions)\n    assert torch.allclose(output_old, output_new), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_forward()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the ORIGINAL FUNCTION and the REVISED FUNCTION, we can see that they are identical in terms of functionality. Both functions perform the same sequence of operations: they check if the lengths of the states and actions sequences are equal, pad the sequences if they are shorter than `max_len`, concatenate the states and actions if `aug_state_contains_actions` is True, and finally pass the resulting tensor through the parent class's `forward` method. The logic and operations are unchanged between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `forward` function in the `VNetwork` class returns a value, which is the output of the neural network. This satisfies the condition.\n- CONDITION 2: The test cases use `torch.allclose` to compare the outputs of the `forward` and `forward_new_implementation` functions, which are return values. There is no checking of printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of the two implementations using `torch.allclose`. This ensures that the new implementation must have the exact same functionality as the original to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use `torch.allclose` to compare the outputs, which is reasonable given that the `forward` function returns a tensor. The test cases do not use inappropriate assertions, satisfying this condition.\n- CONDITION 5: The test cases cover different scenarios, including whether the augmented state contains actions and varying sequence lengths compared to `max_len`. These are non-trivial tests that check different aspects of the functionality, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "669fb18ee47ae5947b578e392be25c1517f6d73b"
    },
    {
        "func_name": "CursorPaginator.get_next_cursor",
        "idx": "712",
        "repo_name": "Mohammad222PR___pagify",
        "func_path": "pagify/core/cursor_paginator.py",
        "orig_func": "def get_next_cursor(self, data: List[dict]) -> Optional[Union[int, str]]:\n    \"\"\"\n        Determines the next cursor based on the last item of the current data.\n\n        :param data: The list of items from the current page.\n        :return: The id of the last item in the data to be used as the cursor for the next page, or None if empty.\n        \"\"\"\n    return data[-1]['id'] if data and 'id' in data[-1] else None",
        "orig_context": "```python\n## pagify/core/cursor_paginator.py\nfrom typing import Optional, List, Union\n\nclass CursorPaginator:\n    def __init__(self, queryset: List[dict], cursor: Optional[Union[int, str]] = None, limit: int = 10):\n        \"\"\"\n        Initializes the paginator with a dataset, cursor, and limit.\n\n        :param queryset: The dataset, typically a list of dictionaries with an 'id' field.\n        :param cursor: The id of the last item from the previous page to continue pagination.\n        :param limit: The number of items per page.\n        \"\"\"\n        self.queryset = queryset\n        self.cursor = cursor\n        self.limit = limit\n\n    def get_paginated_data(self) -> List[dict]:\n        \"\"\"\n        Retrieves a slice of the data based on the cursor and limit.\n\n        :return: A list of items for the current page.\n        \"\"\"\n        if self.cursor is not None:\n            return [item for item in self.queryset if 'id' in item and item['id'] > self.cursor][:self.limit]\n        return self.queryset[:self.limit]\n\n    def get_next_cursor(self, data: List[dict]) -> Optional[Union[int, str]]:\n        \"\"\"\n        Determines the next cursor based on the last item of the current data.\n\n        :param data: The list of items from the current page.\n        :return: The id of the last item in the data to be used as the cursor for the next page, or None if empty.\n        \"\"\"\n        return data[-1]['id'] if data and 'id' in data[-1] else None\n\n```\n\n\n",
        "eval_script": "## pagify/core/cursor_paginator.py\nfrom typing import Optional, List, Union\n\nclass CursorPaginator:\n    def __init__(self, queryset: List[dict], cursor: Optional[Union[int, str]] = None, limit: int = 10):\n        \"\"\"\n        Initializes the paginator with a dataset, cursor, and limit.\n\n        :param queryset: The dataset, typically a list of dictionaries with an 'id' field.\n        :param cursor: The id of the last item from the previous page to continue pagination.\n        :param limit: The number of items per page.\n        \"\"\"\n        self.queryset = queryset\n        self.cursor = cursor\n        self.limit = limit\n\n    def get_paginated_data(self) -> List[dict]:\n        \"\"\"\n        Retrieves a slice of the data based on the cursor and limit.\n\n        :return: A list of items for the current page.\n        \"\"\"\n        if self.cursor is not None:\n            return [item for item in self.queryset if 'id' in item and item['id'] > self.cursor][:self.limit]\n        return self.queryset[:self.limit]\n\n    def get_next_cursor(self, data: List[dict]) -> Optional[Union[int, str]]:\n        \"\"\"\n        Determines the next cursor based on the last item of the current data.\n\n        :param data: The list of items from the current page.\n        :return: The id of the last item in the data to be used as the cursor for the next page, or None if empty.\n        \"\"\"\n        return data[-1]['id'] if data and 'id' in data[-1] else None\n\n\ndef test_get_next_cursor():\n    # Test case 1: Normal case with multiple items\n    data = [{'id': 1}, {'id': 2}, {'id': 3}]\n    paginator = CursorPaginator(queryset=[])\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 2: Empty data list\n    data = []\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 3: Last item without 'id' field\n    data = [{'id': 1}, {'id': 2}, {}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 4: Single item with 'id'\n    data = [{'id': 1}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 5: Single item without 'id'\n    data = [{}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 6: All items without 'id'\n    data = [{}, {}, {}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 7: Mixed 'id' and no 'id'\n    data = [{'id': 1}, {}, {'id': 3}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 8: Non-integer 'id' values\n    data = [{'id': 'a'}, {'id': 'b'}, {'id': 'c'}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 9: Large dataset\n    data = [{'id': i} for i in range(1000)]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\n    # Test case 10: Boundary condition with limit\n    paginator = CursorPaginator(queryset=[], limit=3)\n    data = [{'id': 1}, {'id': 2}, {'id': 3}]\n    assert paginator.get_next_cursor(data) == paginator.get_next_cursor_new_implementation(data)\n\nif __name__ == \"__main__\":\n    test_get_next_cursor()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `get_next_cursor` in the `CursorPaginator` class is identical to the ORIGINAL FUNCTION. Both functions take a list of dictionaries as input and return the 'id' of the last item if it exists, otherwise they return None. The logic and implementation are exactly the same, with no changes in functionality or behavior. The test cases provided in the code also confirm that the behavior of the function remains consistent across various scenarios.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_next_cursor` function returns a value, specifically the 'id' of the last item in the data list or None if the list is empty or the last item lacks an 'id'. Therefore, it satisfies this condition.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `get_next_cursor` and `get_next_cursor_new_implementation`, which means they are checking return values, not printed or logged contents. This condition is satisfied.\n\n- CONDITION 3: The test cases are designed to check various scenarios, including normal cases, edge cases, and cases with missing 'id' fields. If `get_next_cursor_new_implementation` passes all these tests, it would have to have the same functionality as `get_next_cursor`. Thus, this condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `get_next_cursor` returns a value. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a wide range of scenarios, including normal cases, edge cases, and cases with missing or non-integer 'id' fields. This makes the test cases non-trivial. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "8e0e2190a896801ff5fed7a2430a3ffbd902b9be"
    },
    {
        "func_name": "Serializer.serialize",
        "idx": "713",
        "repo_name": "Mohammad222PR___pagify",
        "func_path": "pagify/utils/serializer.py",
        "orig_func": "@staticmethod\ndef serialize(data: List[Any]) -> List[dict]:\n    \"\"\"Converts a list of items into a list of dictionaries.\"\"\"\n    return [dict(item) for item in data]",
        "orig_context": "```python\n## pagify/utils/serializer.py\nfrom typing import List, Any\n\nclass Serializer:\n    @staticmethod\n    def serialize(data: List[Any]) -> List[dict]:\n        \"\"\"Converts a list of items into a list of dictionaries.\"\"\"\n        return [dict(item) for item in data]\n\n```\n\n\n",
        "eval_script": "## pagify/utils/serializer.py\nfrom typing import List, Any\n\nclass Serializer:\n    @staticmethod\n    def serialize(data: List[Any]) -> List[dict]:\n        \"\"\"Converts a list of items into a list of dictionaries.\"\"\"\n        return [dict(item) for item in data]\n\n\ndef test_serialize():\n    # Test with a list of tuples\n    data_tuples = [(('key1', 'value1'), ('key2', 'value2'))]\n    assert Serializer.serialize(data_tuples) == Serializer.serialize_new_implementation(data_tuples)\n    \n    # Test with a list of lists\n    data_lists = [[('key1', 'value1'), ('key2', 'value2')]]\n    assert Serializer.serialize(data_lists) == Serializer.serialize_new_implementation(data_lists)\n    \n    # Test with a list of dictionaries\n    data_dicts = [{'key1': 'value1', 'key2': 'value2'}]\n    assert Serializer.serialize(data_dicts) == Serializer.serialize_new_implementation(data_dicts)\n\nif __name__ == \"__main__\":\n    test_serialize()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function is a static method that takes a list of items and converts each item into a dictionary using the `dict()` constructor. The revised function is implemented within a class named `Serializer` but retains the same static method signature and logic as the original function. Both functions perform the same operation: converting each item in the input list into a dictionary. The test cases provided in the code also confirm that the revised function behaves as expected by comparing its output to another implementation, which is assumed to be equivalent. Since the logic and functionality of the revised function are identical to the original function, the functionality is the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `serialize` function returns a list of dictionaries, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the `serialize` function, not printed or logged contents.\n- CONDITION 3: The test cases compare the output of `serialize` with `serialize_new_implementation` for the same input data. This ensures that `serialize_new_implementation` must have the same functionality as `serialize` to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `serialize` returns a value.\n- CONDITION 5: The test cases cover different types of input data structures (tuples, lists, and dictionaries), making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "8e0e2190a896801ff5fed7a2430a3ffbd902b9be"
    },
    {
        "func_name": "JSONResponseFormatter.success",
        "idx": "714",
        "repo_name": "Mohammad222PR___pagify",
        "func_path": "pagify/utils/response_formatting.py",
        "orig_func": "@staticmethod\ndef success(data: Any, message: str='Success') -> str:\n    response = {'status': 'success', 'message': message, 'data': data}\n    return json.dumps(response)",
        "orig_context": "```python\n## pagify/utils/response_formatting.py\nimport json\n\nfrom typing import Any, Dict, Optional, List, Union\n\nclass JSONResponseFormatter:\n    \"\"\"Provides helper methods to format responses as JSON for consistency across the application.\"\"\"\n\n    @staticmethod\n    def success(data: Any, message: str = \"Success\") -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data\n        }\n        return json.dumps(response)\n\n    @staticmethod\n    def error(message: str, code: int = 400) -> str:\n        response = {\n            \"status\": \"error\",\n            \"message\": message,\n            \"code\": code\n        }\n        return json.dumps(response)\n\n    @staticmethod\n    def paginate(\n        data: List[Any],\n        pagination_info: Dict[str, Union[int, None]],\n        message: str = \"Paginated data\"\n    ) -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data,\n            \"pagination\": pagination_info\n        }\n        return response\n\n    @staticmethod\n    def parse_response(json_response: str) -> Dict[str, Any]:\n        return json.loads(json_response)\n\n```\n\n\n",
        "eval_script": "## pagify/utils/response_formatting.py\nimport json\n\nfrom typing import Any, Dict, Optional, List, Union\n\nclass JSONResponseFormatter:\n    \"\"\"Provides helper methods to format responses as JSON for consistency across the application.\"\"\"\n\n    @staticmethod\n    def success(data: Any, message: str = \"Success\") -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data\n        }\n        return json.dumps(response)\n\n\n    @staticmethod\n    def error(message: str, code: int = 400) -> str:\n        response = {\n            \"status\": \"error\",\n            \"message\": message,\n            \"code\": code\n        }\n        return json.dumps(response)\n\n    @staticmethod\n    def paginate(\n        data: List[Any],\n        pagination_info: Dict[str, Union[int, None]],\n        message: str = \"Paginated data\"\n    ) -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data,\n            \"pagination\": pagination_info\n        }\n        return response\n\n    @staticmethod\n    def parse_response(json_response: str) -> Dict[str, Any]:\n        return json.loads(json_response)\n\ndef test_success():\n    # Test case 1: Default message with simple data\n    data1 = {\"key\": \"value\"}\n    assert JSONResponseFormatter.success(data1) == JSONResponseFormatter.success_new_implementation(data1)\n\n    # Test case 2: Custom message with list data\n    data2 = [1, 2, 3]\n    message2 = \"Custom message\"\n    assert JSONResponseFormatter.success(data2, message2) == JSONResponseFormatter.success_new_implementation(data2, message2)\n\n    # Test case 3: Empty data\n    data3 = None\n    assert JSONResponseFormatter.success(data3) == JSONResponseFormatter.success_new_implementation(data3)\n\n    # Test case 4: Complex nested data structure\n    data4 = {\"key\": {\"subkey\": [1, 2, {\"deepkey\": \"deepvalue\"}]}}\n    assert JSONResponseFormatter.success(data4) == JSONResponseFormatter.success_new_implementation(data4)\n\n    # Test case 5: Different data types\n    data5 = \"string data\"\n    assert JSONResponseFormatter.success(data5) == JSONResponseFormatter.success_new_implementation(data5)\n\n    data6 = 12345\n    assert JSONResponseFormatter.success(data6) == JSONResponseFormatter.success_new_implementation(data6)\n\n    data7 = 123.45\n    assert JSONResponseFormatter.success(data7) == JSONResponseFormatter.success_new_implementation(data7)\n\n    data8 = True\n    assert JSONResponseFormatter.success(data8) == JSONResponseFormatter.success_new_implementation(data8)\n\n    # Test case 6: Empty string message\n    data9 = {\"key\": \"value\"}\n    message9 = \"\"\n    assert JSONResponseFormatter.success(data9, message9) == JSONResponseFormatter.success_new_implementation(data9, message9)\n\n    # Test case 7: Special characters in message\n    data10 = {\"key\": \"value\"}\n    message10 = \"Special characters !@#$%^&*()\"\n    assert JSONResponseFormatter.success(data10, message10) == JSONResponseFormatter.success_new_implementation(data10, message10)\n\n    # Test case 8: Large data\n    data11 = {\"key\": \"value\" * 1000}\n    assert JSONResponseFormatter.success(data11) == JSONResponseFormatter.success_new_implementation(data11)\n\nif __name__ == \"__main__\":\n    test_success()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is part of a class `JSONResponseFormatter` and is defined as a static method, similar to the ORIGINAL FUNCTION. The functionality of the `success` method in both the ORIGINAL FUNCTION and the REVISED FUNCTION is to create a JSON string from a dictionary containing the keys 'status', 'message', and 'data'. The 'status' is set to 'success', the 'message' is set to the provided message or defaults to 'Success', and 'data' is set to the provided data. Both functions use `json.dumps` to convert the dictionary to a JSON string. The test cases provided in the REVISED FUNCTION's code also confirm that the functionality remains consistent across various inputs. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `success` function returns a JSON string, which is a return value. This condition is satisfied.\n- CONDITION 2: The test cases use assertions to compare the return values of `success` and `success_new_implementation`. They do not check printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `success` and `success_new_implementation` for various inputs. If `success_new_implementation` produces the same outputs for all these inputs, it must have the same functionality as `success`. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations. Since `success` returns a value, using assertions to compare these return values is reasonable. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including default and custom messages, different data types, complex nested structures, and special characters. These are non-trivial test cases. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "8e0e2190a896801ff5fed7a2430a3ffbd902b9be"
    },
    {
        "func_name": "JSONResponseFormatter.error",
        "idx": "715",
        "repo_name": "Mohammad222PR___pagify",
        "func_path": "pagify/utils/response_formatting.py",
        "orig_func": "@staticmethod\ndef error(message: str, code: int=400) -> str:\n    response = {'status': 'error', 'message': message, 'code': code}\n    return json.dumps(response)",
        "orig_context": "```python\n## pagify/utils/response_formatting.py\nimport json\n\nfrom typing import Any, Dict, Optional, List, Union\n\nclass JSONResponseFormatter:\n    \"\"\"Provides helper methods to format responses as JSON for consistency across the application.\"\"\"\n\n    @staticmethod\n    def success(data: Any, message: str = \"Success\") -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data\n        }\n        return json.dumps(response)\n\n    @staticmethod\n    def error(message: str, code: int = 400) -> str:\n        response = {\n            \"status\": \"error\",\n            \"message\": message,\n            \"code\": code\n        }\n        return json.dumps(response)\n\n    @staticmethod\n    def paginate(\n        data: List[Any],\n        pagination_info: Dict[str, Union[int, None]],\n        message: str = \"Paginated data\"\n    ) -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data,\n            \"pagination\": pagination_info\n        }\n        return response\n\n    @staticmethod\n    def parse_response(json_response: str) -> Dict[str, Any]:\n        return json.loads(json_response)\n\n```\n\n\n",
        "eval_script": "## pagify/utils/response_formatting.py\nimport json\n\nfrom typing import Any, Dict, Optional, List, Union\n\nclass JSONResponseFormatter:\n    \"\"\"Provides helper methods to format responses as JSON for consistency across the application.\"\"\"\n\n    @staticmethod\n    def success(data: Any, message: str = \"Success\") -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data\n        }\n        return json.dumps(response)\n\n    @staticmethod\n    def error(message: str, code: int = 400) -> str:\n        response = {\n            \"status\": \"error\",\n            \"message\": message,\n            \"code\": code\n        }\n        return json.dumps(response)\n\n\n    @staticmethod\n    def paginate(\n        data: List[Any],\n        pagination_info: Dict[str, Union[int, None]],\n        message: str = \"Paginated data\"\n    ) -> str:\n        response = {\n            \"status\": \"success\",\n            \"message\": message,\n            \"data\": data,\n            \"pagination\": pagination_info\n        }\n        return response\n\n    @staticmethod\n    def parse_response(json_response: str) -> Dict[str, Any]:\n        return json.loads(json_response)\n\ndef test_error():\n    # Test with default error code\n    assert JSONResponseFormatter.error(\"Error occurred\") == JSONResponseFormatter.error_new_implementation(\"Error occurred\")\n\n    # Test with custom error code\n    assert JSONResponseFormatter.error(\"Not Found\", 404) == JSONResponseFormatter.error_new_implementation(\"Not Found\", 404)\n\n    # Test with different error message\n    assert JSONResponseFormatter.error(\"Unauthorized\", 401) == JSONResponseFormatter.error_new_implementation(\"Unauthorized\", 401)\n\n    # Test with empty message\n    assert JSONResponseFormatter.error(\"\", 400) == JSONResponseFormatter.error_new_implementation(\"\", 400)\n\n    # Test with special characters in message\n    assert JSONResponseFormatter.error(\"Error! @#$%^&*()\", 400) == JSONResponseFormatter.error_new_implementation(\"Error! @#$%^&*()\", 400)\n\n    # Test with negative error code\n    assert JSONResponseFormatter.error(\"Negative code\", -1) == JSONResponseFormatter.error_new_implementation(\"Negative code\", -1)\n\n    # Test with large error code\n    assert JSONResponseFormatter.error(\"Large code\", 999999) == JSONResponseFormatter.error_new_implementation(\"Large code\", 999999)\n\n    # Test with non-ASCII characters\n    assert JSONResponseFormatter.error(\"\u041e\u0448\u0438\u0431\u043a\u0430\", 400) == JSONResponseFormatter.error_new_implementation(\"\u041e\u0448\u0438\u0431\u043a\u0430\", 400)\n\nif __name__ == \"__main__\":\n    test_error()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is part of a class called `JSONResponseFormatter` and is defined as a static method. The function takes two parameters: `message` and `code`, with `code` defaulting to 400. It creates a dictionary with keys 'status', 'message', and 'code', and then converts this dictionary to a JSON string using `json.dumps()`. This is identical to the ORIGINAL FUNCTION, which also creates a similar dictionary and returns its JSON representation. Both functions have the same parameters, default values, and return the same JSON structure. The test cases provided in the code also confirm that the functionality of the REVISED FUNCTION matches the expected behavior of the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `error` function returns a JSON string, which is a return value. This condition is satisfied.\n- CONDITION 2: The test cases use assertions to compare the return values of `error` and `error_new_implementation`. They do not check printed or logged content. This condition is satisfied.\n- CONDITION 3: The test cases compare the output of `error` and `error_new_implementation` directly. If `error_new_implementation` has the exact same functionality as `error`, it will pass all test cases. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `error` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover various scenarios, including default and custom error codes, different messages, special characters, negative and large error codes, and non-ASCII characters. These are non-trivial test cases. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "8e0e2190a896801ff5fed7a2430a3ffbd902b9be"
    },
    {
        "func_name": "Analysis._extract_scores",
        "idx": "718",
        "repo_name": "nittybekky___GNDRLens-AI",
        "func_path": "backend/app/models/analysis.py",
        "orig_func": "def _extract_scores(self):\n    \"\"\"Extract key scores from analysis results for querying\"\"\"\n    if isinstance(self.results, str):\n        results = json.loads(self.results)\n    else:\n        results = self.results\n    self.equity_score = results.get('overall_assessment', {}).get('equity_score')\n    if 'industry_context' in results:\n        industry_context = results['industry_context']\n        self.violence_prevention_score = industry_context.get('violence_prevention', {}).get('benchmark_comparison', {}).get('current_score')\n        self.care_support_score = industry_context.get('unpaid_care', {}).get('benchmark_comparison', {}).get('current_score')\n        self.policy_implementation_score = industry_context.get('policy_effectiveness', {}).get('benchmark_comparison', {}).get('current_score')",
        "orig_context": "```python\n## backend/app/models/base.py\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\n```\n\n\n```python\n## backend/app/models/analysis.py\nfrom sqlalchemy import Column, Integer, String, Float, DateTime, Text, JSON\n\nfrom datetime import datetime\n\nfrom .base import Base\n\nimport json\n\nclass Analysis(Base):\n    __tablename__ = 'analyses'\n\n    id = Column(Integer, primary_key=True, index=True)\n    text = Column(Text, nullable=False)\n    analysis_type = Column(String(50), nullable=False)  # e.g., \"gender_equity\"\n    results = Column(JSON, nullable=False)  # Store complete analysis results\n    context = Column(JSON, nullable=True)  # Store analysis context\n    industry = Column(String(50), nullable=True)\n    language = Column(String(10), nullable=True)\n    location = Column(String(100), nullable=True)\n    \n    # Derived metrics for querying and filtering\n    equity_score = Column(Float, nullable=True)\n    violence_prevention_score = Column(Float, nullable=True)\n    care_support_score = Column(Float, nullable=True)\n    policy_implementation_score = Column(Float, nullable=True)\n    \n    created_at = Column(DateTime, default=datetime.utcnow, nullable=False)\n    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        if self.results:\n            self._extract_scores()\n\n    def _extract_scores(self):\n        \"\"\"Extract key scores from analysis results for querying\"\"\"\n        if isinstance(self.results, str):\n            results = json.loads(self.results)\n        else:\n            results = self.results\n\n        self.equity_score = results.get('overall_assessment', {}).get('equity_score')\n        \n        if 'industry_context' in results:\n            industry_context = results['industry_context']\n            self.violence_prevention_score = industry_context.get('violence_prevention', {}).get('benchmark_comparison', {}).get('current_score')\n            self.care_support_score = industry_context.get('unpaid_care', {}).get('benchmark_comparison', {}).get('current_score')\n            self.policy_implementation_score = industry_context.get('policy_effectiveness', {}).get('benchmark_comparison', {}).get('current_score')\n\n```\n\n\n",
        "eval_script": "# backend/app/models/base.py\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\n# backend/app/models/analysis.py\nfrom sqlalchemy import Column, Integer, String, Float, DateTime, Text, JSON\nfrom datetime import datetime\nimport json\n\nclass Analysis(Base):\n    __tablename__ = 'analyses'\n\n    id = Column(Integer, primary_key=True, index=True)\n    text = Column(Text, nullable=False)\n    analysis_type = Column(String(50), nullable=False)  # e.g., \"gender_equity\"\n    results = Column(JSON, nullable=False)  # Store complete analysis results\n    context = Column(JSON, nullable=True)  # Store analysis context\n    industry = Column(String(50), nullable=True)\n    language = Column(String(10), nullable=True)\n    location = Column(String(100), nullable=True)\n    \n    # Derived metrics for querying and filtering\n    equity_score = Column(Float, nullable=True)\n    violence_prevention_score = Column(Float, nullable=True)\n    care_support_score = Column(Float, nullable=True)\n    policy_implementation_score = Column(Float, nullable=True)\n    \n    created_at = Column(DateTime, default=datetime.utcnow, nullable=False)\n    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        if self.results:\n            self._extract_scores()\n\n    def _extract_scores(self):\n        \"\"\"Extract key scores from analysis results for querying\"\"\"\n        if isinstance(self.results, str):\n            results = json.loads(self.results)\n        else:\n            results = self.results\n\n        self.equity_score = results.get('overall_assessment', {}).get('equity_score')\n        \n        if 'industry_context' in results:\n            industry_context = results['industry_context']\n            self.violence_prevention_score = industry_context.get('violence_prevention', {}).get('benchmark_comparison', {}).get('current_score')\n            self.care_support_score = industry_context.get('unpaid_care', {}).get('benchmark_comparison', {}).get('current_score')\n            self.policy_implementation_score = industry_context.get('policy_effectiveness', {}).get('benchmark_comparison', {}).get('current_score')\n\n\ndef test__extract_scores():\n    # Test case 1: results as a dictionary with full data\n    results_dict = {\n        \"overall_assessment\": {\"equity_score\": 85.0},\n        \"industry_context\": {\n            \"violence_prevention\": {\"benchmark_comparison\": {\"current_score\": 70.0}},\n            \"unpaid_care\": {\"benchmark_comparison\": {\"current_score\": 60.0}},\n            \"policy_effectiveness\": {\"benchmark_comparison\": {\"current_score\": 75.0}}\n        }\n    }\n    analysis1 = Analysis(results=results_dict)\n    analysis1._extract_scores()\n    analysis1_new = Analysis(results=results_dict)\n    analysis1_new._extract_scores_new_implementation()\n    assert analysis1.equity_score == analysis1_new.equity_score\n    assert analysis1.violence_prevention_score == analysis1_new.violence_prevention_score\n    assert analysis1.care_support_score == analysis1_new.care_support_score\n    assert analysis1.policy_implementation_score == analysis1_new.policy_implementation_score\n\n    # Test case 2: results as a JSON string with partial data\n    results_json = json.dumps({\n        \"overall_assessment\": {\"equity_score\": 90.0},\n        \"industry_context\": {\n            \"violence_prevention\": {\"benchmark_comparison\": {\"current_score\": 80.0}}\n        }\n    })\n    analysis2 = Analysis(results=results_json)\n    analysis2._extract_scores()\n    analysis2_new = Analysis(results=results_json)\n    analysis2_new._extract_scores_new_implementation()\n    assert analysis2.equity_score == analysis2_new.equity_score\n    assert analysis2.violence_prevention_score == analysis2_new.violence_prevention_score\n    assert analysis2.care_support_score == analysis2_new.care_support_score\n    assert analysis2.policy_implementation_score == analysis2_new.policy_implementation_score\n\n    # Test case 3: results as a dictionary with no industry context\n    results_no_context = {\n        \"overall_assessment\": {\"equity_score\": 95.0}\n    }\n    analysis3 = Analysis(results=results_no_context)\n    analysis3._extract_scores()\n    analysis3_new = Analysis(results=results_no_context)\n    analysis3_new._extract_scores_new_implementation()\n    assert analysis3.equity_score == analysis3_new.equity_score\n    assert analysis3.violence_prevention_score == analysis3_new.violence_prevention_score\n    assert analysis3.care_support_score == analysis3_new.care_support_score\n    assert analysis3.policy_implementation_score == analysis3_new.policy_implementation_score\n\nif __name__ == \"__main__\":\n    test__extract_scores()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing the original and revised functions, both implementations of `_extract_scores` are identical. They both check if `self.results` is a string and parse it as JSON if necessary. They then extract the `equity_score` from `overall_assessment` and, if `industry_context` is present, extract `violence_prevention_score`, `care_support_score`, and `policy_implementation_score` from their respective paths. The test cases provided in the revised code also confirm that the functionality remains consistent across different scenarios. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_extract_scores` function modifies the instance variables of the `Analysis` class, such as `equity_score`, `violence_prevention_score`, `care_support_score`, and `policy_implementation_score`. Therefore, it satisfies this condition.\n  \n- CONDITION 2: The test cases check the values of the instance variables after calling `_extract_scores` and `_extract_scores_new_implementation`. They do not rely on printed or logged outputs, satisfying this condition.\n\n- CONDITION 3: The test cases compare the state of the instance variables between the original and new implementations. If the new implementation passes all tests, it must have the same functionality as the original, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the instance variables of two `Analysis` objects, one using the original implementation and the other using the new implementation. This is a reasonable approach given that `_extract_scores` does not return values but modifies instance variables, satisfying this condition.\n\n- CONDITION 5: The test cases cover various scenarios, including results as a dictionary with full data, results as a JSON string with partial data, and results as a dictionary with no industry context. These cases are non-trivial and cover different potential input structures, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "f48a1f8f77548da0dcfb21a5a8f448b21ee37563"
    },
    {
        "func_name": "AnalysisService._load_violence_indicators",
        "idx": "720",
        "repo_name": "nittybekky___GNDRLens-AI",
        "func_path": "backend/app/services/analysis_service.py",
        "orig_func": "def _load_violence_indicators(self) -> Dict:\n    \"\"\"Load comprehensive violence and harassment indicators\"\"\"\n    return {'physical_violence': ['physical threats', 'intimidation', 'unsafe conditions', 'restricted movement', 'physical isolation'], 'psychological_violence': ['verbal abuse', 'gaslighting', 'manipulation', 'emotional abuse', 'psychological manipulation'], 'economic_violence': ['financial control', 'economic threats', 'withholding resources', 'salary discrimination', 'promotion discrimination'], 'digital_violence': ['online harassment', 'cyberstalking', 'digital surveillance', 'online threats', 'privacy violations'], 'risk_factors': ['power imbalance', 'isolation', 'lack of support systems', 'financial dependency', 'fear of retaliation'], 'protective_factors': ['clear reporting mechanisms', 'support networks', 'financial independence', 'strong policies', 'accountability measures']}",
        "orig_context": "```python\n# backend/app/services/analysis_service.py\n\nclass AnalysisService:\n\n    def __init__(self):\n        try:\n            load_dotenv()\n            script_dir = os.path.dirname(os.path.abspath(__file__))\n            script_dir = os.path.dirname(os.path.abspath(__file__))\n            credentials_path = os.path.join(script_dir, os.getenv('GOOGLE_APPLICATION_CREDENTIALS'))\n            os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credentials_path\n            api_key = os.getenv('GOOGLE_API_KEY')\n            genai.configure(api_key=api_key)\n            self.model = genai.GenerativeModel('gemini-pro')\n            self.translate_client = translate.Client()\n            self.gender_terms = self._load_gender_terms()\n            self.industry_benchmarks = self._load_industry_benchmarks()\n            self.violence_indicators = self._load_violence_indicators()\n            self.care_work_metrics = self._load_care_work_metrics()\n            self.policy_frameworks = self._load_policy_frameworks()\n        except Exception as e:\n            print(f'Error initializing services: {e}')\n            raise\n\n    def detect_language(self, text: str) -> str:\n        \"\"\"Detect the language of the provided text.\"\"\"\n        result = self.translate_client.detect_language(text)\n        return result['language']\n\n    def get_user_location(self) -> str:\n        \"\"\"Detect the user's approximate location based on their IP address.\"\"\"\n        try:\n            response = requests.get('https://ipinfo.io')\n            if response.status_code == 200:\n                data = response.json()\n                return data.get('country', 'global')\n        except Exception as e:\n            print(f'Location detection failed: {e}')\n        return 'global'\n\n    def translate_text(self, text: str, target_language: str) -> str:\n        \"\"\"Translate text to the specified target language.\"\"\"\n        translation = self.translate_client.translate(text, target_language=target_language)\n        return translation['translatedText']\n\n    def _load_gender_terms(self) -> Dict:\n        \"\"\"Load database of gender-related terms and their contexts\"\"\"\n        return {'explicit_bias': [\"women can't\", \"girls shouldn't\", 'male-only', 'feminine weakness', 'maternal risk', 'emotional decision', 'bossy woman', 'hysteric'], 'implicit_bias': ['nurturing role', 'support position', 'office mom', 'aggressive for a woman', 'ambitious woman', 'work-life balance', 'family responsibilities'], 'violence_related': ['threatening', 'intimidating', 'controlling', 'coercive', 'hostile', 'aggressive', 'retaliatory', 'punitive'], 'care_work_related': ['childcare duties', 'eldercare responsibilities', 'domestic duties', 'household management', 'family obligations', 'caretaking role'], 'inclusive_alternatives': {'chairman': 'chairperson', 'businessman': 'business person', 'policeman': 'police officer', 'mankind': 'humanity', 'manpower': 'workforce'}, 'empowerment_terms': ['leadership', 'achievement', 'expertise', 'professional growth', 'equal opportunity', 'mentorship', 'advancement']}\n\n    def _load_care_work_metrics(self) -> Dict:\n        \"\"\"Load metrics for analyzing unpaid care work impact\"\"\"\n        return {'time_allocation': {'childcare_hours': 0, 'eldercare_hours': 0, 'household_maintenance': 0, 'emotional_labor': 0}, 'workplace_policies': {'flexible_hours': False, 'remote_work': False, 'parental_leave': 0, 'caregiving_support': False}, 'economic_impact': {'career_interruptions': 0, 'promotion_delays': 0, 'wage_penalties': 0, 'retirement_impact': 0}, 'support_indicators': {'childcare_facilities': False, 'eldercare_assistance': False, 'domestic_help_allowance': False, 'caregiver_networks': False}}\n\n    def _load_policy_frameworks(self) -> Dict:\n        \"\"\"Load gender equity policy frameworks and recommendations\"\"\"\n        return {'workplace_policies': {'recruitment': ['blind recruitment processes', 'diverse interview panels', 'standardized evaluation criteria'], 'advancement': ['transparent promotion criteria', 'leadership development programs', 'mentorship initiatives'], 'compensation': ['pay equity audits', 'transparent salary bands', 'performance evaluation standardization']}, 'care_support_policies': {'leave_policies': ['paid parental leave', 'flexible caregiving leave', 'sabbatical options'], 'workplace_flexibility': ['flexible hours', 'remote work options', 'compressed work weeks'], 'support_services': ['on-site childcare', 'eldercare referral services', 'caregiver support networks']}, 'safety_policies': {'prevention': ['anti-harassment training', 'bystander intervention programs', 'climate surveys'], 'response': ['reporting mechanisms', 'investigation procedures', 'victim support services'], 'accountability': ['clear consequences', 'regular policy review', 'transparency reports']}}\n\n    def _load_industry_benchmarks(self) -> Dict:\n        \"\"\"Load industry-specific gender equity benchmarks\"\"\"\n        return {'tech': {'leadership_representation': 0.3, 'pay_gap_threshold': 0.05, 'promotion_rate_ratio': 1.0, 'violence_prevention_score': 0.8, 'care_support_score': 0.7, 'policy_implementation_score': 0.75}, 'healthcare': {'leadership_representation': 0.4, 'pay_gap_threshold': 0.03, 'promotion_rate_ratio': 1.0, 'violence_prevention_score': 0.9, 'care_support_score': 0.8, 'policy_implementation_score': 0.85}}\n\n    async def analyze_content(self, text: str, context: Optional[Dict]=None, industry: Optional[str]=None, db: Session=None) -> Dict:\n        \"\"\"\n        Analyze content for gender equity issues with industry-specific context including violence prevention,\n        care work recognition, and policy recommendations\n        \"\"\"\n        source_language = self.detect_language(text)\n        analysis_text = text if source_language == 'en' else self.translate_text(text, 'en')\n        prompt = self._generate_gender_equity_prompt(analysis_text, industry)\n        try:\n            response = await self.model.generate_content_async(prompt, generation_config={'temperature': 0.2, 'top_p': 0.9, 'top_k': 50, 'max_output_tokens': 2048}, safety_settings={'HARASSMENT': 'BLOCK_NONE', 'HATE_SPEECH': 'BLOCK_NONE'})\n            cleaned_response = re.sub('```(?:json)?\\\\n?(.*?)\\\\n?```|^(?:json|JSON)\\\\s*', '\\\\1', response.text, flags=re.DOTALL).strip()\n            analysis = json.loads(cleaned_response)\n            enhanced_analysis = self._enhance_gender_analysis(analysis, industry)\n            if db:\n                self._store_analysis(db, text, enhanced_analysis, context)\n            return enhanced_analysis\n        except Exception as e:\n            print(f'Analysis failed: {str(e)}')\n            return {'status': 'error', 'error': str(e)}\n\n    def _generate_gender_equity_prompt(self, text: str, industry: Optional[str]=None) -> str:\n        \"\"\"Generate a gender equity focused prompt for Gemini\"\"\"\n        industry_context = f'Industry Context: {industry.upper()} sector analysis' if industry else ''\n        return f'\\n        You are an advanced AI system specialized in gender equity analysis. Analyze the following text with special attention to gender-related issues, biases, violence prevention, unpaid care work, policy implications and opportunities for promoting equality.\\n\\n        {industry_context}\\n\\n        1. GENDER BIAS DETECTION\\n        - Identify explicit and implicit gender biases\\n        - Detect stereotypical language and assumptions\\n        - Flag discriminatory patterns in language\\n        - Identify missed opportunities for inclusion\\n        \\n        2. REPRESENTATION ANALYSIS\\n        - Analyze representation of women and girls\\n        - Assess power dynamics and agency\\n        - Evaluate visibility and voice\\n        \\n        3. WORKPLACE EQUITY ASSESSMENT\\n        - Identify barriers to advancement\\n        - Analyze leadership opportunity language\\n        - Detect pay equity issues\\n        - Assess work-life balance assumptions\\n        \\n        4. SAFETY AND DIGNITY\\n        - Flag potential harassment indicators\\n        - Identify undermining or diminishing language\\n        - Assess psychological safety implications\\n        \\n        5. EMPOWERMENT OPPORTUNITIES\\n        - Identify areas for strengthening agency\\n        - Flag mentorship and growth opportunities\\n        - Suggest inclusive alternatives\\n\\n        6. VIOLENCE PREVENTION\\n        - Identify explicit and subtle forms of violence\\n        - Detect harassment risk indicators\\n        - Analyze power dynamics and vulnerabilities\\n        - Assess existing safety measures\\n        - Flag potential escalation patterns\\n        - Evaluate reporting and support mechanisms\\n        \\n        7. UNPAID CARE WORK \\n        - Identify assumptions about care responsibilities\\n        - Analyze work-life balance implications\\n        - Assess recognition of unpaid work\\n        - Evaluate support for caregivers\\n        - Analyze career impact of care duties\\n        - Flag discriminatory patterns\\n        \\n        8. POLICY IMPLICATIONS \\n        - Identify policy gaps and opportunities\\n        - Assess implementation effectiveness\\n        - Evaluate monitoring mechanisms\\n        - Analyze resource allocation\\n        - Flag enforcement challenges\\n        - Suggest policy improvements\\n\\n\\n        Text to analyze: {text}\\n\\n        Please make response really detailed. Any metric that has a score should also have some backup details. If the text request does not qualify for any oof the issues we are analyzing, please say so instead of leaving them blank. There must always be priority actions for overall assessment. Respond ONLY in JSON format with the following structure:\\n        {{\\n            \"bias_detection\": {{\\n                \"explicit_biases\": [\\n                    {{\\n                        \"bias\": <specific bias>,\\n                        \"context\": <explanation>,\\n                        \"impact\": <potential harm>,\\n                        \"suggestion\": <improvement>\\n                    }}\\n                ],\\n                \"implicit_biases\": [<similar structure>],\\n                \"stereotypes\": [<similar structure>],\\n                \"bias_score\": <float 0-1>\\n            }},\\n            \"representation_analysis\": {{\\n                \"visibility_score\": <float 0-1>,\\n                \"agency_score\": <float 0-1>,\\n                \"power_dynamics\": [<specific observations>],\\n                \"improvement_areas\": [<specific suggestions>]\\n            }},\\n            \"workplace_equity\": {{\\n                \"advancement_barriers\": [<specific barriers>],\\n                \"leadership_language\": {{\\n                    \"inclusive_score\": <float 0-1>,\\n                    \"flags\": [<specific issues>]\\n                }},\\n                \"pay_equity_flags\": [<potential issues>],\\n                \"work_life_assumptions\": [<detected assumptions>]\\n            }},\\n            \"safety_assessment\": {{\\n                \"harassment_indicators\": [<potential flags>],\\n                \"undermining_language\": [<specific examples>],\\n                \"psychological_safety\": {{\\n                    \"score\": <float 0-1>,\\n                    \"concerns\": [<specific issues>]\\n                }}\\n            }},\\n            \"empowerment_opportunities\": {{\\n                \"growth_language\": [<positive examples>],\\n                \"mentorship_opportunities\": [<specific suggestions>],\\n                \"inclusive_alternatives\": [<specific improvements>]\\n            }},\\n            \"violence_prevention\": {{\\n                \"risk_indicators\": [\\n                    {{\\n                        \"type\": <violence type>,\\n                        \"indicator\": <specific flag>,\\n                        \"context\": <situation details>,\\n                        \"severity\": <\"high\", \"medium\", \"low\">,\\n                        \"mitigation\": <specific recommendation>\\n                    }}\\n                ],\\n                \"safety_measures\": {{\\n                    \"existing\": [<current measures>],\\n                    \"gaps\": [<missing measures>],\\n                    \"recommendations\": [<specific improvements>]\\n                }},\\n                \"power_dynamics\": {{\\n                    \"risk_factors\": [<specific factors>],\\n                    \"protective_factors\": [<existing protections>],\\n                    \"improvements\": [<recommended changes>]\\n                }},\\n                \"overall_safety_score\": <float 0-1>\\n            }},\\n            \"unpaid_care_analysis\": {{\\n                \"care_responsibilities\": [\\n                    {{\\n                        \"type\": <care type>,\\n                        \"assumption\": <specific assumption>,\\n                        \"impact\": <career/workplace effect>,\\n                        \"support_needed\": <specific support>\\n                    }}\\n                ],\\n                \"work_life_measures\": {{\\n                    \"flexibility_score\": <float 0-1>,\\n                    \"support_score\": <float 0-1>,\\n                    \"gaps\": [<specific gaps>],\\n                    \"recommendations\": [<specific improvements>]\\n                }},\\n                \"economic_impact\": {{\\n                    \"career_progression\": <impact assessment>,\\n                    \"compensation_effects\": [<specific effects>],\\n                    \"mitigation_strategies\": [<specific strategies>]\\n                }},\\n                \"care_recognition_score\": <float 0-1>\\n            }},\\n            \"policy_recommendations\": {{\\n                \"current_policies\": {{\\n                    \"strengths\": [<existing good policies>],\\n                    \"weaknesses\": [<policy gaps>],\\n                    \"implementation_score\": <float 0-1>\\n                }},\\n                \"recommended_policies\": [\\n                    {{\\n                        \"focus_area\": <specific area>,\\n                        \"recommendation\": <policy suggestion>,\\n                        \"rationale\": <justification>,\\n                        \"implementation_steps\": [<specific steps>],\\n                        \"success_metrics\": [<measurement criteria>],\\n                        \"priority\": <\"high\", \"medium\", \"low\">\\n                    }}\\n                ],\\n                \"resource_implications\": {{\\n                    \"required_resources\": [<specific needs>],\\n                    \"potential_challenges\": [<specific challenges>],\\n                    \"mitigation_strategies\": [<specific strategies>]\\n                }},\\n                \"monitoring_framework\": {{\\n                    \"metrics\": [<specific metrics>],\\n                    \"data_needs\": [<required data>],\\n                    \"review_frequency\": <recommended frequency>\\n                }}\\n            }},\\n            \"overall_assessment\": {{\\n                \"equity_score\": <float 0-1>,\\n                \"priority_actions\": [\\n                    {{\\n                        \"issue\": <specific issue>,\\n                        \"impact\": <potential harm>,\\n                        \"recommendation\": <specific suggestion>,\\n                        \"priority\": <\"high\", \"medium\", \"low\">\\n                    }}\\n                ]\\n            }}\\n        }}\\n        '\n\n    def _enhance_gender_analysis(self, analysis: Dict, industry: Optional[str]=None) -> Dict:\n        \"\"\"Enhance the basic analysis with industry-specific insights\"\"\"\n        if not industry or industry not in self.industry_benchmarks:\n            return analysis\n        benchmarks = self.industry_benchmarks[industry]\n        analysis['industry_context'] = {'benchmarks': benchmarks, 'gap_analysis': self._calculate_industry_gaps(analysis, benchmarks), 'recommendations': self._generate_industry_recommendations(analysis, benchmarks), 'violence_prevention': self._analyze_violence_prevention(analysis, benchmarks), 'unpaid_care': self._analyze_care_work(analysis, benchmarks), 'policy_effectiveness': self._analyze_policy_framework(analysis, benchmarks)}\n        return analysis\n\n    def _calculate_industry_gaps(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Calculate comprehensive gaps between current state and industry benchmarks\"\"\"\n        return {'leadership_gaps': {'executive_gap': benchmarks['leadership_representation']['executive_level'] - analysis.get('representation_analysis', {}).get('visibility_score', 0), 'management_gap': benchmarks['leadership_representation']['senior_management'] - analysis.get('representation_analysis', {}).get('agency_score', 0), 'pipeline_gap': benchmarks['advancement_metrics']['leadership_pipeline'] - analysis.get('workplace_equity', {}).get('leadership_language', {}).get('inclusive_score', 0)}, 'pay_equity_gaps': {'base_pay_gap': benchmarks['pay_equity']['gap_threshold'], 'bonus_gap': benchmarks['pay_equity']['bonus_gap_threshold'], 'promotion_gap': benchmarks['pay_equity']['promotion_adjustment']}, 'safety_gaps': {'prevention_gap': benchmarks['safety_metrics']['violence_prevention_score'] - analysis.get('violence_prevention', {}).get('overall_safety_score', 0), 'reporting_gap': benchmarks['safety_metrics']['harassment_reporting_mechanism'] - analysis.get('safety_assessment', {}).get('psychological_safety', {}).get('score', 0), 'response_gap': benchmarks['safety_metrics']['response_effectiveness'] - analysis.get('violence_prevention', {}).get('overall_safety_score', 0)}, 'care_support_gaps': {'leave_gap': benchmarks['care_support']['parental_leave_score'] - analysis.get('unpaid_care_analysis', {}).get('care_recognition_score', 0), 'flexibility_gap': benchmarks['care_support']['flexible_work_score'] - analysis.get('unpaid_care_analysis', {}).get('work_life_measures', {}).get('flexibility_score', 0), 'support_gap': benchmarks['care_support']['caregiving_support'] - analysis.get('unpaid_care_analysis', {}).get('work_life_measures', {}).get('support_score', 0)}, 'policy_gaps': {'implementation_gap': benchmarks['policy_implementation']['overall_score'] - analysis.get('policy_recommendations', {}).get('current_policies', {}).get('implementation_score', 0), 'accountability_gap': benchmarks['policy_implementation']['accountability_measures'], 'resource_gap': benchmarks['policy_implementation']['resource_allocation']}}\n\n    def _generate_industry_recommendations(self, analysis: Dict, benchmarks: Dict) -> List[Dict]:\n        \"\"\"Generate comprehensive industry-specific recommendations based on analysis and benchmarks\"\"\"\n        gaps = self._calculate_industry_gaps(analysis, benchmarks)\n        recommendations = []\n        if gaps['leadership_gaps']['executive_gap'] > 0.1:\n            recommendations.append({'area': 'leadership_representation', 'priority': 'high', 'type': 'structural', 'recommendation': 'Implement targeted executive leadership development program', 'actions': ['Establish sponsorship program pairing senior executives with high-potential women', 'Create rotation opportunities in P&L roles', 'Set clear diversity targets for executive searches'], 'metrics': ['Percentage increase in women executives', 'Promotion rates to executive positions', 'Retention rates of women in leadership pipeline'], 'timeline': '12-18 months', 'resources_needed': ['Executive development budget', 'External leadership coaches', 'Mentorship program infrastructure']})\n        if gaps['pay_equity_gaps']['base_pay_gap'] > benchmarks['pay_equity']['gap_threshold']:\n            recommendations.append({'area': 'pay_equity', 'priority': 'high', 'type': 'immediate', 'recommendation': 'Implement comprehensive pay equity analysis and adjustment program', 'actions': ['Conduct detailed pay equity audit', 'Establish clear salary bands', 'Create standardized promotion criteria', 'Implement regular pay gap monitoring'], 'metrics': ['Reduction in pay gap percentage', 'Pay equity by role and level', 'Promotion rate parity'], 'timeline': '6-12 months', 'resources_needed': ['Compensation analysis tools', 'Budget for pay adjustments', 'HR analytics capabilities']})\n        if gaps['safety_gaps']['prevention_gap'] > 0.1:\n            recommendations.append({'area': 'safety', 'priority': 'high', 'type': 'critical', 'recommendation': 'Enhance workplace safety and violence prevention framework', 'actions': ['Implement comprehensive safety training', 'Establish anonymous reporting system', 'Create rapid response protocol', 'Develop support services network'], 'metrics': ['Incident reporting rates', 'Response time to reports', 'Training completion rates', 'Employee safety perception scores'], 'timeline': '3-6 months', 'resources_needed': ['Training platform', 'Reporting system', 'Support services budget']})\n        if gaps['care_support_gaps']['support_gap'] > 0.15:\n            recommendations.append({'area': 'care_support', 'priority': 'high', 'type': 'structural', 'recommendation': 'Develop comprehensive care support infrastructure', 'actions': ['Implement flexible work policies', 'Establish care support allowances', 'Create return-to-work programs', 'Develop caregiver networks'], 'metrics': ['Utilization of care support programs', 'Return-to-work rates', 'Career progression of caregivers', 'Employee satisfaction scores'], 'timeline': '6-12 months', 'resources_needed': ['Care support budget', 'Policy framework', 'Program management resources']})\n        if gaps['policy_gaps']['implementation_gap'] > 0.1:\n            recommendations.append({'area': 'policy', 'priority': 'medium', 'type': 'systemic', 'recommendation': 'Strengthen policy implementation and monitoring framework', 'actions': ['Establish policy oversight committee', 'Create implementation roadmap', 'Develop monitoring mechanisms', 'Regular policy effectiveness reviews'], 'metrics': ['Policy implementation rates', 'Compliance scores', 'Employee awareness levels', 'Policy effectiveness measures'], 'timeline': '9-12 months', 'resources_needed': ['Policy management system', 'Training resources', 'Monitoring tools']})\n        industry_specific = self._generate_industry_specific_recommendations(analysis, benchmarks, gaps)\n        recommendations.extend(industry_specific)\n        return self._prioritize_recommendations(recommendations, gaps)\n\n    def _generate_industry_specific_recommendations(self, analysis: Dict, benchmarks: Dict, gaps: Dict) -> List[Dict]:\n        \"\"\"Generate industry-specific recommendations based on sector characteristics\"\"\"\n        industry_recs = []\n        industry = analysis.get('industry', '').lower()\n        if industry == 'tech':\n            if gaps['leadership_gaps']['pipeline_gap'] > 0.1:\n                industry_recs.append({'area': 'tech_pipeline', 'priority': 'high', 'type': 'structural', 'recommendation': 'Build inclusive technical leadership pipeline', 'actions': ['Create technical mentorship programs', 'Implement blind coding assessments', 'Establish returnship programs', 'Partner with women in tech organizations'], 'metrics': ['Women in technical leadership roles', 'Technical hiring diversity', 'Technical promotion rates'], 'timeline': '12-18 months'})\n        elif industry == 'healthcare':\n            if gaps['care_support_gaps']['flexibility_gap'] > 0.1:\n                industry_recs.append({'area': 'healthcare_workforce', 'priority': 'high', 'type': 'operational', 'recommendation': 'Implement healthcare-specific flexibility framework', 'actions': ['Create shift-trading platform', 'Establish predictive scheduling', 'Develop emergency care support', 'Implement job-sharing programs'], 'metrics': ['Schedule flexibility usage', 'Staff satisfaction scores', 'Care coverage metrics'], 'timeline': '6-12 months'})\n        elif industry == 'finance':\n            if gaps['pay_equity_gaps']['bonus_gap'] > benchmarks['pay_equity']['bonus_gap_threshold']:\n                industry_recs.append({'area': 'financial_compensation', 'priority': 'high', 'type': 'immediate', 'recommendation': 'Address finance-specific compensation disparities', 'actions': ['Review bonus allocation criteria', 'Analyze client assignment patterns', 'Standardize deal credit attribution', 'Implement transparent commission structures'], 'metrics': ['Bonus gap reduction', 'Deal participation equity', 'Client portfolio diversity'], 'timeline': '3-6 months'})\n        return industry_recs\n\n    def _prioritize_recommendations(self, recommendations: List[Dict], gaps: Dict) -> List[Dict]:\n        \"\"\"Prioritize recommendations based on gap analysis and impact potential\"\"\"\n\n        def calculate_priority_score(rec):\n            priority_scores = {'high': 3, 'medium': 2, 'low': 1}\n            score = priority_scores[rec['priority']]\n            gap_area = rec['area']\n            if gap_area in gaps:\n                gap_severity = max(gaps[gap_area].values())\n                score += gap_severity * 2\n            type_multipliers = {'critical': 2.0, 'immediate': 1.8, 'structural': 1.5, 'systemic': 1.3, 'operational': 1.2}\n            score *= type_multipliers.get(rec['type'], 1.0)\n            return score\n        sorted_recs = sorted(recommendations, key=calculate_priority_score, reverse=True)\n        for i, rec in enumerate(sorted_recs, 1):\n            rec['implementation_sequence'] = i\n        return sorted_recs\n\n    def _analyze_violence_prevention(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Analyze violence prevention measures and gaps\"\"\"\n        violence_indicators = self._extract_violence_indicators(analysis)\n        current_score = self._calculate_violence_prevention_score(violence_indicators)\n        return {'risk_assessment': {'severity_levels': self._evaluate_violence_risks(violence_indicators), 'risk_patterns': self._identify_risk_patterns(violence_indicators), 'vulnerability_factors': self._assess_vulnerabilities(violence_indicators)}, 'prevention_measures': {'existing_measures': self._evaluate_existing_measures(violence_indicators), 'recommended_measures': self._recommend_prevention_measures(violence_indicators), 'implementation_timeline': self._generate_implementation_timeline(violence_indicators)}, 'benchmark_comparison': {'current_score': current_score, 'industry_benchmark': benchmarks['safety_metrics']['violence_prevention_score'], 'gap_analysis': {'prevention_gap': benchmarks['safety_metrics']['violence_prevention_score'] - current_score, 'reporting_gap': benchmarks['safety_metrics']['harassment_reporting_mechanism'] - analysis.get('safety_assessment', {}).get('psychological_safety', {}).get('score', 0), 'response_gap': benchmarks['safety_metrics']['response_effectiveness'] - current_score}, 'improvement_priorities': self._identify_improvement_priorities(violence_indicators, benchmarks)}, 'monitoring_framework': {'metrics': self._define_monitoring_metrics(violence_indicators), 'reporting_schedule': self._create_reporting_schedule(), 'accountability_measures': self._define_accountability_measures()}}\n\n    def _analyze_care_work(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Analyze unpaid care work recognition and support\"\"\"\n        care_indicators = self._extract_care_indicators(analysis)\n        return {'care_burden_assessment': self._evaluate_care_burden(care_indicators), 'support_measures': self._recommend_care_support(care_indicators), 'benchmark_comparison': {'current_score': self._calculate_care_support_score(care_indicators), 'industry_benchmark': benchmarks['care_support_score'], 'gap_analysis': self._calculate_care_support_gaps(care_indicators, benchmarks)}}\n\n    def _analyze_policy_framework(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Analyze policy framework effectiveness\"\"\"\n        policy_indicators = self._extract_policy_indicators(analysis)\n        return {'policy_assessment': self._evaluate_policy_effectiveness(policy_indicators), 'policy_recommendations': self._generate_policy_recommendations(policy_indicators), 'benchmark_comparison': {'current_score': self._calculate_policy_score(policy_indicators), 'industry_benchmark': benchmarks['policy_implementation_score'], 'gap_analysis': self._calculate_policy_gaps(policy_indicators, benchmarks)}}\n\n    def _store_analysis(self, db: Session, text: str, analysis: Dict, context: Optional[Dict]=None):\n        \"\"\"Store analysis results in database\"\"\"\n        analysis_record = Analysis(text=text, analysis_type='gender_equity', results=json.dumps(analysis), context=json.dumps(context) if context else None, created_at=datetime.utcnow())\n        db.add(analysis_record)\n        db.commit()\n        db.refresh(analysis_record)\n\n    def _load_violence_indicators(self) -> Dict:\n        \"\"\"Load comprehensive violence and harassment indicators\"\"\"\n        return {'physical_violence': ['physical threats', 'intimidation', 'unsafe conditions', 'restricted movement', 'physical isolation'], 'psychological_violence': ['verbal abuse', 'gaslighting', 'manipulation', 'emotional abuse', 'psychological manipulation'], 'economic_violence': ['financial control', 'economic threats', 'withholding resources', 'salary discrimination', 'promotion discrimination'], 'digital_violence': ['online harassment', 'cyberstalking', 'digital surveillance', 'online threats', 'privacy violations'], 'risk_factors': ['power imbalance', 'isolation', 'lack of support systems', 'financial dependency', 'fear of retaliation'], 'protective_factors': ['clear reporting mechanisms', 'support networks', 'financial independence', 'strong policies', 'accountability measures']}\n```\n",
        "eval_script": "# Mocking necessary imports and classes for the code to run\nimport os\nfrom typing import Dict, Optional, List\nimport json\nfrom datetime import datetime\n\n# Mocking external dependencies\nclass translate:\n    class Client:\n        def detect_language(self, text):\n            return {'language': 'en'}\n        \n        def translate(self, text, target_language):\n            return {'translatedText': text}\n\nclass genai:\n    @staticmethod\n    def configure(api_key):\n        pass\n    \n    class GenerativeModel:\n        def __init__(self, model_name):\n            pass\n        \n        async def generate_content_async(self, prompt, generation_config, safety_settings):\n            class Response:\n                text = '{}'\n            return Response()\n\nclass requests:\n    @staticmethod\n    def get(url):\n        class Response:\n            status_code = 200\n            def json(self):\n                return {'country': 'US'}\n        return Response()\n\nclass Session:\n    def add(self, record):\n        pass\n    \n    def commit(self):\n        pass\n    \n    def refresh(self, record):\n        pass\n\nclass Analysis:\n    def __init__(self, text, analysis_type, results, context, created_at):\n        pass\n\n# Revised AnalysisService class\nclass AnalysisService:\n\n    def __init__(self):\n        # Mocking the initialization to bypass environment and external service setup\n        self.gender_terms = self._load_gender_terms()\n        self.industry_benchmarks = self._load_industry_benchmarks()\n        self.violence_indicators = self._load_violence_indicators()\n        self.care_work_metrics = self._load_care_work_metrics()\n        self.policy_frameworks = self._load_policy_frameworks()\n\n    def detect_language(self, text: str) -> str:\n        \"\"\"Detect the language of the provided text.\"\"\"\n        result = self.translate_client.detect_language(text)\n        return result['language']\n\n    def get_user_location(self) -> str:\n        \"\"\"Detect the user's approximate location based on their IP address.\"\"\"\n        try:\n            response = requests.get('https://ipinfo.io')\n            if response.status_code == 200:\n                data = response.json()\n                return data.get('country', 'global')\n        except Exception as e:\n            print(f'Location detection failed: {e}')\n        return 'global'\n\n    def translate_text(self, text: str, target_language: str) -> str:\n        \"\"\"Translate text to the specified target language.\"\"\"\n        translation = self.translate_client.translate(text, target_language=target_language)\n        return translation['translatedText']\n\n    def _load_gender_terms(self) -> Dict:\n        \"\"\"Load database of gender-related terms and their contexts\"\"\"\n        return {'explicit_bias': [\"women can't\", \"girls shouldn't\", 'male-only', 'feminine weakness', 'maternal risk', 'emotional decision', 'bossy woman', 'hysteric'], 'implicit_bias': ['nurturing role', 'support position', 'office mom', 'aggressive for a woman', 'ambitious woman', 'work-life balance', 'family responsibilities'], 'violence_related': ['threatening', 'intimidating', 'controlling', 'coercive', 'hostile', 'aggressive', 'retaliatory', 'punitive'], 'care_work_related': ['childcare duties', 'eldercare responsibilities', 'domestic duties', 'household management', 'family obligations', 'caretaking role'], 'inclusive_alternatives': {'chairman': 'chairperson', 'businessman': 'business person', 'policeman': 'police officer', 'mankind': 'humanity', 'manpower': 'workforce'}, 'empowerment_terms': ['leadership', 'achievement', 'expertise', 'professional growth', 'equal opportunity', 'mentorship', 'advancement']}\n\n    def _load_care_work_metrics(self) -> Dict:\n        \"\"\"Load metrics for analyzing unpaid care work impact\"\"\"\n        return {'time_allocation': {'childcare_hours': 0, 'eldercare_hours': 0, 'household_maintenance': 0, 'emotional_labor': 0}, 'workplace_policies': {'flexible_hours': False, 'remote_work': False, 'parental_leave': 0, 'caregiving_support': False}, 'economic_impact': {'career_interruptions': 0, 'promotion_delays': 0, 'wage_penalties': 0, 'retirement_impact': 0}, 'support_indicators': {'childcare_facilities': False, 'eldercare_assistance': False, 'domestic_help_allowance': False, 'caregiver_networks': False}}\n\n    def _load_policy_frameworks(self) -> Dict:\n        \"\"\"Load gender equity policy frameworks and recommendations\"\"\"\n        return {'workplace_policies': {'recruitment': ['blind recruitment processes', 'diverse interview panels', 'standardized evaluation criteria'], 'advancement': ['transparent promotion criteria', 'leadership development programs', 'mentorship initiatives'], 'compensation': ['pay equity audits', 'transparent salary bands', 'performance evaluation standardization']}, 'care_support_policies': {'leave_policies': ['paid parental leave', 'flexible caregiving leave', 'sabbatical options'], 'workplace_flexibility': ['flexible hours', 'remote work options', 'compressed work weeks'], 'support_services': ['on-site childcare', 'eldercare referral services', 'caregiver support networks']}, 'safety_policies': {'prevention': ['anti-harassment training', 'bystander intervention programs', 'climate surveys'], 'response': ['reporting mechanisms', 'investigation procedures', 'victim support services'], 'accountability': ['clear consequences', 'regular policy review', 'transparency reports']}}\n\n    def _load_industry_benchmarks(self) -> Dict:\n        \"\"\"Load industry-specific gender equity benchmarks\"\"\"\n        return {'tech': {'leadership_representation': 0.3, 'pay_gap_threshold': 0.05, 'promotion_rate_ratio': 1.0, 'violence_prevention_score': 0.8, 'care_support_score': 0.7, 'policy_implementation_score': 0.75}, 'healthcare': {'leadership_representation': 0.4, 'pay_gap_threshold': 0.03, 'promotion_rate_ratio': 1.0, 'violence_prevention_score': 0.9, 'care_support_score': 0.8, 'policy_implementation_score': 0.85}}\n\n    async def analyze_content(self, text: str, context: Optional[Dict]=None, industry: Optional[str]=None, db: Session=None) -> Dict:\n        \"\"\"\n        Analyze content for gender equity issues with industry-specific context including violence prevention,\n        care work recognition, and policy recommendations\n        \"\"\"\n        source_language = self.detect_language(text)\n        analysis_text = text if source_language == 'en' else self.translate_text(text, 'en')\n        prompt = self._generate_gender_equity_prompt(analysis_text, industry)\n        try:\n            response = await self.model.generate_content_async(prompt, generation_config={'temperature': 0.2, 'top_p': 0.9, 'top_k': 50, 'max_output_tokens': 2048}, safety_settings={'HARASSMENT': 'BLOCK_NONE', 'HATE_SPEECH': 'BLOCK_NONE'})\n            cleaned_response = re.sub('```(?:json)?\\\\n?(.*?)\\\\n?```|^(?:json|JSON)\\\\s*', '\\\\1', response.text, flags=re.DOTALL).strip()\n            analysis = json.loads(cleaned_response)\n            enhanced_analysis = self._enhance_gender_analysis(analysis, industry)\n            if db:\n                self._store_analysis(db, text, enhanced_analysis, context)\n            return enhanced_analysis\n        except Exception as e:\n            print(f'Analysis failed: {str(e)}')\n            return {'status': 'error', 'error': str(e)}\n\n    def _generate_gender_equity_prompt(self, text: str, industry: Optional[str]=None) -> str:\n        \"\"\"Generate a gender equity focused prompt for Gemini\"\"\"\n        industry_context = f'Industry Context: {industry.upper()} sector analysis' if industry else ''\n        return f'\\n        You are an advanced AI system specialized in gender equity analysis. Analyze the following text with special attention to gender-related issues, biases, violence prevention, unpaid care work, policy implications and opportunities for promoting equality.\\n\\n        {industry_context}\\n\\n        1. GENDER BIAS DETECTION\\n        - Identify explicit and implicit gender biases\\n        - Detect stereotypical language and assumptions\\n        - Flag discriminatory patterns in language\\n        - Identify missed opportunities for inclusion\\n        \\n        2. REPRESENTATION ANALYSIS\\n        - Analyze representation of women and girls\\n        - Assess power dynamics and agency\\n        - Evaluate visibility and voice\\n        \\n        3. WORKPLACE EQUITY ASSESSMENT\\n        - Identify barriers to advancement\\n        - Analyze leadership opportunity language\\n        - Detect pay equity issues\\n        - Assess work-life balance assumptions\\n        \\n        4. SAFETY AND DIGNITY\\n        - Flag potential harassment indicators\\n        - Identify undermining or diminishing language\\n        - Assess psychological safety implications\\n        \\n        5. EMPOWERMENT OPPORTUNITIES\\n        - Identify areas for strengthening agency\\n        - Flag mentorship and growth opportunities\\n        - Suggest inclusive alternatives\\n\\n        6. VIOLENCE PREVENTION\\n        - Identify explicit and subtle forms of violence\\n        - Detect harassment risk indicators\\n        - Analyze power dynamics and vulnerabilities\\n        - Assess existing safety measures\\n        - Flag potential escalation patterns\\n        - Evaluate reporting and support mechanisms\\n        \\n        7. UNPAID CARE WORK \\n        - Identify assumptions about care responsibilities\\n        - Analyze work-life balance implications\\n        - Assess recognition of unpaid work\\n        - Evaluate support for caregivers\\n        - Analyze career impact of care duties\\n        - Flag discriminatory patterns\\n        \\n        8. POLICY IMPLICATIONS \\n        - Identify policy gaps and opportunities\\n        - Assess implementation effectiveness\\n        - Evaluate monitoring mechanisms\\n        - Analyze resource allocation\\n        - Flag enforcement challenges\\n        - Suggest policy improvements\\n\\n\\n        Text to analyze: {text}\\n\\n        Please make response really detailed. Any metric that has a score should also have some backup details. If the text request does not qualify for any oof the issues we are analyzing, please say so instead of leaving them blank. There must always be priority actions for overall assessment. Respond ONLY in JSON format with the following structure:\\n        {{\\n            \"bias_detection\": {{\\n                \"explicit_biases\": [\\n                    {{\\n                        \"bias\": <specific bias>,\\n                        \"context\": <explanation>,\\n                        \"impact\": <potential harm>,\\n                        \"suggestion\": <improvement>\\n                    }}\\n                ],\\n                \"implicit_biases\": [<similar structure>],\\n                \"stereotypes\": [<similar structure>],\\n                \"bias_score\": <float 0-1>\\n            }},\\n            \"representation_analysis\": {{\\n                \"visibility_score\": <float 0-1>,\\n                \"agency_score\": <float 0-1>,\\n                \"power_dynamics\": [<specific observations>],\\n                \"improvement_areas\": [<specific suggestions>]\\n            }},\\n            \"workplace_equity\": {{\\n                \"advancement_barriers\": [<specific barriers>],\\n                \"leadership_language\": {{\\n                    \"inclusive_score\": <float 0-1>,\\n                    \"flags\": [<specific issues>]\\n                }},\\n                \"pay_equity_flags\": [<potential issues>],\\n                \"work_life_assumptions\": [<detected assumptions>]\\n            }},\\n            \"safety_assessment\": {{\\n                \"harassment_indicators\": [<potential flags>],\\n                \"undermining_language\": [<specific examples>],\\n                \"psychological_safety\": {{\\n                    \"score\": <float 0-1>,\\n                    \"concerns\": [<specific issues>]\\n                }}\\n            }},\\n            \"empowerment_opportunities\": {{\\n                \"growth_language\": [<positive examples>],\\n                \"mentorship_opportunities\": [<specific suggestions>],\\n                \"inclusive_alternatives\": [<specific improvements>]\\n            }},\\n            \"violence_prevention\": {{\\n                \"risk_indicators\": [\\n                    {{\\n                        \"type\": <violence type>,\\n                        \"indicator\": <specific flag>,\\n                        \"context\": <situation details>,\\n                        \"severity\": <\"high\", \"medium\", \"low\">,\\n                        \"mitigation\": <specific recommendation>\\n                    }}\\n                ],\\n                \"safety_measures\": {{\\n                    \"existing\": [<current measures>],\\n                    \"gaps\": [<missing measures>],\\n                    \"recommendations\": [<specific improvements>]\\n                }},\\n                \"power_dynamics\": {{\\n                    \"risk_factors\": [<specific factors>],\\n                    \"protective_factors\": [<existing protections>],\\n                    \"improvements\": [<recommended changes>]\\n                }},\\n                \"overall_safety_score\": <float 0-1>\\n            }},\\n            \"unpaid_care_analysis\": {{\\n                \"care_responsibilities\": [\\n                    {{\\n                        \"type\": <care type>,\\n                        \"assumption\": <specific assumption>,\\n                        \"impact\": <career/workplace effect>,\\n                        \"support_needed\": <specific support>\\n                    }}\\n                ],\\n                \"work_life_measures\": {{\\n                    \"flexibility_score\": <float 0-1>,\\n                    \"support_score\": <float 0-1>,\\n                    \"gaps\": [<specific gaps>],\\n                    \"recommendations\": [<specific improvements>]\\n                }},\\n                \"economic_impact\": {{\\n                    \"career_progression\": <impact assessment>,\\n                    \"compensation_effects\": [<specific effects>],\\n                    \"mitigation_strategies\": [<specific strategies>]\\n                }},\\n                \"care_recognition_score\": <float 0-1>\\n            }},\\n            \"policy_recommendations\": {{\\n                \"current_policies\": {{\\n                    \"strengths\": [<existing good policies>],\\n                    \"weaknesses\": [<policy gaps>],\\n                    \"implementation_score\": <float 0-1>\\n                }},\\n                \"recommended_policies\": [\\n                    {{\\n                        \"focus_area\": <specific area>,\\n                        \"recommendation\": <policy suggestion>,\\n                        \"rationale\": <justification>,\\n                        \"implementation_steps\": [<specific steps>],\\n                        \"success_metrics\": [<measurement criteria>],\\n                        \"priority\": <\"high\", \"medium\", \"low\">\\n                    }}\\n                ],\\n                \"resource_implications\": {{\\n                    \"required_resources\": [<specific needs>],\\n                    \"potential_challenges\": [<specific challenges>],\\n                    \"mitigation_strategies\": [<specific strategies>]\\n                }},\\n                \"monitoring_framework\": {{\\n                    \"metrics\": [<specific metrics>],\\n                    \"data_needs\": [<required data>],\\n                    \"review_frequency\": <recommended frequency>\\n                }}\\n            }},\\n            \"overall_assessment\": {{\\n                \"equity_score\": <float 0-1>,\\n                \"priority_actions\": [\\n                    {{\\n                        \"issue\": <specific issue>,\\n                        \"impact\": <potential harm>,\\n                        \"recommendation\": <specific suggestion>,\\n                        \"priority\": <\"high\", \"medium\", \"low\">\\n                    }}\\n                ]\\n            }}\\n        }}\\n        '\n\n    def _enhance_gender_analysis(self, analysis: Dict, industry: Optional[str]=None) -> Dict:\n        \"\"\"Enhance the basic analysis with industry-specific insights\"\"\"\n        if not industry or industry not in self.industry_benchmarks:\n            return analysis\n        benchmarks = self.industry_benchmarks[industry]\n        analysis['industry_context'] = {'benchmarks': benchmarks, 'gap_analysis': self._calculate_industry_gaps(analysis, benchmarks), 'recommendations': self._generate_industry_recommendations(analysis, benchmarks), 'violence_prevention': self._analyze_violence_prevention(analysis, benchmarks), 'unpaid_care': self._analyze_care_work(analysis, benchmarks), 'policy_effectiveness': self._analyze_policy_framework(analysis, benchmarks)}\n        return analysis\n\n    def _calculate_industry_gaps(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Calculate comprehensive gaps between current state and industry benchmarks\"\"\"\n        return {'leadership_gaps': {'executive_gap': benchmarks['leadership_representation']['executive_level'] - analysis.get('representation_analysis', {}).get('visibility_score', 0), 'management_gap': benchmarks['leadership_representation']['senior_management'] - analysis.get('representation_analysis', {}).get('agency_score', 0), 'pipeline_gap': benchmarks['advancement_metrics']['leadership_pipeline'] - analysis.get('workplace_equity', {}).get('leadership_language', {}).get('inclusive_score', 0)}, 'pay_equity_gaps': {'base_pay_gap': benchmarks['pay_equity']['gap_threshold'], 'bonus_gap': benchmarks['pay_equity']['bonus_gap_threshold'], 'promotion_gap': benchmarks['pay_equity']['promotion_adjustment']}, 'safety_gaps': {'prevention_gap': benchmarks['safety_metrics']['violence_prevention_score'] - analysis.get('violence_prevention', {}).get('overall_safety_score', 0), 'reporting_gap': benchmarks['safety_metrics']['harassment_reporting_mechanism'] - analysis.get('safety_assessment', {}).get('psychological_safety', {}).get('score', 0), 'response_gap': benchmarks['safety_metrics']['response_effectiveness'] - analysis.get('violence_prevention', {}).get('overall_safety_score', 0)}, 'care_support_gaps': {'leave_gap': benchmarks['care_support']['parental_leave_score'] - analysis.get('unpaid_care_analysis', {}).get('care_recognition_score', 0), 'flexibility_gap': benchmarks['care_support']['flexible_work_score'] - analysis.get('unpaid_care_analysis', {}).get('work_life_measures', {}).get('flexibility_score', 0), 'support_gap': benchmarks['care_support']['caregiving_support'] - analysis.get('unpaid_care_analysis', {}).get('work_life_measures', {}).get('support_score', 0)}, 'policy_gaps': {'implementation_gap': benchmarks['policy_implementation']['overall_score'] - analysis.get('policy_recommendations', {}).get('current_policies', {}).get('implementation_score', 0), 'accountability_gap': benchmarks['policy_implementation']['accountability_measures'], 'resource_gap': benchmarks['policy_implementation']['resource_allocation']}}\n\n    def _generate_industry_recommendations(self, analysis: Dict, benchmarks: Dict) -> List[Dict]:\n        \"\"\"Generate comprehensive industry-specific recommendations based on analysis and benchmarks\"\"\"\n        gaps = self._calculate_industry_gaps(analysis, benchmarks)\n        recommendations = []\n        if gaps['leadership_gaps']['executive_gap'] > 0.1:\n            recommendations.append({'area': 'leadership_representation', 'priority': 'high', 'type': 'structural', 'recommendation': 'Implement targeted executive leadership development program', 'actions': ['Establish sponsorship program pairing senior executives with high-potential women', 'Create rotation opportunities in P&L roles', 'Set clear diversity targets for executive searches'], 'metrics': ['Percentage increase in women executives', 'Promotion rates to executive positions', 'Retention rates of women in leadership pipeline'], 'timeline': '12-18 months', 'resources_needed': ['Executive development budget', 'External leadership coaches', 'Mentorship program infrastructure']})\n        if gaps['pay_equity_gaps']['base_pay_gap'] > benchmarks['pay_equity']['gap_threshold']:\n            recommendations.append({'area': 'pay_equity', 'priority': 'high', 'type': 'immediate', 'recommendation': 'Implement comprehensive pay equity analysis and adjustment program', 'actions': ['Conduct detailed pay equity audit', 'Establish clear salary bands', 'Create standardized promotion criteria', 'Implement regular pay gap monitoring'], 'metrics': ['Reduction in pay gap percentage', 'Pay equity by role and level', 'Promotion rate parity'], 'timeline': '6-12 months', 'resources_needed': ['Compensation analysis tools', 'Budget for pay adjustments', 'HR analytics capabilities']})\n        if gaps['safety_gaps']['prevention_gap'] > 0.1:\n            recommendations.append({'area': 'safety', 'priority': 'high', 'type': 'critical', 'recommendation': 'Enhance workplace safety and violence prevention framework', 'actions': ['Implement comprehensive safety training', 'Establish anonymous reporting system', 'Create rapid response protocol', 'Develop support services network'], 'metrics': ['Incident reporting rates', 'Response time to reports', 'Training completion rates', 'Employee safety perception scores'], 'timeline': '3-6 months', 'resources_needed': ['Training platform', 'Reporting system', 'Support services budget']})\n        if gaps['care_support_gaps']['support_gap'] > 0.15:\n            recommendations.append({'area': 'care_support', 'priority': 'high', 'type': 'structural', 'recommendation': 'Develop comprehensive care support infrastructure', 'actions': ['Implement flexible work policies', 'Establish care support allowances', 'Create return-to-work programs', 'Develop caregiver networks'], 'metrics': ['Utilization of care support programs', 'Return-to-work rates', 'Career progression of caregivers', 'Employee satisfaction scores'], 'timeline': '6-12 months', 'resources_needed': ['Care support budget', 'Policy framework', 'Program management resources']})\n        if gaps['policy_gaps']['implementation_gap'] > 0.1:\n            recommendations.append({'area': 'policy', 'priority': 'medium', 'type': 'systemic', 'recommendation': 'Strengthen policy implementation and monitoring framework', 'actions': ['Establish policy oversight committee', 'Create implementation roadmap', 'Develop monitoring mechanisms', 'Regular policy effectiveness reviews'], 'metrics': ['Policy implementation rates', 'Compliance scores', 'Employee awareness levels', 'Policy effectiveness measures'], 'timeline': '9-12 months', 'resources_needed': ['Policy management system', 'Training resources', 'Monitoring tools']})\n        industry_specific = self._generate_industry_specific_recommendations(analysis, benchmarks, gaps)\n        recommendations.extend(industry_specific)\n        return self._prioritize_recommendations(recommendations, gaps)\n\n    def _generate_industry_specific_recommendations(self, analysis: Dict, benchmarks: Dict, gaps: Dict) -> List[Dict]:\n        \"\"\"Generate industry-specific recommendations based on sector characteristics\"\"\"\n        industry_recs = []\n        industry = analysis.get('industry', '').lower()\n        if industry == 'tech':\n            if gaps['leadership_gaps']['pipeline_gap'] > 0.1:\n                industry_recs.append({'area': 'tech_pipeline', 'priority': 'high', 'type': 'structural', 'recommendation': 'Build inclusive technical leadership pipeline', 'actions': ['Create technical mentorship programs', 'Implement blind coding assessments', 'Establish returnship programs', 'Partner with women in tech organizations'], 'metrics': ['Women in technical leadership roles', 'Technical hiring diversity', 'Technical promotion rates'], 'timeline': '12-18 months'})\n        elif industry == 'healthcare':\n            if gaps['care_support_gaps']['flexibility_gap'] > 0.1:\n                industry_recs.append({'area': 'healthcare_workforce', 'priority': 'high', 'type': 'operational', 'recommendation': 'Implement healthcare-specific flexibility framework', 'actions': ['Create shift-trading platform', 'Establish predictive scheduling', 'Develop emergency care support', 'Implement job-sharing programs'], 'metrics': ['Schedule flexibility usage', 'Staff satisfaction scores', 'Care coverage metrics'], 'timeline': '6-12 months'})\n        elif industry == 'finance':\n            if gaps['pay_equity_gaps']['bonus_gap'] > benchmarks['pay_equity']['bonus_gap_threshold']:\n                industry_recs.append({'area': 'financial_compensation', 'priority': 'high', 'type': 'immediate', 'recommendation': 'Address finance-specific compensation disparities', 'actions': ['Review bonus allocation criteria', 'Analyze client assignment patterns', 'Standardize deal credit attribution', 'Implement transparent commission structures'], 'metrics': ['Bonus gap reduction', 'Deal participation equity', 'Client portfolio diversity'], 'timeline': '3-6 months'})\n        return industry_recs\n\n    def _prioritize_recommendations(self, recommendations: List[Dict], gaps: Dict) -> List[Dict]:\n        \"\"\"Prioritize recommendations based on gap analysis and impact potential\"\"\"\n\n        def calculate_priority_score(rec):\n            priority_scores = {'high': 3, 'medium': 2, 'low': 1}\n            score = priority_scores[rec['priority']]\n            gap_area = rec['area']\n            if gap_area in gaps:\n                gap_severity = max(gaps[gap_area].values())\n                score += gap_severity * 2\n            type_multipliers = {'critical': 2.0, 'immediate': 1.8, 'structural': 1.5, 'systemic': 1.3, 'operational': 1.2}\n            score *= type_multipliers.get(rec['type'], 1.0)\n            return score\n        sorted_recs = sorted(recommendations, key=calculate_priority_score, reverse=True)\n        for i, rec in enumerate(sorted_recs, 1):\n            rec['implementation_sequence'] = i\n        return sorted_recs\n\n    def _analyze_violence_prevention(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Analyze violence prevention measures and gaps\"\"\"\n        violence_indicators = self._extract_violence_indicators(analysis)\n        current_score = self._calculate_violence_prevention_score(violence_indicators)\n        return {'risk_assessment': {'severity_levels': self._evaluate_violence_risks(violence_indicators), 'risk_patterns': self._identify_risk_patterns(violence_indicators), 'vulnerability_factors': self._assess_vulnerabilities(violence_indicators)}, 'prevention_measures': {'existing_measures': self._evaluate_existing_measures(violence_indicators), 'recommended_measures': self._recommend_prevention_measures(violence_indicators), 'implementation_timeline': self._generate_implementation_timeline(violence_indicators)}, 'benchmark_comparison': {'current_score': current_score, 'industry_benchmark': benchmarks['safety_metrics']['violence_prevention_score'], 'gap_analysis': {'prevention_gap': benchmarks['safety_metrics']['violence_prevention_score'] - current_score, 'reporting_gap': benchmarks['safety_metrics']['harassment_reporting_mechanism'] - analysis.get('safety_assessment', {}).get('psychological_safety', {}).get('score', 0), 'response_gap': benchmarks['safety_metrics']['response_effectiveness'] - current_score}, 'improvement_priorities': self._identify_improvement_priorities(violence_indicators, benchmarks)}, 'monitoring_framework': {'metrics': self._define_monitoring_metrics(violence_indicators), 'reporting_schedule': self._create_reporting_schedule(), 'accountability_measures': self._define_accountability_measures()}}\n\n    def _analyze_care_work(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Analyze unpaid care work recognition and support\"\"\"\n        care_indicators = self._extract_care_indicators(analysis)\n        return {'care_burden_assessment': self._evaluate_care_burden(care_indicators), 'support_measures': self._recommend_care_support(care_indicators), 'benchmark_comparison': {'current_score': self._calculate_care_support_score(care_indicators), 'industry_benchmark': benchmarks['care_support_score'], 'gap_analysis': self._calculate_care_support_gaps(care_indicators, benchmarks)}}\n\n    def _analyze_policy_framework(self, analysis: Dict, benchmarks: Dict) -> Dict:\n        \"\"\"Analyze policy framework effectiveness\"\"\"\n        policy_indicators = self._extract_policy_indicators(analysis)\n        return {'policy_assessment': self._evaluate_policy_effectiveness(policy_indicators), 'policy_recommendations': self._generate_policy_recommendations(policy_indicators), 'benchmark_comparison': {'current_score': self._calculate_policy_score(policy_indicators), 'industry_benchmark': benchmarks['policy_implementation_score'], 'gap_analysis': self._calculate_policy_gaps(policy_indicators, benchmarks)}}\n\n    def _store_analysis(self, db: Session, text: str, analysis: Dict, context: Optional[Dict]=None):\n        \"\"\"Store analysis results in database\"\"\"\n        analysis_record = Analysis(text=text, analysis_type='gender_equity', results=json.dumps(analysis), context=json.dumps(context) if context else None, created_at=datetime.utcnow())\n        db.add(analysis_record)\n        db.commit()\n        db.refresh(analysis_record)\n\n    def _load_violence_indicators(self) -> Dict:\n        \"\"\"Load comprehensive violence and harassment indicators\"\"\"\n        return {'physical_violence': ['physical threats', 'intimidation', 'unsafe conditions', 'restricted movement', 'physical isolation'], 'psychological_violence': ['verbal abuse', 'gaslighting', 'manipulation', 'emotional abuse', 'psychological manipulation'], 'economic_violence': ['financial control', 'economic threats', 'withholding resources', 'salary discrimination', 'promotion discrimination'], 'digital_violence': ['online harassment', 'cyberstalking', 'digital surveillance', 'online threats', 'privacy violations'], 'risk_factors': ['power imbalance', 'isolation', 'lack of support systems', 'financial dependency', 'fear of retaliation'], 'protective_factors': ['clear reporting mechanisms', 'support networks', 'financial independence', 'strong policies', 'accountability measures']}\n\n\ndef test__load_violence_indicators():\n    service = AnalysisService()\n    original = service._load_violence_indicators()\n    new_implementation = service._load_violence_indicators_new_implementation()\n\n    # Assert that both implementations return the same dictionary\n    assert original == new_implementation, \"The new implementation does not match the original.\"\n\n    # List of expected keys\n    expected_keys = [\n        'physical_violence',\n        'psychological_violence',\n        'economic_violence',\n        'digital_violence',\n        'risk_factors',\n        'protective_factors'\n    ]\n\n    # Assert all expected keys exist in both implementations\n    for key in expected_keys:\n        assert key in original, f\"Key '{key}' is missing in the original implementation.\"\n        assert key in new_implementation, f\"Key '{key}' is missing in the new implementation.\"\n\n    # Assert values for each key match between implementations\n    for key in expected_keys:\n        assert original[key] == new_implementation[key], f\"Values for '{key}' do not match.\"\n\n    # Assert the structure of the dictionary (types of values) is consistent\n    for key in expected_keys:\n        assert isinstance(original[key], list), f\"Value for '{key}' in original is not a list.\"\n        assert isinstance(new_implementation[key], list), f\"Value for '{key}' in new implementation is not a list.\"\n\nif __name__ == \"__main__\":\n    test__load_violence_indicators()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_load_violence_indicators` in the `AnalysisService` class is identical to the ORIGINAL FUNCTION. Both functions return the same dictionary containing the same keys and values. The functionality and output of the REVISED FUNCTION are exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `_load_violence_indicators` function returns a dictionary, which satisfies the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases check the return values and do not rely on printed or logged contents. They compare the dictionaries returned by the original and new implementations.\n\n3. **CONDITION 3**: The test cases ensure that `_load_violence_indicators_new_implementation` can pass all tests only if it has the exact same functionality as `_load_violence_indicators`. This is done by asserting that the dictionaries returned by both implementations are identical.\n\n4. **CONDITION 4**: The test cases and assert statements are reasonable. They check for equality of the entire dictionary, presence of expected keys, equality of values for each key, and the type of values (ensuring they are lists). These checks are appropriate given that the function returns a dictionary.\n\n5. **CONDITION 5**: The test cases are non-trivial as they not only check for equality of the entire dictionary but also verify the presence of specific keys and the types of values, ensuring a comprehensive validation of the function's output.",
            "answer": "yes"
        },
        "commit_id": "f48a1f8f77548da0dcfb21a5a8f448b21ee37563"
    },
    {
        "func_name": "CppSuggestions.get_suggestions",
        "idx": "734",
        "repo_name": "beaglesoftware___editor",
        "func_path": "autocomplete.py",
        "orig_func": "@staticmethod\ndef get_suggestions(word_fragment):\n    keywords = ['auto', 'break', 'case', 'char', 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extern', 'float', 'print', 'for', 'goto', 'if', 'inline', 'int', 'long', 'register', 'restrict', 'return', 'short', 'signed', 'sizeof', 'static', 'struct', 'switch', 'typedef', 'union', 'unsigned', 'void', 'volatile', 'while', '_Alignas', '_Alignof', '_Atomic', '_Bool', '_Complex', '_Generic', '_Imaginary', '_Noreturn', '_Static_assert', '_Thread_local']\n    return [kw for kw in keywords if kw.startswith(word_fragment)]",
        "orig_context": "```python\n## autocomplete.py\nclass CppSuggestions:\n    @staticmethod\n    def get_suggestions(word_fragment):\n        keywords = [\n            'auto', 'break', 'case', 'char', 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extern',\n            'float', 'print', 'for', 'goto', 'if', 'inline', 'int', 'long', 'register', 'restrict', 'return', 'short', 'signed',\n            'sizeof', 'static', 'struct', 'switch', 'typedef', 'union', 'unsigned', 'void', 'volatile', 'while', '_Alignas',\n            '_Alignof', '_Atomic', '_Bool', '_Complex', '_Generic', '_Imaginary', '_Noreturn', '_Static_assert', '_Thread_local'\n        ]\n        return [kw for kw in keywords if kw.startswith(word_fragment)]\n\n```\n\n\n",
        "eval_script": "## autocomplete.py\nclass CppSuggestions:\n    @staticmethod\n    def get_suggestions(word_fragment):\n        keywords = [\n            'auto', 'break', 'case', 'char', 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extern',\n            'float', 'print', 'for', 'goto', 'if', 'inline', 'int', 'long', 'register', 'restrict', 'return', 'short', 'signed',\n            'sizeof', 'static', 'struct', 'switch', 'typedef', 'union', 'unsigned', 'void', 'volatile', 'while', '_Alignas',\n            '_Alignof', '_Atomic', '_Bool', '_Complex', '_Generic', '_Imaginary', '_Noreturn', '_Static_assert', '_Thread_local'\n        ]\n        return [kw for kw in keywords if kw.startswith(word_fragment)]\n\n\ndef test_get_suggestions():\n    # Test case 1: Exact match\n    assert CppSuggestions.get_suggestions('auto') == CppSuggestions.get_suggestions_new_implementation('auto')\n\n    # Test case 2: Partial match\n    assert CppSuggestions.get_suggestions('do') == CppSuggestions.get_suggestions_new_implementation('do')\n\n    # Test case 3: No match\n    assert CppSuggestions.get_suggestions('xyz') == CppSuggestions.get_suggestions_new_implementation('xyz')\n\n    # Test case 4: Multiple matches\n    assert CppSuggestions.get_suggestions('st') == CppSuggestions.get_suggestions_new_implementation('st')\n\n    # Test case 5: Empty input\n    assert CppSuggestions.get_suggestions('') == CppSuggestions.get_suggestions_new_implementation('')\n\n    # Test case 6: Special characters\n    assert CppSuggestions.get_suggestions('@') == CppSuggestions.get_suggestions_new_implementation('@')\n\n    # Test case 7: Case sensitivity\n    assert CppSuggestions.get_suggestions('Auto') == CppSuggestions.get_suggestions_new_implementation('Auto')\n\n    # Test case 8: Very long string\n    assert CppSuggestions.get_suggestions('a' * 1000) == CppSuggestions.get_suggestions_new_implementation('a' * 1000)\n\n    # Test case 9: Numeric input\n    assert CppSuggestions.get_suggestions('123') == CppSuggestions.get_suggestions_new_implementation('123')\n\n    # Test case 10: Mixed alphanumeric input\n    assert CppSuggestions.get_suggestions('int1') == CppSuggestions.get_suggestions_new_implementation('int1')\n\nif __name__ == \"__main__\":\n    test_get_suggestions()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions are static methods that take a `word_fragment` as an argument and return a list of keywords that start with the given `word_fragment`. The list of keywords is the same in both functions, and the logic to filter the keywords using `startswith` is also the same. The additional code in the REVISED FUNCTION is related to testing and does not affect the functionality of the `get_suggestions` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_suggestions` function returns a list of keywords that start with the given `word_fragment`. This satisfies the condition as it has a return value.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `get_suggestions` and `get_suggestions_new_implementation`, which satisfies the condition as they do not check printed or logged contents.\n\n- CONDITION 3: The test cases compare the outputs of `get_suggestions` and `get_suggestions_new_implementation` for various inputs. If `get_suggestions_new_implementation` passes all these tests, it must have the same functionality as `get_suggestions`.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `get_suggestions` returns a list. There are no assertions that compare non-return values or use inappropriate methods of comparison.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including exact matches, partial matches, no matches, multiple matches, empty input, special characters, case sensitivity, long strings, numeric input, and mixed alphanumeric input. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "94624db8ddece909124ab906bca4199160cbeb35"
    },
    {
        "func_name": "CSuggestions.get_suggestions",
        "idx": "736",
        "repo_name": "beaglesoftware___editor",
        "func_path": "autocomplete.py",
        "orig_func": "@staticmethod\ndef get_suggestions(word_fragment):\n    keywords = ['auto', 'break', 'case', 'char', 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extern', 'float', 'for', 'goto', 'if', 'inline', 'int', 'long', 'register', 'restrict', 'return', 'short', 'signed', 'sizeof', 'static', 'struct', 'switch', 'typedef', 'union', 'unsigned', 'void', 'volatile', 'while', '_Alignas', '_Alignof', '_Atomic', '_Bool', '_Complex', '_Generic', '_Imaginary', '_Noreturn', '_Static_assert', '_Thread_local']\n    return [kw for kw in keywords if kw.startswith(word_fragment)]",
        "orig_context": "```python\n## autocomplete.py\nclass CSuggestions:\n    @staticmethod\n    def get_suggestions(word_fragment):\n        keywords = [\n            'auto', 'break', 'case', 'char', 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extern',\n            'float', 'for', 'goto', 'if', 'inline', 'int', 'long', 'register', 'restrict', 'return', 'short', 'signed',\n            'sizeof', 'static', 'struct', 'switch', 'typedef', 'union', 'unsigned', 'void', 'volatile', 'while', '_Alignas',\n            '_Alignof', '_Atomic', '_Bool', '_Complex', '_Generic', '_Imaginary', '_Noreturn', '_Static_assert', '_Thread_local'\n        ]\n        return [kw for kw in keywords if kw.startswith(word_fragment)]\n\n```\n\n\n",
        "eval_script": "## autocomplete.py\nclass CSuggestions:\n    @staticmethod\n    def get_suggestions(word_fragment):\n        keywords = [\n            'auto', 'break', 'case', 'char', 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extern',\n            'float', 'for', 'goto', 'if', 'inline', 'int', 'long', 'register', 'restrict', 'return', 'short', 'signed',\n            'sizeof', 'static', 'struct', 'switch', 'typedef', 'union', 'unsigned', 'void', 'volatile', 'while', '_Alignas',\n            '_Alignof', '_Atomic', '_Bool', '_Complex', '_Generic', '_Imaginary', '_Noreturn', '_Static_assert', '_Thread_local'\n        ]\n        return [kw for kw in keywords if kw.startswith(word_fragment)]\n\n\ndef test_get_suggestions():\n    # Test case 1: Word fragment matches multiple keywords\n    fragment1 = 'do'\n    assert CSuggestions.get_suggestions(fragment1) == CSuggestions.get_suggestions_new_implementation(fragment1)\n\n    # Test case 2: Word fragment matches a single keyword\n    fragment2 = 'auto'\n    assert CSuggestions.get_suggestions(fragment2) == CSuggestions.get_suggestions_new_implementation(fragment2)\n\n    # Test case 3: Word fragment matches no keywords\n    fragment3 = 'xyz'\n    assert CSuggestions.get_suggestions(fragment3) == CSuggestions.get_suggestions_new_implementation(fragment3)\n\n    # Test case 4: Empty string should match all keywords\n    fragment4 = ''\n    assert CSuggestions.get_suggestions(fragment4) == CSuggestions.get_suggestions_new_implementation(fragment4)\n\n    # Test case 5: Case sensitivity test\n    fragment5 = 'Auto'\n    assert CSuggestions.get_suggestions(fragment5) == CSuggestions.get_suggestions_new_implementation(fragment5)\n\n    # Test case 6: Special characters\n    fragment6 = '@'\n    assert CSuggestions.get_suggestions(fragment6) == CSuggestions.get_suggestions_new_implementation(fragment6)\n\n    # Test case 7: Partial match that is not a prefix\n    fragment7 = 'uto'\n    assert CSuggestions.get_suggestions(fragment7) == CSuggestions.get_suggestions_new_implementation(fragment7)\n\n    # Test case 8: Long fragment longer than any keyword\n    fragment8 = 'thisisaverylongfragment'\n    assert CSuggestions.get_suggestions(fragment8) == CSuggestions.get_suggestions_new_implementation(fragment8)\n\nif __name__ == \"__main__\":\n    test_get_suggestions()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions are static methods that take a single argument `word_fragment` and return a list of keywords that start with the given `word_fragment`. The list of keywords is the same in both functions, and the list comprehension used to filter the keywords is also the same. The additional code in the REVISED FUNCTION file, such as the `CSuggestions` class and the `test_get_suggestions` function, does not alter the functionality of the `get_suggestions` method itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `get_suggestions` function returns a list of keywords that start with the given `word_fragment`. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `get_suggestions` and `get_suggestions_new_implementation`, which means they are checking return values, not printed or logged content.\n- CONDITION 3: The test cases compare the outputs of `get_suggestions` and `get_suggestions_new_implementation` for various inputs. If `get_suggestions_new_implementation` passes all these tests, it must have the same functionality as `get_suggestions`.\n- CONDITION 4: The assertions are reasonable because they compare the outputs of two functions directly, which is appropriate given that `get_suggestions` has a return value.\n- CONDITION 5: The test cases cover a variety of scenarios including matching multiple keywords, a single keyword, no keywords, an empty string, case sensitivity, special characters, non-prefix partial matches, and excessively long fragments. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "94624db8ddece909124ab906bca4199160cbeb35"
    },
    {
        "func_name": "JavaScriptSuggestions.get_suggestions",
        "idx": "737",
        "repo_name": "beaglesoftware___editor",
        "func_path": "autocomplete.py",
        "orig_func": "@staticmethod\ndef get_suggestions(word_fragment):\n    keywords = ['break', 'case', 'catch', 'class', 'const', 'continue', 'debugger', 'default', 'delete', 'do', 'else', 'export', 'extends', 'finally', 'for', 'function', 'if', 'import', 'in', 'instanceof', 'let', 'new', 'return', 'super', 'switch', 'this', 'throw', 'try', 'typeof', 'var', 'void', 'while', 'with', 'yield']\n    return [kw for kw in keywords if kw.startswith(word_fragment)]",
        "orig_context": "```python\n## autocomplete.py\nclass JavaScriptSuggestions:\n    @staticmethod\n    def get_suggestions(word_fragment):\n        keywords = [\n            'break', 'case', 'catch', 'class', 'const', 'continue', 'debugger', 'default', 'delete', 'do', 'else', 'export',\n            'extends', 'finally', 'for', 'function', 'if', 'import', 'in', 'instanceof', 'let', 'new', 'return', 'super', 'switch',\n            'this', 'throw', 'try', 'typeof', 'var', 'void', 'while', 'with', 'yield'\n        ]\n        return [kw for kw in keywords if kw.startswith(word_fragment)]\n\n```\n\n\n",
        "eval_script": "## autocomplete.py\nclass JavaScriptSuggestions:\n    @staticmethod\n    def get_suggestions(word_fragment):\n        keywords = [\n            'break', 'case', 'catch', 'class', 'const', 'continue', 'debugger', 'default', 'delete', 'do', 'else', 'export',\n            'extends', 'finally', 'for', 'function', 'if', 'import', 'in', 'instanceof', 'let', 'new', 'return', 'super', 'switch',\n            'this', 'throw', 'try', 'typeof', 'var', 'void', 'while', 'with', 'yield'\n        ]\n        return [kw for kw in keywords if kw.startswith(word_fragment)]\n\n\ndef test_get_suggestions():\n    # Test case 1: Exact match\n    assert JavaScriptSuggestions.get_suggestions('for') == JavaScriptSuggestions.get_suggestions_new_implementation('for')\n\n    # Test case 2: Partial match\n    assert JavaScriptSuggestions.get_suggestions('fun') == JavaScriptSuggestions.get_suggestions_new_implementation('fun')\n\n    # Test case 3: No match\n    assert JavaScriptSuggestions.get_suggestions('xyz') == JavaScriptSuggestions.get_suggestions_new_implementation('xyz')\n\n    # Test case 4: Empty string\n    assert JavaScriptSuggestions.get_suggestions('') == JavaScriptSuggestions.get_suggestions_new_implementation('')\n\n    # Test case 5: Single character match\n    assert JavaScriptSuggestions.get_suggestions('c') == JavaScriptSuggestions.get_suggestions_new_implementation('c')\n\n    # Test case 6: Case sensitivity\n    assert JavaScriptSuggestions.get_suggestions('For') == JavaScriptSuggestions.get_suggestions_new_implementation('For')\n\n    # Test case 7: Full keyword\n    assert JavaScriptSuggestions.get_suggestions('return') == JavaScriptSuggestions.get_suggestions_new_implementation('return')\n\n    # Test case 8: Non-keyword characters\n    assert JavaScriptSuggestions.get_suggestions('123') == JavaScriptSuggestions.get_suggestions_new_implementation('123')\n\nif __name__ == \"__main__\":\n    test_get_suggestions()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `JavaScriptSuggestions` class is identical to the ORIGINAL FUNCTION. Both functions are static methods that take a `word_fragment` as input and return a list of JavaScript keywords that start with the given fragment. The list of keywords and the logic to filter them using `startswith` is the same in both functions. The additional code in the revised version, such as the test cases, does not alter the functionality of the `get_suggestions` method itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `get_suggestions` function returns a list of keywords that start with the given `word_fragment`. This satisfies the condition as it has return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `get_suggestions` and `get_suggestions_new_implementation`, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `get_suggestions` and `get_suggestions_new_implementation` for various inputs. If `get_suggestions_new_implementation` passes all tests, it must have the same functionality as `get_suggestions`. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable given that `get_suggestions` returns a list. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including exact matches, partial matches, no matches, empty strings, single characters, case sensitivity, full keywords, and non-keyword characters. This provides a non-trivial set of test cases. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "94624db8ddece909124ab906bca4199160cbeb35"
    },
    {
        "func_name": "_get_system_prompt",
        "idx": "748",
        "repo_name": "Kroks4502___gpt_quiz_tg_bot",
        "func_path": "src/gpt/assistants/question/__init__.py",
        "orig_func": "def _get_system_prompt(prev_answers: list[UserAnswer], number: str):\n    prompt = [PROMPT_SYS.format(number=number)]\n    if prev_answers:\n        correct = []\n        incorrect = []\n        for answer in prev_answers:\n            (correct if answer.correct else incorrect).append(answer.question)\n        if correct:\n            prompt.append(PROMPT_PREV_ANS_CORRECT.format(prev_answers='\\n- '.join(correct)))\n        if incorrect:\n            prompt.append(PROMPT_PREV_ANS_INCORRECT.format(prev_answers='\\n- '.join(incorrect)))\n        prompt.append(PROMPT_NOT_REPEAT)\n    return '\\n'.join(prompt)",
        "orig_context": "```python\n## src/gpt/assistants/question/schemas.py\nfrom pydantic import Field, constr, field_validator, BaseModel\n\nclass UserAnswer(BaseModel):\n    question: str\n    correct: bool = Field(description='Correct answer.')\n\n```\n\n\n```python\n## src/gpt/assistants/question/__init__.py\nfrom gpt.assistants.question.schemas import QuizQuestion, QuizQuestionGpt, UserAnswer\n\nPROMPT_SYS = file.read()\n\nPROMPT_PREV_ANS_CORRECT = file.read()\n\nPROMPT_PREV_ANS_INCORRECT = file.read()\n\nPROMPT_NOT_REPEAT = file.read()\n\ndef _get_system_prompt(prev_answers: list[UserAnswer], number: str):\n    prompt = [PROMPT_SYS.format(number=number)]\n\n    if prev_answers:\n        correct = []\n        incorrect = []\n\n        for answer in prev_answers:\n            (correct if answer.correct else incorrect).append(answer.question)\n\n        if correct:\n            prompt.append(PROMPT_PREV_ANS_CORRECT.format(prev_answers=\"\\n- \".join(correct)))\n        if incorrect:\n            prompt.append(PROMPT_PREV_ANS_INCORRECT.format(prev_answers=\"\\n- \".join(incorrect)))\n        prompt.append(PROMPT_NOT_REPEAT)\n\n    return \"\\n\".join(prompt)\n\n```\n\n\n",
        "eval_script": "# Mock data for file contents\nPROMPT_SYS = \"System prompt for question number {number}.\"\nPROMPT_PREV_ANS_CORRECT = \"Previously correct answers:\\n- {prev_answers}\"\nPROMPT_PREV_ANS_INCORRECT = \"Previously incorrect answers:\\n- {prev_answers}\"\nPROMPT_NOT_REPEAT = \"Please do not repeat the same mistakes.\"\n\n# Pydantic model for UserAnswer\nfrom pydantic import BaseModel, Field\n\nclass UserAnswer(BaseModel):\n    question: str\n    correct: bool = Field(description='Correct answer.')\n\n# Function to get system prompt\ndef _get_system_prompt(prev_answers: list[UserAnswer], number: str):\n    prompt = [PROMPT_SYS.format(number=number)]\n\n    if prev_answers:\n        correct = []\n        incorrect = []\n\n        for answer in prev_answers:\n            (correct if answer.correct else incorrect).append(answer.question)\n\n        if correct:\n            prompt.append(PROMPT_PREV_ANS_CORRECT.format(prev_answers=\"\\n- \".join(correct)))\n        if incorrect:\n            prompt.append(PROMPT_PREV_ANS_INCORRECT.format(prev_answers=\"\\n- \".join(incorrect)))\n        prompt.append(PROMPT_NOT_REPEAT)\n\n    return \"\\n\".join(prompt)\n\n\n# Your revised test__get_system_prompt function\ndef test__get_system_prompt():\n    # Test case 1: No previous answers\n    prev_answers = []\n    number = \"1\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 2: Only correct answers\n    prev_answers = [UserAnswer(question=\"What is Python?\", correct=True)]\n    number = \"2\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 3: Only incorrect answers\n    prev_answers = [UserAnswer(question=\"What is Java?\", correct=False)]\n    number = \"3\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 4: Mix of correct and incorrect answers\n    prev_answers = [\n        UserAnswer(question=\"What is Python?\", correct=True),\n        UserAnswer(question=\"What is Java?\", correct=False)\n    ]\n    number = \"4\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 5: Empty number\n    prev_answers = []\n    number = \"\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 6: Multiple correct answers\n    prev_answers = [\n        UserAnswer(question=\"What is Python?\", correct=True),\n        UserAnswer(question=\"What is a function?\", correct=True)\n    ]\n    number = \"5\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 7: Multiple incorrect answers\n    prev_answers = [\n        UserAnswer(question=\"What is Java?\", correct=False),\n        UserAnswer(question=\"What is a class?\", correct=False)\n    ]\n    number = \"6\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 8: Special characters in questions\n    prev_answers = [\n        UserAnswer(question=\"What is C++?\", correct=True),\n        UserAnswer(question=\"What is C#?\", correct=False)\n    ]\n    number = \"7\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 9: Long question text\n    long_question = \"What is the meaning of life, the universe, and everything?\" * 10\n    prev_answers = [UserAnswer(question=long_question, correct=True)]\n    number = \"8\"\n    assert _get_system_prompt(prev_answers, number) == _get_system_prompt_new_implementation(prev_answers, number)\n\n    # Test case 10: Non-string number\n    prev_answers = [UserAnswer(question=\"What is Python?\", correct=True)]\n    number = 9  # Intentionally using an integer\n    assert _get_system_prompt(prev_answers, str(number)) == _get_system_prompt_new_implementation(prev_answers, str(number))\n\n# Main function\nif __name__ == \"__main__\":\n    test__get_system_prompt()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      8      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      8      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of logic and structure. Both functions initialize a prompt with a formatted system prompt string, then check if there are previous answers. If there are, they separate the answers into correct and incorrect lists based on the 'correct' attribute of each UserAnswer object. They then append formatted strings for correct and incorrect answers to the prompt, followed by a constant string to not repeat mistakes. Finally, they return the joined prompt. There are no changes in logic or functionality between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `_get_system_prompt` function returns a string, which is a return value. Therefore, this condition is satisfied.\n  \n- **CONDITION 2**: The test cases use assertions to compare the return values of `_get_system_prompt` and `_get_system_prompt_new_implementation`. They do not check printed or logged contents. Thus, this condition is satisfied.\n\n- **CONDITION 3**: The test cases compare the outputs of `_get_system_prompt` and `_get_system_prompt_new_implementation` directly. If `_get_system_prompt_new_implementation` produces the same output for all inputs, it must have the same functionality as `_get_system_prompt`. This condition is satisfied.\n\n- **CONDITION 4**: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `_get_system_prompt` returns a value. This condition is satisfied.\n\n- **CONDITION 5**: The test cases cover a variety of scenarios, including no previous answers, only correct answers, only incorrect answers, a mix of correct and incorrect answers, empty number, multiple correct and incorrect answers, special characters, long question text, and non-string number. These are non-trivial and comprehensive. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "63cfe4fd6e755cc31007281749484a2294fedf83"
    },
    {
        "func_name": "LSTMClassifier._test",
        "idx": "752",
        "repo_name": "MunjPatel___Index-Trend-Net",
        "func_path": "lstm.py",
        "orig_func": "def _test(self, lstm_model):\n    x_test_reshaped = self.x_test.reshape((self.x_test.shape[0], 1, self.x_test.shape[1]))\n    y_predicted = lstm_model.predict(x_test_reshaped)\n    y_pred = np.where(y_predicted > 0.99, 1, 0)\n    forecast_accuracy = accuracy_score(self.y_test, y_pred)\n    conf_matrix = confusion_matrix(self.y_test, y_pred)\n    class_report = classification_report(self.y_test, y_pred, output_dict=True)\n    self.save_results(forecast_accuracy, conf_matrix, class_report, self.y_test, y_pred)\n    return (forecast_accuracy, conf_matrix, class_report)",
        "orig_context": "```python\n## lstm.py\nimport pandas as pd\n\nimport numpy as np\n\nimport os\n\nimport json\n\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n\nfrom dataclasses import dataclass\n\nfrom tensorflow.keras import Sequential\n\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout\n\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nclass LSTMClassifier:\n    \n    ticker  : str\n    x_train : np.array\n    x_test  : np.array\n    y_train : pd.DataFrame\n    y_test  : pd.DataFrame\n\n    # def save_model(self, model):\n    #     model_path = f\"./models/{self.ticker}.pkl\"\n    #     os.makedirs(os.path.dirname(model_path), exist_ok=True)\n    #     joblib.dump(model, model_path)\n    #     print(f\"Model saved for ticker: {self.ticker}\")\n        \n    def save_results(self, forecast_accuracy, conf_matrix, class_report, y_test, y_pred):\n        results = {\n            \"forecast_accuracy\": forecast_accuracy,\n            \"confusion_matrix\": conf_matrix.tolist(),  # Convert to list for JSON compatibility\n            \"classification_report\": class_report,\n            \"y_test\": y_test.tolist(),  # Convert to list\n            \"y_pred\": y_pred.tolist()   # Convert to list\n        }\n        result_path = f\"./results/{self.ticker}.json\"\n        os.makedirs(os.path.dirname(result_path), exist_ok=True)\n        with open(result_path, \"w\") as f:\n            json.dump(results, f, indent=4)\n        print(f\"Results saved for ticker: {self.ticker}\")\n        \n    def _train(self):\n        # Reshape input to 3D for LSTM [samples, timesteps, features]\n        x_train_reshaped = self.x_train.reshape((self.x_train.shape[0], 1, self.x_train.shape[1]))\n        # x_test_reshaped = self.x_test.reshape((self.x_test.shape[0], 1, self.x_test.shape[1]))\n        \n        lstm_model = Sequential()\n        lstm_model.add(LSTM(100, input_shape=(x_train_reshaped.shape[1], x_train_reshaped.shape[2]), activation='relu', return_sequences=True))\n        lstm_model.add(Dropout(0.6))\n        lstm_model.add(LSTM(50, activation='relu', return_sequences=True))\n        lstm_model.add(Dropout(0.6))\n        lstm_model.add(LSTM(25, activation='relu'))\n        lstm_model.add(Dropout(0.6))\n        lstm_model.add(Dense(1, activation='sigmoid'))\n        \n        lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n        \n        lstm_history = lstm_model.fit(\n            x_train_reshaped, self.y_train, epochs=100, batch_size=32,\n            validation_split=0.2, callbacks=[EarlyStopping(monitor='val_loss', patience=5)], verbose=1\n        )\n        lstm_metrics = pd.DataFrame(lstm_history.history)\n        # self.save_model(lstm_model)\n        return lstm_model, lstm_metrics\n    \n    def _test(self, lstm_model):\n        x_test_reshaped = self.x_test.reshape((self.x_test.shape[0], 1, self.x_test.shape[1]))\n        y_predicted = lstm_model.predict(x_test_reshaped)\n        y_pred = np.where(y_predicted > 0.99, 1, 0)  # Threshold for binary classification\n        forecast_accuracy = accuracy_score(self.y_test, y_pred)\n        conf_matrix = confusion_matrix(self.y_test, y_pred)\n        class_report = classification_report(self.y_test, y_pred, output_dict=True)\n        \n        # Save results to JSON\n        self.save_results(forecast_accuracy, conf_matrix, class_report, self.y_test, y_pred)\n        \n        return forecast_accuracy, conf_matrix, class_report\n\n```\n\n\n",
        "eval_script": "## lstm.py\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nclass LSTMClassifier:\n    \n    def __init__(self, ticker, x_train, x_test, y_train, y_test):\n        self.ticker = ticker\n        self.x_train = x_train\n        self.x_test = x_test\n        self.y_train = y_train\n        self.y_test = y_test\n\n    def save_results(self, forecast_accuracy, conf_matrix, class_report, y_test, y_pred):\n        results = {\n            \"forecast_accuracy\": forecast_accuracy,\n            \"confusion_matrix\": conf_matrix.tolist(),  # Convert to list for JSON compatibility\n            \"classification_report\": class_report,\n            \"y_test\": y_test.values.tolist(),  # Convert DataFrame to list\n            \"y_pred\": y_pred.tolist()   # Convert to list\n        }\n        result_path = f\"/home/user/tmp/results/{self.ticker}.json\"\n        os.makedirs(os.path.dirname(result_path), exist_ok=True)\n        with open(result_path, \"w\") as f:\n            json.dump(results, f, indent=4)\n        print(f\"Results saved for ticker: {self.ticker}\")\n        \n    def _train(self):\n        # Reshape input to 3D for LSTM [samples, timesteps, features]\n        x_train_reshaped = self.x_train.reshape((self.x_train.shape[0], 1, self.x_train.shape[1]))\n        \n        lstm_model = Sequential()\n        lstm_model.add(LSTM(100, input_shape=(x_train_reshaped.shape[1], x_train_reshaped.shape[2]), activation='relu', return_sequences=True))\n        lstm_model.add(Dropout(0.6))\n        lstm_model.add(LSTM(50, activation='relu', return_sequences=True))\n        lstm_model.add(Dropout(0.6))\n        lstm_model.add(LSTM(25, activation='relu'))\n        lstm_model.add(Dropout(0.6))\n        lstm_model.add(Dense(1, activation='sigmoid'))\n        \n        lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n        \n        lstm_history = lstm_model.fit(\n            x_train_reshaped, self.y_train, epochs=100, batch_size=32,\n            validation_split=0.2, callbacks=[EarlyStopping(monitor='val_loss', patience=5)], verbose=1\n        )\n        lstm_metrics = pd.DataFrame(lstm_history.history)\n        return lstm_model, lstm_metrics\n    \n    def _test(self, lstm_model):\n        x_test_reshaped = self.x_test.reshape((self.x_test.shape[0], 1, self.x_test.shape[1]))\n        y_predicted = lstm_model.predict(x_test_reshaped)\n        y_pred = np.where(y_predicted > 0.99, 1, 0)  # Threshold for binary classification\n        forecast_accuracy = accuracy_score(self.y_test, y_pred)\n        conf_matrix = confusion_matrix(self.y_test, y_pred)\n        class_report = classification_report(self.y_test, y_pred, output_dict=True)\n        \n        # Save results to JSON\n        self.save_results(forecast_accuracy, conf_matrix, class_report, self.y_test, y_pred)\n        \n        return forecast_accuracy, conf_matrix, class_report\n\n\n    \n\n\ndef test__test():\n    # Mock data for testing\n    ticker = \"TEST\"\n    x_train = np.random.rand(100, 10)\n    x_test = np.random.rand(20, 10)\n    y_train = pd.DataFrame(np.random.randint(0, 2, size=(100, 1)))\n    y_test = pd.DataFrame(np.random.randint(0, 2, size=(20, 1)))\n\n    # Instantiate the classifier\n    classifier = LSTMClassifier(ticker, x_train, x_test, y_train, y_test)\n\n    # Train the model\n    model, _ = classifier._train()\n\n    # Test the model using both implementations\n    original_results = classifier._test(model)\n    new_results = classifier._test_new_implementation(model)\n\n    # Assert that the results are the same\n    assert original_results[0] == new_results[0], \"Forecast accuracy differs\"\n    assert np.array_equal(original_results[1], new_results[1]), \"Confusion matrix differs\"\n    assert original_results[2] == new_results[2], \"Classification report differs\"\n\nif __name__ == \"__main__\":\n    test__test()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions reshape the test data, make predictions using the LSTM model, apply a threshold to convert predictions to binary values, calculate accuracy, confusion matrix, and classification report, and save the results. The only difference is the context in which the function is placed, but this does not affect the functionality of the `_test` method itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_test` function returns values (`forecast_accuracy`, `conf_matrix`, `class_report`), satisfying this condition.\n- CONDITION 2: The test function `test__test` checks the return values of `_test` and `_test_new_implementation` using assertions, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test function compares the outputs of `_test` and `_test_new_implementation` directly. If they have the same functionality, their outputs should be identical, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `_test` returns values. This satisfies the condition.\n- CONDITION 5: The test uses random data for training and testing, which is non-trivial as it ensures the model is tested on a variety of inputs, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "38c66c1f8465d254c867973514add7f10f6ab28e"
    },
    {
        "func_name": "ComputeCRC32",
        "idx": "753",
        "repo_name": "fireblade2534___IC-10-Simulator",
        "func_path": "UtilityFunctions/Utility.py",
        "orig_func": "def ComputeCRC32(Input):\n    Hash = zlib.crc32(bytes(Input, 'utf-8'))\n    if Hash & 1 << 32 - 1 != 0:\n        Hash = Hash - (1 << 32)\n    return Hash",
        "orig_context": "```python\n## UtilityFunctions/Utility.py\nimport zlib\n\ndef ComputeCRC32(Input):\n    Hash= zlib.crc32(bytes(Input,\"utf-8\"))\n    if (Hash & (1 << (32 - 1))) != 0:\n        Hash = Hash - (1 << 32)\n    return Hash\n\n```\n\n\n",
        "eval_script": "## UtilityFunctions/Utility.py\nimport zlib\n\ndef ComputeCRC32(Input):\n    Hash= zlib.crc32(bytes(Input,\"utf-8\"))\n    if (Hash & (1 << (32 - 1))) != 0:\n        Hash = Hash - (1 << 32)\n    return Hash\n\n\ndef test_ComputeCRC32():\n    # Test case 1: Positive CRC32 value\n    input1 = \"hello\"\n    assert ComputeCRC32(input1) == ComputeCRC32_new_implementation(input1), \"Test case 1 failed\"\n\n    # Test case 2: Negative CRC32 value (after adjustment)\n    input2 = \"world\"\n    assert ComputeCRC32(input2) == ComputeCRC32_new_implementation(input2), \"Test case 2 failed\"\n\n    # Test case 3: Edge case with empty string\n    input3 = \"\"\n    assert ComputeCRC32(input3) == ComputeCRC32_new_implementation(input3), \"Test case 3 failed\"\n\n    # Additional test case 4: Known input producing negative CRC32 value\n    input4 = \"negative_test\"\n    assert ComputeCRC32(input4) == ComputeCRC32_new_implementation(input4), \"Test case 4 failed\"\n\n    # Additional test case 5: Another input producing negative CRC32 value\n    input5 = \"another_negative_test\"\n    assert ComputeCRC32(input5) == ComputeCRC32_new_implementation(input5), \"Test case 5 failed\"\n\nif __name__ == \"__main__\":\n    test_ComputeCRC32()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is essentially the same as the ORIGINAL FUNCTION. Both functions import the `zlib` module and use the `zlib.crc32` function to compute the CRC32 hash of the input string, converted to bytes using UTF-8 encoding. They both check if the most significant bit (sign bit) of the 32-bit hash is set, indicating a negative number in signed 32-bit integer representation. If it is set, they adjust the hash by subtracting \\(1 << 32\\) to convert it to a signed integer representation. The only difference is in the formatting of the condition check, which does not affect the logic or functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `ComputeCRC32` returns a value, specifically the CRC32 hash of the input string. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `ComputeCRC32` and `ComputeCRC32_new_implementation`, which means they are checking return values, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `ComputeCRC32` and `ComputeCRC32_new_implementation` directly. This ensures that `ComputeCRC32_new_implementation` will pass all tests only if it has the exact same functionality as `ComputeCRC32`.\n- CONDITION 4: The assertions are reasonable because they compare the return values of the two implementations, which is appropriate since `ComputeCRC32` returns a value.\n- CONDITION 5: The test cases cover a variety of inputs, including normal strings, an empty string, and strings that produce negative CRC32 values after adjustment. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "f7581a459d857a9dbd90ed7d5ae50bf50d9669a0"
    },
    {
        "func_name": "MakeIntIfClose",
        "idx": "754",
        "repo_name": "fireblade2534___IC-10-Simulator",
        "func_path": "UtilityFunctions/Utility.py",
        "orig_func": "def MakeIntIfClose(Num):\n    try:\n        float(Num)\n    except:\n        return Num\n    if int(Num) - float(Num) < 1e-10:\n        return int(Num)\n    return Num",
        "orig_context": "```python\n## UtilityFunctions/Utility.py\ndef MakeIntIfClose(Num):\n    try:\n        float(Num)\n    except:\n        return Num\n    if int(Num) - float(Num) < 0.0000000001:\n        return int(Num)\n    return Num\n\n```\n\n\n",
        "eval_script": "## UtilityFunctions/Utility.py\ndef MakeIntIfClose(Num):\n    try:\n        float(Num)\n    except:\n        return Num\n    if int(Num) - float(Num) < 0.0000000001:\n        return int(Num)\n    return Num\n\n\n# Your revised test_MakeIntIfClose function\ndef test_MakeIntIfClose():\n    # Test with an integer\n    assert MakeIntIfClose(5) == MakeIntIfClose_new_implementation(5)\n    # Test with a float close to an integer\n    assert MakeIntIfClose(5.00000000005) == MakeIntIfClose_new_implementation(5.00000000005)\n    # Test with a float not close to an integer\n    assert MakeIntIfClose(5.1) == MakeIntIfClose_new_implementation(5.1)\n    # Test with a non-numeric input\n    assert MakeIntIfClose(\"test\") == MakeIntIfClose_new_implementation(\"test\")\n    # Test with a negative integer\n    assert MakeIntIfClose(-5) == MakeIntIfClose_new_implementation(-5)\n    # Test with a negative float close to an integer\n    assert MakeIntIfClose(-5.00000000005) == MakeIntIfClose_new_implementation(-5.00000000005)\n    # Test with a negative float not close to an integer\n    assert MakeIntIfClose(-5.1) == MakeIntIfClose_new_implementation(-5.1)\n    # Test with zero\n    assert MakeIntIfClose(0) == MakeIntIfClose_new_implementation(0)\n    # Test with a float exactly halfway between two integers\n    assert MakeIntIfClose(5.5) == MakeIntIfClose_new_implementation(5.5)\n    # Test with a float just below the threshold\n    assert MakeIntIfClose(5.00000000009) == MakeIntIfClose_new_implementation(5.00000000009)\n    # Test with a float just above the threshold\n    assert MakeIntIfClose(5.00000000011) == MakeIntIfClose_new_implementation(5.00000000011)\n\nif __name__ == \"__main__\":\n    test_MakeIntIfClose()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       8      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  8      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function are essentially the same in terms of functionality. The only difference between them is the way the threshold value is represented: `1e-10` in the original function and `0.0000000001` in the revised function. These two values are mathematically equivalent, as `1e-10` is simply another way of writing `0.0000000001`. Therefore, the functionality of the two functions is identical.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `MakeIntIfClose` returns a value based on its logic, which is either an integer or the original input. Therefore, it satisfies the condition of having return values.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `MakeIntIfClose` and `MakeIntIfClose_new_implementation`, which means they are checking the return values and not printed or logged contents.\n\n- CONDITION 3: The test cases are designed to compare the outputs of `MakeIntIfClose` and `MakeIntIfClose_new_implementation` for various inputs. This means that `MakeIntIfClose_new_implementation` will pass all the test cases if and only if it behaves exactly like `MakeIntIfClose`.\n\n- CONDITION 4: The test cases use assertions to compare the return values of both implementations, which is reasonable given that `MakeIntIfClose` returns a value. The test cases do not make unreasonable assumptions about the function's behavior.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including integers, floats close to integers, floats not close to integers, non-numeric inputs, negative numbers, zero, and edge cases around the threshold. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "f7581a459d857a9dbd90ed7d5ae50bf50d9669a0"
    },
    {
        "func_name": "SplitNotStringSpaces",
        "idx": "755",
        "repo_name": "fireblade2534___IC-10-Simulator",
        "func_path": "UtilityFunctions/Utility.py",
        "orig_func": "def SplitNotStringSpaces(String, SplitChar):\n    Splits = []\n    CurrentString = ''\n    InBadString = False\n    for X in String:\n        if X == '\"':\n            InBadString = not InBadString\n        if InBadString == False:\n            if X == SplitChar:\n                Splits.append(CurrentString)\n                CurrentString = ''\n                continue\n        CurrentString += X\n    Splits.append(CurrentString)\n    return Splits",
        "orig_context": "```python\n## UtilityFunctions/Utility.py\ndef SplitNotStringSpaces(String,SplitChar):\n    Splits=[]\n    CurrentString=\"\"\n    InBadString=False\n    for X in String:\n        if X == '\"':\n            InBadString=not InBadString\n        if InBadString == False:\n            if X == SplitChar:\n                Splits.append(CurrentString)\n                CurrentString=\"\"\n                continue\n        CurrentString+=X\n    Splits.append(CurrentString)\n    return Splits\n\n```\n\n\n",
        "eval_script": "## UtilityFunctions/Utility.py\ndef SplitNotStringSpaces(String, SplitChar):\n    Splits = []\n    CurrentString = \"\"\n    InBadString = False\n    for X in String:\n        if X == '\"':\n            InBadString = not InBadString\n        if InBadString == False:\n            if X == SplitChar:\n                Splits.append(CurrentString)\n                CurrentString = \"\"\n                continue\n        CurrentString += X\n    Splits.append(CurrentString)\n    return Splits\n\n\ndef test_SplitNotStringSpaces():\n    # Test case 1: No quotes, simple split\n    assert SplitNotStringSpaces(\"a,b,c\", \",\") == SplitNotStringSpaces_new_implementation(\"a,b,c\", \",\")\n\n    # Test case 2: Quotes containing the split character\n    assert SplitNotStringSpaces('a,\"b,c\",d', \",\") == SplitNotStringSpaces_new_implementation('a,\"b,c\",d', \",\")\n\n    # Test case 3: Multiple quoted sections\n    assert SplitNotStringSpaces('a,\"b,c\",\"d,e\"', \",\") == SplitNotStringSpaces_new_implementation('a,\"b,c\",\"d,e\"', \",\")\n\n    # Test case 4: Empty string\n    assert SplitNotStringSpaces(\"\", \",\") == SplitNotStringSpaces_new_implementation(\"\", \",\")\n\n    # Test case 5: No split character present\n    assert SplitNotStringSpaces(\"abc\", \",\") == SplitNotStringSpaces_new_implementation(\"abc\", \",\")\n\n    # Test case 6: Only split character\n    assert SplitNotStringSpaces(\",\", \",\") == SplitNotStringSpaces_new_implementation(\",\", \",\")\n\n    # Test case 7: Nested quotes\n    assert SplitNotStringSpaces('a,\"b,\"c\",d\",e', \",\") == SplitNotStringSpaces_new_implementation('a,\"b,\"c\",d\",e', \",\")\n\n    # Test case 8: Quotes at the start and end\n    assert SplitNotStringSpaces('\"a,b\",c', \",\") == SplitNotStringSpaces_new_implementation('\"a,b\",c', \",\")\n    assert SplitNotStringSpaces('a,\"b,c\"', \",\") == SplitNotStringSpaces_new_implementation('a,\"b,c\"', \",\")\n\n    # Test case 9: Multiple consecutive split characters\n    assert SplitNotStringSpaces(\"a,,b\", \",\") == SplitNotStringSpaces_new_implementation(\"a,,b\", \",\")\n\n    # Test case 10: Escaped quotes (not handled by the function, but test behavior)\n    assert SplitNotStringSpaces('a,\\\\\"b,c\\\\\"', \",\") == SplitNotStringSpaces_new_implementation('a,\\\\\"b,c\\\\\"', \",\")\n\n    # Test case 11: Complex mixed content\n    assert SplitNotStringSpaces('a,\"b,c\",d,e,\"f,g,h\",i', \",\") == SplitNotStringSpaces_new_implementation('a,\"b,c\",d,e,\"f,g,h\",i', \",\")\n\nif __name__ == \"__main__\":\n    test_SplitNotStringSpaces()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      15      0      8      0   100%\n--------------------------------------------------------------------\nTOTAL                                 15      0      8      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions have the same logic and structure, including variable names, control flow, and operations. The test cases provided in the code are meant to compare the original function with a non-existent function `SplitNotStringSpaces_new_implementation`, which seems to be a mistake in the test code. However, since the revised function is exactly the same as the original, the functionality remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. CONDITION 1: The function `SplitNotStringSpaces` returns a list of strings (`Splits`). This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. CONDITION 2: The test cases use assertions to compare the return values of `SplitNotStringSpaces` and `SplitNotStringSpaces_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n\n3. CONDITION 3: The test cases are designed to check various scenarios, including simple splits, quoted sections, empty strings, and complex mixed content. These tests ensure that `SplitNotStringSpaces_new_implementation` must have the exact same functionality as `SplitNotStringSpaces` to pass all tests, satisfying this condition.\n\n4. CONDITION 4: The test cases use assertions to compare the return values of the two implementations. This is reasonable given that `SplitNotStringSpaces` returns a list. There are no assertions that contradict the function's behavior, satisfying this condition.\n\n5. CONDITION 5: The test cases cover a wide range of scenarios, including edge cases like empty strings, no split characters, nested quotes, and complex content. This makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "f7581a459d857a9dbd90ed7d5ae50bf50d9669a0"
    },
    {
        "func_name": "CodeRunner.Instruction_Sin",
        "idx": "757",
        "repo_name": "fireblade2534___IC-10-Simulator",
        "func_path": "EmulatorFunctions/CodeRunner.py",
        "orig_func": "def Instruction_Sin(self, *args):\n    Index1 = self.GetArgIndex(args[1])\n    Value1 = self.GetArgValue(args[2])\n    if self.Parent.Fields['Error'].Value == 1:\n        return\n    if Value1 in Constants.NOT_NUMBER_NUMBERS:\n        self.Registers[Index1] = Value1\n        return\n    try:\n        self.Registers[Index1] = math.sin(Value1)\n    except:\n        self.Registers[Index1] = 'NaN'",
        "orig_context": "```python\n# EmulatorFunctions/CodeRunner.py\n\nclass CodeRunner:\n\n    def __init__(self, Parent, FilePath='Configs/Functions.json', DeviceFile: str='Configs/Devices.json'):\n        self.DevicesList = json.loads(open(DeviceFile, 'r').read())\n        self.FunctionMap = json.load(open(FilePath, 'r'))\n        for X, Y in self.FunctionMap['SpecialTypes'].items():\n            Y['ConfirmFunction'] = getattr(self, Y['ConfirmFunction'])\n            Y['GetArgFunction'] = getattr(self, Y['GetArgFunction'])\n        for X, Y in self.FunctionMap['Functions'].items():\n            Y['Function'] = getattr(self, Y['Function'])\n        self.Parent = Parent\n        self.Code = self.Parent.Code.split('\\n')\n        self.Registers = {f'r{X}': 0 for X in range(0, 18)}\n        self.RegisterAliases = copy.copy(Constants.DEFAULT_REGISTER_ALIAS)\n        self.PinAliases = {}\n        if self.Parent.StackEnabled == True:\n            self.Stack = [0 for X in range(self.Parent.StackLength)]\n        self.Constants = copy.copy(Constants.DEFAULT_CONSTANTS)\n        self.ParseCode()\n        self.HighestSP = 0\n\n    def ParseCode(self):\n        self.LogicTypesList = set(['ReferenceId', 'PrefabHash'] + [f'Channel{X}' for X in range(8)])\n        for X, Y in self.DevicesList.items():\n            for A, B in Y['Fields'].items():\n                if B['Read'] or B['Write']:\n                    self.LogicTypesList.add(A)\n        for X, Y in enumerate(self.Code):\n            if '#' in Y:\n                Location = Y.find('#')\n                self.Code[X] = Y[:Location].strip()\n            YTemp = self.Code[X].strip()\n            if ' ' not in YTemp:\n                if YTemp.endswith(':'):\n                    if YTemp not in self.Constants:\n                        self.Constants[YTemp[:-1]] = X\n                    else:\n                        Log.Warning('You cannot declare two lables with the same name', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    self.Code[X] = ''\n\n    def PrintRegisters(self):\n        Output = ['\\n+------------+-------+\\n|Registers   |       |']\n        for X, Y in self.Registers.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintConstants(self):\n        Output = ['\\n+------------+-------+\\n|Constants   |       |']\n        for X, Y in self.Constants.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintAlias(self):\n        Output = ['\\n+------------+-------+\\n|Aliases     |       |']\n        for X, Y in self.RegisterAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        for X, Y in self.PinAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintStack(self):\n        Output = ['\\n+------------+-------+\\n|Stack       |       |']\n        for X in range(self.HighestSP + 1):\n            Output.append(f'|{X:<12}|{self.Stack[X]:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def ScriptLength(self):\n        return len(self.Script)\n\n    def Special_LogicTypes(self, Value, BaseType):\n        return Value in self.LogicTypesList\n\n    def Special_Get_LogicType(self, Value):\n        return Value\n\n    def Special_BatchMode(self, Value, BaseType):\n        if BaseType == 'String':\n            return Value in list(Constants.BATCH_TYPES_VALUES.keys())\n        return True\n\n    def Special_Get_BatchMode(self, Value):\n        if type(Value) == str:\n            if str(Value) in Constants.BATCH_TYPES_VALUES:\n                return Constants.BATCH_TYPES_VALUES[Value]\n        RawValue = self.GetArgValue(Value)\n        if RawValue >= 0 and RawValue < len(Constants.BATCH_TYPES_VALUES):\n            return RawValue\n        else:\n            Log.Warning(f'Batch mode value must bettween greater then or euqal too 0 and less then {len(Constants.BATCH_TYPES_VALUES)}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n\n    def Special_DeviceHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_DeviceHash(self, Value):\n        return Value\n\n    def Special_NameHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_NameHash(self, Value):\n        return Value\n\n    def Special_ReferenceID(self, Value, BaseType):\n        return True\n\n    def Special_Get_ReferenceID(self, Value):\n        return Value\n\n    def GetArgBaseType(self, Value, TargetTypes=[]):\n        if len(Value) == 0:\n            return 'None'\n        if Value in self.Constants and 'Constant' in TargetTypes:\n            return 'Constant'\n        if Value in self.RegisterAliases and 'Register' in TargetTypes:\n            return 'Register'\n        if Value in self.PinAliases and 'Device' in TargetTypes:\n            return 'Device'\n        if Value[0] == 'd' and len(Value) > 1 and ('Device' in TargetTypes):\n            if 'r' in Value:\n                try:\n                    InValue = int(Value[1:].replace('r', ''))\n                    if InValue >= 0 and InValue < 18:\n                        return 'Device'\n                except:\n                    pass\n            else:\n                try:\n                    DeviceNumberList = [str(X) for X in range(self.Parent.PinsNumber)] + ['b']\n                    if Value[1:] in DeviceNumberList:\n                        return 'Device'\n                except:\n                    pass\n        if Value[0] == 'r' and len(Value) > 1 and ('Register' in TargetTypes):\n            try:\n                InValue = int(Value.replace('r', ''))\n                if InValue >= 0 and InValue < 18:\n                    return 'Register'\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")') and ('Hash' in TargetTypes):\n            return 'Hash'\n        if 'Number' in TargetTypes:\n            if Value[0] == '$':\n                try:\n                    int(Value[1:], 16)\n                    return 'Number'\n                except:\n                    pass\n            try:\n                int(Value)\n                return 'Number'\n            except:\n                try:\n                    float(Value)\n                    return 'Number'\n                except:\n                    pass\n        return 'String'\n\n    def GetArgType(self, Value, TargetTypes=[]):\n        IsSpecialType = TargetTypes[0] in self.FunctionMap['SpecialTypes']\n        if IsSpecialType:\n            SpecialType = TargetTypes[0]\n            TargetTypes = self.FunctionMap['SpecialTypes'][SpecialType]['Types'].split('|')\n        BaseType = self.GetArgBaseType(Value, TargetTypes)\n        if IsSpecialType:\n            if BaseType in TargetTypes:\n                Confirmed = self.FunctionMap['SpecialTypes'][SpecialType]['ConfirmFunction'](Value, BaseType)\n                if Confirmed:\n                    return SpecialType\n                else:\n                    return 'None'\n        return BaseType\n\n    def GetArgIndex(self, Value):\n        if Value in self.Constants:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        if Value in self.RegisterAliases:\n            return self.GetArgIndex(self.RegisterAliases[Value])\n        if Value in self.PinAliases:\n            return self.GetArgIndex(self.PinAliases[Value])\n        if Value[0] == 'd':\n            if 'r' in Value:\n                try:\n                    TempValue = Value[1:]\n                    RegisterIndex = int(TempValue.replace('r', ''))\n                    for X in range(TempValue.count('r')):\n                        if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                            if RegisterIndex >= 0 and RegisterIndex < 18:\n                                RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                            else:\n                                Log.Warning('Indirect device values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                return None\n                        else:\n                            Log.Warning('Indirect device values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    RegisterIndex = f'd{RegisterIndex}'\n                    if RegisterIndex in self.Parent.Pins:\n                        return RegisterIndex\n                    Log.Warning('Indirect device values have to be bettween 0 and 5', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                    self.Parent.Fields['Error'].Value = 1\n                    return None\n                except:\n                    pass\n            elif Value[0] == 'd' and Value[1:] in [str(X) for X in range(self.Parent.PinsNumber)] + ['b']:\n                return Value\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return f'r{RegisterIndex}'\n            except:\n                pass\n        Log.Warning('Unknown value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return None\n\n    def GetArgValue(self, Value, TargetType=[]):\n        Value = str(Value)\n        if Value in self.Constants:\n            return self.Constants[Value]\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return self.Registers[f'r{RegisterIndex}']\n            except:\n                pass\n        if Value in self.RegisterAliases:\n            return self.GetArgValue(self.RegisterAliases[Value])\n        if Value[0] == '$':\n            try:\n                return int(Value[1:], 16)\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")'):\n            Value = Value[6:-2]\n            return ComputeCRC32(Value)\n        try:\n            return int(Value)\n        except:\n            try:\n                return float(Value)\n            except:\n                pass\n        return str(Value)\n\n    def GetSpecialArgValue(self, Value, Type):\n        if Type in self.FunctionMap['SpecialTypes']:\n            ProcessedValue = self.GetArgValue(Value, Type)\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n            return self.FunctionMap['SpecialTypes'][Type]['GetArgFunction'](ProcessedValue)\n        else:\n            Log.Warning(f'Invalid special arg type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def GetDeviceObject(self, RefID: int, DoError: bool=True):\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        RefObject = self.Parent.Network.GetDevice(RefID)\n        if RefObject != None:\n            return RefObject\n        elif DoError:\n            Log.Warning(f'Unknown device at reference id {RefID}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n        else:\n            return None\n\n    def Instruction_Define(self, *args):\n        Value = int(args[2])\n        if args[1] not in self.Constants:\n            self.Constants[args[1]] = Value\n            if args[1] in self.RegisterAliases:\n                del self.RegisterAliases[args[1]]\n        else:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n\n    def Instruction_Move(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = Value2\n\n    def Instruction_Alias(self, *args):\n        if self.GetArgType(args[1], ['String']) == 'String':\n            if args[1] not in self.Constants and args[1] not in self.Parent.Pins and (args[1] not in self.Registers):\n                if args[1] in self.RegisterAliases:\n                    del self.RegisterAliases[args[1]]\n                if args[1] in self.PinAliases:\n                    del self.PinAliases[args[1]]\n                if args[2][0] == 'r':\n                    self.RegisterAliases[args[1]] = args[2]\n                elif args[2][0] == 'd':\n                    self.PinAliases[args[1]] = args[2]\n                else:\n                    Log.Error('Unkown alias type not caught by update')\n            else:\n                Log.Warning('Cannot overwrite a constant/builtin register/builtin device index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                if self.Parent.Fields['Error'].Value == 1:\n                    return\n        else:\n            Log.Warning('You cannot set a register alias to a device name or a register', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Add(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 + Value2\n\n    def Instruction_Sub(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 - Value2\n\n    def Instruction_Mul(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 * Value2\n\n    def Instruction_Div(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        if Value2 == 0:\n            self.Registers[Index1] = 'NaN'\n            return\n        self.Registers[Index1] = Value1 / Value2\n\n    def Instruction_Abs(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = abs(Value1)\n\n    def Instruction_Ceil(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.ceil(Value1)\n\n    def Instruction_Floor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Exp(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.e ** Value1\n\n    def Instruction_Log(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.log(Value1)\n\n    def Instruction_Rand(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = random.random()\n\n    def Instruction_Round(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        Decimal = Value1 - math.floor(Value1)\n        if Decimal >= 0.5:\n            self.Registers[Index1] = math.ceil(Value1)\n        else:\n            self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Sqrt(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.sqrt(Value1)\n\n    def Instruction_Trunc(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.trunc(Value1)\n\n    def Instruction_Asin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.asin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Acos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.acos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.atan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan2(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        try:\n            self.Registers[Index1] = math.atan2(Value1, Value2)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Cos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.cos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Tan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.tan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Peek(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot peek at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 1 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n        else:\n            Log.Warning(f'Peek index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Push(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Value1 = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot push at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 0 and Index1 < self.Parent.StackLength:\n            self.Stack[Index1] = Value1\n            self.Registers[self.RegisterAliases['sp']] += 1\n        else:\n            Log.Warning(f'Push index must be greater then or euqal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Pop(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot pop at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 > 0 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n            self.Registers[self.RegisterAliases['sp']] -= 1\n        else:\n            Log.Warning(f'Pop index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Get(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Index3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if DeviceObject.StackEnabled == False:\n            Log.Warning(f'Device does not have a stack', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot get at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 >= 1 and Index3 <= DeviceObject.StackLength:\n            self.Registers[Index1] = self.Stack[Index3 - 1]\n            Log.Warning(f'Needs further testing', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        else:\n            Log.Warning(f'Get must be greater then 0 and less then or equal to {DeviceObject.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_GetD(self, *args):\n        pass\n\n    def Instruction_Poke(self, *args):\n        Value1 = self.GetArgValue(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot poke at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Value1 >= 0 and Value1 < self.Parent.StackLength:\n            self.Stack[Value1] = Value2\n        else:\n            Log.Warning(f'Pop index must be greater then or equal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Load(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def ApplyBatchOperation(self, Values, BatchMode):\n        try:\n            BatchMode = int(BatchMode)\n            if BatchMode < 0 or BatchMode >= len(Constants.BATCH_NO_RESPONSE):\n                return 0\n        except:\n            return 0\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        if BatchMode == 0:\n            return sum(Values) / len(Values)\n        elif BatchMode == 1:\n            return sum(Values)\n        elif BatchMode == 2:\n            return min(Values)\n        elif BatchMode == 3:\n            return max(Values)\n        return 'NaN'\n\n    def CollectDevicesValueBatch(self, Devices, Value, BatchMode):\n        Values = []\n        for X in Devices:\n            FieldValue = X.GetFieldValue(Value)\n            if FieldValue[0] != None:\n                Values.append(FieldValue[0])\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        return MakeIntIfClose(self.ApplyBatchOperation(Values, BatchMode))\n\n    def Instruction_LoadBatch(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'Nan'\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n        self.Registers[Index1] = Result\n\n    def Instruction_LoadBatchNamed(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[4], 'LogicType')\n        Value4 = self.GetSpecialArgValue(args[5], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'NaN'\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning(f'Cannot get a device with a NameHash of NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        Result = self.CollectDevicesValueBatch(Devices, Value3, Value4)\n        self.Registers[Index1] = Result\n\n    def Instruction_Set(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index1])\n        else:\n            Log.Warning(f'No device at {Index1}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_SetBatch(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        for X in Devices:\n            X.SetFieldValue(Value2, Value3)\n\n    def Instruction_SetBatchNamed(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value4 = self.GetArgValue(args[4])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        for X in Devices:\n            X.SetFieldValue(Value3, Value4)\n\n    def Instruction_SetDevice(self, *args):\n        Index1 = self.GetSpecialArgValue(args[1], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(Index1)\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_LoadDevice(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetSpecialArgValue(args[2], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def Instruction_Yield(self, *args):\n        return\n\n    def Instruction_Hcf(self, *args):\n        Log.Warning('Hcf triggered', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return\n\n    def Instruction_Jump(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpAL(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpR(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump relative to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        NewLineNumber = self.Parent.Fields['LineNumber'].Value + Line - 1\n        if NewLineNumber < 0:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value\n        if NewLineNumber >= len(self.Code) - 1:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value - 1\n        self.Parent.Fields['LineNumber'].Value = NewLineNumber\n\n    def GetBranchRoot(self, FunctionName):\n        if FunctionName.endswith('al'):\n            StoreNextLine = True\n            FunctionName = FunctionName[:-2]\n        else:\n            StoreNextLine = False\n        if FunctionName.startswith('br'):\n            Relative = True\n            FunctionName = FunctionName[2:]\n        else:\n            Relative = False\n            FunctionName = FunctionName[1:]\n        return (FunctionName, StoreNextLine, Relative)\n\n    def Instruction_Branch(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[1:]]\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            JumpLine = Values[2]\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            JumpLine = Values[1]\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            JumpLine = Values[2]\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            JumpLine = Values[2]\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            JumpLine = Values[1]\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            JumpLine = Values[2]\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            JumpLine = Values[1]\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            JumpLine = Values[2]\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            JumpLine = Values[2]\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            JumpLine = Values[1]\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            JumpLine = Values[1]\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'ap':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Branch_Devices(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        JumpLine = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index1], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Set_Conditional_Register(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[2:]]\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'nanz':\n            Matched = Values[0] != 'NaN'\n        elif FunctionName == 'ap':\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Set_Conditional_Register_Devices(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index2], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Select(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Value3 = self.GetArgValue(args[4])\n        Output = 0\n        if Value1 != 0:\n            Output = Value2\n        else:\n            Output = Value3\n        self.Registers[Index1] = Output\n\n    def Instruction_Bitwise_And(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        self.Registers[Index1] = Value1 & Value2\n\n    def Instruction_Bitwise_Nor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOR')\n\n    def Instruction_Bitwise_Not(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOT')\n\n    def Instruction_Bitwise_Or(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL OR')\n        self.Registers[Index1] = Value1 | Value2\n\n    def RunUpdate(self):\n        if self.Parent.Fields['LineNumber'].Value >= len(self.Code) or self.Parent.Fields['Error'].Value != 0:\n            return\n        CurrentLine = self.Code[self.Parent.Fields['LineNumber'].Value].strip()\n        if CurrentLine != '':\n            CurrentLine = SplitNotStringSpaces(CurrentLine, ' ')\n            for CurrentIndex, CurrentFunction in self.FunctionMap['Functions'].items():\n                if CurrentLine[0] in CurrentFunction['Alias']:\n                    if len(CurrentLine) - 1 == CurrentFunction['Alias'][CurrentLine[0]]:\n                        for X in range(0, len(CurrentLine) - 1):\n                            TargetArgTypes = CurrentFunction['Args'][X].split('|')\n                            CurrentArgTypes = self.GetArgType(CurrentLine[X + 1], TargetArgTypes)\n                            if CurrentArgTypes not in TargetArgTypes:\n                                print(CurrentArgTypes)\n                                Log.Warning(f\"Arg {X + 1} of {CurrentLine[0]} must be of type {CurrentFunction['Args'][X]}\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                break\n                        else:\n                            self.FunctionMap['Functions'][CurrentIndex]['Function'](*CurrentLine)\n                    else:\n                        Log.Warning(f\"{CurrentLine[0]} requires {CurrentFunction['Alias'][CurrentLine[0]]} args\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    break\n            else:\n                Log.Warning(f'Unknown function {CurrentLine[0]}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                self.Parent.Fields['Error'].Value = 1\n        self.HighestSP = max(self.HighestSP, self.Registers[self.RegisterAliases['sp']])\n        self.Parent.Fields['LineNumber'].Value += 1\n\n    def Instruction_Sin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.sin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n```\n",
        "eval_script": "import math\nimport json\nimport copy\nimport random\nimport os\n\n# Mock Constants\nclass Constants:\n    NOT_NUMBER_NUMBERS = ['NaN']\n    DEFAULT_REGISTER_ALIAS = {'sp': 'r0', 'ra': 'r1'}\n    DEFAULT_CONSTANTS = {}\n    BATCH_TYPES_VALUES = {}\n    BATCH_NO_RESPONSE = [0]\n\n# Mock Log\nclass Log:\n    @staticmethod\n    def Warning(message, Caller=None):\n        print(f\"Warning: {message}\")\n\n    @staticmethod\n    def Info(message):\n        print(f\"Info: {message}\")\n\n    @staticmethod\n    def Error(message):\n        print(f\"Error: {message}\")\n\n# Mock Parent\nclass MockParent:\n    def __init__(self):\n        self.Code = \"\"\n        self.StackEnabled = True\n        self.StackLength = 10\n        self.PinsNumber = 5\n        self.Fields = {'LineNumber': MockField(0), 'Error': MockField(0)}\n        self.Pins = {}\n        self.Network = MockNetwork()\n\nclass MockField:\n    def __init__(self, value):\n        self.Value = value\n\nclass MockNetwork:\n    def GetDevice(self, RefID):\n        return None\n\n    def GetBatchDevices(self, Value1, Value2=None):\n        return []\n\n# EmulatorFunctions/CodeRunner.py\n\nclass CodeRunner:\n\n    def __init__(self, Parent, FilePath='Configs/Functions.json', DeviceFile: str='Configs/Devices.json'):\n        if os.path.exists(DeviceFile):\n            self.DevicesList = json.loads(open(DeviceFile, 'r').read())\n        else:\n            self.DevicesList = {}\n        if os.path.exists(FilePath):\n            self.FunctionMap = json.load(open(FilePath, 'r'))\n        else:\n            self.FunctionMap = {'SpecialTypes': {}, 'Functions': {}}\n        for X, Y in self.FunctionMap['SpecialTypes'].items():\n            Y['ConfirmFunction'] = getattr(self, Y['ConfirmFunction'])\n            Y['GetArgFunction'] = getattr(self, Y['GetArgFunction'])\n        for X, Y in self.FunctionMap['Functions'].items():\n            Y['Function'] = getattr(self, Y['Function'])\n        self.Parent = Parent\n        self.Code = self.Parent.Code.split('\\n')\n        self.Registers = {f'r{X}': 0 for X in range(0, 18)}\n        self.RegisterAliases = copy.copy(Constants.DEFAULT_REGISTER_ALIAS)\n        self.PinAliases = {}\n        if self.Parent.StackEnabled == True:\n            self.Stack = [0 for X in range(self.Parent.StackLength)]\n        self.Constants = copy.copy(Constants.DEFAULT_CONSTANTS)\n        self.ParseCode()\n        self.HighestSP = 0\n\n    def ParseCode(self):\n        self.LogicTypesList = set(['ReferenceId', 'PrefabHash'] + [f'Channel{X}' for X in range(8)])\n        for X, Y in self.DevicesList.items():\n            for A, B in Y['Fields'].items():\n                if B['Read'] or B['Write']:\n                    self.LogicTypesList.add(A)\n        for X, Y in enumerate(self.Code):\n            if '#' in Y:\n                Location = Y.find('#')\n                self.Code[X] = Y[:Location].strip()\n            YTemp = self.Code[X].strip()\n            if ' ' not in YTemp:\n                if YTemp.endswith(':'):\n                    if YTemp not in self.Constants:\n                        self.Constants[YTemp[:-1]] = X\n                    else:\n                        Log.Warning('You cannot declare two lables with the same name', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    self.Code[X] = ''\n\n    def PrintRegisters(self):\n        Output = ['\\n+------------+-------+\\n|Registers   |       |']\n        for X, Y in self.Registers.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintConstants(self):\n        Output = ['\\n+------------+-------+\\n|Constants   |       |']\n        for X, Y in self.Constants.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintAlias(self):\n        Output = ['\\n+------------+-------+\\n|Aliases     |       |']\n        for X, Y in self.RegisterAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        for X, Y in self.PinAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintStack(self):\n        Output = ['\\n+------------+-------+\\n|Stack       |       |']\n        for X in range(self.HighestSP + 1):\n            Output.append(f'|{X:<12}|{self.Stack[X]:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def ScriptLength(self):\n        return len(self.Script)\n\n    def Special_LogicTypes(self, Value, BaseType):\n        return Value in self.LogicTypesList\n\n    def Special_Get_LogicType(self, Value):\n        return Value\n\n    def Special_BatchMode(self, Value, BaseType):\n        if BaseType == 'String':\n            return Value in list(Constants.BATCH_TYPES_VALUES.keys())\n        return True\n\n    def Special_Get_BatchMode(self, Value):\n        if type(Value) == str:\n            if str(Value) in Constants.BATCH_TYPES_VALUES:\n                return Constants.BATCH_TYPES_VALUES[Value]\n        RawValue = self.GetArgValue(Value)\n        if RawValue >= 0 and RawValue < len(Constants.BATCH_TYPES_VALUES):\n            return RawValue\n        else:\n            Log.Warning(f'Batch mode value must bettween greater then or euqal too 0 and less then {len(Constants.BATCH_TYPES_VALUES)}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n\n    def Special_DeviceHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_DeviceHash(self, Value):\n        return Value\n\n    def Special_NameHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_NameHash(self, Value):\n        return Value\n\n    def Special_ReferenceID(self, Value, BaseType):\n        return True\n\n    def Special_Get_ReferenceID(self, Value):\n        return Value\n\n    def GetArgBaseType(self, Value, TargetTypes=[]):\n        if len(Value) == 0:\n            return 'None'\n        if Value in self.Constants and 'Constant' in TargetTypes:\n            return 'Constant'\n        if Value in self.RegisterAliases and 'Register' in TargetTypes:\n            return 'Register'\n        if Value in self.PinAliases and 'Device' in TargetTypes:\n            return 'Device'\n        if Value[0] == 'd' and len(Value) > 1 and ('Device' in TargetTypes):\n            if 'r' in Value:\n                try:\n                    InValue = int(Value[1:].replace('r', ''))\n                    if InValue >= 0 and InValue < 18:\n                        return 'Device'\n                except:\n                    pass\n            else:\n                try:\n                    DeviceNumberList = [str(X) for X in range(self.Parent.PinsNumber)] + ['b']\n                    if Value[1:] in DeviceNumberList:\n                        return 'Device'\n                except:\n                    pass\n        if Value[0] == 'r' and len(Value) > 1 and ('Register' in TargetTypes):\n            try:\n                InValue = int(Value.replace('r', ''))\n                if InValue >= 0 and InValue < 18:\n                    return 'Register'\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")') and ('Hash' in TargetTypes):\n            return 'Hash'\n        if 'Number' in TargetTypes:\n            if Value[0] == '$':\n                try:\n                    int(Value[1:], 16)\n                    return 'Number'\n                except:\n                    pass\n            try:\n                int(Value)\n                return 'Number'\n            except:\n                try:\n                    float(Value)\n                    return 'Number'\n                except:\n                    pass\n        return 'String'\n\n    def GetArgType(self, Value, TargetTypes=[]):\n        IsSpecialType = TargetTypes[0] in self.FunctionMap['SpecialTypes']\n        if IsSpecialType:\n            SpecialType = TargetTypes[0]\n            TargetTypes = self.FunctionMap['SpecialTypes'][SpecialType]['Types'].split('|')\n        BaseType = self.GetArgBaseType(Value, TargetTypes)\n        if IsSpecialType:\n            if BaseType in TargetTypes:\n                Confirmed = self.FunctionMap['SpecialTypes'][SpecialType]['ConfirmFunction'](Value, BaseType)\n                if Confirmed:\n                    return SpecialType\n                else:\n                    return 'None'\n        return BaseType\n\n    def GetArgIndex(self, Value):\n        if Value in self.Constants:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        if Value in self.RegisterAliases:\n            return self.GetArgIndex(self.RegisterAliases[Value])\n        if Value in self.PinAliases:\n            return self.GetArgIndex(self.PinAliases[Value])\n        if Value[0] == 'd':\n            if 'r' in Value:\n                try:\n                    TempValue = Value[1:]\n                    RegisterIndex = int(TempValue.replace('r', ''))\n                    for X in range(TempValue.count('r')):\n                        if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                            if RegisterIndex >= 0 and RegisterIndex < 18:\n                                RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                            else:\n                                Log.Warning('Indirect device values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                return None\n                        else:\n                            Log.Warning('Indirect device values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    RegisterIndex = f'd{RegisterIndex}'\n                    if RegisterIndex in self.Parent.Pins:\n                        return RegisterIndex\n                    Log.Warning('Indirect device values have to be bettween 0 and 5', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                    self.Parent.Fields['Error'].Value = 1\n                    return None\n                except:\n                    pass\n            elif Value[0] == 'd' and Value[1:] in [str(X) for X in range(self.Parent.PinsNumber)] + ['b']:\n                return Value\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return f'r{RegisterIndex}'\n            except:\n                pass\n        Log.Warning('Unknown value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return None\n\n    def GetArgValue(self, Value, TargetType=[]):\n        Value = str(Value)\n        if Value in self.Constants:\n            return self.Constants[Value]\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return self.Registers[f'r{RegisterIndex}']\n            except:\n                pass\n        if Value in self.RegisterAliases:\n            return self.GetArgValue(self.RegisterAliases[Value])\n        if Value[0] == '$':\n            try:\n                return int(Value[1:], 16)\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")'):\n            Value = Value[6:-2]\n            return ComputeCRC32(Value)\n        try:\n            return int(Value)\n        except:\n            try:\n                return float(Value)\n            except:\n                pass\n        return str(Value)\n\n    def GetSpecialArgValue(self, Value, Type):\n        if Type in self.FunctionMap['SpecialTypes']:\n            ProcessedValue = self.GetArgValue(Value, Type)\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n            return self.FunctionMap['SpecialTypes'][Type]['GetArgFunction'](ProcessedValue)\n        else:\n            Log.Warning(f'Invalid special arg type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def GetDeviceObject(self, RefID: int, DoError: bool=True):\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        RefObject = self.Parent.Network.GetDevice(RefID)\n        if RefObject != None:\n            return RefObject\n        elif DoError:\n            Log.Warning(f'Unknown device at reference id {RefID}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n        else:\n            return None\n\n    def Instruction_Define(self, *args):\n        Value = int(args[2])\n        if args[1] not in self.Constants:\n            self.Constants[args[1]] = Value\n            if args[1] in self.RegisterAliases:\n                del self.RegisterAliases[args[1]]\n        else:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n\n    def Instruction_Move(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = Value2\n\n    def Instruction_Alias(self, *args):\n        if self.GetArgType(args[1], ['String']) == 'String':\n            if args[1] not in self.Constants and args[1] not in self.Parent.Pins and (args[1] not in self.Registers):\n                if args[1] in self.RegisterAliases:\n                    del self.RegisterAliases[args[1]]\n                if args[1] in self.PinAliases:\n                    del self.PinAliases[args[1]]\n                if args[2][0] == 'r':\n                    self.RegisterAliases[args[1]] = args[2]\n                elif args[2][0] == 'd':\n                    self.PinAliases[args[1]] = args[2]\n                else:\n                    Log.Error('Unkown alias type not caught by update')\n            else:\n                Log.Warning('Cannot overwrite a constant/builtin register/builtin device index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                if self.Parent.Fields['Error'].Value == 1:\n                    return\n        else:\n            Log.Warning('You cannot set a register alias to a device name or a register', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Add(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 + Value2\n\n    def Instruction_Sub(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 - Value2\n\n    def Instruction_Mul(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 * Value2\n\n    def Instruction_Div(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        if Value2 == 0:\n            self.Registers[Index1] = 'NaN'\n            return\n        self.Registers[Index1] = Value1 / Value2\n\n    def Instruction_Abs(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = abs(Value1)\n\n    def Instruction_Ceil(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.ceil(Value1)\n\n    def Instruction_Floor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Exp(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.e ** Value1\n\n    def Instruction_Log(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.log(Value1)\n\n    def Instruction_Rand(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = random.random()\n\n    def Instruction_Round(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        Decimal = Value1 - math.floor(Value1)\n        if Decimal >= 0.5:\n            self.Registers[Index1] = math.ceil(Value1)\n        else:\n            self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Sqrt(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.sqrt(Value1)\n\n    def Instruction_Trunc(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.trunc(Value1)\n\n    def Instruction_Asin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.asin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Acos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.acos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.atan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan2(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        try:\n            self.Registers[Index1] = math.atan2(Value1, Value2)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Cos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.cos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Tan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.tan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Peek(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot peek at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 1 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n        else:\n            Log.Warning(f'Peek index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Push(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Value1 = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot push at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 0 and Index1 < self.Parent.StackLength:\n            self.Stack[Index1] = Value1\n            self.Registers[self.RegisterAliases['sp']] += 1\n        else:\n            Log.Warning(f'Push index must be greater then or euqal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Pop(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot pop at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 > 0 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n            self.Registers[self.RegisterAliases['sp']] -= 1\n        else:\n            Log.Warning(f'Pop index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Get(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Index3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if DeviceObject.StackEnabled == False:\n            Log.Warning(f'Device does not have a stack', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot get at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 >= 1 and Index3 <= DeviceObject.StackLength:\n            self.Registers[Index1] = self.Stack[Index3 - 1]\n            Log.Warning(f'Needs further testing', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        else:\n            Log.Warning(f'Get must be greater then 0 and less then or equal to {DeviceObject.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_GetD(self, *args):\n        pass\n\n    def Instruction_Poke(self, *args):\n        Value1 = self.GetArgValue(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot poke at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Value1 >= 0 and Value1 < self.Parent.StackLength:\n            self.Stack[Value1] = Value2\n        else:\n            Log.Warning(f'Pop index must be greater then or equal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Load(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def ApplyBatchOperation(self, Values, BatchMode):\n        try:\n            BatchMode = int(BatchMode)\n            if BatchMode < 0 or BatchMode >= len(Constants.BATCH_NO_RESPONSE):\n                return 0\n        except:\n            return 0\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        if BatchMode == 0:\n            return sum(Values) / len(Values)\n        elif BatchMode == 1:\n            return sum(Values)\n        elif BatchMode == 2:\n            return min(Values)\n        elif BatchMode == 3:\n            return max(Values)\n        return 'NaN'\n\n    def CollectDevicesValueBatch(self, Devices, Value, BatchMode):\n        Values = []\n        for X in Devices:\n            FieldValue = X.GetFieldValue(Value)\n            if FieldValue[0] != None:\n                Values.append(FieldValue[0])\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        return MakeIntIfClose(self.ApplyBatchOperation(Values, BatchMode))\n\n    def Instruction_LoadBatch(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'Nan'\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n        self.Registers[Index1] = Result\n\n    def Instruction_LoadBatchNamed(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[4], 'LogicType')\n        Value4 = self.GetSpecialArgValue(args[5], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'NaN'\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning(f'Cannot get a device with a NameHash of NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        Result = self.CollectDevicesValueBatch(Devices, Value3, Value4)\n        self.Registers[Index1] = Result\n\n    def Instruction_Set(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index1])\n        else:\n            Log.Warning(f'No device at {Index1}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_SetBatch(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        for X in Devices:\n            X.SetFieldValue(Value2, Value3)\n\n    def Instruction_SetBatchNamed(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value4 = self.GetArgValue(args[4])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        for X in Devices:\n            X.SetFieldValue(Value3, Value4)\n\n    def Instruction_SetDevice(self, *args):\n        Index1 = self.GetSpecialArgValue(args[1], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(Index1)\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_LoadDevice(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetSpecialArgValue(args[2], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def Instruction_Yield(self, *args):\n        return\n\n    def Instruction_Hcf(self, *args):\n        Log.Warning('Hcf triggered', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return\n\n    def Instruction_Jump(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpAL(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpR(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump relative to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        NewLineNumber = self.Parent.Fields['LineNumber'].Value + Line - 1\n        if NewLineNumber < 0:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value\n        if NewLineNumber >= len(self.Code) - 1:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value - 1\n        self.Parent.Fields['LineNumber'].Value = NewLineNumber\n\n    def GetBranchRoot(self, FunctionName):\n        if FunctionName.endswith('al'):\n            StoreNextLine = True\n            FunctionName = FunctionName[:-2]\n        else:\n            StoreNextLine = False\n        if FunctionName.startswith('br'):\n            Relative = True\n            FunctionName = FunctionName[2:]\n        else:\n            Relative = False\n            FunctionName = FunctionName[1:]\n        return (FunctionName, StoreNextLine, Relative)\n\n    def Instruction_Branch(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[1:]]\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            JumpLine = Values[2]\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            JumpLine = Values[1]\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            JumpLine = Values[2]\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            JumpLine = Values[2]\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            JumpLine = Values[1]\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            JumpLine = Values[2]\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            JumpLine = Values[1]\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            JumpLine = Values[2]\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            JumpLine = Values[2]\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            JumpLine = Values[1]\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            JumpLine = Values[1]\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'ap':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Branch_Devices(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        JumpLine = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index1], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Set_Conditional_Register(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[2:]]\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'nanz':\n            Matched = Values[0] != 'NaN'\n        elif FunctionName == 'ap':\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Set_Conditional_Register_Devices(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index2], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Select(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Value3 = self.GetArgValue(args[4])\n        Output = 0\n        if Value1 != 0:\n            Output = Value2\n        else:\n            Output = Value3\n        self.Registers[Index1] = Output\n\n    def Instruction_Bitwise_And(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        self.Registers[Index1] = Value1 & Value2\n\n    def Instruction_Bitwise_Nor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOR')\n\n    def Instruction_Bitwise_Not(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOT')\n\n    def Instruction_Bitwise_Or(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL OR')\n        self.Registers[Index1] = Value1 | Value2\n\n    def RunUpdate(self):\n        if self.Parent.Fields['LineNumber'].Value >= len(self.Code) or self.Parent.Fields['Error'].Value != 0:\n            return\n        CurrentLine = self.Code[self.Parent.Fields['LineNumber'].Value].strip()\n        if CurrentLine != '':\n            CurrentLine = SplitNotStringSpaces(CurrentLine, ' ')\n            for CurrentIndex, CurrentFunction in self.FunctionMap['Functions'].items():\n                if CurrentLine[0] in CurrentFunction['Alias']:\n                    if len(CurrentLine) - 1 == CurrentFunction['Alias'][CurrentLine[0]]:\n                        for X in range(0, len(CurrentLine) - 1):\n                            TargetArgTypes = CurrentFunction['Args'][X].split('|')\n                            CurrentArgTypes = self.GetArgType(CurrentLine[X + 1], TargetArgTypes)\n                            if CurrentArgTypes not in TargetArgTypes:\n                                print(CurrentArgTypes)\n                                Log.Warning(f\"Arg {X + 1} of {CurrentLine[0]} must be of type {CurrentFunction['Args'][X]}\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                break\n                        else:\n                            self.FunctionMap['Functions'][CurrentIndex]['Function'](*CurrentLine)\n                    else:\n                        Log.Warning(f\"{CurrentLine[0]} requires {CurrentFunction['Alias'][CurrentLine[0]]} args\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    break\n            else:\n                Log.Warning(f'Unknown function {CurrentLine[0]}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                self.Parent.Fields['Error'].Value = 1\n        self.HighestSP = max(self.HighestSP, self.Registers[self.RegisterAliases['sp']])\n        self.Parent.Fields['LineNumber'].Value += 1\n\n    def Instruction_Sin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.sin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n\ndef test_Instruction_Sin():\n    parent = MockParent()\n    code_runner = CodeRunner(parent)\n\n    # Test case 1: Typical positive input\n    code_runner.Instruction_Sin('sin', 'r2', '0.5')\n    result_old = code_runner.Registers['r2']\n    code_runner.Instruction_Sin_new_implementation('sin', 'r2', '0.5')\n    result_new = code_runner.Registers['r2']\n    assert result_old == result_new, f\"Failed on input 0.5: {result_old} != {result_new}\"\n\n    # Test case 2: Typical negative input\n    code_runner.Instruction_Sin('sin', 'r2', '-0.5')\n    result_old = code_runner.Registers['r2']\n    code_runner.Instruction_Sin_new_implementation('sin', 'r2', '-0.5')\n    result_new = code_runner.Registers['r2']\n    assert result_old == result_new, f\"Failed on input -0.5: {result_old} != {result_new}\"\n\n    # Test case 3: Edge case zero input\n    code_runner.Instruction_Sin('sin', 'r2', '0')\n    result_old = code_runner.Registers['r2']\n    code_runner.Instruction_Sin_new_implementation('sin', 'r2', '0')\n    result_new = code_runner.Registers['r2']\n    assert result_old == result_new, f\"Failed on input 0: {result_old} != {result_new}\"\n\nif __name__ == \"__main__\":\n    test_Instruction_Sin()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions perform the same operations in the same order: they retrieve an index and a value, check for errors, handle special non-numeric values, and compute the sine of the value, storing the result or handling exceptions in the same way. There are no differences in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `Instruction_Sin` function modifies the `Registers` dictionary, which is a global variable within the context of the `CodeRunner` class. Therefore, this condition is satisfied.\n\n- CONDITION 2: The test cases check the state of the `Registers` dictionary after executing the `Instruction_Sin` function, which aligns with the requirement to check variable states rather than printed or logged contents. Thus, this condition is satisfied.\n\n- CONDITION 3: The test cases compare the results of `Instruction_Sin` and `Instruction_Sin_new_implementation` by checking if the values in the `Registers` dictionary are the same after both functions are executed. This ensures that the new implementation has exactly the same functionality as the original, satisfying this condition.\n\n- CONDITION 4: The test cases use assert statements to compare the values in the `Registers` dictionary, which is reasonable given that `Instruction_Sin` modifies this dictionary. Therefore, this condition is satisfied.\n\n- CONDITION 5: The test cases include typical positive and negative inputs, as well as an edge case (zero input), which are non-trivial and cover a range of possible inputs. Thus, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "f7581a459d857a9dbd90ed7d5ae50bf50d9669a0"
    },
    {
        "func_name": "CodeRunner.Instruction_LoadBatch",
        "idx": "758",
        "repo_name": "fireblade2534___IC-10-Simulator",
        "func_path": "EmulatorFunctions/CodeRunner.py",
        "orig_func": "def Instruction_LoadBatch(self, *args):\n    Index1 = self.GetArgIndex(args[1])\n    Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n    Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n    Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n    if self.Parent.Fields['Error'].Value == 1:\n        return\n    if Value1 in Constants.NOT_NUMBER_NUMBERS:\n        self.Registers[Index1] = 'Nan'\n        return\n    Devices = self.Parent.Network.GetBatchDevices(Value1)\n    Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n    self.Registers[Index1] = Result",
        "orig_context": "```python\n# EmulatorFunctions/CodeRunner.py\n\nclass CodeRunner:\n\n    def __init__(self, Parent, FilePath='Configs/Functions.json', DeviceFile: str='Configs/Devices.json'):\n        self.DevicesList = json.loads(open(DeviceFile, 'r').read())\n        self.FunctionMap = json.load(open(FilePath, 'r'))\n        for X, Y in self.FunctionMap['SpecialTypes'].items():\n            Y['ConfirmFunction'] = getattr(self, Y['ConfirmFunction'])\n            Y['GetArgFunction'] = getattr(self, Y['GetArgFunction'])\n        for X, Y in self.FunctionMap['Functions'].items():\n            Y['Function'] = getattr(self, Y['Function'])\n        self.Parent = Parent\n        self.Code = self.Parent.Code.split('\\n')\n        self.Registers = {f'r{X}': 0 for X in range(0, 18)}\n        self.RegisterAliases = copy.copy(Constants.DEFAULT_REGISTER_ALIAS)\n        self.PinAliases = {}\n        if self.Parent.StackEnabled == True:\n            self.Stack = [0 for X in range(self.Parent.StackLength)]\n        self.Constants = copy.copy(Constants.DEFAULT_CONSTANTS)\n        self.ParseCode()\n        self.HighestSP = 0\n\n    def ParseCode(self):\n        self.LogicTypesList = set(['ReferenceId', 'PrefabHash'] + [f'Channel{X}' for X in range(8)])\n        for X, Y in self.DevicesList.items():\n            for A, B in Y['Fields'].items():\n                if B['Read'] or B['Write']:\n                    self.LogicTypesList.add(A)\n        for X, Y in enumerate(self.Code):\n            if '#' in Y:\n                Location = Y.find('#')\n                self.Code[X] = Y[:Location].strip()\n            YTemp = self.Code[X].strip()\n            if ' ' not in YTemp:\n                if YTemp.endswith(':'):\n                    if YTemp not in self.Constants:\n                        self.Constants[YTemp[:-1]] = X\n                    else:\n                        Log.Warning('You cannot declare two lables with the same name', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    self.Code[X] = ''\n\n    def PrintRegisters(self):\n        Output = ['\\n+------------+-------+\\n|Registers   |       |']\n        for X, Y in self.Registers.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintConstants(self):\n        Output = ['\\n+------------+-------+\\n|Constants   |       |']\n        for X, Y in self.Constants.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintAlias(self):\n        Output = ['\\n+------------+-------+\\n|Aliases     |       |']\n        for X, Y in self.RegisterAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        for X, Y in self.PinAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintStack(self):\n        Output = ['\\n+------------+-------+\\n|Stack       |       |']\n        for X in range(self.HighestSP + 1):\n            Output.append(f'|{X:<12}|{self.Stack[X]:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def ScriptLength(self):\n        return len(self.Script)\n\n    def Special_LogicTypes(self, Value, BaseType):\n        return Value in self.LogicTypesList\n\n    def Special_Get_LogicType(self, Value):\n        return Value\n\n    def Special_BatchMode(self, Value, BaseType):\n        if BaseType == 'String':\n            return Value in list(Constants.BATCH_TYPES_VALUES.keys())\n        return True\n\n    def Special_Get_BatchMode(self, Value):\n        if type(Value) == str:\n            if str(Value) in Constants.BATCH_TYPES_VALUES:\n                return Constants.BATCH_TYPES_VALUES[Value]\n        RawValue = self.GetArgValue(Value)\n        if RawValue >= 0 and RawValue < len(Constants.BATCH_TYPES_VALUES):\n            return RawValue\n        else:\n            Log.Warning(f'Batch mode value must bettween greater then or euqal too 0 and less then {len(Constants.BATCH_TYPES_VALUES)}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n\n    def Special_DeviceHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_DeviceHash(self, Value):\n        return Value\n\n    def Special_NameHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_NameHash(self, Value):\n        return Value\n\n    def Special_ReferenceID(self, Value, BaseType):\n        return True\n\n    def Special_Get_ReferenceID(self, Value):\n        return Value\n\n    def GetArgBaseType(self, Value, TargetTypes=[]):\n        if len(Value) == 0:\n            return 'None'\n        if Value in self.Constants and 'Constant' in TargetTypes:\n            return 'Constant'\n        if Value in self.RegisterAliases and 'Register' in TargetTypes:\n            return 'Register'\n        if Value in self.PinAliases and 'Device' in TargetTypes:\n            return 'Device'\n        if Value[0] == 'd' and len(Value) > 1 and ('Device' in TargetTypes):\n            if 'r' in Value:\n                try:\n                    InValue = int(Value[1:].replace('r', ''))\n                    if InValue >= 0 and InValue < 18:\n                        return 'Device'\n                except:\n                    pass\n            else:\n                try:\n                    DeviceNumberList = [str(X) for X in range(self.Parent.PinsNumber)] + ['b']\n                    if Value[1:] in DeviceNumberList:\n                        return 'Device'\n                except:\n                    pass\n        if Value[0] == 'r' and len(Value) > 1 and ('Register' in TargetTypes):\n            try:\n                InValue = int(Value.replace('r', ''))\n                if InValue >= 0 and InValue < 18:\n                    return 'Register'\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")') and ('Hash' in TargetTypes):\n            return 'Hash'\n        if 'Number' in TargetTypes:\n            if Value[0] == '$':\n                try:\n                    int(Value[1:], 16)\n                    return 'Number'\n                except:\n                    pass\n            try:\n                int(Value)\n                return 'Number'\n            except:\n                try:\n                    float(Value)\n                    return 'Number'\n                except:\n                    pass\n        return 'String'\n\n    def GetArgType(self, Value, TargetTypes=[]):\n        IsSpecialType = TargetTypes[0] in self.FunctionMap['SpecialTypes']\n        if IsSpecialType:\n            SpecialType = TargetTypes[0]\n            TargetTypes = self.FunctionMap['SpecialTypes'][SpecialType]['Types'].split('|')\n        BaseType = self.GetArgBaseType(Value, TargetTypes)\n        if IsSpecialType:\n            if BaseType in TargetTypes:\n                Confirmed = self.FunctionMap['SpecialTypes'][SpecialType]['ConfirmFunction'](Value, BaseType)\n                if Confirmed:\n                    return SpecialType\n                else:\n                    return 'None'\n        return BaseType\n\n    def GetArgIndex(self, Value):\n        if Value in self.Constants:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        if Value in self.RegisterAliases:\n            return self.GetArgIndex(self.RegisterAliases[Value])\n        if Value in self.PinAliases:\n            return self.GetArgIndex(self.PinAliases[Value])\n        if Value[0] == 'd':\n            if 'r' in Value:\n                try:\n                    TempValue = Value[1:]\n                    RegisterIndex = int(TempValue.replace('r', ''))\n                    for X in range(TempValue.count('r')):\n                        if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                            if RegisterIndex >= 0 and RegisterIndex < 18:\n                                RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                            else:\n                                Log.Warning('Indirect device values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                return None\n                        else:\n                            Log.Warning('Indirect device values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    RegisterIndex = f'd{RegisterIndex}'\n                    if RegisterIndex in self.Parent.Pins:\n                        return RegisterIndex\n                    Log.Warning('Indirect device values have to be bettween 0 and 5', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                    self.Parent.Fields['Error'].Value = 1\n                    return None\n                except:\n                    pass\n            elif Value[0] == 'd' and Value[1:] in [str(X) for X in range(self.Parent.PinsNumber)] + ['b']:\n                return Value\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return f'r{RegisterIndex}'\n            except:\n                pass\n        Log.Warning('Unknown value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return None\n\n    def GetArgValue(self, Value, TargetType=[]):\n        Value = str(Value)\n        if Value in self.Constants:\n            return self.Constants[Value]\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return self.Registers[f'r{RegisterIndex}']\n            except:\n                pass\n        if Value in self.RegisterAliases:\n            return self.GetArgValue(self.RegisterAliases[Value])\n        if Value[0] == '$':\n            try:\n                return int(Value[1:], 16)\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")'):\n            Value = Value[6:-2]\n            return ComputeCRC32(Value)\n        try:\n            return int(Value)\n        except:\n            try:\n                return float(Value)\n            except:\n                pass\n        return str(Value)\n\n    def GetSpecialArgValue(self, Value, Type):\n        if Type in self.FunctionMap['SpecialTypes']:\n            ProcessedValue = self.GetArgValue(Value, Type)\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n            return self.FunctionMap['SpecialTypes'][Type]['GetArgFunction'](ProcessedValue)\n        else:\n            Log.Warning(f'Invalid special arg type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def GetDeviceObject(self, RefID: int, DoError: bool=True):\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        RefObject = self.Parent.Network.GetDevice(RefID)\n        if RefObject != None:\n            return RefObject\n        elif DoError:\n            Log.Warning(f'Unknown device at reference id {RefID}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n        else:\n            return None\n\n    def Instruction_Define(self, *args):\n        Value = int(args[2])\n        if args[1] not in self.Constants:\n            self.Constants[args[1]] = Value\n            if args[1] in self.RegisterAliases:\n                del self.RegisterAliases[args[1]]\n        else:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n\n    def Instruction_Move(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = Value2\n\n    def Instruction_Alias(self, *args):\n        if self.GetArgType(args[1], ['String']) == 'String':\n            if args[1] not in self.Constants and args[1] not in self.Parent.Pins and (args[1] not in self.Registers):\n                if args[1] in self.RegisterAliases:\n                    del self.RegisterAliases[args[1]]\n                if args[1] in self.PinAliases:\n                    del self.PinAliases[args[1]]\n                if args[2][0] == 'r':\n                    self.RegisterAliases[args[1]] = args[2]\n                elif args[2][0] == 'd':\n                    self.PinAliases[args[1]] = args[2]\n                else:\n                    Log.Error('Unkown alias type not caught by update')\n            else:\n                Log.Warning('Cannot overwrite a constant/builtin register/builtin device index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                if self.Parent.Fields['Error'].Value == 1:\n                    return\n        else:\n            Log.Warning('You cannot set a register alias to a device name or a register', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Add(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 + Value2\n\n    def Instruction_Sub(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 - Value2\n\n    def Instruction_Mul(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 * Value2\n\n    def Instruction_Div(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        if Value2 == 0:\n            self.Registers[Index1] = 'NaN'\n            return\n        self.Registers[Index1] = Value1 / Value2\n\n    def Instruction_Abs(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = abs(Value1)\n\n    def Instruction_Ceil(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.ceil(Value1)\n\n    def Instruction_Floor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Exp(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.e ** Value1\n\n    def Instruction_Log(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.log(Value1)\n\n    def Instruction_Rand(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = random.random()\n\n    def Instruction_Round(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        Decimal = Value1 - math.floor(Value1)\n        if Decimal >= 0.5:\n            self.Registers[Index1] = math.ceil(Value1)\n        else:\n            self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Sqrt(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.sqrt(Value1)\n\n    def Instruction_Trunc(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.trunc(Value1)\n\n    def Instruction_Asin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.asin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Acos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.acos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.atan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan2(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        try:\n            self.Registers[Index1] = math.atan2(Value1, Value2)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Sin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.sin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Cos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.cos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Tan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.tan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Peek(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot peek at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 1 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n        else:\n            Log.Warning(f'Peek index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Push(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Value1 = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot push at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 0 and Index1 < self.Parent.StackLength:\n            self.Stack[Index1] = Value1\n            self.Registers[self.RegisterAliases['sp']] += 1\n        else:\n            Log.Warning(f'Push index must be greater then or euqal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Pop(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot pop at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 > 0 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n            self.Registers[self.RegisterAliases['sp']] -= 1\n        else:\n            Log.Warning(f'Pop index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Get(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Index3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if DeviceObject.StackEnabled == False:\n            Log.Warning(f'Device does not have a stack', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot get at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 >= 1 and Index3 <= DeviceObject.StackLength:\n            self.Registers[Index1] = self.Stack[Index3 - 1]\n            Log.Warning(f'Needs further testing', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        else:\n            Log.Warning(f'Get must be greater then 0 and less then or equal to {DeviceObject.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_GetD(self, *args):\n        pass\n\n    def Instruction_Poke(self, *args):\n        Value1 = self.GetArgValue(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot poke at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Value1 >= 0 and Value1 < self.Parent.StackLength:\n            self.Stack[Value1] = Value2\n        else:\n            Log.Warning(f'Pop index must be greater then or equal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Load(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def ApplyBatchOperation(self, Values, BatchMode):\n        try:\n            BatchMode = int(BatchMode)\n            if BatchMode < 0 or BatchMode >= len(Constants.BATCH_NO_RESPONSE):\n                return 0\n        except:\n            return 0\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        if BatchMode == 0:\n            return sum(Values) / len(Values)\n        elif BatchMode == 1:\n            return sum(Values)\n        elif BatchMode == 2:\n            return min(Values)\n        elif BatchMode == 3:\n            return max(Values)\n        return 'NaN'\n\n    def CollectDevicesValueBatch(self, Devices, Value, BatchMode):\n        Values = []\n        for X in Devices:\n            FieldValue = X.GetFieldValue(Value)\n            if FieldValue[0] != None:\n                Values.append(FieldValue[0])\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        return MakeIntIfClose(self.ApplyBatchOperation(Values, BatchMode))\n\n    def Instruction_LoadBatchNamed(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[4], 'LogicType')\n        Value4 = self.GetSpecialArgValue(args[5], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'NaN'\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning(f'Cannot get a device with a NameHash of NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        Result = self.CollectDevicesValueBatch(Devices, Value3, Value4)\n        self.Registers[Index1] = Result\n\n    def Instruction_Set(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index1])\n        else:\n            Log.Warning(f'No device at {Index1}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_SetBatch(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        for X in Devices:\n            X.SetFieldValue(Value2, Value3)\n\n    def Instruction_SetBatchNamed(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value4 = self.GetArgValue(args[4])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        for X in Devices:\n            X.SetFieldValue(Value3, Value4)\n\n    def Instruction_SetDevice(self, *args):\n        Index1 = self.GetSpecialArgValue(args[1], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(Index1)\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_LoadDevice(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetSpecialArgValue(args[2], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def Instruction_Yield(self, *args):\n        return\n\n    def Instruction_Hcf(self, *args):\n        Log.Warning('Hcf triggered', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return\n\n    def Instruction_Jump(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpAL(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpR(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump relative to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        NewLineNumber = self.Parent.Fields['LineNumber'].Value + Line - 1\n        if NewLineNumber < 0:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value\n        if NewLineNumber >= len(self.Code) - 1:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value - 1\n        self.Parent.Fields['LineNumber'].Value = NewLineNumber\n\n    def GetBranchRoot(self, FunctionName):\n        if FunctionName.endswith('al'):\n            StoreNextLine = True\n            FunctionName = FunctionName[:-2]\n        else:\n            StoreNextLine = False\n        if FunctionName.startswith('br'):\n            Relative = True\n            FunctionName = FunctionName[2:]\n        else:\n            Relative = False\n            FunctionName = FunctionName[1:]\n        return (FunctionName, StoreNextLine, Relative)\n\n    def Instruction_Branch(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[1:]]\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            JumpLine = Values[2]\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            JumpLine = Values[1]\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            JumpLine = Values[2]\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            JumpLine = Values[2]\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            JumpLine = Values[1]\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            JumpLine = Values[2]\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            JumpLine = Values[1]\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            JumpLine = Values[2]\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            JumpLine = Values[2]\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            JumpLine = Values[1]\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            JumpLine = Values[1]\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'ap':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Branch_Devices(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        JumpLine = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index1], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Set_Conditional_Register(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[2:]]\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'nanz':\n            Matched = Values[0] != 'NaN'\n        elif FunctionName == 'ap':\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Set_Conditional_Register_Devices(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index2], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Select(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Value3 = self.GetArgValue(args[4])\n        Output = 0\n        if Value1 != 0:\n            Output = Value2\n        else:\n            Output = Value3\n        self.Registers[Index1] = Output\n\n    def Instruction_Bitwise_And(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        self.Registers[Index1] = Value1 & Value2\n\n    def Instruction_Bitwise_Nor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOR')\n\n    def Instruction_Bitwise_Not(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOT')\n\n    def Instruction_Bitwise_Or(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL OR')\n        self.Registers[Index1] = Value1 | Value2\n\n    def RunUpdate(self):\n        if self.Parent.Fields['LineNumber'].Value >= len(self.Code) or self.Parent.Fields['Error'].Value != 0:\n            return\n        CurrentLine = self.Code[self.Parent.Fields['LineNumber'].Value].strip()\n        if CurrentLine != '':\n            CurrentLine = SplitNotStringSpaces(CurrentLine, ' ')\n            for CurrentIndex, CurrentFunction in self.FunctionMap['Functions'].items():\n                if CurrentLine[0] in CurrentFunction['Alias']:\n                    if len(CurrentLine) - 1 == CurrentFunction['Alias'][CurrentLine[0]]:\n                        for X in range(0, len(CurrentLine) - 1):\n                            TargetArgTypes = CurrentFunction['Args'][X].split('|')\n                            CurrentArgTypes = self.GetArgType(CurrentLine[X + 1], TargetArgTypes)\n                            if CurrentArgTypes not in TargetArgTypes:\n                                print(CurrentArgTypes)\n                                Log.Warning(f\"Arg {X + 1} of {CurrentLine[0]} must be of type {CurrentFunction['Args'][X]}\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                break\n                        else:\n                            self.FunctionMap['Functions'][CurrentIndex]['Function'](*CurrentLine)\n                    else:\n                        Log.Warning(f\"{CurrentLine[0]} requires {CurrentFunction['Alias'][CurrentLine[0]]} args\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    break\n            else:\n                Log.Warning(f'Unknown function {CurrentLine[0]}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                self.Parent.Fields['Error'].Value = 1\n        self.HighestSP = max(self.HighestSP, self.Registers[self.RegisterAliases['sp']])\n        self.Parent.Fields['LineNumber'].Value += 1\n\n    def Instruction_LoadBatch(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'Nan'\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n        self.Registers[Index1] = Result\n```\n",
        "eval_script": "# EmulatorFunctions/CodeRunner.py\n\nimport json\nimport copy\nimport math\nimport random\n\n# Mock Constants\nclass Constants:\n    DEFAULT_REGISTER_ALIAS = {'sp': 0, 'ra': 1}\n    DEFAULT_CONSTANTS = {}\n    BATCH_TYPES_VALUES = {'type1': 0, 'type2': 1}\n    NOT_NUMBER_NUMBERS = ['NaN']\n    BATCH_NO_RESPONSE = [0, 0, 0, 0]\n\n# Mock Log\nclass Log:\n    @staticmethod\n    def Warning(message, Caller=None):\n        print(f\"WARNING: {message} - Caller: {Caller}\")\n\n    @staticmethod\n    def Info(message):\n        print(f\"INFO: {message}\")\n\n    @staticmethod\n    def Error(message):\n        print(f\"ERROR: {message}\")\n\n# Mock Device\nclass MockDevice:\n    def GetFieldValue(self, value):\n        return (42, \"Success\")\n\n# Mock Network\nclass MockNetwork:\n    def GetBatchDevices(self, device_hash, name_hash=None):\n        return [MockDevice()]\n\n# Mock Parent\nclass MockParent:\n    def __init__(self):\n        self.Code = \"Instruction_LoadBatch r1, d0, LogicType, BatchMode\"\n        self.StackEnabled = True\n        self.StackLength = 10\n        self.PinsNumber = 5\n        self.Fields = {\n            'LineNumber': MockField(0),\n            'Error': MockField(0)\n        }\n        self.Network = MockNetwork()\n        self.Pins = {'d0': 0}\n\nclass MockField:\n    def __init__(self, value):\n        self.Value = value\n\n# Create mock JSON files\nimport os\nos.makedirs('/home/user/tmp', exist_ok=True)\nwith open('/home/user/tmp/Functions.json', 'w') as f:\n    json.dump({\n        \"SpecialTypes\": {\n            \"LogicType\": {\n                \"ConfirmFunction\": \"Special_LogicTypes\",\n                \"GetArgFunction\": \"Special_Get_LogicType\",\n                \"Types\": \"String|Number\"\n            },\n            \"BatchMode\": {\n                \"ConfirmFunction\": \"Special_BatchMode\",\n                \"GetArgFunction\": \"Special_Get_BatchMode\",\n                \"Types\": \"String|Number\"\n            }\n        },\n        \"Functions\": {\n            \"Instruction_LoadBatch\": {\n                \"Alias\": {\"Instruction_LoadBatch\": 4},\n                \"Args\": [\"Register|String\", \"Device|String\", \"LogicType|String\", \"BatchMode|String\"],\n                \"Function\": \"Instruction_LoadBatch\"\n            }\n        }\n    }, f)\n\nwith open('/home/user/tmp/Devices.json', 'w') as f:\n    json.dump({\n        \"Device1\": {\n            \"Fields\": {\n                \"Field1\": {\"Read\": True, \"Write\": False}\n            }\n        }\n    }, f)\n\n# Revised CodeRunner class\nclass CodeRunner:\n\n    def __init__(self, Parent, FilePath='/home/user/tmp/Functions.json', DeviceFile: str='/home/user/tmp/Devices.json'):\n        self.DevicesList = json.loads(open(DeviceFile, 'r').read())\n        self.FunctionMap = json.load(open(FilePath, 'r'))\n        for X, Y in self.FunctionMap['SpecialTypes'].items():\n            Y['ConfirmFunction'] = getattr(self, Y['ConfirmFunction'])\n            Y['GetArgFunction'] = getattr(self, Y['GetArgFunction'])\n        for X, Y in self.FunctionMap['Functions'].items():\n            Y['Function'] = getattr(self, Y['Function'])\n        self.Parent = Parent\n        self.Code = self.Parent.Code.split('\\n')\n        self.Registers = {f'r{X}': 0 for X in range(0, 18)}\n        self.RegisterAliases = copy.copy(Constants.DEFAULT_REGISTER_ALIAS)\n        self.PinAliases = {}\n        if self.Parent.StackEnabled == True:\n            self.Stack = [0 for X in range(self.Parent.StackLength)]\n        self.Constants = copy.copy(Constants.DEFAULT_CONSTANTS)\n        self.ParseCode()\n        self.HighestSP = 0\n\n    def ParseCode(self):\n        self.LogicTypesList = set(['ReferenceId', 'PrefabHash'] + [f'Channel{X}' for X in range(8)])\n        for X, Y in self.DevicesList.items():\n            for A, B in Y['Fields'].items():\n                if B['Read'] or B['Write']:\n                    self.LogicTypesList.add(A)\n        for X, Y in enumerate(self.Code):\n            if '#' in Y:\n                Location = Y.find('#')\n                self.Code[X] = Y[:Location].strip()\n            YTemp = self.Code[X].strip()\n            if ' ' not in YTemp:\n                if YTemp.endswith(':'):\n                    if YTemp not in self.Constants:\n                        self.Constants[YTemp[:-1]] = X\n                    else:\n                        Log.Warning('You cannot declare two lables with the same name', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    self.Code[X] = ''\n\n    def PrintRegisters(self):\n        Output = ['\\n+------------+-------+\\n|Registers   |       |']\n        for X, Y in self.Registers.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintConstants(self):\n        Output = ['\\n+------------+-------+\\n|Constants   |       |']\n        for X, Y in self.Constants.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintAlias(self):\n        Output = ['\\n+------------+-------+\\n|Aliases     |       |']\n        for X, Y in self.RegisterAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        for X, Y in self.PinAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintStack(self):\n        Output = ['\\n+------------+-------+\\n|Stack       |       |']\n        for X in range(self.HighestSP + 1):\n            Output.append(f'|{X:<12}|{self.Stack[X]:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def ScriptLength(self):\n        return len(self.Script)\n\n    def Special_LogicTypes(self, Value, BaseType):\n        return Value in self.LogicTypesList\n\n    def Special_Get_LogicType(self, Value):\n        return Value\n\n    def Special_BatchMode(self, Value, BaseType):\n        if BaseType == 'String':\n            return Value in list(Constants.BATCH_TYPES_VALUES.keys())\n        return True\n\n    def Special_Get_BatchMode(self, Value):\n        if type(Value) == str:\n            if str(Value) in Constants.BATCH_TYPES_VALUES:\n                return Constants.BATCH_TYPES_VALUES[Value]\n        RawValue = self.GetArgValue(Value)\n        if RawValue >= 0 and RawValue < len(Constants.BATCH_TYPES_VALUES):\n            return RawValue\n        else:\n            Log.Warning(f'Batch mode value must bettween greater then or euqal too 0 and less then {len(Constants.BATCH_TYPES_VALUES)}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n\n    def Special_DeviceHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_DeviceHash(self, Value):\n        return Value\n\n    def Special_NameHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_NameHash(self, Value):\n        return Value\n\n    def Special_ReferenceID(self, Value, BaseType):\n        return True\n\n    def Special_Get_ReferenceID(self, Value):\n        return Value\n\n    def GetArgBaseType(self, Value, TargetTypes=[]):\n        if len(Value) == 0:\n            return 'None'\n        if Value in self.Constants and 'Constant' in TargetTypes:\n            return 'Constant'\n        if Value in self.RegisterAliases and 'Register' in TargetTypes:\n            return 'Register'\n        if Value in self.PinAliases and 'Device' in TargetTypes:\n            return 'Device'\n        if Value[0] == 'd' and len(Value) > 1 and ('Device' in TargetTypes):\n            if 'r' in Value:\n                try:\n                    InValue = int(Value[1:].replace('r', ''))\n                    if InValue >= 0 and InValue < 18:\n                        return 'Device'\n                except:\n                    pass\n            else:\n                try:\n                    DeviceNumberList = [str(X) for X in range(self.Parent.PinsNumber)] + ['b']\n                    if Value[1:] in DeviceNumberList:\n                        return 'Device'\n                except:\n                    pass\n        if Value[0] == 'r' and len(Value) > 1 and ('Register' in TargetTypes):\n            try:\n                InValue = int(Value.replace('r', ''))\n                if InValue >= 0 and InValue < 18:\n                    return 'Register'\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")') and ('Hash' in TargetTypes):\n            return 'Hash'\n        if 'Number' in TargetTypes:\n            if Value[0] == '$':\n                try:\n                    int(Value[1:], 16)\n                    return 'Number'\n                except:\n                    pass\n            try:\n                int(Value)\n                return 'Number'\n            except:\n                try:\n                    float(Value)\n                    return 'Number'\n                except:\n                    pass\n        return 'String'\n\n    def GetArgType(self, Value, TargetTypes=[]):\n        IsSpecialType = TargetTypes[0] in self.FunctionMap['SpecialTypes']\n        if IsSpecialType:\n            SpecialType = TargetTypes[0]\n            TargetTypes = self.FunctionMap['SpecialTypes'][SpecialType]['Types'].split('|')\n        BaseType = self.GetArgBaseType(Value, TargetTypes)\n        if IsSpecialType:\n            if BaseType in TargetTypes:\n                Confirmed = self.FunctionMap['SpecialTypes'][SpecialType]['ConfirmFunction'](Value, BaseType)\n                if Confirmed:\n                    return SpecialType\n                else:\n                    return 'None'\n        return BaseType\n\n    def GetArgIndex(self, Value):\n        if Value in self.Constants:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        if Value in self.RegisterAliases:\n            return self.GetArgIndex(self.RegisterAliases[Value])\n        if Value in self.PinAliases:\n            return self.GetArgIndex(self.PinAliases[Value])\n        if Value[0] == 'd':\n            if 'r' in Value:\n                try:\n                    TempValue = Value[1:]\n                    RegisterIndex = int(TempValue.replace('r', ''))\n                    for X in range(TempValue.count('r')):\n                        if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                            if RegisterIndex >= 0 and RegisterIndex < 18:\n                                RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                            else:\n                                Log.Warning('Indirect device values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                return None\n                        else:\n                            Log.Warning('Indirect device values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    RegisterIndex = f'd{RegisterIndex}'\n                    if RegisterIndex in self.Parent.Pins:\n                        return RegisterIndex\n                    Log.Warning('Indirect device values have to be bettween 0 and 5', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                    self.Parent.Fields['Error'].Value = 1\n                    return None\n                except:\n                    pass\n            elif Value[0] == 'd' and Value[1:] in [str(X) for X in range(self.Parent.PinsNumber)] + ['b']:\n                return Value\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return f'r{RegisterIndex}'\n            except:\n                pass\n        Log.Warning('Unknown value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return None\n\n    def GetArgValue(self, Value, TargetType=[]):\n        Value = str(Value)\n        if Value in self.Constants:\n            return self.Constants[Value]\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return self.Registers[f'r{RegisterIndex}']\n            except:\n                pass\n        if Value in self.RegisterAliases:\n            return self.GetArgValue(self.RegisterAliases[Value])\n        if Value[0] == '$':\n            try:\n                return int(Value[1:], 16)\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")'):\n            Value = Value[6:-2]\n            return ComputeCRC32(Value)\n        try:\n            return int(Value)\n        except:\n            try:\n                return float(Value)\n            except:\n                pass\n        return str(Value)\n\n    def GetSpecialArgValue(self, Value, Type):\n        if Type in self.FunctionMap['SpecialTypes']:\n            ProcessedValue = self.GetArgValue(Value, Type)\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n            return self.FunctionMap['SpecialTypes'][Type]['GetArgFunction'](ProcessedValue)\n        else:\n            Log.Warning(f'Invalid special arg type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def GetDeviceObject(self, RefID: int, DoError: bool=True):\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        RefObject = self.Parent.Network.GetDevice(RefID)\n        if RefObject != None:\n            return RefObject\n        elif DoError:\n            Log.Warning(f'Unknown device at reference id {RefID}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n        else:\n            return None\n\n    def Instruction_Define(self, *args):\n        Value = int(args[2])\n        if args[1] not in self.Constants:\n            self.Constants[args[1]] = Value\n            if args[1] in self.RegisterAliases:\n                del self.RegisterAliases[args[1]]\n        else:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n\n    def Instruction_Move(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = Value2\n\n    def Instruction_Alias(self, *args):\n        if self.GetArgType(args[1], ['String']) == 'String':\n            if args[1] not in self.Constants and args[1] not in self.Parent.Pins and (args[1] not in self.Registers):\n                if args[1] in self.RegisterAliases:\n                    del self.RegisterAliases[args[1]]\n                if args[1] in self.PinAliases:\n                    del self.PinAliases[args[1]]\n                if args[2][0] == 'r':\n                    self.RegisterAliases[args[1]] = args[2]\n                elif args[2][0] == 'd':\n                    self.PinAliases[args[1]] = args[2]\n                else:\n                    Log.Error('Unkown alias type not caught by update')\n            else:\n                Log.Warning('Cannot overwrite a constant/builtin register/builtin device index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                if self.Parent.Fields['Error'].Value == 1:\n                    return\n        else:\n            Log.Warning('You cannot set a register alias to a device name or a register', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Add(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 + Value2\n\n    def Instruction_Sub(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 - Value2\n\n    def Instruction_Mul(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 * Value2\n\n    def Instruction_Div(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        if Value2 == 0:\n            self.Registers[Index1] = 'NaN'\n            return\n        self.Registers[Index1] = Value1 / Value2\n\n    def Instruction_Abs(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = abs(Value1)\n\n    def Instruction_Ceil(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.ceil(Value1)\n\n    def Instruction_Floor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Exp(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.e ** Value1\n\n    def Instruction_Log(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.log(Value1)\n\n    def Instruction_Rand(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = random.random()\n\n    def Instruction_Round(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        Decimal = Value1 - math.floor(Value1)\n        if Decimal >= 0.5:\n            self.Registers[Index1] = math.ceil(Value1)\n        else:\n            self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Sqrt(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.sqrt(Value1)\n\n    def Instruction_Trunc(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.trunc(Value1)\n\n    def Instruction_Asin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.asin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Acos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.acos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.atan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan2(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        try:\n            self.Registers[Index1] = math.atan2(Value1, Value2)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Sin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.sin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Cos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.cos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Tan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.tan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Peek(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot peek at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 1 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n        else:\n            Log.Warning(f'Peek index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Push(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Value1 = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot push at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 0 and Index1 < self.Parent.StackLength:\n            self.Stack[Index1] = Value1\n            self.Registers[self.RegisterAliases['sp']] += 1\n        else:\n            Log.Warning(f'Push index must be greater then or euqal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Pop(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot pop at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 > 0 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n            self.Registers[self.RegisterAliases['sp']] -= 1\n        else:\n            Log.Warning(f'Pop index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Get(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Index3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if DeviceObject.StackEnabled == False:\n            Log.Warning(f'Device does not have a stack', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot get at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 >= 1 and Index3 <= DeviceObject.StackLength:\n            self.Registers[Index1] = self.Stack[Index3 - 1]\n            Log.Warning(f'Needs further testing', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        else:\n            Log.Warning(f'Get must be greater then 0 and less then or equal to {DeviceObject.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_GetD(self, *args):\n        pass\n\n    def Instruction_Poke(self, *args):\n        Value1 = self.GetArgValue(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot poke at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Value1 >= 0 and Value1 < self.Parent.StackLength:\n            self.Stack[Value1] = Value2\n        else:\n            Log.Warning(f'Pop index must be greater then or equal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Load(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def ApplyBatchOperation(self, Values, BatchMode):\n        try:\n            BatchMode = int(BatchMode)\n            if BatchMode < 0 or BatchMode >= len(Constants.BATCH_NO_RESPONSE):\n                return 0\n        except:\n            return 0\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        if BatchMode == 0:\n            return sum(Values) / len(Values)\n        elif BatchMode == 1:\n            return sum(Values)\n        elif BatchMode == 2:\n            return min(Values)\n        elif BatchMode == 3:\n            return max(Values)\n        return 'NaN'\n\n    def CollectDevicesValueBatch(self, Devices, Value, BatchMode):\n        Values = []\n        for X in Devices:\n            FieldValue = X.GetFieldValue(Value)\n            if FieldValue[0] != None:\n                Values.append(FieldValue[0])\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        return MakeIntIfClose(self.ApplyBatchOperation(Values, BatchMode))\n\n    def Instruction_LoadBatchNamed(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[4], 'LogicType')\n        Value4 = self.GetSpecialArgValue(args[5], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'NaN'\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning(f'Cannot get a device with a NameHash of NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        Result = self.CollectDevicesValueBatch(Devices, Value3, Value4)\n        self.Registers[Index1] = Result\n\n    def Instruction_Set(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index1])\n        else:\n            Log.Warning(f'No device at {Index1}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_SetBatch(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        for X in Devices:\n            X.SetFieldValue(Value2, Value3)\n\n    def Instruction_SetBatchNamed(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value4 = self.GetArgValue(args[4])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        for X in Devices:\n            X.SetFieldValue(Value3, Value4)\n\n    def Instruction_SetDevice(self, *args):\n        Index1 = self.GetSpecialArgValue(args[1], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(Index1)\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_LoadDevice(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetSpecialArgValue(args[2], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def Instruction_Yield(self, *args):\n        return\n\n    def Instruction_Hcf(self, *args):\n        Log.Warning('Hcf triggered', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return\n\n    def Instruction_Jump(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpAL(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpR(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump relative to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        NewLineNumber = self.Parent.Fields['LineNumber'].Value + Line - 1\n        if NewLineNumber < 0:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value\n        if NewLineNumber >= len(self.Code) - 1:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value - 1\n        self.Parent.Fields['LineNumber'].Value = NewLineNumber\n\n    def GetBranchRoot(self, FunctionName):\n        if FunctionName.endswith('al'):\n            StoreNextLine = True\n            FunctionName = FunctionName[:-2]\n        else:\n            StoreNextLine = False\n        if FunctionName.startswith('br'):\n            Relative = True\n            FunctionName = FunctionName[2:]\n        else:\n            Relative = False\n            FunctionName = FunctionName[1:]\n        return (FunctionName, StoreNextLine, Relative)\n\n    def Instruction_Branch(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[1:]]\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            JumpLine = Values[2]\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            JumpLine = Values[1]\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            JumpLine = Values[2]\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            JumpLine = Values[2]\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            JumpLine = Values[1]\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            JumpLine = Values[2]\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            JumpLine = Values[1]\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            JumpLine = Values[2]\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            JumpLine = Values[2]\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            JumpLine = Values[1]\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            JumpLine = Values[1]\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'ap':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Branch_Devices(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        JumpLine = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index1], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Set_Conditional_Register(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[2:]]\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'nanz':\n            Matched = Values[0] != 'NaN'\n        elif FunctionName == 'ap':\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Set_Conditional_Register_Devices(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index2], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Select(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Value3 = self.GetArgValue(args[4])\n        Output = 0\n        if Value1 != 0:\n            Output = Value2\n        else:\n            Output = Value3\n        self.Registers[Index1] = Output\n\n    def Instruction_Bitwise_And(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        self.Registers[Index1] = Value1 & Value2\n\n    def Instruction_Bitwise_Nor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOR')\n\n    def Instruction_Bitwise_Not(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOT')\n\n    def Instruction_Bitwise_Or(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL OR')\n        self.Registers[Index1] = Value1 | Value2\n\n    def RunUpdate(self):\n        if self.Parent.Fields['LineNumber'].Value >= len(self.Code) or self.Parent.Fields['Error'].Value != 0:\n            return\n        CurrentLine = self.Code[self.Parent.Fields['LineNumber'].Value].strip()\n        if CurrentLine != '':\n            CurrentLine = SplitNotStringSpaces(CurrentLine, ' ')\n            for CurrentIndex, CurrentFunction in self.FunctionMap['Functions'].items():\n                if CurrentLine[0] in CurrentFunction['Alias']:\n                    if len(CurrentLine) - 1 == CurrentFunction['Alias'][CurrentLine[0]]:\n                        for X in range(0, len(CurrentLine) - 1):\n                            TargetArgTypes = CurrentFunction['Args'][X].split('|')\n                            CurrentArgTypes = self.GetArgType(CurrentLine[X + 1], TargetArgTypes)\n                            if CurrentArgTypes not in TargetTypes:\n                                print(CurrentArgTypes)\n                                Log.Warning(f\"Arg {X + 1} of {CurrentLine[0]} must be of type {CurrentFunction['Args'][X]}\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                break\n                        else:\n                            self.FunctionMap['Functions'][CurrentIndex]['Function'](*CurrentLine)\n                    else:\n                        Log.Warning(f\"{CurrentLine[0]} requires {CurrentFunction['Alias'][CurrentLine[0]]} args\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    break\n            else:\n                Log.Warning(f'Unknown function {CurrentLine[0]}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                self.Parent.Fields['Error'].Value = 1\n        self.HighestSP = max(self.HighestSP, self.Registers[self.RegisterAliases['sp']])\n        self.Parent.Fields['LineNumber'].Value += 1\n\n    def Instruction_LoadBatch(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'Nan'\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n        self.Registers[Index1] = Result\n\n\ndef test_Instruction_LoadBatch():\n    parent = MockParent()\n    code_runner = CodeRunner(parent)\n\n    # Test case 1: Default case\n    code_runner.Instruction_LoadBatch('Instruction_LoadBatch', 'r1', 'd0', 'LogicType', 'BatchMode')\n    result_old = code_runner.Registers['r1']\n\n    code_runner.Instruction_LoadBatch_new_implementation('Instruction_LoadBatch', 'r1', 'd0', 'LogicType', 'BatchMode')\n    result_new = code_runner.Registers['r1']\n    assert result_old == result_new, f\"Test case 1 failed: {result_old} != {result_new}\"\n\n    # Test case 2: Different LogicType\n    code_runner.Instruction_LoadBatch('Instruction_LoadBatch', 'r1', 'd0', 'Channel0', 'BatchMode')\n    result_old = code_runner.Registers['r1']\n\n    code_runner.Instruction_LoadBatch_new_implementation('Instruction_LoadBatch', 'r1', 'd0', 'Channel0', 'BatchMode')\n    result_new = code_runner.Registers['r1']\n    assert result_old == result_new, f\"Test case 2 failed: {result_old} != {result_new}\"\n\n    # Test case 3: Different BatchMode\n    code_runner.Instruction_LoadBatch('Instruction_LoadBatch', 'r1', 'd0', 'LogicType', 'type1')\n    result_old = code_runner.Registers['r1']\n\n    code_runner.Instruction_LoadBatch_new_implementation('Instruction_LoadBatch', 'r1', 'd0', 'LogicType', 'type1')\n    result_new = code_runner.Registers['r1']\n    assert result_old == result_new, f\"Test case 3 failed: {result_old} != {result_new}\"\n\nif __name__ == \"__main__\":\n    test_Instruction_LoadBatch()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION provided in the CODE is identical to the ORIGINAL FUNCTION. Both functions perform the same operations in the same order, checking for errors, handling special argument values, and updating the registers based on the collected batch device values. There are no differences in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `Instruction_LoadBatch` modifies the `Registers` dictionary, which is a class attribute, thus satisfying this condition.\n- CONDITION 2: The test cases check the values in the `Registers` dictionary after calling both the original and new implementations of `Instruction_LoadBatch`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the results of the original and new implementations of `Instruction_LoadBatch` by asserting that the values in the `Registers` dictionary are equal. This ensures that the new implementation must have the exact same functionality as the original to pass the tests, satisfying this condition.\n- CONDITION 4: The test cases use the `assert` statement to compare the results stored in the `Registers` dictionary, which is reasonable given that `Instruction_LoadBatch` modifies this dictionary. This satisfies the condition.\n- CONDITION 5: The test cases cover different scenarios by varying the `LogicType` and `BatchMode` parameters, making them non-trivial and ensuring that different paths in the code are tested. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "f7581a459d857a9dbd90ed7d5ae50bf50d9669a0"
    },
    {
        "func_name": "CodeRunner.Instruction_Abs",
        "idx": "768",
        "repo_name": "fireblade2534___IC-10-Simulator",
        "func_path": "EmulatorFunctions/CodeRunner.py",
        "orig_func": "def Instruction_Abs(self, *args):\n    Index1 = self.GetArgIndex(args[1])\n    Value1 = self.GetArgValue(args[2])\n    if self.Parent.Fields['Error'].Value == 1:\n        return\n    if Value1 in Constants.NOT_NUMBER_NUMBERS:\n        self.Registers[Index1] = Value1\n        return\n    self.Registers[Index1] = abs(Value1)",
        "orig_context": "```python\n# EmulatorFunctions/CodeRunner.py\n\nclass CodeRunner:\n\n    def __init__(self, Parent, FilePath='Configs/Functions.json', DeviceFile: str='Configs/Devices.json'):\n        self.DevicesList = json.loads(open(DeviceFile, 'r').read())\n        self.FunctionMap = json.load(open(FilePath, 'r'))\n        for X, Y in self.FunctionMap['SpecialTypes'].items():\n            Y['ConfirmFunction'] = getattr(self, Y['ConfirmFunction'])\n            Y['GetArgFunction'] = getattr(self, Y['GetArgFunction'])\n        for X, Y in self.FunctionMap['Functions'].items():\n            Y['Function'] = getattr(self, Y['Function'])\n        self.Parent = Parent\n        self.Code = self.Parent.Code.split('\\n')\n        self.Registers = {f'r{X}': 0 for X in range(0, 18)}\n        self.RegisterAliases = copy.copy(Constants.DEFAULT_REGISTER_ALIAS)\n        self.PinAliases = {}\n        if self.Parent.StackEnabled == True:\n            self.Stack = [0 for X in range(self.Parent.StackLength)]\n        self.Constants = copy.copy(Constants.DEFAULT_CONSTANTS)\n        self.ParseCode()\n        self.HighestSP = 0\n\n    def ParseCode(self):\n        self.LogicTypesList = set(['ReferenceId', 'PrefabHash'] + [f'Channel{X}' for X in range(8)])\n        for X, Y in self.DevicesList.items():\n            for A, B in Y['Fields'].items():\n                if B['Read'] or B['Write']:\n                    self.LogicTypesList.add(A)\n        for X, Y in enumerate(self.Code):\n            if '#' in Y:\n                Location = Y.find('#')\n                self.Code[X] = Y[:Location].strip()\n            YTemp = self.Code[X].strip()\n            if ' ' not in YTemp:\n                if YTemp.endswith(':'):\n                    if YTemp not in self.Constants:\n                        self.Constants[YTemp[:-1]] = X\n                    else:\n                        Log.Warning('You cannot declare two lables with the same name', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    self.Code[X] = ''\n\n    def PrintRegisters(self):\n        Output = ['\\n+------------+-------+\\n|Registers   |       |']\n        for X, Y in self.Registers.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintConstants(self):\n        Output = ['\\n+------------+-------+\\n|Constants   |       |']\n        for X, Y in self.Constants.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintAlias(self):\n        Output = ['\\n+------------+-------+\\n|Aliases     |       |']\n        for X, Y in self.RegisterAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        for X, Y in self.PinAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintStack(self):\n        Output = ['\\n+------------+-------+\\n|Stack       |       |']\n        for X in range(self.HighestSP + 1):\n            Output.append(f'|{X:<12}|{self.Stack[X]:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def ScriptLength(self):\n        return len(self.Script)\n\n    def Special_LogicTypes(self, Value, BaseType):\n        return Value in self.LogicTypesList\n\n    def Special_Get_LogicType(self, Value):\n        return Value\n\n    def Special_BatchMode(self, Value, BaseType):\n        if BaseType == 'String':\n            return Value in list(Constants.BATCH_TYPES_VALUES.keys())\n        return True\n\n    def Special_Get_BatchMode(self, Value):\n        if type(Value) == str:\n            if str(Value) in Constants.BATCH_TYPES_VALUES:\n                return Constants.BATCH_TYPES_VALUES[Value]\n        RawValue = self.GetArgValue(Value)\n        if RawValue >= 0 and RawValue < len(Constants.BATCH_TYPES_VALUES):\n            return RawValue\n        else:\n            Log.Warning(f'Batch mode value must bettween greater then or euqal too 0 and less then {len(Constants.BATCH_TYPES_VALUES)}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n\n    def Special_DeviceHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_DeviceHash(self, Value):\n        return Value\n\n    def Special_NameHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_NameHash(self, Value):\n        return Value\n\n    def Special_ReferenceID(self, Value, BaseType):\n        return True\n\n    def Special_Get_ReferenceID(self, Value):\n        return Value\n\n    def GetArgBaseType(self, Value, TargetTypes=[]):\n        if len(Value) == 0:\n            return 'None'\n        if Value in self.Constants and 'Constant' in TargetTypes:\n            return 'Constant'\n        if Value in self.RegisterAliases and 'Register' in TargetTypes:\n            return 'Register'\n        if Value in self.PinAliases and 'Device' in TargetTypes:\n            return 'Device'\n        if Value[0] == 'd' and len(Value) > 1 and ('Device' in TargetTypes):\n            if 'r' in Value:\n                try:\n                    InValue = int(Value[1:].replace('r', ''))\n                    if InValue >= 0 and InValue < 18:\n                        return 'Device'\n                except:\n                    pass\n            else:\n                try:\n                    DeviceNumberList = [str(X) for X in range(self.Parent.PinsNumber)] + ['b']\n                    if Value[1:] in DeviceNumberList:\n                        return 'Device'\n                except:\n                    pass\n        if Value[0] == 'r' and len(Value) > 1 and ('Register' in TargetTypes):\n            try:\n                InValue = int(Value.replace('r', ''))\n                if InValue >= 0 and InValue < 18:\n                    return 'Register'\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")') and ('Hash' in TargetTypes):\n            return 'Hash'\n        if 'Number' in TargetTypes:\n            if Value[0] == '$':\n                try:\n                    int(Value[1:], 16)\n                    return 'Number'\n                except:\n                    pass\n            try:\n                int(Value)\n                return 'Number'\n            except:\n                try:\n                    float(Value)\n                    return 'Number'\n                except:\n                    pass\n        return 'String'\n\n    def GetArgType(self, Value, TargetTypes=[]):\n        IsSpecialType = TargetTypes[0] in self.FunctionMap['SpecialTypes']\n        if IsSpecialType:\n            SpecialType = TargetTypes[0]\n            TargetTypes = self.FunctionMap['SpecialTypes'][SpecialType]['Types'].split('|')\n        BaseType = self.GetArgBaseType(Value, TargetTypes)\n        if IsSpecialType:\n            if BaseType in TargetTypes:\n                Confirmed = self.FunctionMap['SpecialTypes'][SpecialType]['ConfirmFunction'](Value, BaseType)\n                if Confirmed:\n                    return SpecialType\n                else:\n                    return 'None'\n        return BaseType\n\n    def GetArgIndex(self, Value):\n        if Value in self.Constants:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        if Value in self.RegisterAliases:\n            return self.GetArgIndex(self.RegisterAliases[Value])\n        if Value in self.PinAliases:\n            return self.GetArgIndex(self.PinAliases[Value])\n        if Value[0] == 'd':\n            if 'r' in Value:\n                try:\n                    TempValue = Value[1:]\n                    RegisterIndex = int(TempValue.replace('r', ''))\n                    for X in range(TempValue.count('r')):\n                        if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                            if RegisterIndex >= 0 and RegisterIndex < 18:\n                                RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                            else:\n                                Log.Warning('Indirect device values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                return None\n                        else:\n                            Log.Warning('Indirect device values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    RegisterIndex = f'd{RegisterIndex}'\n                    if RegisterIndex in self.Parent.Pins:\n                        return RegisterIndex\n                    Log.Warning('Indirect device values have to be bettween 0 and 5', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                    self.Parent.Fields['Error'].Value = 1\n                    return None\n                except:\n                    pass\n            elif Value[0] == 'd' and Value[1:] in [str(X) for X in range(self.Parent.PinsNumber)] + ['b']:\n                return Value\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return f'r{RegisterIndex}'\n            except:\n                pass\n        Log.Warning('Unknown value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return None\n\n    def GetArgValue(self, Value, TargetType=[]):\n        Value = str(Value)\n        if Value in self.Constants:\n            return self.Constants[Value]\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return self.Registers[f'r{RegisterIndex}']\n            except:\n                pass\n        if Value in self.RegisterAliases:\n            return self.GetArgValue(self.RegisterAliases[Value])\n        if Value[0] == '$':\n            try:\n                return int(Value[1:], 16)\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")'):\n            Value = Value[6:-2]\n            return ComputeCRC32(Value)\n        try:\n            return int(Value)\n        except:\n            try:\n                return float(Value)\n            except:\n                pass\n        return str(Value)\n\n    def GetSpecialArgValue(self, Value, Type):\n        if Type in self.FunctionMap['SpecialTypes']:\n            ProcessedValue = self.GetArgValue(Value, Type)\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n            return self.FunctionMap['SpecialTypes'][Type]['GetArgFunction'](ProcessedValue)\n        else:\n            Log.Warning(f'Invalid special arg type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def GetDeviceObject(self, RefID: int, DoError: bool=True):\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        RefObject = self.Parent.Network.GetDevice(RefID)\n        if RefObject != None:\n            return RefObject\n        elif DoError:\n            Log.Warning(f'Unknown device at reference id {RefID}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n        else:\n            return None\n\n    def Instruction_Define(self, *args):\n        Value = int(args[2])\n        if args[1] not in self.Constants:\n            self.Constants[args[1]] = Value\n            if args[1] in self.RegisterAliases:\n                del self.RegisterAliases[args[1]]\n        else:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n\n    def Instruction_Move(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = Value2\n\n    def Instruction_Alias(self, *args):\n        if self.GetArgType(args[1], ['String']) == 'String':\n            if args[1] not in self.Constants and args[1] not in self.Parent.Pins and (args[1] not in self.Registers):\n                if args[1] in self.RegisterAliases:\n                    del self.RegisterAliases[args[1]]\n                if args[1] in self.PinAliases:\n                    del self.PinAliases[args[1]]\n                if args[2][0] == 'r':\n                    self.RegisterAliases[args[1]] = args[2]\n                elif args[2][0] == 'd':\n                    self.PinAliases[args[1]] = args[2]\n                else:\n                    Log.Error('Unkown alias type not caught by update')\n            else:\n                Log.Warning('Cannot overwrite a constant/builtin register/builtin device index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                if self.Parent.Fields['Error'].Value == 1:\n                    return\n        else:\n            Log.Warning('You cannot set a register alias to a device name or a register', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Add(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 + Value2\n\n    def Instruction_Sub(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 - Value2\n\n    def Instruction_Mul(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 * Value2\n\n    def Instruction_Div(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        if Value2 == 0:\n            self.Registers[Index1] = 'NaN'\n            return\n        self.Registers[Index1] = Value1 / Value2\n\n    def Instruction_Ceil(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.ceil(Value1)\n\n    def Instruction_Floor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Exp(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.e ** Value1\n\n    def Instruction_Log(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.log(Value1)\n\n    def Instruction_Rand(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = random.random()\n\n    def Instruction_Round(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        Decimal = Value1 - math.floor(Value1)\n        if Decimal >= 0.5:\n            self.Registers[Index1] = math.ceil(Value1)\n        else:\n            self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Sqrt(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.sqrt(Value1)\n\n    def Instruction_Trunc(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.trunc(Value1)\n\n    def Instruction_Asin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.asin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Acos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.acos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.atan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan2(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        try:\n            self.Registers[Index1] = math.atan2(Value1, Value2)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Sin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.sin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Cos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.cos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Tan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.tan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Peek(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot peek at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 1 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n        else:\n            Log.Warning(f'Peek index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Push(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Value1 = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot push at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 0 and Index1 < self.Parent.StackLength:\n            self.Stack[Index1] = Value1\n            self.Registers[self.RegisterAliases['sp']] += 1\n        else:\n            Log.Warning(f'Push index must be greater then or euqal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Pop(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot pop at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 > 0 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n            self.Registers[self.RegisterAliases['sp']] -= 1\n        else:\n            Log.Warning(f'Pop index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Get(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Index3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if DeviceObject.StackEnabled == False:\n            Log.Warning(f'Device does not have a stack', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot get at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 >= 1 and Index3 <= DeviceObject.StackLength:\n            self.Registers[Index1] = self.Stack[Index3 - 1]\n            Log.Warning(f'Needs further testing', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        else:\n            Log.Warning(f'Get must be greater then 0 and less then or equal to {DeviceObject.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_GetD(self, *args):\n        pass\n\n    def Instruction_Poke(self, *args):\n        Value1 = self.GetArgValue(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot poke at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Value1 >= 0 and Value1 < self.Parent.StackLength:\n            self.Stack[Value1] = Value2\n        else:\n            Log.Warning(f'Pop index must be greater then or equal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Load(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def ApplyBatchOperation(self, Values, BatchMode):\n        try:\n            BatchMode = int(BatchMode)\n            if BatchMode < 0 or BatchMode >= len(Constants.BATCH_NO_RESPONSE):\n                return 0\n        except:\n            return 0\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        if BatchMode == 0:\n            return sum(Values) / len(Values)\n        elif BatchMode == 1:\n            return sum(Values)\n        elif BatchMode == 2:\n            return min(Values)\n        elif BatchMode == 3:\n            return max(Values)\n        return 'NaN'\n\n    def CollectDevicesValueBatch(self, Devices, Value, BatchMode):\n        Values = []\n        for X in Devices:\n            FieldValue = X.GetFieldValue(Value)\n            if FieldValue[0] != None:\n                Values.append(FieldValue[0])\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        return MakeIntIfClose(self.ApplyBatchOperation(Values, BatchMode))\n\n    def Instruction_LoadBatch(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'Nan'\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n        self.Registers[Index1] = Result\n\n    def Instruction_LoadBatchNamed(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[4], 'LogicType')\n        Value4 = self.GetSpecialArgValue(args[5], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'NaN'\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning(f'Cannot get a device with a NameHash of NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        Result = self.CollectDevicesValueBatch(Devices, Value3, Value4)\n        self.Registers[Index1] = Result\n\n    def Instruction_Set(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index1])\n        else:\n            Log.Warning(f'No device at {Index1}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_SetBatch(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        for X in Devices:\n            X.SetFieldValue(Value2, Value3)\n\n    def Instruction_SetBatchNamed(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value4 = self.GetArgValue(args[4])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        for X in Devices:\n            X.SetFieldValue(Value3, Value4)\n\n    def Instruction_SetDevice(self, *args):\n        Index1 = self.GetSpecialArgValue(args[1], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(Index1)\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_LoadDevice(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetSpecialArgValue(args[2], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def Instruction_Yield(self, *args):\n        return\n\n    def Instruction_Hcf(self, *args):\n        Log.Warning('Hcf triggered', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return\n\n    def Instruction_Jump(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpAL(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpR(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump relative to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        NewLineNumber = self.Parent.Fields['LineNumber'].Value + Line - 1\n        if NewLineNumber < 0:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value\n        if NewLineNumber >= len(self.Code) - 1:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value - 1\n        self.Parent.Fields['LineNumber'].Value = NewLineNumber\n\n    def GetBranchRoot(self, FunctionName):\n        if FunctionName.endswith('al'):\n            StoreNextLine = True\n            FunctionName = FunctionName[:-2]\n        else:\n            StoreNextLine = False\n        if FunctionName.startswith('br'):\n            Relative = True\n            FunctionName = FunctionName[2:]\n        else:\n            Relative = False\n            FunctionName = FunctionName[1:]\n        return (FunctionName, StoreNextLine, Relative)\n\n    def Instruction_Branch(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[1:]]\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            JumpLine = Values[2]\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            JumpLine = Values[1]\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            JumpLine = Values[2]\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            JumpLine = Values[2]\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            JumpLine = Values[1]\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            JumpLine = Values[2]\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            JumpLine = Values[1]\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            JumpLine = Values[2]\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            JumpLine = Values[2]\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            JumpLine = Values[1]\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            JumpLine = Values[1]\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'ap':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Branch_Devices(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        JumpLine = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index1], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Set_Conditional_Register(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[2:]]\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'nanz':\n            Matched = Values[0] != 'NaN'\n        elif FunctionName == 'ap':\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Set_Conditional_Register_Devices(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index2], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Select(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Value3 = self.GetArgValue(args[4])\n        Output = 0\n        if Value1 != 0:\n            Output = Value2\n        else:\n            Output = Value3\n        self.Registers[Index1] = Output\n\n    def Instruction_Bitwise_And(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        self.Registers[Index1] = Value1 & Value2\n\n    def Instruction_Bitwise_Nor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOR')\n\n    def Instruction_Bitwise_Not(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOT')\n\n    def Instruction_Bitwise_Or(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL OR')\n        self.Registers[Index1] = Value1 | Value2\n\n    def RunUpdate(self):\n        if self.Parent.Fields['LineNumber'].Value >= len(self.Code) or self.Parent.Fields['Error'].Value != 0:\n            return\n        CurrentLine = self.Code[self.Parent.Fields['LineNumber'].Value].strip()\n        if CurrentLine != '':\n            CurrentLine = SplitNotStringSpaces(CurrentLine, ' ')\n            for CurrentIndex, CurrentFunction in self.FunctionMap['Functions'].items():\n                if CurrentLine[0] in CurrentFunction['Alias']:\n                    if len(CurrentLine) - 1 == CurrentFunction['Alias'][CurrentLine[0]]:\n                        for X in range(0, len(CurrentLine) - 1):\n                            TargetArgTypes = CurrentFunction['Args'][X].split('|')\n                            CurrentArgTypes = self.GetArgType(CurrentLine[X + 1], TargetArgTypes)\n                            if CurrentArgTypes not in TargetArgTypes:\n                                print(CurrentArgTypes)\n                                Log.Warning(f\"Arg {X + 1} of {CurrentLine[0]} must be of type {CurrentFunction['Args'][X]}\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                break\n                        else:\n                            self.FunctionMap['Functions'][CurrentIndex]['Function'](*CurrentLine)\n                    else:\n                        Log.Warning(f\"{CurrentLine[0]} requires {CurrentFunction['Alias'][CurrentLine[0]]} args\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    break\n            else:\n                Log.Warning(f'Unknown function {CurrentLine[0]}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                self.Parent.Fields['Error'].Value = 1\n        self.HighestSP = max(self.HighestSP, self.Registers[self.RegisterAliases['sp']])\n        self.Parent.Fields['LineNumber'].Value += 1\n\n    def Instruction_Abs(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = abs(Value1)\n```\n",
        "eval_script": "import json\nimport copy\nimport math\nimport random\n\n# Mock Constants\nclass Constants:\n    DEFAULT_REGISTER_ALIAS = {'sp': 'r0', 'ra': 'r1'}\n    DEFAULT_CONSTANTS = {}\n    NOT_NUMBER_NUMBERS = ['NaN']\n    BATCH_TYPES_VALUES = {'type1': 0, 'type2': 1}\n    BATCH_NO_RESPONSE = [0, 0, 0, 0]\n\n# Mock Log\nclass Log:\n    @staticmethod\n    def Warning(message, Caller=None):\n        print(f\"Warning: {message} Caller: {Caller}\")\n\n    @staticmethod\n    def Info(message):\n        print(f\"Info: {message}\")\n\n    @staticmethod\n    def Error(message):\n        print(f\"Error: {message}\")\n\n# Mock Parent class\nclass MockParent:\n    def __init__(self):\n        self.Code = \"Instruction_Abs r1 5\"\n        self.StackEnabled = True\n        self.StackLength = 10\n        self.PinsNumber = 5\n        self.Fields = {'LineNumber': MockField(0), 'Error': MockField(0)}\n        self.Pins = {'d0': 0, 'd1': 1, 'd2': 2, 'd3': 3, 'd4': 4}\n        self.Network = MockNetwork()\n\nclass MockField:\n    def __init__(self, value):\n        self.Value = value\n\nclass MockNetwork:\n    def GetDevice(self, RefID):\n        return MockDevice()\n\n    def GetBatchDevices(self, Value1, Value2=None):\n        return [MockDevice()]\n\nclass MockDevice:\n    StackEnabled = True\n    StackLength = 10\n\n    def GetFieldValue(self, Value1):\n        return (0, \"Success\")\n\n    def SetFieldValue(self, Value1, Value2):\n        return (0, \"Success\")\n\n# Mock JSON data\nfunction_map = {\n    \"SpecialTypes\": {},\n    \"Functions\": {\n        \"Instruction_Abs\": {\n            \"Alias\": {\"Instruction_Abs\": 2},\n            \"Args\": [\"Register\", \"Number\"],\n            \"Function\": \"Instruction_Abs\"\n        }\n    }\n}\n\ndevices_list = {}\n\n# Mock file reading\ndef mock_open(file, mode='r'):\n    if 'Functions.json' in file:\n        return MockFile(json.dumps(function_map))\n    elif 'Devices.json' in file:\n        return MockFile(json.dumps(devices_list))\n    return open(file, mode)\n\nclass MockFile:\n    def __init__(self, content):\n        self.content = content\n\n    def read(self):\n        return self.content\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        pass\n\n# Override open function in the CodeRunner module\nopen = mock_open\n\n# EmulatorFunctions/CodeRunner.py\nclass CodeRunner:\n\n    def __init__(self, Parent, FilePath='Configs/Functions.json', DeviceFile: str='Configs/Devices.json'):\n        self.DevicesList = json.loads(open(DeviceFile, 'r').read())\n        self.FunctionMap = json.load(open(FilePath, 'r'))\n        for X, Y in self.FunctionMap['SpecialTypes'].items():\n            Y['ConfirmFunction'] = getattr(self, Y['ConfirmFunction'])\n            Y['GetArgFunction'] = getattr(self, Y['GetArgFunction'])\n        for X, Y in self.FunctionMap['Functions'].items():\n            Y['Function'] = getattr(self, Y['Function'])\n        self.Parent = Parent\n        self.Code = self.Parent.Code.split('\\n')\n        self.Registers = {f'r{X}': 0 for X in range(0, 18)}\n        self.RegisterAliases = copy.copy(Constants.DEFAULT_REGISTER_ALIAS)\n        self.PinAliases = {}\n        if self.Parent.StackEnabled == True:\n            self.Stack = [0 for X in range(self.Parent.StackLength)]\n        self.Constants = copy.copy(Constants.DEFAULT_CONSTANTS)\n        self.ParseCode()\n        self.HighestSP = 0\n\n    def ParseCode(self):\n        self.LogicTypesList = set(['ReferenceId', 'PrefabHash'] + [f'Channel{X}' for X in range(8)])\n        for X, Y in self.DevicesList.items():\n            for A, B in Y['Fields'].items():\n                if B['Read'] or B['Write']:\n                    self.LogicTypesList.add(A)\n        for X, Y in enumerate(self.Code):\n            if '#' in Y:\n                Location = Y.find('#')\n                self.Code[X] = Y[:Location].strip()\n            YTemp = self.Code[X].strip()\n            if ' ' not in YTemp:\n                if YTemp.endswith(':'):\n                    if YTemp not in self.Constants:\n                        self.Constants[YTemp[:-1]] = X\n                    else:\n                        Log.Warning('You cannot declare two lables with the same name', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    self.Code[X] = ''\n\n    def PrintRegisters(self):\n        Output = ['\\n+------------+-------+\\n|Registers   |       |']\n        for X, Y in self.Registers.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintConstants(self):\n        Output = ['\\n+------------+-------+\\n|Constants   |       |']\n        for X, Y in self.Constants.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintAlias(self):\n        Output = ['\\n+------------+-------+\\n|Aliases     |       |']\n        for X, Y in self.RegisterAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        for X, Y in self.PinAliases.items():\n            Output.append(f'|{X:<12}|{Y:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def PrintStack(self):\n        Output = ['\\n+------------+-------+\\n|Stack       |       |']\n        for X in range(self.HighestSP + 1):\n            Output.append(f'|{X:<12}|{self.Stack[X]:<7}|')\n        Log.Info('\\n'.join(Output) + '\\n+------------+-------+')\n\n    def ScriptLength(self):\n        return len(self.Script)\n\n    def Special_LogicTypes(self, Value, BaseType):\n        return Value in self.LogicTypesList\n\n    def Special_Get_LogicType(self, Value):\n        return Value\n\n    def Special_BatchMode(self, Value, BaseType):\n        if BaseType == 'String':\n            return Value in list(Constants.BATCH_TYPES_VALUES.keys())\n        return True\n\n    def Special_Get_BatchMode(self, Value):\n        if type(Value) == str:\n            if str(Value) in Constants.BATCH_TYPES_VALUES:\n                return Constants.BATCH_TYPES_VALUES[Value]\n        RawValue = self.GetArgValue(Value)\n        if RawValue >= 0 and RawValue < len(Constants.BATCH_TYPES_VALUES):\n            return RawValue\n        else:\n            Log.Warning(f'Batch mode value must bettween greater then or euqal too 0 and less then {len(Constants.BATCH_TYPES_VALUES)}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n\n    def Special_DeviceHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_DeviceHash(self, Value):\n        return Value\n\n    def Special_NameHash(self, Value, BaseType):\n        return True\n\n    def Special_Get_NameHash(self, Value):\n        return Value\n\n    def Special_ReferenceID(self, Value, BaseType):\n        return True\n\n    def Special_Get_ReferenceID(self, Value):\n        return Value\n\n    def GetArgBaseType(self, Value, TargetTypes=[]):\n        if len(Value) == 0:\n            return 'None'\n        if Value in self.Constants and 'Constant' in TargetTypes:\n            return 'Constant'\n        if Value in self.RegisterAliases and 'Register' in TargetTypes:\n            return 'Register'\n        if Value in self.PinAliases and 'Device' in TargetTypes:\n            return 'Device'\n        if Value[0] == 'd' and len(Value) > 1 and ('Device' in TargetTypes):\n            if 'r' in Value:\n                try:\n                    InValue = int(Value[1:].replace('r', ''))\n                    if InValue >= 0 and InValue < 18:\n                        return 'Device'\n                except:\n                    pass\n            else:\n                try:\n                    DeviceNumberList = [str(X) for X in range(self.Parent.PinsNumber)] + ['b']\n                    if Value[1:] in DeviceNumberList:\n                        return 'Device'\n                except:\n                    pass\n        if Value[0] == 'r' and len(Value) > 1 and ('Register' in TargetTypes):\n            try:\n                InValue = int(Value.replace('r', ''))\n                if InValue >= 0 and InValue < 18:\n                    return 'Register'\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")') and ('Hash' in TargetTypes):\n            return 'Hash'\n        if 'Number' in TargetTypes:\n            if Value[0] == '$':\n                try:\n                    int(Value[1:], 16)\n                    return 'Number'\n                except:\n                    pass\n            try:\n                int(Value)\n                return 'Number'\n            except:\n                try:\n                    float(Value)\n                    return 'Number'\n                except:\n                    pass\n        return 'String'\n\n    def GetArgType(self, Value, TargetTypes=[]):\n        IsSpecialType = TargetTypes[0] in self.FunctionMap['SpecialTypes']\n        if IsSpecialType:\n            SpecialType = TargetTypes[0]\n            TargetTypes = self.FunctionMap['SpecialTypes'][SpecialType]['Types'].split('|')\n        BaseType = self.GetArgBaseType(Value, TargetTypes)\n        if IsSpecialType:\n            if BaseType in TargetTypes:\n                Confirmed = self.FunctionMap['SpecialTypes'][SpecialType]['ConfirmFunction'](Value, BaseType)\n                if Confirmed:\n                    return SpecialType\n                else:\n                    return 'None'\n        return BaseType\n\n    def GetArgIndex(self, Value):\n        if Value in self.Constants:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value} \")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        if Value in self.RegisterAliases:\n            return self.GetArgIndex(self.RegisterAliases[Value])\n        if Value in self.PinAliases:\n            return self.GetArgIndex(self.PinAliases[Value])\n        if Value[0] == 'd':\n            if 'r' in Value:\n                try:\n                    TempValue = Value[1:]\n                    RegisterIndex = int(TempValue.replace('r', ''))\n                    for X in range(TempValue.count('r')):\n                        if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                            if RegisterIndex >= 0 and RegisterIndex < 18:\n                                RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                            else:\n                                Log.Warning('Indirect device values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                return None\n                        else:\n                            Log.Warning('Indirect device values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    RegisterIndex = f'd{RegisterIndex}'\n                    if RegisterIndex in self.Parent.Pins:\n                        return RegisterIndex\n                    Log.Warning('Indirect device values have to be bettween 0 and 5', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                    self.Parent.Fields['Error'].Value = 1\n                    return None\n                except:\n                    pass\n            elif Value[0] == 'd' and Value[1:] in [str(X) for X in range(self.Parent.PinsNumber)] + ['b']:\n                return Value\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return f'r{RegisterIndex}'\n            except:\n                pass\n        Log.Warning('Unknown value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return None\n\n    def GetArgValue(self, Value, TargetType=[]):\n        Value = str(Value)\n        if Value in self.Constants:\n            return self.Constants[Value]\n        if Value[0] == 'r':\n            try:\n                RegisterIndex = int(Value.replace('r', ''))\n                for X in range(Value.count('r') - 1):\n                    if RegisterIndex not in Constants.NOT_NUMBER_NUMBERS:\n                        if RegisterIndex >= 0 and RegisterIndex < 18:\n                            RegisterIndex = self.Registers[f'r{RegisterIndex}']\n                        else:\n                            Log.Warning('Indirect refrences values have to be bettween 0 and 17', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                            self.Parent.Fields['Error'].Value = 1\n                            return None\n                    else:\n                        Log.Warning('Indirect refrences values have to be bettween 0 and 17 not NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                        return None\n                return self.Registers[f'r{RegisterIndex}']\n            except:\n                pass\n        if Value in self.RegisterAliases:\n            return self.GetArgValue(self.RegisterAliases[Value])\n        if Value[0] == '$':\n            try:\n                return int(Value[1:], 16)\n            except:\n                pass\n        if Value.startswith('HASH(\"') and Value.endswith('\")'):\n            Value = Value[6:-2]\n            return ComputeCRC32(Value)\n        try:\n            return int(Value)\n        except:\n            try:\n                return float(Value)\n            except:\n                pass\n        return str(Value)\n\n    def GetSpecialArgValue(self, Value, Type):\n        if Type in self.FunctionMap['SpecialTypes']:\n            ProcessedValue = self.GetArgValue(Value, Type)\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n            return self.FunctionMap['SpecialTypes'][Type]['GetArgFunction'](ProcessedValue)\n        else:\n            Log.Warning(f'Invalid special arg type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def GetDeviceObject(self, RefID: int, DoError: bool=True):\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        RefObject = self.Parent.Network.GetDevice(RefID)\n        if RefObject != None:\n            return RefObject\n        elif DoError:\n            Log.Warning(f'Unknown device at reference id {RefID}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n        else:\n            return None\n\n    def Instruction_Define(self, *args):\n        Value = int(args[2])\n        if args[1] not in self.Constants:\n            self.Constants[args[1]] = Value\n            if args[1] in self.RegisterAliases:\n                del self.RegisterAliases[args[1]]\n        else:\n            Log.Warning('You cannot change a constant value', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            if self.Parent.Fields['Error'].Value == 1:\n                return\n\n    def Instruction_Move(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = Value2\n\n    def Instruction_Alias(self, *args):\n        if self.GetArgType(args[1], ['String']) == 'String':\n            if args[1] not in self.Constants and args[1] not in self.Parent.Pins and (args[1] not in self.Registers):\n                if args[1] in self.RegisterAliases:\n                    del self.RegisterAliases[args[1]]\n                if args[1] in self.PinAliases:\n                    del self.PinAliases[args[1]]\n                if args[2][0] == 'r':\n                    self.RegisterAliases[args[1]] = args[2]\n                elif args[2][0] == 'd':\n                    self.PinAliases[args[1]] = args[2]\n                else:\n                    Log.Error('Unkown alias type not caught by update')\n            else:\n                Log.Warning('Cannot overwrite a constant/builtin register/builtin device index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                if self.Parent.Fields['Error'].Value == 1:\n                    return\n        else:\n            Log.Warning('You cannot set a register alias to a device name or a register', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Add(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 + Value2\n\n    def Instruction_Sub(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 - Value2\n\n    def Instruction_Mul(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        self.Registers[Index1] = Value1 * Value2\n\n    def Instruction_Div(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        if Value2 == 0:\n            self.Registers[Index1] = 'NaN'\n            return\n        self.Registers[Index1] = Value1 / Value2\n\n    def Instruction_Ceil(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.ceil(Value1)\n\n    def Instruction_Floor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Exp(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.e ** Value1\n\n    def Instruction_Log(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.log(Value1)\n\n    def Instruction_Rand(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        self.Registers[Index1] = random.random()\n\n    def Instruction_Round(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        Decimal = Value1 - math.floor(Value1)\n        if Decimal >= 0.5:\n            self.Registers[Index1] = math.ceil(Value1)\n        else:\n            self.Registers[Index1] = math.floor(Value1)\n\n    def Instruction_Sqrt(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.sqrt(Value1)\n\n    def Instruction_Trunc(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = math.trunc(Value1)\n\n    def Instruction_Asin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.asin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Acos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.acos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.atan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Atan2(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value2\n            return\n        try:\n            self.Registers[Index1] = math.atan2(Value1, Value2)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Sin(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.sin(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Cos(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.cos(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Tan(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        try:\n            self.Registers[Index1] = math.tan(Value1)\n        except:\n            self.Registers[Index1] = 'NaN'\n\n    def Instruction_Peek(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot peek at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 1 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n        else:\n            Log.Warning(f'Peek index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Push(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Value1 = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot push at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 >= 0 and Index1 < self.Parent.StackLength:\n            self.Stack[Index1] = Value1\n            self.Registers[self.RegisterAliases['sp']] += 1\n        else:\n            Log.Warning(f'Push index must be greater then or euqal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Pop(self, *args):\n        Index1 = self.Registers[self.RegisterAliases['sp']]\n        Index2 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot pop at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index1 > 0 and Index1 <= self.Parent.StackLength:\n            self.Registers[Index2] = self.Stack[Index1 - 1]\n            self.Registers[self.RegisterAliases['sp']] -= 1\n        else:\n            Log.Warning(f'Pop index must be greater then 0 and less then or equal to {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Get(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Index3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if DeviceObject.StackEnabled == False:\n            Log.Warning(f'Device does not have a stack', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot get at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Index3 >= 1 and Index3 <= DeviceObject.StackLength:\n            self.Registers[Index1] = self.Stack[Index3 - 1]\n            Log.Warning(f'Needs further testing', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        else:\n            Log.Warning(f'Get must be greater then 0 and less then or equal to {DeviceObject.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_GetD(self, *args):\n        pass\n\n    def Instruction_Poke(self, *args):\n        Value1 = self.GetArgValue(args[1])\n        Value2 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('Cannot poke at NaN index', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Value1 >= 0 and Value1 < self.Parent.StackLength:\n            self.Stack[Value1] = Value2\n        else:\n            Log.Warning(f'Pop index must be greater then or equal to 0 and less then {self.Parent.StackLength}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_Load(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        else:\n            Log.Warning(f'No device at {Index2}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def ApplyBatchOperation(self, Values, BatchMode):\n        try:\n            BatchMode = int(BatchMode)\n            if BatchMode < 0 or BatchMode >= len(Constants.BATCH_NO_RESPONSE):\n                return 0\n        except:\n            return 0\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        if BatchMode == 0:\n            return sum(Values) / len(Values)\n        elif BatchMode == 1:\n            return sum(Values)\n        elif BatchMode == 2:\n            return min(Values)\n        elif BatchMode == 3:\n            return max(Values)\n        return 'NaN'\n\n    def CollectDevicesValueBatch(self, Devices, Value, BatchMode):\n        Values = []\n        for X in Devices:\n            FieldValue = X.GetFieldValue(Value)\n            if FieldValue[0] != None:\n                Values.append(FieldValue[0])\n        if len(Values) == 0:\n            return Constants.BATCH_NO_RESPONSE[BatchMode]\n        return MakeIntIfClose(self.ApplyBatchOperation(Values, BatchMode))\n\n    def Instruction_LoadBatch(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value3 = self.GetSpecialArgValue(args[4], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'Nan'\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        Result = self.CollectDevicesValueBatch(Devices, Value2, Value3)\n        self.Registers[Index1] = Result\n\n    def Instruction_LoadBatchNamed(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[3], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[4], 'LogicType')\n        Value4 = self.GetSpecialArgValue(args[5], 'BatchMode')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = 'NaN'\n            return\n        if Value2 in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning(f'Cannot get a device with a NameHash of NaN', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        Result = self.CollectDevicesValueBatch(Devices, Value3, Value4)\n        self.Registers[Index1] = Result\n\n    def Instruction_Set(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index1])\n        else:\n            Log.Warning(f'No device at {Index1}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_SetBatch(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value3 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1)\n        for X in Devices:\n            X.SetFieldValue(Value2, Value3)\n\n    def Instruction_SetBatchNamed(self, *args):\n        Value1 = self.GetSpecialArgValue(args[1], 'DeviceHash')\n        Value2 = self.GetSpecialArgValue(args[2], 'NameHash')\n        Value3 = self.GetSpecialArgValue(args[3], 'LogicType')\n        Value4 = self.GetArgValue(args[4])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Devices = self.Parent.Network.GetBatchDevices(Value1, Value2)\n        for X in Devices:\n            X.SetFieldValue(Value3, Value4)\n\n    def Instruction_SetDevice(self, *args):\n        Index1 = self.GetSpecialArgValue(args[1], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[2], 'LogicType')\n        Value2 = self.GetArgValue(args[3])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(Index1)\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Result = DeviceObject.SetFieldValue(Value1, Value2)\n        if Result[0] == None:\n            Log.Warning(Result[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n\n    def Instruction_LoadDevice(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetSpecialArgValue(args[2], 'ReferenceID')\n        Value1 = self.GetSpecialArgValue(args[3], 'LogicType')\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        DeviceObject = self.GetDeviceObject(self.Parent.Pins[Index2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        FieldValue = DeviceObject.GetFieldValue(Value1)\n        if FieldValue[0] == None:\n            Log.Warning(FieldValue[1], Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return None\n        self.Registers[Index1] = FieldValue[0]\n\n    def Instruction_Yield(self, *args):\n        return\n\n    def Instruction_Hcf(self, *args):\n        Log.Warning('Hcf triggered', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n        self.Parent.Fields['Error'].Value = 1\n        return\n\n    def Instruction_Jump(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpAL(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n        self.Parent.Fields['LineNumber'].Value = Line - 1\n\n    def Instruction_JumpR(self, *args):\n        Line = self.GetArgValue(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Line in Constants.NOT_NUMBER_NUMBERS:\n            Log.Warning('You cannot jump relative to a NaN line', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        NewLineNumber = self.Parent.Fields['LineNumber'].Value + Line - 1\n        if NewLineNumber < 0:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value\n        if NewLineNumber >= len(self.Code) - 1:\n            NewLineNumber = self.Parent.Fields['LineNumber'].Value - 1\n        self.Parent.Fields['LineNumber'].Value = NewLineNumber\n\n    def GetBranchRoot(self, FunctionName):\n        if FunctionName.endswith('al'):\n            StoreNextLine = True\n            FunctionName = FunctionName[:-2]\n        else:\n            StoreNextLine = False\n        if FunctionName.startswith('br'):\n            Relative = True\n            FunctionName = FunctionName[2:]\n        else:\n            Relative = False\n            FunctionName = FunctionName[1:]\n        return (FunctionName, StoreNextLine, Relative)\n\n    def Instruction_Branch(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[1:]]\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            JumpLine = Values[2]\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            JumpLine = Values[1]\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            JumpLine = Values[2]\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            JumpLine = Values[2]\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            JumpLine = Values[1]\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            JumpLine = Values[2]\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            JumpLine = Values[1]\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            JumpLine = Values[2]\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            JumpLine = Values[1]\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            JumpLine = Values[2]\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            JumpLine = Values[1]\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            JumpLine = Values[1]\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'ap':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            JumpLine = Values[3]\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            JumpLine = Values[2]\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Branch_Devices(self, *args):\n        global epsilon\n        FunctionName, StoreNextLine, Relative = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        JumpLine = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index1 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index1], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown branch type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        if Matched == True:\n            if StoreNextLine == True:\n                self.Registers[self.RegisterAliases['ra']] = self.Parent.Fields['LineNumber'].Value + 1\n            if Relative == True:\n                self.Parent.Fields['LineNumber'].Value += JumpLine - 1\n            else:\n                self.Parent.Fields['LineNumber'].Value = JumpLine - 1\n\n    def Instruction_Set_Conditional_Register(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Values = [self.GetArgValue(X) for X in args[2:]]\n        Index1 = self.GetArgIndex(args[1])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        Matched = False\n        if FunctionName == 'eq':\n            Matched = Values[0] == Values[1]\n        elif FunctionName == 'eqz':\n            Matched = Values[0] == 0\n        elif FunctionName == 'ge':\n            Matched = Values[0] >= Values[1]\n        elif FunctionName == 'gez':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'gt':\n            Matched = Values[0] > Values[1]\n        elif FunctionName == 'gtz':\n            Matched = Values[0] > 0\n        elif FunctionName == 'le':\n            Matched = Values[0] <= Values[1]\n        elif FunctionName == 'lez':\n            Matched = Values[0] <= 0\n        elif FunctionName == 'lt':\n            Matched = Values[0] < Values[1]\n        elif FunctionName == 'ltz':\n            Matched = Values[0] >= 0\n        elif FunctionName == 'ne':\n            Matched = Values[0] != Values[1]\n        elif FunctionName == 'nez':\n            Matched = Values[0] != 0\n        elif FunctionName == 'nan':\n            Matched = Values[0] == 'NaN'\n        elif FunctionName == 'nanz':\n            Matched = Values[0] != 'NaN'\n        elif FunctionName == 'ap':\n            Matched = abs(Values[0] - Values[1]) <= max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'apz':\n            Matched = abs(Values[0]) <= max(Values[1] * abs(Values[0]), epsilon * 8)\n        elif FunctionName == 'na':\n            Matched = abs(Values[0] - Values[1]) > max(Values[2] * max(abs(Values[0]), abs(Values[1])), epsilon * 8)\n        elif FunctionName == 'naz':\n            Matched = abs(Values[0]) > max(Values[1] * abs(Values[0]), epsilon * 8)\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Set_Conditional_Register_Devices(self, *args):\n        global epsilon\n        FunctionName, _, _ = self.GetBranchRoot(args[0])\n        Index1 = self.GetArgIndex(args[1])\n        Index2 = self.GetArgIndex(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Index2 in self.Parent.Pins:\n            Matched = self.GetDeviceObject(self.Parent.Pins[Index2], DoError=False) != None\n        else:\n            Matched = False\n        if FunctionName == 'dns':\n            Matched = not Matched\n        elif FunctionName == 'dse':\n            pass\n        else:\n            Log.Warning('Unknown conditional type', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n            self.Parent.Fields['Error'].Value = 1\n            return\n        self.Registers[Index1] = int(Matched)\n\n    def Instruction_Select(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Value3 = self.GetArgValue(args[4])\n        Output = 0\n        if Value1 != 0:\n            Output = Value2\n        else:\n            Output = Value3\n        self.Registers[Index1] = Output\n\n    def Instruction_Bitwise_And(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        self.Registers[Index1] = Value1 & Value2\n\n    def Instruction_Bitwise_Nor(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOR')\n\n    def Instruction_Bitwise_Not(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL NOT')\n\n    def Instruction_Bitwise_Or(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        Value2 = self.GetArgValue(args[3])\n        Log.Error('DO THIS LOL OR')\n        self.Registers[Index1] = Value1 | Value2\n\n    def RunUpdate(self):\n        if self.Parent.Fields['LineNumber'].Value >= len(self.Code) or self.Parent.Fields['Error'].Value != 0:\n            return\n        CurrentLine = self.Code[self.Parent.Fields['LineNumber'].Value].strip()\n        if CurrentLine != '':\n            CurrentLine = SplitNotStringSpaces(CurrentLine, ' ')\n            for CurrentIndex, CurrentFunction in self.FunctionMap['Functions'].items():\n                if CurrentLine[0] in CurrentFunction['Alias']:\n                    if len(CurrentLine) - 1 == CurrentFunction['Alias'][CurrentLine[0]]:\n                        for X in range(0, len(CurrentLine) - 1):\n                            TargetArgTypes = CurrentFunction['Args'][X].split('|')\n                            CurrentArgTypes = self.GetArgType(CurrentLine[X + 1], TargetArgTypes)\n                            if CurrentArgTypes not in TargetArgTypes:\n                                print(CurrentArgTypes)\n                                Log.Warning(f\"Arg {X + 1} of {CurrentLine[0]} must be of type {CurrentFunction['Args'][X]}\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                                self.Parent.Fields['Error'].Value = 1\n                                break\n                        else:\n                            self.FunctionMap['Functions'][CurrentIndex]['Function'](*CurrentLine)\n                    else:\n                        Log.Warning(f\"{CurrentLine[0]} requires {CurrentFunction['Alias'][CurrentLine[0]]} args\", Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                        self.Parent.Fields['Error'].Value = 1\n                    break\n            else:\n                Log.Warning(f'Unknown function {CurrentLine[0]}', Caller=f\"Script line {self.Parent.Fields['LineNumber'].Value}\")\n                self.Parent.Fields['Error'].Value = 1\n        self.HighestSP = max(self.HighestSP, self.Registers[self.RegisterAliases['sp']])\n        self.Parent.Fields['LineNumber'].Value += 1\n\n    def Instruction_Abs(self, *args):\n        Index1 = self.GetArgIndex(args[1])\n        Value1 = self.GetArgValue(args[2])\n        if self.Parent.Fields['Error'].Value == 1:\n            return\n        if Value1 in Constants.NOT_NUMBER_NUMBERS:\n            self.Registers[Index1] = Value1\n            return\n        self.Registers[Index1] = abs(Value1)\n\n\ndef test_Instruction_Abs():\n    parent = MockParent()\n    code_runner = CodeRunner(parent)\n\n    # Test case 1: Positive number\n    code_runner.Instruction_Abs('Instruction_Abs', 'r1', 5)\n    result_old = code_runner.Registers['r1']\n    code_runner.Instruction_Abs_new_implementation('Instruction_Abs', 'r1', 5)\n    result_new = code_runner.Registers['r1']\n    assert result_old == result_new, f\"Test case 1 failed: {result_old} != {result_new}\"\n\n    # Test case 2: Negative number\n    code_runner.Instruction_Abs('Instruction_Abs', 'r1', -10)\n    result_old = code_runner.Registers['r1']\n    code_runner.Instruction_Abs_new_implementation('Instruction_Abs', 'r1', -10)\n    result_new = code_runner.Registers['r1']\n    assert result_old == result_new, f\"Test case 2 failed: {result_old} != {result_new}\"\n\n    # Test case 3: NaN\n    code_runner.Instruction_Abs('Instruction_Abs', 'r1', 'NaN')\n    result_old = code_runner.Registers['r1']\n    code_runner.Instruction_Abs_new_implementation('Instruction_Abs', 'r1', 'NaN')\n    result_new = code_runner.Registers['r1']\n    assert (result_old == result_new) or (math.isnan(result_old) and math.isnan(result_new)), f\"Test case 3 failed: {result_old} != {result_new}\"\n\nif __name__ == \"__main__\":\n    test_Instruction_Abs()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `Instruction_Abs` is identical to the ORIGINAL FUNCTION. Both functions perform the same operations in the same sequence: they retrieve an index and a value using `GetArgIndex` and `GetArgValue`, check for an error condition, handle special \"not a number\" cases, and finally store the absolute value of the input in the specified register. There are no differences in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `Instruction_Abs` function modifies the `Registers` dictionary, which is a global variable within the context of the `CodeRunner` class. Therefore, this condition is satisfied.\n  \n- CONDITION 2: The test cases check the state of the `Registers` dictionary after calling the function, which is a variable state, not printed or logged content. Thus, this condition is satisfied.\n\n- CONDITION 3: The test cases compare the results of `Instruction_Abs` and `Instruction_Abs_new_implementation` by checking the values in the `Registers` dictionary. This ensures that the new implementation must have the exact same functionality as the original to pass all tests. Therefore, this condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the values in the `Registers` dictionary, which is reasonable given that `Instruction_Abs` modifies this dictionary. Therefore, this condition is satisfied.\n\n- CONDITION 5: The test cases cover positive numbers, negative numbers, and the special case of 'NaN', which are non-trivial and cover different scenarios that the `Instruction_Abs` function might encounter. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "f7581a459d857a9dbd90ed7d5ae50bf50d9669a0"
    },
    {
        "func_name": "render_html_report",
        "idx": "771",
        "repo_name": "raestrada___k8sPulse",
        "func_path": "k8spulse/db.py",
        "orig_func": "def render_html_report(template_name, context):\n    console.log('[cyan]Rendering HTML report...[/cyan]')\n    template = env.get_template(template_name)\n    return template.render(context)",
        "orig_context": "```python\n## k8spulse/db.py\nimport os\n\nfrom jinja2 import Environment, FileSystemLoader\n\nfrom rich.console import Console\n\nconsole = Console()\n\ntemplate_dir = os.path.join(os.path.dirname(__file__), \"templates\")\n\nenv = Environment(loader=FileSystemLoader(template_dir))\n\ndef render_html_report(template_name, context):\n    console.log(\"[cyan]Rendering HTML report...[/cyan]\")\n    template = env.get_template(template_name)\n    return template.render(context)\n\n```\n\n\n",
        "eval_script": "import os\nfrom jinja2 import Environment, FileSystemLoader\nfrom rich.console import Console\n\n# Create a mock directory and template for demonstration\nmock_template_dir = \"/home/user/tmp/templates\"\nos.makedirs(mock_template_dir, exist_ok=True)\n\n# Create a sample template file\nsample_template_path = os.path.join(mock_template_dir, \"sample_template.html\")\nwith open(sample_template_path, \"w\") as f:\n    f.write(\"<html><body><h1>{{ title }}</h1><p>{{ content }}</p></body></html>\")\n\nconsole = Console()\n\n# Update the template directory to the mock directory\nenv = Environment(loader=FileSystemLoader(mock_template_dir))\n\ndef render_html_report(template_name, context):\n    console.log(\"[cyan]Rendering HTML report...[/cyan]\")\n    template = env.get_template(template_name)\n    return template.render(context)\n\n\ndef test_render_html_report():\n    # Test case 1: Basic rendering\n    context1 = {'title': 'Test Title', 'content': 'This is a test content.'}\n    assert render_html_report('sample_template.html', context1) == render_html_report_new_implementation('sample_template.html', context1)\n\n    # Test case 2: Empty context\n    context2 = {}\n    assert render_html_report('sample_template.html', context2) == render_html_report_new_implementation('sample_template.html', context2)\n\n    # Test case 3: Different context values\n    context3 = {'title': 'Another Title', 'content': 'Different content.'}\n    assert render_html_report('sample_template.html', context3) == render_html_report_new_implementation('sample_template.html', context3)\n\n    # Test case 4: Missing template\n    try:\n        render_html_report('non_existent_template.html', context1)\n    except Exception as e1:\n        try:\n            render_html_report_new_implementation('non_existent_template.html', context1)\n        except Exception as e2:\n            assert type(e1) == type(e2)\n\n    # Test case 5: Partial context\n    context5 = {'title': 'Partial Title'}\n    assert render_html_report('sample_template.html', context5) == render_html_report_new_implementation('sample_template.html', context5)\n\n    # Test case 6: Special characters in context\n    context6 = {'title': '<Test & Title>', 'content': 'Content with special characters: <, >, &, \\''}\n    assert render_html_report('sample_template.html', context6) == render_html_report_new_implementation('sample_template.html', context6)\n\n    # Test case 7: Large context\n    context7 = {'title': 'Large Title', 'content': 'A' * 10000}\n    assert render_html_report('sample_template.html', context7) == render_html_report_new_implementation('sample_template.html', context7)\n\n    # Test case 8: Unicode characters\n    context8 = {'title': 'Unicode Title', 'content': 'Content with unicode: \u6e2c\u8a66\u5167\u5bb9'}\n    assert render_html_report('sample_template.html', context8) == render_html_report_new_implementation('sample_template.html', context8)\n\nif __name__ == \"__main__\":\n    test_render_html_report()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is essentially the same as the ORIGINAL FUNCTION. Both functions perform the same operations: they log a message to the console and render an HTML template using the Jinja2 environment. The REVISED FUNCTION includes additional setup code for creating a mock template directory and file, as well as a test suite to verify the function's behavior. However, these additions do not alter the functionality of the `render_html_report` function itself. The core logic of rendering a template with a given context remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `render_html_report` function returns a rendered HTML string based on the provided template and context, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `render_html_report` and `render_html_report_new_implementation`, without checking printed or logged contents.\n- CONDITION 3: The test cases are designed to ensure that `render_html_report_new_implementation` must have the exact same functionality as `render_html_report` to pass, as they compare the outputs directly.\n- CONDITION 4: The assertions are reasonable because they compare the outputs of the two implementations directly. The exception handling in test case 4 is also appropriate, as it checks for the same type of exception.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic rendering, empty context, different context values, missing templates, partial context, special characters, large context, and Unicode characters, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "d0b88aca6b5d1beab8b0732e8093a3ac9c3fa46e"
    },
    {
        "func_name": "converter_dolar_para_reais",
        "idx": "779",
        "repo_name": "lucasaccioly___ExPython05",
        "func_path": "ExPython05/ex01.py",
        "orig_func": "def converter_dolar_para_reais(valor_dolar):\n    taxa_conversao = 5.09\n    valor_reais = valor_dolar * taxa_conversao\n    return valor_reais",
        "orig_context": "```python\n## ExPython05/ex01.py\ndef converter_dolar_para_reais(valor_dolar):\n    taxa_conversao = 5.09\n    valor_reais = valor_dolar * taxa_conversao\n    return valor_reais\n\n```\n\n\n",
        "eval_script": "def converter_dolar_para_reais(valor_dolar):\n    taxa_conversao = 5.09\n    valor_reais = valor_dolar * taxa_conversao\n    return valor_reais\n\n\ndef test_converter_dolar_para_reais():\n    # Test case 1: Zero dollars\n    assert converter_dolar_para_reais(0) == converter_dolar_para_reais_new_implementation(0)\n\n    # Test case 2: Positive dollar amount\n    assert converter_dolar_para_reais(10) == converter_dolar_para_reais_new_implementation(10)\n\n    # Test case 3: Another positive dollar amount\n    assert converter_dolar_para_reais(100.5) == converter_dolar_para_reais_new_implementation(100.5)\n\n    # Test case 4: Negative dollar amount\n    assert converter_dolar_para_reais(-10) == converter_dolar_para_reais_new_implementation(-10)\n\n    # Test case 5: Small decimal dollar amount\n    assert converter_dolar_para_reais(0.01) == converter_dolar_para_reais_new_implementation(0.01)\n\n    # Test case 6: Large dollar amount\n    assert converter_dolar_para_reais(1000000) == converter_dolar_para_reais_new_implementation(1000000)\n\n    # Test case 7: Very small positive dollar amount\n    assert converter_dolar_para_reais(1e-10) == converter_dolar_para_reais_new_implementation(1e-10)\n\n    # Test case 8: Very small negative dollar amount\n    assert converter_dolar_para_reais(-1e-10) == converter_dolar_para_reais_new_implementation(-1e-10)\n\nif __name__ == \"__main__\":\n    test_converter_dolar_para_reais()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `converter_dolar_para_reais` is identical to the ORIGINAL FUNCTION. Both functions define a conversion rate of 5.09 and multiply the input `valor_dolar` by this rate to return the equivalent value in reais. The additional code in the revised version includes a test function `test_converter_dolar_para_reais`, which is used to verify the correctness of the conversion function by comparing its output to a non-existent function `converter_dolar_para_reais_new_implementation`. However, the presence of this test function does not alter the functionality of the `converter_dolar_para_reais` function itself. Since the core function remains unchanged and performs the same operation, the functionality is exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `converter_dolar_para_reais` returns a value, specifically `valor_reais`, which is the converted amount in reais. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases use assertions to check the return values of the function `converter_dolar_para_reais` against `converter_dolar_para_reais_new_implementation`. There is no checking of printed or logged content, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `converter_dolar_para_reais` and `converter_dolar_para_reais_new_implementation` for various inputs. If `converter_dolar_para_reais_new_implementation` has the same functionality as `converter_dolar_para_reais`, it will pass all these test cases. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `converter_dolar_para_reais` returns a value. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a range of scenarios, including zero, positive, negative, small decimal, large, and very small amounts. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "d0111b483b10b375edbf818e793cae60398a3363"
    },
    {
        "func_name": "calcular_frete",
        "idx": "780",
        "repo_name": "lucasaccioly___ExPython05",
        "func_path": "ExPython05/ex01.py",
        "orig_func": "def calcular_frete(peso_em_gramas):\n    taxa_frete_por_100g = 1.99\n    peso_em_kg = peso_em_gramas / 1000\n    valor_frete = peso_em_kg * taxa_frete_por_100g\n    return valor_frete",
        "orig_context": "```python\n## ExPython05/ex01.py\ndef calcular_frete(peso_em_gramas):\n    taxa_frete_por_100g = 1.99\n    peso_em_kg = peso_em_gramas / 1000\n    valor_frete = peso_em_kg * taxa_frete_por_100g\n    return valor_frete\n\n```\n\n\n",
        "eval_script": "## ExPython05/ex01.py\ndef calcular_frete(peso_em_gramas):\n    taxa_frete_por_100g = 1.99\n    peso_em_kg = peso_em_gramas / 1000\n    valor_frete = peso_em_kg * taxa_frete_por_100g\n    return valor_frete\n\n\ndef test_calcular_frete():\n    # Test case 1: Zero weight\n    assert calcular_frete(0) == calcular_frete_new_implementation(0), \"Test case 1 failed\"\n\n    # Test case 2: Typical weight\n    assert calcular_frete(500) == calcular_frete_new_implementation(500), \"Test case 2 failed\"\n\n    # Test case 3: Large weight\n    assert calcular_frete(10000) == calcular_frete_new_implementation(10000), \"Test case 3 failed\"\n\n    # Test case 4: Negative weight\n    assert calcular_frete(-100) == calcular_frete_new_implementation(-100), \"Test case 4 failed\"\n\n    # Test case 5: Boundary value just below 1 kg\n    assert calcular_frete(999) == calcular_frete_new_implementation(999), \"Test case 5 failed\"\n\n    # Test case 6: Boundary value just above 1 kg\n    assert calcular_frete(1001) == calcular_frete_new_implementation(1001), \"Test case 6 failed\"\n\n    # Test case 7: Floating point precision\n    assert calcular_frete(1234) == calcular_frete_new_implementation(1234), \"Test case 7 failed\"\n\n    # Test case 8: Very large weight\n    assert calcular_frete(1000000) == calcular_frete_new_implementation(1000000), \"Test case 8 failed\"\n\nif __name__ == \"__main__\":\n    test_calcular_frete()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `calcular_frete` is identical to the ORIGINAL FUNCTION. Both functions take a weight in grams, convert it to kilograms, and then calculate the freight cost using a fixed rate per 100 grams. The additional code in the revised version is a set of test cases that compare the output of `calcular_frete` with a non-existent function `calcular_frete_new_implementation`, which is not defined in the provided code. However, the presence of these test cases does not alter the functionality of the `calcular_frete` function itself. Since the implementation of `calcular_frete` is unchanged, the functionality remains the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `calcular_frete` function returns a value (`valor_frete`), satisfying this condition.\n- [CONDITION 2] The test cases use assertions to check the return values of `calcular_frete` and `calcular_frete_new_implementation`, not printed or logged contents, satisfying this condition.\n- [CONDITION 3] The test cases compare the outputs of `calcular_frete` and `calcular_frete_new_implementation` for various inputs. If both implementations produce the same outputs for all test cases, they have the same functionality, satisfying this condition.\n- [CONDITION 4] The test cases use assertions to compare return values, which is reasonable since `calcular_frete` returns a value. This satisfies the condition.\n- [CONDITION 5] The test cases cover a range of scenarios, including zero, typical, large, negative, boundary, and very large weights, as well as floating-point precision. This makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "d0111b483b10b375edbf818e793cae60398a3363"
    },
    {
        "func_name": "build_response",
        "idx": "794",
        "repo_name": "pedrosribeiro___computer-networks",
        "func_path": "tcp_http/server.py",
        "orig_func": "def build_response(status, content_type, content, connection='keep-alive'):\n    response_header = f'HTTP/1.1 {status}\\r\\n'\n    response_header += f'Content-Type: {content_type}\\r\\n'\n    response_header += f'Content-Length: {len(content)}\\r\\n'\n    response_header += f'Connection: {connection}\\r\\n\\r\\n'\n    return response_header.encode() + content",
        "orig_context": "```python\n## tcp_http/server.py\ndef build_response(status, content_type, content, connection=\"keep-alive\"):\n    response_header = f\"HTTP/1.1 {status}\\r\\n\"\n    response_header += f\"Content-Type: {content_type}\\r\\n\"\n    response_header += f\"Content-Length: {len(content)}\\r\\n\"\n    response_header += f\"Connection: {connection}\\r\\n\\r\\n\"\n    return response_header.encode() + content\n\n```\n\n\n",
        "eval_script": "## tcp_http/server.py\ndef build_response(status, content_type, content, connection=\"keep-alive\"):\n    response_header = f\"HTTP/1.1 {status}\\r\\n\"\n    response_header += f\"Content-Type: {content_type}\\r\\n\"\n    response_header += f\"Content-Length: {len(content)}\\r\\n\"\n    response_header += f\"Connection: {connection}\\r\\n\\r\\n\"\n    return response_header.encode() + content\n\n\ndef test_build_response():\n    # Test case 1: Standard response\n    assert build_response(\"200 OK\", \"text/html\", b\"<html></html>\") == build_response_new_implementation(\"200 OK\", \"text/html\", b\"<html></html>\")\n\n    # Test case 2: Different content type\n    assert build_response(\"200 OK\", \"application/json\", b\"{}\") == build_response_new_implementation(\"200 OK\", \"application/json\", b\"{}\")\n\n    # Test case 3: Different connection type\n    assert build_response(\"200 OK\", \"text/plain\", b\"Hello, World!\", \"close\") == build_response_new_implementation(\"200 OK\", \"text/plain\", b\"Hello, World!\", \"close\")\n\n    # Test case 4: Empty content\n    assert build_response(\"204 No Content\", \"text/plain\", b\"\") == build_response_new_implementation(\"204 No Content\", \"text/plain\", b\"\")\n\n    # Test case 5: Non-standard status message\n    assert build_response(\"418 I'm a teapot\", \"text/plain\", b\"Short and stout\") == build_response_new_implementation(\"418 I'm a teapot\", \"text/plain\", b\"Short and stout\")\n\n    # Test case 6: Large content\n    large_content = b\"a\" * 10000\n    assert build_response(\"200 OK\", \"text/plain\", large_content) == build_response_new_implementation(\"200 OK\", \"text/plain\", large_content)\n\n    # Test case 7: Uncommon content type\n    assert build_response(\"200 OK\", \"application/x-custom\", b\"custom data\") == build_response_new_implementation(\"200 OK\", \"application/x-custom\", b\"custom data\")\n\n    # Test case 8: Different status code\n    assert build_response(\"404 Not Found\", \"text/html\", b\"<html><body>Not Found</body></html>\") == build_response_new_implementation(\"404 Not Found\", \"text/html\", b\"<html><body>Not Found</body></html>\")\n\nif __name__ == \"__main__\":\n    test_build_response()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions have the same name, parameters, and logic for constructing the HTTP response. The only difference is the use of double quotes instead of single quotes in the string literals, which does not affect the functionality. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `build_response` function returns a value, specifically a byte string that represents an HTTP response. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases use assertions to check the return values of `build_response` and `build_response_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `build_response` and `build_response_new_implementation` directly. This means that `build_response_new_implementation` must have the exact same functionality as `build_response` to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `build_response` returns a value. This satisfies the condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including different status codes, content types, connection types, empty content, large content, and non-standard status messages. This makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "f22137e2eb3941ff1ffbc9b56672c33a29ac9805"
    },
    {
        "func_name": "receive_client_id",
        "idx": "795",
        "repo_name": "pedrosribeiro___computer-networks",
        "func_path": "tcp/server.py",
        "orig_func": "def receive_client_id(client_socket, client_address):\n    client_id = client_socket.recv(1024).decode('utf-8').strip()\n    print(f\"[{client_address}] Client '{client_id}' connected.\")\n    return client_id",
        "orig_context": "```python\n## tcp/server.py\ndef receive_client_id(client_socket, client_address):\n    client_id = client_socket.recv(1024).decode(\"utf-8\").strip()\n    print(f\"[{client_address}] Client '{client_id}' connected.\")\n    return client_id\n\n```\n\n\n",
        "eval_script": "# Mock socket class to simulate client socket behavior\nclass MockSocket:\n    def __init__(self, client_id):\n        self.client_id = client_id\n\n    def recv(self, buffer_size):\n        return self.client_id.encode('utf-8')\n\n# Mock client address\nmock_client_address = ('127.0.0.1', 12345)\n\n# Create a mock socket with a predefined client ID\nmock_socket = MockSocket(\"mock_client_id\")\n\n# Call the receive_client_id function with the mock socket and address\ndef receive_client_id(client_socket, client_address):\n    client_id = client_socket.recv(1024).decode(\"utf-8\").strip()\n    print(f\"[{client_address}] Client '{client_id}' connected.\")\n    return client_id\n\n\ndef test_receive_client_id():\n    # Test case 1: Normal client ID\n    mock_socket1 = MockSocket(\"client_123\")\n    assert receive_client_id(mock_socket1, mock_client_address) == receive_client_id_new_implementation(mock_socket1, mock_client_address)\n\n    # Test case 2: Client ID with leading and trailing spaces\n    mock_socket2 = MockSocket(\"  client_456  \")\n    assert receive_client_id(mock_socket2, mock_client_address) == receive_client_id_new_implementation(mock_socket2, mock_client_address)\n\n    # Test case 3: Empty client ID\n    mock_socket3 = MockSocket(\"\")\n    assert receive_client_id(mock_socket3, mock_client_address) == receive_client_id_new_implementation(mock_socket3, mock_client_address)\n\n    # Test case 4: Very long client ID\n    long_client_id = \"a\" * 1024\n    mock_socket4 = MockSocket(long_client_id)\n    assert receive_client_id(mock_socket4, mock_client_address) == receive_client_id_new_implementation(mock_socket4, mock_client_address)\n\n    # Test case 5: Client ID with special characters\n    special_char_client_id = \"!@#$%^&*()_+-=[]{}|;':,.<>/?\"\n    mock_socket5 = MockSocket(special_char_client_id)\n    assert receive_client_id(mock_socket5, mock_client_address) == receive_client_id_new_implementation(mock_socket5, mock_client_address)\n\n    # Test case 6: Client ID with Unicode characters\n    unicode_client_id = \"\u5ba2\u6237\u7aef123\"\n    mock_socket6 = MockSocket(unicode_client_id)\n    assert receive_client_id(mock_socket6, mock_client_address) == receive_client_id_new_implementation(mock_socket6, mock_client_address)\n\n    # Test case 7: Client ID with only spaces\n    spaces_client_id = \"     \"\n    mock_socket7 = MockSocket(spaces_client_id)\n    assert receive_client_id(mock_socket7, mock_client_address) == receive_client_id_new_implementation(mock_socket7, mock_client_address)\n\n    # Test case 8: Client ID with newline characters\n    newline_client_id = \"\\nclient_789\\n\"\n    mock_socket8 = MockSocket(newline_client_id)\n    assert receive_client_id(mock_socket8, mock_client_address) == receive_client_id_new_implementation(mock_socket8, mock_client_address)\n\nif __name__ == \"__main__\":\n    test_receive_client_id()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of its implementation. Both functions perform the same operations: they receive data from a client socket, decode it from UTF-8, strip any leading or trailing whitespace, print a message indicating the client connection, and return the client ID. The additional code in the REVISED FUNCTION is for testing purposes and does not alter the functionality of the `receive_client_id` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `receive_client_id` function returns a value, which is the decoded and stripped client ID. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `receive_client_id` and `receive_client_id_new_implementation`, not printed or logged content. This satisfies the condition.\n- CONDITION 3: The test cases compare the outputs of `receive_client_id` and `receive_client_id_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass. This satisfies the condition.\n- CONDITION 4: The assertions are reasonable because they compare the return values of the two implementations, which is appropriate given that `receive_client_id` returns a value. This satisfies the condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including normal IDs, IDs with spaces, empty IDs, long IDs, special characters, Unicode characters, only spaces, and newline characters. This variety makes the test cases non-trivial. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "f22137e2eb3941ff1ffbc9b56672c33a29ac9805"
    },
    {
        "func_name": "receive_file_data",
        "idx": "799",
        "repo_name": "pedrosribeiro___computer-networks",
        "func_path": "tcp/client.py",
        "orig_func": "def receive_file_data(client_socket, file_size):\n    received_data = b''\n    remaining_size = file_size\n    while remaining_size > 0:\n        chunk = client_socket.recv(min(4096, remaining_size))\n        received_data += chunk\n        remaining_size -= len(chunk)\n    return received_data",
        "orig_context": "```python\n## tcp/client.py\ndef receive_file_data(client_socket, file_size):\n    received_data = b\"\"\n    remaining_size = file_size\n    while remaining_size > 0:\n        chunk = client_socket.recv(min(4096, remaining_size))\n        received_data += chunk\n        remaining_size -= len(chunk)\n    return received_data\n\n```\n\n\n",
        "eval_script": "## tcp/client.py\nclass MockSocket:\n    def __init__(self, data):\n        self.data = data\n        self.index = 0\n\n    def recv(self, buffer_size):\n        if self.index >= len(self.data):\n            return b\"\"\n        chunk = self.data[self.index:self.index + buffer_size]\n        self.index += buffer_size\n        return chunk\n\ndef receive_file_data(client_socket, file_size):\n    received_data = b\"\"\n    remaining_size = file_size\n    while remaining_size > 0:\n        chunk = client_socket.recv(min(4096, remaining_size))\n        received_data += chunk\n        remaining_size -= len(chunk)\n    return received_data\n\n\ndef test_receive_file_data():\n    # Test case 1: Data exactly the buffer size\n    data1 = b'a' * 4096\n    socket1 = MockSocket(data1)\n    assert receive_file_data(socket1, len(data1)) == data1\n    socket1 = MockSocket(data1)  # Recreate socket for new implementation\n    assert receive_file_data_new_implementation(socket1, len(data1)) == data1\n\n    # Test case 2: Data smaller than the buffer size\n    data2 = b'hello'\n    socket2 = MockSocket(data2)\n    assert receive_file_data(socket2, len(data2)) == data2\n    socket2 = MockSocket(data2)  # Recreate socket for new implementation\n    assert receive_file_data_new_implementation(socket2, len(data2)) == data2\n\n    # Test case 3: Data larger than the buffer size\n    data3 = b'b' * 8192\n    socket3 = MockSocket(data3)\n    assert receive_file_data(socket3, len(data3)) == data3\n    socket3 = MockSocket(data3)  # Recreate socket for new implementation\n    assert receive_file_data_new_implementation(socket3, len(data3)) == data3\n\nif __name__ == \"__main__\":\n    test_receive_file_data()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       8      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  8      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of logic and implementation. Both functions initialize an empty byte string `received_data` and a `remaining_size` variable set to `file_size`. They then enter a loop that continues until `remaining_size` is greater than zero. In each iteration, they receive a chunk of data from `client_socket` using the `recv` method, with the buffer size being the minimum of 4096 bytes or the remaining size. This chunk is appended to `received_data`, and the size of the chunk is subtracted from `remaining_size`. Finally, the function returns the accumulated `received_data`. The test cases provided in the code further confirm that the REVISED FUNCTION behaves as expected in different scenarios. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `receive_file_data` function returns a value (`received_data`), satisfying this condition.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `receive_file_data` and `receive_file_data_new_implementation` for the same inputs, ensuring that the new implementation must have the same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of both implementations, which is reasonable given that the function returns data. This satisfies the condition.\n- CONDITION 5: The test cases cover various scenarios: data exactly the buffer size, smaller than the buffer size, and larger than the buffer size. These are non-trivial and comprehensive enough to test the function's behavior under different conditions, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "f22137e2eb3941ff1ffbc9b56672c33a29ac9805"
    },
    {
        "func_name": "generate_post",
        "idx": "805",
        "repo_name": "Zeeshier___GenAI-Posts-Generator",
        "func_path": "postgenerate.py",
        "orig_func": "def generate_post(length, tag):\n    prompt = get_prompt(length, tag)\n    response = llm.invoke(prompt)\n    return response.content",
        "orig_context": "```python\n## llm.py\nfrom langchain_groq import ChatGroq\n\nimport os\n\nllm = ChatGroq(groq_api_key=os.getenv(\"GROQ_API_KEY\"), model_name=\"llama-3.2-90b-text-preview\")\n\n```\n\n\n```python\n## fewshot.py\nimport pandas as pd\n\nimport json\n\nclass FewShot:\n    def __init__(self, file_paths=None):\n        self.df = pd.DataFrame()\n        self.unique_tags = set()\n        \n        if file_paths is None:\n            file_paths = [\"Data/hisham_sarwar_processed.json\",\n                          \"Data/irfan_malik_processed.json\",\n                          \"Data/usman_asif_processed.json\"]\n        \n        self.load_posts(file_paths)\n\n    def load_posts(self, file_paths):\n        for file_path in file_paths:\n            with open(file_path, encoding=\"utf-8\") as f:\n                posts = json.load(f)\n                temp_df = pd.json_normalize(posts)\n                temp_df['length'] = temp_df['line_count'].apply(self.categorize_length)\n                \n                all_tags = temp_df['tags'].apply(lambda x: x).sum()\n                self.unique_tags.update(all_tags)\n                \n\n                self.df = pd.concat([self.df, temp_df], ignore_index=True)\n\n    def get_filtered_posts(self, length, tag):\n        df_filtered = self.df[\n            (self.df['tags'].apply(lambda tags: tag in tags)) &  \n            (self.df['length'] == length)  \n        ]\n        return df_filtered.to_dict(orient='records')\n\n    def categorize_length(self, line_count):\n        if line_count < 5:\n            return \"Short\"\n        elif 5 <= line_count <= 10:\n            return \"Medium\"\n        else:\n            return \"Long\"\n\n    def get_tags(self):\n        return list(self.unique_tags)\n\n```\n\n\n```python\n## postgenerate.py\nfrom llm import llm\n\nfrom fewshot import FewShot\n\nfew_shot = FewShot()\n\ndef get_length_str(length):\n    if length == \"Short\":\n        return \"1 to 5 lines\"\n    if length == \"Medium\":\n        return \"6 to 10 lines\"\n    if length == \"Long\":\n        return \"11 to 15 lines\"\n\ndef get_prompt(length, tag):\n    length_str = get_length_str(length)\n\n    prompt = f'''\n    Generate a LinkedIn post using the below information. No preamble.\n\n    1) Topic: {tag}\n    2) Length: {length_str}\n    The script for the generated post should always be English.\n    '''\n\n    examples = few_shot.get_filtered_posts(length,tag)\n\n    if len(examples) > 0:\n        prompt += \"4) Use the writing style as per the following examples.\"\n\n    for i, post in enumerate(examples):\n        post_text = post['text']\n        prompt += f'\\n\\n Example {i+1}: \\n\\n {post_text}'\n\n        if i == 1: \n            break\n\n    return prompt\n\ndef generate_post(length, tag):\n    prompt = get_prompt(length, tag)\n    response = llm.invoke(prompt)\n    return response.content\n\n```\n\n\n",
        "eval_script": "import pandas as pd\nimport json\n\n# Mocking the ChatGroq class and llm.invoke method\nclass MockResponse:\n    def __init__(self, content):\n        self.content = content\n\nclass ChatGroq:\n    def __init__(self, groq_api_key, model_name):\n        self.groq_api_key = groq_api_key\n        self.model_name = model_name\n\n    def invoke(self, prompt):\n        # Mock response for testing purposes\n        return MockResponse(content=f\"Generated post based on prompt: {prompt}\")\n\n# Initialize the llm object with mock parameters\nllm = ChatGroq(groq_api_key=\"mock_api_key\", model_name=\"llama-3.2-90b-text-preview\")\n\nclass FewShot:\n    def __init__(self, file_paths=None):\n        self.df = pd.DataFrame()\n        self.unique_tags = set()\n        \n        if file_paths is None:\n            file_paths = [\"Data/hisham_sarwar_processed.json\",\n                          \"Data/irfan_malik_processed.json\",\n                          \"Data/usman_asif_processed.json\"]\n        \n        self.load_posts(file_paths)\n\n    def load_posts(self, file_paths):\n        # Mock data for testing purposes\n        mock_data = [\n            {\"text\": \"Example post 1\", \"line_count\": 4, \"tags\": [\"tag1\", \"tag2\"]},\n            {\"text\": \"Example post 2\", \"line_count\": 7, \"tags\": [\"tag2\", \"tag3\"]},\n            {\"text\": \"Example post 3\", \"line_count\": 12, \"tags\": [\"tag1\", \"tag3\"]}\n        ]\n        for file_path in file_paths:\n            temp_df = pd.json_normalize(mock_data)\n            temp_df['length'] = temp_df['line_count'].apply(self.categorize_length)\n            \n            all_tags = temp_df['tags'].apply(lambda x: x).sum()\n            self.unique_tags.update(all_tags)\n\n            self.df = pd.concat([self.df, temp_df], ignore_index=True)\n\n    def get_filtered_posts(self, length, tag):\n        df_filtered = self.df[\n            (self.df['tags'].apply(lambda tags: tag in tags)) &  \n            (self.df['length'] == length)  \n        ]\n        return df_filtered.to_dict(orient='records')\n\n    def categorize_length(self, line_count):\n        if line_count < 5:\n            return \"Short\"\n        elif 5 <= line_count <= 10:\n            return \"Medium\"\n        else:\n            return \"Long\"\n\n    def get_tags(self):\n        return list(self.unique_tags)\n\nfew_shot = FewShot()\n\ndef get_length_str(length):\n    if length == \"Short\":\n        return \"1 to 5 lines\"\n    if length == \"Medium\":\n        return \"6 to 10 lines\"\n    if length == \"Long\":\n        return \"11 to 15 lines\"\n\ndef get_prompt(length, tag):\n    length_str = get_length_str(length)\n\n    prompt = f'''\n    Generate a LinkedIn post using the below information. No preamble.\n\n    1) Topic: {tag}\n    2) Length: {length_str}\n    The script for the generated post should always be English.\n    '''\n\n    examples = few_shot.get_filtered_posts(length, tag)\n\n    if len(examples) > 0:\n        prompt += \"4) Use the writing style as per the following examples.\"\n\n    for i, post in enumerate(examples):\n        post_text = post['text']\n        prompt += f'\\n\\n Example {i+1}: \\n\\n {post_text}'\n\n        if i == 1: \n            break\n\n    return prompt\n\ndef generate_post(length, tag):\n    prompt = get_prompt(length, tag)\n    response = llm.invoke(prompt)\n    return response.content\n\n\ndef test_generate_post():\n    # Test case 1: Medium length, tag2\n    assert generate_post(\"Medium\", \"tag2\") == generate_post_new_implementation(\"Medium\", \"tag2\")\n\n    # Test case 2: Short length, tag1\n    assert generate_post(\"Short\", \"tag1\") == generate_post_new_implementation(\"Short\", \"tag1\")\n\n    # Test case 3: Long length, tag3\n    assert generate_post(\"Long\", \"tag3\") == generate_post_new_implementation(\"Long\", \"tag3\")\n\n    # Test case 4: Boundary case - exactly 5 lines (Short to Medium)\n    assert generate_post(\"Medium\", \"tag1\") == generate_post_new_implementation(\"Medium\", \"tag1\")\n\n    # Test case 5: Boundary case - exactly 10 lines (Medium to Long)\n    assert generate_post(\"Medium\", \"tag3\") == generate_post_new_implementation(\"Medium\", \"tag3\")\n\n    # Test case 6: Non-existent tag\n    assert generate_post(\"Short\", \"non_existent_tag\") == generate_post_new_implementation(\"Short\", \"non_existent_tag\")\n\n    # Test case 7: Empty tag\n    assert generate_post(\"Medium\", \"\") == generate_post_new_implementation(\"Medium\", \"\")\n\n    # Test case 8: Empty length\n    assert generate_post(\"\", \"tag1\") == generate_post_new_implementation(\"\", \"tag1\")\n\n    # Test case 9: Iterate over all unique tags\n    for tag in few_shot.get_tags():\n        assert generate_post(\"Short\", tag) == generate_post_new_implementation(\"Short\", tag)\n        assert generate_post(\"Medium\", tag) == generate_post_new_implementation(\"Medium\", tag)\n        assert generate_post(\"Long\", tag) == generate_post_new_implementation(\"Long\", tag)\n\nif __name__ == \"__main__\":\n    test_generate_post()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `generate_post` is identical to the ORIGINAL FUNCTION. Both functions call `get_prompt` with the same parameters and use `llm.invoke(prompt)` to generate a response, returning `response.content`. The surrounding code in the REVISED FUNCTION, including the mocked classes and test cases, does not alter the functionality of `generate_post` itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `generate_post` function returns a value, specifically the content of the response from the `llm.invoke` method. This satisfies the condition.\n- [CONDITION 2] The test cases use assertions to compare the return values of `generate_post` and `generate_post_new_implementation`, not printed or logged contents. This satisfies the condition.\n- [CONDITION 3] The test cases are designed to compare the outputs of `generate_post` and `generate_post_new_implementation` directly. If `generate_post_new_implementation` has the same functionality, it will produce the same outputs for the same inputs, satisfying this condition.\n- [CONDITION 4] The test cases use assertions to compare the outputs of the two functions. Since `generate_post` returns a value, the use of assertions to compare return values is reasonable. This satisfies the condition.\n- [CONDITION 5] The test cases cover various scenarios, including different lengths, tags, boundary cases, non-existent tags, empty tags, and iterating over all unique tags. This indicates that the test cases are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "73756b37ab273e5b7648f8f4437c4d7e060edfad"
    },
    {
        "func_name": "normalize_window_size",
        "idx": "808",
        "repo_name": "objectiveSquid___Autosubtitles",
        "func_path": "autosubtitles/window/utils_extern.py",
        "orig_func": "def normalize_window_size(root: tk.Misc, geometry: tuple[int, int, int | None, int | None], from_size: tuple[int, int]=(1920, 1080)) -> str:\n    x_relation = root.winfo_screenwidth() / from_size[0]\n    y_relation = root.winfo_screenheight() / from_size[1]\n    width = round(geometry[0] * x_relation)\n    height = round(geometry[1] * y_relation)\n    x = round(geometry[2] * x_relation) if geometry[2] != None else None\n    y = round(geometry[3] * y_relation) if geometry[3] != None else None\n    return f\"{width}x{height}{(f'+{x}' if x else '')}{(f'+{y}' if y else '')}\"",
        "orig_context": "```python\n## autosubtitles/window/utils_extern.py\nimport tkinter as tk\n\ndef normalize_window_size(\n    root: tk.Misc,\n    geometry: tuple[int, int, int | None, int | None],\n    from_size: tuple[int, int] = (1920, 1080),\n) -> str:\n    x_relation = root.winfo_screenwidth() / from_size[0]\n    y_relation = root.winfo_screenheight() / from_size[1]\n\n    width = round(geometry[0] * x_relation)\n    height = round(geometry[1] * y_relation)\n    x = round(geometry[2] * x_relation) if geometry[2] != None else None\n    y = round(geometry[3] * y_relation) if geometry[3] != None else None\n\n    return f\"{width}x{height}{f'+{x}' if x else ''}{f'+{y}' if y else ''}\"\n\n```\n\n\n",
        "eval_script": "## autosubtitles/window/utils_extern.py\nimport tkinter as tk\n\ndef normalize_window_size(\n    root: tk.Misc,\n    geometry: tuple[int, int, int | None, int | None],\n    from_size: tuple[int, int] = (1920, 1080),\n) -> str:\n    x_relation = root.winfo_screenwidth() / from_size[0]\n    y_relation = root.winfo_screenheight() / from_size[1]\n\n    width = round(geometry[0] * x_relation)\n    height = round(geometry[1] * y_relation)\n    x = round(geometry[2] * x_relation) if geometry[2] != None else None\n    y = round(geometry[3] * y_relation) if geometry[3] != None else None\n\n    return f\"{width}x{height}{f'+{x}' if x else ''}{f'+{y}' if y else ''}\"\n\n\ndef test_normalize_window_size():\n    class MockRoot:\n        def winfo_screenwidth(self):\n            return 1920\n\n        def winfo_screenheight(self):\n            return 1080\n\n    root = MockRoot()\n\n    # Test case 1: Basic case with x and y\n    geometry1 = (800, 600, 100, 100)\n    assert normalize_window_size(root, geometry1) == normalize_window_size_new_implementation(root, geometry1)\n\n    # Test case 2: Case without x and y\n    geometry2 = (1024, 768, None, None)\n    assert normalize_window_size(root, geometry2) == normalize_window_size_new_implementation(root, geometry2)\n\n    # Test case 3: Case with only x\n    geometry3 = (1280, 720, 200, None)\n    assert normalize_window_size(root, geometry3) == normalize_window_size_new_implementation(root, geometry3)\n\n    # Test case 4: Edge case with zero dimensions\n    geometry4 = (0, 0, 0, 0)\n    assert normalize_window_size(root, geometry4) == normalize_window_size_new_implementation(root, geometry4)\n\n    # Test case 5: Negative coordinates\n    geometry5 = (800, 600, -100, -100)\n    assert normalize_window_size(root, geometry5) == normalize_window_size_new_implementation(root, geometry5)\n\n    # Test case 6: Non-standard screen size\n    class MockRootNonStandard:\n        def winfo_screenwidth(self):\n            return 2560\n\n        def winfo_screenheight(self):\n            return 1440\n\n    root_non_standard = MockRootNonStandard()\n    geometry6 = (800, 600, 100, 100)\n    assert normalize_window_size(root_non_standard, geometry6) == normalize_window_size_new_implementation(root_non_standard, geometry6)\n\n    # Test case 7: Large dimensions\n    geometry7 = (3840, 2160, 1920, 1080)\n    assert normalize_window_size(root, geometry7) == normalize_window_size_new_implementation(root, geometry7)\n\n    # Test case 8: Fractional scaling\n    class MockRootFractional:\n        def winfo_screenwidth(self):\n            return 1600\n\n        def winfo_screenheight(self):\n            return 900\n\n    root_fractional = MockRootFractional()\n    geometry8 = (800, 600, 100, 100)\n    assert normalize_window_size(root_fractional, geometry8) == normalize_window_size_new_implementation(root_fractional, geometry8)\n\n    # Test case 9: Non-integer scaling\n    geometry9 = (800, 600, 100, 100)\n    from_size = (1280, 720)\n    assert normalize_window_size(root, geometry9, from_size) == normalize_window_size_new_implementation(root, geometry9, from_size)\n\nif __name__ == \"__main__\":\n    test_normalize_window_size()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they are identical in terms of logic and implementation. The function `normalize_window_size` calculates the normalized window size based on the screen dimensions and the provided geometry, and both versions perform the same calculations and return the same formatted string. The revised function includes additional test cases, but these do not alter the functionality of the function itself. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `normalize_window_size` returns a string, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `normalize_window_size` and `normalize_window_size_new_implementation`, and do not check printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `normalize_window_size` and `normalize_window_size_new_implementation` directly. This ensures that `normalize_window_size_new_implementation` must have exactly the same functionality to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable given that the function returns a string. The test cases do not use inappropriate assertions.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic cases, edge cases, and cases with different screen sizes and scaling factors, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "06615ad016af4ee5796117377e1354ccc296081e"
    },
    {
        "func_name": "aes_decrypt",
        "idx": "818",
        "repo_name": "Hamaiz8___Cryptography-and-Data-Protection-",
        "func_path": "client.py",
        "orig_func": "def aes_decrypt(key, ciphertext):\n    iv = ciphertext[:16]\n    actual_ciphertext = ciphertext[16:]\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    decryptor = cipher.decryptor()\n    plaintext = decryptor.update(actual_ciphertext) + decryptor.finalize()\n    return plaintext",
        "orig_context": "```python\n## client.py\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\n\nfrom cryptography.hazmat.backends import default_backend\n\ndef aes_decrypt(key, ciphertext):\n    iv = ciphertext[:16]\n    actual_ciphertext = ciphertext[16:]\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    decryptor = cipher.decryptor()\n    plaintext = decryptor.update(actual_ciphertext) + decryptor.finalize()\n    return plaintext\n\n```\n\n\n",
        "eval_script": "## client.py\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.backends import default_backend\n\ndef aes_decrypt(key, ciphertext):\n    iv = ciphertext[:16]\n    actual_ciphertext = ciphertext[16:]\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    decryptor = cipher.decryptor()\n    plaintext = decryptor.update(actual_ciphertext) + decryptor.finalize()\n    return plaintext\n\n\ndef test_aes_decrypt():\n    key = b'0123456789abcdef'  # 16-byte key for AES-128\n    iv = b'1234567890abcdef'  # 16-byte IV\n    plaintext = b'Hello, World!!!'\n    \n    # Encrypt the plaintext to get ciphertext\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    encryptor = cipher.encryptor()\n    ciphertext = iv + encryptor.update(plaintext) + encryptor.finalize()\n    \n    # Test 1: Basic functionality\n    assert aes_decrypt(key, ciphertext) == aes_decrypt_new_implementation(key, ciphertext)\n    \n    # Test 2: Different plaintext\n    encryptor = cipher.encryptor()  # Create a new encryptor for the new plaintext\n    plaintext2 = b'Another message!'\n    ciphertext2 = iv + encryptor.update(plaintext2) + encryptor.finalize()\n    assert aes_decrypt(key, ciphertext2) == aes_decrypt_new_implementation(key, ciphertext2)\n    \n    # Test 3: Different key\n    key2 = b'abcdef0123456789'\n    cipher2 = Cipher(algorithms.AES(key2), modes.CFB(iv), backend=default_backend())\n    encryptor2 = cipher2.encryptor()\n    ciphertext3 = iv + encryptor2.update(plaintext) + encryptor2.finalize()\n    assert aes_decrypt(key2, ciphertext3) == aes_decrypt_new_implementation(key2, ciphertext3)\n\nif __name__ == \"__main__\":\n    test_aes_decrypt()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining the REVISED FUNCTION within the provided code, it is evident that the function `aes_decrypt` is identical to the ORIGINAL FUNCTION. Both functions extract the initialization vector (IV) from the first 16 bytes of the ciphertext, use the remaining bytes as the actual ciphertext, and perform decryption using the AES algorithm in CFB mode. The REVISED FUNCTION is placed within a larger context that includes a test suite, but the function itself remains unchanged in terms of its logic and implementation.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `aes_decrypt` function returns a value, specifically the decrypted plaintext, which satisfies this condition.\n- CONDITION 2: The test cases use assertions to check the return values of the `aes_decrypt` function, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the output of `aes_decrypt` with `aes_decrypt_new_implementation` for the same inputs. If `aes_decrypt_new_implementation` has the same functionality, it will pass all tests. Thus, this condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `aes_decrypt` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover different scenarios: basic functionality, different plaintext, and different key. These are non-trivial tests as they cover variations in input data, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "9c1ed69bd7a781ac77e0c1b71687c0954706bdbb"
    },
    {
        "func_name": "aes_decrypt",
        "idx": "821",
        "repo_name": "Hamaiz8___Cryptography-and-Data-Protection-",
        "func_path": "app.py",
        "orig_func": "def aes_decrypt(key, ciphertext):\n    iv = ciphertext[:16]\n    actual_ciphertext = ciphertext[16:]\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    decryptor = cipher.decryptor()\n    plaintext = decryptor.update(actual_ciphertext) + decryptor.finalize()\n    return plaintext",
        "orig_context": "```python\n## app.py\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\n\nfrom cryptography.hazmat.backends import default_backend\n\ndef aes_decrypt(key, ciphertext):\n    iv = ciphertext[:16]\n    actual_ciphertext = ciphertext[16:]\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    decryptor = cipher.decryptor()\n    plaintext = decryptor.update(actual_ciphertext) + decryptor.finalize()\n    return plaintext\n\n```\n\n\n",
        "eval_script": "## app.py\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.backends import default_backend\n\ndef aes_decrypt(key, ciphertext):\n    iv = ciphertext[:16]\n    actual_ciphertext = ciphertext[16:]\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    decryptor = cipher.decryptor()\n    plaintext = decryptor.update(actual_ciphertext) + decryptor.finalize()\n    return plaintext\n\n\ndef test_aes_decrypt():\n    key = b'0123456789abcdef'  # 16-byte key for AES-128\n    iv = b'1234567890abcdef'  # 16-byte IV\n    plaintext = b'Hello, World!123'  # 16-byte plaintext\n\n    # Encrypt the plaintext using the original aes_decrypt function setup\n    cipher = Cipher(algorithms.AES(key), modes.CFB(iv), backend=default_backend())\n    encryptor = cipher.encryptor()\n    ciphertext = iv + encryptor.update(plaintext) + encryptor.finalize()\n\n    # Test 1: Check if both implementations return the same plaintext\n    assert aes_decrypt(key, ciphertext) == aes_decrypt_new_implementation(key, ciphertext)\n\n    # Test 2: Test with different plaintext\n    encryptor = cipher.encryptor()  # Create a new encryptor for the new plaintext\n    plaintext2 = b'Another test case!'\n    ciphertext2 = iv + encryptor.update(plaintext2) + encryptor.finalize()\n    assert aes_decrypt(key, ciphertext2) == aes_decrypt_new_implementation(key, ciphertext2)\n\n    # Test 3: Test with a different key\n    key2 = b'fedcba9876543210'\n    cipher2 = Cipher(algorithms.AES(key2), modes.CFB(iv), backend=default_backend())\n    encryptor2 = cipher2.encryptor()\n    ciphertext3 = iv + encryptor2.update(plaintext) + encryptor2.finalize()\n    assert aes_decrypt(key2, ciphertext3) == aes_decrypt_new_implementation(key2, ciphertext3)\n\nif __name__ == \"__main__\":\n    test_aes_decrypt()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided CODE is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: extracting the initialization vector (IV) from the first 16 bytes of the ciphertext, using the remaining bytes as the actual ciphertext, and then decrypting it using AES in CFB mode. The REVISED FUNCTION is placed within a larger context that includes a test function, but the aes_decrypt function itself is unchanged in terms of logic and functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `aes_decrypt` function returns a value, which is the decrypted plaintext. Therefore, it satisfies CONDITION 1.\n- CONDITION 2: The test cases use assertions to check the return values of the `aes_decrypt` function, not printed or logged contents. Thus, CONDITION 2 is satisfied.\n- CONDITION 3: The test cases compare the outputs of `aes_decrypt` and `aes_decrypt_new_implementation` for various inputs. If `aes_decrypt_new_implementation` passes all these tests, it must have the same functionality as `aes_decrypt`. Therefore, CONDITION 3 is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable since `aes_decrypt` returns a value. Thus, CONDITION 4 is satisfied.\n- CONDITION 5: The test cases cover different scenarios, including different plaintexts and keys, making them non-trivial. Therefore, CONDITION 5 is satisfied.",
            "answer": "yes"
        },
        "commit_id": "9c1ed69bd7a781ac77e0c1b71687c0954706bdbb"
    },
    {
        "func_name": "calcularModa",
        "idx": "823",
        "repo_name": "Davi-SC___calculadora-estatistica",
        "func_path": "funcoes.py",
        "orig_func": "def calcularModa(intervalos, frequencias):\n    maiorFrequencia = 0\n    moda = None\n    for i in range(len(frequencias)):\n        if frequencias[i] > maiorFrequencia:\n            maiorFrequencia = frequencias[i]\n            moda = (intervalos[i][0] + intervalos[i][1]) / 2\n    return moda",
        "orig_context": "```python\n## funcoes.py\ndef calcularModa(intervalos, frequencias):\n    maiorFrequencia  = 0\n    moda = None\n\n    for i in range(len(frequencias)):\n        if (frequencias[i] > maiorFrequencia):\n            maiorFrequencia = frequencias[i]\n            moda = (intervalos[i][0]+intervalos[i][1])/2\n    return moda\n\n```\n\n\n",
        "eval_script": "## funcoes.py\ndef calcularModa(intervalos, frequencias):\n    maiorFrequencia  = 0\n    moda = None\n\n    for i in range(len(frequencias)):\n        if (frequencias[i] > maiorFrequencia):\n            maiorFrequencia = frequencias[i]\n            moda = (intervalos[i][0]+intervalos[i][1])/2\n    return moda\n\n\ndef test_calcularModa():\n    # Test case 1: Single interval\n    intervals1 = [(1, 3)]\n    frequencies1 = [5]\n    assert calcularModa(intervals1, frequencies1) == calcularModa_new_implementation(intervals1, frequencies1)\n\n    # Test case 2: Multiple intervals, one with highest frequency\n    intervals2 = [(1, 3), (4, 6), (7, 9)]\n    frequencies2 = [2, 5, 3]\n    assert calcularModa(intervals2, frequencies2) == calcularModa_new_implementation(intervals2, frequencies2)\n\n    # Test case 3: Multiple intervals with equal highest frequencies\n    intervals3 = [(1, 3), (4, 6), (7, 9)]\n    frequencies3 = [5, 5, 3]\n    assert calcularModa(intervals3, frequencies3) == calcularModa_new_implementation(intervals3, frequencies3)\n\n    # Test case 4: Empty intervals and frequencies\n    intervals4 = []\n    frequencies4 = []\n    assert calcularModa(intervals4, frequencies4) == calcularModa_new_implementation(intervals4, frequencies4)\n\n    # Test case 5: Single interval with zero frequency\n    intervals5 = [(1, 3)]\n    frequencies5 = [0]\n    assert calcularModa(intervals5, frequencies5) == calcularModa_new_implementation(intervals5, frequencies5)\n\n    # Test case 6: All zero frequencies\n    intervals6 = [(1, 3), (4, 6), (7, 9)]\n    frequencies6 = [0, 0, 0]\n    assert calcularModa(intervals6, frequencies6) == calcularModa_new_implementation(intervals6, frequencies6)\n\n    # Test case 7: Negative frequencies\n    intervals7 = [(1, 3), (4, 6), (7, 9)]\n    frequencies7 = [-1, -5, -3]\n    assert calcularModa(intervals7, frequencies7) == calcularModa_new_implementation(intervals7, frequencies7)\n\n    # Test case 8: Non-overlapping intervals with varying frequencies\n    intervals8 = [(1, 3), (4, 6), (7, 9)]\n    frequencies8 = [1, 2, 3]\n    assert calcularModa(intervals8, frequencies8) == calcularModa_new_implementation(intervals8, frequencies8)\n\n    # Test case 9: Large numbers\n    intervals9 = [(1000000, 2000000), (3000000, 4000000), (5000000, 6000000)]\n    frequencies9 = [100000, 200000, 300000]\n    assert calcularModa(intervals9, frequencies9) == calcularModa_new_implementation(intervals9, frequencies9)\n\n    # Test case 10: Floating point intervals\n    intervals10 = [(1.5, 3.5), (4.5, 6.5), (7.5, 9.5)]\n    frequencies10 = [2, 5, 3]\n    assert calcularModa(intervals10, frequencies10) == calcularModa_new_implementation(intervals10, frequencies10)\n\nif __name__ == \"__main__\":\n    test_calcularModa()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       8      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  8      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `calcularModa` is identical to the ORIGINAL FUNCTION. Both functions iterate over the list of frequencies, find the interval with the highest frequency, and calculate the mode as the midpoint of that interval. The logic and implementation are exactly the same, including the initialization of variables and the calculation of the mode. The additional test cases in the revised code do not alter the function itself but are used to verify its correctness. Therefore, the functionality and implementation of the REVISED FUNCTION are exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The function `calcularModa` returns a value, which is the mode of the given intervals based on their frequencies. Therefore, this condition is satisfied.\n- [CONDITION 2] The test cases use assertions to check the return values of `calcularModa` and `calcularModa_new_implementation`, not printed or logged outputs. Thus, this condition is satisfied.\n- [CONDITION 3] The test cases compare the outputs of `calcularModa` and `calcularModa_new_implementation` for various inputs. If both functions have the same functionality, they will produce the same outputs for all test cases. Therefore, this condition is satisfied.\n- [CONDITION 4] The assertions are reasonable because they compare the return values of the two implementations, which is appropriate given that `calcularModa` returns a value. Thus, this condition is satisfied.\n- [CONDITION 5] The test cases cover a range of scenarios, including single and multiple intervals, equal frequencies, empty inputs, zero and negative frequencies, non-overlapping intervals, large numbers, and floating-point intervals. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "01a90ee9cad7621bd23b73ce87aad05350ea24be"
    },
    {
        "func_name": "calcularMediana",
        "idx": "824",
        "repo_name": "Davi-SC___calculadora-estatistica",
        "func_path": "funcoes.py",
        "orig_func": "def calcularMediana(intervalos, frequencias):\n    totalFreq = 0\n    for f in frequencias:\n        totalFreq += f\n    freqAcumulada = 0\n    mediana = 0\n    for i in range(len(frequencias)):\n        freqAcumulada += frequencias[i]\n        if freqAcumulada >= totalFreq / 2:\n            a, b = intervalos[i]\n            mediana = (a + b) / 2\n            break\n    return mediana",
        "orig_context": "```python\n## funcoes.py\ndef calcularMediana(intervalos, frequencias):\n    totalFreq = 0\n    for f in frequencias:\n        totalFreq += f\n    \n    freqAcumulada = 0\n    mediana = 0\n    for i in range(len(frequencias)):\n        freqAcumulada += frequencias[i]\n        if freqAcumulada >= totalFreq/2:\n            a, b = intervalos[i]\n            mediana = (a+b) / 2\n            break\n\n    return mediana\n\n```\n\n\n",
        "eval_script": "## funcoes.py\ndef calcularMediana(intervalos, frequencias):\n    totalFreq = 0\n    for f in frequencias:\n        totalFreq += f\n    \n    freqAcumulada = 0\n    mediana = 0\n    for i in range(len(frequencias)):\n        freqAcumulada += frequencias[i]\n        if freqAcumulada >= totalFreq/2:\n            a, b = intervalos[i]\n            mediana = (a+b) / 2\n            break\n\n    return mediana\n\n\ndef test_calcularMediana():\n    # Test case 1: Simple case\n    intervalos1 = [(1, 2), (3, 4), (5, 6)]\n    frequencias1 = [1, 2, 1]\n    assert calcularMediana(intervalos1, frequencias1) == calcularMediana_new_implementation(intervalos1, frequencias1)\n\n    # Test case 2: All frequencies are the same\n    intervalos2 = [(1, 2), (3, 4), (5, 6)]\n    frequencias2 = [1, 1, 1]\n    assert calcularMediana(intervalos2, frequencias2) == calcularMediana_new_implementation(intervalos2, frequencias2)\n\n    # Test case 3: Cumulative frequency reaches half at the last interval\n    intervalos3 = [(1, 2), (3, 4), (5, 6)]\n    frequencias3 = [1, 1, 3]\n    assert calcularMediana(intervalos3, frequencias3) == calcularMediana_new_implementation(intervalos3, frequencias3)\n\n    # Test case 4: Empty intervals and frequencies\n    intervalos4 = []\n    frequencias4 = []\n    assert calcularMediana(intervalos4, frequencias4) == calcularMediana_new_implementation(intervalos4, frequencias4)\n\n    # Test case 5: Cumulative frequency exactly equals half at an earlier interval\n    intervalos5 = [(1, 2), (3, 4), (5, 6)]\n    frequencias5 = [2, 2, 2]\n    assert calcularMediana(intervalos5, frequencias5) == calcularMediana_new_implementation(intervalos5, frequencias5)\n\n    # Test case 6: Negative frequencies (to test robustness)\n    intervalos6 = [(1, 2), (3, 4), (5, 6)]\n    frequencias6 = [-1, 2, 1]\n    assert calcularMediana(intervalos6, frequencias6) == calcularMediana_new_implementation(intervalos6, frequencias6)\n\nif __name__ == \"__main__\":\n    test_calcularMediana()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `calcularMediana` is identical to the ORIGINAL FUNCTION in terms of code structure and logic. Both functions calculate the median by iterating through the frequency list, accumulating the frequencies, and determining the interval where the cumulative frequency reaches or exceeds half of the total frequency. The median is then calculated as the midpoint of this interval. The additional code in the REVISED FUNCTION is a set of test cases and a test function, which do not alter the functionality of the `calcularMediana` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The function `calcularMediana` returns a value, which is the median calculated from the given intervals and frequencies. This satisfies the condition as it has return values.\n- [CONDITION 2] The test cases use assert statements to compare the return values of `calcularMediana` and `calcularMediana_new_implementation`, which means they are checking return values, not printed or logged content.\n- [CONDITION 3] The test cases compare the outputs of `calcularMediana` and `calcularMediana_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- [CONDITION 4] The assert statements are reasonable because they compare the return values of the two implementations. There is no use of `assert calcularMediana() == calcularMediana_new_implementation()` without arguments, which would be inappropriate if the function didn't return values.\n- [CONDITION 5] The test cases cover a variety of scenarios, including simple cases, equal frequencies, cumulative frequency reaching half at different intervals, empty inputs, and negative frequencies. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "01a90ee9cad7621bd23b73ce87aad05350ea24be"
    },
    {
        "func_name": "calcularMedia",
        "idx": "825",
        "repo_name": "Davi-SC___calculadora-estatistica",
        "func_path": "funcoes.py",
        "orig_func": "def calcularMedia(intervalos, frequencias):\n    soma = 0\n    somaFreq = 0\n    for i in range(len(frequencias)):\n        a, b = intervalos[i]\n        pontoMedio = (a + b) / 2\n        freq = frequencias[i]\n        soma += pontoMedio * freq\n        somaFreq += freq\n    if somaFreq != 0:\n        media = soma / somaFreq\n    else:\n        media = 0\n    return media",
        "orig_context": "```python\n## funcoes.py\ndef calcularMedia(intervalos, frequencias):\n    soma = 0 #somatorio dos pontomedios vezes suas frequencias\n    somaFreq = 0 #somatorio das frequencias\n\n    for i in range(len(frequencias)):\n        a, b = intervalos[i]\n        pontoMedio = (a+b)/2\n        freq = frequencias[i]\n        soma += pontoMedio * freq\n        somaFreq += freq\n    \n    if(somaFreq != 0):\n        media = soma/somaFreq\n    else: media = 0\n\n    return media\n\n```\n\n\n",
        "eval_script": "## funcoes.py\ndef calcularMedia(intervalos, frequencias):\n    soma = 0 #somatorio dos pontomedios vezes suas frequencias\n    somaFreq = 0 #somatorio das frequencias\n\n    for i in range(len(frequencias)):\n        a, b = intervalos[i]\n        pontoMedio = (a+b)/2\n        freq = frequencias[i]\n        soma += pontoMedio * freq\n        somaFreq += freq\n    \n    if(somaFreq != 0):\n        media = soma/somaFreq\n    else: media = 0\n\n    return media\n\n\ndef test_calcularMedia():\n    # Test case 1: Normal case\n    intervalos1 = [(1, 3), (4, 6), (7, 9)]\n    frequencias1 = [2, 3, 4]\n    assert calcularMedia(intervalos1, frequencias1) == calcularMedia_new_implementation(intervalos1, frequencias1)\n\n    # Test case 2: Case with zero frequencies\n    intervalos2 = [(1, 3), (4, 6), (7, 9)]\n    frequencias2 = [0, 0, 0]\n    assert calcularMedia(intervalos2, frequencias2) == calcularMedia_new_implementation(intervalos2, frequencias2)\n\n    # Test case 3: Case with negative intervals\n    intervalos3 = [(-3, -1), (-6, -4), (-9, -7)]\n    frequencias3 = [2, 3, 4]\n    assert calcularMedia(intervalos3, frequencias3) == calcularMedia_new_implementation(intervalos3, frequencias3)\n\n    # Test case 4: Empty lists\n    intervalos4 = []\n    frequencias4 = []\n    assert calcularMedia(intervalos4, frequencias4) == calcularMedia_new_implementation(intervalos4, frequencias4)\n\n    # Test case 5: Single interval\n    intervalos5 = [(2, 4)]\n    frequencias5 = [5]\n    assert calcularMedia(intervalos5, frequencias5) == calcularMedia_new_implementation(intervalos5, frequencias5)\n\n    # Test case 6: Large numbers\n    intervalos6 = [(1000000, 2000000), (3000000, 4000000)]\n    frequencias6 = [100000, 200000]\n    assert calcularMedia(intervalos6, frequencias6) == calcularMedia_new_implementation(intervalos6, frequencias6)\n\n    # Test case 7: Mixed positive and negative intervals\n    intervalos7 = [(-5, 5), (-10, 0), (0, 10)]\n    frequencias7 = [1, 2, 3]\n    assert calcularMedia(intervalos7, frequencias7) == calcularMedia_new_implementation(intervalos7, frequencias7)\n\n    # Test case 8: Non-integer frequencies\n    intervalos8 = [(1, 3), (4, 6), (7, 9)]\n    frequencias8 = [2.5, 3.5, 4.5]\n    assert calcularMedia(intervalos8, frequencias8) == calcularMedia_new_implementation(intervalos8, frequencias8)\n\n    # Test case 9: Non-overlapping intervals\n    intervalos9 = [(1, 2), (3, 4), (5, 6)]\n    frequencias9 = [1, 2, 3]\n    assert calcularMedia(intervalos9, frequencias9) == calcularMedia_new_implementation(intervalos9, frequencias9)\n\n    # Test case 10: Identical intervals\n    intervalos10 = [(1, 3), (1, 3), (1, 3)]\n    frequencias10 = [1, 2, 3]\n    assert calcularMedia(intervalos10, frequencias10) == calcularMedia_new_implementation(intervalos10, frequencias10)\n\nif __name__ == \"__main__\":\n    test_calcularMedia()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `calcularMedia` is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions calculate the weighted average of midpoints of intervals, using the given frequencies. The logic and structure of the code are the same, with only minor differences in comments and formatting, which do not affect the functionality. The test cases provided in the revised code are not part of the function itself and are used to validate the function's correctness, but they do not alter the functionality of `calcularMedia`.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n1. **CONDITION 1**: The function `calcularMedia` returns a value (`media`), which satisfies the condition that it should either have return values or modify global variables or input arguments.\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `calcularMedia` and `calcularMedia_new_implementation`, which means they are checking return values, not printed or logged contents.\n3. **CONDITION 3**: The test cases compare the outputs of `calcularMedia` and `calcularMedia_new_implementation` for various inputs. This ensures that `calcularMedia_new_implementation` can pass all the test cases if and only if it has the same functionality as `calcularMedia`.\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is reasonable since `calcularMedia` returns a value. There are no unreasonable assertions.\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including normal cases, zero frequencies, negative intervals, empty lists, single intervals, large numbers, mixed intervals, non-integer frequencies, non-overlapping intervals, and identical intervals. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "01a90ee9cad7621bd23b73ce87aad05350ea24be"
    },
    {
        "func_name": "separaClasses",
        "idx": "828",
        "repo_name": "Davi-SC___calculadora-estatistica",
        "func_path": "funcoes.py",
        "orig_func": "def separaClasses(intervalos):\n    classesSeparadas = []\n    for intervalo in intervalos:\n        classesSeparadas.append(str(intervalo[0]) + ' a ' + str(intervalo[1]))\n    return classesSeparadas",
        "orig_context": "```python\n## funcoes.py\ndef separaClasses(intervalos):\n    classesSeparadas = []\n    for intervalo in intervalos:\n        classesSeparadas.append(str(intervalo[0]) + \" a \" + str(intervalo[1]))\n    return classesSeparadas\n\n```\n\n\n",
        "eval_script": "## funcoes.py\ndef separaClasses(intervalos):\n    classesSeparadas = []\n    for intervalo in intervalos:\n        classesSeparadas.append(str(intervalo[0]) + \" a \" + str(intervalo[1]))\n    return classesSeparadas\n\n\ndef test_separaClasses():\n    # Test case 1: Typical intervals\n    intervals1 = [(1, 2), (3, 4), (5, 6)]\n    assert separaClasses(intervals1) == separaClasses_new_implementation(intervals1), \"Test case 1 failed\"\n\n    # Test case 2: Single interval\n    intervals2 = [(10, 20)]\n    assert separaClasses(intervals2) == separaClasses_new_implementation(intervals2), \"Test case 2 failed\"\n\n    # Test case 3: Empty list\n    intervals3 = []\n    assert separaClasses(intervals3) == separaClasses_new_implementation(intervals3), \"Test case 3 failed\"\n\n    # Test case 4: Negative intervals\n    intervals4 = [(-5, -3), (-2, -1)]\n    assert separaClasses(intervals4) == separaClasses_new_implementation(intervals4), \"Test case 4 failed\"\n\n    # Test case 5: Zero intervals\n    intervals5 = [(0, 0), (0, 5)]\n    assert separaClasses(intervals5) == separaClasses_new_implementation(intervals5), \"Test case 5 failed\"\n\n    # Test case 6: Identical start and end\n    intervals6 = [(7, 7), (8, 8)]\n    assert separaClasses(intervals6) == separaClasses_new_implementation(intervals6), \"Test case 6 failed\"\n\n    # Test case 7: Large numbers\n    intervals7 = [(1000000, 2000000), (3000000, 4000000)]\n    assert separaClasses(intervals7) == separaClasses_new_implementation(intervals7), \"Test case 7 failed\"\n\n    # Test case 8: Non-integer intervals\n    intervals8 = [(1.5, 2.5), (3.0, 4.0)]\n    assert separaClasses(intervals8) == separaClasses_new_implementation(intervals8), \"Test case 8 failed\"\n\n    # Test case 9: Mixed intervals\n    intervals9 = [(-1, 0), (0, 1), (1, 2)]\n    assert separaClasses(intervals9) == separaClasses_new_implementation(intervals9), \"Test case 9 failed\"\n\nif __name__ == \"__main__\":\n    test_separaClasses()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       5      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  5      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions iterate over a list of tuples called `intervalos`, convert each tuple into a string with the format \"start a end\", and append this string to a new list called `classesSeparadas`. The function then returns this list. The only difference is the use of double quotes instead of single quotes in the string concatenation, which does not affect the functionality of the code. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `separaClasses` function returns a list of strings, which satisfies the condition of having return values.\n- [CONDITION 2] The test cases use assertions to check the return values of the function, not printed or logged contents.\n- [CONDITION 3] The test cases compare the outputs of `separaClasses` and `separaClasses_new_implementation` directly, ensuring that the new implementation must have the same functionality to pass.\n- [CONDITION 4] The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `separaClasses` returns a list.\n- [CONDITION 5] The test cases cover a variety of scenarios, including typical intervals, single intervals, empty lists, negative intervals, zero intervals, identical start and end, large numbers, non-integer intervals, and mixed intervals. This variety ensures that the test cases are non-trivial.",
            "answer": "yes"
        },
        "commit_id": "01a90ee9cad7621bd23b73ce87aad05350ea24be"
    },
    {
        "func_name": "converterDicionarios",
        "idx": "829",
        "repo_name": "Davi-SC___calculadora-estatistica",
        "func_path": "funcoes.py",
        "orig_func": "def converterDicionarios(intervalos, frequencias):\n    dados = []\n    for i in range(len(intervalos)):\n        classe = f'{intervalos[i][0]}|-{intervalos[i][1]}'\n        frequencia = str(frequencias[i])\n        dados.append({'Classes': classe, 'Frequencia': frequencia})\n    return dados",
        "orig_context": "```python\n## funcoes.py\ndef converterDicionarios(intervalos, frequencias):\n    dados = []\n    for i in range(len(intervalos)):\n        classe = f\"{intervalos[i][0]}|-{intervalos[i][1]}\"\n        frequencia = str(frequencias[i])\n        dados.append({\"Classes\": classe, \"Frequencia\": frequencia})\n    return dados\n\n```\n\n\n",
        "eval_script": "## funcoes.py\ndef converterDicionarios(intervalos, frequencias):\n    dados = []\n    for i in range(len(intervalos)):\n        classe = f\"{intervalos[i][0]}|-{intervalos[i][1]}\"\n        frequencia = str(frequencias[i])\n        dados.append({\"Classes\": classe, \"Frequencia\": frequencia})\n    return dados\n\n\ndef test_converterDicionarios():\n    # Test case 1: Simple case\n    intervals1 = [(1, 2), (3, 4)]\n    frequencies1 = [5, 6]\n    assert converterDicionarios(intervals1, frequencies1) == converterDicionarios_new_implementation(intervals1, frequencies1)\n\n    # Test case 2: Different intervals and frequencies\n    intervals2 = [(0, 1), (2, 3), (4, 5)]\n    frequencies2 = [10, 20, 30]\n    assert converterDicionarios(intervals2, frequencies2) == converterDicionarios_new_implementation(intervals2, frequencies2)\n\n    # Test case 3: Edge case with empty lists\n    intervals3 = []\n    frequencies3 = []\n    assert converterDicionarios(intervals3, frequencies3) == converterDicionarios_new_implementation(intervals3, frequencies3)\n\n    # Test case 4: Single interval and frequency\n    intervals4 = [(10, 20)]\n    frequencies4 = [15]\n    assert converterDicionarios(intervals4, frequencies4) == converterDicionarios_new_implementation(intervals4, frequencies4)\n\n    # Test case 5: Large numbers\n    intervals5 = [(1000000, 2000000), (3000000, 4000000)]\n    frequencies5 = [5000000, 6000000]\n    assert converterDicionarios(intervals5, frequencies5) == converterDicionarios_new_implementation(intervals5, frequencies5)\n\n    # Test case 6: Negative numbers\n    intervals6 = [(-10, -5), (-3, -1)]\n    frequencies6 = [-15, -20]\n    assert converterDicionarios(intervals6, frequencies6) == converterDicionarios_new_implementation(intervals6, frequencies6)\n\n    # Test case 7: Non-integer frequencies\n    intervals7 = [(1, 2), (3, 4)]\n    frequencies7 = [5.5, 6.7]\n    assert converterDicionarios(intervals7, frequencies7) == converterDicionarios_new_implementation(intervals7, frequencies7)\n\n    # Test case 8: Non-sequential intervals\n    intervals8 = [(5, 6), (1, 2)]\n    frequencies8 = [7, 8]\n    assert converterDicionarios(intervals8, frequencies8) == converterDicionarios_new_implementation(intervals8, frequencies8)\n\n    # Test case 9: Overlapping intervals\n    intervals9 = [(1, 5), (4, 8)]\n    frequencies9 = [10, 15]\n    assert converterDicionarios(intervals9, frequencies9) == converterDicionarios_new_implementation(intervals9, frequencies9)\n\nif __name__ == \"__main__\":\n    test_converterDicionarios()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       7      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  7      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions iterate over the `intervalos` list, construct a string `classe` using the elements of each tuple, convert the corresponding element in `frequencias` to a string, and append a dictionary with these values to the `dados` list. The function then returns this list. The changes in the REVISED FUNCTION are purely stylistic (e.g., using double quotes instead of single quotes) and do not affect the functionality. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `converterDicionarios` returns a list of dictionaries, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assert statements to check the return values of the function, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `converterDicionarios` and `converterDicionarios_new_implementation` directly, ensuring that they must have the same functionality to pass.\n- CONDITION 4: The test cases use assert statements to compare the outputs of the two implementations, which is reasonable given that the function returns a value.\n- CONDITION 5: The test cases cover a variety of scenarios, including simple cases, different intervals and frequencies, edge cases with empty lists, single interval and frequency, large numbers, negative numbers, non-integer frequencies, non-sequential intervals, and overlapping intervals. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "01a90ee9cad7621bd23b73ce87aad05350ea24be"
    },
    {
        "func_name": "collate_fn",
        "idx": "831",
        "repo_name": "ArtanVafaei___Image-Caption-Pre-training",
        "func_path": "data_load.py",
        "orig_func": "def collate_fn(data):\n    images, captions = zip(*data)\n    images = torch.stack(images, 0)\n    captions = [[tokenizer.bos_token_id] + cap + [tokenizer.eos_token_id] for cap in captions]\n    lengths = [len(cap) for cap in captions]\n    targets = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n    masks = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n    for i, cap in enumerate(captions):\n        end = lengths[i]\n        targets[i, :end] = torch.LongTensor(cap)\n        masks[i, :end] = 1\n    return (images, targets, masks)",
        "orig_context": "```python\n## model_load.py\nfrom transformers import GPT2Tokenizer\n\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n\n```\n\n\n```python\n## data_load.py\nimport torch\n\nfrom model_load import tokenizer\n\ndef collate_fn(data):\n    images, captions = zip(*data)\n    images = torch.stack(images, 0)\n\n    captions = [[tokenizer.bos_token_id] + cap + [tokenizer.eos_token_id] for cap in captions]\n\n    lengths = [len(cap) for cap in captions]\n    targets = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n    # Create attention masks\n    masks = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n\n    for i, cap in enumerate(captions):\n        end = lengths[i]\n        targets[i, :end] = torch.LongTensor(cap)\n        masks[i, :end] = 1\n\n    return images, targets, masks\n\n```\n\n\n",
        "eval_script": "# Import necessary libraries\nimport torch\nfrom transformers import GPT2Tokenizer\n\n# Initialize the tokenizer\ntokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n\n# Define the collate function\ndef collate_fn(data):\n    images, captions = zip(*data)\n    images = torch.stack(images, 0)\n\n    captions = [[tokenizer.bos_token_id] + cap + [tokenizer.eos_token_id] for cap in captions]\n\n    lengths = [len(cap) for cap in captions]\n    targets = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n    # Create attention masks\n    masks = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n\n    for i, cap in enumerate(captions):\n        end = lengths[i]\n        targets[i, :end] = torch.LongTensor(cap)\n        masks[i, :end] = 1\n\n    return images, targets, masks\n\n\n# Test function to compare both implementations\ndef test_collate_fn():\n    # Mock data\n    data = [\n        (torch.rand(3, 224, 224), [1, 2, 3]),\n        (torch.rand(3, 224, 224), [4, 5]),\n        (torch.rand(3, 224, 224), [6, 7, 8, 9])\n    ]\n\n    # Get results from both functions\n    images1, targets1, masks1 = collate_fn(data)\n    images2, targets2, masks2 = collate_fn_new_implementation(data)\n\n    # Assert statements to check for equality\n    assert torch.equal(images1, images2), \"Images do not match\"\n    assert torch.equal(targets1, targets2), \"Targets do not match\"\n    assert torch.equal(masks1, masks2), \"Masks do not match\"\n\n# Main function to run the test\nif __name__ == \"__main__\":\n    test_collate_fn()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      12      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                 12      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they take a list of tuples containing images and captions, stack the images, add special tokens to the captions, calculate the lengths of the captions, and create target and mask tensors. The logic and sequence of operations are the same in both functions. The test function provided in the code is designed to compare the outputs of the REVISED FUNCTION with another implementation, but since the REVISED FUNCTION is the one being tested, it confirms that the functionality remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `collate_fn` function returns three values: `images`, `targets`, and `masks`. Therefore, it satisfies this condition.\n- [CONDITION 2] The test function checks the return values of `collate_fn` and `collate_fn_new_implementation` using assert statements. It does not rely on printed or logged content, satisfying this condition.\n- [CONDITION 3] The test cases compare the outputs of `collate_fn` and `collate_fn_new_implementation` for equality. This ensures that `collate_fn_new_implementation` must have the exact same functionality as `collate_fn` to pass the tests, satisfying this condition.\n- [CONDITION 4] The assert statements are reasonable as they compare the outputs of the two functions directly. There is no use of `assert collate_fn() == collate_fn_new_implementation()` without return values, satisfying this condition.\n- [CONDITION 5] The test cases use non-trivial mock data with varying lengths of captions, which tests the padding and attention mask creation logic of the `collate_fn` function. This satisfies the condition of being non-trivial.",
            "answer": "yes"
        },
        "commit_id": "29c327de534a850609be8efb9240ff111fe1afea"
    },
    {
        "func_name": "parse_nmap_output",
        "idx": "834",
        "repo_name": "ShadowSpecter88___ScriptedRecon",
        "func_path": "kev_scanner.py",
        "orig_func": "def parse_nmap_output(nmap_output):\n    devices = []\n    lines = nmap_output.split('\\n')\n    current_device = {}\n    for line in lines:\n        if 'Nmap scan report for' in line:\n            if current_device:\n                devices.append(current_device)\n                current_device = {}\n            current_device['ip'] = line.split(' ')[-1]\n        elif 'OS details:' in line:\n            current_device['os'] = line.replace('OS details: ', '').strip()\n        elif 'Device type:' in line:\n            current_device['device_type'] = line.replace('Device type: ', '').strip()\n    if current_device:\n        devices.append(current_device)\n    return devices",
        "orig_context": "```python\n## kev_scanner.py\ndef parse_nmap_output(nmap_output):\n    devices = []\n    lines = nmap_output.split('\\n')\n    current_device = {}\n    for line in lines:\n        if 'Nmap scan report for' in line:\n            if current_device:\n                devices.append(current_device)\n                current_device = {}\n            current_device['ip'] = line.split(' ')[-1]\n        elif 'OS details:' in line:\n            current_device['os'] = line.replace('OS details: ', '').strip()\n        elif 'Device type:' in line:\n            current_device['device_type'] = line.replace('Device type: ', '').strip()\n    if current_device:\n        devices.append(current_device)\n    return devices\n\n```\n\n\n",
        "eval_script": "## kev_scanner.py\ndef parse_nmap_output(nmap_output):\n    devices = []\n    lines = nmap_output.split('\\n')\n    current_device = {}\n    for line in lines:\n        if 'Nmap scan report for' in line:\n            if current_device:\n                devices.append(current_device)\n                current_device = {}\n            current_device['ip'] = line.split(' ')[-1]\n        elif 'OS details:' in line:\n            current_device['os'] = line.replace('OS details: ', '').strip()\n        elif 'Device type:' in line:\n            current_device['device_type'] = line.replace('Device type: ', '').strip()\n    if current_device:\n        devices.append(current_device)\n    return devices\n\n\ndef test_parse_nmap_output():\n    # Test case 1: Basic test with IP and OS details\n    nmap_output_1 = \"\"\"Nmap scan report for 192.168.1.1\nOS details: Linux 3.10 - 4.11\nDevice type: general purpose\"\"\"\n    assert parse_nmap_output(nmap_output_1) == parse_nmap_output_new_implementation(nmap_output_1)\n\n    # Test case 2: Test with multiple devices\n    nmap_output_2 = \"\"\"Nmap scan report for 192.168.1.1\nOS details: Linux 3.10 - 4.11\nDevice type: general purpose\nNmap scan report for 192.168.1.2\nOS details: Windows 7 or 8\nDevice type: workstation\"\"\"\n    assert parse_nmap_output(nmap_output_2) == parse_nmap_output_new_implementation(nmap_output_2)\n\n    # Test case 3: Test with missing OS details\n    nmap_output_3 = \"\"\"Nmap scan report for 192.168.1.1\nDevice type: general purpose\"\"\"\n    assert parse_nmap_output(nmap_output_3) == parse_nmap_output_new_implementation(nmap_output_3)\n\n    # Test case 4: Test with missing device type\n    nmap_output_4 = \"\"\"Nmap scan report for 192.168.1.1\nOS details: Linux 3.10 - 4.11\"\"\"\n    assert parse_nmap_output(nmap_output_4) == parse_nmap_output_new_implementation(nmap_output_4)\n\n    # Test case 5: Test with no devices\n    nmap_output_5 = \"\"\n    assert parse_nmap_output(nmap_output_5) == parse_nmap_output_new_implementation(nmap_output_5)\n\n    # Test case 6: Test with incomplete device information (only IP)\n    nmap_output_6 = \"\"\"Nmap scan report for 192.168.1.1\"\"\"\n    assert parse_nmap_output(nmap_output_6) == parse_nmap_output_new_implementation(nmap_output_6)\n\n    # Test case 7: Test with additional unexpected lines\n    nmap_output_7 = \"\"\"Random text\nNmap scan report for 192.168.1.1\nOS details: Linux 3.10 - 4.11\nDevice type: general purpose\nAnother random line\"\"\"\n    assert parse_nmap_output(nmap_output_7) == parse_nmap_output_new_implementation(nmap_output_7)\n\nif __name__ == \"__main__\":\n    test_parse_nmap_output()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      17      0     12      0   100%\n--------------------------------------------------------------------\nTOTAL                                 17      0     12      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions parse the nmap output to extract device information such as IP address, OS details, and device type. The logic and structure of the code are the same, including how they handle the input lines and append the current device to the devices list. The additional code in the revised version is a set of test cases that validate the function's behavior but do not alter the function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `parse_nmap_output` function returns a list of dictionaries, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `parse_nmap_output` and `parse_nmap_output_new_implementation`, and do not check printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `parse_nmap_output` and `parse_nmap_output_new_implementation` for various inputs, ensuring that the new implementation must have the same functionality as the original to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values, which is appropriate given that `parse_nmap_output` returns a value. The assertions are reasonable.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic input, multiple devices, missing details, no devices, incomplete information, and unexpected lines. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "595cd6d7f388a77cbd9a9983b7ad9cdc1864e08f"
    },
    {
        "func_name": "filter_data",
        "idx": "836",
        "repo_name": "ShadowSpecter88___ScriptedRecon",
        "func_path": "kev_scanner.py",
        "orig_func": "def filter_data(df, search_input):\n    search_input_parts = search_input.split(':')\n    if len(search_input_parts) == 2:\n        start_date, end_date = search_input_parts\n        return df[(df['dateAdded'] >= start_date) & (df['dateAdded'] <= end_date)]\n    return df[(df['cveID'] == search_input) | (df['vendorProject'] == search_input) | df['product'].str.contains(search_input)]",
        "orig_context": "```python\n## kev_scanner.py\ndef filter_data(df, search_input):\n    search_input_parts = search_input.split(\":\")\n    if len(search_input_parts) == 2:\n        start_date, end_date = search_input_parts\n        return df[(df[\"dateAdded\"] >= start_date) & (df[\"dateAdded\"] <= end_date)]\n    return df[(df[\"cveID\"] == search_input) | (df[\"vendorProject\"] == search_input) | (df[\"product\"].str.contains(search_input))]\n\n```\n\n\n",
        "eval_script": "import pandas as pd\n\n# Create a mock DataFrame\ndata = {\n    \"dateAdded\": [\"2023-01-01\", \"2023-02-15\", \"2023-03-10\"],\n    \"cveID\": [\"CVE-2023-0001\", \"CVE-2023-0002\", \"CVE-2023-0003\"],\n    \"vendorProject\": [\"VendorA\", \"VendorB\", \"VendorC\"],\n    \"product\": [\"ProductX\", \"ProductY\", \"ProductZ\"]\n}\n\ndf = pd.DataFrame(data)\n\ndef filter_data(df, search_input):\n    search_input_parts = search_input.split(\":\")\n    if len(search_input_parts) == 2:\n        start_date, end_date = search_input_parts\n        return df[(df[\"dateAdded\"] >= start_date) & (df[\"dateAdded\"] <= end_date)]\n    return df[(df[\"cveID\"] == search_input) | (df[\"vendorProject\"] == search_input) | (df[\"product\"].str.contains(search_input))]\n\n\ndef test_filter_data():\n    # Test filtering by date range\n    assert filter_data(df, \"2023-01-01:2023-02-28\").equals(filter_data_new_implementation(df, \"2023-01-01:2023-02-28\"))\n\n    # Test filtering by specific CVE ID\n    assert filter_data(df, \"CVE-2023-0002\").equals(filter_data_new_implementation(df, \"CVE-2023-0002\"))\n\n    # Test filtering by vendor project name\n    assert filter_data(df, \"VendorB\").equals(filter_data_new_implementation(df, \"VendorB\"))\n\n    # Test filtering by product name\n    assert filter_data(df, \"ProductY\").equals(filter_data_new_implementation(df, \"ProductY\"))\n\n    # Test filtering by partial product name\n    assert filter_data(df, \"Product\").equals(filter_data_new_implementation(df, \"Product\"))\n\n    # Test with an empty DataFrame\n    empty_df = pd.DataFrame(columns=[\"dateAdded\", \"cveID\", \"vendorProject\", \"product\"])\n    assert filter_data(empty_df, \"2023-01-01:2023-02-28\").equals(filter_data_new_implementation(empty_df, \"2023-01-01:2023-02-28\"))\n\n    # Test with no matches\n    assert filter_data(df, \"NonExistent\").equals(filter_data_new_implementation(df, \"NonExistent\"))\n\n    # Test with invalid date range\n    assert filter_data(df, \"2023-03-10:2023-01-01\").equals(filter_data_new_implementation(df, \"2023-03-10:2023-01-01\"))\n\n    # Test with a single date\n    assert filter_data(df, \"2023-01-01\").equals(filter_data_new_implementation(df, \"2023-01-01\"))\n\n    # Test case sensitivity\n    assert filter_data(df, \"vendorb\").equals(filter_data_new_implementation(df, \"vendorb\"))\n\n    # Test with non-existent CVE ID\n    assert filter_data(df, \"CVE-2023-9999\").equals(filter_data_new_implementation(df, \"CVE-2023-9999\"))\n\n    # Test with special characters in product name\n    assert filter_data(df, \"ProductX!\").equals(filter_data_new_implementation(df, \"ProductX!\"))\n\nif __name__ == \"__main__\":\n    test_filter_data()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION in terms of logic and functionality. Both functions split the `search_input` by a colon to determine if it is a date range. If it is a date range, they filter the DataFrame based on the `dateAdded` column. If it is not a date range, they filter the DataFrame based on whether the `search_input` matches the `cveID`, `vendorProject`, or is contained within the `product` column. The only differences are stylistic, such as the use of double quotes instead of single quotes and the addition of parentheses around the `str.contains` condition, which does not affect functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `filter_data` function returns a DataFrame, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use the `equals` method to compare the DataFrames returned by `filter_data` and `filter_data_new_implementation`, which means they are checking return values, not printed or logged contents.\n- CONDITION 3: The test cases cover various scenarios such as filtering by date range, CVE ID, vendor project name, product name, partial product name, empty DataFrame, no matches, invalid date range, single date, case sensitivity, non-existent CVE ID, and special characters. These tests ensure that `filter_data_new_implementation` must have the same functionality as `filter_data` to pass all tests.\n- CONDITION 4: The use of `equals` to compare DataFrames is reasonable, as it checks for equality in content and structure. The test cases do not use inappropriate assertions.\n- CONDITION 5: The test cases are non-trivial as they cover a wide range of input scenarios, including edge cases like empty DataFrame, no matches, invalid date range, and case sensitivity.",
            "answer": "yes"
        },
        "commit_id": "595cd6d7f388a77cbd9a9983b7ad9cdc1864e08f"
    },
    {
        "func_name": "analyze_network",
        "idx": "837",
        "repo_name": "ShadowSpecter88___ScriptedRecon",
        "func_path": "kev_scanner.py",
        "orig_func": "def analyze_network(df, network):\n    nmap_output = scan_network(network)\n    devices = parse_nmap_output(nmap_output)\n    vulnerable_devices = []\n    for device in devices:\n        vendor = device.get('os', '').split(' ')[0].lower()\n        if vendor:\n            matching_vulns = df[df['vendorProject'] == vendor]\n            if not matching_vulns.empty:\n                vulnerable_devices.append({'ip': device['ip'], 'vendor': vendor, 'vulnerabilities': matching_vulns['cveID'].tolist()})\n    return vulnerable_devices",
        "orig_context": "```python\n## kev_scanner.py\nimport subprocess\n\ndef scan_network(network_range):\n    result = subprocess.run(['nmap', '-O', network_range], capture_output=True, text=True)\n    return result.stdout\n\ndef parse_nmap_output(nmap_output):\n    devices = []\n    lines = nmap_output.split('\\n')\n    current_device = {}\n    for line in lines:\n        if 'Nmap scan report for' in line:\n            if current_device:\n                devices.append(current_device)\n                current_device = {}\n            current_device['ip'] = line.split(' ')[-1]\n        elif 'OS details:' in line:\n            current_device['os'] = line.replace('OS details: ', '').strip()\n        elif 'Device type:' in line:\n            current_device['device_type'] = line.replace('Device type: ', '').strip()\n    if current_device:\n        devices.append(current_device)\n    return devices\n\ndef analyze_network(df, network):\n    nmap_output = scan_network(network)\n    devices = parse_nmap_output(nmap_output)\n    vulnerable_devices = []\n    for device in devices:\n        vendor = device.get('os', '').split(' ')[0].lower()\n        if vendor:\n            matching_vulns = df[df['vendorProject'] == vendor]\n            if not matching_vulns.empty:\n                vulnerable_devices.append({'ip': device['ip'], 'vendor': vendor, 'vulnerabilities': matching_vulns['cveID'].tolist()})\n    return vulnerable_devices\n\n```\n\n\n",
        "eval_script": "## kev_scanner.py\nimport subprocess\nimport pandas as pd\n\n# Mocking the subprocess.run to simulate nmap output\ndef mock_subprocess_run(command, capture_output, text):\n    class MockCompletedProcess:\n        def __init__(self):\n            self.stdout = (\n                \"Nmap scan report for 192.168.1.1\\n\"\n                \"Device type: general purpose\\n\"\n                \"OS details: Linux 3.10 - 3.13\\n\"\n                \"Nmap scan report for 192.168.1.2\\n\"\n                \"Device type: general purpose\\n\"\n                \"OS details: Windows 7 or 8\\n\"\n            )\n    return MockCompletedProcess()\n\n# Replace subprocess.run with the mock function\nsubprocess.run = mock_subprocess_run\n\ndef scan_network(network_range):\n    result = subprocess.run(['nmap', '-O', network_range], capture_output=True, text=True)\n    return result.stdout\n\ndef parse_nmap_output(nmap_output):\n    devices = []\n    lines = nmap_output.split('\\n')\n    current_device = {}\n    for line in lines:\n        if 'Nmap scan report for' in line:\n            if current_device:\n                devices.append(current_device)\n                current_device = {}\n            current_device['ip'] = line.split(' ')[-1]\n        elif 'OS details:' in line:\n            current_device['os'] = line.replace('OS details: ', '').strip()\n        elif 'Device type:' in line:\n            current_device['device_type'] = line.replace('Device type: ', '').strip()\n    if current_device:\n        devices.append(current_device)\n    return devices\n\ndef analyze_network(df, network):\n    nmap_output = scan_network(network)\n    devices = parse_nmap_output(nmap_output)\n    vulnerable_devices = []\n    for device in devices:\n        vendor = device.get('os', '').split(' ')[0].lower()\n        if vendor:\n            matching_vulns = df[df['vendorProject'] == vendor]\n            if not matching_vulns.empty:\n                vulnerable_devices.append({'ip': device['ip'], 'vendor': vendor, 'vulnerabilities': matching_vulns['cveID'].tolist()})\n    return vulnerable_devices\n\n# Sample DataFrame for testing\nsample_data = {\n    'vendorProject': ['linux', 'windows'],\n    'cveID': ['CVE-2021-1234', 'CVE-2021-5678']\n}\ndf = pd.DataFrame(sample_data)\n\n\ndef test_analyze_network():\n    # Test case 1: Basic functionality test\n    network = '192.168.1.0/24'\n    result_old = analyze_network(df, network)\n    result_new = analyze_network_new_implementation(df, network)\n    assert result_old == result_new, \"Test case 1 failed\"\n\n    # Test case 2: No vulnerabilities found\n    df_empty = pd.DataFrame(columns=['vendorProject', 'cveID'])\n    result_old = analyze_network(df_empty, network)\n    result_new = analyze_network_new_implementation(df_empty, network)\n    assert result_old == result_new, \"Test case 2 failed\"\n\n    # Test case 3: Different network range\n    network_different = '192.168.2.0/24'\n    result_old = analyze_network(df, network_different)\n    result_new = analyze_network_new_implementation(df, network_different)\n    assert result_old == result_new, \"Test case 3 failed\"\n\n    # Test case 4: Vendor not in DataFrame\n    df_no_vendor = pd.DataFrame({'vendorProject': ['macos'], 'cveID': ['CVE-2021-9999']})\n    result_old = analyze_network(df_no_vendor, network)\n    result_new = analyze_network_new_implementation(df_no_vendor, network)\n    assert result_old == result_new, \"Test case 4 failed\"\n\n    # Test case 5: Malformed OS details\n    def mock_subprocess_run_malformed(command, capture_output, text):\n        class MockCompletedProcess:\n            def __init__(self):\n                self.stdout = (\n                    \"Nmap scan report for 192.168.1.3\\n\"\n                    \"Device type: general purpose\\n\"\n                    \"OS details: \\n\"\n                )\n        return MockCompletedProcess()\n\n    # Replace subprocess.run with the mock function for malformed OS\n    subprocess.run = mock_subprocess_run_malformed\n    result_old = analyze_network(df, network)\n    result_new = analyze_network_new_implementation(df, network)\n    assert result_old == result_new, \"Test case 5 failed\"\n\n    # Restore the original mock function\n    subprocess.run = mock_subprocess_run\n\nif __name__ == \"__main__\":\n    test_analyze_network()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they scan a network, parse the nmap output to extract device information, and then check for vulnerabilities based on the vendor information extracted from the OS details. The logic and flow of the function remain unchanged. The additional code in the REVISED FUNCTION is related to mocking the subprocess call for testing purposes and does not alter the functionality of the `analyze_network` function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- **CONDITION 1**: The `analyze_network` function returns a list of dictionaries (`vulnerable_devices`), satisfying the condition of having return values.\n  \n- **CONDITION 2**: The test cases use assertions to compare the return values of `analyze_network` and `analyze_network_new_implementation`, without checking printed or logged content. This condition is satisfied.\n\n- **CONDITION 3**: The test cases compare the outputs of `analyze_network` and `analyze_network_new_implementation` directly. If `analyze_network_new_implementation` has the same functionality, it will produce the same output for the same input, satisfying this condition.\n\n- **CONDITION 4**: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `analyze_network` returns a value. This condition is satisfied.\n\n- **CONDITION 5**: The test cases cover various scenarios, including basic functionality, no vulnerabilities, different network ranges, vendors not in the DataFrame, and malformed OS details. These are non-trivial and cover a range of potential edge cases, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "595cd6d7f388a77cbd9a9983b7ad9cdc1864e08f"
    },
    {
        "func_name": "selection_sort",
        "idx": "840",
        "repo_name": "Akash671___MNC_DSA_Test",
        "func_path": "sorting_algo.py",
        "orig_func": "def selection_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        min_idx = i\n        for j in range(i + 1, n):\n            if arr[j] < arr[min_idx]:\n                min_idx = j\n        arr[i], arr[min_idx] = (arr[min_idx], arr[i])\n    return arr",
        "orig_context": "```python\n## sorting_algo.py\ndef selection_sort(arr):\n    n=len(arr)\n    \n    for i in range(n):\n        min_idx=i\n        \n        for j in range(i+1,n):\n            if arr[j]<arr[min_idx]:\n                min_idx=j\n        arr[i],arr[min_idx]=arr[min_idx],arr[i]\n        \n    return arr\n\n```\n\n\n",
        "eval_script": "## sorting_algo.py\ndef selection_sort(arr):\n    n=len(arr)\n    \n    for i in range(n):\n        min_idx=i\n        \n        for j in range(i+1,n):\n            if arr[j]<arr[min_idx]:\n                min_idx=j\n        arr[i],arr[min_idx]=arr[min_idx],arr[i]\n        \n    return arr\n\n\ndef test_selection_sort():\n    # Test case 1: Already sorted list\n    arr1 = [1, 2, 3, 4, 5]\n    assert selection_sort(arr1[:]) == selection_sort_new_implementation(arr1[:]), \"Test case 1 failed\"\n\n    # Test case 2: Reverse sorted list\n    arr2 = [5, 4, 3, 2, 1]\n    assert selection_sort(arr2[:]) == selection_sort_new_implementation(arr2[:]), \"Test case 2 failed\"\n\n    # Test case 3: List with duplicate elements\n    arr3 = [3, 1, 2, 3, 1]\n    assert selection_sort(arr3[:]) == selection_sort_new_implementation(arr3[:]), \"Test case 3 failed\"\n\n    # Test case 4: Empty list\n    arr4 = []\n    assert selection_sort(arr4[:]) == selection_sort_new_implementation(arr4[:]), \"Test case 4 failed\"\n\n    # Test case 5: Single element list\n    arr5 = [42]\n    assert selection_sort(arr5[:]) == selection_sort_new_implementation(arr5[:]), \"Test case 5 failed\"\n\n    # Test case 6: All elements identical\n    arr6 = [7, 7, 7, 7, 7]\n    assert selection_sort(arr6[:]) == selection_sort_new_implementation(arr6[:]), \"Test case 6 failed\"\n\n    # Test case 7: Large numbers\n    arr7 = [1000000, 999999, 1000001]\n    assert selection_sort(arr7[:]) == selection_sort_new_implementation(arr7[:]), \"Test case 7 failed\"\n\n    # Test case 8: Negative numbers\n    arr8 = [-1, -3, -2, -5, -4]\n    assert selection_sort(arr8[:]) == selection_sort_new_implementation(arr8[:]), \"Test case 8 failed\"\n\n    # Test case 9: Mixed numbers\n    arr9 = [0, -1, 1, -2, 2]\n    assert selection_sort(arr9[:]) == selection_sort_new_implementation(arr9[:]), \"Test case 9 failed\"\n\n    # Test case 10: Floating point numbers\n    arr10 = [3.1, 2.4, 5.6, 1.2, 4.8]\n    assert selection_sort(arr10[:]) == selection_sort_new_implementation(arr10[:]), \"Test case 10 failed\"\n\nif __name__ == \"__main__\":\n    test_selection_sort()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       9      0      6      0   100%\n--------------------------------------------------------------------\nTOTAL                                  9      0      6      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions implement the selection sort algorithm in the same way. They iterate over the array, find the minimum element in the unsorted portion, and swap it with the first unsorted element. The syntax and logic are the same, including the use of indices and swapping mechanism. The test cases provided in the code are irrelevant to the comparison of the functions themselves, as they are meant to test the functionality of the sorting algorithm, which is already confirmed to be the same in both implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `selection_sort` function returns the sorted array, satisfying the condition that it should have return values or modify global variables or input arguments.\n- [CONDITION 2] The test cases use assert statements to check the return values of the `selection_sort` and `selection_sort_new_implementation` functions, not printed or logged contents.\n- [CONDITION 3] The test cases compare the outputs of `selection_sort` and `selection_sort_new_implementation` for various inputs, ensuring that the new implementation must have exactly the same functionality to pass all tests.\n- [CONDITION 4] The test cases use assert statements to compare the return values of the two functions, which is reasonable given that `selection_sort` returns a sorted list.\n- [CONDITION 5] The test cases cover a variety of scenarios, including sorted lists, reverse sorted lists, lists with duplicates, empty lists, single-element lists, identical elements, large numbers, negative numbers, mixed numbers, and floating-point numbers, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "7a14edd936c3d9212fe83c092bd2ea9249790cf7"
    },
    {
        "func_name": "insertion_sort",
        "idx": "841",
        "repo_name": "Akash671___MNC_DSA_Test",
        "func_path": "sorting_algo.py",
        "orig_func": "def insertion_sort(arr):\n    n = len(arr)\n    for i in range(1, n):\n        key = arr[i]\n        j = i - 1\n        while j >= 0 and arr[j] > key:\n            arr[j + 1] = arr[j]\n            j -= 1\n        arr[j + 1] = key\n    return arr",
        "orig_context": "```python\n## sorting_algo.py\ndef insertion_sort(arr):\n    n=len(arr)\n    \n    for i in range(1,n):\n        key=arr[i]\n        j=i-1\n        \n        while j>=0 and arr[j]>key:\n            #4 5 3 7 6 1\n            #4 3 5\n            #4 4 5\n            #3 4 5 7 6 1\n            arr[j+1]=arr[j]\n            j-=1\n        arr[j+1]=key\n    return arr\n\n```\n\n\n",
        "eval_script": "## sorting_algo.py\ndef insertion_sort(arr):\n    n = len(arr)\n    \n    for i in range(1, n):\n        key = arr[i]\n        j = i - 1\n        \n        while j >= 0 and arr[j] > key:\n            arr[j + 1] = arr[j]\n            j -= 1\n        arr[j + 1] = key\n    return arr\n\n\ndef test_insertion_sort():\n    # Test case 1: Typical unsorted list\n    assert insertion_sort([4, 5, 3, 7, 6, 1]) == insertion_sort_new_implementation([4, 5, 3, 7, 6, 1])\n    \n    # Test case 2: Already sorted list\n    assert insertion_sort([1, 2, 3, 4, 5]) == insertion_sort_new_implementation([1, 2, 3, 4, 5])\n    \n    # Test case 3: List with duplicate elements\n    assert insertion_sort([3, 1, 2, 3, 1]) == insertion_sort_new_implementation([3, 1, 2, 3, 1])\n    \n    # Test case 4: Empty list\n    assert insertion_sort([]) == insertion_sort_new_implementation([])\n    \n    # Test case 5: Single element list\n    assert insertion_sort([42]) == insertion_sort_new_implementation([42])\n\nif __name__ == \"__main__\":\n    test_insertion_sort()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      10      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 10      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions implement the insertion sort algorithm in the same way, with the same logic and structure. The additional code in the CODE is a test suite that compares the output of the `insertion_sort` function with another function `insertion_sort_new_implementation`, which is not provided. However, this test suite does not affect the functionality of the `insertion_sort` function itself. Since the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `insertion_sort` function modifies the input list `arr` in place and returns it. This satisfies the condition as it modifies the input argument and has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `insertion_sort` and `insertion_sort_new_implementation`, which means they are checking the return values, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `insertion_sort` and `insertion_sort_new_implementation` for various inputs. If `insertion_sort_new_implementation` has the same functionality as `insertion_sort`, it will pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two functions. Since `insertion_sort` returns the sorted list, using assertions to compare the return values is reasonable.\n- CONDITION 5: The test cases cover a variety of scenarios: a typical unsorted list, an already sorted list, a list with duplicates, an empty list, and a single-element list. These are non-trivial and comprehensive enough to test the sorting functionality.",
            "answer": "yes"
        },
        "commit_id": "7a14edd936c3d9212fe83c092bd2ea9249790cf7"
    },
    {
        "func_name": "download_urls",
        "idx": "844",
        "repo_name": "LooFifteen___mathematics",
        "func_path": "main.py",
        "orig_func": "def download_urls(urls) -> list[BytesIO]:\n    total = len(urls)\n    progress_bar = ProgressBar(total)\n    data = []\n    with ThreadPoolExecutor() as executor:\n        futures = [executor.submit(download_url, url, progress_bar) for url in urls]\n        for future in futures:\n            data.append(future.result())\n    print()\n    return data",
        "orig_context": "```python\n## main.py\nimport requests\n\nimport sys\n\nimport time\n\nfrom io import BytesIO\n\nfrom concurrent.futures import ThreadPoolExecutor\n\nclass ProgressBar:\n    def __init__(self, total: int):\n        self.__total = total\n        self.__current = 0\n        self.__start_time = time.time()\n\n    def increment(self):\n        self.__current += 1\n        self.display()\n\n    def display(self):\n        progress = int((self.__current / self.__total) * 100)\n        sys.stdout.write(f\"\\r\\033[K[{('=' * progress).ljust(100)}] {self.__current}/{self.__total} ({time.time() - self.__start_time:.2f}s)\")\n        sys.stdout.flush()\n\ndef download_url(url: str, progress_bar: ProgressBar) -> BytesIO:\n    response = requests.get(url)\n    progress_bar.increment()\n    return BytesIO(response.content)\n\ndef download_urls(urls) -> list[BytesIO]:\n    total = len(urls)\n    progress_bar = ProgressBar(total)\n    data = []\n\n    with ThreadPoolExecutor() as executor:\n        futures = [executor.submit(download_url, url, progress_bar) for url in urls]\n        for future in futures:\n            data.append(future.result())\n    print()\n\n    return data\n\n```\n\n\n",
        "eval_script": "## main.py\nimport requests\nfrom unittest.mock import patch, Mock\n\nimport sys\n\nimport time\n\nfrom io import BytesIO\n\nfrom concurrent.futures import ThreadPoolExecutor\n\nclass ProgressBar:\n    def __init__(self, total: int):\n        self.__total = total\n        self.__current = 0\n        self.__start_time = time.time()\n\n    def increment(self):\n        self.__current += 1\n        self.display()\n\n    def display(self):\n        progress = int((self.__current / self.__total) * 100)\n        sys.stdout.write(f\"\\r\\033[K[{('=' * progress).ljust(100)}] {self.__current}/{self.__total} ({time.time() - self.__start_time:.2f}s)\")\n        sys.stdout.flush()\n\ndef download_url(url: str, progress_bar: ProgressBar) -> BytesIO:\n    response = requests.get(url)\n    progress_bar.increment()\n    return BytesIO(response.content)\n\ndef download_urls(urls) -> list[BytesIO]:\n    total = len(urls)\n    progress_bar = ProgressBar(total)\n    data = []\n\n    with ThreadPoolExecutor() as executor:\n        futures = [executor.submit(download_url, url, progress_bar) for url in urls]\n        for future in futures:\n            data.append(future.result())\n    print()\n\n    return data\n\n# Mocking requests.get to simulate downloading without internet access\ndef mock_requests_get(url):\n    mock_response = Mock()\n    mock_response.content = f\"Content of {url}\".encode()\n    return mock_response\n\n\ndef test_download_urls():\n    test_urls = [\"http://example.com/file1\", \"http://example.com/file2\", \"http://example.com/file3\"]\n\n    with patch('requests.get', side_effect=mock_requests_get):\n        # Test with the original set of URLs\n        original_result = download_urls(test_urls)\n        new_result = download_urls_new_implementation(test_urls)\n\n        # Assert that the lengths of the results are the same\n        assert len(original_result) == len(new_result), \"Length mismatch\"\n\n        # Assert that the content of each result is the same\n        for original, new in zip(original_result, new_result):\n            assert original.getvalue() == new.getvalue(), \"Content mismatch\"\n\n        # Assert that the types of the results are the same\n        assert all(isinstance(item, BytesIO) for item in original_result), \"Type mismatch in original\"\n        assert all(isinstance(item, BytesIO) for item in new_result), \"Type mismatch in new\"\n\n        # Test with an empty list of URLs\n        empty_urls = []\n        original_result_empty = download_urls(empty_urls)\n        new_result_empty = download_urls_new_implementation(empty_urls)\n        assert original_result_empty == new_result_empty == [], \"Mismatch with empty URL list\"\n\n        # Test with a single URL\n        single_url = [\"http://example.com/single\"]\n        original_result_single = download_urls(single_url)\n        new_result_single = download_urls_new_implementation(single_url)\n        assert len(original_result_single) == len(new_result_single) == 1, \"Length mismatch with single URL\"\n        assert original_result_single[0].getvalue() == new_result_single[0].getvalue(), \"Content mismatch with single URL\"\n\n        # Test with invalid URLs (syntactically incorrect)\n        invalid_urls = [\"invalid_url\"]\n        original_result_invalid = download_urls(invalid_urls)\n        new_result_invalid = download_urls_new_implementation(invalid_urls)\n        assert len(original_result_invalid) == len(new_result_invalid) == 1, \"Length mismatch with invalid URL\"\n        assert original_result_invalid[0].getvalue() == new_result_invalid[0].getvalue(), \"Content mismatch with invalid URL\"\n\n        # Test with a large number of URLs\n        large_number_of_urls = [f\"http://example.com/file{i}\" for i in range(100)]\n        original_result_large = download_urls(large_number_of_urls)\n        new_result_large = download_urls_new_implementation(large_number_of_urls)\n        assert len(original_result_large) == len(new_result_large) == 100, \"Length mismatch with large number of URLs\"\n        for original, new in zip(original_result_large, new_result_large):\n            assert original.getvalue() == new.getvalue(), \"Content mismatch with large number of URLs\"\n\n        # Test with duplicate URLs\n        duplicate_urls = [\"http://example.com/file1\", \"http://example.com/file1\"]\n        original_result_duplicate = download_urls(duplicate_urls)\n        new_result_duplicate = download_urls_new_implementation(duplicate_urls)\n        assert len(original_result_duplicate) == len(new_result_duplicate) == 2, \"Length mismatch with duplicate URLs\"\n        assert original_result_duplicate[0].getvalue() == new_result_duplicate[0].getvalue(), \"Content mismatch with duplicate URLs\"\n        assert original_result_duplicate[1].getvalue() == new_result_duplicate[1].getvalue(), \"Content mismatch with duplicate URLs\"\n\nif __name__ == \"__main__\":\n    test_download_urls()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      10      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                 10      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions, `download_urls`, perform the same operations: they initialize a `ProgressBar`, use a `ThreadPoolExecutor` to download content from a list of URLs concurrently, and return a list of `BytesIO` objects containing the downloaded content. The REVISED FUNCTION includes additional context and testing code, but the core function itself is unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `download_urls` function returns a list of `BytesIO` objects, which satisfies the condition as it has return values.\n- [CONDITION 2] The test cases check the return values and do not rely on printed or logged content. They use assertions to compare the results of `download_urls` and `download_urls_new_implementation`.\n- [CONDITION 3] The test cases compare the results of `download_urls` and `download_urls_new_implementation` for various scenarios, ensuring that the new implementation must have the same functionality to pass all tests.\n- [CONDITION 4] The test cases use appropriate assertions to compare the lengths and contents of the results, ensuring they are reasonable and aligned with the function's behavior.\n- [CONDITION 5] The test cases cover a range of scenarios, including normal URLs, empty lists, single URLs, invalid URLs, large numbers of URLs, and duplicate URLs, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "b7dc802821e20806a66ddfad6f7f70f04b3df5a6"
    },
    {
        "func_name": "NodoArbol.reemplazarDatoDeNodo",
        "idx": "851",
        "repo_name": "ezefigueroa___TP_Final_prog2_2024",
        "func_path": "gps_simulation/routes/utils.py",
        "orig_func": "def reemplazarDatoDeNodo(self, clave, valor, hizq, hder):\n    self.clave = clave\n    self.cargaUtil = valor\n    self.hijoIzquierdo = hizq\n    self.hijoDerecho = hder\n    if self.tieneHijoIzquierdo():\n        self.hijoIzquierdo.padre = self\n    if self.tieneHijoDerecho():\n        self.hijoDerecho.padre = self",
        "orig_context": "```python\n## gps_simulation/routes/utils.py\nclass NodoArbol: \n    def __init__(self,clave,valor,izquierdo=None,derecho=None,padre=None): #se define el constructor de la clase para inicializar un \u00e1rbol con sus respectivos atributos.\n        self.clave = clave #atributo que va a almacenar la clave del nodo\n        self.cargaUtil = valor #atributo que va a almacenar el valor del nodo\n        self.hijoIzquierdo = izquierdo #hijo izquierdo del nodo\n        self.hijoDerecho = derecho #hijo derecho del nodo\n        self.padre = padre #nodo padre\n        self.factorEquilibrio = 0\n\n    def tieneHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo, devolviendo True o False.\n        return self.hijoIzquierdo \n\n    def tieneHijoDerecho(self): #m\u00e9todo que verifica si el nodo tiene un hijo derecho, devolviendo True o False.\n        return self.hijoDerecho\n\n    def esHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo es hijo izquierdo de su padre.\n        return self.padre and self.padre.hijoIzquierdo == self\n\n    def esHijoDerecho(self): #m\u00e9todo que verifica si el nodo es hijo derecho de su padre.\n        return self.padre and self.padre.hijoDerecho == self\n\n    def esRaiz(self): #m\u00e9todo que verifica si el nodo es la ra\u00edz del \u00e1rbol (no tiene padre)\n        return not self.padre\n\n    def esHoja(self): #m\u00e9todo que verifica si el nodo es una hoja (si no tiene hijos ni izquierdo ni derecho)\n        return not (self.hijoDerecho or self.hijoIzquierdo)\n\n    def tieneAlgunHijo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo o un hijo derecho\n        return self.hijoDerecho or self.hijoIzquierdo\n\n    def tieneAmbosHijos(self): #m\u00e9todo que verifica si el nodo tiene ambos hijos izquierdo y derecho\n        return self.hijoDerecho and self.hijoIzquierdo\n\n    def reemplazarDatoDeNodo(self,clave,valor,hizq,hder): #m\u00e9todo que reemplaza los datos del nodo (clave, valor) y sus hijos izquierdo y derecho.\n        self.clave = clave # Reemplaza la clave del nodo.\n        self.cargaUtil = valor # Reemplaza el valor asociado a la clave.\n        self.hijoIzquierdo = hizq # Reemplaza el hijo izquierdo del nodo.\n        self.hijoDerecho = hder # Reemplaza el hijo derecho del nodo.\n        if self.tieneHijoIzquierdo(): #si el nodo tiene hijo izquierdo, actualiza el padre de ese hijo.\n            self.hijoIzquierdo.padre = self\n        if self.tieneHijoDerecho(): #si el nodo tiene hijo derecho, actualiza el padre de ese hijo.\n            self.hijoDerecho.padre = self\n\n    def encontrarSucesor(self): #m\u00e9todo que busca el sucesor inorden del nodo (es decir, el nodo con la siguiente clave mayor).\n        suc = None\n        if self.tieneHijoDerecho(): # Si el nodo tiene hijo derecho, el sucesor es el nodo con el valor m\u00ednimo en el sub\u00e1rbol derecho.\n            suc = self.hijoDerecho.encontrarMin()\n        else: # Si no tiene hijo derecho, sube por el \u00e1rbol buscando el primer ancestro que sea hijo izquierdo de su padre.\n            if self.padre:\n                if self.esHijoIzquierdo():\n                    suc = self.padre.encontrarSucesor() # El sucesor es el padre si este nodo es hijo izquierdo.\n                else: # Si el nodo es hijo derecho, sube al padre y busca su sucesor.\n                    self.padre.hijoDerecho = None\n                    suc = self.padre.encontrarSucesor()\n                    self.padre.hijoDerecho = self\n        return suc\n\n    def empalmar(self):\n        # Elimina el nodo del \u00e1rbol, \"empalmando\" sus hijos con su padre.\n        if self.esHoja(): # Si el nodo es una hoja, simplemente elimina la referencia de su padre a \u00e9l.\n            if self.esHijoIzquierdo():\n                self.padre.hijoIzquierdo = None\n            else:\n                self.padre.hijoDerecho = None\n        elif self.tieneAlgunHijo(): # Si el nodo tiene un hijo (izquierdo o derecho), reemplaza el nodo con su \u00fanico hijo.\n            if self.tieneHijoIzquierdo():\n                if self.esHijoIzquierdo():\n                    self.padre.hijoIzquierdo = self.hijoIzquierdo # Si es hijo izquierdo, el padre apunta al hijo izquierdo.\n                else:\n                    self.padre.hijoDerecho = self.hijoIzquierdo # Si es hijo derecho, el padre apunta al hijo izquierdo.\n                self.hijoIzquierdo.padre = self.padre # Actualiza el padre del hijo izquierdo.\n            else:\n                if self.esHijoIzquierdo(): \n                    self.padre.hijoIzquierdo = self.hijoDerecho # Si es hijo izquierdo, el padre apunta al hijo derecho.\n                else:\n                    self.padre.hijoDerecho = self.hijoDerecho # Si es hijo derecho, el padre apunta al hijo derecho.\n                self.hijoDerecho.padre = self.padre # Actualiza el padre del hijo derecho.\n\n    def encontrarMin(self): #m\u00e9todo que # Encuentra el nodo con la clave m\u00ednima en el sub\u00e1rbol (el m\u00e1s a la izquierda).\n        actual = self\n        while actual.tieneHijoIzquierdo():\n            actual = actual.hijoIzquierdo # Baja al hijo izquierdo hasta llegar al nodo m\u00e1s a la izquierda.\n        return actual\n\n    def __iter__(self): # Permite la iteraci\u00f3n sobre el \u00e1rbol inorden (izquierda, ra\u00edz, derecha).\n        if self:\n            if self.tieneHijoIzquierdo(): # Si tiene hijo izquierdo, itera sobre \u00e9l.\n                for elem in self.hijoIzquierdo:\n                    yield elem\n            yield self.clave # Luego devuelve la clave del nodo actual.\n            if self.tieneHijoDerecho():\n                for elem in self.hijoDerecho: # Si tiene hijo derecho, itera sobre \u00e9l.\n                    yield elem\n\n```\n\n\n",
        "eval_script": "## gps_simulation/routes/utils.py\nclass NodoArbol: \n    def __init__(self,clave,valor,izquierdo=None,derecho=None,padre=None): #se define el constructor de la clase para inicializar un \u00e1rbol con sus respectivos atributos.\n        self.clave = clave #atributo que va a almacenar la clave del nodo\n        self.cargaUtil = valor #atributo que va a almacenar el valor del nodo\n        self.hijoIzquierdo = izquierdo #hijo izquierdo del nodo\n        self.hijoDerecho = derecho #hijo derecho del nodo\n        self.padre = padre #nodo padre\n        self.factorEquilibrio = 0\n\n    def tieneHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo, devolviendo True o False.\n        return self.hijoIzquierdo \n\n    def tieneHijoDerecho(self): #m\u00e9todo que verifica si el nodo tiene un hijo derecho, devolviendo True o False.\n        return self.hijoDerecho\n\n    def esHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo es hijo izquierdo de su padre.\n        return self.padre and self.padre.hijoIzquierdo == self\n\n    def esHijoDerecho(self): #m\u00e9todo que verifica si el nodo es hijo derecho de su padre.\n        return self.padre and self.padre.hijoDerecho == self\n\n    def esRaiz(self): #m\u00e9todo que verifica si el nodo es la ra\u00edz del \u00e1rbol (no tiene padre)\n        return not self.padre\n\n    def esHoja(self): #m\u00e9todo que verifica si el nodo es una hoja (si no tiene hijos ni izquierdo ni derecho)\n        return not (self.hijoDerecho or self.hijoIzquierdo)\n\n    def tieneAlgunHijo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo o un hijo derecho\n        return self.hijoDerecho or self.hijoIzquierdo\n\n    def tieneAmbosHijos(self): #m\u00e9todo que verifica si el nodo tiene ambos hijos izquierdo y derecho\n        return self.hijoDerecho and self.hijoIzquierdo\n\n    def reemplazarDatoDeNodo(self,clave,valor,hizq,hder): #m\u00e9todo que reemplaza los datos del nodo (clave, valor) y sus hijos izquierdo y derecho.\n        self.clave = clave # Reemplaza la clave del nodo.\n        self.cargaUtil = valor # Reemplaza el valor asociado a la clave.\n        self.hijoIzquierdo = hizq # Reemplaza el hijo izquierdo del nodo.\n        self.hijoDerecho = hder # Reemplaza el hijo derecho del nodo.\n        if self.tieneHijoIzquierdo(): #si el nodo tiene hijo izquierdo, actualiza el padre de ese hijo.\n            self.hijoIzquierdo.padre = self\n        if self.tieneHijoDerecho(): #si el nodo tiene hijo derecho, actualiza el padre de ese hijo.\n            self.hijoDerecho.padre = self\n\n\n    def encontrarSucesor(self): #m\u00e9todo que busca el sucesor inorden del nodo (es decir, el nodo con la siguiente clave mayor).\n        suc = None\n        if self.tieneHijoDerecho(): # Si el nodo tiene hijo derecho, el sucesor es el nodo con el valor m\u00ednimo en el sub\u00e1rbol derecho.\n            suc = self.hijoDerecho.encontrarMin()\n        else: # Si no tiene hijo derecho, sube por el \u00e1rbol buscando el primer ancestro que sea hijo izquierdo de su padre.\n            if self.padre:\n                if self.esHijoIzquierdo():\n                    suc = self.padre.encontrarSucesor() # El sucesor es el padre si este nodo es hijo izquierdo.\n                else: # Si el nodo es hijo derecho, sube al padre y busca su sucesor.\n                    self.padre.hijoDerecho = None\n                    suc = self.padre.encontrarSucesor()\n                    self.padre.hijoDerecho = self\n        return suc\n\n    def empalmar(self):\n        # Elimina el nodo del \u00e1rbol, \"empalmando\" sus hijos con su padre.\n        if self.esHoja(): # Si el nodo es una hoja, simplemente elimina la referencia de su padre a \u00e9l.\n            if self.esHijoIzquierdo():\n                self.padre.hijoIzquierdo = None\n            else:\n                self.padre.hijoDerecho = None\n        elif self.tieneAlgunHijo(): # Si el nodo tiene un hijo (izquierdo o derecho), reemplaza el nodo con su \u00fanico hijo.\n            if self.tieneHijoIzquierdo():\n                if self.esHijoIzquierdo():\n                    self.padre.hijoIzquierdo = self.hijoIzquierdo # Si es hijo izquierdo, el padre apunta al hijo izquierdo.\n                else:\n                    self.padre.hijoDerecho = self.hijoIzquierdo # Si es hijo derecho, el padre apunta al hijo izquierdo.\n                self.hijoIzquierdo.padre = self.padre # Actualiza el padre del hijo izquierdo.\n            else:\n                if self.esHijoIzquierdo(): \n                    self.padre.hijoIzquierdo = self.hijoDerecho # Si es hijo izquierdo, el padre apunta al hijo derecho.\n                else:\n                    self.padre.hijoDerecho = self.hijoDerecho # Si es hijo derecho, el padre apunta al hijo derecho.\n                self.hijoDerecho.padre = self.padre # Actualiza el padre del hijo derecho.\n\n    def encontrarMin(self): #m\u00e9todo que # Encuentra el nodo con la clave m\u00ednima en el sub\u00e1rbol (el m\u00e1s a la izquierda).\n        actual = self\n        while actual.tieneHijoIzquierdo():\n            actual = actual.hijoIzquierdo # Baja al hijo izquierdo hasta llegar al nodo m\u00e1s a la izquierda.\n        return actual\n\n    def __iter__(self): # Permite la iteraci\u00f3n sobre el \u00e1rbol inorden (izquierda, ra\u00edz, derecha).\n        if self:\n            if self.tieneHijoIzquierdo(): # Si tiene hijo izquierdo, itera sobre \u00e9l.\n                for elem in self.hijoIzquierdo:\n                    yield elem\n            yield self.clave # Luego devuelve la clave del nodo actual.\n            if self.tieneHijoDerecho():\n                for elem in self.hijoDerecho: # Si tiene hijo derecho, itera sobre \u00e9l.\n                    yield elem\n\ndef test_reemplazarDatoDeNodo():\n    # Test case 1: Node with no children\n    node1 = NodoArbol(1, 'a')\n    node1.reemplazarDatoDeNodo(2, 'b', None, None)\n    node1_new = NodoArbol(1, 'a')\n    node1_new.reemplazarDatoDeNodo_new_implementation(2, 'b', None, None)\n    assert node1.clave == node1_new.clave\n    assert node1.cargaUtil == node1_new.cargaUtil\n    assert node1.hijoIzquierdo == node1_new.hijoIzquierdo\n    assert node1.hijoDerecho == node1_new.hijoDerecho\n\n    # Test case 2: Node with one left child\n    left_child = NodoArbol(3, 'c')\n    node2 = NodoArbol(1, 'a', izquierdo=left_child)\n    node2.reemplazarDatoDeNodo(2, 'b', left_child, None)\n    node2_new = NodoArbol(1, 'a', izquierdo=left_child)\n    node2_new.reemplazarDatoDeNodo_new_implementation(2, 'b', left_child, None)\n    assert node2.clave == node2_new.clave\n    assert node2.cargaUtil == node2_new.cargaUtil\n    assert node2.hijoIzquierdo == node2_new.hijoIzquierdo\n    assert node2.hijoDerecho == node2_new.hijoDerecho\n    assert node2.hijoIzquierdo.padre == node2_new.hijoIzquierdo.padre\n\n    # Test case 3: Node with both children\n    right_child = NodoArbol(4, 'd')\n    node3 = NodoArbol(1, 'a', izquierdo=left_child, derecho=right_child)\n    node3.reemplazarDatoDeNodo(2, 'b', left_child, right_child)\n    node3_new = NodoArbol(1, 'a', izquierdo=left_child, derecho=right_child)\n    node3_new.reemplazarDatoDeNodo_new_implementation(2, 'b', left_child, right_child)\n    assert node3.clave == node3_new.clave\n    assert node3.cargaUtil == node3_new.cargaUtil\n    assert node3.hijoIzquierdo == node3_new.hijoIzquierdo\n    assert node3.hijoDerecho == node3_new.hijoDerecho\n    assert node3.hijoIzquierdo.padre == node3_new.hijoIzquierdo.padre\n    assert node3.hijoDerecho.padre == node3_new.hijoDerecho.padre\n\n    # Test case 4: Node with one right child\n    node4 = NodoArbol(1, 'a', derecho=right_child)\n    node4.reemplazarDatoDeNodo(2, 'b', None, right_child)\n    node4_new = NodoArbol(1, 'a', derecho=right_child)\n    node4_new.reemplazarDatoDeNodo_new_implementation(2, 'b', None, right_child)\n    assert node4.clave == node4_new.clave\n    assert node4.cargaUtil == node4_new.cargaUtil\n    assert node4.hijoIzquierdo == node4_new.hijoIzquierdo\n    assert node4.hijoDerecho == node4_new.hijoDerecho\n    assert node4.hijoDerecho.padre == node4_new.hijoDerecho.padre\n\n    # Test case 5: Node with self-replacement\n    node5 = NodoArbol(5, 'e')\n    node5.reemplazarDatoDeNodo(5, 'e', None, None)\n    node5_new = NodoArbol(5, 'e')\n    node5_new.reemplazarDatoDeNodo_new_implementation(5, 'e', None, None)\n    assert node5.clave == node5_new.clave\n    assert node5.cargaUtil == node5_new.cargaUtil\n    assert node5.hijoIzquierdo == node5_new.hijoIzquierdo\n    assert node5.hijoDerecho == node5_new.hijoDerecho\n\n    # Test case 6: Node with complex tree structure\n    complex_left_child = NodoArbol(6, 'f')\n    complex_right_child = NodoArbol(7, 'g')\n    node6 = NodoArbol(5, 'e', izquierdo=complex_left_child, derecho=complex_right_child)\n    node6.reemplazarDatoDeNodo(8, 'h', complex_left_child, complex_right_child)\n    node6_new = NodoArbol(5, 'e', izquierdo=complex_left_child, derecho=complex_right_child)\n    node6_new.reemplazarDatoDeNodo_new_implementation(8, 'h', complex_left_child, complex_right_child)\n    assert node6.clave == node6_new.clave\n    assert node6.cargaUtil == node6_new.cargaUtil\n    assert node6.hijoIzquierdo == node6_new.hijoIzquierdo\n    assert node6.hijoDerecho == node6_new.hijoDerecho\n    assert node6.hijoIzquierdo.padre == node6_new.hijoIzquierdo.padre\n    assert node6.hijoDerecho.padre == node6_new.hijoDerecho.padre\n\n    # Test case 7: Node with None values\n    node7 = NodoArbol(None, None)\n    node7.reemplazarDatoDeNodo(None, None, None, None)\n    node7_new = NodoArbol(None, None)\n    node7_new.reemplazarDatoDeNodo_new_implementation(None, None, None, None)\n    assert node7.clave == node7_new.clave\n    assert node7.cargaUtil == node7_new.cargaUtil\n    assert node7.hijoIzquierdo == node7_new.hijoIzquierdo\n    assert node7.hijoDerecho == node7_new.hijoDerecho\n\nif __name__ == \"__main__\":\n    test_reemplazarDatoDeNodo()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `reemplazarDatoDeNodo` in the `NodoArbol` class is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions update the node's key, value, left child, and right child. They also update the parent reference of the left and right children if they exist. The logic and sequence of operations are the same in both versions. The additional code provided in the revised version, such as the class definition and other methods, does not affect the functionality of the `reemplazarDatoDeNodo` method itself. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The function `reemplazarDatoDeNodo` modifies the attributes of the `NodoArbol` instance, which is an input argument. Therefore, it satisfies this condition.\n  \n- [CONDITION 2] The test cases use assertions to check the state of the `NodoArbol` instances after calling the function, rather than checking printed or logged outputs. This condition is satisfied.\n\n- [CONDITION 3] The test cases compare the states of two `NodoArbol` instances after calling `reemplazarDatoDeNodo` and `reemplazarDatoDeNodo_new_implementation`, respectively. This ensures that both implementations must have the same functionality to pass the tests, satisfying this condition.\n\n- [CONDITION 4] The test cases do not use return values for assertions, as `reemplazarDatoDeNodo` does not return anything. Instead, they check the state of the object, which is reasonable. This condition is satisfied.\n\n- [CONDITION 5] The test cases cover various scenarios, including nodes with no children, one child, both children, self-replacement, complex tree structures, and nodes with `None` values. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "36b96be36e67b0ae88c9ed39851025f2503af2d5"
    },
    {
        "func_name": "NodoArbol.encontrarMin",
        "idx": "852",
        "repo_name": "ezefigueroa___TP_Final_prog2_2024",
        "func_path": "gps_simulation/routes/utils.py",
        "orig_func": "def encontrarMin(self):\n    actual = self\n    while actual.tieneHijoIzquierdo():\n        actual = actual.hijoIzquierdo\n    return actual",
        "orig_context": "```python\n## gps_simulation/routes/utils.py\nclass NodoArbol: \n    def __init__(self,clave,valor,izquierdo=None,derecho=None,padre=None): #se define el constructor de la clase para inicializar un \u00e1rbol con sus respectivos atributos.\n        self.clave = clave #atributo que va a almacenar la clave del nodo\n        self.cargaUtil = valor #atributo que va a almacenar el valor del nodo\n        self.hijoIzquierdo = izquierdo #hijo izquierdo del nodo\n        self.hijoDerecho = derecho #hijo derecho del nodo\n        self.padre = padre #nodo padre\n        self.factorEquilibrio = 0\n\n    def tieneHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo, devolviendo True o False.\n        return self.hijoIzquierdo \n\n    def tieneHijoDerecho(self): #m\u00e9todo que verifica si el nodo tiene un hijo derecho, devolviendo True o False.\n        return self.hijoDerecho\n\n    def esHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo es hijo izquierdo de su padre.\n        return self.padre and self.padre.hijoIzquierdo == self\n\n    def esHijoDerecho(self): #m\u00e9todo que verifica si el nodo es hijo derecho de su padre.\n        return self.padre and self.padre.hijoDerecho == self\n\n    def esRaiz(self): #m\u00e9todo que verifica si el nodo es la ra\u00edz del \u00e1rbol (no tiene padre)\n        return not self.padre\n\n    def esHoja(self): #m\u00e9todo que verifica si el nodo es una hoja (si no tiene hijos ni izquierdo ni derecho)\n        return not (self.hijoDerecho or self.hijoIzquierdo)\n\n    def tieneAlgunHijo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo o un hijo derecho\n        return self.hijoDerecho or self.hijoIzquierdo\n\n    def tieneAmbosHijos(self): #m\u00e9todo que verifica si el nodo tiene ambos hijos izquierdo y derecho\n        return self.hijoDerecho and self.hijoIzquierdo\n\n    def reemplazarDatoDeNodo(self,clave,valor,hizq,hder): #m\u00e9todo que reemplaza los datos del nodo (clave, valor) y sus hijos izquierdo y derecho.\n        self.clave = clave # Reemplaza la clave del nodo.\n        self.cargaUtil = valor # Reemplaza el valor asociado a la clave.\n        self.hijoIzquierdo = hizq # Reemplaza el hijo izquierdo del nodo.\n        self.hijoDerecho = hder # Reemplaza el hijo derecho del nodo.\n        if self.tieneHijoIzquierdo(): #si el nodo tiene hijo izquierdo, actualiza el padre de ese hijo.\n            self.hijoIzquierdo.padre = self\n        if self.tieneHijoDerecho(): #si el nodo tiene hijo derecho, actualiza el padre de ese hijo.\n            self.hijoDerecho.padre = self\n\n    def encontrarSucesor(self): #m\u00e9todo que busca el sucesor inorden del nodo (es decir, el nodo con la siguiente clave mayor).\n        suc = None\n        if self.tieneHijoDerecho(): # Si el nodo tiene hijo derecho, el sucesor es el nodo con el valor m\u00ednimo en el sub\u00e1rbol derecho.\n            suc = self.hijoDerecho.encontrarMin()\n        else: # Si no tiene hijo derecho, sube por el \u00e1rbol buscando el primer ancestro que sea hijo izquierdo de su padre.\n            if self.padre:\n                if self.esHijoIzquierdo():\n                    suc = self.padre.encontrarSucesor() # El sucesor es el padre si este nodo es hijo izquierdo.\n                else: # Si el nodo es hijo derecho, sube al padre y busca su sucesor.\n                    self.padre.hijoDerecho = None\n                    suc = self.padre.encontrarSucesor()\n                    self.padre.hijoDerecho = self\n        return suc\n\n    def empalmar(self):\n        # Elimina el nodo del \u00e1rbol, \"empalmando\" sus hijos con su padre.\n        if self.esHoja(): # Si el nodo es una hoja, simplemente elimina la referencia de su padre a \u00e9l.\n            if self.esHijoIzquierdo():\n                self.padre.hijoIzquierdo = None\n            else:\n                self.padre.hijoDerecho = None\n        elif self.tieneAlgunHijo(): # Si el nodo tiene un hijo (izquierdo o derecho), reemplaza el nodo con su \u00fanico hijo.\n            if self.tieneHijoIzquierdo():\n                if self.esHijoIzquierdo():\n                    self.padre.hijoIzquierdo = self.hijoIzquierdo # Si es hijo izquierdo, el padre apunta al hijo izquierdo.\n                else:\n                    self.padre.hijoDerecho = self.hijoIzquierdo # Si es hijo derecho, el padre apunta al hijo izquierdo.\n                self.hijoIzquierdo.padre = self.padre # Actualiza el padre del hijo izquierdo.\n            else:\n                if self.esHijoIzquierdo(): \n                    self.padre.hijoIzquierdo = self.hijoDerecho # Si es hijo izquierdo, el padre apunta al hijo derecho.\n                else:\n                    self.padre.hijoDerecho = self.hijoDerecho # Si es hijo derecho, el padre apunta al hijo derecho.\n                self.hijoDerecho.padre = self.padre # Actualiza el padre del hijo derecho.\n\n    def encontrarMin(self): #m\u00e9todo que # Encuentra el nodo con la clave m\u00ednima en el sub\u00e1rbol (el m\u00e1s a la izquierda).\n        actual = self\n        while actual.tieneHijoIzquierdo():\n            actual = actual.hijoIzquierdo # Baja al hijo izquierdo hasta llegar al nodo m\u00e1s a la izquierda.\n        return actual\n\n    def __iter__(self): # Permite la iteraci\u00f3n sobre el \u00e1rbol inorden (izquierda, ra\u00edz, derecha).\n        if self:\n            if self.tieneHijoIzquierdo(): # Si tiene hijo izquierdo, itera sobre \u00e9l.\n                for elem in self.hijoIzquierdo:\n                    yield elem\n            yield self.clave # Luego devuelve la clave del nodo actual.\n            if self.tieneHijoDerecho():\n                for elem in self.hijoDerecho: # Si tiene hijo derecho, itera sobre \u00e9l.\n                    yield elem\n\n```\n\n\n",
        "eval_script": "## gps_simulation/routes/utils.py\nclass NodoArbol: \n    def __init__(self,clave,valor,izquierdo=None,derecho=None,padre=None): #se define el constructor de la clase para inicializar un \u00e1rbol con sus respectivos atributos.\n        self.clave = clave #atributo que va a almacenar la clave del nodo\n        self.cargaUtil = valor #atributo que va a almacenar el valor del nodo\n        self.hijoIzquierdo = izquierdo #hijo izquierdo del nodo\n        self.hijoDerecho = derecho #hijo derecho del nodo\n        self.padre = padre #nodo padre\n        self.factorEquilibrio = 0\n\n    def tieneHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo, devolviendo True o False.\n        return self.hijoIzquierdo \n\n    def tieneHijoDerecho(self): #m\u00e9todo que verifica si el nodo tiene un hijo derecho, devolviendo True o False.\n        return self.hijoDerecho\n\n    def esHijoIzquierdo(self): #m\u00e9todo que verifica si el nodo es hijo izquierdo de su padre.\n        return self.padre and self.padre.hijoIzquierdo == self\n\n    def esHijoDerecho(self): #m\u00e9todo que verifica si el nodo es hijo derecho de su padre.\n        return self.padre and self.padre.hijoDerecho == self\n\n    def esRaiz(self): #m\u00e9todo que verifica si el nodo es la ra\u00edz del \u00e1rbol (no tiene padre)\n        return not self.padre\n\n    def esHoja(self): #m\u00e9todo que verifica si el nodo es una hoja (si no tiene hijos ni izquierdo ni derecho)\n        return not (self.hijoDerecho or self.hijoIzquierdo)\n\n    def tieneAlgunHijo(self): #m\u00e9todo que verifica si el nodo tiene un hijo izquierdo o un hijo derecho\n        return self.hijoDerecho or self.hijoIzquierdo\n\n    def tieneAmbosHijos(self): #m\u00e9todo que verifica si el nodo tiene ambos hijos izquierdo y derecho\n        return self.hijoDerecho and self.hijoIzquierdo\n\n    def reemplazarDatoDeNodo(self,clave,valor,hizq,hder): #m\u00e9todo que reemplaza los datos del nodo (clave, valor) y sus hijos izquierdo y derecho.\n        self.clave = clave # Reemplaza la clave del nodo.\n        self.cargaUtil = valor # Reemplaza el valor asociado a la clave.\n        self.hijoIzquierdo = hizq # Reemplaza el hijo izquierdo del nodo.\n        self.hijoDerecho = hder # Reemplaza el hijo derecho del nodo.\n        if self.tieneHijoIzquierdo(): #si el nodo tiene hijo izquierdo, actualiza el padre de ese hijo.\n            self.hijoIzquierdo.padre = self\n        if self.tieneHijoDerecho(): #si el nodo tiene hijo derecho, actualiza el padre de ese hijo.\n            self.hijoDerecho.padre = self\n\n    def encontrarSucesor(self): #m\u00e9todo que busca el sucesor inorden del nodo (es decir, el nodo con la siguiente clave mayor).\n        suc = None\n        if self.tieneHijoDerecho(): # Si el nodo tiene hijo derecho, el sucesor es el nodo con el valor m\u00ednimo en el sub\u00e1rbol derecho.\n            suc = self.hijoDerecho.encontrarMin()\n        else: # Si no tiene hijo derecho, sube por el \u00e1rbol buscando el primer ancestro que sea hijo izquierdo de su padre.\n            if self.padre:\n                if self.esHijoIzquierdo():\n                    suc = self.padre.encontrarSucesor() # El sucesor es el padre si este nodo es hijo izquierdo.\n                else: # Si el nodo es hijo derecho, sube al padre y busca su sucesor.\n                    self.padre.hijoDerecho = None\n                    suc = self.padre.encontrarSucesor()\n                    self.padre.hijoDerecho = self\n        return suc\n\n    def empalmar(self):\n        # Elimina el nodo del \u00e1rbol, \"empalmando\" sus hijos con su padre.\n        if self.esHoja(): # Si el nodo es una hoja, simplemente elimina la referencia de su padre a \u00e9l.\n            if self.esHijoIzquierdo():\n                self.padre.hijoIzquierdo = None\n            else:\n                self.padre.hijoDerecho = None\n        elif self.tieneAlgunHijo(): # Si el nodo tiene un hijo (izquierdo o derecho), reemplaza el nodo con su \u00fanico hijo.\n            if self.tieneHijoIzquierdo():\n                if self.esHijoIzquierdo():\n                    self.padre.hijoIzquierdo = self.hijoIzquierdo # Si es hijo izquierdo, el padre apunta al hijo izquierdo.\n                else:\n                    self.padre.hijoDerecho = self.hijoIzquierdo # Si es hijo derecho, el padre apunta al hijo izquierdo.\n                self.hijoIzquierdo.padre = self.padre # Actualiza el padre del hijo izquierdo.\n            else:\n                if self.esHijoIzquierdo(): \n                    self.padre.hijoIzquierdo = self.hijoDerecho # Si es hijo izquierdo, el padre apunta al hijo derecho.\n                else:\n                    self.padre.hijoDerecho = self.hijoDerecho # Si es hijo derecho, el padre apunta al hijo derecho.\n                self.hijoDerecho.padre = self.padre # Actualiza el padre del hijo derecho.\n\n    def encontrarMin(self): #m\u00e9todo que # Encuentra el nodo con la clave m\u00ednima en el sub\u00e1rbol (el m\u00e1s a la izquierda).\n        actual = self\n        while actual.tieneHijoIzquierdo():\n            actual = actual.hijoIzquierdo # Baja al hijo izquierdo hasta llegar al nodo m\u00e1s a la izquierda.\n        return actual\n\n\n    def __iter__(self): # Permite la iteraci\u00f3n sobre el \u00e1rbol inorden (izquierda, ra\u00edz, derecha).\n        if self:\n            if self.tieneHijoIzquierdo(): # Si tiene hijo izquierdo, itera sobre \u00e9l.\n                for elem in self.hijoIzquierdo:\n                    yield elem\n            yield self.clave # Luego devuelve la clave del nodo actual.\n            if self.tieneHijoDerecho():\n                for elem in self.hijoDerecho: # Si tiene hijo derecho, itera sobre \u00e9l.\n                    yield elem\n\ndef test_encontrarMin():\n    # Test case 1: Single node tree\n    root = NodoArbol(10, \"root\")\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 2: Tree with left child\n    left_child = NodoArbol(5, \"left\", padre=root)\n    root.hijoIzquierdo = left_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 3: Tree with multiple levels\n    left_left_child = NodoArbol(2, \"left left\", padre=left_child)\n    left_child.hijoIzquierdo = left_left_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 4: Tree with right child only\n    root = NodoArbol(10, \"root\")\n    right_child = NodoArbol(15, \"right\", padre=root)\n    root.hijoDerecho = right_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 5: Balanced tree\n    root = NodoArbol(10, \"root\")\n    left_child = NodoArbol(5, \"left\", padre=root)\n    right_child = NodoArbol(15, \"right\", padre=root)\n    root.hijoIzquierdo = left_child\n    root.hijoDerecho = right_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 6: Unbalanced tree to the left\n    root = NodoArbol(10, \"root\")\n    left_child = NodoArbol(5, \"left\", padre=root)\n    left_left_child = NodoArbol(2, \"left left\", padre=left_child)\n    left_left_left_child = NodoArbol(1, \"left left left\", padre=left_left_child)\n    root.hijoIzquierdo = left_child\n    left_child.hijoIzquierdo = left_left_child\n    left_left_child.hijoIzquierdo = left_left_left_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 7: Complex tree\n    root = NodoArbol(10, \"root\")\n    left_child = NodoArbol(5, \"left\", padre=root)\n    right_child = NodoArbol(15, \"right\", padre=root)\n    left_left_child = NodoArbol(2, \"left left\", padre=left_child)\n    left_right_child = NodoArbol(7, \"left right\", padre=left_child)\n    right_left_child = NodoArbol(12, \"right left\", padre=right_child)\n    right_right_child = NodoArbol(20, \"right right\", padre=right_child)\n    root.hijoIzquierdo = left_child\n    root.hijoDerecho = right_child\n    left_child.hijoIzquierdo = left_left_child\n    left_child.hijoDerecho = left_right_child\n    right_child.hijoIzquierdo = right_left_child\n    right_child.hijoDerecho = right_right_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\n    # Test case 8: Tree with duplicate keys\n    root = NodoArbol(10, \"root\")\n    left_child = NodoArbol(10, \"left\", padre=root)\n    root.hijoIzquierdo = left_child\n    assert root.encontrarMin() == root.encontrarMin_new_implementation()\n\nif __name__ == \"__main__\":\n    test_encontrarMin()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `encontrarMin` is a method that traverses the left children of a binary tree node until it reaches the leftmost node, which is the node with the minimum key in the subtree. The revised function in the provided code does exactly the same thing. It starts from the current node (`self`), checks if there is a left child using the `tieneHijoIzquierdo` method, and continues moving to the left child (`hijoIzquierdo`) until there are no more left children. The functionality of both the original and revised functions is identical, as they both aim to find the minimum key node in a binary tree by traversing the left children.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `encontrarMin` function returns a value, specifically the node with the minimum key in the subtree. This satisfies the condition as it has a return value.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `encontrarMin` and `encontrarMin_new_implementation`. There is no checking of printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `encontrarMin` and `encontrarMin_new_implementation` directly using assertions. This ensures that `encontrarMin_new_implementation` can only pass all tests if it has the same functionality as `encontrarMin`, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations. This is reasonable given that `encontrarMin` returns a node, and the comparison checks if both implementations return the same node. Thus, this condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including single node trees, trees with left and right children, balanced and unbalanced trees, complex trees, and trees with duplicate keys. This variety ensures that the test cases are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "36b96be36e67b0ae88c9ed39851025f2503af2d5"
    },
    {
        "func_name": "generate_recommendation",
        "idx": "856",
        "repo_name": "krik8235___multi-rag-agent-system",
        "func_path": "src/components/app.py",
        "orig_func": "def generate_recommendation(clause, analysis):\n    recommendations = []\n    if 'payment' in analysis:\n        recommendations.append('Ensure payment terms are clearly defined and favorable.')\n    if 'deadline' in analysis:\n        recommendations.append('Review deadlines to ensure they are realistic and include buffer time.')\n    if 'confidentiality' in analysis:\n        recommendations.append('Verify that confidentiality clauses protect your interests adequately.')\n    if 'termination' in analysis:\n        recommendations.append('Check termination conditions and ensure they are fair to both parties.')\n    return recommendations if recommendations else ['No specific recommendations. The clause appears standard.']",
        "orig_context": "```python\n## src/components/app.py\ndef generate_recommendation(clause, analysis):\n    recommendations = []\n    if \"payment\" in analysis:\n        recommendations.append(\n            \"Ensure payment terms are clearly defined and favorable.\"\n        )\n    if \"deadline\" in analysis:\n        recommendations.append(\n            \"Review deadlines to ensure they are realistic and include buffer time.\"\n        )\n    if \"confidentiality\" in analysis:\n        recommendations.append(\n            \"Verify that confidentiality clauses protect your interests adequately.\"\n        )\n    if \"termination\" in analysis:\n        recommendations.append(\n            \"Check termination conditions and ensure they are fair to both parties.\"\n        )\n\n    return (\n        recommendations\n        if recommendations\n        else [\"No specific recommendations. The clause appears standard.\"]\n    )\n\n```\n\n\n",
        "eval_script": "## src/components/app.py\ndef generate_recommendation(clause, analysis):\n    recommendations = []\n    if \"payment\" in analysis:\n        recommendations.append(\n            \"Ensure payment terms are clearly defined and favorable.\"\n        )\n    if \"deadline\" in analysis:\n        recommendations.append(\n            \"Review deadlines to ensure they are realistic and include buffer time.\"\n        )\n    if \"confidentiality\" in analysis:\n        recommendations.append(\n            \"Verify that confidentiality clauses protect your interests adequately.\"\n        )\n    if \"termination\" in analysis:\n        recommendations.append(\n            \"Check termination conditions and ensure they are fair to both parties.\"\n        )\n\n    return (\n        recommendations\n        if recommendations\n        else [\"No specific recommendations. The clause appears standard.\"]\n    )\n\n\ndef test_generate_recommendation():\n    # Test case 1: Analysis contains \"payment\"\n    clause = \"Clause 1\"\n    analysis = \"This clause involves payment terms.\"\n    assert generate_recommendation(clause, analysis) == generate_recommendation_new_implementation(clause, analysis)\n\n    # Test case 2: Analysis contains \"deadline\" and \"confidentiality\"\n    analysis = \"This clause involves deadline and confidentiality.\"\n    assert generate_recommendation(clause, analysis) == generate_recommendation_new_implementation(clause, analysis)\n\n    # Test case 3: Analysis contains none of the keywords\n    analysis = \"This clause is about general terms.\"\n    assert generate_recommendation(clause, analysis) == generate_recommendation_new_implementation(clause, analysis)\n\n    # Test case 4: Analysis contains \"termination\"\n    analysis = \"This clause involves termination conditions.\"\n    assert generate_recommendation(clause, analysis) == generate_recommendation_new_implementation(clause, analysis)\n\nif __name__ == \"__main__\":\n    test_generate_recommendation()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      8      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      8      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical in functionality to the ORIGINAL FUNCTION. Both functions check for the presence of specific keywords ('payment', 'deadline', 'confidentiality', 'termination') in the 'analysis' parameter and append corresponding recommendations to the 'recommendations' list. If no keywords are found, both functions return a default message indicating no specific recommendations. The differences in formatting and string style (single vs. double quotes) do not affect the functionality. The test cases provided in the code verify that the function behaves as expected for various inputs, further confirming that the functionality remains unchanged.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `generate_recommendation` function returns a list of recommendations based on the analysis input. This satisfies the condition as it has return values.\n- CONDITION 2: The test cases use assertions to check the return values of the function, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the return values of `generate_recommendation` and `generate_recommendation_new_implementation` for the same inputs. This ensures that the new implementation must have the exact same functionality to pass the tests. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that the function returns values. This condition is satisfied.\n- CONDITION 5: The test cases cover different scenarios: when the analysis contains \"payment\", \"deadline\" and \"confidentiality\", none of the keywords, and \"termination\". These are non-trivial as they test different branches of the function logic. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "566c013615e55377ef8d29f2c16e355f6436721d"
    },
    {
        "func_name": "get_tasks",
        "idx": "857",
        "repo_name": "krik8235___multi-rag-agent-system",
        "func_path": "src/components/clause_tasks.py",
        "orig_func": "def get_tasks(input_document):\n    tasks = []\n    ingest_documents_task = Task(description=\"Ingest benchmark NDAs that will be used as a yardstick to compare NDAs we will judge later.\\n        Check all the files with NDA in their title in the ndas folder inside the current directory and ingest all the documents using the RAG tool.\\n        Don't bother with the files inside the uploads folder.\\n        Only ingest files with docx, doc, and pdf extensions. You don't need to analyze these documents.\\n        If you pass the path of the documents to the RAG tool, it should be able to parse the documents.\", expected_output=EXPECTED_TASK_OUTPUT, agent=corporate_lawyer_agent)\n    tasks.append(create_accumulating_task(ingest_documents_task, 'ingest_documents'))\n    identify_parties = Task(description=f\"Take the current parties clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\\n    Your task is to identify the parties in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\\n    There is a party that offers services, and there's a party that consumes services. This should be well defined within the clauses.\", expected_output=EXPECTED_TASK_OUTPUT, agent=parties_corporate_lawyer, output_pydantic=AgentOutput)\n    tasks.append(create_accumulating_task(identify_parties, 'identify_parties'))\n    identify_obligations_of_receiving_party = Task(description=f'Take the current obligations of receiving party clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\\n        Your task is to identify the obligations of receiving party in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.', expected_output=EXPECTED_TASK_OUTPUT, agent=obligation_information_lawyer, output_pydantic=AgentOutput)\n    tasks.append(create_accumulating_task(identify_obligations_of_receiving_party, 'obligations'))\n    identify_terms_and_termination = Task(description=f'Take the current terms and termination clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\\n        Your task is to identify the terms and termination in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.', expected_output=EXPECTED_TASK_OUTPUT, agent=terms_and_termination_lawyer, output_pydantic=AgentOutput)\n    tasks.append(create_accumulating_task(identify_terms_and_termination, 'terms_and_termination'))\n    identify_remedies = Task(description=f'Take the current remedies clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\\n        Your task is to identify the remedies in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.', expected_output=EXPECTED_TASK_OUTPUT, agent=remedies_lawyer, output_pydantic=AgentOutput)\n    tasks.append(create_accumulating_task(identify_remedies, 'remedies'))\n    identify_additional_information = Task(description=f'Take the current additional important information clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\\n        Your task is to identify the additional important information in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.', expected_output=EXPECTED_TASK_OUTPUT, agent=additional_information_lawyer, output_pydantic=AgentOutput)\n    tasks.append(create_accumulating_task(identify_additional_information, 'additional_info'))\n    return tasks",
        "orig_context": "```python\n## src/components/models.py\nfrom pydantic import BaseModel, Field\n\nclass AgentOutput(BaseModel):\n    \"\"\"Output of each clause agent\"\"\"\n\n    analysis: str = Field(\n        description=\"An analysis of the section in laymen terms\", max_length=256\n    )\n    recommendation: str = Field(\n        description=\"How the current clause deviates from the benchmark documents\",\n        max_length=256,\n    )\n\n```\n\n\n```python\n## src/components/tools.py\nimport os\n\nfrom composio_crewai import Action, App, ComposioToolSet\n\ncomposio_toolset = ComposioToolSet(api_key=os.environ.get(\"COMPOSIO_API_KEY\"))\n\nrag_tools = composio_toolset.get_tools(\n    apps=[App.RAGTOOL],\n    actions=[\n        Action.FILETOOL_LIST_FILES,\n        Action.FILETOOL_CHANGE_WORKING_DIRECTORY,\n        Action.FILETOOL_FIND_FILE,\n    ],\n)\n\nrag_query_tools = composio_toolset.get_tools(\n    apps=[App.RAGTOOL],\n)\n\n```\n\n\n```python\n## src/components/clause_agents.py\nimport os\n\nfrom crewai import Agent\n\nfrom langchain_openai import ChatOpenAI\n\nfrom components.tools import rag_query_tools, rag_tools\n\nAI_API_KEY_LITELLM = os.environ.get(\"AI_API_KEY_LITELLM\")\n\nAI_API_BASE_LITELLM = os.environ.get(\"AI_API_BASE_LITELLM\")\n\nAI_MODEL_NAME_LITELLM = os.environ.get(\"AI_MODEL_NAME_LITELLM\")\n\nllm = ChatOpenAI(\n    openai_api_key=AI_API_KEY_LITELLM,\n    openai_api_base=AI_API_BASE_LITELLM,\n    model_name=AI_MODEL_NAME_LITELLM,\n    temperature=0.1,\n)\n\ncorporate_lawyer_agent = Agent(\n    role=\"Corporate Lawyer\",\n    goal=\"Use the documents you're given and the tools you have to build a knowledge base of NDAs that you can refer later. First, check if the documents have already been added.\",\n    backstory=\"\"\"You are a corporate lawyer who has vast knowledge of NDAs, different sections within them, and how they are supposed to work.\n    You also have the ability to call the RAG tool to ingest new documents that using the paths of files given to you and building a knowledge base of NDAs.\"\"\",\n    # tools=rag_tools,\n    verbose=True,\n    llm=llm,\n)\n\ncorporate_lawyer_agent.tools = rag_tools\n\nparties_corporate_lawyer = Agent(\n    role=\"Parties Corporate Lawyer\",\n    goal=\"To compare the current NDA parties clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are a corporate lawyer who specialises in identifying who the parties in a certain NDA are.\n    There's no one who does it as well as you do. Things that others miss, you don't.\"\"\",\n    # tools=rag_query_tools,\n    verbose=True,\n    llm=llm,\n)\n\nparties_corporate_lawyer.tools = rag_query_tools\n\nobligation_information_lawyer = Agent(\n    role=\"Obligations of Receiving Party Lawyer\",\n    goal=\"To compare the current NDA obligations of receiving party clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are an obligations of receiving party lawyer who is an expert in identifying what the obligations of receiving party is in a certain NDA.\n    You have never failed to identify obligations of receiving party in an NDA. You are a lawyer with many years of experience and know how to identify obligations of receiving party.\n    \"\"\",\n    # tools=rag_query_tools,\n    verbose=True,\n    llm=llm,\n)\n\nobligation_information_lawyer.tools = rag_query_tools\n\nterms_and_termination_lawyer = Agent(\n    role=\"Terms and Termination Lawyer\",\n    goal=\"To compare the current NDA terms and termination clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are a terms and termination lawyer who is an expert in identifying what the terms and termination is in a certain NDA.\n    Terms and terminatioin are in your DNA. When given an NDA, you're eyes first go to terms and termination clause and you can identify fallacies well.\n    \"\"\",\n    # tools=rag_query_tools,\n    verbose=True,\n    llm=llm,\n)\n\nterms_and_termination_lawyer.tools = rag_query_tools\n\nremedies_lawyer = Agent(\n    role=\"Remedies Lawyer\",\n    goal=\"To compare the current NDA remedies clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are a remedies lawyer who is an expert in identifying what the remedies is in a certain NDA.\n    You craft perfect remedies in an NDA in the case of breach or conflict. You are the go to person for remedies in an NDA.\n    \"\"\",\n    # tools=rag_query_tools,\n    verbose=True,\n    llm=llm,\n)\n\nremedies_lawyer.tools = rag_query_tools\n\nadditional_information_lawyer = Agent(\n    role=\"Additional Important Information Lawyer\",\n    goal=\"To compare the current NDA additional important information clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are an additional important information lawyer who is an expert in identifying what the additional important information is in a certain NDA.\n    You identify up all the missing information in an NDA. You carefully craft perfect additional important information in an NDA.\n    \"\"\",\n    # tools=rag_query_tools,\n    verbose=True,\n    llm=llm,\n)\n\nadditional_information_lawyer.tools = rag_query_tools\n\n```\n\n\n```python\n## src/components/clause_tasks.py\nfrom crewai import Task\n\nfrom components.clause_agents import (\n    additional_information_lawyer,\n    corporate_lawyer_agent,\n    obligation_information_lawyer,\n    parties_corporate_lawyer,\n    remedies_lawyer,\n    terms_and_termination_lawyer,\n)\n\nfrom components.models import AgentOutput\n\nEXPECTED_TASK_OUTPUT = \"\"\"\nA JSON that has two keys: an `analysis` of the current clause in laymen terms as a paragraph as well as a `recommendation` of how the current clause deviates from the benchmark clauses (in short, numbered points).\"\"\"\n\ndef create_accumulating_task(original_task, key):\n    def accumulating_task(agent, context):\n        result = original_task.function(agent, context)\n        if \"accumulated_results\" not in context:\n            context[\"accumulated_results\"] = {}\n        context[\"accumulated_results\"][key] = result\n        return context[\"accumulated_results\"]\n\n    return Task(\n        description=original_task.description,\n        agent=original_task.agent,\n        function=accumulating_task,\n        expected_output=original_task.expected_output,\n        output_pydantic=original_task.output_pydantic,\n        context=original_task.context,\n    )\n\ndef get_tasks(input_document):\n    tasks = []\n    ingest_documents_task = Task(\n        description=\"\"\"Ingest benchmark NDAs that will be used as a yardstick to compare NDAs we will judge later.\n        Check all the files with NDA in their title in the ndas folder inside the current directory and ingest all the documents using the RAG tool.\n        Don't bother with the files inside the uploads folder.\n        Only ingest files with docx, doc, and pdf extensions. You don't need to analyze these documents.\n        If you pass the path of the documents to the RAG tool, it should be able to parse the documents.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=corporate_lawyer_agent,\n    )\n    tasks.append(create_accumulating_task(ingest_documents_task, \"ingest_documents\"))\n\n    identify_parties = Task(\n        description=f\"\"\"Take the current parties clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n    Your task is to identify the parties in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\n    There is a party that offers services, and there's a party that consumes services. This should be well defined within the clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=parties_corporate_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(create_accumulating_task(identify_parties, \"identify_parties\"))\n\n    identify_obligations_of_receiving_party = Task(\n        description=f\"\"\"Take the current obligations of receiving party clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the obligations of receiving party in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=obligation_information_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(\n        create_accumulating_task(identify_obligations_of_receiving_party, \"obligations\")\n    )\n\n    identify_terms_and_termination = Task(\n        description=f\"\"\"Take the current terms and termination clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the terms and termination in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=terms_and_termination_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(\n        create_accumulating_task(\n            identify_terms_and_termination, \"terms_and_termination\"\n        )\n    )\n\n    identify_remedies = Task(\n        description=f\"\"\"Take the current remedies clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the remedies in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=remedies_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(create_accumulating_task(identify_remedies, \"remedies\"))\n\n    identify_additional_information = Task(\n        description=f\"\"\"Take the current additional important information clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the additional important information in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=additional_information_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(\n        create_accumulating_task(identify_additional_information, \"additional_info\")\n    )\n\n    return tasks\n\n```\n\n\n",
        "eval_script": "# Mock implementations for external dependencies\nclass Task:\n    def __init__(self, description, agent, function=None, expected_output=None, output_pydantic=None, context=None):\n        self.description = description\n        self.agent = agent\n        self.function = function\n        self.expected_output = expected_output\n        self.output_pydantic = output_pydantic\n        self.context = context\n\nclass Agent:\n    def __init__(self, role, goal, backstory, verbose, llm):\n        self.role = role\n        self.goal = goal\n        self.backstory = backstory\n        self.verbose = verbose\n        self.llm = llm\n        self.tools = None\n\nclass ChatOpenAI:\n    def __init__(self, openai_api_key, openai_api_base, model_name, temperature):\n        self.openai_api_key = openai_api_key\n        self.openai_api_base = openai_api_base\n        self.model_name = model_name\n        self.temperature = temperature\n\n# Mock environment variables\nimport os\nos.environ[\"COMPOSIO_API_KEY\"] = \"mock_composio_api_key\"\nos.environ[\"AI_API_KEY_LITELLM\"] = \"mock_ai_api_key\"\nos.environ[\"AI_API_BASE_LITELLM\"] = \"mock_ai_api_base\"\nos.environ[\"AI_MODEL_NAME_LITELLM\"] = \"mock_ai_model_name\"\n\n# Mock tools\nclass ComposioToolSet:\n    def __init__(self, api_key):\n        pass\n\n    def get_tools(self, apps, actions=None):\n        return \"mock_tools\"\n\nclass App:\n    RAGTOOL = \"RAGTOOL\"\n\nclass Action:\n    FILETOOL_LIST_FILES = \"FILETOOL_LIST_FILES\"\n    FILETOOL_CHANGE_WORKING_DIRECTORY = \"FILETOOL_CHANGE_WORKING_DIRECTORY\"\n    FILETOOL_FIND_FILE = \"FILETOOL_FIND_FILE\"\n\ncomposio_toolset = ComposioToolSet(api_key=os.environ.get(\"COMPOSIO_API_KEY\"))\n\nrag_tools = composio_toolset.get_tools(\n    apps=[App.RAGTOOL],\n    actions=[\n        Action.FILETOOL_LIST_FILES,\n        Action.FILETOOL_CHANGE_WORKING_DIRECTORY,\n        Action.FILETOOL_FIND_FILE,\n    ],\n)\n\nrag_query_tools = composio_toolset.get_tools(\n    apps=[App.RAGTOOL],\n)\n\n# AgentOutput model\nfrom pydantic import BaseModel, Field\n\nclass AgentOutput(BaseModel):\n    \"\"\"Output of each clause agent\"\"\"\n    analysis: str = Field(\n        description=\"An analysis of the section in laymen terms\", max_length=256\n    )\n    recommendation: str = Field(\n        description=\"How the current clause deviates from the benchmark documents\",\n        max_length=256,\n    )\n\n# Agents\nllm = ChatOpenAI(\n    openai_api_key=os.environ.get(\"AI_API_KEY_LITELLM\"),\n    openai_api_base=os.environ.get(\"AI_API_BASE_LITELLM\"),\n    model_name=os.environ.get(\"AI_MODEL_NAME_LITELLM\"),\n    temperature=0.1,\n)\n\ncorporate_lawyer_agent = Agent(\n    role=\"Corporate Lawyer\",\n    goal=\"Use the documents you're given and the tools you have to build a knowledge base of NDAs that you can refer later. First, check if the documents have already been added.\",\n    backstory=\"\"\"You are a corporate lawyer who has vast knowledge of NDAs, different sections within them, and how they are supposed to work.\n    You also have the ability to call the RAG tool to ingest new documents that using the paths of files given to you and building a knowledge base of NDAs.\"\"\",\n    verbose=True,\n    llm=llm,\n)\ncorporate_lawyer_agent.tools = rag_tools\n\nparties_corporate_lawyer = Agent(\n    role=\"Parties Corporate Lawyer\",\n    goal=\"To compare the current NDA parties clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are a corporate lawyer who specialises in identifying who the parties in a certain NDA are.\n    There's no one who does it as well as you do. Things that others miss, you don't.\"\"\",\n    verbose=True,\n    llm=llm,\n)\nparties_corporate_lawyer.tools = rag_query_tools\n\nobligation_information_lawyer = Agent(\n    role=\"Obligations of Receiving Party Lawyer\",\n    goal=\"To compare the current NDA obligations of receiving party clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are an obligations of receiving party lawyer who is an expert in identifying what the obligations of receiving party is in a certain NDA.\n    You have never failed to identify obligations of receiving party in an NDA. You are a lawyer with many years of experience and know how to identify obligations of receiving party.\n    \"\"\",\n    verbose=True,\n    llm=llm,\n)\nobligation_information_lawyer.tools = rag_query_tools\n\nterms_and_termination_lawyer = Agent(\n    role=\"Terms and Termination Lawyer\",\n    goal=\"To compare the current NDA terms and termination clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are a terms and termination lawyer who is an expert in identifying what the terms and termination is in a certain NDA.\n    Terms and terminatioin are in your DNA. When given an NDA, you're eyes first go to terms and termination clause and you can identify fallacies well.\n    \"\"\",\n    verbose=True,\n    llm=llm,\n)\nterms_and_termination_lawyer.tools = rag_query_tools\n\nremedies_lawyer = Agent(\n    role=\"Remedies Lawyer\",\n    goal=\"To compare the current NDA remedies clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are a remedies lawyer who is an expert in identifying what the remedies is in a certain NDA.\n    You craft perfect remedies in an NDA in the case of breach or conflict. You are the go to person for remedies in an NDA.\n    \"\"\",\n    verbose=True,\n    llm=llm,\n)\nremedies_lawyer.tools = rag_query_tools\n\nadditional_information_lawyer = Agent(\n    role=\"Additional Important Information Lawyer\",\n    goal=\"To compare the current NDA additional important information clause to the ones in our RAG database and identify how good it is.\",\n    backstory=\"\"\"You are an additional important information lawyer who is an expert in identifying what the additional important information is in a certain NDA.\n    You identify up all the missing information in an NDA. You carefully craft perfect additional important information in an NDA.\n    \"\"\",\n    verbose=True,\n    llm=llm,\n)\nadditional_information_lawyer.tools = rag_query_tools\n\n# Main function\nEXPECTED_TASK_OUTPUT = \"\"\"\nA JSON that has two keys: an `analysis` of the current clause in laymen terms as a paragraph as well as a `recommendation` of how the current clause deviates from the benchmark clauses (in short, numbered points).\"\"\"\n\ndef create_accumulating_task(original_task, key):\n    def accumulating_task(agent, context):\n        result = original_task.function(agent, context)\n        if \"accumulated_results\" not in context:\n            context[\"accumulated_results\"] = {}\n        context[\"accumulated_results\"][key] = result\n        return context[\"accumulated_results\"]\n\n    return Task(\n        description=original_task.description,\n        agent=original_task.agent,\n        function=accumulating_task,\n        expected_output=original_task.expected_output,\n        output_pydantic=original_task.output_pydantic,\n        context=original_task.context,\n    )\n\ndef get_tasks(input_document):\n    tasks = []\n    ingest_documents_task = Task(\n        description=\"\"\"Ingest benchmark NDAs that will be used as a yardstick to compare NDAs we will judge later.\n        Check all the files with NDA in their title in the ndas folder inside the current directory and ingest all the documents using the RAG tool.\n        Don't bother with the files inside the uploads folder.\n        Only ingest files with docx, doc, and pdf extensions. You don't need to analyze these documents.\n        If you pass the path of the documents to the RAG tool, it should be able to parse the documents.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=corporate_lawyer_agent,\n    )\n    tasks.append(create_accumulating_task(ingest_documents_task, \"ingest_documents\"))\n\n    identify_parties = Task(\n        description=f\"\"\"Take the current parties clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n    Your task is to identify the parties in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\n    There is a party that offers services, and there's a party that consumes services. This should be well defined within the clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=parties_corporate_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(create_accumulating_task(identify_parties, \"identify_parties\"))\n\n    identify_obligations_of_receiving_party = Task(\n        description=f\"\"\"Take the current obligations of receiving party clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the obligations of receiving party in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=obligation_information_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(\n        create_accumulating_task(identify_obligations_of_receiving_party, \"obligations\")\n    )\n\n    identify_terms_and_termination = Task(\n        description=f\"\"\"Take the current terms and termination clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the terms and termination in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=terms_and_termination_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(\n        create_accumulating_task(\n            identify_terms_and_termination, \"terms_and_termination\"\n        )\n    )\n\n    identify_remedies = Task(\n        description=f\"\"\"Take the current remedies clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the remedies in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=remedies_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(create_accumulating_task(identify_remedies, \"remedies\"))\n\n    identify_additional_information = Task(\n        description=f\"\"\"Take the current additional important information clause, which is inside this: `{input_document}`, and compare it with similar clauses in our RAG database to check how good it is.\n        Your task is to identify the additional important information in our NDA, and see if the current NDA clause abides by all the best practices of similar clauses.\"\"\",\n        expected_output=EXPECTED_TASK_OUTPUT,\n        agent=additional_information_lawyer,\n        output_pydantic=AgentOutput,\n    )\n    tasks.append(\n        create_accumulating_task(identify_additional_information, \"additional_info\")\n    )\n\n    return tasks\n\n\ndef test_get_tasks():\n    input_document = \"Sample NDA Document\"\n    tasks_old = get_tasks(input_document)\n    tasks_new = get_tasks_new_implementation(input_document)\n\n    assert len(tasks_old) == len(tasks_new), \"Number of tasks do not match\"\n\n    for task_old, task_new in zip(tasks_old, tasks_new):\n        assert task_old.description == task_new.description, \"Task descriptions do not match\"\n        assert task_old.agent.role == task_new.agent.role, \"Agent roles do not match\"\n        assert task_old.expected_output == task_new.expected_output, \"Expected outputs do not match\"\n        assert task_old.output_pydantic == task_new.output_pydantic, \"Output pydantic models do not match\"\n        assert task_old.context == task_new.context, \"Task contexts do not match\"\n        assert task_old.function.__code__.co_code == task_new.function.__code__.co_code, \"Task functions do not match\"\n\nif __name__ == \"__main__\":\n    test_get_tasks()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The revised function `get_tasks` is identical to the original function in terms of functionality. Both functions create a list of tasks, each with a specific description, expected output, agent, and output model. The tasks are appended to the list using the `create_accumulating_task` function, which is also implemented in the same way in both versions. The mock implementations and environment setup in the revised code do not affect the core functionality of the `get_tasks` function. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `get_tasks` function returns a list of `Task` objects, which satisfies the condition of having return values.\n\n2. **CONDITION 2**: The test function `test_get_tasks` checks the return values by comparing the properties of the `Task` objects returned by `get_tasks` and `get_tasks_new_implementation`. It does not rely on printed or logged content.\n\n3. **CONDITION 3**: The test cases compare the length of the task lists, descriptions, agent roles, expected outputs, output pydantic models, contexts, and the bytecode of the task functions. This ensures that `get_tasks_new_implementation` must have the same functionality as `get_tasks` to pass the tests.\n\n4. **CONDITION 4**: The test cases use appropriate assertions to compare the properties of the `Task` objects, which is reasonable given that `get_tasks` returns a list of tasks.\n\n5. **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the `Task` objects, including their descriptions, agents, expected outputs, and the function logic itself.",
            "answer": "yes"
        },
        "commit_id": "566c013615e55377ef8d29f2c16e355f6436721d"
    },
    {
        "func_name": "make_hist_simple",
        "idx": "858",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab3.py",
        "orig_func": "def make_hist_simple(img):\n    c_full = np.zeros(256)\n    u, c = np.unique(img, return_counts=True)\n    c_full[u] = c\n    c_prob = c_full / np.sum(c_full)\n    return (np.linspace(0, 255, 256), c_prob)",
        "orig_context": "```python\n## lab3.py\nimport numpy as np\n\ndef make_hist_simple(img):\n    c_full = np.zeros(256)\n    u, c = np.unique(img, return_counts=True)\n    c_full[u] = c\n\n    c_prob = c_full / np.sum(c_full)\n    return np.linspace(0, 255, 256), c_prob\n\n```\n\n\n",
        "eval_script": "## lab3.py\nimport numpy as np\n\ndef make_hist_simple(img):\n    c_full = np.zeros(256)\n    u, c = np.unique(img, return_counts=True)\n    c_full[u] = c\n\n    c_prob = c_full / np.sum(c_full)\n    return np.linspace(0, 255, 256), c_prob\n\n\ndef test_make_hist_simple():\n    # Test case 1: Uniform image\n    img1 = np.zeros((10, 10), dtype=int)\n    assert np.array_equal(make_hist_simple(img1), make_hist_simple_new_implementation(img1))\n\n    # Test case 2: Image with a range of pixel values\n    img2 = np.arange(100).reshape((10, 10))\n    assert np.array_equal(make_hist_simple(img2), make_hist_simple_new_implementation(img2))\n\n    # Test case 3: Random image\n    np.random.seed(0)\n    img3 = np.random.randint(0, 256, (10, 10))\n    assert np.array_equal(make_hist_simple(img3), make_hist_simple_new_implementation(img3))\n\nif __name__ == \"__main__\":\n    test_make_hist_simple()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are named `make_hist_simple` and perform the same operations: they initialize a zero array `c_full` of size 256, use `np.unique` to find unique pixel values and their counts in the input image `img`, update `c_full` with these counts, normalize `c_full` to get probabilities `c_prob`, and return a tuple containing a linearly spaced array from 0 to 255 and `c_prob`. The structure, logic, and operations are exactly the same in both functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `make_hist_simple` returns a tuple of two numpy arrays, so it satisfies this condition.\n- CONDITION 2: The test cases use `np.array_equal` to check the equality of the return values from `make_hist_simple` and `make_hist_simple_new_implementation`, which means they are checking return values, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `make_hist_simple` and `make_hist_simple_new_implementation` for the same inputs. If `make_hist_simple_new_implementation` has the same functionality, it will pass these tests. This condition is satisfied.\n- CONDITION 4: The test cases use `np.array_equal` to compare the outputs, which is appropriate for numpy arrays. The tests are checking the return values, not using inappropriate assertions. This condition is satisfied.\n- CONDITION 5: The test cases cover different scenarios: a uniform image, an image with a range of pixel values, and a random image. These are non-trivial and provide a reasonable coverage of possible inputs. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "make_hist",
        "idx": "859",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab3.py",
        "orig_func": "def make_hist(img):\n    ur, cr = np.unique(img[:, :, 0], return_counts=True)\n    cr_prob = cr / np.sum(cr)\n    ug, cg = np.unique(img[:, :, 1], return_counts=True)\n    cg_prob = cg / np.sum(cg)\n    ub, cb = np.unique(img[:, :, 2], return_counts=True)\n    cb_prob = cb / np.sum(cb)\n    return (ur, cr_prob, ug, cg_prob, ub, cb_prob)",
        "orig_context": "```python\n## lab3.py\nimport numpy as np\n\ndef make_hist(img):\n    ur, cr = np.unique(img[:, :, 0], return_counts=True)\n    cr_prob = cr / np.sum(cr)\n\n    ug, cg = np.unique(img[:, :, 1], return_counts=True)\n    cg_prob = cg / np.sum(cg)\n\n    ub, cb = np.unique(img[:, :, 2], return_counts=True)\n    cb_prob = cb / np.sum(cb)\n\n    return ur, cr_prob, ug, cg_prob, ub, cb_prob\n\n```\n\n\n",
        "eval_script": "## lab3.py\nimport numpy as np\n\ndef make_hist(img):\n    ur, cr = np.unique(img[:, :, 0], return_counts=True)\n    cr_prob = cr / np.sum(cr)\n\n    ug, cg = np.unique(img[:, :, 1], return_counts=True)\n    cg_prob = cg / np.sum(cg)\n\n    ub, cb = np.unique(img[:, :, 2], return_counts=True)\n    cb_prob = cb / np.sum(cb)\n\n    return ur, cr_prob, ug, cg_prob, ub, cb_prob\n\n\ndef test_make_hist():\n    # Test case 1: Simple 2x2 image with distinct colors\n    img1 = np.array([[[255, 0, 0], [0, 255, 0]], [[0, 0, 255], [255, 255, 0]]])\n    assert all(np.array_equal(a, b) for a, b in zip(make_hist(img1), make_hist_new_implementation(img1)))\n\n    # Test case 2: 3x3 image with repeated colors\n    img2 = np.array([[[255, 0, 0], [255, 0, 0], [0, 255, 0]],\n                     [[0, 255, 0], [0, 0, 255], [0, 0, 255]],\n                     [[255, 255, 0], [255, 255, 0], [255, 255, 0]]])\n    assert all(np.array_equal(a, b) for a, b in zip(make_hist(img2), make_hist_new_implementation(img2)))\n\n    # Test case 3: 2x2 image with all pixels the same\n    img3 = np.array([[[128, 128, 128], [128, 128, 128]], [[128, 128, 128], [128, 128, 128]]])\n    assert all(np.array_equal(a, b) for a, b in zip(make_hist(img3), make_hist_new_implementation(img3)))\n\nif __name__ == \"__main__\":\n    test_make_hist()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions, `make_hist`, perform the same operations: they calculate the unique values and their counts for each color channel (red, green, blue) in a given image, then compute the probability of each unique value by dividing the counts by the total sum of counts for that channel. The return values are the same in both functions, consisting of tuples of unique values and their probabilities for each channel. The additional code in the REVISED FUNCTION, such as the `test_make_hist` function, does not alter the functionality of `make_hist` itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- [CONDITION 1] The `make_hist` function returns six values: unique red values and their probabilities, unique green values and their probabilities, and unique blue values and their probabilities. Therefore, it satisfies CONDITION 1 as it has return values.\n  \n- [CONDITION 2] The test cases use assertions to compare the return values of `make_hist` and `make_hist_new_implementation`. They do not check printed or logged contents, satisfying CONDITION 2.\n  \n- [CONDITION 3] The test cases compare the outputs of `make_hist` and `make_hist_new_implementation` using `np.array_equal` for each corresponding pair of outputs. This ensures that `make_hist_new_implementation` must have the exact same functionality as `make_hist` to pass all tests, satisfying CONDITION 3.\n  \n- [CONDITION 4] The test cases use `assert` statements to compare the return values of the two functions, which is appropriate given that `make_hist` returns values. Therefore, CONDITION 4 is satisfied.\n  \n- [CONDITION 5] The test cases cover various scenarios: a simple image with distinct colors, an image with repeated colors, and an image where all pixels are the same. These are non-trivial and cover different potential edge cases, satisfying CONDITION 5.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "task_3",
        "idx": "861",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab1.py",
        "orig_func": "def task_3(fun, targets: []):\n    noise = np.random.normal(size=fun.shape)\n    fun_noisy = fun + noise\n    results = []\n    for number in targets:\n        noisy_signals = []\n        for i in range(number):\n            noise = np.random.normal(size=fun.shape)\n            noisy_signals.append(noise + fun)\n        noisy_signals = np.array(noisy_signals)\n        avg_signal = np.mean(noisy_signals, axis=0)\n        results.append((avg_signal, number))\n    return (fun_noisy, results)",
        "orig_context": "```python\n## lab1.py\nimport numpy as np\n\ndef task_3(fun, targets: []):\n    noise = np.random.normal(size=(fun.shape))\n    fun_noisy = fun + noise\n\n    results = []\n    for number in targets:\n        noisy_signals = []\n        for i in range(number):\n            noise = np.random.normal(size=(fun.shape))\n            noisy_signals.append(noise + fun)\n\n        noisy_signals = np.array(noisy_signals)\n        avg_signal = np.mean(noisy_signals, axis=0)\n        results.append((avg_signal, number))\n\n    return fun_noisy, results\n\n```\n\n\n",
        "eval_script": "## lab1.py\nimport numpy as np\n\ndef task_3(fun, targets: []):\n    noise = np.random.normal(size=(fun.shape))\n    fun_noisy = fun + noise\n\n    results = []\n    for number in targets:\n        noisy_signals = []\n        for i in range(number):\n            noise = np.random.normal(size=(fun.shape))\n            noisy_signals.append(noise + fun)\n\n        noisy_signals = np.array(noisy_signals)\n        avg_signal = np.mean(noisy_signals, axis=0)\n        results.append((avg_signal, number))\n\n    return fun_noisy, results\n\n\ndef test_task_3():\n    fun = np.array([1.0, 2.0, 3.0])\n    targets = [1, 2, 3]\n\n    # Test 1: Basic functionality\n    np.random.seed(0)  # Set seed for reproducibility\n    result_original = task_3(fun, targets)\n    np.random.seed(0)  # Reset seed for reproducibility\n    result_new = task_3_new_implementation(fun, targets)\n    assert np.allclose(result_original[0], result_new[0]), \"Test 1 failed: fun_noisy mismatch\"\n    assert len(result_original[1]) == len(result_new[1]), \"Test 1 failed: results length mismatch\"\n    for (avg_orig, num_orig), (avg_new, num_new) in zip(result_original[1], result_new[1]):\n        assert np.allclose(avg_orig, avg_new), \"Test 1 failed: avg_signal mismatch\"\n        assert num_orig == num_new, \"Test 1 failed: number mismatch\"\n\n    # Test 2: Edge case with empty targets\n    np.random.seed(0)  # Reset seed for reproducibility\n    targets = []\n    result_original = task_3(fun, targets)\n    np.random.seed(0)  # Reset seed for reproducibility\n    result_new = task_3_new_implementation(fun, targets)\n    assert np.allclose(result_original[0], result_new[0]), \"Test 2 failed: fun_noisy mismatch\"\n    assert result_original[1] == result_new[1], \"Test 2 failed: results mismatch\"\n\n    # Test 3: Single target\n    np.random.seed(0)  # Reset seed for reproducibility\n    targets = [5]\n    result_original = task_3(fun, targets)\n    np.random.seed(0)  # Reset seed for reproducibility\n    result_new = task_3_new_implementation(fun, targets)\n    assert np.allclose(result_original[0], result_new[0]), \"Test 3 failed: fun_noisy mismatch\"\n    assert len(result_original[1]) == len(result_new[1]), \"Test 3 failed: results length mismatch\"\n    for (avg_orig, num_orig), (avg_new, num_new) in zip(result_original[1], result_new[1]):\n        assert np.allclose(avg_orig, avg_new), \"Test 3 failed: avg_signal mismatch\"\n        assert num_orig == num_new, \"Test 3 failed: number mismatch\"\n\nif __name__ == \"__main__\":\n    test_task_3()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they generate a noisy version of the input array `fun` and then, for each target number in `targets`, they generate that many noisy signals, compute their average, and store the result along with the target number. The only difference in the provided code is the inclusion of a test function `test_task_3`, which is not part of the function itself and does not affect the functionality of `task_3`. The test function is used to verify the correctness of the function but does not alter its behavior.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `task_3` function returns a tuple containing `fun_noisy` and `results`, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `task_3` and `task_3_new_implementation`, not printed or logged outputs.\n- CONDITION 3: The test cases use a fixed random seed to ensure reproducibility, allowing `task_3_new_implementation` to pass only if it has the same functionality as `task_3`.\n- CONDITION 4: The assertions compare the return values of both implementations, which is reasonable given that `task_3` returns values.\n- CONDITION 5: The test cases cover basic functionality, an edge case with empty targets, and a single target, providing a non-trivial set of tests.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "normalize",
        "idx": "862",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab10.py",
        "orig_func": "def normalize(img):\n    img -= np.min(img)\n    img /= np.max(img)\n    return img",
        "orig_context": "```python\n## lab10.py\nimport numpy as np\n\ndef normalize(img):\n    img -= np.min(img)\n    img /= np.max(img)\n    return img\n\n```\n\n\n",
        "eval_script": "## lab10.py\nimport numpy as np\n\ndef normalize(img):\n    img -= np.min(img)\n    img /= np.max(img)\n    return img\n\n\ndef test_normalize():\n    # Test case 1: Simple case with positive integers\n    img1 = np.array([1, 2, 3, 4, 5], dtype=float)\n    assert np.allclose(normalize(img1.copy()), normalize_new_implementation(img1.copy())), \"Test case 1 failed\"\n\n    # Test case 2: Case with zero and positive integers\n    img2 = np.array([0, 10, 20, 30, 40], dtype=float)\n    assert np.allclose(normalize(img2.copy()), normalize_new_implementation(img2.copy())), \"Test case 2 failed\"\n\n    # Test case 3: Case with negative and positive integers\n    img3 = np.array([-5, 0, 5, 10, 15], dtype=float)\n    assert np.allclose(normalize(img3.copy()), normalize_new_implementation(img3.copy())), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_normalize()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they subtract the minimum value of the image array from each element and then divide each element by the maximum value of the array. The test cases provided in the code are designed to verify the functionality of the `normalize` function, but they do not affect the equivalence of the two function implementations. Since the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION, the answer is \"same\".",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `normalize` function returns a modified version of the input array `img`, satisfying the condition that it should have return values or modify input arguments.\n- CONDITION 2: The test cases in `test_normalize` use `assert` statements to check the return values of `normalize` and `normalize_new_implementation` using `np.allclose`, which compares the numerical values and not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `normalize` and `normalize_new_implementation` using `np.allclose`, which ensures that `normalize_new_implementation` must have the same functionality as `normalize` to pass all tests.\n- CONDITION 4: The test cases use `assert np.allclose(...)` to compare the outputs of both implementations, which is reasonable since `normalize` returns a value.\n- CONDITION 5: The test cases cover a variety of scenarios, including positive integers, zero and positive integers, and negative and positive integers, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "make_hist_simple",
        "idx": "863",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab10.py",
        "orig_func": "def make_hist_simple(img):\n    c_full = np.zeros(256)\n    u, c = np.unique(img, return_counts=True)\n    c_full[u] = c\n    return (np.linspace(0, 255, 256), c_full)",
        "orig_context": "```python\n## lab10.py\nimport numpy as np\n\ndef make_hist_simple(img):\n    c_full = np.zeros(256)\n    u, c = np.unique(img, return_counts=True)\n    c_full[u] = c\n\n    return np.linspace(0, 255, 256), c_full\n\n```\n\n\n",
        "eval_script": "## lab10.py\nimport numpy as np\n\ndef make_hist_simple(img):\n    c_full = np.zeros(256)\n    u, c = np.unique(img, return_counts=True)\n    c_full[u] = c\n\n    return np.linspace(0, 255, 256), c_full\n\n\ndef test_make_hist_simple():\n    # Test case 1: Image with uniform pixel values\n    img1 = np.full((10, 10), 128, dtype=np.uint8)\n    bins1, counts1 = make_hist_simple(img1)\n    bins1_new, counts1_new = make_hist_simple_new_implementation(img1)\n    assert np.array_equal(bins1, bins1_new), \"Bins do not match for uniform image\"\n    assert np.array_equal(counts1, counts1_new), \"Counts do not match for uniform image\"\n\n    # Test case 2: Image with a range of pixel values\n    img2 = np.arange(100, dtype=np.uint8).reshape((10, 10))\n    bins2, counts2 = make_hist_simple(img2)\n    bins2_new, counts2_new = make_hist_simple_new_implementation(img2)\n    assert np.array_equal(bins2, bins2_new), \"Bins do not match for range image\"\n    assert np.array_equal(counts2, counts2_new), \"Counts do not match for range image\"\n\n    # Test case 3: Image with random pixel values\n    img3 = np.random.randint(0, 256, size=(10, 10), dtype=np.uint8)\n    bins3, counts3 = make_hist_simple(img3)\n    bins3_new, counts3_new = make_hist_simple_new_implementation(img3)\n    assert np.array_equal(bins3, bins3_new), \"Bins do not match for random image\"\n    assert np.array_equal(counts3, counts3_new), \"Counts do not match for random image\"\n\n    # Test case 4: Empty image\n    img4 = np.array([], dtype=np.uint8).reshape((0, 0))\n    bins4, counts4 = make_hist_simple(img4)\n    bins4_new, counts4_new = make_hist_simple_new_implementation(img4)\n    assert np.array_equal(bins4, bins4_new), \"Bins do not match for empty image\"\n    assert np.array_equal(counts4, counts4_new), \"Counts do not match for empty image\"\n\n    # Test case 5: Single pixel image\n    img5 = np.array([[42]], dtype=np.uint8)\n    bins5, counts5 = make_hist_simple(img5)\n    bins5_new, counts5_new = make_hist_simple_new_implementation(img5)\n    assert np.array_equal(bins5, bins5_new), \"Bins do not match for single pixel image\"\n    assert np.array_equal(counts5, counts5_new), \"Counts do not match for single pixel image\"\n\n    # Test case 6: Image with max and min values\n    img6 = np.array([[0, 255], [255, 0]], dtype=np.uint8)\n    bins6, counts6 = make_hist_simple(img6)\n    bins6_new, counts6_new = make_hist_simple_new_implementation(img6)\n    assert np.array_equal(bins6, bins6_new), \"Bins do not match for max and min values image\"\n    assert np.array_equal(counts6, counts6_new), \"Counts do not match for max and min values image\"\n\n    # Test case 7: Large image\n    img7 = np.random.randint(0, 256, size=(1000, 1000), dtype=np.uint8)\n    bins7, counts7 = make_hist_simple(img7)\n    bins7_new, counts7_new = make_hist_simple_new_implementation(img7)\n    assert np.array_equal(bins7, bins7_new), \"Bins do not match for large image\"\n    assert np.array_equal(counts7, counts7_new), \"Counts do not match for large image\"\n\n    # Test case 8: Non-square image\n    img8 = np.random.randint(0, 256, size=(10, 20), dtype=np.uint8)\n    bins8, counts8 = make_hist_simple(img8)\n    bins8_new, counts8_new = make_hist_simple_new_implementation(img8)\n    assert np.array_equal(bins8, bins8_new), \"Bins do not match for non-square image\"\n    assert np.array_equal(counts8, counts8_new), \"Counts do not match for non-square image\"\n\nif __name__ == \"__main__\":\n    test_make_hist_simple()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of both code and functionality. Both functions create a histogram of an image by counting the occurrences of each pixel value (from 0 to 255) and return a tuple containing an array of bin edges and the counts. The test cases provided in the revised code are not part of the function itself and do not alter its functionality. They are merely used to verify the correctness of the function. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `make_hist_simple` function returns two values: `bins` and `counts`, which are arrays. This satisfies the condition as the function has return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `make_hist_simple` and `make_hist_simple_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `make_hist_simple` and `make_hist_simple_new_implementation` for various inputs. If the new implementation passes all tests, it must have the same functionality as the original, satisfying this condition.\n- CONDITION 4: The test cases use `assert` statements to compare the outputs of the two implementations. Since `make_hist_simple` returns values, using `assert` to compare these outputs is reasonable, satisfying this condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including uniform images, images with a range of values, random images, empty images, single pixel images, images with extreme values, large images, and non-square images. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "correlate_kerneln",
        "idx": "866",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab4.py",
        "orig_func": "def correlate_kerneln(img, kernel):\n    img_x, img_y = img.shape\n    kernel_x, kernel_y = kernel.shape\n    pad = int((kernel_x - 1) / 2)\n    img_pad = np.pad(img, pad, 'edge')\n    new_img = np.zeros(img.shape)\n    for x in range(pad, img_x + pad):\n        for y in range(pad, img_y + pad):\n            part = img_pad[x - pad:x + pad + 1, y - pad:y + pad + 1]\n            new_img[x - pad][y - pad] = np.sum(part * kernel)\n    return new_img",
        "orig_context": "```python\n## lab4.py\nimport numpy as np\n\ndef correlate_kerneln(img, kernel):\n    img_x, img_y = img.shape\n    kernel_x, kernel_y = kernel.shape\n\n    pad = int((kernel_x - 1) / 2)\n    img_pad = np.pad(img, pad, 'edge')\n    new_img = np.zeros(img.shape)\n    for x in range(pad, img_x + pad):\n        for y in range(pad, img_y + pad):\n            part = img_pad[x-pad:x + pad + 1, y - pad: y + pad + 1]\n            new_img[x-pad][y-pad] = np.sum(part * kernel)\n    return new_img\n\n```\n\n\n",
        "eval_script": "## lab4.py\nimport numpy as np\n\ndef correlate_kerneln(img, kernel):\n    img_x, img_y = img.shape\n    kernel_x, kernel_y = kernel.shape\n\n    pad = int((kernel_x - 1) / 2)\n    img_pad = np.pad(img, pad, 'edge')\n    new_img = np.zeros(img.shape)\n    for x in range(pad, img_x + pad):\n        for y in range(pad, img_y + pad):\n            part = img_pad[x-pad:x + pad + 1, y - pad: y + pad + 1]\n            new_img[x-pad][y-pad] = np.sum(part * kernel)\n    return new_img\n\n\ndef test_correlate_kerneln():\n    # Test case 1: Simple 3x3 image with 3x3 kernel\n    img1 = np.array([[1, 2, 3],\n                     [4, 5, 6],\n                     [7, 8, 9]])\n    kernel1 = np.array([[1, 0, -1],\n                        [1, 0, -1],\n                        [1, 0, -1]])\n    assert np.allclose(correlate_kerneln(img1, kernel1), correlate_kerneln_new_implementation(img1, kernel1))\n\n    # Test case 2: Larger image with 3x3 kernel\n    img2 = np.array([[1, 2, 3, 4],\n                     [5, 6, 7, 8],\n                     [9, 10, 11, 12],\n                     [13, 14, 15, 16]])\n    kernel2 = np.array([[0, 1, 0],\n                        [1, -4, 1],\n                        [0, 1, 0]])\n    assert np.allclose(correlate_kerneln(img2, kernel2), correlate_kerneln_new_implementation(img2, kernel2))\n\n    # Test case 3: 5x5 image with 1x1 kernel (should return the same image)\n    img3 = np.array([[1, 2, 3, 4, 5],\n                     [6, 7, 8, 9, 10],\n                     [11, 12, 13, 14, 15],\n                     [16, 17, 18, 19, 20],\n                     [21, 22, 23, 24, 25]])\n    kernel3 = np.array([[1]])\n    assert np.allclose(correlate_kerneln(img3, kernel3), correlate_kerneln_new_implementation(img3, kernel3))\n\n    # Test case 4: Non-square image and kernel\n    img4 = np.array([[1, 2, 3],\n                     [4, 5, 6]])\n    kernel4 = np.array([[1, 0],\n                        [0, -1]])\n    assert np.allclose(correlate_kerneln(img4, kernel4), correlate_kerneln_new_implementation(img4, kernel4))\n\n    # Test case 5: Even-sized kernel\n    img5 = np.array([[1, 2, 3, 4],\n                     [5, 6, 7, 8],\n                     [9, 10, 11, 12],\n                     [13, 14, 15, 16]])\n    kernel5 = np.array([[1, 1],\n                        [1, 1]])\n    assert np.allclose(correlate_kerneln(img5, kernel5), correlate_kerneln_new_implementation(img5, kernel5))\n\n    # Test case 6: Image with zeros\n    img6 = np.array([[0, 0, 0],\n                     [0, 1, 0],\n                     [0, 0, 0]])\n    kernel6 = np.array([[1, 0, -1],\n                        [1, 0, -1],\n                        [1, 0, -1]])\n    assert np.allclose(correlate_kerneln(img6, kernel6), correlate_kerneln_new_implementation(img6, kernel6))\n\n    # Test case 7: Image with negative values\n    img7 = np.array([[-1, -2, -3],\n                     [-4, -5, -6],\n                     [-7, -8, -9]])\n    kernel7 = np.array([[1, 0, -1],\n                        [1, 0, -1],\n                        [1, 0, -1]])\n    assert np.allclose(correlate_kerneln(img7, kernel7), correlate_kerneln_new_implementation(img7, kernel7))\n\n    # Test case 8: Empty image and kernel\n    img8 = np.array([[]])\n    kernel8 = np.array([[]])\n    assert np.allclose(correlate_kerneln(img8, kernel8), correlate_kerneln_new_implementation(img8, kernel8))\n\n    # Test case 9: Single-element image and kernel\n    img9 = np.array([[42]])\n    kernel9 = np.array([[1]])\n    assert np.allclose(correlate_kerneln(img9, kernel9), correlate_kerneln_new_implementation(img9, kernel9))\n\nif __name__ == \"__main__\":\n    test_correlate_kerneln()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      11      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 11      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations in the same sequence: they calculate the padding size, pad the image, and then iterate over the padded image to apply the kernel and compute the resulting image. The logic and implementation details, such as the use of `np.pad`, the nested loops, and the computation of the new image values using `np.sum(part * kernel)`, are exactly the same. There are no changes in the logic or functionality between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The function `correlate_kerneln` returns a new image array (`new_img`), satisfying the condition of having return values.\n- [CONDITION 2] The test cases use `assert np.allclose(...)` to compare the return values of `correlate_kerneln` and `correlate_kerneln_new_implementation`, which checks the return values and not printed or logged contents.\n- [CONDITION 3] The test cases compare the outputs of `correlate_kerneln` and `correlate_kerneln_new_implementation` using `np.allclose`, ensuring that `correlate_kerneln_new_implementation` must have the exact same functionality to pass all tests.\n- [CONDITION 4] The test cases use `assert np.allclose(...)` to compare the outputs, which is reasonable given that `correlate_kerneln` returns a value.\n- [CONDITION 5] The test cases cover a variety of scenarios including different image and kernel sizes, edge cases like empty arrays, and images with negative values, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "normalize",
        "idx": "867",
        "repo_name": "kAleks12___PSW_LAB",
        "func_path": "lab6/lab6.py",
        "orig_func": "def normalize(vector):\n    vector -= np.min(vector)\n    vector /= np.max(vector)\n    return vector",
        "orig_context": "```python\n## lab6/lab6.py\nimport numpy as np\n\ndef normalize(vector):\n    vector -= np.min(vector)\n    vector /= np.max(vector)\n    return vector\n\n```\n\n\n",
        "eval_script": "## lab6/lab6.py\nimport numpy as np\n\ndef normalize(vector):\n    vector -= np.min(vector)\n    vector /= np.max(vector)\n    return vector\n\n\ndef test_normalize():\n    # Test case 1: Vector with positive numbers\n    vector1 = np.array([1, 2, 3, 4, 5], dtype=float)\n    assert np.allclose(normalize(vector1.copy()), normalize_new_implementation(vector1.copy())), \"Test case 1 failed\"\n\n    # Test case 2: Vector with negative numbers\n    vector2 = np.array([-5, -4, -3, -2, -1], dtype=float)\n    assert np.allclose(normalize(vector2.copy()), normalize_new_implementation(vector2.copy())), \"Test case 2 failed\"\n\n    # Test case 3: Vector with mixed positive and negative numbers\n    vector3 = np.array([-3, -1, 0, 1, 3], dtype=float)\n    assert np.allclose(normalize(vector3.copy()), normalize_new_implementation(vector3.copy())), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_normalize()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they subtract the minimum value of the vector from each element and then divide each element by the maximum value of the vector. The test cases provided in the code are meant to compare the output of this function with another function called `normalize_new_implementation`, which is not provided. However, the task is to compare the ORIGINAL FUNCTION with the REVISED FUNCTION, which are exactly the same in this case.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `normalize` function returns a modified version of the input vector, satisfying this condition.\n- CONDITION 2: The test cases use `np.allclose` to compare the return values of `normalize` and `normalize_new_implementation`, which means they are checking the return values, not printed or logged contents. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `normalize` and `normalize_new_implementation` using `np.allclose`, which ensures that `normalize_new_implementation` must have the same functionality as `normalize` to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use `np.allclose` to compare the outputs of the two functions, which is reasonable given that `normalize` returns a modified vector. This condition is satisfied.\n- CONDITION 5: The test cases cover vectors with positive numbers, negative numbers, and mixed numbers, which are non-trivial and provide a good range of scenarios to test the functionality. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "0346ec4ce02407a277cd0899547236cecf252065"
    },
    {
        "func_name": "convert_time_to_seconds",
        "idx": "869",
        "repo_name": "AHammoudeh___energy",
        "func_path": "colab.py",
        "orig_func": "def convert_time_to_seconds(time_series):\n    time_format = '%H:%M:%S.%f'\n    times = pd.to_datetime(time_series, format=time_format)\n    min_time = times.min()\n    time_in_seconds = (times - min_time).dt.total_seconds()\n    return time_in_seconds",
        "orig_context": "```python\n## colab.py\nimport pandas as pd\n\ndef convert_time_to_seconds(time_series):\n    # Convert the time strings to datetime objects\n    time_format = '%H:%M:%S.%f'\n    times = pd.to_datetime(time_series, format=time_format)\n    # Get the first (minimum) time to use as the reference point\n    min_time = times.min()\n    # Subtract the minimum time and convert the time differences to seconds\n    time_in_seconds = (times - min_time).dt.total_seconds()\n    return time_in_seconds\n\n```\n\n\n",
        "eval_script": "## colab.py\nimport pandas as pd\n\ndef convert_time_to_seconds(time_series):\n    # Convert the time strings to datetime objects\n    time_format = '%H:%M:%S.%f'\n    times = pd.to_datetime(time_series, format=time_format)\n    # Get the first (minimum) time to use as the reference point\n    min_time = times.min()\n    # Subtract the minimum time and convert the time differences to seconds\n    time_in_seconds = (times - min_time).dt.total_seconds()\n    return time_in_seconds\n\n\ndef test_convert_time_to_seconds():\n    # Test case 1: Typical case with multiple times\n    time_series_1 = pd.Series(['00:00:01.000', '00:00:02.000', '00:00:03.000'])\n    assert convert_time_to_seconds(time_series_1).equals(convert_time_to_seconds_new_implementation(time_series_1))\n    \n    # Test case 2: Edge case with a single time entry\n    time_series_2 = pd.Series(['00:00:01.000'])\n    assert convert_time_to_seconds(time_series_2).equals(convert_time_to_seconds_new_implementation(time_series_2))\n    \n    # Test case 3: Case with varying time intervals\n    time_series_3 = pd.Series(['00:00:01.000', '00:00:01.500', '00:00:02.000'])\n    assert convert_time_to_seconds(time_series_3).equals(convert_time_to_seconds_new_implementation(time_series_3))\n\nif __name__ == \"__main__\":\n    test_convert_time_to_seconds()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they convert a series of time strings into datetime objects using the format '%H:%M:%S.%f', find the minimum time to use as a reference, and then calculate the time difference in seconds from this reference point. The additional code in the REVISED FUNCTION is for testing purposes and does not alter the functionality of the `convert_time_to_seconds` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `convert_time_to_seconds` returns a value, specifically a pandas Series of time differences in seconds. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n- CONDITION 2: The test cases use the `equals` method to compare the return values of `convert_time_to_seconds` and `convert_time_to_seconds_new_implementation`, which checks the return values rather than printed or logged content. This satisfies the condition.\n- CONDITION 3: The test cases compare the outputs of `convert_time_to_seconds` and `convert_time_to_seconds_new_implementation` using the `equals` method, which ensures that the new implementation must have exactly the same functionality to pass all tests. This condition is satisfied.\n- CONDITION 4: The test cases use the `equals` method to compare the return values, which is reasonable given that the function returns a pandas Series. There are no unreasonable assertions present. This condition is satisfied.\n- CONDITION 5: The test cases cover typical, edge, and varying interval scenarios, which are non-trivial and provide a reasonable coverage of potential input cases. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "db897074b98da9f219756072e2473d92124e5d60"
    },
    {
        "func_name": "calc_energy",
        "idx": "870",
        "repo_name": "AHammoudeh___energy",
        "func_path": "colab.py",
        "orig_func": "def calc_energy(runtime, utilization, P100=61, P0=10, num_cpu=2):\n    power = num_cpu * (P0 + (P100 - P0) * utilization / 100)\n    energy = power * runtime\n    return (energy, power)",
        "orig_context": "```python\n## colab.py\ndef calc_energy(runtime,utilization, P100=61, P0=10, num_cpu=2):\n  power = num_cpu*(P0 + (P100-P0)*utilization/100)\n  energy = power*runtime\n  #print('cpu_energy:',cpu_energy, 'Joul')\n  return energy, power\n\n```\n\n\n",
        "eval_script": "## colab.py\ndef calc_energy(runtime, utilization, P100=61, P0=10, num_cpu=2):\n    power = num_cpu * (P0 + (P100 - P0) * utilization / 100)\n    energy = power * runtime\n    return energy, power\n\n\ndef test_calc_energy():\n    # Test case 1: Default parameters\n    assert calc_energy(5, 50) == calc_energy_new_implementation(5, 50), \"Test case 1 failed\"\n\n    # Test case 2: Different utilization\n    assert calc_energy(10, 75) == calc_energy_new_implementation(10, 75), \"Test case 2 failed\"\n\n    # Test case 3: Different number of CPUs\n    assert calc_energy(3, 30, num_cpu=4) == calc_energy_new_implementation(3, 30, num_cpu=4), \"Test case 3 failed\"\n\n    # Test case 4: Different P100 and P0 values\n    assert calc_energy(8, 60, P100=70, P0=15) == calc_energy_new_implementation(8, 60, P100=70, P0=15), \"Test case 4 failed\"\n\n    # Test case 5: Edge case with zero utilization\n    assert calc_energy(5, 0) == calc_energy_new_implementation(5, 0), \"Test case 5 failed\"\n\n    # Test case 6: Edge case with zero runtime\n    assert calc_energy(0, 50) == calc_energy_new_implementation(0, 50), \"Test case 6 failed\"\n\n    # Test case 7: Edge case with maximum utilization\n    assert calc_energy(5, 100) == calc_energy_new_implementation(5, 100), \"Test case 7 failed\"\n\n    # Test case 8: Negative utilization\n    assert calc_energy(5, -50) == calc_energy_new_implementation(5, -50), \"Test case 8 failed\"\n\n    # Test case 9: Negative runtime\n    assert calc_energy(-5, 50) == calc_energy_new_implementation(-5, 50), \"Test case 9 failed\"\n\n    # Test case 10: Non-integer utilization and runtime\n    assert calc_energy(5.5, 50.5) == calc_energy_new_implementation(5.5, 50.5), \"Test case 10 failed\"\n\nif __name__ == \"__main__\":\n    test_calc_energy()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions calculate the power and energy using the same formula and return the same tuple of (energy, power). The order of the returned values is the same, and there are no changes in the logic or parameters. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `calc_energy` function returns a tuple containing `energy` and `power`. Therefore, it satisfies the condition of having return values.\n  \n- CONDITION 2: The test cases use assertions to compare the return values of `calc_energy` and `calc_energy_new_implementation`. There is no checking of printed or logged contents, so this condition is satisfied.\n\n- CONDITION 3: The test cases compare the outputs of `calc_energy` and `calc_energy_new_implementation` for various inputs. If `calc_energy_new_implementation` passes all these tests, it must have the same functionality as `calc_energy`, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable given that `calc_energy` returns values. The assertions are correctly structured, so this condition is satisfied.\n\n- CONDITION 5: The test cases cover a range of scenarios, including default parameters, different utilizations, different numbers of CPUs, different `P100` and `P0` values, edge cases (zero utilization, zero runtime, maximum utilization), negative values, and non-integer inputs. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "db897074b98da9f219756072e2473d92124e5d60"
    },
    {
        "func_name": "create_dataset",
        "idx": "872",
        "repo_name": "Princeguam___igbo-translator",
        "func_path": "mainfile.py",
        "orig_func": "def create_dataset(path, num_examples):\n    lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n    word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n    return word_pairs",
        "orig_context": "```python\n## mainfile.py\nimport unicodedata\n\nimport re\n\ndef unicode_to_ascii(the_file):\n    return ''.join(c for c in unicodedata.normalize('NFD', the_file)\n                   if unicodedata.category(c) != 'MN')\n\ndef preprocess_sentence(w):\n    w = unicode_to_ascii(w.lower().strip())\n    # creating a space between a word and the punctuation following it\n    w = re.sub(r\"([?.!,])\", r\" \\1 \", w)\n\n    # adding a start and an end token to the sentence\n    # so that the model knows when to start and stop predicting\n    w = '<start> ' + w + ' <end>'\n    return w\n\ndef create_dataset(path,num_examples):\n    lines = open(path,encoding='UTF-8').read().strip().split('\\n')\n    word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n    return word_pairs\n\n```\n\n\n",
        "eval_script": "## mainfile.py\nimport unicodedata\nimport re\nimport os\n\ndef unicode_to_ascii(the_file):\n    return ''.join(c for c in unicodedata.normalize('NFD', the_file)\n                   if unicodedata.category(c) != 'MN')\n\ndef preprocess_sentence(w):\n    w = unicode_to_ascii(w.lower().strip())\n    # creating a space between a word and the punctuation following it\n    w = re.sub(r\"([?.!,])\", r\" \\1 \", w)\n\n    # adding a start and an end token to the sentence\n    # so that the model knows when to start and stop predicting\n    w = '<start> ' + w + ' <end>'\n    return w\n\ndef create_dataset(path, num_examples):\n    lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n    word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n    return word_pairs\n\n\ndef test_create_dataset():\n    # Ensure the directory exists\n    os.makedirs('/home/user/tmp', exist_ok=True)\n\n    # Create a mock data file\n    mock_data_path = '/home/user/tmp/mock_data.txt'\n    with open(mock_data_path, 'w', encoding='UTF-8') as f:\n        f.write(\"Hello\\tWorld\\nHow are you?\\tI am fine.\\nGoodbye\\tSee you later.\\n\")\n\n    # Test with different number of examples\n    result_original = create_dataset(mock_data_path, 1)\n    result_new = create_dataset_new_implementation(mock_data_path, 1)\n    assert result_original == result_new, \"Test failed for num_examples=1\"\n\n    result_original = create_dataset(mock_data_path, 2)\n    result_new = create_dataset_new_implementation(mock_data_path, 2)\n    assert result_original == result_new, \"Test failed for num_examples=2\"\n\n    result_original = create_dataset(mock_data_path, 3)\n    result_new = create_dataset_new_implementation(mock_data_path, 3)\n    assert result_original == result_new, \"Test failed for num_examples=3\"\n\nif __name__ == \"__main__\":\n    test_create_dataset()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `create_dataset` is identical to the ORIGINAL FUNCTION in terms of its implementation. Both functions read lines from a file, split them by tab characters, preprocess each part of the split using the `preprocess_sentence` function, and return the processed word pairs. The additional code in the revised version, such as the `unicode_to_ascii` function and the `test_create_dataset` function, does not alter the functionality of the `create_dataset` function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `create_dataset` function returns a list of word pairs, satisfying the condition of having return values.\n- CONDITION 2: The test cases check the return values of the `create_dataset` function and do not rely on printed or logged content.\n- CONDITION 3: The test cases compare the outputs of `create_dataset` and `create_dataset_new_implementation` for equivalence, ensuring that they must have the same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations, which is reasonable given that `create_dataset` has return values.\n- CONDITION 5: The test cases are non-trivial as they test the function with different numbers of examples, ensuring that the function handles varying input sizes correctly.",
            "answer": "yes"
        },
        "commit_id": "2ea4f0adacac18469ae867c0911ef20e4eb60c3f"
    },
    {
        "func_name": "loss_function",
        "idx": "874",
        "repo_name": "Princeguam___igbo-translator",
        "func_path": "mainfile.py",
        "orig_func": "def loss_function(real, pred):\n    mask = 1 - np.equal(real, 0)\n    loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n    return tf.reduce_mean(loss_)",
        "orig_context": "```python\n## mainfile.py\nimport tensorflow as tf\n\nimport numpy as np\n\ndef loss_function(real, pred):\n    mask = 1 - np.equal(real, 0)\n    loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n    return tf.reduce_mean(loss_)\n\n```\n\n\n",
        "eval_script": "## mainfile.py\nimport tensorflow as tf\nimport numpy as np\n\ndef loss_function(real, pred):\n    mask = 1 - np.equal(real, 0)\n    loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n    return tf.reduce_mean(loss_)\n\n\ndef test_loss_function():\n    # Test case 1: real contains zeros, pred is arbitrary\n    real1 = np.array([0, 1, 2])\n    pred1 = np.array([[0.1, 0.9, 0.0], [0.1, 0.8, 0.1], [0.1, 0.1, 0.8]])\n    assert np.isclose(loss_function(real1, pred1), loss_function_new_implementation(real1, pred1))\n\n    # Test case 2: real contains no zeros, pred is arbitrary\n    real2 = np.array([1, 2, 1])\n    pred2 = np.array([[0.1, 0.9, 0.0], [0.1, 0.1, 0.8], [0.8, 0.1, 0.1]])\n    assert np.isclose(loss_function(real2, pred2), loss_function_new_implementation(real2, pred2))\n\n    # Test case 3: real and pred are larger arrays\n    real3 = np.array([1, 0, 2, 1, 0])\n    pred3 = np.array([[0.2, 0.8, 0.0], [0.3, 0.3, 0.4], [0.1, 0.2, 0.7], [0.6, 0.3, 0.1], [0.4, 0.4, 0.2]])\n    assert np.isclose(loss_function(real3, pred3), loss_function_new_implementation(real3, pred3))\n\nif __name__ == \"__main__\":\n    test_loss_function()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the CODE is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they create a mask to ignore zero values in the 'real' array, calculate the sparse softmax cross-entropy loss, and return the mean of the masked loss. There is no difference in the implementation or logic between the two functions. The test cases provided in the CODE are meant to verify the functionality but do not alter the function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `loss_function` returns a value, which is the mean of the masked sparse softmax cross-entropy loss. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use `assert` statements to compare the return values of `loss_function` and `loss_function_new_implementation`, which means they are checking return values, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `loss_function` and `loss_function_new_implementation` using `np.isclose`, which checks for numerical closeness. This ensures that `loss_function_new_implementation` must have the same functionality as `loss_function` to pass the tests.\n- CONDITION 4: The test cases use `np.isclose` to compare the return values, which is appropriate for comparing floating-point numbers. The use of `assert` is reasonable given that `loss_function` returns a value.\n- CONDITION 5: The test cases cover different scenarios: when `real` contains zeros, when it does not, and when both `real` and `pred` are larger arrays. This provides a non-trivial set of test cases.",
            "answer": "yes"
        },
        "commit_id": "2ea4f0adacac18469ae867c0911ef20e4eb60c3f"
    },
    {
        "func_name": "remove_background",
        "idx": "878",
        "repo_name": "zachysaur___Text-Behind-Image",
        "func_path": "app.py",
        "orig_func": "def remove_background(image):\n    img_bytes = io.BytesIO()\n    image.save(img_bytes, format='PNG')\n    img_bytes = img_bytes.getvalue()\n    output = remove(img_bytes)\n    return Image.open(io.BytesIO(output))",
        "orig_context": "```python\n## app.py\nfrom PIL import Image, ImageDraw, ImageFont, ImageColor\n\nimport io\n\nfrom rembg import remove\n\ndef remove_background(image):\n    img_bytes = io.BytesIO()\n    image.save(img_bytes, format=\"PNG\")\n    img_bytes = img_bytes.getvalue()\n    output = remove(img_bytes)\n    return Image.open(io.BytesIO(output))\n\n```\n\n\n",
        "eval_script": "## app.py\nfrom PIL import Image, ImageDraw, ImageFont, ImageColor\nimport io\n\n# Mock implementation of the `remove` function from the `rembg` module\ndef remove(image_bytes):\n    # Simulate the removal of the background by returning the original image bytes\n    # In a real scenario, this function would process the image bytes to remove the background\n    return image_bytes\n\ndef remove_background(image):\n    img_bytes = io.BytesIO()\n    image.save(img_bytes, format=\"PNG\")\n    img_bytes = img_bytes.getvalue()\n    output = remove(img_bytes)\n    return Image.open(io.BytesIO(output))\n\n\ndef test_remove_background():\n    # Create a simple image for testing\n    image1 = Image.new(\"RGB\", (10, 10), color=\"red\")\n    image2 = Image.new(\"RGB\", (10, 10), color=\"green\")\n    image3 = Image.new(\"RGB\", (10, 10), color=\"blue\")\n    \n    # Test case 1: Red image\n    result_original = remove_background(image1)\n    result_new = remove_background_new_implementation(image1)\n    assert list(result_original.getdata()) == list(result_new.getdata()), \"Test case 1 failed\"\n    \n    # Test case 2: Green image\n    result_original = remove_background(image2)\n    result_new = remove_background_new_implementation(image2)\n    assert list(result_original.getdata()) == list(result_new.getdata()), \"Test case 2 failed\"\n    \n    # Test case 3: Blue image\n    result_original = remove_background(image3)\n    result_new = remove_background_new_implementation(image3)\n    assert list(result_original.getdata()) == list(result_new.getdata()), \"Test case 3 failed\"\n\nif __name__ == \"__main__\":\n    test_remove_background()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions convert an image to bytes, pass those bytes to the `remove` function, and then convert the output back to an image. The `remove` function is mocked to return the original image bytes, which means the functionality of the `remove_background` function remains unchanged. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `remove_background` function returns a value, specifically an image object. This satisfies the condition that the function should either have return values or modify global variables or input arguments.\n\n- CONDITION 2: The test cases check the return values of the `remove_background` function by comparing the pixel data of the images returned by the original and new implementations. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the pixel data of the images returned by both implementations. If the new implementation has the exact same functionality as the original, it will pass all the test cases. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the pixel data of the images, which is reasonable given that `remove_background` returns an image object. The test cases do not use inappropriate assertions, satisfying this condition.\n\n- CONDITION 5: The test cases use three different images with distinct colors (red, green, blue), which is non-trivial as it tests the function with different inputs. This satisfies the condition for non-trivial test cases.",
            "answer": "yes"
        },
        "commit_id": "e20f0e97dee6b38d9c3cecc5e3a01e6829a11e8d"
    },
    {
        "func_name": "superimpose",
        "idx": "879",
        "repo_name": "zachysaur___Text-Behind-Image",
        "func_path": "app.py",
        "orig_func": "def superimpose(image_with_text, overlay_image):\n    overlay_image = overlay_image.convert('RGBA')\n    image_with_text.paste(overlay_image, (0, 0), overlay_image)\n    return image_with_text",
        "orig_context": "```python\n## app.py\ndef superimpose(image_with_text, overlay_image):\n    overlay_image = overlay_image.convert(\"RGBA\")\n    image_with_text.paste(overlay_image, (0, 0), overlay_image)\n    return image_with_text\n\n```\n\n\n",
        "eval_script": "from PIL import Image, ImageDraw\n\ndef superimpose(image_with_text, overlay_image):\n    overlay_image = overlay_image.convert(\"RGBA\")\n    image_with_text.paste(overlay_image, (0, 0), overlay_image)\n    return image_with_text\n\n\n# Create mock images for testing\ndef create_mock_image_with_text():\n    # Create a blank image with white background\n    image = Image.new('RGB', (200, 200), color='white')\n    draw = ImageDraw.Draw(image)\n    draw.text((10, 10), \"Hello\", fill='black')\n    return image\n\ndef create_mock_overlay_image():\n    # Create a blank image with transparent background\n    overlay = Image.new('RGBA', (100, 100), color=(255, 0, 0, 128))  # Red with 50% transparency\n    return overlay\n\ndef test_superimpose():\n    image_with_text = create_mock_image_with_text()\n    overlay_image = create_mock_overlay_image()\n\n    # Test case 1\n    result_original = superimpose(image_with_text.copy(), overlay_image.copy())\n    result_new = superimpose_new_implementation(image_with_text.copy(), overlay_image.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 1 failed\"\n\n    # Test case 2: Different overlay size\n    overlay_image_small = Image.new('RGBA', (50, 50), color=(0, 255, 0, 128))  # Green with 50% transparency\n    result_original = superimpose(image_with_text.copy(), overlay_image_small.copy())\n    result_new = superimpose_new_implementation(image_with_text.copy(), overlay_image_small.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 2 failed\"\n\n    # Test case 3: Different overlay color\n    overlay_image_blue = Image.new('RGBA', (100, 100), color=(0, 0, 255, 128))  # Blue with 50% transparency\n    result_original = superimpose(image_with_text.copy(), overlay_image_blue.copy())\n    result_new = superimpose_new_implementation(image_with_text.copy(), overlay_image_blue.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 3 failed\"\n\n    # Test case 4: Empty overlay\n    overlay_image_empty = Image.new('RGBA', (100, 100), color=(0, 0, 0, 0))  # Fully transparent\n    result_original = superimpose(image_with_text.copy(), overlay_image_empty.copy())\n    result_new = superimpose_new_implementation(image_with_text.copy(), overlay_image_empty.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 4 failed\"\n\n    # Test case 5: Fully opaque overlay\n    overlay_image_opaque = Image.new('RGBA', (100, 100), color=(255, 255, 255, 255))  # Fully opaque white\n    result_original = superimpose(image_with_text.copy(), overlay_image_opaque.copy())\n    result_new = superimpose_new_implementation(image_with_text.copy(), overlay_image_opaque.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 5 failed\"\n\n    # Test case 6: Non-square overlay\n    overlay_image_rect = Image.new('RGBA', (150, 50), color=(255, 255, 0, 128))  # Yellow with 50% transparency\n    result_original = superimpose(image_with_text.copy(), overlay_image_rect.copy())\n    result_new = superimpose_new_implementation(image_with_text.copy(), overlay_image_rect.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 6 failed\"\n\n    # Test case 7: Different image formats\n    image_with_text_jpeg = image_with_text.convert('RGB')\n    overlay_image_jpeg = overlay_image.convert('RGB')\n    result_original = superimpose(image_with_text_jpeg.copy(), overlay_image_jpeg.copy())\n    result_new = superimpose_new_implementation(image_with_text_jpeg.copy(), overlay_image_jpeg.copy())\n    assert result_original.tobytes() == result_new.tobytes(), \"Test case 7 failed\"\n\nif __name__ == \"__main__\":\n    test_superimpose()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions convert the `overlay_image` to 'RGBA' mode and then paste it onto `image_with_text` at position (0, 0) using the overlay image as a mask. The functionality remains unchanged, and the code structure is the same. The additional code provided is for testing purposes and does not alter the function itself.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `superimpose` function returns the modified image, satisfying the condition that it has return values.\n- CONDITION 2: The test cases use assertions to compare the byte representation of the images returned by `superimpose` and `superimpose_new_implementation`, ensuring they check return values rather than printed or logged content.\n- CONDITION 3: The test cases compare the byte representation of the images, which is a comprehensive way to ensure that the two implementations have exactly the same functionality. If the images are identical, the functionality is the same.\n- CONDITION 4: The test cases use `assert` statements to compare the results of `superimpose` and `superimpose_new_implementation`, which is reasonable given that `superimpose` returns a value.\n- CONDITION 5: The test cases cover various scenarios, including different overlay sizes, colors, transparency levels, shapes, and image formats, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "e20f0e97dee6b38d9c3cecc5e3a01e6829a11e8d"
    },
    {
        "func_name": "createSymbol",
        "idx": "880",
        "repo_name": "lusiess01___Netlist2StateSpace",
        "func_path": "Netlist2StateSpace.py",
        "orig_func": "def createSymbol(symbolList, prefix):\n    count = sum((1 for s in symbolList if str(s).startswith(prefix)))\n    newSymbol = sym.Symbol(prefix + str(count + 1))\n    symbolList.append(newSymbol)\n    return newSymbol",
        "orig_context": "```python\n## Netlist2StateSpace.py\nimport sympy as sym\n\ndef createSymbol(symbolList, prefix):\n    count = sum(1 for s in symbolList if str(s).startswith(prefix))\n    newSymbol = sym.Symbol(prefix + str(count + 1))\n    symbolList.append(newSymbol)\n    return newSymbol\n\n```\n\n\n",
        "eval_script": "## Netlist2StateSpace.py\nimport sympy as sym\n\ndef createSymbol(symbolList, prefix):\n    count = sum(1 for s in symbolList if str(s).startswith(prefix))\n    newSymbol = sym.Symbol(prefix + str(count + 1))\n    symbolList.append(newSymbol)\n    return newSymbol\n\n\ndef test_createSymbol():\n    # Test case 1: Empty symbolList\n    symbolList1 = []\n    prefix1 = \"x\"\n    assert createSymbol(symbolList1[:], prefix1) == createSymbol_new_implementation(symbolList1[:], prefix1)\n    \n    # Test case 2: symbolList with symbols with the same prefix\n    symbolList2 = [sym.Symbol(\"x1\"), sym.Symbol(\"x2\")]\n    prefix2 = \"x\"\n    assert createSymbol(symbolList2[:], prefix2) == createSymbol_new_implementation(symbolList2[:], prefix2)\n    \n    # Test case 3: symbolList with symbols with different prefixes\n    symbolList3 = [sym.Symbol(\"y1\"), sym.Symbol(\"y2\")]\n    prefix3 = \"x\"\n    assert createSymbol(symbolList3[:], prefix3) == createSymbol_new_implementation(symbolList3[:], prefix3)\n\nif __name__ == \"__main__\":\n    test_createSymbol()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of functionality. The only difference is the import statement for the sympy module, which is necessary for the function to work in an isolated environment. The logic of counting symbols with the specified prefix, creating a new symbol, appending it to the list, and returning it remains unchanged. The test cases provided in the revised code further confirm that the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `createSymbol` function returns a new symbol and modifies the `symbolList` by appending the new symbol to it. This satisfies the condition as it has a return value and modifies an input argument.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `createSymbol` and `createSymbol_new_implementation`, which means they are checking return values and not printed or logged contents. This condition is satisfied.\n\n- CONDITION 3: The test cases compare the outputs of `createSymbol` and `createSymbol_new_implementation` for the same inputs. If `createSymbol_new_implementation` passes all these tests, it must have the same functionality as `createSymbol`. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `createSymbol` returns a value. This condition is satisfied.\n\n- CONDITION 5: The test cases cover different scenarios: an empty list, a list with symbols having the same prefix, and a list with symbols having different prefixes. These are non-trivial and cover a range of possible inputs. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "3dd56b3cc681596a5274d6f4eba487bbdbe007cc"
    },
    {
        "func_name": "parseNetlist",
        "idx": "881",
        "repo_name": "lusiess01___Netlist2StateSpace",
        "func_path": "Netlist2StateSpace.py",
        "orig_func": "def parseNetlist(netlist):\n    nodes = set()\n    currentSymbols = []\n    currentCount = 0\n    voltageSourceCount = 0\n    for line in netlist:\n        parts = line.split()\n        nodes.update(parts[1:3])\n        element = parts[0]\n        if element in ('L', 'D', 'S', 'E'):\n            currentSymbols.append(element)\n            currentCount += 1\n            if element == 'E':\n                voltageSourceCount += 1\n    if '0' in nodes:\n        nodes.remove('0')\n    return (len(nodes), currentCount, voltageSourceCount, currentSymbols)",
        "orig_context": "```python\n## Netlist2StateSpace.py\ndef parseNetlist(netlist):\n    nodes = set()  # Set to store all unique nodes\n    currentSymbols = []  # List to store symbols for currents and inductors\n    currentCount = 0\n    voltageSourceCount = 0\n\n    for line in netlist:\n        parts = line.split()\n        nodes.update(parts[1:3])  # Add nodes from each line\n        element = parts[0]\n        if element in ('L', 'D', 'S', 'E'):  # Elements that require additional equations\n            currentSymbols.append(element)\n            currentCount += 1\n            if element == 'E':  # Count voltage sources separately\n                voltageSourceCount += 1\n\n    if '0' in nodes:  # Remove ground node from node count\n        nodes.remove('0')\n\n    return len(nodes), currentCount, voltageSourceCount, currentSymbols\n\n```\n\n\n",
        "eval_script": "## Netlist2StateSpace.py\ndef parseNetlist(netlist):\n    nodes = set()  # Set to store all unique nodes\n    currentSymbols = []  # List to store symbols for currents and inductors\n    currentCount = 0\n    voltageSourceCount = 0\n\n    for line in netlist:\n        parts = line.split()\n        nodes.update(parts[1:3])  # Add nodes from each line\n        element = parts[0]\n        if element in ('L', 'D', 'S', 'E'):  # Elements that require additional equations\n            currentSymbols.append(element)\n            currentCount += 1\n            if element == 'E':  # Count voltage sources separately\n                voltageSourceCount += 1\n\n    if '0' in nodes:  # Remove ground node from node count\n        nodes.remove('0')\n\n    return len(nodes), currentCount, voltageSourceCount, currentSymbols\n\n\ndef test_parseNetlist():\n    # Test case 1: Basic netlist with different elements\n    netlist1 = [\"L 1 0\", \"D 2 3\", \"S 3 4\", \"E 4 5\"]\n    assert parseNetlist(netlist1) == parseNetlist_new_implementation(netlist1)\n\n    # Test case 2: Netlist with ground node and repeated elements\n    netlist2 = [\"L 1 0\", \"L 2 0\", \"D 3 0\", \"E 4 5\", \"E 5 6\"]\n    assert parseNetlist(netlist2) == parseNetlist_new_implementation(netlist2)\n\n    # Test case 3: Netlist without ground node\n    netlist3 = [\"L 1 2\", \"D 2 3\", \"S 3 4\", \"E 4 5\"]\n    assert parseNetlist(netlist3) == parseNetlist_new_implementation(netlist3)\n\n    # Test case 4: Empty netlist\n    netlist4 = []\n    assert parseNetlist(netlist4) == parseNetlist_new_implementation(netlist4)\n\n    # Test case 5: Netlist with only ground nodes\n    netlist5 = [\"R 0 0\", \"C 0 0\"]\n    assert parseNetlist(netlist5) == parseNetlist_new_implementation(netlist5)\n\n    # Test case 6: Netlist with elements not requiring additional equations\n    netlist6 = [\"R 1 2\", \"C 2 3\"]\n    assert parseNetlist(netlist6) == parseNetlist_new_implementation(netlist6)\n\n    # Test case 7: Netlist with nodes not connected to any ground node\n    netlist7 = [\"L 1 2\", \"D 3 4\", \"S 5 6\", \"E 7 8\"]\n    assert parseNetlist(netlist7) == parseNetlist_new_implementation(netlist7)\n\nif __name__ == \"__main__\":\n    test_parseNetlist()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      17      0      8      0   100%\n--------------------------------------------------------------------\nTOTAL                                 17      0      8      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon comparing the ORIGINAL FUNCTION and the REVISED FUNCTION, they are identical in terms of functionality. Both functions initialize the same variables, iterate over the netlist to parse each line, update the nodes set, and count specific elements ('L', 'D', 'S', 'E') to update the currentSymbols list, currentCount, and voltageSourceCount. They both remove the ground node '0' from the nodes set and return the same tuple. The REVISED FUNCTION includes additional comments for clarity, but these do not affect the logic or functionality of the code.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `parseNetlist` function returns a tuple containing the number of nodes, current count, voltage source count, and current symbols. Thus, it satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `parseNetlist` and `parseNetlist_new_implementation`, which means they are checking return values and not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `parseNetlist` and `parseNetlist_new_implementation` directly. If `parseNetlist_new_implementation` has the same functionality as `parseNetlist`, it will pass all the test cases. Therefore, this condition is satisfied.\n- CONDITION 4: The assertions are reasonable as they compare the return values of both implementations. There is no use of `assert parseNetlist() == parseNetlist_new_implementation()` without arguments, which would be unreasonable if there were no return values.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic netlists, netlists with ground nodes, empty netlists, netlists with only ground nodes, and netlists with elements that do not require additional equations. This makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "3dd56b3cc681596a5274d6f4eba487bbdbe007cc"
    },
    {
        "func_name": "get_period_alg",
        "idx": "883",
        "repo_name": "yusshu___aernctrack",
        "func_path": "aernctrack/period/alg.py",
        "orig_func": "def get_period_alg(xs, ys):\n    peaks_times = [xs[i] for i in range(1, len(ys) - 1) if ys[i - 1] < ys[i] > ys[i + 1]]\n    if len(peaks_times) > 1:\n        period = numpy.mean([peaks_times[i + 1] - peaks_times[i] for i in range(len(peaks_times) - 1)])\n    else:\n        period = None\n    return period",
        "orig_context": "```python\n## aernctrack/period/alg.py\nimport numpy\n\ndef get_period_alg(xs, ys):\n    # Find the peaks\n    peaks_times = [xs[i] for i in range(1, len(ys) - 1) if ys[i - 1] < ys[i] > ys[i + 1]]\n    if len(peaks_times) > 1:\n        period = numpy.mean([peaks_times[i + 1] - peaks_times[i] for i in range(len(peaks_times) - 1)])\n    else:\n        period = None\n    return period\n\n```\n\n\n",
        "eval_script": "## aernctrack/period/alg.py\nimport numpy\n\ndef get_period_alg(xs, ys):\n    # Find the peaks\n    peaks_times = [xs[i] for i in range(1, len(ys) - 1) if ys[i - 1] < ys[i] > ys[i + 1]]\n    if len(peaks_times) > 1:\n        period = numpy.mean([peaks_times[i + 1] - peaks_times[i] for i in range(len(peaks_times) - 1)])\n    else:\n        period = None\n    return period\n\n\ndef test_get_period_alg():\n    # Test case 1: Multiple peaks\n    xs1 = [0, 1, 2, 3, 4, 5, 6]\n    ys1 = [0, 1, 0, 1, 0, 1, 0]\n    assert get_period_alg(xs1, ys1) == get_period_alg_new_implementation(xs1, ys1)\n\n    # Test case 2: No peaks\n    xs2 = [0, 1, 2, 3, 4]\n    ys2 = [0, 0, 0, 0, 0]\n    assert get_period_alg(xs2, ys2) == get_period_alg_new_implementation(xs2, ys2)\n\n    # Test case 3: Single peak\n    xs3 = [0, 1, 2, 3, 4]\n    ys3 = [0, 1, 0, 0, 0]\n    assert get_period_alg(xs3, ys3) == get_period_alg_new_implementation(xs3, ys3)\n\n    # Test case 4: Minimum input size\n    xs4 = [0]\n    ys4 = [0]\n    assert get_period_alg(xs4, ys4) == get_period_alg_new_implementation(xs4, ys4)\n\n    # Test case 5: Peaks at the beginning and end\n    xs5 = [0, 1, 2, 3, 4]\n    ys5 = [1, 0, 0, 0, 1]\n    assert get_period_alg(xs5, ys5) == get_period_alg_new_implementation(xs5, ys5)\n\n    # Test case 6: Non-uniform X intervals\n    xs6 = [0, 1, 3, 4, 7]\n    ys6 = [0, 1, 0, 1, 0]\n    assert get_period_alg(xs6, ys6) == get_period_alg_new_implementation(xs6, ys6)\n\n    # Test case 7: Negative values\n    xs7 = [0, 1, 2, 3, 4]\n    ys7 = [-1, 0, -1, 0, -1]\n    assert get_period_alg(xs7, ys7) == get_period_alg_new_implementation(xs7, ys7)\n\n    # Test case 8: Floating point values\n    xs8 = [0, 1, 2, 3, 4]\n    ys8 = [0.0, 1.0, 0.0, 1.0, 0.0]\n    assert get_period_alg(xs8, ys8) == get_period_alg_new_implementation(xs8, ys8)\n\n    # Test case 9: Repeated peaks\n    xs9 = [0, 1, 2, 3, 4, 5, 6]\n    ys9 = [0, 1, 1, 0, 1, 1, 0]\n    assert get_period_alg(xs9, ys9) == get_period_alg_new_implementation(xs9, ys9)\n\n    # Test case 10: Large input\n    xs10 = list(range(1000))\n    ys10 = [0, 1] * 500\n    assert get_period_alg(xs10, ys10) == get_period_alg_new_implementation(xs10, ys10)\n\nif __name__ == \"__main__\":\n    test_get_period_alg()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      2      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      2      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is identical to the ORIGINAL FUNCTION in terms of its code. Both functions perform the same operations: they identify peaks in the `ys` list, calculate the time differences between consecutive peaks using the `xs` list, and return the mean of these differences if there is more than one peak, or `None` otherwise. The additional code in the revised version is a set of test cases to verify the function's behavior, but it does not alter the function itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `get_period_alg` returns a value, specifically the calculated period or `None`. This satisfies the condition as it has return values.\n- CONDITION 2: The test cases use assertions to check the return values of `get_period_alg` and `get_period_alg_new_implementation`, not printed or logged content. This condition is satisfied.\n- CONDITION 3: The test cases compare the outputs of `get_period_alg` and `get_period_alg_new_implementation` for various inputs. If both implementations produce the same outputs for all test cases, they have the same functionality. This condition is satisfied.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `get_period_alg` returns a value. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including multiple peaks, no peaks, single peak, minimum input size, peaks at the beginning and end, non-uniform X intervals, negative values, floating point values, repeated peaks, and large input. These are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "1943079544b2c4e1ac0abb358bbbbe2f54bf9a40"
    },
    {
        "func_name": "get_period_fft",
        "idx": "884",
        "repo_name": "yusshu___aernctrack",
        "func_path": "aernctrack/period/fft.py",
        "orig_func": "def get_period_fft(xs, ys):\n    if len(xs) != len(ys) or len(xs) < 2:\n        return None\n    t = xs[1] - xs[0]\n    fft_values = np.fft.fft(ys)\n    frequencies = np.fft.fftfreq(len(ys), d=t)\n    magnitudes = np.abs(fft_values)\n    magnitudes[0] = 0\n    dominant_frequency = frequencies[np.argmax(magnitudes)]\n    if dominant_frequency == 0:\n        return None\n    period = 1 / abs(dominant_frequency)\n    return period",
        "orig_context": "```python\n## aernctrack/period/fft.py\nimport numpy as np\n\ndef get_period_fft(xs, ys):\n    # Ensure that xs and ys have the same length and there are at least two points\n    if len(xs) != len(ys) or len(xs) < 2:\n        return None\n\n    t = xs[1] - xs[0]  # sample spacing, assumes uniform time spacing\n    fft_values = np.fft.fft(ys)  # fft\n    frequencies = np.fft.fftfreq(len(ys), d=t)  # get the frequencies\n    magnitudes = np.abs(fft_values)\n\n    # exclude the zero frequency and find the dominant frequency\n    magnitudes[0] = 0\n    dominant_frequency = frequencies[np.argmax(magnitudes)]\n\n    # if the dominant frequency is zero, the period is undetermined\n    if dominant_frequency == 0:\n        return None\n\n    # calculate the period as the inverse of the dominant frequency\n    period = 1 / abs(dominant_frequency)\n    return period\n\n```\n\n\n",
        "eval_script": "## aernctrack/period/fft.py\nimport numpy as np\n\ndef get_period_fft(xs, ys):\n    # Ensure that xs and ys have the same length and there are at least two points\n    if len(xs) != len(ys) or len(xs) < 2:\n        return None\n\n    t = xs[1] - xs[0]  # sample spacing, assumes uniform time spacing\n    fft_values = np.fft.fft(ys)  # fft\n    frequencies = np.fft.fftfreq(len(ys), d=t)  # get the frequencies\n    magnitudes = np.abs(fft_values)\n\n    # exclude the zero frequency and find the dominant frequency\n    magnitudes[0] = 0\n    dominant_frequency = frequencies[np.argmax(magnitudes)]\n\n    # if the dominant frequency is zero, the period is undetermined\n    if dominant_frequency == 0:\n        return None\n\n    # calculate the period as the inverse of the dominant frequency\n    period = 1 / abs(dominant_frequency)\n    return period\n\n\ndef test_get_period_fft():\n    # Test case 1: Normal case with a clear dominant frequency\n    xs = np.linspace(0, 1, 100)\n    ys = np.sin(2 * np.pi * 5 * xs)  # 5 Hz signal\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 2: Dominant frequency is zero\n    xs = np.linspace(0, 1, 100)\n    ys = np.ones(100)  # Constant signal\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 3: Input arrays have less than two points\n    xs = np.array([0])\n    ys = np.array([1])\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 4: Non-uniform sampling\n    xs = np.array([0, 0.1, 0.4, 0.9])\n    ys = np.sin(2 * np.pi * 5 * xs)\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 5: Multiple frequencies\n    xs = np.linspace(0, 1, 100)\n    ys = np.sin(2 * np.pi * 5 * xs) + 0.5 * np.sin(2 * np.pi * 10 * xs)\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 6: Negative values\n    xs = np.linspace(0, 1, 100)\n    ys = -np.sin(2 * np.pi * 5 * xs)\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 7: Complex signal\n    xs = np.linspace(0, 1, 100)\n    ys = np.sin(2 * np.pi * 5 * xs) + 1j * np.cos(2 * np.pi * 5 * xs)\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 8: Very large values\n    xs = np.linspace(0, 1, 100)\n    ys = 1e10 * np.sin(2 * np.pi * 5 * xs)\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 9: Very small values\n    xs = np.linspace(0, 1, 100)\n    ys = 1e-10 * np.sin(2 * np.pi * 5 * xs)\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 10: Edge case of two points\n    xs = np.array([0, 0.5])\n    ys = np.array([0, 1])\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\n    # Test case 11: Zero-length input\n    xs = np.array([])\n    ys = np.array([])\n    assert get_period_fft(xs, ys) == get_period_fft_new_implementation(xs, ys)\n\nif __name__ == \"__main__\":\n    test_get_period_fft()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      13      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                 13      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION and the ORIGINAL FUNCTION are identical in terms of functionality. Both functions perform the same steps: they check if the input arrays `xs` and `ys` have the same length and contain at least two elements, calculate the sample spacing `t`, compute the FFT of `ys`, determine the frequencies, calculate the magnitudes of the FFT values, set the magnitude of the zero frequency to zero, find the dominant frequency, and return the period as the inverse of the dominant frequency. The comments in the REVISED FUNCTION do not alter the functionality. Therefore, the functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The function `get_period_fft` returns a value, specifically the period calculated from the dominant frequency of the input signal. Therefore, it satisfies this condition.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `get_period_fft` and `get_period_fft_new_implementation`. They do not rely on printed or logged outputs, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `get_period_fft` and `get_period_fft_new_implementation` directly. If `get_period_fft_new_implementation` has the same functionality as `get_period_fft`, it will pass all tests. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two functions, which is reasonable given that `get_period_fft` returns a value. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including normal cases, edge cases, and cases with different signal characteristics (e.g., constant signal, non-uniform sampling, multiple frequencies, complex signals, very large/small values, and zero-length input). This variety ensures that the test cases are non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "1943079544b2c4e1ac0abb358bbbbe2f54bf9a40"
    },
    {
        "func_name": "_attrs_data_class",
        "idx": "888",
        "repo_name": "shayzi3___json_orm",
        "func_path": "orm_json/utils/checking.py",
        "orig_func": "def _attrs_data_class(data_class: type | None, name_class: str) -> dict[str, Any]:\n    default_values = {'path': 'base.json', 'primary': None, 'free': False}\n    metadata = {}\n    if not data_class:\n        metadata['tablename'] = name_class\n        metadata['path'] = default_values.get('path')\n        metadata['primary'] = default_values.get('primary')\n        metadata['free'] = default_values.get('free')\n    else:\n        data = data_class.__dict__\n        for keyword in ['path', 'tablename', 'free', 'primary']:\n            if data.get(keyword):\n                metadata[keyword] = data.get(keyword)\n            elif keyword == 'tablename':\n                metadata['tablename'] = name_class\n            else:\n                metadata[keyword] = default_values.get(keyword)\n    return metadata",
        "orig_context": "```python\n## orm_json/utils/checking.py\nfrom typing_extensions import (\n     Any, \n     Sequence, \n     Callable\n)\n\ndef _attrs_data_class(\n     data_class: type | None,\n     name_class: str\n     \n) -> dict[str, Any]:\n     default_values = {\n          'path': 'base.json',\n          'primary': None,\n          'free': False\n     }\n     \n     metadata = {}\n     if not data_class:\n          metadata['tablename'] = name_class\n          metadata['path'] = default_values.get('path')\n          metadata['primary'] = default_values.get('primary')\n          metadata['free'] = default_values.get('free')\n          \n     else:\n          data = data_class.__dict__\n          for keyword in ['path', 'tablename', 'free', 'primary']:\n               if data.get(keyword):\n                    metadata[keyword] = data.get(keyword)\n                    \n               else:\n                    if keyword == 'tablename':\n                         metadata['tablename'] = name_class\n                    \n                    else: metadata[keyword] = default_values.get(keyword)\n     return metadata\n\n```\n\n\n",
        "eval_script": "## orm_json/utils/checking.py\nfrom typing_extensions import (\n     Any, \n     Sequence, \n     Callable\n)\n\ndef _attrs_data_class(\n     data_class: type | None,\n     name_class: str\n     \n) -> dict[str, Any]:\n     default_values = {\n          'path': 'base.json',\n          'primary': None,\n          'free': False\n     }\n     \n     metadata = {}\n     if not data_class:\n          metadata['tablename'] = name_class\n          metadata['path'] = default_values.get('path')\n          metadata['primary'] = default_values.get('primary')\n          metadata['free'] = default_values.get('free')\n          \n     else:\n          data = data_class.__dict__\n          for keyword in ['path', 'tablename', 'free', 'primary']:\n               if data.get(keyword):\n                    metadata[keyword] = data.get(keyword)\n                    \n               else:\n                    if keyword == 'tablename':\n                         metadata['tablename'] = name_class\n                    \n                    else: metadata[keyword] = default_values.get(keyword)\n     return metadata\n\n\ndef test__attrs_data_class():\n    # Test with a data class\n    class ExampleDataClass:\n        path = 'example.json'\n        tablename = 'example_table'\n        free = True\n\n    result_original = _attrs_data_class(ExampleDataClass, 'ExampleClass')\n    result_new = _attrs_data_class_new_implementation(ExampleDataClass, 'ExampleClass')\n    assert result_original == result_new, \"Test with ExampleDataClass failed\"\n\n    # Test without a data class\n    result_original = _attrs_data_class(None, 'ExampleClass')\n    result_new = _attrs_data_class_new_implementation(None, 'ExampleClass')\n    assert result_original == result_new, \"Test without data class failed\"\n\n    # Test with a data class with missing attributes\n    class PartialDataClass:\n        path = 'partial.json'\n\n    result_original = _attrs_data_class(PartialDataClass, 'PartialClass')\n    result_new = _attrs_data_class_new_implementation(PartialDataClass, 'PartialClass')\n    assert result_original == result_new, \"Test with PartialDataClass failed\"\n\n    # Test with a data class having all attributes set to None\n    class NoneAttributesDataClass:\n        path = None\n        tablename = None\n        free = None\n        primary = None\n\n    result_original = _attrs_data_class(NoneAttributesDataClass, 'NoneAttributesClass')\n    result_new = _attrs_data_class_new_implementation(NoneAttributesDataClass, 'NoneAttributesClass')\n    assert result_original == result_new, \"Test with NoneAttributesDataClass failed\"\n\n    # Test with a data class having some attributes missing and others set to None\n    class MixedAttributesDataClass:\n        path = None\n        primary = None\n\n    result_original = _attrs_data_class(MixedAttributesDataClass, 'MixedAttributesClass')\n    result_new = _attrs_data_class_new_implementation(MixedAttributesDataClass, 'MixedAttributesClass')\n    assert result_original == result_new, \"Test with MixedAttributesDataClass failed\"\n\n    # Test with a data class having attributes with non-default values\n    class NonDefaultDataClass:\n        path = 'non_default.json'\n        tablename = 'non_default_table'\n        free = True\n        primary = 'id'\n\n    result_original = _attrs_data_class(NonDefaultDataClass, 'NonDefaultClass')\n    result_new = _attrs_data_class_new_implementation(NonDefaultDataClass, 'NonDefaultClass')\n    assert result_original == result_new, \"Test with NonDefaultDataClass failed\"\n\n    # Test with a data class having additional attributes not relevant to the function\n    class ExtraAttributesDataClass:\n        path = 'extra.json'\n        tablename = 'extra_table'\n        free = False\n        primary = 'id'\n        extra = 'extra_value'\n\n    result_original = _attrs_data_class(ExtraAttributesDataClass, 'ExtraAttributesClass')\n    result_new = _attrs_data_class_new_implementation(ExtraAttributesDataClass, 'ExtraAttributesClass')\n    assert result_original == result_new, \"Test with ExtraAttributesDataClass failed\"\n\n    # Test with an empty data class\n    class EmptyDataClass:\n        pass\n\n    result_original = _attrs_data_class(EmptyDataClass, 'EmptyClass')\n    result_new = _attrs_data_class_new_implementation(EmptyDataClass, 'EmptyClass')\n    assert result_original == result_new, \"Test with EmptyDataClass failed\"\n\nif __name__ == \"__main__\":\n    test__attrs_data_class()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py      16      0      8      0   100%\n--------------------------------------------------------------------\nTOTAL                                 16      0      8      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The revised function `_attrs_data_class` is almost identical to the original function. The only difference is in the formatting of the `else` block inside the for loop. In the original function, the `elif` statement is used for the 'tablename' keyword, while in the revised function, the `if` statement is used. This change does not affect the functionality because the logic remains the same: if the keyword is 'tablename', it assigns `name_class` to `metadata['tablename']`, otherwise, it assigns the default value. The test cases provided in the code also confirm that the revised function behaves the same as the original function across various scenarios.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `_attrs_data_class` function returns a dictionary, satisfying the condition that it has return values.\n- CONDITION 2: The test cases check the return values of the function, not printed or logged contents, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_attrs_data_class` and `_attrs_data_class_new_implementation` for various inputs, ensuring that the new implementation must have the exact same functionality to pass all tests.\n- CONDITION 4: The test cases use assertions to compare the return values of the original and new implementations, which is reasonable given that the function returns a dictionary.\n- CONDITION 5: The test cases cover a variety of scenarios, including data classes with all attributes, missing attributes, attributes set to `None`, non-default values, additional irrelevant attributes, and an empty data class. This variety makes the test cases non-trivial.",
            "answer": "yes"
        },
        "commit_id": "58262f4a343e7d719e962ca0d346261ba2bc4513"
    },
    {
        "func_name": "_custom",
        "idx": "889",
        "repo_name": "shayzi3___json_orm",
        "func_path": "orm_json/utils/checking.py",
        "orig_func": "def _custom(data: Sequence, option: Callable) -> tuple[dict[str, dict[str, Any]], list[dict[str, Any]]]:\n    if isinstance(data, dict):\n        data = list(data.values())\n    if not callable(option) and (not isinstance(option, type)):\n        raise CallableError(f'{option} its not callable object')\n    return (option(), data)",
        "orig_context": "```python\n## orm_json/utils/exception.py\nclass CallableError(Exception):\n     def __init__(self, *args: object) -> None:\n          super().__init__(*args)\n\n```\n\n\n```python\n## orm_json/utils/checking.py\nfrom typing_extensions import (\n     Any, \n     Sequence, \n     Callable\n)\n\nfrom orm_json.utils.exception import (\n     TableNotExists,\n     TableColumnNotExists,\n     RequiredArgument,\n     PrimaryNotExists,\n     JsonFileEmpty,\n     CallableError\n)\n\ndef _custom(\n     data: Sequence,\n     option: Callable,\n) -> tuple[\n     dict[str, dict[str, Any]], \n     list[dict[str, Any]]]:\n     \n     if isinstance(data, dict):\n          data = list(data.values())\n          \n     if not callable(option) and not isinstance(option, type):\n          raise CallableError(f\"{option} its not callable object\")\n     return option(), data\n\n```\n\n\n",
        "eval_script": "from typing import Any, Sequence, Callable\n\nclass CallableError(Exception):\n    def __init__(self, *args: object) -> None:\n        super().__init__(*args)\n\ndef _custom(\n    data: Sequence,\n    option: Callable,\n) -> tuple[\n    dict[str, dict[str, Any]], \n    list[dict[str, Any]]]:\n    \n    if isinstance(data, dict):\n        data = list(data.values())\n        \n    if not callable(option) and not isinstance(option, type):\n        raise CallableError(f\"{option} its not callable object\")\n    return option(), data\n\n\ndef test__custom():\n    # Test case 1: data is a dictionary and option is callable\n    data_dict = {'a': 1, 'b': 2}\n    option_callable = lambda: {'result': 'success'}\n    assert _custom(data_dict, option_callable) == _custom_new_implementation(data_dict, option_callable)\n\n    # Test case 2: data is a list and option is callable\n    data_list = [1, 2, 3]\n    assert _custom(data_list, option_callable) == _custom_new_implementation(data_list, option_callable)\n\n    # Test case 3: option is not callable, should raise CallableError\n    try:\n        _custom(data_list, \"not_callable\")\n    except CallableError:\n        pass\n    else:\n        assert False, \"CallableError was not raised in _custom\"\n\n    try:\n        _custom_new_implementation(data_list, \"not_callable\")\n    except CallableError:\n        pass\n    else:\n        assert False, \"CallableError was not raised in _custom_new_implementation\"\n\n    # Test case 4: data is an empty list\n    empty_list = []\n    assert _custom(empty_list, option_callable) == _custom_new_implementation(empty_list, option_callable)\n\n    # Test case 5: data is an empty dictionary\n    empty_dict = {}\n    assert _custom(empty_dict, option_callable) == _custom_new_implementation(empty_dict, option_callable)\n\n    # Test case 6: data is a nested dictionary\n    nested_dict = {'a': {'b': 2}, 'c': {'d': 4}}\n    assert _custom(nested_dict, option_callable) == _custom_new_implementation(nested_dict, option_callable)\n\n    # Test case 7: data is a nested list\n    nested_list = [[1, 2], [3, 4]]\n    assert _custom(nested_list, option_callable) == _custom_new_implementation(nested_list, option_callable)\n\n    # Test case 8: option is a function\n    def func_option():\n        return {'result': 'function'}\n    assert _custom(data_list, func_option) == _custom_new_implementation(data_list, func_option)\n\n    # Test case 9: option is a class instance with __call__\n    class CallableClass:\n        def __call__(self):\n            return {'result': 'callable_class'}\n    callable_instance = CallableClass()\n    assert _custom(data_list, callable_instance) == _custom_new_implementation(data_list, callable_instance)\n\n    # Test case 10: option returns different types\n    option_callable_diff = lambda: [1, 2, 3]\n    assert _custom(data_list, option_callable_diff) == _custom_new_implementation(data_list, option_callable_diff)\n\n    # Test case 11: option is an integer, should raise CallableError\n    try:\n        _custom(data_list, 123)\n    except CallableError:\n        pass\n    else:\n        assert False, \"CallableError was not raised in _custom\"\n\n    try:\n        _custom_new_implementation(data_list, 123)\n    except CallableError:\n        pass\n    else:\n        assert False, \"CallableError was not raised in _custom_new_implementation\"\n\nif __name__ == \"__main__\":\n    test__custom()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       6      0      4      0   100%\n--------------------------------------------------------------------\nTOTAL                                  6      0      4      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining the original and revised functions, they are identical in terms of logic and functionality. Both functions check if the `data` is a dictionary and convert it to a list of its values if so. They then verify if `option` is callable or a type, raising a `CallableError` if not. Finally, they return the result of calling `option` and the `data`. The test cases provided also confirm that both functions behave the same under various scenarios. The only difference is the formatting and structure of the code, which does not affect functionality.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `_custom` function returns a tuple, so it satisfies this condition.\n- CONDITION 2: The test cases use assertions to check return values and exceptions, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_custom` and `_custom_new_implementation` for various inputs, ensuring that both implementations must have the same functionality to pass the tests. This condition is satisfied.\n- CONDITION 4: The test cases and assertions are reasonable. They check for correct return values and ensure exceptions are raised when expected, without using inappropriate assertions. This condition is satisfied.\n- CONDITION 5: The test cases cover a variety of scenarios, including different data types, callable and non-callable options, and nested structures, making them non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "58262f4a343e7d719e962ca0d346261ba2bc4513"
    },
    {
        "func_name": "_get_custom_args",
        "idx": "890",
        "repo_name": "shayzi3___json_orm",
        "func_path": "orm_json/utils/checking.py",
        "orig_func": "def _get_custom_args(data: dict[str, dict[str, Any]], tablename: str) -> tuple:\n    arguments = data.get(tablename).get('args')\n    func = data.get(tablename).get('function')\n    return_type = data.get(tablename).get('return_type')\n    return (arguments, func, return_type)",
        "orig_context": "```python\n## orm_json/utils/checking.py\nfrom typing_extensions import (\n     Any, \n     Sequence, \n     Callable\n)\n\ndef _get_custom_args(\n     data: dict[str, dict[str, Any]],\n     tablename: str\n) -> tuple:\n\n     arguments = data.get(tablename).get('args')\n     func = data.get(tablename).get('function')\n     return_type = data.get(tablename).get('return_type')\n     \n     return arguments, func, return_type\n\n```\n\n\n",
        "eval_script": "## orm_json/utils/checking.py\nfrom typing_extensions import (\n     Any, \n     Sequence, \n     Callable\n)\n\ndef _get_custom_args(\n     data: dict[str, dict[str, Any]],\n     tablename: str\n) -> tuple:\n\n     arguments = data.get(tablename).get('args')\n     func = data.get(tablename).get('function')\n     return_type = data.get(tablename).get('return_type')\n     \n     return arguments, func, return_type\n\n\ndef test__get_custom_args():\n    # Test case 1: Normal case with all fields present\n    data1 = {\n        \"table1\": {\n            \"args\": [1, 2, 3],\n            \"function\": lambda x: x + 1,\n            \"return_type\": int\n        }\n    }\n    assert _get_custom_args(data1, \"table1\") == _get_custom_args_new_implementation(data1, \"table1\")\n\n    # Test case 2: Missing 'args' field\n    data2 = {\n        \"table2\": {\n            \"function\": lambda x: x * 2,\n            \"return_type\": int\n        }\n    }\n    assert _get_custom_args(data2, \"table2\") == _get_custom_args_new_implementation(data2, \"table2\")\n\n    # Test case 3: Missing 'function' field\n    data3 = {\n        \"table3\": {\n            \"args\": [4, 5, 6],\n            \"return_type\": str\n        }\n    }\n    assert _get_custom_args(data3, \"table3\") == _get_custom_args_new_implementation(data3, \"table3\")\n\nif __name__ == \"__main__\":\n    test__get_custom_args()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original and revised functions are identical in terms of their logic and implementation. Both functions take a dictionary `data` and a string `tablename` as inputs. They retrieve the values associated with the keys 'args', 'function', and 'return_type' from the dictionary corresponding to the given `tablename`. Both functions return these values as a tuple. The test cases provided in the revised code further confirm that the functionality remains unchanged, as they compare the output of the revised function with the original function's output and expect them to be equal.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "```\nREASONING: \n- CONDITION 1: The function `_get_custom_args` returns a tuple containing values from the input dictionary, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of `_get_custom_args` and `_get_custom_args_new_implementation`, not printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `_get_custom_args` and `_get_custom_args_new_implementation` directly. If the new implementation has the same functionality, it will pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values, which is reasonable given that `_get_custom_args` returns a tuple. This condition is satisfied.\n- CONDITION 5: The test cases cover different scenarios, such as missing fields in the input dictionary, making them non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "58262f4a343e7d719e962ca0d346261ba2bc4513"
    },
    {
        "func_name": "BtrfsManager.list_subvolumes",
        "idx": "898",
        "repo_name": "Ranjit-Pokharel___BTRFS_Manager",
        "func_path": "project.py",
        "orig_func": "def list_subvolumes(self):\n    command = ['btrfs', 'subvolume', 'list', self.path]\n    result = sp.run(command, capture_output=True)\n    message = result.stdout.decode().strip()\n    if result.returncode != 0:\n        raise BtrfsError(f'Error: {message}')\n    return message",
        "orig_context": "```python\n## project.py\nimport subprocess as sp\n\nclass BtrfsError(Exception):\n    def __init__(self, message):\n        self.message = message\n        super().__init__(self.message)\n\n    def __str__(self):\n        return self.message\n\nclass BtrfsManager:\n    def __init__(self, filesystem_path):\n        self.path = filesystem_path\n\n    # def create_snapshot(self, snapshot_name):\n    #     try:\n    #         command = f\"btrfs subvolume snapshot {self.filesystem_path} {snapshot_name}\"\n    #         sp.run(command, check=True, shell=True)\n    #         return f\"Snapshot '{snapshot_name}' created successfully\"\n    #     except sp.CalledProcessError:\n    #         return f\"Error: Snapshot '{snapshot_name}' creation failed\"\n\n    def list_subvolumes(self):\n        command = [\"btrfs\", \"subvolume\", \"list\", self.path]\n\n        result = sp.run(command, capture_output=True)\n        message = result.stdout.decode().strip()\n        if result.returncode != 0:\n            raise BtrfsError(f\"Error: {message}\")\n        return message\n\n```\n\n\n",
        "eval_script": "## project.py\nimport subprocess as sp\nfrom unittest.mock import patch\n\nclass BtrfsError(Exception):\n    def __init__(self, message):\n        self.message = message\n        super().__init__(self.message)\n\n    def __str__(self):\n        return self.message\n\nclass BtrfsManager:\n    def __init__(self, filesystem_path):\n        self.path = filesystem_path\n\n    def list_subvolumes(self):\n        command = [\"btrfs\", \"subvolume\", \"list\", self.path]\n\n        result = sp.run(command, capture_output=True)\n        message = result.stdout.decode().strip()\n        if result.returncode != 0:\n            raise BtrfsError(f\"Error: {message}\")\n        return message\n\n\ndef mock_subprocess_run(command, capture_output):\n    class MockCompletedProcess:\n        def __init__(self, returncode=0, stdout=b\"\"):\n            self.returncode = returncode\n            self.stdout = stdout\n\n    if command == [\"btrfs\", \"subvolume\", \"list\", \"/mock/path\"]:\n        return MockCompletedProcess(stdout=b\"ID 256 gen 5 top level 5 path @\\nID 257 gen 6 top level 5 path @home\")\n    elif command == [\"btrfs\", \"subvolume\", \"list\", \"/empty/path\"]:\n        return MockCompletedProcess(stdout=b\"\")\n    else:\n        return MockCompletedProcess(returncode=1, stdout=b\"Error: Command failed\")\n\n# Your revised test_list_subvolumes function\ndef test_list_subvolumes():\n    with patch('subprocess.run', mock_subprocess_run):\n        # Standard case\n        manager = BtrfsManager(\"/mock/path\")\n        assert manager.list_subvolumes() == manager.list_subvolumes_new_implementation(), \"Test failed for standard case\"\n\n        # Empty list case\n        manager_empty = BtrfsManager(\"/empty/path\")\n        assert manager_empty.list_subvolumes() == manager_empty.list_subvolumes_new_implementation(), \"Test failed for empty list case\"\n\n        # Error case\n        manager_error = BtrfsManager(\"/error/path\")\n        try:\n            manager_error.list_subvolumes()\n        except BtrfsError as e:\n            error_message = str(e)\n        try:\n            manager_error.list_subvolumes_new_implementation()\n        except BtrfsError as e:\n            new_error_message = str(e)\n        assert error_message == new_error_message, \"Test failed for error case\"\n\n        # Multiple subvolumes case\n        manager_multiple = BtrfsManager(\"/multiple/path\")\n        try:\n            assert manager_multiple.list_subvolumes() == manager_multiple.list_subvolumes_new_implementation(), \"Test failed for multiple subvolumes case\"\n        except BtrfsError:\n            pass  # Handle if mock_subprocess_run is not set up for this path\n\n        # Non-standard output case\n        manager_non_standard = BtrfsManager(\"/nonstandard/path\")\n        try:\n            assert manager_non_standard.list_subvolumes() == manager_non_standard.list_subvolumes_new_implementation(), \"Test failed for non-standard output case\"\n        except BtrfsError:\n            pass  # Handle if mock_subprocess_run is not set up for this path\n\n        # Large output case\n        manager_large = BtrfsManager(\"/large/path\")\n        try:\n            assert manager_large.list_subvolumes() == manager_large.list_subvolumes_new_implementation(), \"Test failed for large output case\"\n        except BtrfsError:\n            pass  # Handle if mock_subprocess_run is not set up for this path\n\n        # Different filesystem path case\n        manager_different = BtrfsManager(\"/different/path\")\n        try:\n            assert manager_different.list_subvolumes() == manager_different.list_subvolumes_new_implementation(), \"Test failed for different filesystem path case\"\n        except BtrfsError:\n            pass  # Handle if mock_subprocess_run is not set up for this path\n\nif __name__ == \"__main__\":\n    test_list_subvolumes()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `BtrfsManager` class is identical to the ORIGINAL FUNCTION. Both functions execute the same command using `subprocess.run`, capture the output, decode it, and check for errors in the same manner. The additional code provided in the revised version, such as the mock function and test cases, does not alter the functionality of the `list_subvolumes` method itself. Therefore, the functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `list_subvolumes` function returns a value (a message string), so this condition is satisfied.\n- CONDITION 2: The test cases use assertions to check the return values of the functions and do not rely on printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the outputs of `list_subvolumes` and `list_subvolumes_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the outputs of the two implementations and handle exceptions appropriately, which is reasonable given the function's behavior, satisfying this condition.\n- CONDITION 5: The test cases cover various scenarios, including standard, empty, error, multiple subvolumes, non-standard output, large output, and different filesystem paths, making them non-trivial and satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "115f25b0861eed83991c86b95f82882927a6de55"
    },
    {
        "func_name": "HistoryEntry.from_string",
        "idx": "904",
        "repo_name": "Chicken___elem-prog-minesweeper",
        "func_path": "src/history.py",
        "orig_func": "@staticmethod\ndef from_string(s):\n    date, duration, moves, win, width, height, mines, remaining_mines = s.split(', ')\n    return HistoryEntry(int(date), int(duration), int(moves), win == 'True', int(width), int(height), int(mines), int(remaining_mines))",
        "orig_context": "```python\n## src/history.py\nfrom datetime import datetime\n\nclass HistoryEntry:\n    def __init__(self, date: int, duration: int, moves: int, win: bool, width: int, height: int, mines: int, remaining_mines: int):\n        self.date = datetime.fromtimestamp(date)\n        self.duration = duration\n        self.moves = moves\n        self.win = win\n        self.width = width\n        self.height = height\n        self.mines = mines\n        self.remaining_mines = remaining_mines\n    \n    def __str__(self):\n        return f\"{int(self.date.timestamp())}, {self.duration}, {self.moves}, {self.win}, {self.width}, {self.height}, {self.mines}, {self.remaining_mines}\"\n    \n    @staticmethod\n    def from_string(s):\n        date, duration, moves, win, width, height, mines, remaining_mines = s.split(\", \")\n        return HistoryEntry(int(date), int(duration), int(moves), win == \"True\", int(width), int(height), int(mines), int(remaining_mines))\n\n```\n\n\n",
        "eval_script": "## src/history.py\nfrom datetime import datetime\n\nclass HistoryEntry:\n    def __init__(self, date: int, duration: int, moves: int, win: bool, width: int, height: int, mines: int, remaining_mines: int):\n        self.date = datetime.fromtimestamp(date)\n        self.duration = duration\n        self.moves = moves\n        self.win = win\n        self.width = width\n        self.height = height\n        self.mines = mines\n        self.remaining_mines = remaining_mines\n    \n    def __str__(self):\n        return f\"{int(self.date.timestamp())}, {self.duration}, {self.moves}, {self.win}, {self.width}, {self.height}, {self.mines}, {self.remaining_mines}\"\n    \n    @staticmethod\n    def from_string(s):\n        date, duration, moves, win, width, height, mines, remaining_mines = s.split(\", \")\n        return HistoryEntry(int(date), int(duration), int(moves), win == \"True\", int(width), int(height), int(mines), int(remaining_mines))\n\n\ndef test_from_string():\n    # Test case 1: Basic test case\n    s1 = \"1625097600, 120, 30, True, 10, 10, 20, 5\"\n    entry1_old = HistoryEntry.from_string(s1)\n    entry1_new = HistoryEntry.from_string_new_implementation(s1)\n    assert entry1_old.__dict__ == entry1_new.__dict__\n\n    # Test case 2: Different values\n    s2 = \"1625097601, 300, 50, False, 15, 15, 40, 10\"\n    entry2_old = HistoryEntry.from_string(s2)\n    entry2_new = HistoryEntry.from_string_new_implementation(s2)\n    assert entry2_old.__dict__ == entry2_new.__dict__\n\n    # Test case 3: Edge case with zero values\n    s3 = \"0, 0, 0, False, 0, 0, 0, 0\"\n    entry3_old = HistoryEntry.from_string(s3)\n    entry3_new = HistoryEntry.from_string_new_implementation(s3)\n    assert entry3_old.__dict__ == entry3_new.__dict__\n\n    # Test case 4: Negative values\n    s4 = \"1625097602, -120, -30, True, -10, -10, -20, -5\"\n    entry4_old = HistoryEntry.from_string(s4)\n    entry4_new = HistoryEntry.from_string_new_implementation(s4)\n    assert entry4_old.__dict__ == entry4_new.__dict__\n\n    # Test case 5: Boolean edge cases\n    s5 = \"1625097603, 100, 20, true, 5, 5, 10, 2\"\n    entry5_old = HistoryEntry.from_string(s5)\n    entry5_new = HistoryEntry.from_string_new_implementation(s5)\n    assert entry5_old.__dict__ == entry5_new.__dict__\n\n    s6 = \"1625097604, 200, 40, FALSE, 8, 8, 16, 4\"\n    entry6_old = HistoryEntry.from_string(s6)\n    entry6_new = HistoryEntry.from_string_new_implementation(s6)\n    assert entry6_old.__dict__ == entry6_new.__dict__\n\n    # Test case 6: Large numbers\n    s7 = \"1625097605, 999999999, 999999999, True, 9999, 9999, 9999, 9999\"\n    entry7_old = HistoryEntry.from_string(s7)\n    entry7_new = HistoryEntry.from_string_new_implementation(s7)\n    assert entry7_old.__dict__ == entry7_new.__dict__\n\n    # Test case 7: Whitespace handling\n    s8 = \" 1625097606 ,  150 , 25 , True , 12 , 12 , 24 , 6 \"\n    entry8_old = HistoryEntry.from_string(s8.strip())\n    entry8_new = HistoryEntry.from_string_new_implementation(s8.strip())\n    assert entry8_old.__dict__ == entry8_new.__dict__\n\nif __name__ == \"__main__\":\n    test_from_string()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are static methods named `from_string` and take a string `s` as input. They split the string by \", \" and convert the resulting values into the appropriate types to create a `HistoryEntry` object. The logic and implementation details are exactly the same in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `from_string` function returns a `HistoryEntry` object, which is a return value. This satisfies the condition.\n- CONDITION 2: The test cases use assertions to compare the `__dict__` attributes of the objects returned by `from_string` and `from_string_new_implementation`. This checks the state of the objects rather than printed or logged content, satisfying the condition.\n- CONDITION 3: The test cases compare the entire state of the objects (`__dict__`), ensuring that `from_string_new_implementation` must have exactly the same functionality as `from_string` to pass all tests. This satisfies the condition.\n- CONDITION 4: The test cases use reasonable assertions by comparing the state of the objects returned by both implementations. This is appropriate given that `from_string` returns an object, satisfying the condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including basic cases, different values, edge cases with zero and negative values, boolean edge cases, large numbers, and whitespace handling. This variety makes the test cases non-trivial, satisfying the condition.",
            "answer": "yes"
        },
        "commit_id": "a9e9e13b7a043be512e422b17e4c5a4e41e7d5e7"
    },
    {
        "func_name": "PlotLidar.separate_clusters",
        "idx": "911",
        "repo_name": "alejotoro-o___lidar_object_detection_ros2",
        "func_path": "src/l_shape_vis_amcl_test.py",
        "orig_func": "def separate_clusters(self, points, labels):\n    unique_labels = set(labels)\n    clusters = []\n    for l in unique_labels:\n        indices = np.where(labels == l)\n        cluster = points[indices[0]]\n        clusters.append(cluster)\n    return clusters",
        "orig_context": "```python\n## src/l_shape_vis_amcl_test.py\nfrom rclpy.node import Node\n\nfrom geometry_msgs.msg import PoseWithCovarianceStamped\n\nfrom sensor_msgs.msg import LaserScan\n\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN\n\nimport matplotlib\n\nimport matplotlib.pyplot as plt\n\nimport matplotlib.animation as anim\n\nfrom scipy.spatial.transform import Rotation as R\n\nclass PlotLidar(Node):\n\n    def __init__(self):\n\n        super().__init__('plot_lidar')\n\n        self.declare_parameter(\"lidar_angular_resolution\", 0.00872665)\n        self.lidar_ang_res = self.get_parameter('lidar_angular_resolution').get_parameter_value().double_value\n        self.declare_parameter(\"x_lim\", 5.0)\n        self.declare_parameter(\"y_lim\", 5.0)\n        self.x_lim = self.get_parameter('x_lim').get_parameter_value().double_value\n        self.y_lim = self.get_parameter('y_lim').get_parameter_value().double_value\n\n        self.pose = [0,0,0]\n        self.ranges = []\n        self.create_subscription(PoseWithCovarianceStamped, \"amcl_pose\", self.pose_callback, 10)\n        self.create_subscription(LaserScan, \"scan\", self.scan_callback, 10)\n\n        self.fig, self.ax = plt.subplots()\n\n    def pose_callback(self, pose_msg):\n\n        r = R.from_quat([pose_msg.pose.pose.orientation.x, pose_msg.pose.pose.orientation.y, pose_msg.pose.pose.orientation.z, pose_msg.pose.pose.orientation.w])\n        theta = r.as_rotvec()[-1]\n\n        self.pose = [pose_msg.pose.pose.position.x,\n                    pose_msg.pose.pose.position.y,\n                    theta]\n        \n    def scan_callback(self, scan_msg):\n        \n        self.ranges = []\n\n        for range in scan_msg.ranges:       \n            if range != float(\"+inf\"):\n                self.ranges.append(range)\n\n    def update_plot(self, frame):\n\n        current_lidar_angle = 0\n        points_x = []\n        points_y = []\n\n        for range in self.ranges:\n\n            point_x = self.pose[0] + range*np.cos(self.pose[2] + current_lidar_angle)\n            point_y = self.pose[1] + range*np.sin(self.pose[2] + current_lidar_angle)\n\n            points_x.append(point_x)\n            points_y.append(point_y)\n            \n            current_lidar_angle += self.lidar_ang_res\n        \n        points_x = np.array(points_x).reshape((-1,1))\n        points_y = np.array(points_y).reshape((-1,1))\n\n        if points_x.shape[0] > 0 and points_y.shape[0] > 0:\n\n            ## Clustering\n            lidar_data = np.concatenate((points_x,points_y),axis=1)\n\n            dbscan = DBSCAN(eps=0.1, min_samples=5)\n            labels = dbscan.fit_predict(lidar_data)\n\n            ## Plot Data\n            self.ax.clear()\n            self.ax.autoscale(False)\n            self.ax.set_xlim(-self.x_lim,self.x_lim)\n            self.ax.set_ylim(-self.y_lim,self.y_lim)\n\n            unique_labels = set(labels)\n            core_samples_mask = np.zeros_like(labels, dtype=bool)\n            core_samples_mask[dbscan.core_sample_indices_] = True\n\n            colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n\n            for k, col in zip(unique_labels, colors):\n                if k == -1:\n                    # Black used for noise.\n                    col = [0, 0, 0, 1]\n\n                class_member_mask = labels == k            \n\n                xy = lidar_data[class_member_mask & core_samples_mask]\n                self.ax.scatter(\n                    xy[:, 0],\n                    xy[:, 1],\n                    s=2,\n                    c=np.array([[col]])\n                )\n\n                ## Obtain L-Shapes\n                if k != -1 and xy.shape[0] > 20:\n                    angle, c, pos = self.cal_l_shape(xy)\n                    rect = matplotlib.patches.Rectangle(pos[0], c[2] - c[0], c[3] - c[1], np.rad2deg(angle), color = \"b\", fill = False)\n                    self.ax.add_patch(rect)\n\n                    ## Rectangle center\n                    x_cent = pos[0][0] + ((c[2] - c[0])*np.cos(angle) - (c[3] - c[1])*np.sin(angle))/2\n                    y_cent = pos[0][1] + ((c[2] - c[0])*np.sin(angle) + (c[3] - c[1])*np.cos(angle))/2\n                    self.ax.scatter([x_cent], [y_cent], s=10, c=\"r\", marker=\"x\")\n\n                xy = lidar_data[class_member_mask & ~core_samples_mask]\n                self.ax.scatter(\n                    xy[:, 0],\n                    xy[:, 1],\n                    s=2,\n                    c=np.array([[col]])\n                )\n\n            self.ax.scatter(self.pose[0],self.pose[1],s=14,c='r')       \n\n        return self.ax\n    \n    def variance_criterion(self, C1, C2):\n\n        c1_max = np.max(C1)\n        c1_min = np.min(C1)\n        c2_max = np.max(C2)\n        c2_min = np.min(C2)\n\n        # Calculate distances d1 and d2\n        d1 = np.minimum(np.abs(c1_max - C1), np.abs(C1 - c1_min))\n        d2 = np.minimum(np.abs(c2_max - C2), np.abs(C2 - c2_min))\n\n        e1 = []\n        e2 = []\n\n        # Compare distances\n        for i in range(len(d1)):\n            if d1[i] < d2[i]:\n                e1.append(d1[i])\n            else:\n                e2.append(d2[i])\n\n        v1 = -np.var(e1) if e1 else 0.0\n        v2 = -np.var(e2) if e2 else 0.0\n\n        gamma = v1 + v2\n\n        return gamma\n    \n    def cal_l_shape(self, points):\n\n        Q = []\n        angle_step = 0.0174533\n\n        for search_theta in np.arange(0, np.pi/2 - angle_step, angle_step):\n\n            e1 = np.array([np.cos(search_theta),np.sin(search_theta)]).T\n            e2 = np.array([-np.sin(search_theta),np.cos(search_theta)]).T\n            C1 = np.dot(points,e1)\n            C2 = np.dot(points,e2)\n            q = self.variance_criterion(C1,C2)\n            Q.append([search_theta,q])\n\n        Q = np.array(Q)\n        i = np.argmax(Q[:,1],axis=0)\n        theta_star = Q[i,0]\n\n        C1_star = np.dot(points, np.array([np.cos(theta_star),np.sin(theta_star)]).T)\n        C2_star = np.dot(points, np.array([-np.sin(theta_star),np.cos(theta_star)]).T)\n\n        c1 = np.min(C1_star)\n        c2 = np.min(C2_star)\n        c3 = np.max(C1_star)\n        c4 = np.max(C2_star)\n\n        a1 = np.cos(theta_star)\n        b1 = np.sin(theta_star)\n\n        a2 = -np.sin(theta_star)\n        b2 = np.cos(theta_star)\n\n        a3 = np.cos(theta_star)\n        b3 = np.sin(theta_star)\n\n        a4 = -np.sin(theta_star)\n        b4 = np.cos(theta_star)\n\n        x1 = (b2*c1 - b1*c2)/(b2*a1 - b1*a2)\n        y1 = (c2 - a2*x1)/b2\n\n        x2 = (b4*c1 - b1*c4)/(b4*a1 - b1*a4)\n        y2 = (c4 - a4*x2)/b4\n\n        x3 = (b4*c3 - b3*c4)/(b4*a3 - b3*a4)\n        y3 = (c4 - a4*x3)/b4\n\n        x4 = (b2*c3 - b3*c2)/(b2*a3 - b3*a2)\n        y4 = (c2 - a2*x4)/b2\n\n        return theta_star, [c1,c2,c3,c4], [(x1,y1), (x2,y2), (x3,y3), (x4,y4)]\n    \n    def separate_clusters(self, points, labels):\n\n        unique_labels = set(labels)\n        clusters = []\n\n        for l in unique_labels:\n\n            indices = np.where(labels == l)\n\n            cluster = points[indices[0]]\n            clusters.append(cluster)\n\n        return clusters\n            \n\n    def _plt(self):\n            \n        self.ani = anim.FuncAnimation(self.fig, self.update_plot, interval=10)\n        plt.show()\n\n```\n\n\n",
        "eval_script": "# Mocking necessary parts to execute the code without ROS 2 environment\nimport numpy as np\nfrom sklearn.cluster import DBSCAN\nimport matplotlib.pyplot as plt\nimport matplotlib.patches\nfrom scipy.spatial.transform import Rotation as R\n\n# Mock Node class to bypass ROS 2 dependencies\nclass Node:\n    def __init__(self, name):\n        self.name = name\n\n    def declare_parameter(self, name, value):\n        setattr(self, name, value)\n\n    def get_parameter(self, name):\n        return self\n\n    def get_parameter_value(self):\n        return self\n\n    def double_value(self):\n        return getattr(self, self.name)\n\n    def create_subscription(self, msg_type, topic, callback, qos):\n        pass\n\n# Original PlotLidar class with minimal modifications\nclass PlotLidar(Node):\n\n    def __init__(self):\n        super().__init__('plot_lidar')\n\n        self.declare_parameter(\"lidar_angular_resolution\", 0.00872665)\n        self.lidar_ang_res = self.get_parameter('lidar_angular_resolution').get_parameter_value().double_value\n        self.declare_parameter(\"x_lim\", 5.0)\n        self.declare_parameter(\"y_lim\", 5.0)\n        self.x_lim = self.get_parameter('x_lim').get_parameter_value().double_value\n        self.y_lim = self.get_parameter('y_lim').get_parameter_value().double_value\n\n        self.pose = [0,0,0]\n        self.ranges = []\n        self.create_subscription(None, \"amcl_pose\", self.pose_callback, 10)\n        self.create_subscription(None, \"scan\", self.scan_callback, 10)\n\n        self.fig, self.ax = plt.subplots()\n\n    def pose_callback(self, pose_msg):\n        r = R.from_quat([pose_msg.pose.pose.orientation.x, pose_msg.pose.pose.orientation.y, pose_msg.pose.pose.orientation.z, pose_msg.pose.pose.orientation.w])\n        theta = r.as_rotvec()[-1]\n\n        self.pose = [pose_msg.pose.pose.position.x,\n                    pose_msg.pose.pose.position.y,\n                    theta]\n        \n    def scan_callback(self, scan_msg):\n        self.ranges = []\n\n        for range in scan_msg.ranges:       \n            if range != float(\"+inf\"):\n                self.ranges.append(range)\n\n    def update_plot(self, frame):\n        current_lidar_angle = 0\n        points_x = []\n        points_y = []\n\n        for range in self.ranges:\n            point_x = self.pose[0] + range*np.cos(self.pose[2] + current_lidar_angle)\n            point_y = self.pose[1] + range*np.sin(self.pose[2] + current_lidar_angle)\n\n            points_x.append(point_x)\n            points_y.append(point_y)\n            \n            current_lidar_angle += self.lidar_ang_res\n        \n        points_x = np.array(points_x).reshape((-1,1))\n        points_y = np.array(points_y).reshape((-1,1))\n\n        if points_x.shape[0] > 0 and points_y.shape[0] > 0:\n            lidar_data = np.concatenate((points_x,points_y),axis=1)\n\n            dbscan = DBSCAN(eps=0.1, min_samples=5)\n            labels = dbscan.fit_predict(lidar_data)\n\n            self.ax.clear()\n            self.ax.autoscale(False)\n            self.ax.set_xlim(-self.x_lim,self.x_lim)\n            self.ax.set_ylim(-self.y_lim,self.y_lim)\n\n            unique_labels = set(labels)\n            core_samples_mask = np.zeros_like(labels, dtype=bool)\n            core_samples_mask[dbscan.core_sample_indices_] = True\n\n            colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n\n            for k, col in zip(unique_labels, colors):\n                if k == -1:\n                    col = [0, 0, 0, 1]\n\n                class_member_mask = labels == k            \n\n                xy = lidar_data[class_member_mask & core_samples_mask]\n                self.ax.scatter(\n                    xy[:, 0],\n                    xy[:, 1],\n                    s=2,\n                    c=np.array([[col]])\n                )\n\n                if k != -1 and xy.shape[0] > 20:\n                    angle, c, pos = self.cal_l_shape(xy)\n                    rect = matplotlib.patches.Rectangle(pos[0], c[2] - c[0], c[3] - c[1], np.rad2deg(angle), color = \"b\", fill = False)\n                    self.ax.add_patch(rect)\n\n                    x_cent = pos[0][0] + ((c[2] - c[0])*np.cos(angle) - (c[3] - c[1])*np.sin(angle))/2\n                    y_cent = pos[0][1] + ((c[2] - c[0])*np.sin(angle) + (c[3] - c[1])*np.cos(angle))/2\n                    self.ax.scatter([x_cent], [y_cent], s=10, c=\"r\", marker=\"x\")\n\n                xy = lidar_data[class_member_mask & ~core_samples_mask]\n                self.ax.scatter(\n                    xy[:, 0],\n                    xy[:, 1],\n                    s=2,\n                    c=np.array([[col]])\n                )\n\n            self.ax.scatter(self.pose[0],self.pose[1],s=14,c='r')       \n\n        return self.ax\n    \n    def variance_criterion(self, C1, C2):\n        c1_max = np.max(C1)\n        c1_min = np.min(C1)\n        c2_max = np.max(C2)\n        c2_min = np.min(C2)\n\n        d1 = np.minimum(np.abs(c1_max - C1), np.abs(C1 - c1_min))\n        d2 = np.minimum(np.abs(c2_max - C2), np.abs(C2 - c2_min))\n\n        e1 = []\n        e2 = []\n\n        for i in range(len(d1)):\n            if d1[i] < d2[i]:\n                e1.append(d1[i])\n            else:\n                e2.append(d2[i])\n\n        v1 = -np.var(e1) if e1 else 0.0\n        v2 = -np.var(e2) if e2 else 0.0\n\n        gamma = v1 + v2\n\n        return gamma\n    \n    def cal_l_shape(self, points):\n        Q = []\n        angle_step = 0.0174533\n\n        for search_theta in np.arange(0, np.pi/2 - angle_step, angle_step):\n            e1 = np.array([np.cos(search_theta),np.sin(search_theta)]).T\n            e2 = np.array([-np.sin(search_theta),np.cos(search_theta)]).T\n            C1 = np.dot(points,e1)\n            C2 = np.dot(points,e2)\n            q = self.variance_criterion(C1,C2)\n            Q.append([search_theta,q])\n\n        Q = np.array(Q)\n        i = np.argmax(Q[:,1],axis=0)\n        theta_star = Q[i,0]\n\n        C1_star = np.dot(points, np.array([np.cos(theta_star),np.sin(theta_star)]).T)\n        C2_star = np.dot(points, np.array([-np.sin(theta_star),np.cos(theta_star)]).T)\n\n        c1 = np.min(C1_star)\n        c2 = np.min(C2_star)\n        c3 = np.max(C1_star)\n        c4 = np.max(C2_star)\n\n        a1 = np.cos(theta_star)\n        b1 = np.sin(theta_star)\n\n        a2 = -np.sin(theta_star)\n        b2 = np.cos(theta_star)\n\n        a3 = np.cos(theta_star)\n        b3 = np.sin(theta_star)\n\n        a4 = -np.sin(theta_star)\n        b4 = np.cos(theta_star)\n\n        x1 = (b2*c1 - b1*c2)/(b2*a1 - b1*a2)\n        y1 = (c2 - a2*x1)/b2\n\n        x2 = (b4*c1 - b1*c4)/(b4*a1 - b1*a4)\n        y2 = (c4 - a4*x2)/b4\n\n        x3 = (b4*c3 - b3*c4)/(b4*a3 - b3*a4)\n        y3 = (c4 - a4*x3)/b4\n\n        x4 = (b2*c3 - b3*c2)/(b2*a3 - b3*a2)\n        y4 = (c2 - a2*x4)/b2\n\n        return theta_star, [c1,c2,c3,c4], [(x1,y1), (x2,y2), (x3,y3), (x4,y4)]\n    \n    def separate_clusters(self, points, labels):\n        unique_labels = set(labels)\n        clusters = []\n\n        for l in unique_labels:\n            indices = np.where(labels == l)\n            cluster = points[indices[0]]\n            clusters.append(cluster)\n\n        return clusters\n\n\n    def _plt(self):\n        self.ani = anim.FuncAnimation(self.fig, self.update_plot, interval=10)\n        plt.show()\n\ndef test_separate_clusters():\n    plot_lidar = PlotLidar()\n\n    # Test case 1: Multiple clusters\n    points = np.array([[1, 2], [2, 3], [3, 4], [8, 8], [9, 9]])\n    labels = np.array([0, 0, 0, 1, 1])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\n    # Test case 2: Single cluster\n    points = np.array([[1, 2], [2, 3], [3, 4]])\n    labels = np.array([0, 0, 0])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\n    # Test case 3: No points\n    points = np.array([])\n    labels = np.array([])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n\n    # Test case 4: Single point\n    points = np.array([[1, 2]])\n    labels = np.array([0])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\n    # Test case 5: All noise points\n    points = np.array([[1, 2], [2, 3], [3, 4]])\n    labels = np.array([-1, -1, -1])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n\n    # Test case 6: Mixed noise and clusters\n    points = np.array([[1, 2], [2, 3], [3, 4], [8, 8], [9, 9]])\n    labels = np.array([0, 0, -1, 1, 1])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\n    # Test case 7: Non-sequential labels\n    points = np.array([[1, 2], [2, 3], [3, 4], [8, 8], [9, 9]])\n    labels = np.array([10, 10, 10, 20, 20])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\n    # Test case 8: Large number of clusters\n    points = np.array([[i, i] for i in range(100)])\n    labels = np.array([i // 10 for i in range(100)])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\n    # Test case 9: Negative and positive coordinates\n    points = np.array([[-1, -2], [-2, -3], [3, 4], [8, 8], [-9, 9]])\n    labels = np.array([0, 0, 1, 1, 2])\n    original_clusters = plot_lidar.separate_clusters(points, labels)\n    new_clusters = plot_lidar.separate_clusters_new_implementation(points, labels)\n    assert len(original_clusters) == len(new_clusters)\n    for oc, nc in zip(original_clusters, new_clusters):\n        assert np.array_equal(oc, nc)\n\nif __name__ == \"__main__\":\n    test_separate_clusters()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `separate_clusters` is identical to the ORIGINAL FUNCTION. Both functions iterate over unique labels, find indices where the labels match, extract the corresponding points, and append them to a list of clusters. The logic and implementation are the same in both versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `separate_clusters` function returns a list of clusters, which satisfies the condition of having return values.\n- [CONDITION 2] The test cases use assertions to check the return values of the function, not printed or logged contents.\n- [CONDITION 3] The test cases compare the outputs of `separate_clusters` and `separate_clusters_new_implementation` to ensure they have the same functionality.\n- [CONDITION 4] The test cases use assertions to compare the lengths and contents of the clusters returned by both implementations, which is reasonable given that the function returns values.\n- [CONDITION 5] The test cases cover a variety of scenarios, including multiple clusters, single clusters, no points, noise points, non-sequential labels, and large numbers of clusters, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "75479d06aa3928e3760406020a2ba86d4768dd42"
    },
    {
        "func_name": "Blockchain.hash",
        "idx": "916",
        "repo_name": "tinesime___blockchain",
        "func_path": "main.py",
        "orig_func": "@staticmethod\ndef hash(block):\n    block_string = json.dumps(block, sort_keys=True).encode()\n    return hashlib.sha256(block_string).hexdigest()",
        "orig_context": "```python\n## main.py\nimport hashlib\n\nimport json\n\nfrom time import time\n\nfrom urllib.parse import urlparse\n\nimport requests\n\nclass Blockchain(object):\n    def __init__(self):\n        self.chain = [] # Blockchain\n        self.current_transactions = []\n        self.nodes = set()\n\n        self.new_block(previous_hash=1, proof=1000)\n\n    def new_block(self, proof, previous_hash=None):\n        block = {\n            'index': len(self.chain) + 1,\n            'timestamp': time(),\n            'transactions': self.current_transactions,\n            'proof': proof,\n            'previous_hash': previous_hash or self.hash(self.chain[-1]),\n        }\n\n        self.current_transactions = []\n\n        self.chain.append(block)\n        return block\n\n    def new_transaction(self, sender, recipient, data):\n        self.current_transactions.append({\n            'sender': sender,\n            'recipient': recipient,\n            'data': data,\n        })\n\n        return self.last_block['index'] + 1\n\n    @staticmethod\n    def hash(block):\n        block_string = json.dumps(block, sort_keys=True).encode()\n        return hashlib.sha256(block_string).hexdigest()\n\n    @property\n    def last_block(self):\n        return self.chain[-1]\n\n    def proof_of_work(self, last_proof):\n        proof = 0\n        while not self.valid_proof(last_proof, proof):\n            proof += 1\n\n        return proof\n\n    @staticmethod\n    def valid_proof(last_proof, proof):\n        guess = f'{last_proof}{proof}'.encode()\n        guess_hash = hashlib.sha256(guess).hexdigest()\n        return guess_hash[:6] == \"000000\"\n\n    def register_node(self, address):\n        parsed_url = urlparse(address)\n        self.nodes.add(parsed_url.netloc)\n\n    def valid_chain(self, chain):\n        last_block = chain[0]\n        current_index = 1\n\n        while current_index < len(chain):\n            block = chain[current_index]\n\n            if block['previous_hash'] != self.hash(last_block):\n                return False\n\n            if not self.valid_proof(last_block['proof'], block['proof']):\n                return False\n\n            last_block = block\n            current_index += 1\n\n        return True\n\n    def resolve_conflicts(self):\n        neighbours = self.nodes\n        new_chain = None\n\n        max_length = len(self.chain)\n\n        for node in neighbours:\n            response = requests.get(f'http://{node}/chain')\n\n            if response.status_code == 200:\n                length = response.json()['length']\n                chain = response.json()['chain']\n\n                if length > max_length and self.valid_chain(chain):\n                    max_length = length\n                    new_chain = chain\n\n        if new_chain:\n            self.chain = new_chain\n            return True\n\n        return False\n\n```\n\n\n",
        "eval_script": "## main.py\nimport hashlib\n\nimport json\n\nfrom time import time\n\nfrom urllib.parse import urlparse\n\nimport requests\n\nclass Blockchain(object):\n    def __init__(self):\n        self.chain = [] # Blockchain\n        self.current_transactions = []\n        self.nodes = set()\n\n        self.new_block(previous_hash=1, proof=1000)\n\n    def new_block(self, proof, previous_hash=None):\n        block = {\n            'index': len(self.chain) + 1,\n            'timestamp': time(),\n            'transactions': self.current_transactions,\n            'proof': proof,\n            'previous_hash': previous_hash or self.hash(self.chain[-1]),\n        }\n\n        self.current_transactions = []\n\n        self.chain.append(block)\n        return block\n\n    def new_transaction(self, sender, recipient, data):\n        self.current_transactions.append({\n            'sender': sender,\n            'recipient': recipient,\n            'data': data,\n        })\n\n        return self.last_block['index'] + 1\n\n    @staticmethod\n    def hash(block):\n        block_string = json.dumps(block, sort_keys=True).encode()\n        return hashlib.sha256(block_string).hexdigest()\n\n\n    @property\n    def last_block(self):\n        return self.chain[-1]\n\n    def proof_of_work(self, last_proof):\n        proof = 0\n        while not self.valid_proof(last_proof, proof):\n            proof += 1\n\n        return proof\n\n    @staticmethod\n    def valid_proof(last_proof, proof):\n        guess = f'{last_proof}{proof}'.encode()\n        guess_hash = hashlib.sha256(guess).hexdigest()\n        return guess_hash[:6] == \"000000\"\n\n    def register_node(self, address):\n        parsed_url = urlparse(address)\n        self.nodes.add(parsed_url.netloc)\n\n    def valid_chain(self, chain):\n        last_block = chain[0]\n        current_index = 1\n\n        while current_index < len(chain):\n            block = chain[current_index]\n\n            if block['previous_hash'] != self.hash(last_block):\n                return False\n\n            if not self.valid_proof(last_block['proof'], block['proof']):\n                return False\n\n            last_block = block\n            current_index += 1\n\n        return True\n\n    def resolve_conflicts(self):\n        neighbours = self.nodes\n        new_chain = None\n\n        max_length = len(self.chain)\n\n        for node in neighbours:\n            response = requests.get(f'http://{node}/chain')\n\n            if response.status_code == 200:\n                length = response.json()['length']\n                chain = response.json()['chain']\n\n                if length > max_length and self.valid_chain(chain):\n                    max_length = length\n                    new_chain = chain\n\n        if new_chain:\n            self.chain = new_chain\n            return True\n\n        return False\n\ndef test_hash():\n    blockchain = Blockchain()\n\n    # Test case 1: Hashing an empty block\n    empty_block = {}\n    assert Blockchain.hash(empty_block) == Blockchain.hash_new_implementation(empty_block)\n\n    # Test case 2: Hashing a block with transactions\n    blockchain.new_transaction(\"Alice\", \"Bob\", \"10 BTC\")\n    block_with_transactions = blockchain.new_block(12345)\n    assert Blockchain.hash(block_with_transactions) == Blockchain.hash_new_implementation(block_with_transactions)\n\n    # Test case 3: Hashing a block with a specific proof value\n    block_with_proof = {\n        'index': 1,\n        'timestamp': time(),\n        'transactions': [],\n        'proof': 6789,\n        'previous_hash': 'abc123'\n    }\n    assert Blockchain.hash(block_with_proof) == Blockchain.hash_new_implementation(block_with_proof)\n\n    # Test case 4: Hashing a block with various data types\n    block_with_various_types = {\n        'index': 2,\n        'timestamp': time(),\n        'transactions': [{'amount': 100, 'valid': True}],\n        'proof': 1234,\n        'previous_hash': 'def456'\n    }\n    assert Blockchain.hash(block_with_various_types) == Blockchain.hash_new_implementation(block_with_various_types)\n\n    # Test case 5: Hashing a block with nested structures\n    block_with_nested_structures = {\n        'index': 3,\n        'timestamp': time(),\n        'transactions': [{'details': {'amount': 50, 'currency': 'USD'}}],\n        'proof': 5678,\n        'previous_hash': 'ghi789'\n    }\n    assert Blockchain.hash(block_with_nested_structures) == Blockchain.hash_new_implementation(block_with_nested_structures)\n\n    # Test case 6: Hashing a block with special characters\n    block_with_special_characters = {\n        'index': 4,\n        'timestamp': time(),\n        'transactions': [{'message': 'Hello, world! \ud83d\ude0a'}],\n        'proof': 91011,\n        'previous_hash': 'jkl012'\n    }\n    assert Blockchain.hash(block_with_special_characters) == Blockchain.hash_new_implementation(block_with_special_characters)\n\n    # Test case 7: Hashing a block with large data\n    large_data = 'x' * 10000  # 10,000 characters\n    block_with_large_data = {\n        'index': 5,\n        'timestamp': time(),\n        'transactions': [{'data': large_data}],\n        'proof': 131415,\n        'previous_hash': 'mno345'\n    }\n    assert Blockchain.hash(block_with_large_data) == Blockchain.hash_new_implementation(block_with_large_data)\n\n    # Test case 8: Hashing a block with None values\n    block_with_none_values = {\n        'index': 6,\n        'timestamp': time(),\n        'transactions': [{'sender': None, 'recipient': None, 'amount': None}],\n        'proof': 161718,\n        'previous_hash': 'pqr678'\n    }\n    assert Blockchain.hash(block_with_none_values) == Blockchain.hash_new_implementation(block_with_none_values)\n\nif __name__ == \"__main__\":\n    test_hash()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are static methods named `hash`, which take a `block` as input, convert it to a JSON string with sorted keys, encode it, and then compute and return the SHA-256 hash of the encoded string. There are no differences in the implementation or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `hash` function returns a hash value, satisfying the condition of having return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `hash` and `hash_new_implementation`, not printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `hash` and `hash_new_implementation` for various inputs. If `hash_new_implementation` produces the same outputs for all these inputs, it must have the same functionality as `hash`.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `hash` has a return value.\n- CONDITION 5: The test cases cover a wide range of scenarios, including empty blocks, blocks with transactions, blocks with various data types, nested structures, special characters, large data, and None values, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "4e8087ff8547e7a02c7b5cdf7045cc66024481f7"
    },
    {
        "func_name": "Blockchain.valid_proof",
        "idx": "917",
        "repo_name": "tinesime___blockchain",
        "func_path": "main.py",
        "orig_func": "@staticmethod\ndef valid_proof(last_proof, proof):\n    guess = f'{last_proof}{proof}'.encode()\n    guess_hash = hashlib.sha256(guess).hexdigest()\n    return guess_hash[:6] == '000000'",
        "orig_context": "```python\n## main.py\nimport hashlib\n\nimport json\n\nfrom time import time\n\nfrom urllib.parse import urlparse\n\nimport requests\n\nclass Blockchain(object):\n    def __init__(self):\n        self.chain = [] # Blockchain\n        self.current_transactions = []\n        self.nodes = set()\n\n        self.new_block(previous_hash=1, proof=1000)\n\n    def new_block(self, proof, previous_hash=None):\n        block = {\n            'index': len(self.chain) + 1,\n            'timestamp': time(),\n            'transactions': self.current_transactions,\n            'proof': proof,\n            'previous_hash': previous_hash or self.hash(self.chain[-1]),\n        }\n\n        self.current_transactions = []\n\n        self.chain.append(block)\n        return block\n\n    def new_transaction(self, sender, recipient, data):\n        self.current_transactions.append({\n            'sender': sender,\n            'recipient': recipient,\n            'data': data,\n        })\n\n        return self.last_block['index'] + 1\n\n    @staticmethod\n    def hash(block):\n        block_string = json.dumps(block, sort_keys=True).encode()\n        return hashlib.sha256(block_string).hexdigest()\n\n    @property\n    def last_block(self):\n        return self.chain[-1]\n\n    def proof_of_work(self, last_proof):\n        proof = 0\n        while not self.valid_proof(last_proof, proof):\n            proof += 1\n\n        return proof\n\n    @staticmethod\n    def valid_proof(last_proof, proof):\n        guess = f'{last_proof}{proof}'.encode()\n        guess_hash = hashlib.sha256(guess).hexdigest()\n        return guess_hash[:6] == \"000000\"\n\n    def register_node(self, address):\n        parsed_url = urlparse(address)\n        self.nodes.add(parsed_url.netloc)\n\n    def valid_chain(self, chain):\n        last_block = chain[0]\n        current_index = 1\n\n        while current_index < len(chain):\n            block = chain[current_index]\n\n            if block['previous_hash'] != self.hash(last_block):\n                return False\n\n            if not self.valid_proof(last_block['proof'], block['proof']):\n                return False\n\n            last_block = block\n            current_index += 1\n\n        return True\n\n    def resolve_conflicts(self):\n        neighbours = self.nodes\n        new_chain = None\n\n        max_length = len(self.chain)\n\n        for node in neighbours:\n            response = requests.get(f'http://{node}/chain')\n\n            if response.status_code == 200:\n                length = response.json()['length']\n                chain = response.json()['chain']\n\n                if length > max_length and self.valid_chain(chain):\n                    max_length = length\n                    new_chain = chain\n\n        if new_chain:\n            self.chain = new_chain\n            return True\n\n        return False\n\n```\n\n\n",
        "eval_script": "## main.py\nimport hashlib\n\nimport json\n\nfrom time import time\n\nfrom urllib.parse import urlparse\n\nimport requests\n\nclass Blockchain(object):\n    def __init__(self):\n        self.chain = [] # Blockchain\n        self.current_transactions = []\n        self.nodes = set()\n\n        self.new_block(previous_hash=1, proof=1000)\n\n    def new_block(self, proof, previous_hash=None):\n        block = {\n            'index': len(self.chain) + 1,\n            'timestamp': time(),\n            'transactions': self.current_transactions,\n            'proof': proof,\n            'previous_hash': previous_hash or self.hash(self.chain[-1]),\n        }\n\n        self.current_transactions = []\n\n        self.chain.append(block)\n        return block\n\n    def new_transaction(self, sender, recipient, data):\n        self.current_transactions.append({\n            'sender': sender,\n            'recipient': recipient,\n            'data': data,\n        })\n\n        return self.last_block['index'] + 1\n\n    @staticmethod\n    def hash(block):\n        block_string = json.dumps(block, sort_keys=True).encode()\n        return hashlib.sha256(block_string).hexdigest()\n\n    @property\n    def last_block(self):\n        return self.chain[-1]\n\n    def proof_of_work(self, last_proof):\n        proof = 0\n        while not self.valid_proof(last_proof, proof):\n            proof += 1\n\n        return proof\n\n    @staticmethod\n    def valid_proof(last_proof, proof):\n        guess = f'{last_proof}{proof}'.encode()\n        guess_hash = hashlib.sha256(guess).hexdigest()\n        return guess_hash[:6] == \"000000\"\n\n\n    def register_node(self, address):\n        parsed_url = urlparse(address)\n        self.nodes.add(parsed_url.netloc)\n\n    def valid_chain(self, chain):\n        last_block = chain[0]\n        current_index = 1\n\n        while current_index < len(chain):\n            block = chain[current_index]\n\n            if block['previous_hash'] != self.hash(last_block):\n                return False\n\n            if not self.valid_proof(last_block['proof'], block['proof']):\n                return False\n\n            last_block = block\n            current_index += 1\n\n        return True\n\n    def resolve_conflicts(self):\n        neighbours = self.nodes\n        new_chain = None\n\n        max_length = len(self.chain)\n\n        for node in neighbours:\n            response = requests.get(f'http://{node}/chain')\n\n            if response.status_code == 200:\n                length = response.json()['length']\n                chain = response.json()['chain']\n\n                if length > max_length and self.valid_chain(chain):\n                    max_length = length\n                    new_chain = chain\n\n        if new_chain:\n            self.chain = new_chain\n            return True\n\n        return False\n\ndef test_valid_proof():\n    # Test case 1: Valid proof\n    last_proof = 100\n    proof = 35293  # Assume this is a valid proof\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 2: Invalid proof\n    last_proof = 100\n    proof = 35294  # Assume this is an invalid proof\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 3: Edge case with zero proof\n    last_proof = 0\n    proof = 0\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 4: Negative proof values\n    last_proof = -100\n    proof = -35293\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 5: Large proof values\n    last_proof = 10**6\n    proof = 10**6 + 1\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 6: Boundary condition (just below valid)\n    last_proof = 100\n    proof = 35292  # Assume this is just below a valid proof\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 7: Boundary condition (just above valid)\n    last_proof = 100\n    proof = 35294  # Assume this is just above a valid proof\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\n    # Test case 8: Random proof values\n    import random\n    last_proof = random.randint(0, 100000)\n    proof = random.randint(0, 100000)\n    assert Blockchain.valid_proof(last_proof, proof) == Blockchain.valid_proof_new_implementation(last_proof, proof)\n\nif __name__ == \"__main__\":\n    test_valid_proof()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are static methods named `valid_proof`, and they perform the same operations: they concatenate `last_proof` and `proof` into a string, encode it, compute its SHA-256 hash, and check if the first six characters of the hash are '000000'. There are no changes in logic or functionality between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `valid_proof` function returns a boolean value indicating whether the proof is valid. This satisfies the condition as it has a return value.\n  \n- CONDITION 2: The test cases use assertions to check the return values of `valid_proof` and `valid_proof_new_implementation`, not printed or logged contents. This condition is satisfied.\n\n- CONDITION 3: The test cases compare the outputs of `valid_proof` and `valid_proof_new_implementation` for various inputs. If `valid_proof_new_implementation` has the same functionality as `valid_proof`, it will pass all tests. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `valid_proof` returns a value. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a range of scenarios, including valid and invalid proofs, edge cases, negative values, large values, boundary conditions, and random values. This makes the test cases non-trivial. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "4e8087ff8547e7a02c7b5cdf7045cc66024481f7"
    },
    {
        "func_name": "UserSignupSerializers.validate",
        "idx": "931",
        "repo_name": "parsarezaee___TaskMate",
        "func_path": "users/serializers.py",
        "orig_func": "def validate(self, attrs):\n    if attrs['password'] != attrs['password2']:\n        raise serializers.ValidationError({'password': \"password fields didn't match.\"})\n    return attrs",
        "orig_context": "```python\n## users/models.py\nfrom django.contrib.auth.base_user import BaseUserManager\n\nfrom django.utils.translation import gettext_lazy as _\n\nfrom django.contrib.auth.models import AbstractUser\n\nfrom django.db import models\n\nclass CustomManagerUser(BaseUserManager):\n\n    def create_user(self, first_name, last_name, email, password, **extra_fields):\n        if not first_name:\n            raise ValueError(_(\"Your first name must be set\"))\n        if not last_name:\n            raise ValueError(_(\"Your last name must be set\"))\n        if not email:\n            raise ValueError(_(\"The Email must be set\"))\n        email = self.normalize_email(email)\n        user = self.model(first_name=first_name,\n                          last_name= last_name,\n                          email=email,\n                          **extra_fields)\n        user.set_password(password)\n        user.save()\n        return user\n    \n    def create_superuser(self, first_name, last_name, email, password, **extra_fields):\n\n        extra_fields.setdefault(\"is_staff\", True)\n        extra_fields.setdefault(\"is_superuser\", True)\n        extra_fields.setdefault(\"is_active\", True)\n\n        if extra_fields.get(\"is_staff\") is not True:\n            raise ValueError(_(\"Superuser must have is_staff=True\"))\n        if extra_fields.get(\"is_superuser\") is not True:\n            raise ValueError(_(\"Superuser must have is_superuser=True\"))\n        return self.create_user(first_name, last_name, email, password, **extra_fields)\n\nclass CustomUser(AbstractUser):\n    username = None\n    first_name = models.CharField(_(\"first name\"), max_length=50)\n    last_name = models.CharField(_(\"last name\"), max_length=50)\n    email = models.EmailField(_(\"email address\"), unique=True)\n    USERNAME_FIELD = \"email\"\n    REQUIRED_FIELDS = ['first_name', 'last_name',]\n\n    objects = CustomManagerUser()\n\n    def __str__(self):\n        return self.email\n\n```\n\n\n```python\n## users/serializers.py\nfrom rest_framework import serializers\n\nfrom .models import CustomUser\n\nclass UserSignupSerializers(serializers.ModelSerializer):\n    password = serializers.CharField(write_only=True, required=True)\n    password2 = serializers.CharField(write_only=True, required=True)\n\n    class Meta:\n        model = CustomUser\n        fields = ('first_name', 'last_name', 'email', 'password', 'password2')\n    \n    def validate(self, attrs):\n        if attrs['password'] != attrs['password2']:\n            raise serializers.ValidationError({\"password\": \"password fields didn't match.\"})\n        return attrs\n    \n    def create(self, validated_data):\n        validated_data.pop('password2')\n        user = CustomUser.objects.create_user(**validated_data)\n        return user\n\n```\n\n\n",
        "eval_script": "# Mock setup for Django ORM and REST framework\n\n# Mocking Django's translation function\ndef gettext_lazy(s):\n    return s\n\n# Alias gettext_lazy to _\n_ = gettext_lazy\n\n# Mocking Django's models and serializers\nclass MockModel:\n    def save(self):\n        pass\n\nclass MockBaseUserManager:\n    def normalize_email(self, email):\n        return email.lower()\n\n# Mocking Django's AbstractUser\nclass AbstractUser(MockModel):\n    pass\n\n# Mocking Django's CharField and EmailField\nclass CharField:\n    def __init__(self, *args, **kwargs):\n        pass\n\nclass EmailField:\n    def __init__(self, *args, **kwargs):\n        pass\n\n# Mocking Django's serializers\nclass serializers:\n    class ModelSerializer:\n        pass\n    \n    class CharField:\n        def __init__(self, write_only=False, required=False):\n            pass\n    \n    class ValidationError(Exception):\n        pass\n\n# Mocking the CustomManagerUser\nclass CustomManagerUser(MockBaseUserManager):\n    def create_user(self, first_name, last_name, email, password, **extra_fields):\n        if not first_name:\n            raise ValueError(gettext_lazy(\"Your first name must be set\"))\n        if not last_name:\n            raise ValueError(gettext_lazy(\"Your last name must be set\"))\n        if not email:\n            raise ValueError(gettext_lazy(\"The Email must be set\"))\n        email = self.normalize_email(email)\n        user = CustomUser(first_name=first_name, last_name=last_name, email=email, **extra_fields)\n        user.set_password(password)\n        user.save()\n        return user\n\n# Mocking the CustomUser model\nclass CustomUser(AbstractUser):\n    username = None\n    first_name = CharField(_(\"first name\"), max_length=50)\n    last_name = CharField(_(\"last name\"), max_length=50)\n    email = EmailField(_(\"email address\"), unique=True)\n    USERNAME_FIELD = \"email\"\n    REQUIRED_FIELDS = ['first_name', 'last_name']\n\n    objects = CustomManagerUser()\n\n    def __str__(self):\n        return self.email\n\n    def set_password(self, password):\n        self.password = password\n\n# UserSignupSerializers definition\nclass UserSignupSerializers(serializers.ModelSerializer):\n    password = serializers.CharField(write_only=True, required=True)\n    password2 = serializers.CharField(write_only=True, required=True)\n\n    class Meta:\n        model = CustomUser\n        fields = ('first_name', 'last_name', 'email', 'password', 'password2')\n    \n    def validate(self, attrs):\n        if attrs['password'] != attrs['password2']:\n            raise serializers.ValidationError({\"password\": \"password fields didn't match.\"})\n        return attrs\n\n\n    \n    def create(self, validated_data):\n        validated_data.pop('password2')\n        user = CustomUser.objects.create_user(**validated_data)\n        return user\n\n\ndef test_validate():\n    serializer = UserSignupSerializers()\n    \n    # Test case 1: Matching passwords\n    data1 = {\n        'first_name': 'John',\n        'last_name': 'Doe',\n        'email': 'john.doe@example.com',\n        'password': 'password123',\n        'password2': 'password123'\n    }\n    assert serializer.validate(data1) == serializer.validate_new_implementation(data1)\n    \n    # Test case 2: Non-matching passwords\n    data2 = {\n        'first_name': 'Jane',\n        'last_name': 'Doe',\n        'email': 'jane.doe@example.com',\n        'password': 'password123',\n        'password2': 'password321'\n    }\n    try:\n        serializer.validate(data2)\n    except serializers.ValidationError as e1:\n        try:\n            serializer.validate_new_implementation(data2)\n        except serializers.ValidationError as e2:\n            assert str(e1) == str(e2)\n    \n    # Test case 3: Different data types\n    data3 = {\n        'first_name': 'Alice',\n        'last_name': 'Smith',\n        'email': 'alice.smith@example.com',\n        'password': 123456,\n        'password2': 123456\n    }\n    assert serializer.validate(data3) == serializer.validate_new_implementation(data3)\n\nif __name__ == \"__main__\":\n    test_validate()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `UserSignupSerializers` class is the `validate` method, which checks if the `password` and `password2` fields in the `attrs` dictionary match. If they do not match, it raises a `serializers.ValidationError`. Otherwise, it returns the `attrs` dictionary. This functionality is identical to the ORIGINAL FUNCTION, which performs the same check and raises the same error under the same conditions. There are no changes in logic or behavior between the two functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `validate` function returns a value (the `attrs` dictionary) if the passwords match, and raises an exception if they don't. This satisfies the condition as it has return values or modifies input arguments.\n  \n- CONDITION 2: The test cases use assertions to compare return values and exceptions, not printed or logged content. This satisfies the condition.\n\n- CONDITION 3: The test cases compare the outputs of `validate` and `validate_new_implementation` directly for both matching and non-matching passwords. This ensures that `validate_new_implementation` must have the same functionality as `validate` to pass all tests. This satisfies the condition.\n\n- CONDITION 4: The test cases use assertions appropriately to compare return values and exceptions, which is reasonable given the behavior of `validate`. This satisfies the condition.\n\n- CONDITION 5: The test cases cover different scenarios: matching passwords, non-matching passwords, and different data types for passwords. This provides a non-trivial test suite. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "512c4a3315919cc84a54fde302cdff26b8899f12"
    },
    {
        "func_name": "BookingReport.pull_deal_boxes",
        "idx": "945",
        "repo_name": "SelyamiDurmush___selenium-booking-automation",
        "func_path": "booking/booking_report.py",
        "orig_func": "def pull_deal_boxes(self):\n    deal_boxes = self.boxes_section_element.find_elements(By.XPATH, './/div[@data-testid=\"property-card-container\"]')\n    return deal_boxes",
        "orig_context": "```python\n## booking/booking_report.py\nimport re\n\nfrom selenium.webdriver.common.by import By\n\nfrom selenium.webdriver.remote.webdriver import WebElement\n\nclass BookingReport:\n    def __init__(self, boxes_section_element: WebElement):\n        self.boxes_section_element = boxes_section_element\n        self.deal_boxes = self.pull_deal_boxes()\n\n    def pull_deal_boxes(self):\n        deal_boxes = self.boxes_section_element.find_elements(By.XPATH, './/div[@data-testid=\"property-card-container\"]')\n        return deal_boxes\n    \n    def pull_deal_box_attributes(self):\n        collected_data = []\n\n        for deal_box in self.deal_boxes:\n            hotel_name = deal_box.find_element(By.XPATH, './/div[@data-testid=\"title\"]')\n            hotel_name = hotel_name.get_attribute('innerHTML').strip()\n            \n            hotel_price = deal_box.find_element(By.XPATH, './/span[@data-testid=\"price-and-discounted-price\"]').text.strip()\n            \n            hotel_score = deal_box.find_element(By.XPATH, './/div[@data-testid=\"review-score\"]')\n            score_text = hotel_score.find_element(By.XPATH, './/div').text.strip()          \n            score_text = re.search(r'\\d+\\.\\d+', score_text)\n            if score_text:\n                score_text = score_text.group()\n            else:\n                score_text = None  # Handle the case if no score is found\n\n            collected_data.append([hotel_name, hotel_price, score_text])\n        print(\"Number of hotel numbers:\", len(self.deal_boxes))\n        return collected_data\n\n```\n\n\n",
        "eval_script": "# booking/booking_report.py\nimport re\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.remote.webdriver import WebElement\n\nclass MockWebElement:\n    def __init__(self, tag_name='', text='', attributes=None, children=None):\n        self.tag_name = tag_name\n        self.text = text\n        self.attributes = attributes or {}\n        self.children = children or []\n\n    def find_elements(self, by=By.XPATH, value=None):\n        # Return a list of mock deal boxes\n        if value == './/div[@data-testid=\"property-card-container\"]':\n            return [MockWebElement(children=[\n                MockWebElement(tag_name='div', attributes={'data-testid': 'title'}, text='Hotel Mock Name'),\n                MockWebElement(tag_name='span', attributes={'data-testid': 'price-and-discounted-price'}, text='$100'),\n                MockWebElement(tag_name='div', attributes={'data-testid': 'review-score'}, children=[\n                    MockWebElement(tag_name='div', text='8.5')\n                ])\n            ])]\n        return []\n\n    def find_element(self, by=By.XPATH, value=None):\n        # Return the first child that matches the criteria\n        for child in self.children:\n            if value in child.attributes.values():\n                return child\n        return MockWebElement()\n\n    def get_attribute(self, attr_name):\n        return self.attributes.get(attr_name, '')\n\n    def __eq__(self, other):\n        if not isinstance(other, MockWebElement):\n            return False\n        return (self.tag_name == other.tag_name and\n                self.text == other.text and\n                self.attributes == other.attributes and\n                self.children == other.children)\n\nclass BookingReport:\n    def __init__(self, boxes_section_element: WebElement):\n        self.boxes_section_element = boxes_section_element\n        self.deal_boxes = self.pull_deal_boxes()\n\n    def pull_deal_boxes(self):\n        deal_boxes = self.boxes_section_element.find_elements(By.XPATH, './/div[@data-testid=\"property-card-container\"]')\n        return deal_boxes\n\n\n    def pull_deal_box_attributes(self):\n        collected_data = []\n\n        for deal_box in self.deal_boxes:\n            hotel_name = deal_box.find_element(By.XPATH, './/div[@data-testid=\"title\"]')\n            hotel_name = hotel_name.get_attribute('innerHTML').strip()\n            \n            hotel_price = deal_box.find_element(By.XPATH, './/span[@data-testid=\"price-and-discounted-price\"]').text.strip()\n            \n            hotel_score = deal_box.find_element(By.XPATH, './/div[@data-testid=\"review-score\"]')\n            score_text = hotel_score.find_element(By.XPATH, './/div').text.strip()          \n            score_text = re.search(r'\\d+\\.\\d+', score_text)\n            if score_text:\n                score_text = score_text.group()\n            else:\n                score_text = None  # Handle the case if no score is found\n\n            collected_data.append([hotel_name, hotel_price, score_text])\n        print(\"Number of hotel numbers:\", len(self.deal_boxes))\n        return collected_data\n\ndef test_pull_deal_boxes():\n    mock_element = MockWebElement()\n    report = BookingReport(mock_element)\n    \n    original_deal_boxes = report.pull_deal_boxes()\n    new_deal_boxes = report.pull_deal_boxes_new_implementation()\n    \n    # Assert that the number of deal boxes is the same\n    assert len(original_deal_boxes) == len(new_deal_boxes), \"Mismatch in number of deal boxes\"\n    \n    # Assert that each deal box is the same\n    for original, new in zip(original_deal_boxes, new_deal_boxes):\n        assert original.text == new.text, \"Mismatch in deal box text\"\n        assert original.attributes == new.attributes, \"Mismatch in deal box attributes\"\n        assert original.children == new.children, \"Mismatch in deal box children\"\n\nif __name__ == \"__main__\":\n    test_pull_deal_boxes()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `BookingReport` class is identical to the ORIGINAL FUNCTION. Both functions are named `pull_deal_boxes` and perform the same operation: they call the `find_elements` method on `self.boxes_section_element` with the same XPath to find elements with the `data-testid=\"property-card-container\"` attribute. The functionality of the REVISED FUNCTION is exactly the same as the ORIGINAL FUNCTION, as it retrieves the same set of elements using the same method and criteria.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `pull_deal_boxes` function returns a list of deal boxes, satisfying the condition that it has return values.\n- CONDITION 2: The test cases in `test_pull_deal_boxes` check the return values of the `pull_deal_boxes` function and do not rely on printed or logged content.\n- CONDITION 3: The test function compares the outputs of `pull_deal_boxes` and `pull_deal_boxes_new_implementation` to ensure they have the same functionality. This satisfies the condition.\n- CONDITION 4: The test cases use assertions to compare the lengths and contents of the deal boxes returned by the two implementations, which is reasonable given that `pull_deal_boxes` returns a list.\n- CONDITION 5: The test cases check multiple aspects of the deal boxes (text, attributes, children), making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "88328ff5dcdc79c5f034e3ae3558537590c98143"
    },
    {
        "func_name": "BookingFiltration.apply_star_rating",
        "idx": "947",
        "repo_name": "SelyamiDurmush___selenium-booking-automation",
        "func_path": "booking/booking_filtration.py",
        "orig_func": "def apply_star_rating(self, *star_values):\n    for star_value in star_values:\n        star_filtration_box = self.driver.find_elements(By.XPATH, '//div[@data-filters-group=\"class\"]/fieldset//input[@type=\"checkbox\"]')[star_value - 1]\n        star_filtration_box.click()\n        print(f'Selected {star_value} star(s) rating.')\n    time.sleep(2)\n    return",
        "orig_context": "```python\n## booking/booking_filtration.py\nfrom selenium.webdriver.common.by import By\n\nfrom selenium.webdriver.remote.webdriver import WebDriver\n\nimport time\n\nclass BookingFiltration:\n    def __init__(self, driver: WebDriver):\n        self.driver = driver\n        \n\n    def apply_star_rating(self, *star_values):\n        for star_value in star_values:\n            star_filtration_box = self.driver.find_elements(By.XPATH, '//div[@data-filters-group=\"class\"]/fieldset//input[@type=\"checkbox\"]')[star_value - 1]\n            star_filtration_box.click()\n            print(f\"Selected {star_value} star(s) rating.\")\n        # wait for the page to load after applying the filtration\n        time.sleep(2)\n        return\n    \n    def sort_price_lowest_first(self):\n        sort_by_button = self.driver.find_element(By.CSS_SELECTOR, 'button[data-testid=\"sorters-dropdown-trigger\"]')\n        sort_by_button.click()\n        \n        lowest_price_button = self.driver.find_element(By.CSS_SELECTOR, 'button[aria-label=\"Price (lowest first)\"]')\n        lowest_price_button.click()\n        print(\"Sorted by price lowest first.\")\n        #input(\"Press Enter to continue...\")\n        time.sleep(3)\n        return\n\n```\n\n\n",
        "eval_script": "# booking/booking_filtration.py\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.remote.webdriver import WebDriver\nimport time\n\nclass BookingFiltration:\n    def __init__(self, driver: WebDriver):\n        self.driver = driver\n\n    def apply_star_rating(self, *star_values):\n        for star_value in star_values:\n            star_filtration_box = self.driver.find_elements(By.XPATH, '//div[@data-filters-group=\"class\"]/fieldset//input[@type=\"checkbox\"]')[star_value - 1]\n            star_filtration_box.click()\n            print(f\"Selected {star_value} star(s) rating.\")\n        # wait for the page to load after applying the filtration\n        time.sleep(2)\n        return\n    \n\n\n    def sort_price_lowest_first(self):\n        sort_by_button = self.driver.find_element(By.CSS_SELECTOR, 'button[data-testid=\"sorters-dropdown-trigger\"]')\n        sort_by_button.click()\n        \n        lowest_price_button = self.driver.find_element(By.CSS_SELECTOR, 'button[aria-label=\"Price (lowest first)\"]')\n        lowest_price_button.click()\n        print(\"Sorted by price lowest first.\")\n        #input(\"Press Enter to continue...\")\n        time.sleep(3)\n        return\n\n# Mock setup for testing\nfrom unittest.mock import MagicMock\n\ndef test_apply_star_rating():\n    # Create a mock WebDriver\n    mock_driver = MagicMock(spec=WebDriver)\n\n    # Create a mock element for the checkboxes\n    mock_checkbox = MagicMock()\n    mock_checkbox.click = MagicMock()\n\n    # Mock the find_elements method to return a list of mock checkboxes\n    mock_driver.find_elements.return_value = [mock_checkbox] * 5  # Assume there are 5 checkboxes\n\n    # Instantiate BookingFiltration with the mock driver\n    booking_filtration = BookingFiltration(mock_driver)\n\n    # Call the original apply_star_rating method\n    booking_filtration.apply_star_rating(1, 3, 5)\n    original_calls = mock_checkbox.click.call_count\n\n    # Reset the mock\n    mock_checkbox.click.reset_mock()\n\n    # Call the new apply_star_rating_new_implementation method\n    booking_filtration.apply_star_rating_new_implementation(1, 3, 5)\n    new_implementation_calls = mock_checkbox.click.call_count\n\n    # Assert that both implementations result in the same number of clicks\n    assert original_calls == new_implementation_calls, \"The number of clicks should be the same\"\n\n    # Assert that the click method was called the expected number of times\n    assert mock_checkbox.click.call_count == 3, \"Click should be called three times for ratings 1, 3, and 5\"\n\n    # Assert that the find_elements method was called with the correct XPath\n    expected_xpath = '//div[@data-filters-group=\"class\"]/fieldset//input[@type=\"checkbox\"]'\n    mock_driver.find_elements.assert_called_with(By.XPATH, expected_xpath)\n\nif __name__ == \"__main__\":\n    test_apply_star_rating()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `BookingFiltration` class is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions iterate over the `star_values`, find the corresponding checkbox elements using the same XPath, click on the checkboxes, print a message indicating the selected star rating, and then pause for 2 seconds. The test code provided verifies that the number of clicks and the XPath used are consistent with the original implementation. There is no difference in the logic or behavior of the function between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `apply_star_rating` function modifies the state of the mock checkboxes by calling their `click` method. It doesn't return any values but affects the state of the mock objects, which is a valid form of output for testing purposes.\n\n- CONDITION 2: The test cases check the state of the mock objects (i.e., the number of times the `click` method is called) rather than printed or logged content. Therefore, this condition is satisfied.\n\n- CONDITION 3: The test cases compare the number of clicks between the original and new implementations. If the new implementation has the same functionality, it should result in the same number of clicks, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the state changes (click counts) and ensure the same XPath is used, which is reasonable given that the function modifies the state of the mock objects rather than returning values.\n\n- CONDITION 5: The test cases are non-trivial as they check multiple aspects: the number of clicks and the correct XPath usage, ensuring comprehensive testing of the function's behavior.",
            "answer": "yes"
        },
        "commit_id": "88328ff5dcdc79c5f034e3ae3558537590c98143"
    },
    {
        "func_name": "UsuarioGenerico.correo_es_valido",
        "idx": "950",
        "repo_name": "AlexColladodev___MovieBuster",
        "func_path": "hito2/api/src/models/usuario_generico.py",
        "orig_func": "@classmethod\ndef correo_es_valido(cls, email):\n    patron = '\\\\w+@\\\\w+\\\\.\\\\w+'\n    return re.match(patron, email) is not None",
        "orig_context": "```python\n## hito2/api/src/models/usuario_generico.py\nimport re\n\nfrom flask import jsonify\n\nfrom db import mongo\n\nfrom werkzeug.security import generate_password_hash\n\nfrom bson import json_util\n\nfrom bson.objectid import ObjectId\n\nfrom pymongo.errors import PyMongoError\n\nfrom datetime import datetime\n\nclass UsuarioGenerico:\n    def __init__(self, data: dict) -> None:\n        self.nombre = data.get(\"nombre\")\n        self.nombre_usuario = data.get(\"nombre_usuario\")\n        self.password = data.get(\"password\")\n        self.email = data.get(\"email\")\n        self.telefono = data.get(\"telefono\")\n        self.seguidos = data.get(\"seguidos\", [])\n        self.preferencias = data.get(\"preferencias\", [])\n        self.actividades_creadas = data.get(\"actividades_creadas\", [])\n        self.reviews = data.get(\"reviews\", [])\n        self.fecha_nac = data.get(\"fecha_nac\")\n\n    def insertar_usuario_generico(self):\n        try:\n            if not self.correo_es_valido(self.email):\n                raise ValueError(\"Usuario no encontrado\")\n            if self.fecha_nac:\n                if isinstance(self.fecha_nac, str):\n                    self.fecha_nac = datetime.fromisoformat(self.fecha_nac)\n            data_insertar = self.__dict__\n            data_insertar[\"password\"] = generate_password_hash(data_insertar[\"password\"])\n            id = str(mongo.db.usuarios_genericos.insert_one(data_insertar).inserted_id)\n            return {\"message\": \"Usuario creado con \u00e9xito\", \"id\": id}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al crear un usuario: {e}\")\n\n    @staticmethod\n    def eliminar_usuario_generico(id):\n        try:\n            usuario_a_eliminar = mongo.db.usuarios_genericos.find_one({\"_id\": ObjectId(id)})\n            if not usuario_a_eliminar:\n                raise ValueError(\"Usuario no encontrado\")\n            resultado = mongo.db.usuarios_genericos.delete_one({\"_id\": ObjectId(id)})\n            if resultado.deleted_count == 0:\n                raise RuntimeError(\"No se pudo eliminar al usuario\")\n            return {\"message\": \"Usuario eliminado con \u00e9xito\"}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al eliminar a un usuario: {e}\")\n    \n    @staticmethod\n    def consultar_usuarios():\n        try:\n            usuarios_genericos = mongo.db.usuarios_genericos.find()\n            return json_util.dumps(usuarios_genericos)\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error de base de datos al consultar usuarios genericos de establecimientos: {e}\")\n    \n    @staticmethod\n    def consultar_usuario(id):\n        try:\n            \n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            \n            \n            usuario = mongo.db.usuarios_genericos.find_one({\"_id\": ObjectId(id)})\n            if not usuario:\n                raise ValueError(\"Usuario no encontrado\")\n            \n            \n            return json_util.dumps(usuario)\n\n        except ValueError as e:\n            return jsonify({\"error\": str(e)}), 404\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al consultar al usuario: {e}\")\n        \n    @staticmethod\n    def actualizar_usuario(id, data):\n        try:\n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            \n            data.pop(\"password\", None)\n\n            if 'fecha_nac' in data and isinstance(data['fecha_nac'], str):\n                data['fecha_nac'] = datetime.fromisoformat(data['fecha_nac'])\n\n            if 'preferencias' in data and isinstance(data['preferencias'], str):\n                data['preferencias'] = data['preferencias'].split(',')\n\n            resultado = mongo.db.usuarios_genericos.update_one({\"_id\": ObjectId(id)}, {\"$set\": data})\n\n            if resultado.modified_count == 0:\n                return {\"message\": \"No se realizaron cambios\"}\n\n            return {\"message\": \"Usuario actualizado con \u00e9xito\"}\n        \n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al actualizar al usuario: {e}\")\n\n    @classmethod\n    def correo_es_valido(cls, email):\n        patron = r'\\w+@\\w+\\.\\w+'\n        return re.match(patron, email) is not None\n\n    @classmethod\n    def existe_nombre_usuario(cls, nombre):\n        try:\n            usuario = mongo.db.usuarios_genericos.find_one({\"nombre_usuario\": nombre})\n            if usuario:\n                id_usuario = str(usuario.get(\"_id\"))\n                return id_usuario\n            else:\n                return None\n        except Exception as e:\n            raise RuntimeError(f\"Error en la base de datos al consultar nombre de usuario: {e}\")\n\n    @staticmethod\n    def invita_actividad(id_usuario, id_actividad):\n        try:\n            actividad = mongo.db.actividades.find_one({\"_id\": ObjectId(id_actividad)})\n            if not actividad:\n                raise ValueError(\"La actividad especificada no existe\")\n\n            usuario = mongo.db.usuarios_genericos.find_one({\"_id\": ObjectId(id_usuario)})\n            if not usuario:\n                raise ValueError(\"El usuario especificado no existe\")\n\n            resultado = mongo.db.actividades.update_one(\n                {\"_id\": ObjectId(id_actividad)},\n                {\"$addToSet\": {\"participantes\": id_usuario}}\n            )\n\n            if resultado.modified_count > 0:\n                return {\"message\": \"Actividad a\u00f1adida con \u00e9xito al usuario\"}\n            else:\n                raise ValueError(\"No se pudo a\u00f1adir la actividad al usuario\")\n\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al asociar usuario-actividad: {e}\")\n\n```\n\n\n",
        "eval_script": "# Mocking the mongo object to avoid database interactions\nclass MockMongo:\n    class MockCollection:\n        def find_one(self, query):\n            return None\n\n        def insert_one(self, data):\n            class InsertResult:\n                @property\n                def inserted_id(self):\n                    return \"mock_id\"\n            return InsertResult()\n\n        def delete_one(self, query):\n            class DeleteResult:\n                @property\n                def deleted_count(self):\n                    return 1\n            return DeleteResult()\n\n        def update_one(self, query, update):\n            class UpdateResult:\n                @property\n                def modified_count(self):\n                    return 1\n            return UpdateResult()\n\n        def find(self):\n            return []\n\n    db = MockCollection()\n\n# Replacing the mongo object with the mock\nmongo = MockMongo()\n\n# The original code with minimal changes\nimport re\nfrom werkzeug.security import generate_password_hash\nfrom bson import json_util\nfrom bson.objectid import ObjectId\nfrom pymongo.errors import PyMongoError\nfrom datetime import datetime\n\nclass UsuarioGenerico:\n    def __init__(self, data: dict) -> None:\n        self.nombre = data.get(\"nombre\")\n        self.nombre_usuario = data.get(\"nombre_usuario\")\n        self.password = data.get(\"password\")\n        self.email = data.get(\"email\")\n        self.telefono = data.get(\"telefono\")\n        self.seguidos = data.get(\"seguidos\", [])\n        self.preferencias = data.get(\"preferencias\", [])\n        self.actividades_creadas = data.get(\"actividades_creadas\", [])\n        self.reviews = data.get(\"reviews\", [])\n        self.fecha_nac = data.get(\"fecha_nac\")\n\n    def insertar_usuario_generico(self):\n        try:\n            if not self.correo_es_valido(self.email):\n                raise ValueError(\"Usuario no encontrado\")\n            if self.fecha_nac:\n                if isinstance(self.fecha_nac, str):\n                    self.fecha_nac = datetime.fromisoformat(self.fecha_nac)\n            data_insertar = self.__dict__\n            data_insertar[\"password\"] = generate_password_hash(data_insertar[\"password\"])\n            id = str(mongo.db.usuarios_genericos.insert_one(data_insertar).inserted_id)\n            return {\"message\": \"Usuario creado con \u00e9xito\", \"id\": id}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al crear un usuario: {e}\")\n\n    @staticmethod\n    def eliminar_usuario_generico(id):\n        try:\n            usuario_a_eliminar = mongo.db.usuarios_genericos.find_one({\"_id\": ObjectId(id)})\n            if not usuario_a_eliminar:\n                raise ValueError(\"Usuario no encontrado\")\n            resultado = mongo.db.usuarios_genericos.delete_one({\"_id\": ObjectId(id)})\n            if resultado.deleted_count == 0:\n                raise RuntimeError(\"No se pudo eliminar al usuario\")\n            return {\"message\": \"Usuario eliminado con \u00e9xito\"}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al eliminar a un usuario: {e}\")\n    \n    @staticmethod\n    def consultar_usuarios():\n        try:\n            usuarios_genericos = mongo.db.usuarios_genericos.find()\n            return json_util.dumps(usuarios_genericos)\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error de base de datos al consultar usuarios genericos de establecimientos: {e}\")\n    \n    @staticmethod\n    def consultar_usuario(id):\n        try:\n            \n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            \n            \n            usuario = mongo.db.usuarios_genericos.find_one({\"_id\": ObjectId(id)})\n            if not usuario:\n                raise ValueError(\"Usuario no encontrado\")\n            \n            \n            return json_util.dumps(usuario)\n\n        except ValueError as e:\n            return {\"error\": str(e)}, 404\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al consultar al usuario: {e}\")\n        \n    @staticmethod\n    def actualizar_usuario(id, data):\n        try:\n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            \n            data.pop(\"password\", None)\n\n            if 'fecha_nac' in data and isinstance(data['fecha_nac'], str):\n                data['fecha_nac'] = datetime.fromisoformat(data['fecha_nac'])\n\n            if 'preferencias' in data and isinstance(data['preferencias'], str):\n                data['preferencias'] = data['preferencias'].split(',')\n\n            resultado = mongo.db.usuarios_genericos.update_one({\"_id\": ObjectId(id)}, {\"$set\": data})\n\n            if resultado.modified_count == 0:\n                return {\"message\": \"No se realizaron cambios\"}\n\n            return {\"message\": \"Usuario actualizado con \u00e9xito\"}\n        \n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al actualizar al usuario: {e}\")\n\n    @classmethod\n    def correo_es_valido(cls, email):\n        patron = r'\\w+@\\w+\\.\\w+'\n        return re.match(patron, email) is not None\n\n\n    @classmethod\n    def existe_nombre_usuario(cls, nombre):\n        try:\n            usuario = mongo.db.usuarios_genericos.find_one({\"nombre_usuario\": nombre})\n            if usuario:\n                id_usuario = str(usuario.get(\"_id\"))\n                return id_usuario\n            else:\n                return None\n        except Exception as e:\n            raise RuntimeError(f\"Error en la base de datos al consultar nombre de usuario: {e}\")\n\n    @staticmethod\n    def invita_actividad(id_usuario, id_actividad):\n        try:\n            actividad = mongo.db.actividades.find_one({\"_id\": ObjectId(id_actividad)})\n            if not actividad:\n                raise ValueError(\"La actividad especificada no existe\")\n\n            usuario = mongo.db.usuarios_genericos.find_one({\"_id\": ObjectId(id_usuario)})\n            if not usuario:\n                raise ValueError(\"El usuario especificado no existe\")\n\n            resultado = mongo.db.actividades.update_one(\n                {\"_id\": ObjectId(id_actividad)},\n                {\"$addToSet\": {\"participantes\": id_usuario}}\n            )\n\n            if resultado.modified_count > 0:\n                return {\"message\": \"Actividad a\u00f1adida con \u00e9xito al usuario\"}\n            else:\n                raise ValueError(\"No se pudo a\u00f1adir la actividad al usuario\")\n\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al asociar usuario-actividad: {e}\")\n\ndef test_correo_es_valido():\n    # Test cases\n    valid_email = \"test@example.com\"\n    invalid_email_no_at = \"testexample.com\"\n    invalid_email_no_dot = \"test@examplecom\"\n\n    # Assertions\n    assert UsuarioGenerico.correo_es_valido(valid_email) == UsuarioGenerico.correo_es_valido_new_implementation(valid_email)\n    assert UsuarioGenerico.correo_es_valido(invalid_email_no_at) == UsuarioGenerico.correo_es_valido_new_implementation(invalid_email_no_at)\n    assert UsuarioGenerico.correo_es_valido(invalid_email_no_dot) == UsuarioGenerico.correo_es_valido_new_implementation(invalid_email_no_dot)\n\nif __name__ == \"__main__\":\n    test_correo_es_valido()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `correo_es_valido` is a class method that checks if an email matches a specific regex pattern. The revised function in the provided code is also a class method with the same name and uses the same regex pattern to validate the email. Both functions return `True` if the email matches the pattern and `False` otherwise. There are no changes in the logic or functionality of the method between the original and revised versions. The test cases provided also confirm that the behavior of the function remains consistent.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `correo_es_valido` returns a boolean value indicating whether the email is valid based on a regex pattern. This satisfies the condition as it has a return value.\n\n2. **CONDITION 2**: The test cases in `test_correo_es_valido` use assertions to check the return values of `correo_es_valido` and `correo_es_valido_new_implementation`. There is no checking of printed or logged contents, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the results of `correo_es_valido` and `correo_es_valido_new_implementation` for the same inputs. If both implementations return the same results for all test cases, they are functionally equivalent. Thus, this condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate given that `correo_es_valido` returns a boolean. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases include a valid email and two invalid emails (one missing '@' and one missing a dot), which are non-trivial and cover different scenarios of email validation. This satisfies the condition.",
            "answer": "yes"
        },
        "commit_id": "0671d3fe12d7e5cd119783e8f0e02c8e47187820"
    },
    {
        "func_name": "AdministradorCine.correo_es_valido",
        "idx": "955",
        "repo_name": "AlexColladodev___MovieBuster",
        "func_path": "hito2/api/src/models/administrador_cine.py",
        "orig_func": "@staticmethod\ndef correo_es_valido(email):\n    patron = '\\\\w+@\\\\w+\\\\.\\\\w+'\n    return re.match(patron, email) is not None",
        "orig_context": "```python\n## hito2/api/src/models/administrador_cine.py\nimport re\n\nfrom db import mongo\n\nfrom werkzeug.security import generate_password_hash\n\nfrom bson import json_util\n\nfrom bson.objectid import ObjectId\n\nfrom pymongo.errors import PyMongoError\n\nclass AdministradorCine:\n    def __init__(self, data: dict) -> None:\n        self.nombre = data.get(\"nombre\")\n        self.nombre_usuario = data.get(\"nombre_usuario\")\n        self.password = data.get(\"password\")\n        self.email = data.get(\"email\")\n        self.telefono = data.get(\"telefono\")\n        self.dni = data.get(\"dni\")\n\n    def insertar_administrador_cine(self):\n        try:\n            if not self.correo_es_valido(self.email):\n                raise ValueError(\"Administrador no encontrado\")\n            data_insertar = self.__dict__\n            data_insertar[\"password\"] = generate_password_hash(data_insertar[\"password\"])\n            id = str(mongo.db.administradores_cines.insert_one(data_insertar).inserted_id)\n            return {\"message\": \"Administrador creado con \u00e9xito\", \"id\": id}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al crear un administrador: {e}\")\n\n    @staticmethod\n    def eliminar_administrador_cine(id):\n        try:\n            administrador_a_eliminar = mongo.db.administradores_cines.find_one({\"_id\": ObjectId(id)})\n            if not administrador_a_eliminar:\n                raise ValueError(\"administrador no encontrado\")\n            resultado = mongo.db.administradores_cines.delete_one({\"_id\": ObjectId(id)})\n            if resultado.deleted_count == 0:\n                raise RuntimeError(\"No se pudo eliminar al administrador\")\n            return {\"message\": \"administrador eliminado con \u00e9xito\"}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al eliminar a un administrador: {e}\")\n    \n    @staticmethod\n    def consultar_administradores_cines():\n        try:\n            administradores_cines = mongo.db.administradores_cines.find()\n            return json_util.dumps(administradores_cines)\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error de base de datos al consultar administradores_cines genericos de establecimientos: {e}\")\n    \n    @staticmethod\n    def consultar_administrador(id):\n        try:\n            \n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            \n            \n            administrador = mongo.db.administradores_cines.find_one({\"_id\": ObjectId(id)})\n            if not administrador:\n                raise ValueError(\"administrador no encontrado\")\n            \n            \n            return json_util.dumps(administrador)\n        \n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al consultar al administrador: {e}\")\n\n    @staticmethod\n    def actualizar_administrador(id, data):\n        try:\n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            \n            data.pop(\"password\", None)\n\n            resultado = mongo.db.administradores_cines.update_one({\"_id\": ObjectId(id)}, {\"$set\": data})\n\n            if resultado.modified_count == 0:\n                return {\"message\": \"No se realizaron cambios o administrador no encontrado\"}\n\n            return {\"message\": \"administrador actualizado con \u00e9xito\"}\n        \n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al actualizar al administrador: {e}\")\n\n    @staticmethod\n    def correo_es_valido(email):\n        patron = r'\\w+@\\w+\\.\\w+'\n        return re.match(patron, email) is not None\n\n```\n\n\n",
        "eval_script": "# Mock implementation for the mongo database object\nclass MockMongoDB:\n    class AdministradoresCinesCollection:\n        def insert_one(self, data):\n            return MockInsertResult()\n\n        def find_one(self, query):\n            return None\n\n        def delete_one(self, query):\n            return MockDeleteResult()\n\n        def find(self):\n            return []\n\n        def update_one(self, query, update):\n            return MockUpdateResult()\n\n    db = AdministradoresCinesCollection()\n\nclass MockInsertResult:\n    @property\n    def inserted_id(self):\n        return \"mock_id\"\n\nclass MockDeleteResult:\n    @property\n    def deleted_count(self):\n        return 1\n\nclass MockUpdateResult:\n    @property\n    def modified_count(self):\n        return 1\n\n# Mock the mongo object\nmongo = MockMongoDB()\n\n# Original code with the mock implementation\nimport re\nfrom werkzeug.security import generate_password_hash\nfrom bson import json_util\nfrom bson.objectid import ObjectId\nfrom pymongo.errors import PyMongoError\n\nclass AdministradorCine:\n    def __init__(self, data: dict) -> None:\n        self.nombre = data.get(\"nombre\")\n        self.nombre_usuario = data.get(\"nombre_usuario\")\n        self.password = data.get(\"password\")\n        self.email = data.get(\"email\")\n        self.telefono = data.get(\"telefono\")\n        self.dni = data.get(\"dni\")\n\n    def insertar_administrador_cine(self):\n        try:\n            if not self.correo_es_valido(self.email):\n                raise ValueError(\"Administrador no encontrado\")\n            data_insertar = self.__dict__\n            data_insertar[\"password\"] = generate_password_hash(data_insertar[\"password\"])\n            id = str(mongo.db.administradores_cines.insert_one(data_insertar).inserted_id)\n            return {\"message\": \"Administrador creado con \u00e9xito\", \"id\": id}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al crear un administrador: {e}\")\n\n    @staticmethod\n    def eliminar_administrador_cine(id):\n        try:\n            administrador_a_eliminar = mongo.db.administradores_cines.find_one({\"_id\": ObjectId(id)})\n            if not administrador_a_eliminar:\n                raise ValueError(\"administrador no encontrado\")\n            resultado = mongo.db.administradores_cines.delete_one({\"_id\": ObjectId(id)})\n            if resultado.deleted_count == 0:\n                raise RuntimeError(\"No se pudo eliminar al administrador\")\n            return {\"message\": \"administrador eliminado con \u00e9xito\"}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al eliminar a un administrador: {e}\")\n    \n    @staticmethod\n    def consultar_administradores_cines():\n        try:\n            administradores_cines = mongo.db.administradores_cines.find()\n            return json_util.dumps(administradores_cines)\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error de base de datos al consultar administradores_cines genericos de establecimientos: {e}\")\n    \n    @staticmethod\n    def consultar_administrador(id):\n        try:\n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            administrador = mongo.db.administradores_cines.find_one({\"_id\": ObjectId(id)})\n            if not administrador:\n                raise ValueError(\"administrador no encontrado\")\n            return json_util.dumps(administrador)\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al consultar al administrador: {e}\")\n\n    @staticmethod\n    def actualizar_administrador(id, data):\n        try:\n            if not ObjectId.is_valid(id):\n                raise ValueError(\"El ID proporcionado no es un ObjectId v\u00e1lido\")\n            data.pop(\"password\", None)\n            resultado = mongo.db.administradores_cines.update_one({\"_id\": ObjectId(id)}, {\"$set\": data})\n            if resultado.modified_count == 0:\n                return {\"message\": \"No se realizaron cambios o administrador no encontrado\"}\n            return {\"message\": \"administrador actualizado con \u00e9xito\"}\n        except PyMongoError as e:\n            raise RuntimeError(f\"Error en la base de datos al actualizar al administrador: {e}\")\n\n    @staticmethod\n    def correo_es_valido(email):\n        patron = r'\\w+@\\w+\\.\\w+'\n        return re.match(patron, email) is not None\n\n\ndef test_correo_es_valido():\n    # Test cases\n    valid_email = \"test@example.com\"\n    invalid_email_no_at = \"testexample.com\"\n    invalid_email_no_domain = \"test@.com\"\n    invalid_email_no_tld = \"test@example\"\n    valid_email_with_numbers = \"test123@example.com\"\n    valid_email_with_subdomain = \"test@mail.example.com\"\n    valid_email_with_special_chars = \"test.email+alex@leetcode.com\"\n    valid_email_with_hyphen_domain = \"test@ex-ample.com\"\n    valid_email_long_tld = \"test@example.co.uk\"\n    invalid_email_consecutive_dots = \"test..email@example.com\"\n    invalid_email_with_spaces = \" test @example.com \"\n    invalid_email_invalid_chars = \"test@exa!mple.com\"\n    valid_email_unusual_tld = \"test@example.museum\"\n    empty_email = \"\"\n    email_with_leading_trailing_spaces = \" test@example.com \"\n\n    # Assertions\n    assert AdministradorCine.correo_es_valido(valid_email) == AdministradorCine.correo_es_valido_new_implementation(valid_email)\n    assert AdministradorCine.correo_es_valido(invalid_email_no_at) == AdministradorCine.correo_es_valido_new_implementation(invalid_email_no_at)\n    assert AdministradorCine.correo_es_valido(invalid_email_no_domain) == AdministradorCine.correo_es_valido_new_implementation(invalid_email_no_domain)\n    assert AdministradorCine.correo_es_valido(invalid_email_no_tld) == AdministradorCine.correo_es_valido_new_implementation(invalid_email_no_tld)\n    assert AdministradorCine.correo_es_valido(valid_email_with_numbers) == AdministradorCine.correo_es_valido_new_implementation(valid_email_with_numbers)\n    assert AdministradorCine.correo_es_valido(valid_email_with_subdomain) == AdministradorCine.correo_es_valido_new_implementation(valid_email_with_subdomain)\n    assert AdministradorCine.correo_es_valido(valid_email_with_special_chars) == AdministradorCine.correo_es_valido_new_implementation(valid_email_with_special_chars)\n    assert AdministradorCine.correo_es_valido(valid_email_with_hyphen_domain) == AdministradorCine.correo_es_valido_new_implementation(valid_email_with_hyphen_domain)\n    assert AdministradorCine.correo_es_valido(valid_email_long_tld) == AdministradorCine.correo_es_valido_new_implementation(valid_email_long_tld)\n    assert AdministradorCine.correo_es_valido(invalid_email_consecutive_dots) == AdministradorCine.correo_es_valido_new_implementation(invalid_email_consecutive_dots)\n    assert AdministradorCine.correo_es_valido(invalid_email_with_spaces) == AdministradorCine.correo_es_valido_new_implementation(invalid_email_with_spaces)\n    assert AdministradorCine.correo_es_valido(invalid_email_invalid_chars) == AdministradorCine.correo_es_valido_new_implementation(invalid_email_invalid_chars)\n    assert AdministradorCine.correo_es_valido(valid_email_unusual_tld) == AdministradorCine.correo_es_valido_new_implementation(valid_email_unusual_tld)\n    assert AdministradorCine.correo_es_valido(empty_email) == AdministradorCine.correo_es_valido_new_implementation(empty_email)\n    assert AdministradorCine.correo_es_valido(email_with_leading_trailing_spaces) == AdministradorCine.correo_es_valido_new_implementation(email_with_leading_trailing_spaces)\n\nif __name__ == \"__main__\":\n    test_correo_es_valido()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `correo_es_valido` uses a regex pattern `\\\\w+@\\\\w+\\\\.\\\\w+` to validate email addresses. The revised function in the provided code uses the same regex pattern `r'\\w+@\\w+\\.\\w+'`, which is equivalent to the original pattern. Both patterns match a sequence of word characters, followed by an '@' symbol, followed by another sequence of word characters, a dot, and another sequence of word characters. This pattern checks for a basic email structure but does not account for more complex valid email formats (e.g., emails with special characters or subdomains).\n\nThe functionality of the revised function is the same as the original function because they both use the same regex pattern to validate email addresses. The test cases provided in the code also confirm that the revised function behaves the same as the original function for various email formats.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The function `correo_es_valido` returns a boolean value indicating whether the email is valid according to a regex pattern. This satisfies the condition as it has return values.\n\n2. **CONDITION 2**: The test cases in `test_correo_es_valido` use assertions to compare the return values of `correo_es_valido` and `correo_es_valido_new_implementation`. They do not check printed or logged contents, thus satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `correo_es_valido` and `correo_es_valido_new_implementation` for various email inputs. This ensures that the new implementation must have the exact same functionality to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations, which is appropriate since `correo_es_valido` returns a value. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a wide range of email formats, including valid and invalid cases with different characteristics (e.g., missing '@', missing domain, special characters, etc.). This makes the test cases non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "0671d3fe12d7e5cd119783e8f0e02c8e47187820"
    },
    {
        "func_name": "TransThing.load",
        "idx": "966",
        "repo_name": "hirmiura___elin-yar-craft",
        "func_path": "src/elin_yar_craft/translate.py",
        "orig_func": "@classmethod\ndef load(cls, file: str) -> TransThing:\n    self = cls()\n    with warnings.catch_warnings():\n        warnings.simplefilter('ignore')\n        wb = openpyxl.load_workbook(file)\n    ws = wb['Thing']\n    header = ws[1]\n    for row in ws.iter_rows(min_row=3):\n        row_dic = {}\n        for k, v in zip(header, row):\n            row_dic[k.value] = v.value\n        self.trans_list.append(row_dic)\n    return self",
        "orig_context": "```python\n## src/elin_yar_craft/translate.py\nimport copy\n\nimport re\n\nimport warnings\n\nfrom typing import Any, ClassVar\n\nimport openpyxl\n\nfrom pydantic import BaseModel\n\ndef gen_key_name(lang: str, id: str, name: str) -> str:\n    # \u672b\u5c3e\u304c _q\\d \u304b?\n    suffix_match = re.search(r\"(_q\\d)$\", id)\n    if not suffix_match:\n        return name  # \u30de\u30c3\u30c1\u3057\u306a\u3051\u308c\u3070\u30ea\u30bf\u30fc\u30f3\n\n    # \u30e9\u30f3\u30af\u5206\u3051\n    new_name: str = name + \" \"\n    match suffix_match[0]:\n        case \"_q1\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u4f18\u8d28\u54c1\"\n                case \"zhtw\":\n                    new_name += \"\u512a\u8cea\u54c1\"\n                case _:\n                    new_name += \"good\"\n        case \"_q2\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u5947\u8ff9\"\n                case \"zhtw\":\n                    new_name += \"\u5947\u8e5f\"\n                case _:\n                    new_name += \"miracle\"\n        case \"_q3\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u795e\u5668\"\n                case \"zhtw\":\n                    new_name += \"\u795e\u5668\"\n                case _:\n                    new_name += \"godly\"\n        case \"_q4\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u7279\u5236\"\n                case \"zhtw\":\n                    new_name += \"\u7279\u88fd\"\n                case _:\n                    new_name += \"special\"\n    return new_name\n\nclass TransThing(BaseModel):\n    key_id: ClassVar[str] = \"id\"\n    key_name: ClassVar[str] = \"name\"\n    key_name_EN: ClassVar[str] = \"name_EN\"  # noqa: N815\n    key_detail: ClassVar[str] = \"detail\"\n    key_unit: ClassVar[str] = \"unit\"\n    key_unknown: ClassVar[str] = \"unknown\"\n    key_roomName: ClassVar[str] = \"roomName\"  # noqa: N815\n    key_name2: ClassVar[str] = \"name2\"\n    trans_list: list[dict[str, Any]] = []\n\n    @classmethod\n    def load(cls, file: str) -> TransThing:\n        # \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u751f\u6210\n        self = cls()\n        # \u30d5\u30a1\u30a4\u30eb\u8aad\u307f\u8fbc\u307f\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            wb = openpyxl.load_workbook(file)\n        ws = wb[\"Thing\"]\n        # 1\u884c\u76ee(\u30d8\u30c3\u30c0)\n        header = ws[1]\n        # 3\u884c\u76ee\u4ee5\u964d(2\u884c\u76ee\u306f\u30b4\u30df)\n        for row in ws.iter_rows(min_row=3):\n            row_dic = {}\n            for k, v in zip(header, row):\n                row_dic[k.value] = v.value\n            self.trans_list.append(row_dic)\n        return self\n\n    def translate(self, lang: str, line: dict[str, Any]) -> dict[str, Any] | None:\n        for trans in self.trans_list:\n            trans_id: str = trans[TransThing.key_id]\n            line_id: str = line[TransThing.key_id]\n            pure_line_id = re.sub(r\"^YarCraft_\", \"\", line_id)\n            pure_line_id = re.sub(r\"_(wood|stone|metal|cloth|glass)?(_q\\d)?$\", \"\", pure_line_id)\n            if (  # id\u304c\u4e00\u81f4\u3059\u308b\u304b\n                trans_id.casefold() == pure_line_id.casefold()\n            ):\n                new = copy.deepcopy(line)\n                for key in [\n                    TransThing.key_name,\n                    TransThing.key_detail,\n                    TransThing.key_unit,\n                    TransThing.key_unknown,\n                    TransThing.key_roomName,\n                ]:\n                    new[key] = trans[key]\n                    if key == TransThing.key_name:\n                        new[key] = gen_key_name(lang, new[TransThing.key_id], new[key])\n\n                return new\n        return None\n\n```\n\n\n",
        "eval_script": "import os\nimport openpyxl\nfrom typing import Any, ClassVar\nfrom pydantic import BaseModel\nimport re\nimport copy\nimport warnings\n\ndef gen_key_name(lang: str, id: str, name: str) -> str:\n    suffix_match = re.search(r\"(_q\\d)$\", id)\n    if not suffix_match:\n        return name\n\n    new_name: str = name + \" \"\n    match suffix_match[0]:\n        case \"_q1\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u4f18\u8d28\u54c1\"\n                case \"zhtw\":\n                    new_name += \"\u512a\u8cea\u54c1\"\n                case _:\n                    new_name += \"good\"\n        case \"_q2\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u5947\u8ff9\"\n                case \"zhtw\":\n                    new_name += \"\u5947\u8e5f\"\n                case _:\n                    new_name += \"miracle\"\n        case \"_q3\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u795e\u5668\"\n                case \"zhtw\":\n                    new_name += \"\u795e\u5668\"\n                case _:\n                    new_name += \"godly\"\n        case \"_q4\":\n            match lang:\n                case \"cn\":\n                    new_name += \"\u7279\u5236\"\n                case \"zhtw\":\n                    new_name += \"\u7279\u88fd\"\n                case _:\n                    new_name += \"special\"\n    return new_name\n\nclass TransThing(BaseModel):\n    key_id: ClassVar[str] = \"id\"\n    key_name: ClassVar[str] = \"name\"\n    key_name_EN: ClassVar[str] = \"name_EN\"\n    key_detail: ClassVar[str] = \"detail\"\n    key_unit: ClassVar[str] = \"unit\"\n    key_unknown: ClassVar[str] = \"unknown\"\n    key_roomName: ClassVar[str] = \"roomName\"\n    key_name2: ClassVar[str] = \"name2\"\n    trans_list: list[dict[str, Any]] = []\n\n    @classmethod\n    def load(cls, file: str) -> 'TransThing':\n        self = cls()\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            wb = openpyxl.load_workbook(file)\n        ws = wb[\"Thing\"]\n        header = ws[1]\n        for row in ws.iter_rows(min_row=3):\n            row_dic = {}\n            for k, v in zip(header, row):\n                row_dic[k.value] = v.value\n            self.trans_list.append(row_dic)\n        return self\n\n\n    def translate(self, lang: str, line: dict[str, Any]) -> dict[str, Any] | None:\n        for trans in self.trans_list:\n            trans_id: str = trans[TransThing.key_id]\n            line_id: str = line[TransThing.key_id]\n            pure_line_id = re.sub(r\"^YarCraft_\", \"\", line_id)\n            pure_line_id = re.sub(r\"_(wood|stone|metal|cloth|glass)?(_q\\d)?$\", \"\", pure_line_id)\n            if trans_id.casefold() == pure_line_id.casefold():\n                new = copy.deepcopy(line)\n                for key in [\n                    TransThing.key_name,\n                    TransThing.key_detail,\n                    TransThing.key_unit,\n                    TransThing.key_unknown,\n                    TransThing.key_roomName,\n                ]:\n                    new[key] = trans[key]\n                    if key == TransThing.key_name:\n                        new[key] = gen_key_name(lang, new[TransThing.key_id], new[key])\n                return new\n        return None\n\ndef create_mock_excel_file():\n    os.makedirs('/home/user/tmp', exist_ok=True)\n    file_path = '/home/user/tmp/mock_data.xlsx'\n    wb = openpyxl.Workbook()\n    ws = wb.active\n    ws.title = \"Thing\"\n    ws.append([\"id\", \"name\", \"detail\", \"unit\", \"unknown\", \"roomName\"])  # Header\n    ws.append([\"\", \"\", \"\", \"\", \"\", \"\"])  # Dummy row\n    ws.append([\"item1\", \"Item 1 Name\", \"Detail 1\", \"Unit 1\", \"Unknown 1\", \"Room 1\"])\n    wb.save(file_path)\n    return file_path\n\ndef test_load():\n    # Test with the original mock file\n    mock_file_path = create_mock_excel_file()\n    trans_thing_instance_old = TransThing.load(mock_file_path)\n    trans_thing_instance_new = TransThing.load_new_implementation(mock_file_path)\n\n    assert len(trans_thing_instance_old.trans_list) == len(trans_thing_instance_new.trans_list), \"Length mismatch\"\n    assert trans_thing_instance_old.trans_list[0] == trans_thing_instance_new.trans_list[0], \"First item mismatch\"\n    assert trans_thing_instance_old.trans_list == trans_thing_instance_new.trans_list, \"List content mismatch\"\n\n    # Test with an empty Excel file\n    empty_file_path = '/home/user/tmp/empty_data.xlsx'\n    wb = openpyxl.Workbook()\n    ws = wb.active\n    ws.title = \"Thing\"\n    ws.append([\"id\", \"name\", \"detail\", \"unit\", \"unknown\", \"roomName\"])  # Header only\n    wb.save(empty_file_path)\n\n    trans_thing_instance_old = TransThing.load(empty_file_path)\n    trans_thing_instance_new = TransThing.load_new_implementation(empty_file_path)\n\n    assert len(trans_thing_instance_old.trans_list) == len(trans_thing_instance_new.trans_list), \"Empty file length mismatch\"\n    assert trans_thing_instance_old.trans_list == trans_thing_instance_new.trans_list, \"Empty file list content mismatch\"\n\n    # Test with multiple data rows\n    multi_row_file_path = '/home/user/tmp/multi_row_data.xlsx'\n    wb = openpyxl.Workbook()\n    ws = wb.active\n    ws.title = \"Thing\"\n    ws.append([\"id\", \"name\", \"detail\", \"unit\", \"unknown\", \"roomName\"])  # Header\n    ws.append([\"\", \"\", \"\", \"\", \"\", \"\"])  # Dummy row\n    ws.append([\"item1\", \"Item 1 Name\", \"Detail 1\", \"Unit 1\", \"Unknown 1\", \"Room 1\"])\n    ws.append([\"item2\", \"Item 2 Name\", \"Detail 2\", \"Unit 2\", \"Unknown 2\", \"Room 2\"])\n    wb.save(multi_row_file_path)\n\n    trans_thing_instance_old = TransThing.load(multi_row_file_path)\n    trans_thing_instance_new = TransThing.load_new_implementation(multi_row_file_path)\n\n    assert len(trans_thing_instance_old.trans_list) == len(trans_thing_instance_new.trans_list), \"Multi-row file length mismatch\"\n    assert trans_thing_instance_old.trans_list == trans_thing_instance_new.trans_list, \"Multi-row file list content mismatch\"\n\n    # Test with special characters\n    special_char_file_path = '/home/user/tmp/special_char_data.xlsx'\n    wb = openpyxl.Workbook()\n    ws = wb.active\n    ws.title = \"Thing\"\n    ws.append([\"id\", \"name\", \"detail\", \"unit\", \"unknown\", \"roomName\"])  # Header\n    ws.append([\"\", \"\", \"\", \"\", \"\", \"\"])  # Dummy row\n    ws.append([\"item1\", \"Item 1 Name\", \"Detail 1\", \"Unit 1\", \"Unknown 1\", \"Room 1\"])\n    ws.append([\"item2\", \"Item 2 Name\", \"Detail 2\", \"Unit 2\", \"Unknown 2\", \"Room 2\"])\n    ws.append([\"item3\", \"Item 3 Name\", \"Detail 3\", \"Unit 3\", \"Unknown 3\", \"Room 3\"])\n    ws.append([\"item4\", \"Item 4 Name\", \"Detail 4\", \"Unit 4\", \"Unknown 4\", \"Room 4\"])\n    ws.append([\"item5\", \"Item 5 Name\", \"Detail 5\", \"Unit 5\", \"Unknown 5\", \"Room 5\"])\n    wb.save(special_char_file_path)\n\n    trans_thing_instance_old = TransThing.load(special_char_file_path)\n    trans_thing_instance_new = TransThing.load_new_implementation(special_char_file_path)\n\n    assert len(trans_thing_instance_old.trans_list) == len(trans_thing_instance_new.trans_list), \"Special char file length mismatch\"\n    assert trans_thing_instance_old.trans_list == trans_thing_instance_new.trans_list, \"Special char file list content mismatch\"\n\nif __name__ == \"__main__\":\n    test_load()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are class methods of the `TransThing` class and perform the same operations: they create an instance of the class, suppress warnings, load an Excel workbook, iterate over the rows starting from the third row, and append dictionaries of row data to the `trans_list` attribute. The logic and flow of operations are unchanged between the two versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `load` function modifies the `trans_list` attribute of the `TransThing` class, which is a class variable. This satisfies the condition as it modifies a global variable (in the context of the class).\n\n2. **CONDITION 2**: The test cases check the state of the `trans_list` attribute of the `TransThing` instances returned by `load` and `load_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the `trans_list` of instances created by `load` and `load_new_implementation` for equality. This ensures that `load_new_implementation` must have the exact same functionality as `load` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the lengths and contents of `trans_list` between instances created by `load` and `load_new_implementation`. These assertions are reasonable because they directly test the functionality of the `load` method by examining the state of the `trans_list`.\n\n5. **CONDITION 5**: The test cases cover various scenarios, including a mock file with data, an empty file, a file with multiple data rows, and a file with special characters. This variety makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "ff5acc16420d54bbfca0836842c1cf34d64d4deb"
    },
    {
        "func_name": "Migrator.resolve_db_path",
        "idx": "971",
        "repo_name": "dataclipse___ClipseTicker",
        "func_path": "backend/src/tools/migrator.py",
        "orig_func": "@staticmethod\ndef resolve_db_path(db_path: Union[str, Path]) -> Path:\n    db_path = Path(db_path)\n    if db_path.is_absolute():\n        return db_path\n    return Path(__file__).parents[2] / 'db' / db_path",
        "orig_context": "```python\n## backend/src/tools/migrator.py\nimport sqlite3\n\nimport pandas as pd\n\nfrom pathlib import Path\n\nimport logging\n\nimport time\n\nfrom typing import Optional, Union\n\nclass Migrator:\n    @staticmethod\n    def resolve_db_path(db_path: Union[str, Path]) -> Path:\n        db_path = Path(db_path)\n        if db_path.is_absolute():\n            return db_path\n        \n        return Path(__file__).parents[2] / 'db' / db_path\n    \n    def __init__(self, source_db: Union[str, Path], target_db: Union[str, Path], max_retries: int = 3, retry_delay: int = 1):\n        # Initialize the migrator variables\n        self.source_db = self.resolve_db_path(source_db)\n        self.target_db = self.resolve_db_path(target_db)\n        self.max_retries = max_retries\n        self.retry_delay = retry_delay\n        \n        # Set up basic logging\n        logging.basicConfig(level=logging.INFO)\n        self.logger = logging.getLogger(__name__)\n    \n    def _verify_source_exists(self) -> bool:\n        return Path(self.source_db).exists()\n    \n    def _create_target_if_not_exists(self):\n        Path(self.target_db).parent.mkdir(parents=True, exist_ok=True)\n        if not Path(self.target_db).exists():\n            try:\n                Path(self.target_db).touch()\n                self.logger.info(f\"Created new target database: {self.target_db}\")\n            except Exception as e:\n                self.logger.error(f\"Failed to create target database: {str(e)}\")\n                raise\n    \n    def migrate_table(self, source_table: str, target_table: Optional[str] = None) -> bool:\n        if target_table is None:\n            target_table = source_table\n            \n        if not self._verify_source_exists():\n            self.logger.error(f\"Source database not found: {self.source_db}\")\n            return False\n        \n        self._create_target_if_not_exists()\n        \n        retry_count = 0\n        while retry_count < self.max_retries:\n            try:\n                source_query = f\"SELECT * FROM {source_table}\"\n                df = pd.read_sql_query(source_query, sqlite3.connect(self.source_db))\n                \n                if df.empty:\n                    self.logger.info(f\"Source table '{source_table}' is empty\")\n                    return True\n                \n                with sqlite3.connect(self.target_db) as conn:\n                    df.to_sql(\n                        name=target_table,\n                        con=conn,\n                        if_exists='replace',\n                        index=False\n                    )\n                    \n                self.logger.info(\n                    f\"Successfully migrated table '{len(df)}' rows from '{source_table}' to '{target_table}'\"\n                )\n                return True\n            \n            except sqlite3.OperationalError as e:\n                self.logger.error(f\"SQLite operational error: {str(e)}\")\n                retry_count += 1\n                if retry_count < self.max_retries:\n                    self.logger.info(f\"Retrying in {self.retry_delay} seconds...\"\n                                    f\"(Attempt {retry_count + 1}/{self.max_retries})\")\n                    \n                    time.sleep(self.retry_delay)\n                continue\n            \n            except pd.io.sql.DatabaseError as e:\n                self.logger.error(f\"Database error: {str(e)}\")\n                return False\n            \n            except Exception as e:\n                self.logger.error(f\"Unexpected error during migration: {str(e)}\")\n                return False\n            \n        self.logger.error(f\"Failed to migrate table '{source_table}' after {self.max_retries} attempts\")\n        return False\n\n```\n\n\n",
        "eval_script": "## backend/src/tools/migrator.py\nimport sqlite3\n\nimport pandas as pd\n\nfrom pathlib import Path\n\nimport logging\n\nimport time\n\nfrom typing import Optional, Union\n\nclass Migrator:\n    @staticmethod\n    def resolve_db_path(db_path: Union[str, Path]) -> Path:\n        db_path = Path(db_path)\n        if db_path.is_absolute():\n            return db_path\n        \n        return Path(__file__).parents[2] / 'db' / db_path\n    \n\n\n    \n    def __init__(self, source_db: Union[str, Path], target_db: Union[str, Path], max_retries: int = 3, retry_delay: int = 1):\n        # Initialize the migrator variables\n        self.source_db = self.resolve_db_path(source_db)\n        self.target_db = self.resolve_db_path(target_db)\n        self.max_retries = max_retries\n        self.retry_delay = retry_delay\n        \n        # Set up basic logging\n        logging.basicConfig(level=logging.INFO)\n        self.logger = logging.getLogger(__name__)\n    \n    def _verify_source_exists(self) -> bool:\n        return Path(self.source_db).exists()\n    \n    def _create_target_if_not_exists(self):\n        Path(self.target_db).parent.mkdir(parents=True, exist_ok=True)\n        if not Path(self.target_db).exists():\n            try:\n                Path(self.target_db).touch()\n                self.logger.info(f\"Created new target database: {self.target_db}\")\n            except Exception as e:\n                self.logger.error(f\"Failed to create target database: {str(e)}\")\n                raise\n    \n    def migrate_table(self, source_table: str, target_table: Optional[str] = None) -> bool:\n        if target_table is None:\n            target_table = source_table\n            \n        if not self._verify_source_exists():\n            self.logger.error(f\"Source database not found: {self.source_db}\")\n            return False\n        \n        self._create_target_if_not_exists()\n        \n        retry_count = 0\n        while retry_count < self.max_retries:\n            try:\n                source_query = f\"SELECT * FROM {source_table}\"\n                df = pd.read_sql_query(source_query, sqlite3.connect(self.source_db))\n                \n                if df.empty:\n                    self.logger.info(f\"Source table '{source_table}' is empty\")\n                    return True\n                \n                with sqlite3.connect(self.target_db) as conn:\n                    df.to_sql(\n                        name=target_table,\n                        con=conn,\n                        if_exists='replace',\n                        index=False\n                    )\n                    \n                self.logger.info(\n                    f\"Successfully migrated table '{len(df)}' rows from '{source_table}' to '{target_table}'\"\n                )\n                return True\n            \n            except sqlite3.OperationalError as e:\n                self.logger.error(f\"SQLite operational error: {str(e)}\")\n                retry_count += 1\n                if retry_count < self.max_retries:\n                    self.logger.info(f\"Retrying in {self.retry_delay} seconds...\"\n                                    f\"(Attempt {retry_count + 1}/{self.max_retries})\")\n                    \n                    time.sleep(self.retry_delay)\n                continue\n            \n            except pd.io.sql.DatabaseError as e:\n                self.logger.error(f\"Database error: {str(e)}\")\n                return False\n            \n            except Exception as e:\n                self.logger.error(f\"Unexpected error during migration: {str(e)}\")\n                return False\n            \n        self.logger.error(f\"Failed to migrate table '{source_table}' after {self.max_retries} attempts\")\n        return False\n\ndef test_resolve_db_path():\n    # Test with an absolute path\n    abs_path = Path('/home/user/tmp/database.db')\n    assert Migrator.resolve_db_path(abs_path) == Migrator.resolve_db_path_new_implementation(abs_path)\n\n    # Test with a relative path\n    rel_path = 'database.db'\n    assert Migrator.resolve_db_path(rel_path) == Migrator.resolve_db_path_new_implementation(rel_path)\n\n    # Test with another relative path\n    another_rel_path = Path('db/database.db')\n    assert Migrator.resolve_db_path(another_rel_path) == Migrator.resolve_db_path_new_implementation(another_rel_path)\n\n    # Test with an empty path\n    empty_path = ''\n    assert Migrator.resolve_db_path(empty_path) == Migrator.resolve_db_path_new_implementation(empty_path)\n\n    # Test with a path containing special characters\n    special_char_path = 'db/special!@#$.db'\n    assert Migrator.resolve_db_path(special_char_path) == Migrator.resolve_db_path_new_implementation(special_char_path)\n\n    # Test with a path containing parent directory reference\n    parent_dir_path = '../db/parent_dir.db'\n    assert Migrator.resolve_db_path(parent_dir_path) == Migrator.resolve_db_path_new_implementation(parent_dir_path)\n\n    # Test with a path containing current directory reference\n    current_dir_path = './db/current_dir.db'\n    assert Migrator.resolve_db_path(current_dir_path) == Migrator.resolve_db_path_new_implementation(current_dir_path)\n\n    # Test with a non-existent path\n    non_existent_path = 'non_existent_dir/non_existent.db'\n    assert Migrator.resolve_db_path(non_existent_path) == Migrator.resolve_db_path_new_implementation(non_existent_path)\n\nif __name__ == \"__main__\":\n    test_resolve_db_path()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions are static methods that take a `db_path` argument, convert it to a `Path` object, and check if it is an absolute path. If it is absolute, they return it as is. If it is not absolute, they construct a new path by navigating two directories up from the current file's location and appending 'db' and the `db_path`. There are no differences in logic, structure, or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `resolve_db_path` function returns a `Path` object, satisfying the condition that it has return values.\n- CONDITION 2: The test cases use assertions to compare the return values of `resolve_db_path` and `resolve_db_path_new_implementation`, without checking printed or logged contents.\n- CONDITION 3: The test cases compare the outputs of `resolve_db_path` and `resolve_db_path_new_implementation` for various inputs, ensuring that the new implementation must have the exact same functionality to pass.\n- CONDITION 4: The test cases use assertions to compare return values, which is reasonable given that `resolve_db_path` returns a value.\n- CONDITION 5: The test cases cover a variety of scenarios, including absolute paths, relative paths, empty paths, paths with special characters, and paths with directory references, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "7406b416543d40f5d8a808e23ab5a4964e1a6d73"
    },
    {
        "func_name": "Entity.tick",
        "idx": "978",
        "repo_name": "KaurHuko___Rock-Paper-Scissors",
        "func_path": "src/game.py",
        "orig_func": "def tick(self):\n    self.move()\n    self.collide_with_others()\n    self.draw()",
        "orig_context": "```python\n## src/game.py\nimport pygame\n\nfrom random import *\n\nimport shared\n\nimport math\n\nall_entities = None\n\nentity_size = None\n\ndef find_entity_size(count):\n    area_per_entity = 75000 / count\n    global entity_size\n    entity_size = math.sqrt(area_per_entity)\n\ndef find_winner():\n    potential_winner = all_entities[0]\n    for entity in all_entities:\n        if (entity.type.id != potential_winner.type.id):\n            return None\n    return potential_winner\n\ndef game_tick(events):\n    shared.display_surf.fill(shared.VALTER_VALGE)\n    \n    for entity in all_entities:\n        entity.tick()\n        \n    potential_winner = find_winner()\n    if (potential_winner != None):\n        shared.win_setup(potential_winner.type)\n\ndef game_setup():\n    shared.uireset()\n    \n    find_entity_size(shared.ENTITY_TYPES[0].count + shared.ENTITY_TYPES[1].count + shared.ENTITY_TYPES[2].count)\n    \n    global all_entities\n    all_entities = []\n    \n    for entity_type in shared.ENTITY_TYPES:\n        for i in range(entity_type.count):\n            new_entity = Entity(entity_type)\n            all_entities.append(new_entity)\n    \n    \n    shared.current_tick = game_tick\n\nshared.game_setup = game_setup\n\nclass Entity(pygame.sprite.Sprite): # Entity on kas kivi/paber/k\u00e4\u00e4rid\n\n    def __init__(self, type):\n        super().__init__()\n        \n        self.set_type(type)\n        \n        self.rect = self.image.get_rect()\n        w = self.rect.width\n        h = self.rect.height\n        self.rect.center = (randint(w, shared.SCREEN_WIDTH - w), randint(h, shared.SCREEN_HEIGHT - h))\n        \n        self.dx = randint(-3, 3)\n        self.dy = randint(-3, 3)\n\n    def set_type(self, type):\n        self.type = type\n        \n        self.image = pygame.image.load(type.image())\n        self.image = pygame.transform.scale(self.image, (entity_size, entity_size))\n        \n    \n    def tick(self):\n        self.move()\n        self.collide_with_others()\n        self.draw()\n\n    def move(self):\n        # Muuda kiirust v\u00e4hehaaval, et sujuvam liikumine\n        if randint(0, 20) == 0:  # V\u00e4ike v\u00f5imalus suuna muutmiseks\n            self.dx += randint(-1, 1)\n            self.dy += randint(-1, 1)\n\n        # Piira kiirus maksimaalselt 3-ga\n        self.dx = max(-3, min(self.dx, 3))\n        self.dy = max(-3, min(self.dy, 3))\n\n        # Kontrolli piire ja p\u00f5rka tagasi, kui on servas\n        if self.rect.top < 0 or self.rect.bottom > shared.SCREEN_HEIGHT:\n            self.dy *= -1\n        if self.rect.left < 0 or self.rect.right > shared.SCREEN_WIDTH:\n            self.dx *= -1\n        \n        # Uuenda positsiooni\n        self.rect.move_ip(self.dx, self.dy)\n        \n    def collide_with_others(self):\n        for other in all_entities:\n            if (not pygame.sprite.collide_rect(self, other)):\n                continue\n            \n            if (self.is_weaker_than(other)):\n                self.set_type(other.type)\n    \n    def is_weaker_than(self, other):\n        self_type = self.type.id\n        other_type = other.type.id\n        \n        if (other_type == \"rock\" and self_type == \"scissors\"): return True\n        if (other_type == \"scissors\" and self_type == \"paper\"): return True\n        if (other_type == \"paper\" and self_type == \"rock\"): return True\n        \n        return False\n        \n    def draw(self):\n        shared.display_surf.blit(self.image, self.rect)\n\n```\n\n\n",
        "eval_script": "# Mock shared module\nimport pygame\n\nclass MockType:\n    def __init__(self, id, count):\n        self.id = id\n        self.count = count\n    \n    def image(self):\n        # Return a placeholder image path\n        return '/home/user/tmp/placeholder.png'\n\nclass MockShared:\n    SCREEN_WIDTH = 800\n    SCREEN_HEIGHT = 600\n    VALTER_VALGE = (255, 255, 255)\n    \n    ENTITY_TYPES = [\n        MockType('rock', 5),\n        MockType('paper', 5),\n        MockType('scissors', 5)\n    ]\n    \n    def __init__(self):\n        pygame.init()\n        self.display_surf = pygame.display.set_mode((self.SCREEN_WIDTH, self.SCREEN_HEIGHT))\n        self.current_tick = None\n    \n    def uireset(self):\n        pass\n    \n    def win_setup(self, winner_type):\n        print(f\"Winner is {winner_type}\")\n\n# Create a placeholder image for testing\npygame.image.save(pygame.Surface((50, 50)), '/home/user/tmp/placeholder.png')\n\n# Initialize the shared module\nshared = MockShared()\n\n# Original code with slight modifications for testing\nimport math\nfrom random import randint, seed\n\nall_entities = None\nentity_size = None\n\ndef find_entity_size(count):\n    area_per_entity = 75000 / count\n    global entity_size\n    entity_size = math.sqrt(area_per_entity)\n\ndef find_winner():\n    potential_winner = all_entities[0]\n    for entity in all_entities:\n        if (entity.type.id != potential_winner.type.id):\n            return None\n    return potential_winner\n\ndef game_tick(events):\n    shared.display_surf.fill(shared.VALTER_VALGE)\n    \n    for entity in all_entities:\n        entity.tick()\n        \n    potential_winner = find_winner()\n    if (potential_winner != None):\n        shared.win_setup(potential_winner.type)\n\ndef game_setup():\n    shared.uireset()\n    \n    find_entity_size(shared.ENTITY_TYPES[0].count + shared.ENTITY_TYPES[1].count + shared.ENTITY_TYPES[2].count)\n    \n    global all_entities\n    all_entities = []\n    \n    for entity_type in shared.ENTITY_TYPES:\n        for i in range(entity_type.count):\n            new_entity = Entity(entity_type)\n            all_entities.append(new_entity)\n    \n    shared.current_tick = game_tick\n\nshared.game_setup = game_setup\n\nclass Entity(pygame.sprite.Sprite): # Entity on kas kivi/paber/k\u00e4\u00e4rid\n\n    def __init__(self, type):\n        super().__init__()\n        \n        self.set_type(type)\n        \n        self.rect = self.image.get_rect()\n        w = self.rect.width\n        h = self.rect.height\n        self.rect.center = (randint(w, shared.SCREEN_WIDTH - w), randint(h, shared.SCREEN_HEIGHT - h))\n        \n        self.dx = randint(-3, 3)\n        self.dy = randint(-3, 3)\n\n    def set_type(self, type):\n        self.type = type\n        \n        self.image = pygame.image.load(type.image())\n        self.image = pygame.transform.scale(self.image, (entity_size, entity_size))\n        \n    \n    def tick(self):\n        self.move()\n        self.collide_with_others()\n        self.draw()\n\n\n    def move(self):\n        # Muuda kiirust v\u00e4hehaaval, et sujuvam liikumine\n        if randint(0, 20) == 0:  # V\u00e4ike v\u00f5imalus suuna muutmiseks\n            self.dx += randint(-1, 1)\n            self.dy += randint(-1, 1)\n\n        # Piira kiirus maksimaalselt 3-ga\n        self.dx = max(-3, min(self.dx, 3))\n        self.dy = max(-3, min(self.dy, 3))\n\n        # Kontrolli piire ja p\u00f5rka tagasi, kui on servas\n        if self.rect.top < 0 or self.rect.bottom > shared.SCREEN_HEIGHT:\n            self.dy *= -1\n        if self.rect.left < 0 or self.rect.right > shared.SCREEN_WIDTH:\n            self.dx *= -1\n        \n        # Uuenda positsiooni\n        self.rect.move_ip(self.dx, self.dy)\n        \n    def collide_with_others(self):\n        for other in all_entities:\n            if (not pygame.sprite.collide_rect(self, other)):\n                continue\n            \n            if (self.is_weaker_than(other)):\n                self.set_type(other.type)\n    \n    def is_weaker_than(self, other):\n        self_type = self.type.id\n        other_type = other.type.id\n        \n        if (other_type == \"rock\" and self_type == \"scissors\"): return True\n        if (other_type == \"scissors\" and self_type == \"paper\"): return True\n        if (other_type == \"paper\" and self_type == \"rock\"): return True\n        \n        return False\n        \n    def draw(self):\n        shared.display_surf.blit(self.image, self.rect)\n\ndef test_tick():\n    # Setup the game\n    game_setup()\n    \n    # Test 1: Check if positions are the same after tick\n    for entity in all_entities:\n        original_rect = entity.rect.copy()\n        seed(0)  # Seed the random number generator\n        entity.tick()\n        new_rect = entity.rect.copy()\n        entity.rect = original_rect  # Reset to original for new implementation\n        seed(0)  # Seed again for consistent results\n        entity.tick_new_implementation()\n        assert entity.rect == new_rect, f\"Position mismatch: {entity.rect} vs {new_rect}\"\n    \n    # Test 2: Check if types are the same after collision\n    for entity in all_entities:\n        original_type = entity.type\n        seed(0)  # Seed the random number generator\n        entity.tick()\n        new_type = entity.type\n        entity.type = original_type  # Reset to original for new implementation\n        seed(0)  # Seed again for consistent results\n        entity.tick_new_implementation()\n        assert entity.type == new_type, f\"Type mismatch: {entity.type} vs {new_type}\"\n    \n    # Test 3: Check if dx, dy are the same after tick\n    for entity in all_entities:\n        original_dx, original_dy = entity.dx, entity.dy\n        seed(0)  # Seed the random number generator\n        entity.tick()\n        new_dx, new_dy = entity.dx, entity.dy\n        entity.dx, entity.dy = original_dx, original_dy  # Reset to original for new implementation\n        seed(0)  # Seed again for consistent results\n        entity.tick_new_implementation()\n        assert (entity.dx, entity.dy) == (new_dx, new_dy), f\"Velocity mismatch: {(entity.dx, entity.dy)} vs {(new_dx, new_dy)}\"\n\nif __name__ == \"__main__\":\n    test_tick()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `tick` in the provided code is identical to the ORIGINAL FUNCTION `tick`. Both functions call the methods `move`, `collide_with_others`, and `draw` in the same order and with the same logic. There are no changes in the functionality or the sequence of operations between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `tick` function modifies the state of the `Entity` objects, specifically their position (`rect`), type, and velocity (`dx`, `dy`). These are instance variables of the `Entity` class, which are effectively global in the context of the game state. Therefore, this condition is satisfied.\n\n2. **CONDITION 2**: The test cases in `test_tick` do not check printed or logged content. They only check the state of the `Entity` objects after calling `tick` and `tick_new_implementation`, specifically their `rect`, `type`, `dx`, and `dy` attributes. This condition is satisfied.\n\n3. **CONDITION 3**: The test cases are designed to compare the state of the `Entity` objects after executing both `tick` and `tick_new_implementation`. By seeding the random number generator, the tests ensure that both implementations are subjected to the same conditions. If `tick_new_implementation` passes all tests, it must have the same functionality as `tick`. This condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the state of the entities after calling both implementations. They do not use inappropriate assertions like comparing return values when there are none. The assertions are reasonable given the context of the function. This condition is satisfied.\n\n5. **CONDITION 5**: The test cases are non-trivial as they check multiple aspects of the `Entity` state: position, type, and velocity. These checks cover the core functionalities of the `tick` method, ensuring that movement, collision, and type changes are correctly implemented. This condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "ff78e0c57e69fc93e4d0092df20acdc1bdd790ec"
    },
    {
        "func_name": "TestGF2Polynomial.setUp",
        "idx": "980",
        "repo_name": "MykytaShashenok___GF2PolyBasis",
        "func_path": "src/test.py",
        "orig_func": "def setUp(self):\n    self.poly1 = GF2Polynomial([1, 0, 1])\n    self.poly2 = GF2Polynomial([1, 1])\n    self.poly3 = GF2Polynomial([1, 0, 1, 0])",
        "orig_context": "```python\n## src/poly.py\nimport numpy as np\n\nimport os\n\ngenerator_coeffs     = np.array(eval(os.getenv(\"GENERATOR_POLYNOMIAL\")), dtype=np.int8)\n\nclass GF2Polynomial:\n    def __init__(self, coeffs):\n        self.coeffs = np.array(coeffs, dtype=np.int8) & 1\n        self.trim()\n\n    def trim(self):\n        self.coeffs = np.trim_zeros(self.coeffs, 'f')\n\n    def __add__(self, other):\n        max_len = max(len(self.coeffs), len(other.coeffs))\n        padded_self = np.pad(self.coeffs, (max_len - len(self.coeffs), 0))\n        padded_other = np.pad(other.coeffs, (max_len - len(other.coeffs), 0))\n        result_coeffs = np.bitwise_xor(padded_self, padded_other)\n        return GF2Polynomial(result_coeffs)\n\n    def __mul__(self, other):\n        result_coeffs = np.zeros(len(self.coeffs) + len(other.coeffs) - 1, dtype=np.int8)\n        for i in range(len(self.coeffs)):\n            result_coeffs[i:i + len(other.coeffs)] ^= self.coeffs[i] * other.coeffs\n        return self.reduce(result_coeffs)\n\n    def square(self):\n        squared_coeffs = np.zeros(2 * len(self.coeffs) - 1, dtype=np.int8)\n        squared_coeffs[::2] = self.coeffs\n        return self.reduce(squared_coeffs)\n\n    def reduce(self, coeffs):\n        while len(coeffs) >= len(generator_polynomial.coeffs):\n            if coeffs[0] == 1:\n                coeffs[:len(generator_polynomial.coeffs)] ^= generator_polynomial.coeffs\n            coeffs = np.trim_zeros(coeffs, 'f')\n            if coeffs.size == 0:\n                coeffs = np.array([0], dtype=np.int8)\n        return GF2Polynomial(coeffs)\n\n    def __repr__(self):\n        return f\"GF2Polynomial({self.coeffs.tolist()})\"\n    \n    def power(self, exp):\n        result = GF2Polynomial([1])\n        base = self\n        \n        while exp > 0:\n            if exp % 2 == 1:\n                result = result * base\n            base = base.square()\n            exp //= 2\n        \n        return result\n    \n    def trace(self) :\n        # just need to summarize powers of 2 \n        pass\n\ngenerator_polynomial = GF2Polynomial(generator_coeffs)\n\n```\n\n\n```python\n## src/test.py\nimport unittest\n\nimport numpy as np\n\nfrom poly import GF2Polynomial\n\nclass TestGF2Polynomial(unittest.TestCase):\n\n    def setUp(self):\n        # \u0417\u0430\u0432\u0436\u0434\u0438 \u043f\u0435\u0440\u0435\u0434 \u043a\u043e\u0436\u043d\u0438\u043c \u0442\u0435\u0441\u0442\u043e\u043c \u0456\u043d\u0456\u0446\u0456\u0430\u043b\u0456\u0437\u0443\u0454\u043c\u043e \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u0438\u0439 \u043f\u043e\u043b\u0456\u043d\u043e\u043c\n        self.poly1 = GF2Polynomial([1, 0, 1])  # x^2 + 1\n        self.poly2 = GF2Polynomial([1, 1])  # x + 1\n        self.poly3 = GF2Polynomial([1, 0, 1, 0])  # x^3 + x + 1\n\n    def test_addition(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0434\u043e\u0434\u0430\u0432\u0430\u043d\u043d\u044f \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0432\n        result = self.poly1 + self.poly2\n        expected = GF2Polynomial([1, 1, 0])  # x^2 + x + \n        self.assertEqual(result, expected)\n\n    def test_multiplication(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u043c\u043d\u043e\u0436\u0435\u043d\u043d\u044f \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0432\n        result = self.poly1 * self.poly2\n        expected = GF2Polynomial([1, 1, 1])  # x^2 + x + 1\n        self.assertEqual(result, expected)\n\n    def test_square(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0432\u043e\u0437\u0432\u0435\u0434\u0435\u043d\u043d\u044f \u0432 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\n        result = self.poly1.square()\n        expected = GF2Polynomial([1, 0, 1, 0])  # x^4 + x^2 + 1\n        self.assertEqual(result, expected)\n\n    def test_power(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u043f\u0456\u0434\u043d\u0435\u0441\u0435\u043d\u043d\u044f \u0434\u043e \u0441\u0442\u0435\u043f\u0435\u043d\u044f\n        result = self.poly1.power(3)\n        expected = GF2Polynomial([1, 0, 1, 0])  # x^3 + x + 1 (\u043f\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0456\u0457 \u0437 \u043c\u0435\u0442\u043e\u0434\u043e\u043c reduce)\n        self.assertEqual(result, expected)\n\n    def test_reduce(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0440\u0435\u0434\u0443\u043a\u0446\u0456\u044e \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0430\n        coeffs = np.array([1, 1, 0, 0, 1, 1])  # x^5 + x^4 + x^1 + 1\n        reduced_poly = self.poly1.reduce(coeffs)\n        expected = GF2Polynomial([1, 1, 1])  # \u041f\u0456\u0441\u043b\u044f \u0440\u0435\u0434\u0443\u043a\u0446\u0456\u0457 \u043c\u0430\u0454 \u0432\u0438\u0439\u0442\u0438 x^2 + x + 1\n        self.assertEqual(reduced_poly, expected)\n\n    def test_trim(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e, \u0447\u0438 \u043f\u0440\u0430\u0446\u044e\u0454 trim \u0434\u043b\u044f \u0443\u0441\u0443\u043d\u0435\u043d\u043d\u044f \u0437\u0430\u0439\u0432\u0438\u0445 \u043d\u0443\u043b\u0456\u0432\n        poly_with_trailing_zeros = GF2Polynomial([1, 0, 0, 1, 0])  # x^4 + 1\n        #need to \n                #need to rewright\n        #incorrect tests\n        poly_with_trailing_zeros.trim()\n        expected = GF2Polynomial([1, 0, 0, 1])  # x^4 + 1\n        self.assertEqual(poly_with_trailing_zeros, expected)\n\n    def test_empty_polynomial(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0432\u0438\u043f\u0430\u0434\u043e\u043a \u0437 \u043f\u043e\u0440\u043e\u0436\u043d\u0456\u043c \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u043e\u043c\n        empty_poly = GF2Polynomial([0])  # \u041f\u043e\u0440\u043e\u0436\u043d\u0456\u0439 \u043f\u043e\u043b\u0456\u043d\u043e\u043c (0)\n\n        \n        self.assertEqual(empty_poly, GF2Polynomial([0]))\n\n```\n\n\n",
        "eval_script": "import unittest\nimport numpy as np\n\n# Mocking the generator polynomial coefficients\ngenerator_coeffs = np.array([1, 0, 1, 1], dtype=np.int8)  # Example polynomial x^3 + x + 1\n\nclass GF2Polynomial:\n    def __init__(self, coeffs):\n        self.coeffs = np.array(coeffs, dtype=np.int8) & 1\n        self.trim()\n\n    def trim(self):\n        self.coeffs = np.trim_zeros(self.coeffs, 'f')\n\n    def __add__(self, other):\n        max_len = max(len(self.coeffs), len(other.coeffs))\n        padded_self = np.pad(self.coeffs, (max_len - len(self.coeffs), 0))\n        padded_other = np.pad(other.coeffs, (max_len - len(other.coeffs), 0))\n        result_coeffs = np.bitwise_xor(padded_self, padded_other)\n        return GF2Polynomial(result_coeffs)\n\n    def __mul__(self, other):\n        result_coeffs = np.zeros(len(self.coeffs) + len(other.coeffs) - 1, dtype=np.int8)\n        for i in range(len(self.coeffs)):\n            result_coeffs[i:i + len(other.coeffs)] ^= self.coeffs[i] * other.coeffs\n        return self.reduce(result_coeffs)\n\n    def square(self):\n        squared_coeffs = np.zeros(2 * len(self.coeffs) - 1, dtype=np.int8)\n        squared_coeffs[::2] = self.coeffs\n        return self.reduce(squared_coeffs)\n\n    def reduce(self, coeffs):\n        while len(coeffs) >= len(generator_polynomial.coeffs):\n            if coeffs[0] == 1:\n                coeffs[:len(generator_polynomial.coeffs)] ^= generator_polynomial.coeffs\n            coeffs = np.trim_zeros(coeffs, 'f')\n            if coeffs.size == 0:\n                coeffs = np.array([0], dtype=np.int8)\n        return GF2Polynomial(coeffs)\n\n    def __repr__(self):\n        return f\"GF2Polynomial({self.coeffs.tolist()})\"\n    \n    def power(self, exp):\n        result = GF2Polynomial([1])\n        base = self\n        \n        while exp > 0:\n            if exp % 2 == 1:\n                result = result * base\n            base = base.square()\n            exp //= 2\n        \n        return result\n    \n    def trace(self):\n        # just need to summarize powers of 2 \n        pass\n\ngenerator_polynomial = GF2Polynomial(generator_coeffs)\n\nclass TestGF2Polynomial(unittest.TestCase):\n\n    def setUp(self):\n        # \u0417\u0430\u0432\u0436\u0434\u0438 \u043f\u0435\u0440\u0435\u0434 \u043a\u043e\u0436\u043d\u0438\u043c \u0442\u0435\u0441\u0442\u043e\u043c \u0456\u043d\u0456\u0446\u0456\u0430\u043b\u0456\u0437\u0443\u0454\u043c\u043e \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u0438\u0439 \u043f\u043e\u043b\u0456\u043d\u043e\u043c\n        self.poly1 = GF2Polynomial([1, 0, 1])  # x^2 + 1\n        self.poly2 = GF2Polynomial([1, 1])  # x + 1\n        self.poly3 = GF2Polynomial([1, 0, 1, 0])  # x^3 + x + 1\n\n\n    def test_setUp(self):\n        # Test to compare the original setUp and the new implementation\n        self.setUp()\n        original_poly1 = self.poly1\n        original_poly2 = self.poly2\n        original_poly3 = self.poly3\n    \n        self.setUp_new_implementation()\n        new_poly1 = self.poly1\n        new_poly2 = self.poly2\n        new_poly3 = self.poly3\n    \n        # Check if coefficients are the same\n        assert original_poly1.coeffs.tolist() == new_poly1.coeffs.tolist(), \"poly1 differs\"\n        assert original_poly2.coeffs.tolist() == new_poly2.coeffs.tolist(), \"poly2 differs\"\n        assert original_poly3.coeffs.tolist() == new_poly3.coeffs.tolist(), \"poly3 differs\"\n    \n        # Check if the objects are instances of GF2Polynomial\n        assert isinstance(new_poly1, GF2Polynomial), \"new_poly1 is not a GF2Polynomial\"\n        assert isinstance(new_poly2, GF2Polynomial), \"new_poly2 is not a GF2Polynomial\"\n        assert isinstance(new_poly3, GF2Polynomial), \"new_poly3 is not a GF2Polynomial\"\n    \n        # Check if the polynomials behave the same in addition\n        assert (original_poly1 + original_poly2).coeffs.tolist() == (new_poly1 + new_poly2).coeffs.tolist(), \"Addition result differs\"\n    \n        # Check if the polynomials behave the same in multiplication\n        assert (original_poly1 * original_poly2).coeffs.tolist() == (new_poly1 * new_poly2).coeffs.tolist(), \"Multiplication result differs\"\n    \n        # Check if the polynomials behave the same in squaring\n        assert original_poly1.square().coeffs.tolist() == new_poly1.square().coeffs.tolist(), \"Square result differs\"\n    \n        # Check if the polynomials behave the same in power\n        assert original_poly1.power(2).coeffs.tolist() == new_poly1.power(2).coeffs.tolist(), \"Power result differs\"\n\n    def test_addition(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0434\u043e\u0434\u0430\u0432\u0430\u043d\u043d\u044f \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0432\n        result = self.poly1 + self.poly2\n        expected = GF2Polynomial([1, 1, 0])  # x^2 + x + \n        self.assertEqual(result, expected)\n\n    def test_multiplication(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u043c\u043d\u043e\u0436\u0435\u043d\u043d\u044f \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0432\n        result = self.poly1 * self.poly2\n        expected = GF2Polynomial([1, 1, 1])  # x^2 + x + 1\n        self.assertEqual(result, expected)\n\n    def test_square(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0432\u043e\u0437\u0432\u0435\u0434\u0435\u043d\u043d\u044f \u0432 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\n        result = self.poly1.square()\n        expected = GF2Polynomial([1, 0, 1, 0])  # x^4 + x^2 + 1\n        self.assertEqual(result, expected)\n\n    def test_power(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u043f\u0456\u0434\u043d\u0435\u0441\u0435\u043d\u043d\u044f \u0434\u043e \u0441\u0442\u0435\u043f\u0435\u043d\u044f\n        result = self.poly1.power(3)\n        expected = GF2Polynomial([1, 0, 1, 0])  # x^3 + x + 1 (\u043f\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0456\u0457 \u0437 \u043c\u0435\u0442\u043e\u0434\u043e\u043c reduce)\n        self.assertEqual(result, expected)\n\n    def test_reduce(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0440\u0435\u0434\u0443\u043a\u0446\u0456\u044e \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0430\n        coeffs = np.array([1, 1, 0, 0, 1, 1])  # x^5 + x^4 + x^1 + 1\n        reduced_poly = self.poly1.reduce(coeffs)\n        expected = GF2Polynomial([1, 1, 1])  # \u041f\u0456\u0441\u043b\u044f \u0440\u0435\u0434\u0443\u043a\u0446\u0456\u0457 \u043c\u0430\u0454 \u0432\u0438\u0439\u0442\u0438 x^2 + x + 1\n        self.assertEqual(reduced_poly, expected)\n\n    def test_trim(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e, \u0447\u0438 \u043f\u0440\u0430\u0446\u044e\u0454 trim \u0434\u043b\u044f \u0443\u0441\u0443\u043d\u0435\u043d\u043d\u044f \u0437\u0430\u0439\u0432\u0438\u0445 \u043d\u0443\u043b\u0456\u0432\n        poly_with_trailing_zeros = GF2Polynomial([1, 0, 0, 1, 0])  # x^4 + 1\n        poly_with_trailing_zeros.trim()\n        expected = GF2Polynomial([1, 0, 0, 1])  # x^4 + 1\n        self.assertEqual(poly_with_trailing_zeros, expected)\n\n    def test_empty_polynomial(self):\n        # \u041f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u043c\u043e \u0432\u0438\u043f\u0430\u0434\u043e\u043a \u0437 \u043f\u043e\u0440\u043e\u0436\u043d\u0456\u043c \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u043e\u043c\n        empty_poly = GF2Polynomial([0])  # \u041f\u043e\u0440\u043e\u0436\u043d\u0456\u0439 \u043f\u043e\u043b\u0456\u043d\u043e\u043c (0)\n        self.assertEqual(empty_poly, GF2Polynomial([0]))\n\nif __name__ == \"__main__\":\n    test = TestGF2Polynomial()\n    test.test_setUp()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `setUp` initializes three polynomial objects `poly1`, `poly2`, and `poly3` with specific coefficients. The revised function `setUp` in the `TestGF2Polynomial` class does the same initialization with the same coefficients for `poly1`, `poly2`, and `poly3`. The revised function is part of a test class and is intended to set up the test environment before each test case, which is consistent with the purpose of the original function. The functionality of initializing the polynomials with the same coefficients is preserved in the revised function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `setUp` function initializes instance variables `self.poly1`, `self.poly2`, and `self.poly3` with instances of `GF2Polynomial`. It modifies these global variables (in the context of the class), satisfying this condition.\n\n- CONDITION 2: The test function `test_setUp` checks the coefficients of the polynomials and their behavior in operations like addition, multiplication, squaring, and power. It does not rely on printed or logged content, thus satisfying this condition.\n\n- CONDITION 3: The test function compares the state of the polynomials and their behavior after using both `setUp` and `setUp_new_implementation`. This ensures that `setUp_new_implementation` must have the same functionality as `setUp` to pass all tests, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the coefficients and behavior of polynomial operations, which are reasonable given that `setUp` does not return values but modifies instance variables. This condition is satisfied.\n\n- CONDITION 5: The test cases in `test_setUp` are non-trivial as they test not only the equality of polynomial coefficients but also their behavior in various operations, ensuring comprehensive coverage of the functionality.",
            "answer": "yes"
        },
        "commit_id": "4122805efb129a41080286a7cb8c9aeb365bf7c2"
    },
    {
        "func_name": "GF2Polynomial.power",
        "idx": "982",
        "repo_name": "MykytaShashenok___GF2PolyBasis",
        "func_path": "src/poly.py",
        "orig_func": "def power(self, exp):\n    result = GF2Polynomial([1])\n    base = self\n    while exp > 0:\n        if exp % 2 == 1:\n            result = result * base\n        base = base.square()\n        exp //= 2\n    return result",
        "orig_context": "```python\n## src/poly.py\nimport numpy as np\n\nimport os\n\ngenerator_coeffs     = np.array(eval(os.getenv(\"GENERATOR_POLYNOMIAL\")), dtype=np.int8)\n\ngenerator_polynomial = GF2Polynomial(generator_coeffs)\n\nclass GF2Polynomial:\n    def __init__(self, coeffs):\n        self.coeffs = np.array(coeffs, dtype=np.int8) & 1\n        self.trim()\n\n    def trim(self):\n        self.coeffs = np.trim_zeros(self.coeffs, 'f')\n\n    def __add__(self, other):\n        max_len = max(len(self.coeffs), len(other.coeffs))\n        padded_self = np.pad(self.coeffs, (max_len - len(self.coeffs), 0))\n        padded_other = np.pad(other.coeffs, (max_len - len(other.coeffs), 0))\n        result_coeffs = np.bitwise_xor(padded_self, padded_other)\n        return GF2Polynomial(result_coeffs)\n\n    def __mul__(self, other):\n        result_coeffs = np.zeros(len(self.coeffs) + len(other.coeffs) - 1, dtype=np.int8)\n        for i in range(len(self.coeffs)):\n            result_coeffs[i:i + len(other.coeffs)] ^= self.coeffs[i] * other.coeffs\n        return self.reduce(result_coeffs)\n\n    def square(self):\n        squared_coeffs = np.zeros(2 * len(self.coeffs) - 1, dtype=np.int8)\n        squared_coeffs[::2] = self.coeffs\n        return self.reduce(squared_coeffs)\n\n    def reduce(self, coeffs):\n        while len(coeffs) >= len(generator_polynomial.coeffs):\n            if coeffs[0] == 1:\n                coeffs[:len(generator_polynomial.coeffs)] ^= generator_polynomial.coeffs\n            coeffs = np.trim_zeros(coeffs, 'f')\n            if coeffs.size == 0:\n                coeffs = np.array([0], dtype=np.int8)\n        return GF2Polynomial(coeffs)\n\n    def __repr__(self):\n        return f\"GF2Polynomial({self.coeffs.tolist()})\"\n    \n    def power(self, exp):\n        result = GF2Polynomial([1])\n        base = self\n        \n        while exp > 0:\n            if exp % 2 == 1:\n                result = result * base\n            base = base.square()\n            exp //= 2\n        \n        return result\n    \n    def trace(self) :\n        # just need to summarize powers of 2 \n        pass\n\n```\n\n\n",
        "eval_script": "## src/poly.py\nimport numpy as np\n\nimport os\n\nclass GF2Polynomial:\n    def __init__(self, coeffs):\n        self.coeffs = np.array(coeffs, dtype=np.int8) & 1\n        self.trim()\n\n    def trim(self):\n        self.coeffs = np.trim_zeros(self.coeffs, 'f')\n\n    def __add__(self, other):\n        max_len = max(len(self.coeffs), len(other.coeffs))\n        padded_self = np.pad(self.coeffs, (max_len - len(self.coeffs), 0))\n        padded_other = np.pad(other.coeffs, (max_len - len(other.coeffs), 0))\n        result_coeffs = np.bitwise_xor(padded_self, padded_other)\n        return GF2Polynomial(result_coeffs)\n\n    def __mul__(self, other):\n        result_coeffs = np.zeros(len(self.coeffs) + len(other.coeffs) - 1, dtype=np.int8)\n        for i in range(len(self.coeffs)):\n            result_coeffs[i:i + len(other.coeffs)] ^= self.coeffs[i] * other.coeffs\n        return self.reduce(result_coeffs)\n\n    def square(self):\n        squared_coeffs = np.zeros(2 * len(self.coeffs) - 1, dtype=np.int8)\n        squared_coeffs[::2] = self.coeffs\n        return self.reduce(squared_coeffs)\n\n    def reduce(self, coeffs):\n        while len(coeffs) >= len(generator_polynomial.coeffs):\n            if coeffs[0] == 1:\n                coeffs[:len(generator_polynomial.coeffs)] ^= generator_polynomial.coeffs\n            coeffs = np.trim_zeros(coeffs, 'f')\n            if coeffs.size == 0:\n                coeffs = np.array([0], dtype=np.int8)\n        return GF2Polynomial(coeffs)\n\n    def __repr__(self):\n        return f\"GF2Polynomial({self.coeffs.tolist()})\"\n    \n    def power(self, exp):\n        result = GF2Polynomial([1])\n        base = self\n        \n        while exp > 0:\n            if exp % 2 == 1:\n                result = result * base\n            base = base.square()\n            exp //= 2\n        \n        return result\n\n\n    \n    def trace(self) :\n        # just need to summarize powers of 2 \n        pass\n\n# Provide a default value for GENERATOR_POLYNOMIAL if the environment variable is not set\ndefault_generator_polynomial = \"[1, 0, 1, 1]\"  # Example polynomial x^3 + x + 1\ngenerator_coeffs = np.array(eval(os.getenv(\"GENERATOR_POLYNOMIAL\", default_generator_polynomial)), dtype=np.int8)\n\ngenerator_polynomial = GF2Polynomial(generator_coeffs)\n\ndef test_power():\n    poly1 = GF2Polynomial([1, 0, 1])  # x^2 + 1\n    poly2 = GF2Polynomial([1, 1])     # x + 1\n    poly3 = GF2Polynomial([1, 0, 0, 1])  # x^3 + 1\n\n    # Test power of 0\n    assert poly1.power(0).coeffs.tolist() == poly1.power_new_implementation(0).coeffs.tolist()\n    \n    # Test power of 1\n    assert poly2.power(1).coeffs.tolist() == poly2.power_new_implementation(1).coeffs.tolist()\n    \n    # Test power of 3\n    assert poly3.power(3).coeffs.tolist() == poly3.power_new_implementation(3).coeffs.tolist()\n\nif __name__ == \"__main__\":\n    test_power()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `power` in the `GF2Polynomial` class is identical to the ORIGINAL FUNCTION in terms of functionality. Both functions perform exponentiation using the same algorithm, which involves repeated squaring and multiplication. The logic and operations within the function are unchanged, and the context provided by the surrounding code (such as the `GF2Polynomial` class and its methods) does not alter the behavior of the `power` function itself. The test cases provided in the `test_power` function also confirm that the functionality remains consistent.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `power` function returns a `GF2Polynomial` object, which contains the result of the polynomial raised to the given exponent. Therefore, it satisfies this condition as it has a return value.\n\n- CONDITION 2: The test cases in `test_power` use assertions to compare the `coeffs` attribute of the `GF2Polynomial` objects returned by `power` and `power_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n\n- CONDITION 3: The test cases check the results of raising polynomials to different powers (0, 1, and 3). These tests ensure that `power_new_implementation` must have the same functionality as `power` to pass all tests, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the `coeffs` attribute of the results from `power` and `power_new_implementation`. This is reasonable because `power` returns a `GF2Polynomial` object, and comparing the `coeffs` attribute is a valid way to check for equality of the results. Thus, this condition is satisfied.\n\n- CONDITION 5: The test cases cover different scenarios: raising a polynomial to the power of 0 (which should return the identity element), to the power of 1 (which should return the polynomial itself), and to the power of 3 (a non-trivial power). These tests are non-trivial as they check for different behaviors of the `power` function, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "4122805efb129a41080286a7cb8c9aeb365bf7c2"
    },
    {
        "func_name": "Player.shoot",
        "idx": "986",
        "repo_name": "peshala-prabhapoorna___asteroids",
        "func_path": "player.py",
        "orig_func": "def shoot(self):\n    if self.shoot_timer > 0:\n        return\n    self.shoot_timer = PLAYER_SHOOT_COOLDOWN\n    shot = Shot(self.position.x, self.position.y)\n    shot.velocity = pg.Vector2(0, 1).rotate(self.rotation) * PLAYER_SHOOT_SPEED",
        "orig_context": "```python\n## constants.py\nPLAYER_RADIUS = 20\n\nPLAYER_TURN_SPEED = 300\n\nPLAYER_SPEED = 200\n\nPLAYER_SHOOT_SPEED = 500\n\nPLAYER_SHOOT_COOLDOWN = 0.3\n\nSHOT_RADIUS=5\n\n```\n\n\n```python\n## circleshape.py\nimport pygame as pg\n\nclass CircleShape(pg.sprite.Sprite):\n    def __init__(self, x, y, radius):\n        # we will be using this later\n        if hasattr(self, \"containers\"):\n            super().__init__(self.containers)\n        else:\n            super().__init__()\n\n        self.position = pg.Vector2(x, y)\n        self.velocity = pg.Vector2(0, 0)\n        self.radius = radius\n\n    def draw(self, screen):\n        # sub-classes must override\n        pass\n\n    def update(self, dt):\n        # sub-classes must override\n        pass\n\n    def is_colliding(self, asteroid):\n        distance = self.position.distance_to(asteroid.position)\n\n        if distance <= self.radius + asteroid.radius:\n            return True\n        return False\n\n```\n\n\n```python\n## shot.py\nimport pygame as pg\n\nfrom circleshape import CircleShape\n\nfrom constants import SHOT_RADIUS\n\nclass Shot(CircleShape):\n    def __init__(self, x, y):\n        super().__init__(x, y, SHOT_RADIUS)\n\n    def draw(self, screen):\n        pg.draw.circle(screen, \"white\", self.position, self.radius, width=2)\n\n    def update(self, dt):\n        self.position += self.velocity * dt\n\n```\n\n\n```python\n## player.py\nimport pygame as pg\n\nfrom circleshape import CircleShape\n\nfrom constants import (\n    PLAYER_RADIUS,\n    PLAYER_SHOOT_COOLDOWN,\n    PLAYER_SHOOT_SPEED,\n    PLAYER_SPEED,\n    PLAYER_TURN_SPEED,\n)\n\nfrom shot import Shot\n\nclass Player(CircleShape):\n    def __init__(self, x, y):\n        super().__init__(x, y, PLAYER_RADIUS)\n        self.rotation = 0\n        self.shoot_timer = 0\n    \n    def triangle(self):\n        forward = pg.Vector2(0, 1).rotate(self.rotation)\n        right = pg.Vector2(0, 1).rotate(self.rotation + 90) * self.radius / 1.5\n        a = self.position + forward * self.radius\n        b = self.position - forward * self.radius - right\n        c = self.position - forward * self.radius + right\n        return [a, b, c]\n\n    def draw(self, screen):\n        pg.draw.polygon(screen, \"white\", self.triangle(), width=2)\n\n    def rotate(self, dt):\n        self.rotation += PLAYER_TURN_SPEED * dt\n\n    def move(self, dt):\n        forward = pg.Vector2(0, 1).rotate(self.rotation)\n        self.position += forward * PLAYER_SPEED * dt\n\n    def shoot(self):\n        if self.shoot_timer > 0:\n            return\n        self.shoot_timer = PLAYER_SHOOT_COOLDOWN\n        shot = Shot(self.position.x, self.position.y)\n        shot.velocity = pg.Vector2(0, 1).rotate(self.rotation) * \\\n            PLAYER_SHOOT_SPEED\n\n    def update(self, dt):\n        self.shoot_timer -= dt\n        keys = pg.key.get_pressed()\n\n        if keys[pg.K_a]:\n            self.rotate(-dt)\n        if keys[pg.K_d]:\n            self.rotate(dt)\n        if keys[pg.K_w]:\n            self.move(dt)\n        if keys[pg.K_s]:\n            self.move(-dt)\n        if keys[pg.K_SPACE]:\n            self.shoot()\n\n```\n\n\n",
        "eval_script": "import pygame as pg\n\n# Constants\nPLAYER_RADIUS = 20\nPLAYER_TURN_SPEED = 300\nPLAYER_SPEED = 200\nPLAYER_SHOOT_SPEED = 500\nPLAYER_SHOOT_COOLDOWN = 0.3\nSHOT_RADIUS = 5\n\n# CircleShape class\nclass CircleShape(pg.sprite.Sprite):\n    def __init__(self, x, y, radius):\n        if hasattr(self, \"containers\"):\n            super().__init__(self.containers)\n        else:\n            super().__init__()\n\n        self.position = pg.Vector2(x, y)\n        self.velocity = pg.Vector2(0, 0)\n        self.radius = radius\n\n    def draw(self, screen):\n        pass\n\n    def update(self, dt):\n        pass\n\n    def is_colliding(self, asteroid):\n        distance = self.position.distance_to(asteroid.position)\n        if distance <= self.radius + asteroid.radius:\n            return True\n        return False\n\n# Shot class\nclass Shot(CircleShape):\n    def __init__(self, x, y):\n        super().__init__(x, y, SHOT_RADIUS)\n\n    def draw(self, screen):\n        pg.draw.circle(screen, \"white\", self.position, self.radius, width=2)\n\n    def update(self, dt):\n        self.position += self.velocity * dt\n\n# Player class\nclass Player(CircleShape):\n    def __init__(self, x, y):\n        super().__init__(x, y, PLAYER_RADIUS)\n        self.rotation = 0\n        self.shoot_timer = 0\n    \n    def triangle(self):\n        forward = pg.Vector2(0, 1).rotate(self.rotation)\n        right = pg.Vector2(0, 1).rotate(self.rotation + 90) * self.radius / 1.5\n        a = self.position + forward * self.radius\n        b = self.position - forward * self.radius - right\n        c = self.position - forward * self.radius + right\n        return [a, b, c]\n\n    def draw(self, screen):\n        pg.draw.polygon(screen, \"white\", self.triangle(), width=2)\n\n    def rotate(self, dt):\n        self.rotation += PLAYER_TURN_SPEED * dt\n\n    def move(self, dt):\n        forward = pg.Vector2(0, 1).rotate(self.rotation)\n        self.position += forward * PLAYER_SPEED * dt\n\n    def shoot(self):\n        if self.shoot_timer > 0:\n            return\n        self.shoot_timer = PLAYER_SHOOT_COOLDOWN\n        shot = Shot(self.position.x, self.position.y)\n        shot.velocity = pg.Vector2(0, 1).rotate(self.rotation) * PLAYER_SHOOT_SPEED\n\n\n    def update(self, dt):\n        self.shoot_timer -= dt\n        keys = pg.key.get_pressed()\n\n        if keys[pg.K_a]:\n            self.rotate(-dt)\n        if keys[pg.K_d]:\n            self.rotate(dt)\n        if keys[pg.K_w]:\n            self.move(dt)\n        if keys[pg.K_s]:\n            self.move(-dt)\n        if keys[pg.K_SPACE]:\n            self.shoot()\n\ndef test_shoot():\n    player = Player(0, 0)\n    \n    # Test 1: shoot_timer > 0, no shot should be fired\n    player.shoot_timer = 0.1\n    player.shoot()\n    player.shoot_new_implementation()\n    assert player.shoot_timer == 0.1, \"Test 1 failed: shoot_timer should not change\"\n\n    # Test 2: shoot_timer == 0, shot should be fired and shoot_timer reset\n    player.shoot_timer = 0\n    player.shoot()\n    assert player.shoot_timer == PLAYER_SHOOT_COOLDOWN, \"Test 2 failed: shoot_timer should be reset\"\n    player.shoot_timer = 0\n    player.shoot_new_implementation()\n    assert player.shoot_timer == PLAYER_SHOOT_COOLDOWN, \"Test 2 failed: shoot_timer should be reset\"\n\n    # Test 3: Check shot velocity\n    player.rotation = 45\n    player.shoot_timer = 0\n    player.shoot()\n    shot_velocity = pg.Vector2(0, 1).rotate(45) * PLAYER_SHOOT_SPEED\n    player.shoot_timer = 0\n    player.shoot_new_implementation()\n    assert shot_velocity == pg.Vector2(0, 1).rotate(45) * PLAYER_SHOOT_SPEED, \"Test 3 failed: shot velocity should match\"\n\nif __name__ == \"__main__\":\n    test_shoot()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions check if `self.shoot_timer` is greater than 0 and return if true. If not, they set `self.shoot_timer` to `PLAYER_SHOOT_COOLDOWN`, create a `Shot` object at the player's current position, and set the shot's velocity based on the player's rotation and `PLAYER_SHOOT_SPEED`. There are no differences in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `shoot` function modifies the `shoot_timer` attribute of the `Player` class, which is a global variable in the context of the class instance. Therefore, this condition is satisfied.\n\n- CONDITION 2: The test cases check the state of the `shoot_timer` attribute and the expected shot velocity, not any printed or logged output. Thus, this condition is satisfied.\n\n- CONDITION 3: The test cases are designed to check the functionality of the `shoot` method by verifying the `shoot_timer` and the shot velocity. If `shoot_new_implementation` behaves differently, it would fail these tests. Therefore, this condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to check the state of `shoot_timer` and the expected shot velocity, which are reasonable checks given the functionality of the `shoot` method. Thus, this condition is satisfied.\n\n- CONDITION 5: The test cases are non-trivial as they check different scenarios: when `shoot_timer` is greater than zero, when it is zero, and the velocity of the shot when fired. These cover the essential functionality of the `shoot` method. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "f8afc1765c06333ee4aa8031574fcfa64bd91900"
    },
    {
        "func_name": "Ball.update",
        "idx": "989",
        "repo_name": "AnnyaB___Catch-The-Ball",
        "func_path": "CatchTheBall.py",
        "orig_func": "def update(self):\n    self.rect.x += self.x_velocity\n    self.rect.y += self.y_velocity\n    if self.rect.left <= 0 or self.rect.right >= WIDTH:\n        self.x_velocity *= -1\n        wall_hit_sound.play()\n    if self.rect.top <= 0 or self.rect.bottom >= HEIGHT:\n        self.y_velocity *= -1\n        wall_hit_sound.play()",
        "orig_context": "```python\n## CatchTheBall.py\nimport pygame\n\nimport random\n\nRED = (255, 0, 0)\n\nwall_hit_sound = pygame.mixer.Sound(\"wall_hit_sound.wav\")\n\nclass Ball(pygame.sprite.Sprite):\n    def __init__(self):\n        super().__init__()\n        self.image = pygame.Surface((15, 15))\n        self.image.fill(RED)\n        self.rect = self.image.get_rect()\n        self.rect.center = (WIDTH // 2, HEIGHT // 2)\n        self.x_velocity = random.choice([3, -3])\n        self.y_velocity = random.choice([3, -3])\n\n    def update(self):\n        self.rect.x += self.x_velocity\n        self.rect.y += self.y_velocity\n\n        if self.rect.left <= 0 or self.rect.right >= WIDTH:\n            self.x_velocity *= -1\n            wall_hit_sound.play()  # Play wall hit sound\n\n        if self.rect.top <= 0 or self.rect.bottom >= HEIGHT:\n            self.y_velocity *= -1\n            wall_hit_sound.play()\n\n```\n\n\n",
        "eval_script": "## CatchTheBall.py\nimport pygame\nimport random\n\n# Initialize Pygame\npygame.init()\n\n# Define screen dimensions\nWIDTH, HEIGHT = 800, 600\n\nRED = (255, 0, 0)\n\n# Mock sound object\nclass MockSound:\n    def play(self):\n        pass  # Do nothing\n\nwall_hit_sound = MockSound()\n\nclass Ball(pygame.sprite.Sprite):\n    def __init__(self):\n        super().__init__()\n        self.image = pygame.Surface((15, 15))\n        self.image.fill(RED)\n        self.rect = self.image.get_rect()\n        self.rect.center = (WIDTH // 2, HEIGHT // 2)\n        self.x_velocity = random.choice([3, -3])\n        self.y_velocity = random.choice([3, -3])\n\n    def update(self):\n        self.rect.x += self.x_velocity\n        self.rect.y += self.y_velocity\n\n        if self.rect.left <= 0 or self.rect.right >= WIDTH:\n            self.x_velocity *= -1\n            wall_hit_sound.play()  # Play wall hit sound\n\n        if self.rect.top <= 0 or self.rect.bottom >= HEIGHT:\n            self.y_velocity *= -1\n            wall_hit_sound.play()\n\n\ndef test_update():\n    # Test 1: Ball moves without hitting any walls\n    ball1 = Ball()\n    ball1.rect.x = WIDTH // 2\n    ball1.rect.y = HEIGHT // 2\n    ball1.x_velocity = 3\n    ball1.y_velocity = 3\n\n    ball2 = Ball()\n    ball2.rect.x = WIDTH // 2\n    ball2.rect.y = HEIGHT // 2\n    ball2.x_velocity = 3\n    ball2.y_velocity = 3\n\n    ball1.update()\n    ball2.update_new_implementation()\n\n    assert ball1.rect.x == ball2.rect.x\n    assert ball1.rect.y == ball2.rect.y\n    assert ball1.x_velocity == ball2.x_velocity\n    assert ball1.y_velocity == ball2.y_velocity\n\n    # Test 2: Ball hits the right wall\n    ball1.rect.x = WIDTH - 1\n    ball1.x_velocity = 3\n\n    ball2.rect.x = WIDTH - 1\n    ball2.x_velocity = 3\n\n    ball1.update()\n    ball2.update_new_implementation()\n\n    assert ball1.x_velocity == ball2.x_velocity\n\n    # Test 3: Ball hits the bottom wall\n    ball1.rect.y = HEIGHT - 1\n    ball1.y_velocity = 3\n\n    ball2.rect.y = HEIGHT - 1\n    ball2.y_velocity = 3\n\n    ball1.update()\n    ball2.update_new_implementation()\n\n    assert ball1.y_velocity == ball2.y_velocity\n\n    # Test 4: Ball hits the left wall\n    ball1.rect.x = 0\n    ball1.x_velocity = -3\n\n    ball2.rect.x = 0\n    ball2.x_velocity = -3\n\n    ball1.update()\n    ball2.update_new_implementation()\n\n    assert ball1.x_velocity == ball2.x_velocity\n\n    # Test 5: Ball hits the top wall\n    ball1.rect.y = 0\n    ball1.y_velocity = -3\n\n    ball2.rect.y = 0\n    ball2.y_velocity = -3\n\n    ball1.update()\n    ball2.update_new_implementation()\n\n    assert ball1.y_velocity == ball2.y_velocity\n\n    # Test 6: Ball hits the top-right corner\n    ball1.rect.x = WIDTH - 1\n    ball1.rect.y = 0\n    ball1.x_velocity = 3\n    ball1.y_velocity = -3\n\n    ball2.rect.x = WIDTH - 1\n    ball2.rect.y = 0\n    ball2.x_velocity = 3\n    ball2.y_velocity = -3\n\n    ball1.update()\n    ball2.update_new_implementation()\n\n    assert ball1.x_velocity == ball2.x_velocity\n    assert ball1.y_velocity == ball2.y_velocity\n\n    # Test 7: Multiple updates\n    ball1.rect.x = WIDTH // 2\n    ball1.rect.y = HEIGHT // 2\n    ball1.x_velocity = 3\n    ball1.y_velocity = 3\n\n    ball2.rect.x = WIDTH // 2\n    ball2.rect.y = HEIGHT // 2\n    ball2.x_velocity = 3\n    ball2.y_velocity = 3\n\n    for _ in range(10):\n        ball1.update()\n        ball2.update_new_implementation()\n\n    assert ball1.rect.x == ball2.rect.x\n    assert ball1.rect.y == ball2.rect.y\n    assert ball1.x_velocity == ball2.x_velocity\n    assert ball1.y_velocity == ball2.y_velocity\n\n    # Test 8: Random initial velocities\n    for _ in range(5):\n        ball1 = Ball()\n        ball2 = Ball()\n        ball1.x_velocity = random.choice([3, -3])\n        ball1.y_velocity = random.choice([3, -3])\n        ball2.x_velocity = ball1.x_velocity\n        ball2.y_velocity = ball1.y_velocity\n\n        ball1.update()\n        ball2.update_new_implementation()\n\n        assert ball1.rect.x == ball2.rect.x\n        assert ball1.rect.y == ball2.rect.y\n        assert ball1.x_velocity == ball2.x_velocity\n        assert ball1.y_velocity == ball2.y_velocity\n\nif __name__ == \"__main__\":\n    test_update()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is the `update` method within the `Ball` class. Upon examining the code, the `update` method in the REVISED FUNCTION is identical to the ORIGINAL FUNCTION. Both functions update the position of the ball by adding the `x_velocity` and `y_velocity` to the `rect.x` and `rect.y` respectively. They also check for collisions with the walls (left, right, top, bottom) and reverse the respective velocity while playing a sound when a collision is detected. The functionality of both functions is exactly the same.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `update` function modifies the state of the `Ball` object by updating its position and velocity. This satisfies the condition as it modifies input arguments (the `Ball` object).\n- CONDITION 2: The test cases use assertions to check the state of the `Ball` objects (position and velocity) after calling `update` and `update_new_implementation`. They do not rely on printed or logged content.\n- CONDITION 3: The test cases compare the state of two `Ball` objects after calling `update` and `update_new_implementation`. If `update_new_implementation` has the same functionality as `update`, the states will match, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the state of the `Ball` objects, which is reasonable given that `update` modifies the object's state.\n- CONDITION 5: The test cases cover various scenarios, including hitting different walls, corners, multiple updates, and random initial velocities, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "64f81fb9374c99847f657e7d1195ba0f9c264306"
    },
    {
        "func_name": "YouTubeAPIClient.get_playlist_videos",
        "idx": "992",
        "repo_name": "Lewyyy00___SentimentTube",
        "func_path": "data_processing/api.py",
        "orig_func": "def get_playlist_videos(self, playlist_id):\n    request = youtube.playlistItems().list(part='snippet', playlistId=playlist_id, maxResults=500)\n    response = request.execute()\n    video_urls = []\n    for item in response['items']:\n        video_id = item['snippet']['resourceId']['videoId']\n        video_urls.append(f'https://www.youtube.com/watch?v={video_id}')\n    return video_urls",
        "orig_context": "```python\n## data_processing/api.py\nimport os\n\nfrom googleapiclient.discovery import build\n\nfrom googleapiclient.errors import HttpError\n\nimport isodate\n\nimport requests\n\nAPI_KEY = os.getenv(\"API_KEY\")\n\nyoutube = build('youtube', 'v3', developerKey=API_KEY)\n\nclass YouTubeAPIClient:\n    \n    \"\"\"\n    The YouTubeAPIClient class interacts with the YouTube API to retrieve video details, comments, playlist videos, and search results. \n    It provides methods to gather comments from specific videos, fetch various statistics like views and likes, retrieve video URLs from \n    playlists, and conduct YouTube search queries. \n    \n    This class requires a video ID or playlist ID as input to operate.\n    \n    \"\"\"\n    def __init__(self, video_id):\n        self.video_id = video_id\n        \n    def get_video_comments(self, max_results=100):\n\n        try:\n            comments = []\n            request = youtube.commentThreads().list(\n                part='snippet',\n                videoId=self.video_id,\n                maxResults=max_results,\n                textFormat='plainText'\n            )\n            \n            while request:\n                response = request.execute()\n            \n                for item in response['items']:\n                    comment = item['snippet']['topLevelComment']['snippet']['textDisplay']\n                    comments.append(comment)\n                    \n                request = youtube.commentThreads().list_next(request, response)\n                \n        except HttpError as e:\n            if e.resp.status == 403 and \"commentsDisabled\" in str(e):\n                print(\"Comments are disabled for this video.\")\n                \n        return comments\n\n    def get_video_details(self):\n\n        request = youtube.videos().list(\n            part=\"snippet,statistics,contentDetails\",\n            id=self.video_id\n        )\n        response = request.execute()\n\n        if response['items']:\n            video_info = response['items'][0]\n            title = video_info['snippet']['title']\n            duration = isodate.parse_duration(video_info['contentDetails'].get('duration', 0))\n            \n            likes = video_info['statistics'].get('likeCount', 0)\n            dislikes = video_info['statistics'].get('dislikeCount', 0)\n            views = video_info['statistics'].get('viewCount', 0)\n            comment_count = video_info['statistics'].get('commentCount', 0)\n\n            results = {\n                'title': title,\n                'likes': likes,\n                'duration': duration,\n                #'dislikes': dislikes,\n                'views': views,\n                'comment_count': comment_count\n            }\n            \n            return results\n        else:\n            return None\n        \n    def get_playlist_videos(self,playlist_id):\n        request = youtube.playlistItems().list(\n            part='snippet',\n            playlistId=playlist_id,\n            maxResults=500\n        )\n        \n        response = request.execute()\n\n        video_urls = []\n        for item in response['items']:\n            video_id = item['snippet']['resourceId']['videoId']\n            video_urls.append(f'https://www.youtube.com/watch?v={video_id}')\n        \n        return video_urls\n\n    def get_youtube_search_result(query, api_key = API_KEY, max_results=10):\n        \n        url = f\"https://www.googleapis.com/youtube/v3/search?part=snippet&q={query}&maxResults={max_results}&type=video&key={api_key}\"\n        response = requests.get(url)\n\n        if response.status_code == 200:\n            results = response.json().get('items', []) #if error then []\n            links = [item['id']['videoId'] for item in results]\n            return links\n        \n        else:\n            f'error:{response.status_code}'\n\n```\n\n\n",
        "eval_script": "## data_processing/api.py\nimport os\nfrom unittest.mock import Mock\n\n# Mocking the googleapiclient.discovery.build function\ndef mock_build(service_name, version, developerKey=None):\n    # Mocking the playlistItems().list().execute() method chain\n    mock_playlist_items = Mock()\n    mock_playlist_items.list.return_value.execute.return_value = {\n        'items': [\n            {'snippet': {'resourceId': {'videoId': 'video1'}}},\n            {'snippet': {'resourceId': {'videoId': 'video2'}}},\n            {'snippet': {'resourceId': {'videoId': 'video3'}}}\n        ]\n    }\n    return Mock(playlistItems=Mock(return_value=mock_playlist_items))\n\n# Replacing the build function with our mock\nyoutube = mock_build('youtube', 'v3', developerKey='DUMMY_API_KEY')\n\nclass YouTubeAPIClient:\n    \n    \"\"\"\n    The YouTubeAPIClient class interacts with the YouTube API to retrieve video details, comments, playlist videos, and search results. \n    It provides methods to gather comments from specific videos, fetch various statistics like views and likes, retrieve video URLs from \n    playlists, and conduct YouTube search queries. \n    \n    This class requires a video ID or playlist ID as input to operate.\n    \n    \"\"\"\n    def __init__(self, video_id):\n        self.video_id = video_id\n        \n    def get_video_comments(self, max_results=100):\n\n        try:\n            comments = []\n            request = youtube.commentThreads().list(\n                part='snippet',\n                videoId=self.video_id,\n                maxResults=max_results,\n                textFormat='plainText'\n            )\n            \n            while request:\n                response = request.execute()\n            \n                for item in response['items']:\n                    comment = item['snippet']['topLevelComment']['snippet']['textDisplay']\n                    comments.append(comment)\n                    \n                request = youtube.commentThreads().list_next(request, response)\n                \n        except HttpError as e:\n            if e.resp.status == 403 and \"commentsDisabled\" in str(e):\n                print(\"Comments are disabled for this video.\")\n                \n        return comments\n\n    def get_video_details(self):\n\n        request = youtube.videos().list(\n            part=\"snippet,statistics,contentDetails\",\n            id=self.video_id\n        )\n        response = request.execute()\n\n        if response['items']:\n            video_info = response['items'][0]\n            title = video_info['snippet']['title']\n            duration = isodate.parse_duration(video_info['contentDetails'].get('duration', 0))\n            \n            likes = video_info['statistics'].get('likeCount', 0)\n            dislikes = video_info['statistics'].get('dislikeCount', 0)\n            views = video_info['statistics'].get('viewCount', 0)\n            comment_count = video_info['statistics'].get('commentCount', 0)\n\n            results = {\n                'title': title,\n                'likes': likes,\n                'duration': duration,\n                #'dislikes': dislikes,\n                'views': views,\n                'comment_count': comment_count\n            }\n            \n            return results\n        else:\n            return None\n        \n    def get_playlist_videos(self,playlist_id):\n        request = youtube.playlistItems().list(\n            part='snippet',\n            playlistId=playlist_id,\n            maxResults=500\n        )\n        \n        response = request.execute()\n\n        video_urls = []\n        for item in response['items']:\n            video_id = item['snippet']['resourceId']['videoId']\n            video_urls.append(f'https://www.youtube.com/watch?v={video_id}')\n        \n        return video_urls\n\n\n    API_KEY = 'DUMMY_API_KEY'  # Define API_KEY here\n\n    def get_youtube_search_result(query, api_key = API_KEY, max_results=10):\n        \n        url = f\"https://www.googleapis.com/youtube/v3/search?part=snippet&q={query}&maxResults={max_results}&type=video&key={api_key}\"\n        response = requests.get(url)\n\n        if response.status_code == 200:\n            results = response.json().get('items', []) #if error then []\n            links = [item['id']['videoId'] for item in results]\n            return links\n        \n        else:\n            f'error:{response.status_code}'\n\ndef test_get_playlist_videos():\n    client = YouTubeAPIClient(video_id='dummy_video_id')\n    playlist_id = 'dummy_playlist_id'\n    \n    original_result = client.get_playlist_videos(playlist_id)\n    new_result = client.get_playlist_videos_new_implementation(playlist_id)\n    \n    # Assert that both implementations return the same list of video URLs\n    assert original_result == new_result, \"The video URLs do not match between implementations.\"\n    \n    # Assert that the length of the results is the same\n    assert len(original_result) == len(new_result), \"The number of videos does not match between implementations.\"\n    \n    # Assert that each video URL in the original result is in the new result\n    for url in original_result:\n        assert url in new_result, f\"The URL {url} is missing in the new implementation.\"\n\nif __name__ == \"__main__\":\n    test_get_playlist_videos()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `YouTubeAPIClient` class is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they create a request to the YouTube API to list playlist items, execute the request, and then iterate over the response to extract video IDs, which are used to construct YouTube video URLs. The logic and sequence of operations are the same in both functions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `get_playlist_videos` function returns a list of video URLs, satisfying the requirement of having return values.\n\n2. **CONDITION 2**: The test function `test_get_playlist_videos` checks the return values of the function by comparing the results of the original and new implementations. It does not rely on printed or logged content.\n\n3. **CONDITION 3**: The test cases compare the results of `get_playlist_videos` and `get_playlist_videos_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass the tests.\n\n4. **CONDITION 4**: The test cases use assertions to compare the results of the two implementations. The assertions are reasonable because they check for equality of the lists, the length of the lists, and the presence of each URL in both results.\n\n5. **CONDITION 5**: The test cases are non-trivial as they not only check for equality but also ensure that each URL in the original result is present in the new result, which is a meaningful test of the functionality.",
            "answer": "yes"
        },
        "commit_id": "5dcb55667b5330a36ea4513f8bcf69102800d640"
    },
    {
        "func_name": "YouTubeCommentAnalyzer.data_connector",
        "idx": "998",
        "repo_name": "Lewyyy00___SentimentTube",
        "func_path": "src/models.py",
        "orig_func": "def data_connector(self):\n    data = self.video_detalis\n    data['Result'] = self.sentiment_data\n    comment_count = int(data['comment_count']) if int(data['comment_count']) > 0 else 1\n    views = int(data['views']) if int(data['views']) > 0 else 1\n    likes = int(data['likes']) if int(data['likes']) > 0 else 1\n    data['Engagement'] = round((likes / views * self.WEIGHT_LIKES_VIEWS + int(data['Result'].get('positive', 0)) / comment_count * self.WEIGHT_POSITIVE + int(data['Result'].get('negative', 0)) / comment_count) * self.WEIGHT_NEGATIVE + math.log(views) * self.WEIGHT_LOG_VIEWS, 5)\n    return data",
        "orig_context": "```python\n## src/models.py\nfrom data_processing import processing\n\nimport os\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n\nfrom collections import Counter\n\nimport matplotlib.pyplot as plt\n\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\nimport math\n\nclass YouTubeCommentAnalyzer:\n\n    \"\"\"\n    This class contains a set of methods that are used to perform sentiment analysis. It also calculates engagement metrics based on video details \n    and sentiment data to assess the video's potential quality.\n    \n    \"\"\"\n\n    WEIGHT_LIKES_VIEWS = 0.1 \n    WEIGHT_POSITIVE = 1.5  \n    WEIGHT_NEGATIVE = 1.5 \n    WEIGHT_LOG_VIEWS = 0.002 #vievs are not as important as likes or comments \n\n    def __init__(self, video_id):\n\n        self.max_workers = os.cpu_count()\n        self.data_cleanner = processing.DataCleaning(video_id)\n        self.analyser = SentimentIntensityAnalyzer()\n        self.comments = self.data_cleanner.sentence_tokenize() \n        self.sentiment_data = self.analyze_sentiment()\n        self.video_detalis = self.data_cleanner.otherapidata\n        \n\n    def vectorize_data(self, max_features=5000):\n\n        vectorizer = TfidfVectorizer(max_features=max_features)        \n        return vectorizer.fit_transform(self.comments)\n    \n    def analyze_sentiment(self):\n        \n        sentiment_labels = []\n\n        def analyze_comment(comment):\n            sentiment_scores = self.analyser.polarity_scores(comment)\n            if sentiment_scores['compound'] >= 0.05:\n                sentiment_labels.append('positive')\n            elif sentiment_scores['compound'] <= -0.05:\n                sentiment_labels.append('negative')\n            else:\n                sentiment_labels.append('neutral')\n\n        with ThreadPoolExecutor(max_workers=10) as executor:\n            futures = {executor.submit(analyze_comment, comment): comment for comment in self.comments}\n\n            for future in as_completed(futures):\n                try:\n                    result = future.result()  \n                    sentiment_labels.append(result)\n                except Exception as exc:\n                    print(f'Comment generated an exception: {exc}')\n\n        return Counter(sentiment_labels)\n    \n\n    def bar_chart_maker(self):\n\n        sentiment_count = self.sentiment_data\n        sentiments = list(sentiment_count.keys())\n        counts = list(sentiment_count.values())\n\n        plt.figure(figsize=(8, 6))\n        plt.bar(sentiments, counts, color=['red', 'green', 'gray'])\n        plt.title('Sentiment Distribution')\n        plt.xlabel('Sentiment')\n        plt.ylabel('Amount of comments')\n        plt.show()\n        plt.close()\n\n    def data_connector(self):\n\n        data = self.video_detalis\n        data[\"Result\"] = self.sentiment_data\n\n        comment_count = int(data['comment_count']) if int(data['comment_count']) > 0 else 1\n        views = int(data['views']) if int(data['views']) > 0 else 1\n        likes = int(data['likes']) if int(data['likes']) > 0 else 1\n\n        data['Engagement'] = round(((likes / views) * self.WEIGHT_LIKES_VIEWS + \n                                    (int(data['Result'].get('positive', 0)) / comment_count) * self.WEIGHT_POSITIVE + \n                                    (int(data['Result'].get('negative', 0)) / comment_count)) * self.WEIGHT_NEGATIVE + \n                                    math.log(views) * self.WEIGHT_LOG_VIEWS, 5) \n\n        return data\n\n```\n\n\n",
        "eval_script": "## src/models.py\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nimport math\nimport os\n\n# Mocking the data_processing module and DataCleaning class\nclass MockDataCleaning:\n    def __init__(self, video_id):\n        self.video_id = video_id\n        self.otherapidata = {\n            'comment_count': '100',\n            'views': '1000',\n            'likes': '100'\n        }\n    \n    def sentence_tokenize(self):\n        # Mocking a list of comments\n        return [\n            \"I love this video!\",\n            \"This is terrible.\",\n            \"Not bad, could be better.\",\n            \"Amazing content!\",\n            \"I didn't like it.\"\n        ]\n\n# Mocking the processing module\nclass processing:\n    DataCleaning = MockDataCleaning\n\nclass YouTubeCommentAnalyzer:\n    WEIGHT_LIKES_VIEWS = 0.1 \n    WEIGHT_POSITIVE = 1.5  \n    WEIGHT_NEGATIVE = 1.5 \n    WEIGHT_LOG_VIEWS = 0.002 \n\n    def __init__(self, video_id):\n        self.max_workers = os.cpu_count()\n        self.data_cleanner = processing.DataCleaning(video_id)\n        self.analyser = SentimentIntensityAnalyzer()\n        self.comments = self.data_cleanner.sentence_tokenize() \n        self.sentiment_data = self.analyze_sentiment()\n        self.video_detalis = self.data_cleanner.otherapidata\n\n    def vectorize_data(self, max_features=5000):\n        vectorizer = TfidfVectorizer(max_features=max_features)        \n        return vectorizer.fit_transform(self.comments)\n    \n    def analyze_sentiment(self):\n        sentiment_labels = []\n\n        def analyze_comment(comment):\n            sentiment_scores = self.analyser.polarity_scores(comment)\n            if sentiment_scores['compound'] >= 0.05:\n                sentiment_labels.append('positive')\n            elif sentiment_scores['compound'] <= -0.05:\n                sentiment_labels.append('negative')\n            else:\n                sentiment_labels.append('neutral')\n\n        with ThreadPoolExecutor(max_workers=10) as executor:\n            futures = {executor.submit(analyze_comment, comment): comment for comment in self.comments}\n\n            for future in as_completed(futures):\n                try:\n                    result = future.result()  \n                    sentiment_labels.append(result)\n                except Exception as exc:\n                    print(f'Comment generated an exception: {exc}')\n\n        return Counter(sentiment_labels)\n    \n    def bar_chart_maker(self):\n        sentiment_count = self.sentiment_data\n        sentiments = list(sentiment_count.keys())\n        counts = list(sentiment_count.values())\n\n        plt.figure(figsize=(8, 6))\n        plt.bar(sentiments, counts, color=['red', 'green', 'gray'])\n        plt.title('Sentiment Distribution')\n        plt.xlabel('Sentiment')\n        plt.ylabel('Amount of comments')\n        plt.show()\n        plt.close()\n\n    def data_connector(self):\n        data = self.video_detalis\n        data[\"Result\"] = self.sentiment_data\n\n        comment_count = int(data['comment_count']) if int(data['comment_count']) > 0 else 1\n        views = int(data['views']) if int(data['views']) > 0 else 1\n        likes = int(data['likes']) if int(data['likes']) > 0 else 1\n\n        data['Engagement'] = round(((likes / views) * self.WEIGHT_LIKES_VIEWS + \n                                    (int(data['Result'].get('positive', 0)) / comment_count) * self.WEIGHT_POSITIVE + \n                                    (int(data['Result'].get('negative', 0)) / comment_count)) * self.WEIGHT_NEGATIVE + \n                                    math.log(views) * self.WEIGHT_LOG_VIEWS, 5) \n\n        return data\n\n\ndef test_data_connector():\n    # Test with default mock data\n    analyzer = YouTubeCommentAnalyzer(video_id=\"dummy_video_id\")\n    original_result = analyzer.data_connector()\n    new_result = analyzer.data_connector_new_implementation()\n\n    assert original_result['comment_count'] == new_result['comment_count'], \"Comment count mismatch\"\n    assert original_result['views'] == new_result['views'], \"Views count mismatch\"\n    assert original_result['likes'] == new_result['likes'], \"Likes count mismatch\"\n    assert original_result['Result'] == new_result['Result'], \"Sentiment result mismatch\"\n    assert original_result['Engagement'] == new_result['Engagement'], \"Engagement calculation mismatch\"\n\n    # Test with different video ID\n    analyzer_different_id = YouTubeCommentAnalyzer(video_id=\"another_video_id\")\n    original_result_different_id = analyzer_different_id.data_connector()\n    new_result_different_id = analyzer_different_id.data_connector_new_implementation()\n\n    assert original_result_different_id['comment_count'] == new_result_different_id['comment_count'], \"Comment count mismatch for different video ID\"\n    assert original_result_different_id['views'] == new_result_different_id['views'], \"Views count mismatch for different video ID\"\n    assert original_result_different_id['likes'] == new_result_different_id['likes'], \"Likes count mismatch for different video ID\"\n    assert original_result_different_id['Result'] == new_result_different_id['Result'], \"Sentiment result mismatch for different video ID\"\n    assert original_result_different_id['Engagement'] == new_result_different_id['Engagement'], \"Engagement calculation mismatch for different video ID\"\n\n    # Test with edge cases for counts\n    analyzer.data_cleanner.otherapidata['comment_count'] = '0'\n    analyzer.data_cleanner.otherapidata['views'] = '0'\n    analyzer.data_cleanner.otherapidata['likes'] = '0'\n    original_result_edge = analyzer.data_connector()\n    new_result_edge = analyzer.data_connector_new_implementation()\n\n    assert original_result_edge['comment_count'] == new_result_edge['comment_count'], \"Comment count mismatch for edge case\"\n    assert original_result_edge['views'] == new_result_edge['views'], \"Views count mismatch for edge case\"\n    assert original_result_edge['likes'] == new_result_edge['likes'], \"Likes count mismatch for edge case\"\n    assert original_result_edge['Result'] == new_result_edge['Result'], \"Sentiment result mismatch for edge case\"\n    assert original_result_edge['Engagement'] == new_result_edge['Engagement'], \"Engagement calculation mismatch for edge case\"\n\n    # Test with large numbers\n    analyzer.data_cleanner.otherapidata['comment_count'] = '1000000'\n    analyzer.data_cleanner.otherapidata['views'] = '1000000000'\n    analyzer.data_cleanner.otherapidata['likes'] = '100000'\n    original_result_large = analyzer.data_connector()\n    new_result_large = analyzer.data_connector_new_implementation()\n\n    assert original_result_large['comment_count'] == new_result_large['comment_count'], \"Comment count mismatch for large numbers\"\n    assert original_result_large['views'] == new_result_large['views'], \"Views count mismatch for large numbers\"\n    assert original_result_large['likes'] == new_result_large['likes'], \"Likes count mismatch for large numbers\"\n    assert original_result_large['Result'] == new_result_large['Result'], \"Sentiment result mismatch for large numbers\"\n    assert original_result_large['Engagement'] == new_result_large['Engagement'], \"Engagement calculation mismatch for large numbers\"\n\n    # Test with different sentiment distribution\n    analyzer.comments = [\n        \"I love this video!\", \"I love this video!\", \"I love this video!\",\n        \"This is terrible.\", \"This is terrible.\"\n    ]\n    analyzer.sentiment_data = analyzer.analyze_sentiment()\n    original_result_sentiment = analyzer.data_connector()\n    new_result_sentiment = analyzer.data_connector_new_implementation()\n\n    assert original_result_sentiment['comment_count'] == new_result_sentiment['comment_count'], \"Comment count mismatch for sentiment distribution\"\n    assert original_result_sentiment['views'] == new_result_sentiment['views'], \"Views count mismatch for sentiment distribution\"\n    assert original_result_sentiment['likes'] == new_result_sentiment['likes'], \"Likes count mismatch for sentiment distribution\"\n    assert original_result_sentiment['Result'] == new_result_sentiment['Result'], \"Sentiment result mismatch for sentiment distribution\"\n    assert original_result_sentiment['Engagement'] == new_result_sentiment['Engagement'], \"Engagement calculation mismatch for sentiment distribution\"\n\nif __name__ == \"__main__\":\n    test_data_connector()\n    print(\"All tests passed.\")",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining both the original and revised functions, they perform the same operations. Both functions retrieve video details, calculate engagement metrics using the same formula, and return the modified data dictionary. The formula for calculating engagement is identical in both functions, and the handling of potential zero values for comment count, views, and likes is consistent. The revised function is embedded within a class structure, but this does not alter the functionality of the `data_connector` method itself. The test cases provided in the revised code also confirm that the functionality remains unchanged across various scenarios.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `data_connector` function returns a dictionary containing video details and sentiment analysis results, fulfilling the requirement of having return values or modifying global variables or input arguments.\n\n- CONDITION 2: The test cases in `test_data_connector` check the return values of `data_connector` and `data_connector_new_implementation` by comparing the contents of the dictionaries returned by these functions. There is no checking of printed or logged contents.\n\n- CONDITION 3: The test cases compare the results of `data_connector` and `data_connector_new_implementation` for various scenarios, ensuring that the new implementation must have the exact same functionality as the original to pass all tests.\n\n- CONDITION 4: The test cases use assertions to compare the values in the dictionaries returned by both implementations. These assertions are reasonable given that `data_connector` returns a dictionary, and the comparisons are made on specific keys within these dictionaries.\n\n- CONDITION 5: The test cases include various scenarios such as default data, different video IDs, edge cases with zero counts, large numbers, and different sentiment distributions. These scenarios ensure that the tests are non-trivial and cover a wide range of possible inputs and conditions.",
            "answer": "yes"
        },
        "commit_id": "5dcb55667b5330a36ea4513f8bcf69102800d640"
    },
    {
        "func_name": "SubcategoryListing.refresh_new_token",
        "idx": "1002",
        "repo_name": "jeffemart___automation-python-ticket-desk-manager",
        "func_path": "subcategory/subcategory.py",
        "orig_func": "def refresh_new_token(self):\n    auth_instance = Auth()\n    new_token = auth_instance.token()\n    if new_token:\n        logger.info(f'{os.path.basename(__file__)}: Token atualizado com sucesso: {new_token}')\n        self.token = new_token\n        self.header = {'Authorization': f'{self.token}'}\n    else:\n        logger.warning(f'{os.path.basename(__file__)}: Falha ao obter o token.')",
        "orig_context": "```python\n## subcategory/subcategory.py\nimport requests\n\nimport json\n\nimport os\n\nfrom auth.auth import Auth\n\nfrom utils.logger import configure_logger\n\nlogger = configure_logger()\n\nclass SubcategoryListing:\n    def __init__(self):\n        # Verifica se a vari\u00e1vel de ambiente 'TOKEN' existe e a usa\n        self.token = os.getenv(\"TOKEN\")\n        if not self.token:\n            logger.warning(\n                f\"{os.path.basename(__file__)}: Token n\u00e3o encontrado nas vari\u00e1veis de ambiente.\"\n            )\n            # Tenta obter o token se n\u00e3o houver\n            auth = Auth()\n            self.token = auth.token()\n\n        self.header = {\"Authorization\": f\"{self.token}\"}\n        self.refresh_token()\n\n    def refresh_token(self):\n        # Atualiza o header com o novo token se necess\u00e1rio\n        if not self.token:\n            logger.warning(f\"{os.path.basename(__file__)}: Token n\u00e3o definido.\")\n        else:\n            self.header = {\"Authorization\": f\"{self.token}\"}\n\n    def refresh_new_token(self):\n        # Tenta obter um novo token se necess\u00e1rio\n        auth_instance = Auth()\n        new_token = auth_instance.token()\n        if new_token:\n            logger.info(\n                f\"{os.path.basename(__file__)}: Token atualizado com sucesso: {new_token}\"\n            )\n            self.token = new_token\n            self.header = {\"Authorization\": f\"{self.token}\"}\n        else:\n            logger.warning(f\"{os.path.basename(__file__)}: Falha ao obter o token.\")\n\n    def make_api_request(self, url, params):\n        try:\n            response = requests.post(url, headers=self.header, data=params)\n            response.raise_for_status()  # Raises HTTPError for bad responses\n            response_data = response.json()\n\n            logger.info(\n                f\"{os.path.basename(__file__)}: API Request to {url} with params: {params}\"\n            )\n\n            if response_data != {\"erro\": \"Token expirado ou n\u00e3o existe\"}:\n                return response_data[\"root\"]\n            else:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado ou n\u00e3o dispon\u00edvel.\"\n                )\n                self.refresh_new_token()  # Atualiza o token\n                return None\n\n        except requests.RequestException as e:\n            if e.response and e.response.status_code == 401:  # Verifica erro 401\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado. Atualizando token e tentando novamente...\"\n                )\n                self.refresh_new_token()  # Atualiza o token e tenta novamente\n                return self.make_api_request(\n                    url, params\n                )  # Recursivamente tenta novamente a requisi\u00e7\u00e3o\n            else:\n                logger.error(\n                    f\"{os.path.basename(__file__)}: Error in API request: {e}\"\n                )\n                raise  # Rethrow the exception after logger\n            raise\n\n    def get_subcategory_list(self):\n        url = \"https://api.desk.ms/SubCategorias/lista\"\n        parameters = {\n            \"Pesquisa\": \"\",\n            \"Ativo\": \"S\",\n            \"Ordem\": [{\"Coluna\": \"SubCategoria\", \"Direcao\": \"true\"}],\n        }\n\n        try:\n            response_data = self.make_api_request(url, parameters)\n\n            if response_data:\n                logger.info(\"Subcategory list obtained successfully\")\n\n                # Process the response_data as needed\n\n                # Save response_data to a JSON file\n                self.save_to_json(response_data, \"subcategory_list.json\")\n\n                return response_data\n            else:\n                logger.warning(\"Failed to obtain subcategory list.\")\n                return None\n\n        except requests.HTTPError as http_err:\n            if http_err.response.status_code == 401:\n                logger.warning(\"Token expired. Refreshing token and retrying...\")\n                # Adicione a l\u00f3gica para atualizar o token, se necess\u00e1rio\n                return (\n                    self.get_subcategory_list()\n                )  # Tentar novamente ap\u00f3s a atualiza\u00e7\u00e3o do token\n            else:\n                logger.error(\"HTTPError: %s\", http_err)\n                raise  # Rethrow the exception after logger\n        except Exception as e:\n            logger.error(\"Error: %s\", e)\n            raise  # Rethrow the exception after logger\n\n    def save_to_json(self, data, filename):\n        with open(filename, \"w\", encoding=\"utf-8\") as json_file:\n            json.dump(data, json_file, ensure_ascii=False, indent=4)\n\n```\n\n\n",
        "eval_script": "## subcategory/subcategory.py\nimport requests\nimport json\nimport os\n\n# Mock Auth class\nclass Auth:\n    def token(self):\n        return \"mocked_token\"\n\n# Mock logger\nclass MockLogger:\n    def info(self, message):\n        print(f\"INFO: {message}\")\n\n    def warning(self, message):\n        print(f\"WARNING: {message}\")\n\n    def error(self, message):\n        print(f\"ERROR: {message}\")\n\ndef configure_logger():\n    return MockLogger()\n\nlogger = configure_logger()\n\nclass SubcategoryListing:\n    def __init__(self):\n        # Verifica se a vari\u00e1vel de ambiente 'TOKEN' existe e a usa\n        self.token = os.getenv(\"TOKEN\")\n        if not self.token:\n            logger.warning(\n                f\"{os.path.basename(__file__)}: Token n\u00e3o encontrado nas vari\u00e1veis de ambiente.\"\n            )\n            # Tenta obter o token se n\u00e3o houver\n            auth = Auth()\n            self.token = auth.token()\n\n        self.header = {\"Authorization\": f\"{self.token}\"}\n        self.refresh_token()\n\n    def refresh_token(self):\n        # Atualiza o header com o novo token se necess\u00e1rio\n        if not self.token:\n            logger.warning(f\"{os.path.basename(__file__)}: Token n\u00e3o definido.\")\n        else:\n            self.header = {\"Authorization\": f\"{self.token}\"}\n\n    def refresh_new_token(self):\n        # Tenta obter um novo token se necess\u00e1rio\n        auth_instance = Auth()\n        new_token = auth_instance.token()\n        if new_token:\n            logger.info(\n                f\"{os.path.basename(__file__)}: Token atualizado com sucesso: {new_token}\"\n            )\n            self.token = new_token\n            self.header = {\"Authorization\": f\"{self.token}\"}\n        else:\n            logger.warning(f\"{os.path.basename(__file__)}: Falha ao obter o token.\")\n\n\n    def make_api_request(self, url, params):\n        try:\n            response = requests.post(url, headers=self.header, data=params)\n            response.raise_for_status()  # Raises HTTPError for bad responses\n            response_data = response.json()\n\n            logger.info(\n                f\"{os.path.basename(__file__)}: API Request to {url} with params: {params}\"\n            )\n\n            if response_data != {\"erro\": \"Token expirado ou n\u00e3o existe\"}:\n                return response_data[\"root\"]\n            else:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado ou n\u00e3o dispon\u00edvel.\"\n                )\n                self.refresh_new_token()  # Atualiza o token\n                return None\n\n        except requests.RequestException as e:\n            if e.response and e.response.status_code == 401:  # Verifica erro 401\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado. Atualizando token e tentando novamente...\"\n                )\n                self.refresh_new_token()  # Atualiza o token e tenta novamente\n                return self.make_api_request(\n                    url, params\n                )  # Recursivamente tenta novamente a requisi\u00e7\u00e3o\n            else:\n                logger.error(\n                    f\"{os.path.basename(__file__)}: Error in API request: {e}\"\n                )\n                raise  # Rethrow the exception after logger\n            raise\n\n    def get_subcategory_list(self):\n        url = \"https://api.desk.ms/SubCategorias/lista\"\n        parameters = {\n            \"Pesquisa\": \"\",\n            \"Ativo\": \"S\",\n            \"Ordem\": [{\"Coluna\": \"SubCategoria\", \"Direcao\": \"true\"}],\n        }\n\n        try:\n            response_data = self.make_api_request(url, parameters)\n\n            if response_data:\n                logger.info(\"Subcategory list obtained successfully\")\n\n                # Process the response_data as needed\n\n                # Save response_data to a JSON file\n                self.save_to_json(response_data, \"/home/user/tmp/subcategory_list.json\")\n\n                return response_data\n            else:\n                logger.warning(\"Failed to obtain subcategory list.\")\n                return None\n\n        except requests.HTTPError as http_err:\n            if http_err.response.status_code == 401:\n                logger.warning(\"Token expired. Refreshing token and retrying...\")\n                # Adicione a l\u00f3gica para atualizar o token, se necess\u00e1rio\n                return (\n                    self.get_subcategory_list()\n                )  # Tentar novamente ap\u00f3s a atualiza\u00e7\u00e3o do token\n            else:\n                logger.error(\"HTTPError: %s\", http_err)\n                raise  # Rethrow the exception after logger\n        except Exception as e:\n            logger.error(\"Error: %s\", e)\n            raise  # Rethrow the exception after logger\n\n    def save_to_json(self, data, filename):\n        with open(filename, \"w\", encoding=\"utf-8\") as json_file:\n            json.dump(data, json_file, ensure_ascii=False, indent=4)\n\ndef test_refresh_new_token():\n    # Mock the Auth class to return different tokens\n    original_auth_token = Auth.token\n    try:\n        # Test case 1: New token is obtained\n        Auth.token = lambda self: \"new_mocked_token\"\n        subcategory = SubcategoryListing()\n        subcategory.refresh_new_token()\n        original_token = subcategory.token\n        original_header = subcategory.header\n\n        subcategory.refresh_new_token_new_implementation()\n        new_token = subcategory.token\n        new_header = subcategory.header\n\n        assert original_token == new_token, \"Tokens do not match\"\n        assert original_header == new_header, \"Headers do not match\"\n\n        # Test case 2: No new token is obtained\n        Auth.token = lambda self: None\n        subcategory.refresh_new_token()\n        original_token = subcategory.token\n        original_header = subcategory.header\n\n        subcategory.refresh_new_token_new_implementation()\n        new_token = subcategory.token\n        new_header = subcategory.header\n\n        assert original_token == new_token, \"Tokens do not match when no new token is obtained\"\n        assert original_header == new_header, \"Headers do not match when no new token is obtained\"\n\n        # Test case 3: Check logger output (mocked)\n        # This is a placeholder for actual logger testing, which would require a more complex setup\n        print(\"Logger output test passed\")\n\n    finally:\n        Auth.token = original_auth_token\n\nif __name__ == \"__main__\":\n    test_refresh_new_token()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `refresh_new_token` in the `SubcategoryListing` class is functionally identical to the ORIGINAL FUNCTION. Both functions create an instance of the `Auth` class, retrieve a new token using the `token()` method, and update the `token` and `header` attributes if a new token is obtained. If no new token is obtained, both functions log a warning message. The functionality and logic of the REVISED FUNCTION match exactly with the ORIGINAL FUNCTION.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `refresh_new_token` function modifies the `token` and `header` attributes of the `SubcategoryListing` class, which are instance variables. This satisfies the condition as it modifies the state of the object.\n\n2. **CONDITION 2**: The test function checks the state of the `token` and `header` attributes after calling `refresh_new_token` and `refresh_new_token_new_implementation`. It uses assertions to compare these states, not printed or logged content, thus satisfying this condition.\n\n3. **CONDITION 3**: The test cases check if the `token` and `header` attributes are the same after calling both `refresh_new_token` and `refresh_new_token_new_implementation`. This ensures that the new implementation must have the same functionality as the original to pass the tests.\n\n4. **CONDITION 4**: The test cases use assertions to compare the `token` and `header` attributes, which are reasonable given that `refresh_new_token` modifies these attributes. The test does not use inappropriate assertions like comparing return values when there are none.\n\n5. **CONDITION 5**: The test cases cover scenarios where a new token is obtained and where no new token is obtained. These are non-trivial cases as they test different branches of the `refresh_new_token` logic.",
            "answer": "yes"
        },
        "commit_id": "4792896d7769ebd0e67e13d99b87454f9a7db3ea"
    },
    {
        "func_name": "Listing.refresh_new_token",
        "idx": "1003",
        "repo_name": "jeffemart___automation-python-ticket-desk-manager",
        "func_path": "listing/listing.py",
        "orig_func": "def refresh_new_token(self):\n    auth_instance = Auth()\n    new_token = auth_instance.token()\n    if new_token:\n        logger.info(f'{os.path.basename(__file__)}: Token atualizado com sucesso: {new_token}')\n        self.token = new_token\n        self.header = {'Authorization': f'{self.token}'}\n    else:\n        logger.warning(f'{os.path.basename(__file__)}: Falha ao obter o token.')",
        "orig_context": "```python\n## listing/listing.py\nimport requests\n\nimport json\n\nimport os\n\nfrom auth.auth import Auth\n\nfrom utils.logger import configure_logger\n\nlogger = configure_logger()\n\nclass Listing:\n    def __init__(self):\n        # Verifica se a vari\u00e1vel de ambiente 'TOKEN' existe e a usa\n        self.token = os.getenv(\"TOKEN\")\n        if not self.token:\n            logger.warning(\n                f\"{os.path.basename(__file__)}: Token n\u00e3o encontrado nas vari\u00e1veis de ambiente.\"\n            )\n            # Tenta obter o token se n\u00e3o houver\n            auth = Auth()\n            self.token = auth.token()\n\n        self.header = {\"Authorization\": f\"{self.token}\"}\n        self.refresh_token()\n\n    def refresh_token(self):\n        # Atualiza o header com o novo token se necess\u00e1rio\n        if not self.token:\n            logger.warning(f\"{os.path.basename(__file__)}: Token n\u00e3o definido.\")\n        else:\n            self.header = {\"Authorization\": f\"{self.token}\"}\n\n    def refresh_new_token(self):\n        # Tenta obter um novo token se necess\u00e1rio\n        auth_instance = Auth()\n        new_token = auth_instance.token()\n        if new_token:\n            logger.info(\n                f\"{os.path.basename(__file__)}: Token atualizado com sucesso: {new_token}\"\n            )\n            self.token = new_token\n            self.header = {\"Authorization\": f\"{self.token}\"}\n        else:\n            logger.warning(f\"{os.path.basename(__file__)}: Falha ao obter o token.\")\n\n    def make_api_request(self, url, params):\n        try:\n            response = requests.post(url, headers=self.header, data=params)\n            response.raise_for_status()  # Raises HTTPError for bad responses\n            response_data = response.json()\n\n            logger.info(\n                f\"{os.path.basename(__file__)}: API Request to {url} with params: {params}\"\n            )\n\n            if response_data != {\"erro\": \"Token expirado ou n\u00e3o existe\"}:\n                return response_data[\"root\"]\n            else:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado ou n\u00e3o dispon\u00edvel.\"\n                )\n                self.refresh_new_token()  # Atualiza o token\n                return None\n\n        except requests.RequestException as e:\n            if e.response and e.response.status_code == 401:  # Verifica erro 401\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado. Atualizando token e tentando novamente...\"\n                )\n                self.refresh_new_token()  # Atualiza o token e tenta novamente\n                return self.make_api_request(\n                    url, params\n                )  # Recursivamente tenta novamente a requisi\u00e7\u00e3o\n            else:\n                logger.error(\n                    f\"{os.path.basename(__file__)}: Error in API request: {e}\"\n                )\n                raise  # Rethrow the exception after logger\n\n    def get_ticket_list(self):\n        url = \"https://api.desk.ms/ChamadosSuporte/lista\"\n        parameters = json.dumps(\n            {\n                \"Pesquisa\": \"\",\n                \"Tatual\": \"\",\n                \"Ativo\": \"NaFila\",\n                \"StatusSLA\": \"N\",\n                \"Colunas\": {\n                    \"Chave\": \"on\",\n                    \"CodChamado\": \"on\",\n                    \"NomePrioridade\": \"on\",\n                    \"DataCriacao\": \"on\",\n                    \"HoraCriacao\": \"on\",\n                    \"DataFinalizacao\": \"on\",\n                    \"HoraFinalizacao\": \"on\",\n                    \"DataAlteracao\": \"on\",\n                    \"HoraAlteracao\": \"on\",\n                    \"NomeStatus\": \"on\",\n                    \"Assunto\": \"on\",\n                    \"Descricao\": \"on\",\n                    \"ChaveUsuario\": \"on\",\n                    \"NomeUsuario\": \"on\",\n                    \"SobrenomeUsuario\": \"on\",\n                    \"NomeOperador\": \"on\",\n                    \"SobrenomeOperador\": \"on\",\n                    \"TotalAcoes\": \"on\",\n                    \"TotalAnexos\": \"on\",\n                    \"Sla\": \"on\",\n                    \"CodGrupo\": \"on\",\n                    \"NomeGrupo\": \"on\",\n                    \"CodSolicitacao\": \"on\",\n                    \"CodSubCategoria\": \"on\",\n                    \"CodTipoOcorrencia\": \"on\",\n                    \"CodCategoriaTipo\": \"on\",\n                    \"CodPrioridadeAtual\": \"on\",\n                    \"CodStatusAtual\": \"on\"\n                },\n                \"Ordem\": [{\"Coluna\": \"Chave\", \"Direcao\": \"true\"}]\n            }\n        )\n        try:\n            response_data = self.make_api_request(url, parameters)\n\n            if response_data:\n                logger.info(f\"{os.path.basename(__file__)}: Requisi\u00e7\u00e3o bem-sucedida.\")\n                return response_data\n            else:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Falha ao obter a lista de tickets.\"\n                )\n                return None\n\n        except requests.HTTPError as http_err:\n            if http_err.response.status_code == 401:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado. Atualizando token e tentando novamente...\"\n                )\n                self.refresh_token()  # Tenta atualizar o token\n                return None  # Retorna None ap\u00f3s a falha\n            else:\n                logger.error(f\"{os.path.basename(__file__)}: HTTPError: {http_err}\")\n                raise  # Re-levanta o erro\n                return None\n\n```\n\n\n",
        "eval_script": "# Mocking the necessary components\n\n# Mocking the Auth class\nclass Auth:\n    def token(self):\n        return \"mocked_token\"\n\n# Mocking the configure_logger function\ndef configure_logger():\n    import logging\n    logging.basicConfig(level=logging.INFO)\n    return logging.getLogger(__name__)\n\n# The revised Listing class with the mocked components\nimport requests\nimport json\nimport os\n\nlogger = configure_logger()\n\nclass Listing:\n    def __init__(self):\n        # Verifica se a vari\u00e1vel de ambiente 'TOKEN' existe e a usa\n        self.token = os.getenv(\"TOKEN\")\n        if not self.token:\n            logger.warning(\n                f\"{os.path.basename(__file__)}: Token n\u00e3o encontrado nas vari\u00e1veis de ambiente.\"\n            )\n            # Tenta obter o token se n\u00e3o houver\n            auth = Auth()\n            self.token = auth.token()\n\n        self.header = {\"Authorization\": f\"{self.token}\"}\n        self.refresh_token()\n\n    def refresh_token(self):\n        # Atualiza o header com o novo token se necess\u00e1rio\n        if not self.token:\n            logger.warning(f\"{os.path.basename(__file__)}: Token n\u00e3o definido.\")\n        else:\n            self.header = {\"Authorization\": f\"{self.token}\"}\n\n    def refresh_new_token(self):\n        # Tenta obter um novo token se necess\u00e1rio\n        auth_instance = Auth()\n        new_token = auth_instance.token()\n        if new_token:\n            logger.info(\n                f\"{os.path.basename(__file__)}: Token atualizado com sucesso: {new_token}\"\n            )\n            self.token = new_token\n            self.header = {\"Authorization\": f\"{self.token}\"}\n        else:\n            logger.warning(f\"{os.path.basename(__file__)}: Falha ao obter o token.\")\n\n\n    def make_api_request(self, url, params):\n        try:\n            response = requests.post(url, headers=self.header, data=params)\n            response.raise_for_status()  # Raises HTTPError for bad responses\n            response_data = response.json()\n\n            logger.info(\n                f\"{os.path.basename(__file__)}: API Request to {url} with params: {params}\"\n            )\n\n            if response_data != {\"erro\": \"Token expirado ou n\u00e3o existe\"}:\n                return response_data[\"root\"]\n            else:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado ou n\u00e3o dispon\u00edvel.\"\n                )\n                self.refresh_new_token()  # Atualiza o token\n                return None\n\n        except requests.RequestException as e:\n            if e.response and e.response.status_code == 401:  # Verifica erro 401\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado. Atualizando token e tentando novamente...\"\n                )\n                self.refresh_new_token()  # Atualiza o token e tenta novamente\n                return self.make_api_request(\n                    url, params\n                )  # Recursivamente tenta novamente a requisi\u00e7\u00e3o\n            else:\n                logger.error(\n                    f\"{os.path.basename(__file__)}: Error in API request: {e}\"\n                )\n                raise  # Rethrow the exception after logger\n\n    def get_ticket_list(self):\n        url = \"https://api.desk.ms/ChamadosSuporte/lista\"\n        parameters = json.dumps(\n            {\n                \"Pesquisa\": \"\",\n                \"Tatual\": \"\",\n                \"Ativo\": \"NaFila\",\n                \"StatusSLA\": \"N\",\n                \"Colunas\": {\n                    \"Chave\": \"on\",\n                    \"CodChamado\": \"on\",\n                    \"NomePrioridade\": \"on\",\n                    \"DataCriacao\": \"on\",\n                    \"HoraCriacao\": \"on\",\n                    \"DataFinalizacao\": \"on\",\n                    \"HoraFinalizacao\": \"on\",\n                    \"DataAlteracao\": \"on\",\n                    \"HoraAlteracao\": \"on\",\n                    \"NomeStatus\": \"on\",\n                    \"Assunto\": \"on\",\n                    \"Descricao\": \"on\",\n                    \"ChaveUsuario\": \"on\",\n                    \"NomeUsuario\": \"on\",\n                    \"SobrenomeUsuario\": \"on\",\n                    \"NomeOperador\": \"on\",\n                    \"SobrenomeOperador\": \"on\",\n                    \"TotalAcoes\": \"on\",\n                    \"TotalAnexos\": \"on\",\n                    \"Sla\": \"on\",\n                    \"CodGrupo\": \"on\",\n                    \"NomeGrupo\": \"on\",\n                    \"CodSolicitacao\": \"on\",\n                    \"CodSubCategoria\": \"on\",\n                    \"CodTipoOcorrencia\": \"on\",\n                    \"CodCategoriaTipo\": \"on\",\n                    \"CodPrioridadeAtual\": \"on\",\n                    \"CodStatusAtual\": \"on\"\n                },\n                \"Ordem\": [{\"Coluna\": \"Chave\", \"Direcao\": \"true\"}]\n            }\n        )\n        try:\n            response_data = self.make_api_request(url, parameters)\n\n            if response_data:\n                logger.info(f\"{os.path.basename(__file__)}: Requisi\u00e7\u00e3o bem-sucedida.\")\n                return response_data\n            else:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Falha ao obter a lista de tickets.\"\n                )\n                return None\n\n        except requests.HTTPError as http_err:\n            if http_err.response.status_code == 401:\n                logger.warning(\n                    f\"{os.path.basename(__file__)}: Token expirado. Atualizando token e tentando novamente...\"\n                )\n                self.refresh_token()  # Tenta atualizar o token\n                return None  # Retorna None ap\u00f3s a falha\n            else:\n                logger.error(f\"{os.path.basename(__file__)}: HTTPError: {http_err}\")\n                raise  # Re-levanta o erro\n                return None\n\n# Your revised test_refresh_new_token function\ndef test_refresh_new_token():\n    # Test 1: Basic functionality test\n    listing_original = Listing()\n    listing_original.refresh_new_token()\n    original_token = listing_original.token\n    original_header = listing_original.header\n\n    listing_new = Listing()\n    listing_new.refresh_new_token_new_implementation()\n    new_token = listing_new.token\n    new_header = listing_new.header\n\n    # Assert that both tokens are the same\n    assert original_token == new_token, \"Tokens do not match\"\n\n    # Assert that both headers are the same\n    assert original_header == new_header, \"Headers do not match\"\n\n    # Assert that the token is not None\n    assert new_token is not None, \"Token should not be None\"\n\n    # Test 2: Environment variable token test\n    os.environ['TOKEN'] = 'env_token'\n    listing_env = Listing()\n    assert listing_env.token == 'env_token', \"Token should be fetched from environment variable\"\n    assert listing_env.header == {\"Authorization\": \"env_token\"}, \"Header should be set with environment token\"\n    del os.environ['TOKEN']  # Clean up\n\n    # Test 3: Token update test\n    listing_update = Listing()\n    listing_update.refresh_new_token()\n    updated_token = listing_update.token\n    updated_header = listing_update.header\n    assert updated_token == \"mocked_token\", \"Token should be updated to mocked_token\"\n    assert updated_header == {\"Authorization\": \"mocked_token\"}, \"Header should be updated with new token\"\n\n    # Test 4: Multiple calls consistency test\n    listing_consistent = Listing()\n    listing_consistent.refresh_new_token()\n    token_after_first_call = listing_consistent.token\n    header_after_first_call = listing_consistent.header\n    listing_consistent.refresh_new_token()\n    token_after_second_call = listing_consistent.token\n    header_after_second_call = listing_consistent.header\n    assert token_after_first_call == token_after_second_call, \"Token should remain consistent across multiple calls\"\n    assert header_after_first_call == header_after_second_call, \"Header should remain consistent across multiple calls\"\n\n    # Test 5: Token format test\n    assert isinstance(new_token, str) and new_token, \"Token should be a non-empty string\"\n\nif __name__ == \"__main__\":\n    test_refresh_new_token()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `refresh_new_token` in the `Listing` class is functionally identical to the ORIGINAL FUNCTION. Both functions create an instance of the `Auth` class, retrieve a new token using the `token` method, and update the `token` and `header` attributes of the class if a new token is obtained. If no token is obtained, both functions log a warning message. The logic and flow of operations are preserved in the revised version, ensuring the same functionality as the original.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `refresh_new_token` function modifies the `token` and `header` attributes of the `Listing` class, which are instance variables. Therefore, it satisfies this condition.\n\n2. **CONDITION 2**: The test cases in `test_refresh_new_token` check the state of the `token` and `header` attributes, which are the return values or variable states. They do not check printed or logged contents. Thus, this condition is satisfied.\n\n3. **CONDITION 3**: The test cases compare the `token` and `header` attributes of the original and new implementations of `refresh_new_token`. This ensures that `refresh_new_token_new_implementation` must have the exact same functionality as `refresh_new_token` to pass all tests. Therefore, this condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to check the state of the `token` and `header` attributes, which are reasonable given that `refresh_new_token` modifies these attributes. The test cases do not use inappropriate assertions, so this condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover various scenarios, including basic functionality, environment variable usage, token updating, consistency across multiple calls, and token format. These tests are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "4792896d7769ebd0e67e13d99b87454f9a7db3ea"
    },
    {
        "func_name": "game_controller._reveal",
        "idx": "1007",
        "repo_name": "MonochromaticSilver___python-minesweeper",
        "func_path": "minesweeper/game_controller.py",
        "orig_func": "def _reveal(self, x, y):\n    queue = deque()\n    queue.append((x, y))\n    while len(queue) > 0:\n        item = queue.popleft()\n        if item in self.game_state.revealed:\n            continue\n        self.game_state.revealed.append(item)\n        if self.game_state.number_mines(item[0], item[1]) > 0:\n            continue\n        directions = [(0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (-1, -1), (1, -1), (-1, 1)]\n        for dx, dy in directions:\n            mine_check_x = item[0] + dx\n            mine_check_y = item[1] + dy\n            if mine_check_x < 0 or mine_check_x >= self.game_state.width:\n                continue\n            if mine_check_y < 0 or mine_check_y >= self.game_state.height:\n                continue\n            queue.append((mine_check_x, mine_check_y))",
        "orig_context": "```python\n## minesweeper/game_state.py\nfrom enum import Enum\n\nimport random\n\nclass GameState(Enum):\n    PLAYING = 1\n    LOST = 2\n    WON = 3\n\nclass game_state:\n    def __init__(self, width = 9, height = 9, mine_count = 10):\n        self.width = width\n        self.height = height\n        self.mines = []\n        self.flags = []\n        self.revealed = []\n        self.cursor = (0,0)\n        self.game_state = GameState.PLAYING\n        self._generate_mines(mine_count)\n\n    def _generate_mines(self, mine_count):\n        # Task: Implement the method that generates mines based on width/height/mine_count\n        # This would involve generating a list of random (x, y) coordinates\n        rangeX = (0, self.width)\n        rangeY = (0, self.height)\n\n        randPoints = []\n        current_generated_mines = 0\n        while current_generated_mines < mine_count:\n            x = random.randrange(*rangeX)\n            y = random.randrange(*rangeY)\n            if (x, y) not in randPoints:\n                randPoints.append((x, y))\n                current_generated_mines += 1\n        \n        self.mines = randPoints\n\n    def number_mines(self, x, y):\n        directions = [(0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (-1, -1), (1, -1), (-1, 1)]\n\n        counted_mines = 0\n\n        for dx, dy in directions:\n            mine_check_x = x + dx\n            mine_check_y = y + dy\n\n            if (mine_check_x, mine_check_y) in self.mines:\n                counted_mines += 1\n                # self.shown_listed_mines.append((mine_check_x, mine_check_y))\n\n        return counted_mines\n\n```\n\n\n```python\n## minesweeper/ui.py\nfrom .game_state import game_state, GameState\n\nimport os\n\ndef print_board(game_state: game_state):\n    # print(f\" TODO print board (mines: {game_state.mines})\")\n    # Task: Implement the method that prints the board\n    # 1\ufe0f\u20e32\ufe0f\u20e33\ufe0f\u20e34\ufe0f\u20e35\ufe0f\u20e36\ufe0f\u20e37\ufe0f\u20e38\ufe0f\u20e3\ud83d\udfe8 <-- copy and paste\n    for y in range(game_state.height):\n        for x in range(game_state.width):\n            if (x, y) == game_state.cursor:\n                print(\"\ud83d\udfe8\", end=\"\")\n            elif (x, y) in game_state.mines and game_state.game_state != GameState.PLAYING:\n                print(\"\ud83d\udca3\", end=\"\")\n            elif (x, y) in game_state.flags:\n                print(\"\ud83d\udea9\", end=\"\")\n            elif (x, y) in game_state.revealed:\n                number_mines = game_state.number_mines(x, y)\n                if number_mines == 0:\n                    print(\"\ud83d\udfeb\", end=\"\")\n                if number_mines == 1:\n                    print(\"\\u0031\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 2:\n                    print(\"\\u0032\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 3:\n                    print(\"\\u0033\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 4:\n                    print(\"\\u0034\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 5:\n                    print(\"\\u0035\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 6:\n                    print(\"\\u0036\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 7:\n                    print(\"\\u0037\\ufe0f\\u20e3 \", end ='')\n                if number_mines == 8:\n                    print(\"\\u0038\\ufe0f\\u20e3 \", end ='')\n            else:\n                print(\"\ud83d\udfe9\", end=\"\")\n        print()\n\ndef clear_console():\n    # For Windows\n    if os.name == 'nt':\n        os.system('cls')\n    # For macOS and Linux\n    print('\\n\\n')\n\n```\n\n\n```python\n## minesweeper/game_controller.py\nfrom collections import deque\n\nfrom minesweeper.game_state import game_state, GameState\n\nfrom minesweeper.ui import clear_console, print_board\n\nimport readchar\n\nclass game_controller:\n    def __init__(self, game_state: game_state):\n        self.game_state = game_state\n\n    def play_until_end(self):\n        while self.game_state.game_state == GameState.PLAYING:\n            self.play_game_turn()\n        self.print_end_game()\n\n    def play_game_turn(self):\n        clear_console()\n        print_board(self.game_state)\n        # Task: Implement the method that plays a single turn of the game (including printing the UI)\n        \n        #for now, we'll just loose the game\n        #self.game_state.game_state = GameState.LOST\n        key = readchar.readkey()\n        if key == readchar.key.LEFT:\n            self.game_state.cursor = (max(0, self.game_state.cursor[0] - 1), self.game_state.cursor[1])\n        if key == readchar.key.RIGHT:\n            self.game_state.cursor = (min(self.game_state.width - 1, self.game_state.cursor[0] + 1), self.game_state.cursor[1])\n        if key == readchar.key.DOWN:\n            self.game_state.cursor = (self.game_state.cursor[0], min(self.game_state.height - 1, self.game_state.cursor[1] + 1))\n        if key == readchar.key.UP:\n            self.game_state.cursor = (self.game_state.cursor[0], max( 0, self.game_state.cursor[1] - 1))\n        if key == 'f' or key == 'F':\n            if self.game_state.cursor in self.game_state.flags:\n                self.game_state.flags.remove(self.game_state.cursor)\n            else:   \n                self.game_state.flags.append(self.game_state.cursor)\n        if key == 'r' or key == 'R':\n            if self.game_state.cursor in self.game_state.mines:\n                self.game_state.game_state = 2\n            else:\n                self._reveal(self.game_state.cursor[0], self.game_state.cursor[1])\n\n    def _reveal(self, x, y):\n        queue = deque()\n        queue.append((x, y))\n        while len(queue) > 0:\n            item = queue.popleft()\n            if item in self.game_state.revealed:\n                continue\n            self.game_state.revealed.append(item)\n            if self.game_state.number_mines(item[0], item[1]) > 0:\n                continue\n            directions = [(0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (-1, -1), (1, -1), (-1, 1)]\n\n            for dx, dy in directions:\n                mine_check_x = item[0] + dx\n                mine_check_y = item[1] + dy\n\n                if mine_check_x < 0 or mine_check_x >= self.game_state.width:\n                    continue\n                if mine_check_y < 0 or mine_check_y >= self.game_state.height:\n                    continue\n                queue.append((mine_check_x, mine_check_y))\n                    \n    def print_end_game(self):\n        # Task: Implement the method that prints the end game message then waits for the user to push a key (to return to the menu loop)\n        clear_console()\n        print_board(self.game_state)\n        print(\"Game Over\")\n        input(\"Press enter key to return to the main menu\")\n        pass\n\n```\n\n\n",
        "eval_script": "from collections import deque\nfrom enum import Enum\nimport random\nimport os\n\n# Mock readchar module for testing purposes\nclass MockReadChar:\n    key = type('key', (), {'LEFT': 'a', 'RIGHT': 'd', 'UP': 'w', 'DOWN': 's'})\n\n    @staticmethod\n    def readkey():\n        # Simulate key press for testing\n        return input(\"Enter a key (a=LEFT, d=RIGHT, w=UP, s=DOWN, f=FLAG, r=REVEAL): \").strip()\n\nreadchar = MockReadChar()\n\n# GameState Enum\nclass GameState(Enum):\n    PLAYING = 1\n    LOST = 2\n    WON = 3\n\n# game_state class\nclass game_state:\n    def __init__(self, width=9, height=9, mine_count=10):\n        self.width = width\n        self.height = height\n        self.mines = []\n        self.flags = []\n        self.revealed = []\n        self.cursor = (0, 0)\n        self.game_state = GameState.PLAYING\n        self._generate_mines(mine_count)\n\n    def _generate_mines(self, mine_count):\n        rangeX = (0, self.width)\n        rangeY = (0, self.height)\n\n        randPoints = []\n        current_generated_mines = 0\n        while current_generated_mines < mine_count:\n            x = random.randrange(*rangeX)\n            y = random.randrange(*rangeY)\n            if (x, y) not in randPoints:\n                randPoints.append((x, y))\n                current_generated_mines += 1\n\n        self.mines = randPoints\n\n    def number_mines(self, x, y):\n        directions = [(0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (-1, -1), (1, -1), (-1, 1)]\n\n        counted_mines = 0\n\n        for dx, dy in directions:\n            mine_check_x = x + dx\n            mine_check_y = y + dy\n\n            if (mine_check_x, mine_check_y) in self.mines:\n                counted_mines += 1\n\n        return counted_mines\n\n# UI functions\ndef print_board(game_state: game_state):\n    for y in range(game_state.height):\n        for x in range(game_state.width):\n            if (x, y) == game_state.cursor:\n                print(\"\ud83d\udfe8\", end=\"\")\n            elif (x, y) in game_state.mines and game_state.game_state != GameState.PLAYING:\n                print(\"\ud83d\udca3\", end=\"\")\n            elif (x, y) in game_state.flags:\n                print(\"\ud83d\udea9\", end=\"\")\n            elif (x, y) in game_state.revealed:\n                number_mines = game_state.number_mines(x, y)\n                if number_mines == 0:\n                    print(\"\ud83d\udfeb\", end=\"\")\n                if number_mines == 1:\n                    print(\"\\u0031\\ufe0f\\u20e3 \", end='')\n                if number_mines == 2:\n                    print(\"\\u0032\\ufe0f\\u20e3 \", end='')\n                if number_mines == 3:\n                    print(\"\\u0033\\ufe0f\\u20e3 \", end='')\n                if number_mines == 4:\n                    print(\"\\u0034\\ufe0f\\u20e3 \", end='')\n                if number_mines == 5:\n                    print(\"\\u0035\\ufe0f\\u20e3 \", end='')\n                if number_mines == 6:\n                    print(\"\\u0036\\ufe0f\\u20e3 \", end='')\n                if number_mines == 7:\n                    print(\"\\u0037\\ufe0f\\u20e3 \", end='')\n                if number_mines == 8:\n                    print(\"\\u0038\\ufe0f\\u20e3 \", end='')\n            else:\n                print(\"\ud83d\udfe9\", end=\"\")\n        print()\n\ndef clear_console():\n    # For Windows\n    if os.name == 'nt':\n        os.system('cls')\n    # For macOS and Linux\n    print('\\n\\n')\n\n# game_controller class\nclass game_controller:\n    def __init__(self, game_state: game_state):\n        self.game_state = game_state\n\n    def play_until_end(self):\n        while self.game_state.game_state == GameState.PLAYING:\n            self.play_game_turn()\n        self.print_end_game()\n\n    def play_game_turn(self):\n        clear_console()\n        print_board(self.game_state)\n        \n        key = readchar.readkey()\n        if key == readchar.key.LEFT:\n            self.game_state.cursor = (max(0, self.game_state.cursor[0] - 1), self.game_state.cursor[1])\n        if key == readchar.key.RIGHT:\n            self.game_state.cursor = (min(self.game_state.width - 1, self.game_state.cursor[0] + 1), self.game_state.cursor[1])\n        if key == readchar.key.DOWN:\n            self.game_state.cursor = (self.game_state.cursor[0], min(self.game_state.height - 1, self.game_state.cursor[1] + 1))\n        if key == readchar.key.UP:\n            self.game_state.cursor = (self.game_state.cursor[0], max(0, self.game_state.cursor[1] - 1))\n        if key == 'f' or key == 'F':\n            if self.game_state.cursor in self.game_state.flags:\n                self.game_state.flags.remove(self.game_state.cursor)\n            else:\n                self.game_state.flags.append(self.game_state.cursor)\n        if key == 'r' or key == 'R':\n            if self.game_state.cursor in self.game_state.mines:\n                self.game_state.game_state = 2\n            else:\n                self._reveal(self.game_state.cursor[0], self.game_state.cursor[1])\n\n    def _reveal(self, x, y):\n        queue = deque()\n        queue.append((x, y))\n        while len(queue) > 0:\n            item = queue.popleft()\n            if item in self.game_state.revealed:\n                continue\n            self.game_state.revealed.append(item)\n            if self.game_state.number_mines(item[0], item[1]) > 0:\n                continue\n            directions = [(0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (-1, -1), (1, -1), (-1, 1)]\n\n            for dx, dy in directions:\n                mine_check_x = item[0] + dx\n                mine_check_y = item[1] + dy\n\n                if mine_check_x < 0 or mine_check_x >= self.game_state.width:\n                    continue\n                if mine_check_y < 0 or mine_check_y >= self.game_state.height:\n                    continue\n                queue.append((mine_check_x, mine_check_y))\n                    \n    def print_end_game(self):\n        clear_console()\n        print_board(self.game_state)\n        print(\"Game Over\")\n        input(\"Press enter key to return to the main menu\")\n        pass\n\n\ndef test__reveal():\n    # Test case 1: Reveal a cell with no adjacent mines\n    gs1 = game_state(width=3, height=3, mine_count=0)\n    gc1 = game_controller(gs1)\n    gc1._reveal(1, 1)\n    revealed1 = gs1.revealed.copy()\n\n    gs1.revealed = []\n    gc1._reveal_new_implementation(1, 1)\n    assert revealed1 == gs1.revealed, \"Test case 1 failed\"\n\n    # Test case 2: Reveal a cell with adjacent mines\n    gs2 = game_state(width=3, height=3, mine_count=0)\n    gs2.mines = [(0, 0)]\n    gc2 = game_controller(gs2)\n    gc2._reveal(1, 1)\n    revealed2 = gs2.revealed.copy()\n\n    gs2.revealed = []\n    gc2._reveal_new_implementation(1, 1)\n    assert revealed2 == gs2.revealed, \"Test case 2 failed\"\n\n    # Test case 3: Reveal a cell that is a mine (should not affect revealed)\n    gs3 = game_state(width=3, height=3, mine_count=0)\n    gs3.mines = [(1, 1)]\n    gc3 = game_controller(gs3)\n    gc3._reveal(1, 1)\n    revealed3 = gs3.revealed.copy()\n\n    gs3.revealed = []\n    gc3._reveal_new_implementation(1, 1)\n    assert revealed3 == gs3.revealed, \"Test case 3 failed\"\n\n    # Test case 4: Reveal a cell at the edge of the board\n    gs4 = game_state(width=3, height=3, mine_count=0)\n    gc4 = game_controller(gs4)\n    gc4._reveal(0, 1)\n    revealed4 = gs4.revealed.copy()\n\n    gs4.revealed = []\n    gc4._reveal_new_implementation(0, 1)\n    assert revealed4 == gs4.revealed, \"Test case 4 failed\"\n\n    # Test case 5: Reveal a cell at the corner of the board\n    gs5 = game_state(width=3, height=3, mine_count=0)\n    gc5 = game_controller(gs5)\n    gc5._reveal(0, 0)\n    revealed5 = gs5.revealed.copy()\n\n    gs5.revealed = []\n    gc5._reveal_new_implementation(0, 0)\n    assert revealed5 == gs5.revealed, \"Test case 5 failed\"\n\n    # Test case 6: Reveal a cell with multiple adjacent mines\n    gs6 = game_state(width=3, height=3, mine_count=0)\n    gs6.mines = [(0, 0), (2, 2)]\n    gc6 = game_controller(gs6)\n    gc6._reveal(1, 1)\n    revealed6 = gs6.revealed.copy()\n\n    gs6.revealed = []\n    gc6._reveal_new_implementation(1, 1)\n    assert revealed6 == gs6.revealed, \"Test case 6 failed\"\n\n    # Test case 7: Reveal a cell that has already been revealed\n    gs7 = game_state(width=3, height=3, mine_count=0)\n    gc7 = game_controller(gs7)\n    gc7._reveal(1, 1)\n    gc7._reveal(1, 1)  # Reveal again\n    revealed7 = gs7.revealed.copy()\n\n    gs7.revealed = []\n    gc7._reveal_new_implementation(1, 1)\n    gc7._reveal_new_implementation(1, 1)  # Reveal again\n    assert revealed7 == gs7.revealed, \"Test case 7 failed\"\n\n    # Test case 8: Reveal a flagged cell (should not affect revealed)\n    gs8 = game_state(width=3, height=3, mine_count=0)\n    gs8.flags = [(1, 1)]\n    gc8 = game_controller(gs8)\n    gc8._reveal(1, 1)\n    revealed8 = gs8.revealed.copy()\n\n    gs8.revealed = []\n    gc8._reveal_new_implementation(1, 1)\n    assert revealed8 == gs8.revealed, \"Test case 8 failed\"\n\n    # Test case 9: Complex board with mixed mines and reveals\n    gs9 = game_state(width=5, height=5, mine_count=0)\n    gs9.mines = [(0, 0), (4, 4), (2, 2)]\n    gc9 = game_controller(gs9)\n    gc9._reveal(1, 1)\n    revealed9 = gs9.revealed.copy()\n\n    gs9.revealed = []\n    gc9._reveal_new_implementation(1, 1)\n    assert revealed9 == gs9.revealed, \"Test case 9 failed\"\n\nif __name__ == \"__main__\":\n    test__reveal()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon reviewing the original and revised `_reveal` functions, they are identical in terms of logic and implementation. Both functions use a queue to manage the cells to be revealed, check if a cell has already been revealed, append it to the revealed list if not, and continue to reveal neighboring cells if there are no adjacent mines. The directions for neighboring cells and boundary checks are also the same. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `_reveal` function modifies the `revealed` attribute of the `game_state` object, which is a global variable in the context of the `game_controller` class. Therefore, this condition is satisfied.\n\n2. **CONDITION 2**: The test cases check the state of the `revealed` list after calling `_reveal` and `_reveal_new_implementation`. They do not check printed or logged contents. Thus, this condition is satisfied.\n\n3. **CONDITION 3**: The test cases compare the state of the `revealed` list after executing both `_reveal` and `_reveal_new_implementation`. If both functions result in the same `revealed` list, they are considered to have the same functionality. Therefore, this condition is satisfied.\n\n4. **CONDITION 4**: The test cases use assertions to compare the `revealed` list after calling both implementations. This is reasonable because `_reveal` modifies the `revealed` list, and the test cases do not use inappropriate assertions like comparing return values when there are none. Thus, this condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including revealing cells with no adjacent mines, with adjacent mines, cells that are mines, edge and corner cells, cells with multiple adjacent mines, already revealed cells, flagged cells, and complex boards. These scenarios are non-trivial and provide comprehensive coverage of potential edge cases. Therefore, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "ae0f687652bdb33ba6c91a96b5cab08c92bdfb6e"
    },
    {
        "func_name": "AppUserManager.create_superuser",
        "idx": "1016",
        "repo_name": "MariyaKristova___Petstagram-App",
        "func_path": "petstagram_app/accounts/managers.py",
        "orig_func": "def create_superuser(self, email, password=None, **extra_fields):\n    extra_fields.setdefault('is_staff', True)\n    extra_fields.setdefault('is_superuser', True)\n    return self.create_user(email, password, **extra_fields)",
        "orig_context": "```python\n## petstagram_app/accounts/managers.py\nfrom django.contrib.auth.base_user import BaseUserManager\n\nclass AppUserManager(BaseUserManager):\n    def create_user(self, email, password=None, **extra_fields):\n        if not email:\n            raise ValueError('The Email field must be set!')\n        email = self.normalize_email(email)\n        user = self.model(email=email, **extra_fields)\n        user.set_password(password)\n        user.save(using=self._db)\n        return user\n\n    def create_superuser(self, email, password=None, **extra_fields):\n        extra_fields.setdefault('is_staff', True)\n        extra_fields.setdefault('is_superuser', True)\n        return self.create_user(email, password, **extra_fields)\n\n```\n\n\n",
        "eval_script": "# The debugged PYTHON CODE in one piece.\n\n# Mock user model to simulate the Django model\nclass MockUser:\n    def __init__(self, email, **extra_fields):\n        self.email = email\n        self.extra_fields = extra_fields\n        self.password = None\n\n    def set_password(self, password):\n        self.password = password\n\n    def save(self, using=None):\n        print(f\"User {self.email} saved with fields: {self.extra_fields} and password: {self.password}\")\n\nclass AppUserManager:\n    def __init__(self):\n        self.model = MockUser  # Use the mock user model\n\n    def create_user(self, email, password=None, **extra_fields):\n        if not email:\n            raise ValueError('The Email field must be set!')\n        email = email.lower()  # Simulate normalize_email\n        user = self.model(email=email, **extra_fields)\n        user.set_password(password)\n        user.save(using=None)  # No database, so using=None\n        return user\n\n    def create_superuser(self, email, password=None, **extra_fields):\n        extra_fields.setdefault('is_staff', True)\n        extra_fields.setdefault('is_superuser', True)\n        return self.create_user(email, password, **extra_fields)\n\n\ndef test_create_superuser():\n    manager = AppUserManager()\n\n    # Test case 1: Default superuser creation\n    user1_old = manager.create_superuser('test1@example.com', 'password123')\n    user1_new = manager.create_superuser_new_implementation('test1@example.com', 'password123')\n    assert user1_old.email == user1_new.email\n    assert user1_old.extra_fields == user1_new.extra_fields\n    assert user1_old.password == user1_new.password\n\n    # Test case 2: Superuser creation with additional fields\n    user2_old = manager.create_superuser('test2@example.com', 'password123', first_name='John', last_name='Doe')\n    user2_new = manager.create_superuser_new_implementation('test2@example.com', 'password123', first_name='John', last_name='Doe')\n    assert user2_old.email == user2_new.email\n    assert user2_old.extra_fields == user2_new.extra_fields\n    assert user2_old.password == user2_new.password\n\n    # Test case 3: Superuser creation with no password\n    user3_old = manager.create_superuser('test3@example.com')\n    user3_new = manager.create_superuser_new_implementation('test3@example.com')\n    assert user3_old.email == user3_new.email\n    assert user3_old.extra_fields == user3_new.extra_fields\n    assert user3_old.password == user3_new.password\n\nif __name__ == \"__main__\":\n    test_create_superuser()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the `AppUserManager` class is identical to the ORIGINAL FUNCTION. Both functions set the 'is_staff' and 'is_superuser' fields to True by default and then call the `create_user` method with the provided email, password, and any additional fields. The functionality remains unchanged, as both functions perform the same operations in the same order.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `create_superuser` function returns a `MockUser` object, which means it has a return value. This satisfies CONDITION 1.\n  \n- CONDITION 2: The test cases in `test_create_superuser` compare the attributes of the returned `MockUser` objects from both `create_superuser` and `create_superuser_new_implementation`. They do not rely on printed or logged content. This satisfies CONDITION 2.\n\n- CONDITION 3: The test cases check for equality of the email, extra_fields, and password attributes of the `MockUser` objects returned by both implementations. If `create_superuser_new_implementation` has the same functionality as `create_superuser`, it will pass these tests. This satisfies CONDITION 3.\n\n- CONDITION 4: The test cases use assertions to compare the attributes of the returned objects, which is reasonable given that `create_superuser` returns a `MockUser` object. This satisfies CONDITION 4.\n\n- CONDITION 5: The test cases cover different scenarios: default superuser creation, superuser creation with additional fields, and superuser creation with no password. These are non-trivial test cases that check different aspects of the function's behavior. This satisfies CONDITION 5.",
            "answer": "yes"
        },
        "commit_id": "d669b3fb8e327f8bc8b6e60c82c3254f1046eb2d"
    },
    {
        "func_name": "DataTransformation._validate_order_config",
        "idx": "1023",
        "repo_name": "acmoudleysa___Bioprocess-DL-MLOps",
        "func_path": "src/bioprocess_mlops/components/data_transformation.py",
        "orig_func": "def _validate_order_config(self) -> bool:\n    enabled_steps = {name for name, config in self.preprocessing_config.steps.items() if config['enabled']}\n    order_steps = self.preprocessing_config.order\n    if len(enabled_steps) != len(order_steps) or enabled_steps != set(order_steps):\n        raise ValueError(f'Mismatch in configuration. Enabled steps: {enabled_steps}, Order specified: {self.preprocessing_config.order}')",
        "orig_context": "```python\n## src/bioprocess_mlops/utils/utils.py\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nclass SavitzkyGolayFilter:\n    ...\n\nclass SNV(BaseEstimator, TransformerMixin):\n    def __init__(self):\n        pass\n\n    def fit(self, X, y=None):\n        pass\n\n    def transform(self, X, y=None):\n        return X\n\n    def fit_transform(self, X, y=None):\n        return X\n\n```\n\n\n```python\n## src/bioprocess_mlops/utils/__init__.py\nfrom .utils import (CustomFormatter,\n                    load_yaml,\n                    SavitzkyGolayFilter,\n                    SNV,\n                    Metrics)\n\n```\n\n\n```python\n## src/bioprocess_mlops/config/config.py\nfrom dataclasses import dataclass\n\nfrom typing import Any, Dict\n\nclass PreprocessingConfig:\n    steps: Dict[str, Dict[str, Any]]\n    order: list\n    artifacts_path: Dict[str, str]\n\n```\n\n\n```python\n## src/bioprocess_mlops/components/data_transformation.py\nimport logging\n\nfrom sklearn.preprocessing import StandardScaler, FunctionTransformer\n\nfrom sklearn.pipeline import Pipeline\n\nimport skops.io as sio\n\nfrom typing import List, Tuple, Any\n\nfrom bioprocess_mlops.config.config import PreprocessingConfig\n\nfrom bioprocess_mlops.utils import SavitzkyGolayFilter, SNV\n\nlogger = logging.getLogger(__name__)\n\nclass DataTransformation:\n    def __init__(self,\n                 preprocessing_config: PreprocessingConfig):\n        self.preprocessing_config = preprocessing_config\n\n    def _validate_order_config(self) -> bool:\n        enabled_steps = {\n            name for name, config in self.preprocessing_config.steps.items()\n            if config['enabled']\n        }\n        order_steps = self.preprocessing_config.order\n\n        if ((len(enabled_steps) != len(order_steps)) or (enabled_steps !=\n                                                         set(order_steps))):\n            raise ValueError(\n                f\"Mismatch in configuration. Enabled steps: {enabled_steps}, \"\n                f\"Order specified: {self.preprocessing_config.order}\"\n                )\n\n    def get_transformer_object(self) -> Pipeline:\n        try:\n            self._validate_order_config()\n            preprocessing_steps: List[Tuple[str, Any]] = []\n            steps_config = self.preprocessing_config.steps\n\n            for step_name in self.preprocessing_config.order:\n                config = steps_config[step_name]\n                if config['enabled']:\n                    if step_name == 'sg_smooth':\n                        sg_params = config['params']\n                        preprocessing_steps.append(\n                            ('smoothing', SavitzkyGolayFilter(\n                                window_length=sg_params['window_length'],\n                                polyorder=sg_params['polyorder'],\n                                deriv=sg_params['deriv']\n                            ))\n                        )\n                        logger.info(\"Added SG-smoothing to the pipeline\")\n\n                    elif step_name == 'snv':\n                        preprocessing_steps.append(('snv', SNV()))\n                        logger.info(\"Added SNV to pipeline\")\n\n                    elif step_name == 'standard_scaler':\n                        preprocessing_steps.append(\n                            ('scaler', StandardScaler()))\n                        logger.info(\"Added StandardScaler to pipeline\")\n\n                    else:\n                        logger.warning(f\"{step_name} has not be added yet\")\n\n            if not preprocessing_steps:\n                logger.warning(\"No preprocessing steps enabled - returning \"\n                               \"passthrough pipeline\")\n                preprocessing_steps.append(\n                    ('passthrough', FunctionTransformer(func=None,\n                                                        validate=False))\n                )\n            preprocessor = Pipeline(preprocessing_steps)\n            logger.debug(f\"Pipeline steps: \"\n                         f\"{[step[0]for step in preprocessor.steps]}\")\n            return preprocessor\n\n        except Exception:\n            logger.exception(\"Error in getting the preprocessor object!\")\n            raise\n\n    def create_preprocessor_object(self):\n        try:\n            pp_template = self.get_transformer_object()\n            logger.info(f\"Created preprocessing pipeline with \"\n                        f\"{len(pp_template.steps)} steps\")\n            preprocessor_template_path = self.preprocessing_config.artifacts_path['preprocesser_template_path']  # noqa E51\n            logger.info(f\"Saving preprocessing object at {preprocessor_template_path}\")  # noqa E51\n            sio.dump(pp_template, preprocessor_template_path)\n\n        except Exception:\n            logger.error(\"Error in creating preprocessor pipeline template\")\n            raise\n\n```\n\n\n",
        "eval_script": "import logging\nfrom sklearn.preprocessing import StandardScaler, FunctionTransformer\nfrom sklearn.pipeline import Pipeline\nimport skops.io as sio\nfrom typing import List, Tuple, Any, Dict\n\n# Mock implementations of the classes and functions from the context\n\nclass SavitzkyGolayFilter:\n    pass\n\nclass SNV:\n    def __init__(self):\n        pass\n\n    def fit(self, X, y=None):\n        pass\n\n    def transform(self, X, y=None):\n        return X\n\n    def fit_transform(self, X, y=None):\n        return X\n\nclass PreprocessingConfig:\n    def __init__(self, steps: Dict[str, Dict[str, Any]], order: list, artifacts_path: Dict[str, str]):\n        self.steps = steps\n        self.order = order\n        self.artifacts_path = artifacts_path\n\n# Main class from the provided code\n\nlogger = logging.getLogger(__name__)\n\nclass DataTransformation:\n    def __init__(self, preprocessing_config: PreprocessingConfig):\n        self.preprocessing_config = preprocessing_config\n\n    def _validate_order_config(self) -> bool:\n        enabled_steps = {\n            name for name, config in self.preprocessing_config.steps.items()\n            if config['enabled']\n        }\n        order_steps = self.preprocessing_config.order\n\n        if ((len(enabled_steps) != len(order_steps)) or (enabled_steps !=\n                                                         set(order_steps))):\n            raise ValueError(\n                f\"Mismatch in configuration. Enabled steps: {enabled_steps}, \"\n                f\"Order specified: {self.preprocessing_config.order}\"\n                )\n\n\n    def get_transformer_object(self) -> Pipeline:\n        try:\n            self._validate_order_config()\n            preprocessing_steps: List[Tuple[str, Any]] = []\n            steps_config = self.preprocessing_config.steps\n\n            for step_name in self.preprocessing_config.order:\n                config = steps_config[step_name]\n                if config['enabled']:\n                    if step_name == 'sg_smooth':\n                        sg_params = config['params']\n                        preprocessing_steps.append(\n                            ('smoothing', SavitzkyGolayFilter(\n                                window_length=sg_params['window_length'],\n                                polyorder=sg_params['polyorder'],\n                                deriv=sg_params['deriv']\n                            ))\n                        )\n                        logger.info(\"Added SG-smoothing to the pipeline\")\n\n                    elif step_name == 'snv':\n                        preprocessing_steps.append(('snv', SNV()))\n                        logger.info(\"Added SNV to pipeline\")\n\n                    elif step_name == 'standard_scaler':\n                        preprocessing_steps.append(\n                            ('scaler', StandardScaler()))\n                        logger.info(\"Added StandardScaler to pipeline\")\n\n                    else:\n                        logger.warning(f\"{step_name} has not be added yet\")\n\n            if not preprocessing_steps:\n                logger.warning(\"No preprocessing steps enabled - returning \"\n                               \"passthrough pipeline\")\n                preprocessing_steps.append(\n                    ('passthrough', FunctionTransformer(func=None,\n                                                        validate=False))\n                )\n            preprocessor = Pipeline(preprocessing_steps)\n            logger.debug(f\"Pipeline steps: \"\n                         f\"{[step[0]for step in preprocessor.steps]}\")\n            return preprocessor\n\n        except Exception:\n            logger.exception(\"Error in getting the preprocessor object!\")\n            raise\n\n    def create_preprocessor_object(self):\n        try:\n            pp_template = self.get_transformer_object()\n            logger.info(f\"Created preprocessing pipeline with \"\n                        f\"{len(pp_template.steps)} steps\")\n            preprocessor_template_path = self.preprocessing_config.artifacts_path['preprocesser_template_path']  # noqa E51\n            logger.info(f\"Saving preprocessing object at {preprocessor_template_path}\")  # noqa E51\n            sio.dump(pp_template, preprocessor_template_path)\n\n        except Exception:\n            logger.error(\"Error in creating preprocessor pipeline template\")\n            raise\n\ndef test__validate_order_config():\n    # Test case 1: Matching enabled steps and order\n    config1 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': True, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': True},\n            'standard_scaler': {'enabled': True}\n        },\n        order=['sg_smooth', 'snv', 'standard_scaler'],\n        artifacts_path={}\n    )\n    dt1 = DataTransformation(config1)\n    assert dt1._validate_order_config() == dt1._validate_order_config_new_implementation()\n\n    # Test case 2: Mismatched enabled steps and order\n    config2 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': True, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': False},\n            'standard_scaler': {'enabled': True}\n        },\n        order=['sg_smooth', 'standard_scaler'],\n        artifacts_path={}\n    )\n    dt2 = DataTransformation(config2)\n    assert dt2._validate_order_config() == dt2._validate_order_config_new_implementation()\n\n    # Test case 3: No enabled steps\n    config3 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': False, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': False},\n            'standard_scaler': {'enabled': False}\n        },\n        order=[],\n        artifacts_path={}\n    )\n    dt3 = DataTransformation(config3)\n    assert dt3._validate_order_config() == dt3._validate_order_config_new_implementation()\n\n    # Test case 4: Empty order with enabled steps\n    config4 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': True, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': True},\n            'standard_scaler': {'enabled': True}\n        },\n        order=[],\n        artifacts_path={}\n    )\n    dt4 = DataTransformation(config4)\n    try:\n        dt4._validate_order_config()\n    except ValueError:\n        pass\n    try:\n        dt4._validate_order_config_new_implementation()\n    except ValueError:\n        pass\n\n    # Test case 5: Order with disabled steps\n    config5 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': False, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': True},\n            'standard_scaler': {'enabled': True}\n        },\n        order=['sg_smooth', 'snv', 'standard_scaler'],\n        artifacts_path={}\n    )\n    dt5 = DataTransformation(config5)\n    try:\n        dt5._validate_order_config()\n    except ValueError:\n        pass\n    try:\n        dt5._validate_order_config_new_implementation()\n    except ValueError:\n        pass\n\n    # Test case 6: Duplicate steps in order\n    config6 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': True, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': True},\n            'standard_scaler': {'enabled': True}\n        },\n        order=['sg_smooth', 'snv', 'snv', 'standard_scaler'],\n        artifacts_path={}\n    )\n    dt6 = DataTransformation(config6)\n    try:\n        dt6._validate_order_config()\n    except ValueError:\n        pass\n    try:\n        dt6._validate_order_config_new_implementation()\n    except ValueError:\n        pass\n\n    # Test case 7: Order with extra steps\n    config7 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': True, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': True}\n        },\n        order=['sg_smooth', 'snv', 'standard_scaler'],\n        artifacts_path={}\n    )\n    dt7 = DataTransformation(config7)\n    try:\n        dt7._validate_order_config()\n    except ValueError:\n        pass\n    try:\n        dt7._validate_order_config_new_implementation()\n    except ValueError:\n        pass\n\n    # Test case 8: Enabled steps with different order\n    config8 = PreprocessingConfig(\n        steps={\n            'sg_smooth': {'enabled': True, 'params': {'window_length': 5, 'polyorder': 2, 'deriv': 0}},\n            'snv': {'enabled': True},\n            'standard_scaler': {'enabled': True}\n        },\n        order=['standard_scaler', 'snv', 'sg_smooth'],\n        artifacts_path={}\n    )\n    dt8 = DataTransformation(config8)\n    try:\n        dt8._validate_order_config()\n    except ValueError:\n        pass\n    try:\n        dt8._validate_order_config_new_implementation()\n    except ValueError:\n        pass\n\nif __name__ == \"__main__\":\n    test__validate_order_config()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `_validate_order_config` in the `DataTransformation` class is identical to the ORIGINAL FUNCTION. Both functions perform the same operations: they create a set of enabled steps from the `steps` dictionary in `preprocessing_config`, compare it with the `order` list, and raise a `ValueError` if there is a mismatch in length or content. The logic, conditions, and error message are the same in both implementations. There is no difference in functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The function `_validate_order_config` does not return any value, but it raises a `ValueError` if the conditions are not met. This implies that the function modifies the state by raising exceptions, which is a form of output.\n- CONDITION 2: The test cases do not check printed or logged contents; they only check for exceptions raised by the function.\n- CONDITION 3: The test cases compare the behavior of `_validate_order_config` and `_validate_order_config_new_implementation` by checking if both raise exceptions under the same conditions. This ensures that both implementations have the same functionality.\n- CONDITION 4: The test cases use try-except blocks to check for exceptions, which is appropriate since `_validate_order_config` does not return values. The test cases do not use inappropriate assertions.\n- CONDITION 5: The test cases cover various scenarios, including matching and mismatched steps and order, no enabled steps, empty order with enabled steps, duplicate steps, extra steps, and different order. These are non-trivial and comprehensive.",
            "answer": "yes"
        },
        "commit_id": "6dd63acbc0fd8760d0122f338da49d86a69a0f91"
    },
    {
        "func_name": "database.user_exists",
        "idx": "1024",
        "repo_name": "mifizz___kitis_schedule_bot",
        "func_path": "db.py",
        "orig_func": "def user_exists(self, user_id):\n    result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n    return bool(len(result.fetchall()))",
        "orig_context": "```python\n## db.py\nimport sqlite3\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\n```\n\n\n",
        "eval_script": "import sqlite3\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n\n\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\ndef test_user_exists():\n    db = database(':memory:')  # Use an in-memory database for testing\n\n    # Test with an empty database\n    assert db.user_exists(1) == db.user_exists_new_implementation(1), \"Test case 1 failed: Empty database\"\n\n    # Add users\n    db.add_user(1, 'Alice')\n    db.add_user(2, 'Bob')\n\n    # Test cases for existing users\n    assert db.user_exists(1) == db.user_exists_new_implementation(1), \"Test case 2 failed: Existing user 1\"\n    assert db.user_exists(2) == db.user_exists_new_implementation(2), \"Test case 3 failed: Existing user 2\"\n\n    # Test case for non-existing user\n    assert db.user_exists(3) == db.user_exists_new_implementation(3), \"Test case 4 failed: Non-existing user\"\n\n    # Test with negative user ID\n    assert db.user_exists(-1) == db.user_exists_new_implementation(-1), \"Test case 5 failed: Negative user ID\"\n\n    # Test with zero user ID\n    assert db.user_exists(0) == db.user_exists_new_implementation(0), \"Test case 6 failed: Zero user ID\"\n\n    # Test with a large user ID\n    large_user_id = 10**6\n    assert db.user_exists(large_user_id) == db.user_exists_new_implementation(large_user_id), \"Test case 7 failed: Large user ID\"\n\n    # Test with non-integer user ID (should handle or reject appropriately)\n    try:\n        db.user_exists('string_id')\n        db.user_exists_new_implementation('string_id')\n        print(\"Test case 8 passed: String user ID handled\")\n    except Exception as e:\n        print(f\"Test case 8 failed: String user ID raised an exception: {e}\")\n\n    try:\n        db.user_exists(1.5)\n        db.user_exists_new_implementation(1.5)\n        print(\"Test case 9 passed: Float user ID handled\")\n    except Exception as e:\n        print(f\"Test case 9 failed: Float user ID raised an exception: {e}\")\n\n    db.close()\n\nif __name__ == \"__main__\":\n    test_user_exists()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical to the ORIGINAL FUNCTION. Both functions execute the same SQL query to check if a user exists in the database by selecting the `id` from the `users` table where the `user_id` matches the provided `user_id`. They both return a boolean value based on whether the result set is non-empty, using `bool(len(result.fetchall()))`. There are no changes in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `user_exists` function returns a boolean value indicating whether a user exists in the database. This satisfies the condition as it has a return value.\n- CONDITION 2: The test cases use assertions to compare the return values of `user_exists` and `user_exists_new_implementation`, which satisfies this condition as they do not rely on printed or logged content.\n- CONDITION 3: The test cases compare the outputs of `user_exists` and `user_exists_new_implementation` directly. If both functions have the same functionality, they should return the same results for the same inputs, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `user_exists` returns a boolean. The test cases also handle exceptions for non-integer user IDs, which is appropriate.\n- CONDITION 5: The test cases cover various scenarios, including an empty database, existing users, non-existing users, negative and zero user IDs, large user IDs, and non-integer user IDs. This provides a comprehensive and non-trivial set of test cases.",
            "answer": "yes"
        },
        "commit_id": "2e6eaf350a54ddeb2f6c04caab845a8f823aa87d"
    },
    {
        "func_name": "database.user_has_group",
        "idx": "1025",
        "repo_name": "mifizz___kitis_schedule_bot",
        "func_path": "db.py",
        "orig_func": "def user_has_group(self, user_id):\n    result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n    return bool(len(result.fetchall()))",
        "orig_context": "```python\n## db.py\nimport sqlite3\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\n```\n\n\n",
        "eval_script": "# Revised Code with Mock Interface\n\nimport sqlite3\nimport os\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\n\ndef test_user_has_group():\n    # Mock database setup\n    mock_db_file = '/home/user/tmp/mock_database.db'\n\n    # Ensure the directory exists\n    os.makedirs('/home/user/tmp', exist_ok=True)\n\n    # Create a new mock database\n    db = database(mock_db_file)\n\n    # Add a user with a group\n    db.add_user(1, 'test_user')\n    db.update_value(1, 'user_group', 'test_group')\n\n    # Add a user without a group\n    db.add_user(2, 'test_user_no_group')\n\n    # Add multiple users with groups\n    db.add_user(3, 'test_user_2')\n    db.update_value(3, 'user_group', 'group_2')\n    db.add_user(4, 'test_user_3')\n    db.update_value(4, 'user_group', 'group_3')\n\n    # Edge case: Add a user with a very large user ID\n    db.add_user(999999999, 'large_id_user')\n    db.update_value(999999999, 'user_group', 'large_group')\n\n    # Edge case: Add a user with an empty group\n    db.add_user(5, 'empty_group_user')\n    db.update_value(5, 'user_group', '')\n\n    # Test cases\n    assert db.user_has_group(1) == db.user_has_group_new_implementation(1)  # User with a group\n    assert db.user_has_group(2) == db.user_has_group_new_implementation(2)  # User without a group\n    assert db.user_has_group(3) == db.user_has_group_new_implementation(3)  # Another user with a group\n    assert db.user_has_group(4) == db.user_has_group_new_implementation(4)  # Yet another user with a group\n    assert db.user_has_group(999999999) == db.user_has_group_new_implementation(999999999)  # User with a large ID\n    assert db.user_has_group(5) == db.user_has_group_new_implementation(5)  # User with an empty group\n    assert db.user_has_group(6) == db.user_has_group_new_implementation(6)  # Non-existent user\n\n    # Clean up\n    db.close()\n    os.remove(mock_db_file)\n\nif __name__ == \"__main__\":\n    test_user_has_group()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `user_has_group` in the provided code is identical to the ORIGINAL FUNCTION. Both functions execute the same SQL query to check if a user has a group by selecting the `user_group` from the `users` table where the `user_id` matches the given `user_id`. They both return a boolean value based on whether the result set from the query has any rows. There are no changes in logic or functionality between the two implementations.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `user_has_group` function returns a boolean value indicating whether a user has a group or not. This satisfies the condition as it has a return value.\n  \n- CONDITION 2: The test cases use assert statements to compare the return values of `user_has_group` and `user_has_group_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the outputs of `user_has_group` and `user_has_group_new_implementation` for various scenarios, ensuring that the new implementation must have the exact same functionality to pass all tests. This condition is satisfied.\n\n- CONDITION 4: The test cases use assert statements to compare the return values of the two implementations, which is reasonable given that `user_has_group` returns a boolean. This condition is satisfied.\n\n- CONDITION 5: The test cases cover a variety of scenarios, including users with and without groups, multiple users with groups, edge cases with large user IDs, empty groups, and non-existent users. This makes the test cases non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "2e6eaf350a54ddeb2f6c04caab845a8f823aa87d"
    },
    {
        "func_name": "database.get_value",
        "idx": "1026",
        "repo_name": "mifizz___kitis_schedule_bot",
        "func_path": "db.py",
        "orig_func": "def get_value(self, user_id: int, column: str):\n    result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n    return result.fetchone()[0]",
        "orig_context": "```python\n## db.py\nimport sqlite3\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\n```\n\n\n",
        "eval_script": "import os\nimport sqlite3\n\n# Ensure the directory exists\nos.makedirs('/home/user/tmp', exist_ok=True)\n\n# Define the path for the mock database\nmock_db_path = '/home/user/tmp/mock_database.db'\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\n\ndef test_get_value():\n    db = database(mock_db_path)\n    db.add_user(1, 'user1')\n    db.update_value(1, 'user_group', 'group1')\n    \n    # Test 1: Check username retrieval\n    assert db.get_value(1, 'username') == db.get_value_new_implementation(1, 'username')\n    \n    # Test 2: Check user_group retrieval\n    assert db.get_value(1, 'user_group') == db.get_value_new_implementation(1, 'user_group')\n    \n    # Test 3: Check last_schedule_request_time retrieval\n    assert db.get_value(1, 'last_schedule_request_time') == db.get_value_new_implementation(1, 'last_schedule_request_time')\n    \n    db.close()\n\nif __name__ == \"__main__\":\n    test_get_value()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining the provided code, the REVISED FUNCTION `get_value` in the `database` class is identical to the ORIGINAL FUNCTION. Both functions execute the same SQL query to retrieve a specified column value for a given `user_id` from the `users` table and return the first element of the fetched result. There are no changes in the logic or functionality between the two versions of the function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `get_value` function returns a value from the database, satisfying the condition that it should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to check the return values of `get_value` and `get_value_new_implementation`, not printed or logged contents. Thus, this condition is satisfied.\n\n3. **CONDITION 3**: The test cases compare the outputs of `get_value` and `get_value_new_implementation` for the same inputs. This ensures that `get_value_new_implementation` must have the exact same functionality as `get_value` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of `get_value` and `get_value_new_implementation`, which is reasonable given that `get_value` returns values. Therefore, this condition is satisfied.\n\n5. **CONDITION 5**: The test cases cover different columns (`username`, `user_group`, `last_schedule_request_time`) for the same user, which is non-trivial as it checks multiple aspects of the function's behavior. Thus, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "2e6eaf350a54ddeb2f6c04caab845a8f823aa87d"
    },
    {
        "func_name": "database.get_all_values",
        "idx": "1027",
        "repo_name": "mifizz___kitis_schedule_bot",
        "func_path": "db.py",
        "orig_func": "def get_all_values(self, column: str):\n    result = self.cursor.execute(f'SELECT {column} FROM users')\n    return result.fetchall()",
        "orig_context": "```python\n## db.py\nimport sqlite3\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\n```\n\n\n",
        "eval_script": "## db.py\nimport sqlite3\n\nclass database:\n\n    def __init__(self, db_file):\n        self.connection = sqlite3.connect(db_file, check_same_thread=False)\n        self.cursor = self.connection.cursor()\n        # create a new database table if not exists\n        self.cursor.execute(\"\"\"\nCREATE TABLE IF NOT EXISTS users (\n                            id INTEGER PRIMARY KEY, \n                            user_id INTEGER UNIQUE NOT NULL, \n                            username TEXT, \n                            user_group TEXT, \n                            join_date DATETIME NOT NULL DEFAULT ((DATETIME('now'))), \n                            last_schedule_request_time REAL DEFAULT 0, \n                            last_group_request_time REAL DEFAULT 0,\n                            last_ping_request_time REAL DEFAULT 0)\n\"\"\")\n        # here goes checks for all new columns that were added with updates\n        self.add_column_if_not_exists('last_ping_request_time', 'REAL', 0)\n    \n    # guess what does this do\n    def add_column_if_not_exists(self, c_name: str, c_type: str, c_default=None):\n        # fetch all columns from database\n        columns = [info[1] for info in self.cursor.execute('PRAGMA table_info(users)').fetchall()]\n        # add new column if there is not\n        if c_name not in columns:\n            # DEFAULT is set\n            if c_default is not None:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type} DEFAULT {c_default}')\n            # DEFAULT is not set\n            else:\n                self.cursor.execute(f'ALTER TABLE users ADD COLUMN {c_name} {c_type}')\n        return self.connection.commit()\n\n    # Check if user exists\n    def user_exists(self, user_id):\n        result = self.cursor.execute('SELECT id FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Check if user has group\n    def user_has_group(self, user_id):\n        result = self.cursor.execute('SELECT user_group FROM users WHERE user_id = ?', (user_id,))\n        return bool(len(result.fetchall()))\n    \n    # Add user to database\n    def add_user(self, user_id, username):\n        self.cursor.execute('INSERT INTO users (`user_id`, `username`, `last_schedule_request_time`, `last_group_request_time`) VALUES (?, ?, ?, ?)', (user_id, username, 0, 0))\n        return self.connection.commit()\n    \n    # set value in given column\n    def update_value(self, user_id: int, column: str, value: None):\n        self.cursor.execute(f'UPDATE users SET {column} = \\'{value}\\' WHERE user_id = {user_id}')\n        return self.connection.commit()\n\n    # get value from given column\n    def get_value(self, user_id: int, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users WHERE user_id = {user_id}')\n        return result.fetchone()[0]\n\n    # get all values of given column\n    def get_all_values(self, column: str):\n        result = self.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # CLose database connection. Just for fun i guess\n    def close(self):\n        self.connection.close()\n\ndef test_get_all_values():\n    db = database(':memory:')\n    db.add_user(1, 'Alice')\n    db.add_user(2, 'Bob')\n    db.update_value(1, 'user_group', 'admin')\n    db.update_value(2, 'user_group', 'user')\n\n    # Assuming database.get_all_values_new_implementation is defined elsewhere\n    def get_all_values_new_implementation(column: str):\n        result = db.cursor.execute(f'SELECT {column} FROM users')\n        return result.fetchall()\n\n    # Test 1: Check 'username' column\n    assert db.get_all_values('username') == get_all_values_new_implementation('username')\n\n    # Test 2: Check 'user_group' column\n    assert db.get_all_values('user_group') == get_all_values_new_implementation('user_group')\n\n    # Test 3: Check 'user_id' column\n    assert db.get_all_values('user_id') == get_all_values_new_implementation('user_id')\n\n    db.close()\n\nif __name__ == \"__main__\":\n    test_get_all_values()",
        "coverage_rate": 1.0,
        "coverage_report": "No Branch, Coverage Rate = 100%.",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function both perform the same task: they execute a SQL query to select all values from a specified column in the 'users' table and return the results using `fetchall()`. The revised function is part of a class and is tested within a test function, but the core functionality remains unchanged. Both functions use the same SQL command and method to fetch the data.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `get_all_values` function returns a list of tuples containing the values from the specified column in the database. Therefore, it satisfies this condition as it has return values.\n\n- CONDITION 2: The test cases use assertions to compare the return values of `get_all_values` and `get_all_values_new_implementation`. They do not check printed or logged contents, satisfying this condition.\n\n- CONDITION 3: The test cases compare the results of `get_all_values` and `get_all_values_new_implementation` for different columns. If both functions return the same results for all these columns, they have the same functionality. Thus, this condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the return values of the two implementations, which is reasonable given that `get_all_values` returns values. Therefore, this condition is satisfied.\n\n- CONDITION 5: The test cases check multiple columns ('username', 'user_group', 'user_id'), which are non-trivial as they cover different aspects of the database table. Thus, this condition is satisfied.",
            "answer": "yes"
        },
        "commit_id": "2e6eaf350a54ddeb2f6c04caab845a8f823aa87d"
    },
    {
        "func_name": "WebApp.add_route_sync",
        "idx": "1028",
        "repo_name": "anonyxbiz___Predator",
        "func_path": "Predator.py",
        "orig_func": "def add_route_sync(app, route_name: str, incoming_data: dict):\n    if not (func := incoming_data.get('func')):\n        raise Error('func is required')\n    if not isinstance(func, (Callable,)):\n        raise Error('func is not Callable')\n    signature = sig(func)\n    params = dict(signature.parameters)\n    methods_param = params.get('methods', None)\n    if methods_param:\n        methods = methods_param.default\n    else:\n        methods = app.default_methods\n    if isinstance(methods, list):\n        methods = {method: True for method in methods}\n    elif isinstance(methods, str):\n        methods = {methods: True}\n    data = {'func': func, 'methods': methods, 'params': incoming_data.get('params', {})}\n    app.routes.__dict__[route_name] = data",
        "orig_context": "```python\n## Predator.py\nfrom typing import Callable, List, Dict, Optional\n\nfrom inspect import signature as sig, Parameter, stack as inspect_stack\n\nfrom os import path\n\nfrom asyncio import CancelledError, to_thread, run, sleep, create_task\n\nfrom aiohttp import web, ClientConnectionError\n\nfrom datetime import datetime as dt, timedelta\n\nimport time\n\np = print\n\nclass MyDict:\n    async def init(app, **kwargs):\n        app.__dict__.update(kwargs)\n        return app\n\n    async def get(app):\n        return app.__dict__\n\nclass Stuff:\n    @classmethod\n    async def headers(app, **kwargs):\n        response_headers = {\n            'Server': 'Predator',\n            'Strict-Transport-Security': 'max-age=63072000; includeSubdomains', \n            'X-Frame-Options': 'SAMEORIGIN',\n            'X-XSS-Protection': '1; mode=block',\n            'Referrer-Policy': 'origin-when-cross-origin'\n        }\n\n        if kwargs:\n            response_headers.update(**kwargs)\n\n        return response_headers\n\nclass Error(Exception):\n    def __init__(app, message=None):\n        super().__init__(message)\n        app.message = str(message)\n\n    def __str__(app) -> str:\n        return app.message\n\nclass Abort(Exception):\n    def __init__(app, message=\"Something went wrong\", **kwargs):\n        super().__init__(message)\n        app.message = str(message)\n        app.kwargs = kwargs\n\n    def __str__(app) -> str:\n        return app.message\n\n    async def text(app, r):\n        await Log.out(app.message)\n        response = web.Response(\n            status = app.kwargs.get(\"status\", 403),\n            text = app.message,\n            headers = app.kwargs.get(\"headers\", {})\n        )\n\n        r.response = response\n        return response\n\nclass Pack:\n    @classmethod\n    async def set(app, **kwargs):\n        app = app()\n        app.__dict__.update(kwargs)\n        return app\n\nclass Log:\n    @classmethod\n    async def out_(app, e):\n        try:\n            e = str(e).strip()\n            fname = inspect_stack()[1].function\n            log = False\n\n            known_exceps = [\n                \"transport\",\n                \"Task\",\n                \"Cannot write\",\n                \"closing transport\",\n                \"Cannot write to closing transport\"\n            ]\n            for a in known_exceps:\n                if a in e:\n                    log = False\n                    break\n                else:\n                    log = True\n\n            if log:\n                e = \"[%s]:: %s ::\" % (\n                    fname,\n                    e\n                )\n                print(\"$: %s\" % (e))\n        except Exception as e:\n            print(e)\n    \n    @classmethod\n    async def out(app, e):\n        try:\n            e = str(e).strip()\n            log = False\n\n            known_exceps = [\n                \"transport\",\n                \"Task\",\n                \"Cannot write\",\n                \"closing transport\",\n                \"Cannot write to closing transport\"\n            ]\n            for a in known_exceps:\n                if a in e:\n                    log = False\n                    break\n                else:\n                    log = True\n\n            if log:\n                print(\"$ (%s): %s\" % (dt.now(), e))\n        except Exception as e:\n            print(e)\n\nclass Request:\n    @classmethod\n    async def gen(app, request):\n        app = app() # Immutable dict\n        app.request = request\n        app.json = request.json\n        app.content = request.content\n        app.response = None\n        app.tail = request.path\n        app.params = request.query\n        app.headers = request.headers\n        app.method = request.method\n        app.ip = request.remote\n        app.route_name = request.path\n\n        return app\n\nclass WebApp:\n    environment = \"development\"\n    @classmethod\n    async def init(app, **kwargs):\n        app = app()\n        app.__dict__.update(kwargs)\n        app.web = web\n        app.response_headers = await Stuff.headers()\n        app.dev = 1\n\n        app.routes = await Pack.set()\n        app.ddos_protection = 0\n        app.throttle_at_ram = 0.20\n        app.secure_host = 0\n        app.requests_count = 0\n        app.default_methods = [\"GET\", \"POST\", \"OPTIONS\", \"PUT\", \"PATCH\", \"HEAD\", \"DELETE\"]\n\n        return app\n    \n    def add_route_sync(app, route_name: str, incoming_data: dict):\n        if not (func := incoming_data.get(\"func\")):\n            raise Error(\"func is required\")\n        \n        if not isinstance(func, (Callable,)):\n            raise Error(\"func is not Callable\")\n\n        signature = sig(func)\n        params = dict(signature.parameters)\n\n        methods_param = params.get(\"methods\", None)\n        if methods_param:\n            methods = methods_param.default\n        else:\n            methods = app.default_methods\n\n        if isinstance(methods, list):\n            methods = {method: True for method in methods}\n        elif isinstance(methods, str):\n            methods = {methods: True}\n\n        data = {\n            \"func\": func,\n            \"methods\": methods,\n            \"params\": incoming_data.get(\"params\", {})\n        }\n    \n        app.routes.__dict__[route_name] = data\n\n    async def add_route(app, route_name: str, incoming_data: dict):\n        app.add_route_sync(route_name, incoming_data)\n\n    def route(app, func: Callable):\n        route_name = str(func.__name__).replace(\"_\", \"/\")\n        app.add_route_sync(route_name, {\"func\": func})\n        \n        return func\n\n    async def serve_route(app, r, route=None):\n        if \"before_middleware\" in app.routes.__dict__:\n            route = app.routes.__dict__[\"before_middleware\"]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n\n        if route is None and r.route_name in app.routes.__dict__:\n            route = app.routes.__dict__[r.route_name]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n        else:\n            if route is None and \"dynamic_routes\" in app.__dict__:\n                for key, val in app.routes.__dict__:\n                    if r.route_name.startswith(key):\n                        route = val\n\n        if route is None and \"handle_all\" in app.routes.__dict__:\n            route = app.routes.__dict__[\"handle_all\"]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n\n        if \"after_middleware\" in app.routes.__dict__:\n            route = app.routes.__dict__[\"after_middleware\"]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n\n        if route is not None:\n            await route.get(\"func\")(r, **route.get(\"params\"))\n        else:\n            raise Abort(\"Not Found\", status=404)\n\n    async def handle_preqs(app, r):\n        if r.method in \"HEAD\":\n            raise pd.Abort(\n                \"OK\",\n                status = 200,\n                headers = {\n                    \"time\": str(int(time.time())),\n                },\n            )\n\n    async def router(app, aiohttp_request, r=None):\n        try:\n            r = await Request.gen(aiohttp_request)\n\n            await Log.out(\"[%s] => %s@ %s\" % (r.ip, r.method, r.tail))\n\n            await app.handle_preqs(r)\n            await app.serve_route(r)\n            if r.response is None:\n                raise Abort()\n\n        except KeyboardInterrupt:\n            return\n        except Abort as e:\n            await e.text(r)\n        except Error as e:\n            try:\n                raise Abort(str(e), status=403)\n            except Abort as e:\n                await e.text(r)\n\n        except (CancelledError, AttributeError, Exception) as e:\n            await Log.out(e)\n        finally:\n            return await app.finalize(r)\n\n    async def finalize(app, r):\n        if r is not None and \"response\" in r.__dict__ and r.response is not None:\n                r.response.headers.update(await Stuff.headers())\n                return r.response\n\n    async def config_ssl(app):\n        def setup_ssl(system = None):\n            try:\n                if not path.exists(f\"{app.app_config.certfile}\") or not path.exists(f\"{app.app_config.keyfile}\"):\n                    from os import system\n                    system(f'openssl req -x509 -newkey rsa:2048 -keyout {app.app_config.keyfile} -out {app.app_config.certfile} -days 365 -nodes -subj \"/CN={app.app_config.host}\"')\n\n                from ssl import create_default_context, Purpose \n                app.app_config.ssl_context = create_default_context(Purpose.CLIENT_AUTH)\n                app.app_config.ssl_context.load_cert_chain(certfile=app.app_config.certfile, keyfile=app.app_config.keyfile)\n            except Exception as e:\n                p(e)\n            finally:\n                del system\n                return\n\n        if (ssl_data := app.app_config.__dict__.get(\"ssl\")) is not None:\n            app.web_protocol = \"https\"\n            app.app_config.certfile, app.app_config.keyfile = ssl_data[\"certfile\"], ssl_data[\"keyfile\"]\n\n            setup_ssl()\n            app.app_config.site = web.TCPSite(app.app_config.runner, app.app_config.host, app.app_config.port, ssl_context=app.app_config.ssl_context)\n        else:\n            app.web_protocol = \"http\"\n            app.app_config.site = web.TCPSite(app.app_config.runner, app.app_config.host, app.app_config.port)\n\n    async def run(app, app_config):\n        if not isinstance(app_config, (MyDict,)):\n            if isinstance(app_config, (dict,)):\n                config = MyDict()\n                config.__dict__.update(app_config)\n                app_config = config\n            else:\n                raise Error(\"app_config must be a valid dict\")\n\n        for a in [\"host\", \"port\"]:\n            if not app_config.__dict__.get(a, None):\n                raise Error(f\"{a} is required.\")\n\n        app.app_config = app_config\n        server = web.Server(app.router)\n        server.client_max_size = None\n\n        app.app_config.runner = web.ServerRunner(server)\n            \n        await app.app_config.runner.setup()\n        await app.config_ssl()\n\n        await app.app_config.site.start()\n\n        await Log.out(\n            \"=== Predator Is Serving %s On %s://%s:%s ===\" % (\n                app.app_config.host,\n                app.web_protocol,\n                app.app_config.host,\n                app.app_config.port\n            )\n        )\n        \n        await sleep(100*3600)\n        \n    def runner(app, app_config):\n        try:\n            run(app.run(app_config))\n        except KeyboardInterrupt:\n            exit(\"[%s]:: KeyboardInterrupted\" % (str(dt.now())))\n        except CancelledError:\n            pass\n        except Exception as e:\n            p(\"Exception caught: %s\" % str(e))\n\n```\n\n\n",
        "eval_script": "# The debugged PYTHON CODE in one piece.\n\nfrom typing import Callable, List, Dict, Optional\nfrom inspect import signature as sig, Parameter, stack as inspect_stack\nfrom os import path\nfrom asyncio import CancelledError, to_thread, run, sleep, create_task\nfrom aiohttp import web, ClientConnectionError\nfrom datetime import datetime as dt, timedelta\nimport time\n\np = print\n\nclass MyDict:\n    async def init(app, **kwargs):\n        app.__dict__.update(kwargs)\n        return app\n\n    async def get(app):\n        return app.__dict__\n\nclass Stuff:\n    @classmethod\n    async def headers(app, **kwargs):\n        response_headers = {\n            'Server': 'Predator',\n            'Strict-Transport-Security': 'max-age=63072000; includeSubdomains', \n            'X-Frame-Options': 'SAMEORIGIN',\n            'X-XSS-Protection': '1; mode=block',\n            'Referrer-Policy': 'origin-when-cross-origin'\n        }\n\n        if kwargs:\n            response_headers.update(**kwargs)\n\n        return response_headers\n\nclass Error(Exception):\n    def __init__(app, message=None):\n        super().__init__(message)\n        app.message = str(message)\n\n    def __str__(app) -> str:\n        return app.message\n\nclass Abort(Exception):\n    def __init__(app, message=\"Something went wrong\", **kwargs):\n        super().__init__(message)\n        app.message = str(message)\n        app.kwargs = kwargs\n\n    def __str__(app) -> str:\n        return app.message\n\n    async def text(app, r):\n        await Log.out(app.message)\n        response = web.Response(\n            status = app.kwargs.get(\"status\", 403),\n            text = app.message,\n            headers = app.kwargs.get(\"headers\", {})\n        )\n\n        r.response = response\n        return response\n\nclass Pack:\n    @classmethod\n    async def set(app, **kwargs):\n        app = app()\n        app.__dict__.update(kwargs)\n        return app\n\nclass Log:\n    @classmethod\n    async def out_(app, e):\n        try:\n            e = str(e).strip()\n            fname = inspect_stack()[1].function\n            log = False\n\n            known_exceps = [\n                \"transport\",\n                \"Task\",\n                \"Cannot write\",\n                \"closing transport\",\n                \"Cannot write to closing transport\"\n            ]\n            for a in known_exceps:\n                if a in e:\n                    log = False\n                    break\n                else:\n                    log = True\n\n            if log:\n                e = \"[%s]:: %s ::\" % (\n                    fname,\n                    e\n                )\n                print(\"$: %s\" % (e))\n        except Exception as e:\n            print(e)\n    \n    @classmethod\n    async def out(app, e):\n        try:\n            e = str(e).strip()\n            log = False\n\n            known_exceps = [\n                \"transport\",\n                \"Task\",\n                \"Cannot write\",\n                \"closing transport\",\n                \"Cannot write to closing transport\"\n            ]\n            for a in known_exceps:\n                if a in e:\n                    log = False\n                    break\n                else:\n                    log = True\n\n            if log:\n                print(\"$ (%s): %s\" % (dt.now(), e))\n        except Exception as e:\n            print(e)\n\nclass Request:\n    @classmethod\n    async def gen(app, request):\n        app = app() # Immutable dict\n        app.request = request\n        app.json = request.json\n        app.content = request.content\n        app.response = None\n        app.tail = request.path\n        app.params = request.query\n        app.headers = request.headers\n        app.method = request.method\n        app.ip = request.remote\n        app.route_name = request.path\n\n        return app\n\nclass WebApp:\n    environment = \"development\"\n    @classmethod\n    async def init(app, **kwargs):\n        app = app()\n        app.__dict__.update(kwargs)\n        app.web = web\n        app.response_headers = await Stuff.headers()\n        app.dev = 1\n\n        app.routes = await Pack.set()\n        app.ddos_protection = 0\n        app.throttle_at_ram = 0.20\n        app.secure_host = 0\n        app.requests_count = 0\n        app.default_methods = [\"GET\", \"POST\", \"OPTIONS\", \"PUT\", \"PATCH\", \"HEAD\", \"DELETE\"]\n\n        return app\n    \n    def add_route_sync(app, route_name: str, incoming_data: dict):\n        if not (func := incoming_data.get(\"func\")):\n            raise Error(\"func is required\")\n        \n        if not isinstance(func, (Callable,)):\n            raise Error(\"func is not Callable\")\n\n        signature = sig(func)\n        params = dict(signature.parameters)\n\n        methods_param = params.get(\"methods\", None)\n        if methods_param:\n            methods = methods_param.default\n        else:\n            methods = app.default_methods\n\n        if isinstance(methods, list):\n            methods = {method: True for method in methods}\n        elif isinstance(methods, str):\n            methods = {methods: True}\n\n        data = {\n            \"func\": func,\n            \"methods\": methods,\n            \"params\": incoming_data.get(\"params\", {})\n        }\n    \n        app.routes.__dict__[route_name] = data\n\n\n    async def add_route(app, route_name: str, incoming_data: dict):\n        app.add_route_sync(route_name, incoming_data)\n\n    def route(app, func: Callable):\n        route_name = str(func.__name__).replace(\"_\", \"/\")\n        app.add_route_sync(route_name, {\"func\": func})\n        \n        return func\n\n    async def serve_route(app, r, route=None):\n        if \"before_middleware\" in app.routes.__dict__:\n            route = app.routes.__dict__[\"before_middleware\"]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n\n        if route is None and r.route_name in app.routes.__dict__:\n            route = app.routes.__dict__[r.route_name]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n        else:\n            if route is None and \"dynamic_routes\" in app.__dict__:\n                for key, val in app.routes.__dict__:\n                    if r.route_name.startswith(key):\n                        route = val\n\n        if route is None and \"handle_all\" in app.routes.__dict__:\n            route = app.routes.__dict__[\"handle_all\"]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n\n        if \"after_middleware\" in app.routes.__dict__:\n            route = app.routes.__dict__[\"after_middleware\"]\n            if not r.method in route.get(\"methods\", {}):\n                raise Abort(\"Illegal method\")\n\n        if route is not None:\n            await route.get(\"func\")(r, **route.get(\"params\"))\n        else:\n            raise Abort(\"Not Found\", status=404)\n\n    async def handle_preqs(app, r):\n        if r.method in \"HEAD\":\n            raise pd.Abort(\n                \"OK\",\n                status = 200,\n                headers = {\n                    \"time\": str(int(time.time())),\n                },\n            )\n\n    async def router(app, aiohttp_request, r=None):\n        try:\n            r = await Request.gen(aiohttp_request)\n\n            await Log.out(\"[%s] => %s@ %s\" % (r.ip, r.method, r.tail))\n\n            await app.handle_preqs(r)\n            await app.serve_route(r)\n            if r.response is None:\n                raise Abort()\n\n        except KeyboardInterrupt:\n            return\n        except Abort as e:\n            await e.text(r)\n        except Error as e:\n            try:\n                raise Abort(str(e), status=403)\n            except Abort as e:\n                await e.text(r)\n\n        except (CancelledError, AttributeError, Exception) as e:\n            await Log.out(e)\n        finally:\n            return await app.finalize(r)\n\n    async def finalize(app, r):\n        if r is not None and \"response\" in r.__dict__ and r.response is not None:\n                r.response.headers.update(await Stuff.headers())\n                return r.response\n\n    async def config_ssl(app):\n        def setup_ssl(system = None):\n            try:\n                if not path.exists(f\"{app.app_config.certfile}\") or not path.exists(f\"{app.app_config.keyfile}\"):\n                    from os import system\n                    system(f'openssl req -x509 -newkey rsa:2048 -keyout {app.app_config.keyfile} -out {app.app_config.certfile} -days 365 -nodes -subj \"/CN={app.app_config.host}\"')\n\n                from ssl import create_default_context, Purpose \n                app.app_config.ssl_context = create_default_context(Purpose.CLIENT_AUTH)\n                app.app_config.ssl_context.load_cert_chain(certfile=app.app_config.certfile, keyfile=app.app_config.keyfile)\n            except Exception as e:\n                p(e)\n            finally:\n                del system\n                return\n\n        if (ssl_data := app.app_config.__dict__.get(\"ssl\")) is not None:\n            app.web_protocol = \"https\"\n            app.app_config.certfile, app.app_config.keyfile = ssl_data[\"certfile\"], ssl_data[\"keyfile\"]\n\n            setup_ssl()\n            app.app_config.site = web.TCPSite(app.app_config.runner, app.app_config.host, app.app_config.port, ssl_context=app.app_config.ssl_context)\n        else:\n            app.web_protocol = \"http\"\n            app.app_config.site = web.TCPSite(app.app_config.runner, app.app_config.host, app.app_config.port)\n\n    async def run(app, app_config):\n        if not isinstance(app_config, (MyDict,)):\n            if isinstance(app_config, (dict,)):\n                config = MyDict()\n                config.__dict__.update(app_config)\n                app_config = config\n            else:\n                raise Error(\"app_config must be a valid dict\")\n\n        for a in [\"host\", \"port\"]:\n            if not app_config.__dict__.get(a, None):\n                raise Error(f\"{a} is required.\")\n\n        app.app_config = app_config\n        server = web.Server(app.router)\n        server.client_max_size = None\n\n        app.app_config.runner = web.ServerRunner(server)\n            \n        await app.app_config.runner.setup()\n        await app.config_ssl()\n\n        await app.app_config.site.start()\n\n        await Log.out(\n            \"=== Predator Is Serving %s On %s://%s:%s ===\" % (\n                app.app_config.host,\n                app.web_protocol,\n                app.app_config.host,\n                app.app_config.port\n            )\n        )\n        \n        await sleep(100*3600)\n        \n    def runner(app, app_config):\n        try:\n            run(app.run(app_config))\n        except KeyboardInterrupt:\n            exit(\"[%s]:: KeyboardInterrupted\" % (str(dt.now())))\n        except CancelledError:\n            pass\n        except Exception as e:\n            p(\"Exception caught: %s\" % str(e))\n\ndef test_add_route_sync():\n    async def test():\n        web_app_instance = await WebApp.init()\n        route_name = \"test_route\"\n        func = lambda x: x\n        incoming_data = {\"func\": func}\n\n        # Test with original implementation\n        web_app_instance.add_route_sync(route_name, incoming_data)\n        original_route_data = web_app_instance.routes.__dict__[route_name]\n\n        # Reset routes for new implementation test\n        web_app_instance.routes = await Pack.set()\n\n        # Test with new implementation\n        web_app_instance.add_route_sync_new_implementation(route_name, incoming_data)\n        new_route_data = web_app_instance.routes.__dict__[route_name]\n\n        # Assertions to compare both implementations\n        assert original_route_data[\"func\"] == new_route_data[\"func\"], \"Function mismatch\"\n        assert original_route_data[\"methods\"] == new_route_data[\"methods\"], \"Methods mismatch\"\n        assert original_route_data[\"params\"] == new_route_data[\"params\"], \"Params mismatch\"\n\n    run(test())\n\nif __name__ == \"__main__\":\n    test_add_route_sync()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: Upon examining the original and revised functions, `add_route_sync`, we can see that both functions perform the same operations. They both:\n\n1. Retrieve the 'func' key from the `incoming_data` dictionary and check if it exists. If not, they raise an `Error`.\n2. Verify that the 'func' is callable, raising an `Error` if it is not.\n3. Use the `signature` function to obtain the function's signature and check for a 'methods' parameter.\n4. If 'methods' is found, they use its default value; otherwise, they use `app.default_methods`.\n5. Convert the 'methods' to a dictionary format where each method is a key with a value of `True`.\n6. Construct a `data` dictionary containing 'func', 'methods', and 'params' from `incoming_data`.\n7. Store this `data` dictionary in `app.routes.__dict__` under the key `route_name`.\n\nThe revised function is placed within a class and uses the same logic and steps as the original function. Therefore, the functionality is identical.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "```\nREASONING: \n- CONDITION 1: The `add_route_sync` function modifies the `app.routes.__dict__` by adding a new route with the given `route_name` and `incoming_data`. This satisfies the condition as it modifies a global variable or input argument.\n- CONDITION 2: The test function `test_add_route_sync` checks the state of `web_app_instance.routes.__dict__` after calling `add_route_sync` and `add_route_sync_new_implementation`. It does not rely on printed or logged content, satisfying this condition.\n- CONDITION 3: The test cases compare the `func`, `methods`, and `params` attributes of the route data added by both implementations. This ensures that `add_route_sync_new_implementation` must have the exact same functionality as `add_route_sync` to pass the tests, satisfying this condition.\n- CONDITION 4: The assertions in the test function are reasonable as they compare the relevant attributes of the route data. The test does not use inappropriate assertions like comparing return values when there are none, satisfying this condition.\n- CONDITION 5: The test cases are non-trivial as they involve setting up a `WebApp` instance, adding routes, and comparing the resulting route data, which involves multiple steps and checks.",
            "answer": "yes"
        },
        "commit_id": "c2ce18dd33e64458b55634f1c99e6e695194130c"
    },
    {
        "func_name": "LinkedList.nodo_de_medio",
        "idx": "1030",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/dataStructures.py",
        "orig_func": "def nodo_de_medio(self, start, end):\n    slow = start\n    fast = start\n    while fast != end and fast.next != end:\n        slow = slow.next\n        fast = fast.next.next\n    return slow",
        "orig_context": "```python\n## LinkedList/dataStructures.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n        else:\n            current = self.head\n\n            while current.next:\n                current = current.next\n            current.next = new_node\n\n    def nodo_de_medio(self, start, end):\n        slow = start\n        fast = start\n\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n\n        return slow\n\n    def busqueda_binaria(self, start, end, value_objetivo):\n        if start == end:\n            return False\n\n        medio = self.nodo_de_medio(start, end)\n\n        if medio.value == value_objetivo:\n            return True\n        elif medio.value < value_objetivo:\n            return self.busqueda_binaria(medio.next, end, value_objetivo)\n        elif medio.value > value_objetivo:\n            return self.busqueda_binaria(start, medio, value_objetivo)\n\n```\n\n\n",
        "eval_script": "## LinkedList/dataStructures.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n        else:\n            current = self.head\n\n            while current.next:\n                current = current.next\n            current.next = new_node\n\n    def nodo_de_medio(self, start, end):\n        slow = start\n        fast = start\n\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n\n        return slow\n\n\n    def busqueda_binaria(self, start, end, value_objetivo):\n        if start == end:\n            return False\n\n        medio = self.nodo_de_medio(start, end)\n\n        if medio.value == value_objetivo:\n            return True\n        elif medio.value < value_objetivo:\n            return self.busqueda_binaria(medio.next, end, value_objetivo)\n        elif medio.value > value_objetivo:\n            return self.busqueda_binaria(start, medio, value_objetivo)\n\ndef test_nodo_de_medio():\n    # Test case 1: Odd number of elements\n    linked_list = LinkedList()\n    linked_list.append(1)\n    linked_list.append(2)\n    linked_list.append(3)\n    linked_list.append(4)\n    linked_list.append(5)\n    start_node = linked_list.head\n    end_node = None\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original.value == middle_node_new.value\n\n    # Test case 2: Even number of elements\n    linked_list = LinkedList()\n    linked_list.append(1)\n    linked_list.append(2)\n    linked_list.append(3)\n    linked_list.append(4)\n    start_node = linked_list.head\n    end_node = None\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original.value == middle_node_new.value\n\n    # Test case 3: Single element\n    linked_list = LinkedList()\n    linked_list.append(1)\n    start_node = linked_list.head\n    end_node = None\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original.value == middle_node_new.value\n\n    # Test case 4: Empty list\n    linked_list = LinkedList()\n    start_node = linked_list.head\n    end_node = None\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original == middle_node_new\n\n    # Test case 5: Two elements\n    linked_list = LinkedList()\n    linked_list.append(1)\n    linked_list.append(2)\n    start_node = linked_list.head\n    end_node = None\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original.value == middle_node_new.value\n\n    # Test case 6: Large list\n    linked_list = LinkedList()\n    for i in range(1, 101):  # 100 elements\n        linked_list.append(i)\n    start_node = linked_list.head\n    end_node = None\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original.value == middle_node_new.value\n\n    # Test case 7: Sublist\n    linked_list = LinkedList()\n    linked_list.append(1)\n    linked_list.append(2)\n    linked_list.append(3)\n    linked_list.append(4)\n    linked_list.append(5)\n    start_node = linked_list.head.next  # Start from second node\n    end_node = linked_list.head.next.next.next  # End at fourth node\n    middle_node_original = linked_list.nodo_de_medio(start_node, end_node)\n    middle_node_new = linked_list.nodo_de_medio_new_implementation(start_node, end_node)\n    assert middle_node_original.value == middle_node_new.value\n\nif __name__ == \"__main__\":\n    test_nodo_de_medio()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `nodo_de_medio` is implemented within a `LinkedList` class, which is a common practice for organizing related functions. The logic of the function remains unchanged from the ORIGINAL FUNCTION. Both functions use the two-pointer technique (slow and fast pointers) to find the middle node of a linked list segment defined by `start` and `end`. The while loop condition and the pointer updates are identical in both versions. The REVISED FUNCTION is tested with various test cases in the `test_nodo_de_medio` function, which confirms that the functionality is preserved. The test cases cover different scenarios, including lists with odd and even numbers of elements, single-element lists, empty lists, and sublists. Since the logic and behavior of the function remain the same, the functionality is equivalent.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `nodo_de_medio` function returns a node, which is a return value, satisfying this condition.\n- CONDITION 2: The test cases use assertions to compare the return values of `nodo_de_medio` and `nodo_de_medio_new_implementation`, and do not rely on printed or logged output, satisfying this condition.\n- CONDITION 3: The test cases are designed to compare the outputs of `nodo_de_medio` and `nodo_de_medio_new_implementation` directly, ensuring that the new implementation must have the exact same functionality to pass all tests, satisfying this condition.\n- CONDITION 4: The test cases use assertions to compare the values of the nodes returned by both implementations, which is reasonable given that `nodo_de_medio` returns a node. The test for an empty list correctly checks for equality of the nodes themselves, satisfying this condition.\n- CONDITION 5: The test cases cover a variety of scenarios, including odd and even numbers of elements, single-element lists, empty lists, two-element lists, large lists, and sublists. This variety ensures that the test cases are non-trivial, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    },
    {
        "func_name": "LinkedList.append",
        "idx": "1031",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/dataStrucutreBinarySearchGraphically.py",
        "orig_func": "def append(self, data):\n    new_node = Node(data)\n    if not self.head:\n        self.head = new_node\n        return\n    last = self.head\n    while last.next:\n        last = last.next\n    last.next = new_node",
        "orig_context": "```python\n## LinkedList/dataStrucutreBinarySearchGraphically.py\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, data):\n        new_node = Node(data)\n        if not self.head:\n            self.head = new_node\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node\n\n    def medio(self, start, end):\n        slow = start\n        fast = start\n\n        # Find the middle element between start and end\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n\n    def binary_search(self, start, end, target):\n        if start == end:\n            print(\"Reached the end of the search range. Element not found.\")\n            return None\n\n        # Find the middle node\n        middle = self.medio(start, end)\n\n        # Visualization of the current step\n        start_data = start.data if start else \"None\"\n        end_data = end.data if end else \"None\"\n        middle_data = middle.data if middle else \"None\"\n        print(\n            f\"\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}\")\n\n        # Check if the middle node contains the target\n        if middle.data == target:\n            print(f\"Element {target} found at node with value {middle.data}.\")\n            return middle\n        elif middle.data < target:\n            print(\n                f\"Target {target} is greater than {middle.data}. Searching in the right half.\")\n            # Recursively search in the right half\n            return self.binary_search(middle.next, end, target)\n        else:\n            print(\n                f\"Target {target} is less than {middle.data}. Searching in the left half.\")\n            # Recursively search in the left half\n            return self.binary_search(start, middle, target)\n\n```\n\n\n",
        "eval_script": "## LinkedList/dataStrucutreBinarySearchGraphically.py\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, data):\n        new_node = Node(data)\n        if not self.head:\n            self.head = new_node\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node\n\n\n    def medio(self, start, end):\n        slow = start\n        fast = start\n\n        # Find the middle element between start and end\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n\n    def binary_search(self, start, end, target):\n        if start == end:\n            print(\"Reached the end of the search range. Element not found.\")\n            return None\n\n        # Find the middle node\n        middle = self.medio(start, end)\n\n        # Visualization of the current step\n        start_data = start.data if start else \"None\"\n        end_data = end.data if end else \"None\"\n        middle_data = middle.data if middle else \"None\"\n        print(\n            f\"\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}\")\n\n        # Check if the middle node contains the target\n        if middle.data == target:\n            print(f\"Element {target} found at node with value {middle.data}.\")\n            return middle\n        elif middle.data < target:\n            print(\n                f\"Target {target} is greater than {middle.data}. Searching in the right half.\")\n            # Recursively search in the right half\n            return self.binary_search(middle.next, end, target)\n        else:\n            print(\n                f\"Target {target} is less than {middle.data}. Searching in the left half.\")\n            # Recursively search in the left half\n            return self.binary_search(start, middle, target)\n\ndef test_append():\n    # Test case 1: Append to an empty list\n    ll1 = LinkedList()\n    ll2 = LinkedList()\n    ll1.append(1)\n    ll2.append_new_implementation(1)\n    assert ll1.head.data == ll2.head.data, \"Test case 1 failed\"\n\n    # Test case 2: Append multiple elements\n    ll1.append(2)\n    ll1.append(3)\n    ll2.append_new_implementation(2)\n    ll2.append_new_implementation(3)\n    current1 = ll1.head\n    current2 = ll2.head\n    while current1 and current2:\n        assert current1.data == current2.data, \"Test case 2 failed\"\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None, \"Test case 2 failed\"\n\n    # Test case 3: Append to a non-empty list\n    ll1.append(4)\n    ll2.append_new_implementation(4)\n    current1 = ll1.head\n    current2 = ll2.head\n    while current1 and current2:\n        assert current1.data == current2.data, \"Test case 3 failed\"\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None, \"Test case 3 failed\"\n\n    # Test case 4: Append duplicate elements\n    ll1.append(5)\n    ll1.append(5)\n    ll2.append_new_implementation(5)\n    ll2.append_new_implementation(5)\n    current1 = ll1.head\n    current2 = ll2.head\n    while current1 and current2:\n        assert current1.data == current2.data, \"Test case 4 failed\"\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None, \"Test case 4 failed\"\n\n    # Test case 5: Append different data types\n    ll1.append(\"string\")\n    ll1.append(3.14)\n    ll2.append_new_implementation(\"string\")\n    ll2.append_new_implementation(3.14)\n    current1 = ll1.head\n    current2 = ll2.head\n    while current1 and current2:\n        assert current1.data == current2.data, \"Test case 5 failed\"\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None, \"Test case 5 failed\"\n\n    # Test case 6: Append a large number of elements\n    for i in range(1000):\n        ll1.append(i)\n        ll2.append_new_implementation(i)\n    current1 = ll1.head\n    current2 = ll2.head\n    while current1 and current2:\n        assert current1.data == current2.data, \"Test case 6 failed\"\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None, \"Test case 6 failed\"\n\n    # Test case 7: Append to a list with a single element\n    ll1 = LinkedList()\n    ll2 = LinkedList()\n    ll1.append(10)\n    ll2.append_new_implementation(10)\n    ll1.append(20)\n    ll2.append_new_implementation(20)\n    current1 = ll1.head\n    current2 = ll2.head\n    while current1 and current2:\n        assert current1.data == current2.data, \"Test case 7 failed\"\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None, \"Test case 7 failed\"\n\nif __name__ == \"__main__\":\n    test_append()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION is implemented within a class `LinkedList`, which includes a `Node` class for creating nodes of the linked list. The `append` method in the `LinkedList` class is identical to the ORIGINAL FUNCTION. It creates a new node with the given data, checks if the list is empty, and if so, sets the head to the new node. Otherwise, it traverses to the end of the list and appends the new node. The functionality of the REVISED FUNCTION is the same as the ORIGINAL FUNCTION, as it performs the same operations in the same sequence.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `append` function modifies the state of the `LinkedList` object by adding a new node to it. This satisfies the condition as it modifies an input argument (the linked list itself).\n\n- CONDITION 2: The test cases check the state of the linked list after appending elements by comparing the data in the nodes of two linked lists (`ll1` and `ll2`). They do not rely on printed or logged content, satisfying this condition.\n\n- CONDITION 3: The test cases compare the state of the linked lists after using both `append` and `append_new_implementation`. If `append_new_implementation` has the same functionality as `append`, the states of the linked lists will be identical, and all assertions will pass. This condition is satisfied.\n\n- CONDITION 4: The test cases use assertions to compare the data in the nodes of the linked lists. They do not compare return values since `append` does not return anything. This is reasonable given the functionality of `append`, satisfying this condition.\n\n- CONDITION 5: The test cases cover various scenarios, including appending to an empty list, appending multiple elements, appending to a non-empty list, appending duplicate elements, appending different data types, appending a large number of elements, and appending to a list with a single element. These are non-trivial test cases that thoroughly test the functionality of the `append` method.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    },
    {
        "func_name": "LinkedList.medio",
        "idx": "1032",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/dataStrucutreBinarySearchGraphically.py",
        "orig_func": "def medio(self, start, end):\n    slow = start\n    fast = start\n    while fast != end and fast.next != end:\n        slow = slow.next\n        fast = fast.next.next\n    return slow",
        "orig_context": "```python\n## LinkedList/dataStrucutreBinarySearchGraphically.py\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, data):\n        new_node = Node(data)\n        if not self.head:\n            self.head = new_node\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node\n\n    def medio(self, start, end):\n        slow = start\n        fast = start\n\n        # Find the middle element between start and end\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n\n    def binary_search(self, start, end, target):\n        if start == end:\n            print(\"Reached the end of the search range. Element not found.\")\n            return None\n\n        # Find the middle node\n        middle = self.medio(start, end)\n\n        # Visualization of the current step\n        start_data = start.data if start else \"None\"\n        end_data = end.data if end else \"None\"\n        middle_data = middle.data if middle else \"None\"\n        print(\n            f\"\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}\")\n\n        # Check if the middle node contains the target\n        if middle.data == target:\n            print(f\"Element {target} found at node with value {middle.data}.\")\n            return middle\n        elif middle.data < target:\n            print(\n                f\"Target {target} is greater than {middle.data}. Searching in the right half.\")\n            # Recursively search in the right half\n            return self.binary_search(middle.next, end, target)\n        else:\n            print(\n                f\"Target {target} is less than {middle.data}. Searching in the left half.\")\n            # Recursively search in the left half\n            return self.binary_search(start, middle, target)\n\n```\n\n\n",
        "eval_script": "## LinkedList/dataStrucutreBinarySearchGraphically.py\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n    def __str__(self):\n        return str(self.data)\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, data):\n        new_node = Node(data)\n        if not self.head:\n            self.head = new_node\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node\n\n    def __str__(self):\n        values = []\n        current = self.head\n        while current:\n            values.append(str(current.data))\n            current = current.next\n        return \" -> \".join(values)\n\n    def medio(self, start, end):\n        slow = start\n        fast = start\n\n        # Find the middle element between start and end\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n\n\n    def binary_search(self, start, end, target):\n        if start == end:\n            print(\"Reached the end of the search range. Element not found.\")\n            return None\n\n        # Find the middle node\n        middle = self.medio(start, end)\n\n        # Visualization of the current step\n        start_data = start.data if start else \"None\"\n        end_data = end.data if end else \"None\"\n        middle_data = middle.data if middle else \"None\"\n        print(\n            f\"\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}\")\n\n        # Check if the middle node contains the target\n        if middle.data == target:\n            print(f\"Element {target} found at node with value {middle.data}.\")\n            return middle\n        elif middle.data < target:\n            print(\n                f\"Target {target} is greater than {middle.data}. Searching in the right half.\")\n            # Recursively search in the right half\n            return self.binary_search(middle.next, end, target)\n        else:\n            print(\n                f\"Target {target} is less than {middle.data}. Searching in the left half.\")\n            # Recursively search in the left half\n            return self.binary_search(start, middle, target)\n\ndef test_medio():\n    ll = LinkedList()\n    # Test case 1: Odd number of elements\n    for i in range(1, 6):  # Linked list: 1 -> 2 -> 3 -> 4 -> 5\n        ll.append(i)\n    start = ll.head\n    end = None\n    assert ll.medio(start, end).data == ll.medio_new_implementation(start, end).data\n\n    # Test case 2: Even number of elements\n    ll = LinkedList()\n    for i in range(1, 5):  # Linked list: 1 -> 2 -> 3 -> 4\n        ll.append(i)\n    start = ll.head\n    end = None\n    assert ll.medio(start, end).data == ll.medio_new_implementation(start, end).data\n\n    # Test case 3: Single element\n    ll = LinkedList()\n    ll.append(1)  # Linked list: 1\n    start = ll.head\n    end = None\n    assert ll.medio(start, end).data == ll.medio_new_implementation(start, end).data\n\nif __name__ == \"__main__\":\n    test_medio()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `medio` and the revised function `medio` in the `LinkedList` class are identical in terms of functionality. Both functions are designed to find the middle node of a linked list segment defined by `start` and `end` nodes. They both use the \"tortoise and hare\" approach, where `slow` moves one step at a time and `fast` moves two steps at a time, stopping when `fast` reaches the `end` or the node before `end`. The revised function is placed within a class context, but its logic remains unchanged from the original function. Therefore, the functionality of the revised function is exactly the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- CONDITION 1: The `medio` function returns a node, which satisfies the condition of having return values.\n- CONDITION 2: The test cases use assertions to check the return values of the `medio` function, not printed or logged contents.\n- CONDITION 3: The test cases compare the data of the nodes returned by `medio` and `medio_new_implementation`, ensuring that both implementations must have the same functionality to pass the tests.\n- CONDITION 4: The test cases use assertions to compare the data of the nodes returned by both implementations, which is reasonable since `medio` returns a node.\n- CONDITION 5: The test cases cover different scenarios: odd number of elements, even number of elements, and a single element, which are non-trivial and sufficient to test the functionality of finding the middle node.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    },
    {
        "func_name": "LinkedList.binary_search",
        "idx": "1033",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/dataStrucutreBinarySearchGraphically.py",
        "orig_func": "def binary_search(self, start, end, target):\n    if start == end:\n        print('Reached the end of the search range. Element not found.')\n        return None\n    middle = self.medio(start, end)\n    start_data = start.data if start else 'None'\n    end_data = end.data if end else 'None'\n    middle_data = middle.data if middle else 'None'\n    print(f'\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}')\n    if middle.data == target:\n        print(f'Element {target} found at node with value {middle.data}.')\n        return middle\n    elif middle.data < target:\n        print(f'Target {target} is greater than {middle.data}. Searching in the right half.')\n        return self.binary_search(middle.next, end, target)\n    else:\n        print(f'Target {target} is less than {middle.data}. Searching in the left half.')\n        return self.binary_search(start, middle, target)",
        "orig_context": "```python\n## LinkedList/dataStrucutreBinarySearchGraphically.py\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, data):\n        new_node = Node(data)\n        if not self.head:\n            self.head = new_node\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node\n\n    def medio(self, start, end):\n        slow = start\n        fast = start\n\n        # Find the middle element between start and end\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n\n    def binary_search(self, start, end, target):\n        if start == end:\n            print(\"Reached the end of the search range. Element not found.\")\n            return None\n\n        # Find the middle node\n        middle = self.medio(start, end)\n\n        # Visualization of the current step\n        start_data = start.data if start else \"None\"\n        end_data = end.data if end else \"None\"\n        middle_data = middle.data if middle else \"None\"\n        print(\n            f\"\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}\")\n\n        # Check if the middle node contains the target\n        if middle.data == target:\n            print(f\"Element {target} found at node with value {middle.data}.\")\n            return middle\n        elif middle.data < target:\n            print(\n                f\"Target {target} is greater than {middle.data}. Searching in the right half.\")\n            # Recursively search in the right half\n            return self.binary_search(middle.next, end, target)\n        else:\n            print(\n                f\"Target {target} is less than {middle.data}. Searching in the left half.\")\n            # Recursively search in the left half\n            return self.binary_search(start, middle, target)\n\n```\n\n\n",
        "eval_script": "## LinkedList/dataStrucutreBinarySearchGraphically.py\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, data):\n        new_node = Node(data)\n        if not self.head:\n            self.head = new_node\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node\n\n    def medio(self, start, end):\n        slow = start\n        fast = start\n\n        # Find the middle element between start and end\n        while fast != end and fast.next != end:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n\n    def binary_search(self, start, end, target):\n        if start == end:\n            print(\"Reached the end of the search range. Element not found.\")\n            return None\n\n        # Find the middle node\n        middle = self.medio(start, end)\n\n        # Visualization of the current step\n        start_data = start.data if start else \"None\"\n        end_data = end.data if end else \"None\"\n        middle_data = middle.data if middle else \"None\"\n        print(\n            f\"\\nSearching in range: Start={start_data}, Middle={middle_data}, End={end_data}\")\n\n        # Check if the middle node contains the target\n        if middle.data == target:\n            print(f\"Element {target} found at node with value {middle.data}.\")\n            return middle\n        elif middle.data < target:\n            print(\n                f\"Target {target} is greater than {middle.data}. Searching in the right half.\")\n            # Recursively search in the right half\n            return self.binary_search(middle.next, end, target)\n        else:\n            print(\n                f\"Target {target} is less than {middle.data}. Searching in the left half.\")\n            # Recursively search in the left half\n            return self.binary_search(start, middle, target)\n\n\ndef test_binary_search():\n    linked_list = LinkedList()\n    for value in [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]:\n        linked_list.append(value)\n\n    # Test case 1: Element exists in the list\n    target_value = 7\n    assert linked_list.binary_search(linked_list.head, None, target_value) == linked_list.binary_search_new_implementation(linked_list.head, None, target_value)\n\n    # Test case 2: Element does not exist in the list\n    target_value = 8\n    assert linked_list.binary_search(linked_list.head, None, target_value) == linked_list.binary_search_new_implementation(linked_list.head, None, target_value)\n\n    # Test case 3: Empty list\n    empty_list = LinkedList()\n    target_value = 1\n    assert empty_list.binary_search(empty_list.head, None, target_value) == empty_list.binary_search_new_implementation(empty_list.head, None, target_value)\n\n    # Test case 4: Single element list where element is the target\n    single_element_list = LinkedList()\n    single_element_list.append(5)\n    target_value = 5\n    assert single_element_list.binary_search(single_element_list.head, None, target_value) == single_element_list.binary_search_new_implementation(single_element_list.head, None, target_value)\n\n    # Test case 5: Single element list where element is not the target\n    target_value = 10\n    assert single_element_list.binary_search(single_element_list.head, None, target_value) == single_element_list.binary_search_new_implementation(single_element_list.head, None, target_value)\n\n    # Test case 6: All elements the same and target is the same\n    same_elements_list = LinkedList()\n    for _ in range(5):\n        same_elements_list.append(4)\n    target_value = 4\n    assert same_elements_list.binary_search(same_elements_list.head, None, target_value) == same_elements_list.binary_search_new_implementation(same_elements_list.head, None, target_value)\n\n    # Test case 7: Target is the first element\n    target_value = 1\n    assert linked_list.binary_search(linked_list.head, None, target_value) == linked_list.binary_search_new_implementation(linked_list.head, None, target_value)\n\n    # Test case 8: Target is the last element\n    target_value = 19\n    assert linked_list.binary_search(linked_list.head, None, target_value) == linked_list.binary_search_new_implementation(linked_list.head, None, target_value)\n\n    # Test case 9: Large list\n    large_list = LinkedList()\n    for value in range(1, 1001, 2):  # Odd numbers from 1 to 999\n        large_list.append(value)\n    target_value = 501\n    assert large_list.binary_search(large_list.head, None, target_value) == large_list.binary_search_new_implementation(large_list.head, None, target_value)\n\n    # Test case 10: Negative numbers\n    negative_list = LinkedList()\n    for value in [-10, -5, 0, 5, 10]:\n        negative_list.append(value)\n    target_value = -5\n    assert negative_list.binary_search(negative_list.head, None, target_value) == negative_list.binary_search_new_implementation(negative_list.head, None, target_value)\n\nif __name__ == \"__main__\":\n    test_binary_search()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION in the provided code is identical in logic and functionality to the ORIGINAL FUNCTION. Both functions perform a binary search on a linked list by recursively finding the middle node and comparing it to the target value. They print the same debug statements and handle the search range and conditions in the same manner. The test cases in the code are designed to verify the correctness of the binary search implementation, but they reference a non-existent method `binary_search_new_implementation`, which is likely intended to be the revised function. Despite this oversight in the test cases, the functionality of the binary search itself remains unchanged between the original and revised versions.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n1. **CONDITION 1**: The `binary_search` function is expected to return a node if the target is found, or `None` if it is not found. This satisfies the condition that the function should have return values or modify global variables or input arguments.\n\n2. **CONDITION 2**: The test cases use assertions to compare the return values of `binary_search` and `binary_search_new_implementation`. They do not rely on printed or logged content, satisfying this condition.\n\n3. **CONDITION 3**: The test cases compare the outputs of `binary_search` and `binary_search_new_implementation` directly. This ensures that `binary_search_new_implementation` must have exactly the same functionality as `binary_search` to pass all tests, satisfying this condition.\n\n4. **CONDITION 4**: The test cases use assertions to compare the return values of the two implementations. This is appropriate given that `binary_search` returns a node or `None`. Therefore, the test cases and assert statements are reasonable.\n\n5. **CONDITION 5**: The test cases cover a variety of scenarios, including:\n   - Element exists in the list.\n   - Element does not exist in the list.\n   - Empty list.\n   - Single element list where the element is the target.\n   - Single element list where the element is not the target.\n   - All elements the same and target is the same.\n   - Target is the first element.\n   - Target is the last element.\n   - Large list.\n   - List with negative numbers.\n   \n   These scenarios are non-trivial and cover a wide range of possible edge cases, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    },
    {
        "func_name": "LinkedList.append",
        "idx": "1034",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/linked_list_ayudantia1.py",
        "orig_func": "def append(self, value):\n    new_node = Node(value)\n    if self.head == None:\n        self.head = new_node\n        return\n    current = self.head\n    while current.next != None:\n        current = current.next\n    current.next = new_node",
        "orig_context": "```python\n## LinkedList/linked_list_ayudantia1.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n    \n        current = self.head\n        while current.next != None:\n            current = current.next\n        \n        current.next = new_node\n\n\n    def append_recursive(self, nodo, value ):\n        if nodo == None:\n            return Node(value)\n        \n        nodo.next = self.append_recursive(nodo.next, value)\n        return nodo\n\n    def print_list(self):\n        if self.head is None:\n            print(\"La lista est\u00e1 vac\u00eda\")\n            return\n\n        current = self.head\n        while current is not None:\n            print(current.value, end=\" -> \")\n            current = current.next\n        print(\"None\")\n\n\n    def print_list_recurive(self, nodo):\n        if nodo == None:\n            print(\"None:\")\n            return\n        print(nodo.value, end=\" -> \")\n        self.print_list_recurive(nodo.next)\n        \n        \n    def buscar_recursivamente(self, nodo, value):\n        if nodo == None:\n            return False\n        if nodo.value == value: #4 == 7\n            return True\n        \n        return self.buscar_recursivamente(nodo.next, value)\n\n\n    def delete_value(self, value):\n        if self.head == None:\n            print(\"La lista no contiene ning\u00f9n nodo\")\n            return\n        \n        if self.head.value == value:\n            self.head = self.head.next\n\n        current = self.head\n        while current.next is not None:\n            if current.next.value == value:\n                current.next = current.next.next\n                return\n            current = current.next\n\n\n    def insertar_valor(self, valor, index):\n        new_node = Node(valor)\n\n        if index == 0:\n            new_node.next = self.head\n            self.head = new_node\n            return\n\n        contador = 0\n        current = self.head\n        while current.next != None and contador < index:\n            current = current.next\n            contador += 1\n\n        if current.next == None:\n            print(\"No es posible ingresa un nuevo nodo con un nuevo valor, puesto que la lista es m\u00e0s peque\u00f1a\")\n            return\n\n\n        new_node.next = current.next\n        current.next = new_node\n        print(\"nodo con su valor agregado correctamente a la lista\")\n        return\n\n    def largo(self):\n        if self.head == None:\n            print(\"La lista esta vacia, no hay nodos\")\n            return\n        \n        contador = 0\n        current = self.head\n        while current.next != None:\n            current = current.next\n            contador += 1\n        print(f\"El largo de la lista es: {contador}\")\n        return\n\n\n    def largo_recursivo(self, nodo):\n        if nodo == None:\n            return 0\n        \n        return 1 + self.largo_recursivo(nodo.next) # 1 + (1 + (1 + (1 +(1 + (1 + (0))))))\n\n\n    def encontrar_mayor(self):\n        if self.head == None:\n            return None\n\n        mayor = self.head.value\n\n        current = self.head.next\n\n        while current != None:\n            if current.value > mayor:\n                mayor = current.value\n\n            current = current.next\n\n        return mayor\n   \n    def encontrar_mayor_recurisvo(self, nodo):\n        if nodo == None:\n            return float('-inf')\n\n        mayor = self.encontrar_mayor_recurisvo(nodo.next)\n\n        return max(nodo.value, mayor)\n\n```\n\n\n",
        "eval_script": "## LinkedList/linked_list_ayudantia1.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n    \n        current = self.head\n        while current.next != None:\n            current = current.next\n        \n        current.next = new_node\n\n\n    def append_recursive(self, nodo, value ):\n        if nodo == None:\n            return Node(value)\n        \n        nodo.next = self.append_recursive(nodo.next, value)\n        return nodo\n\n    def print_list(self):\n        if self.head is None:\n            print(\"La lista est\u00e1 vac\u00eda\")\n            return\n\n        current = self.head\n        while current is not None:\n            print(current.value, end=\" -> \")\n            current = current.next\n        print(\"None\")\n\n    def print_list_recurive(self, nodo):\n        if nodo == None:\n            print(\"None:\")\n            return\n        print(nodo.value, end=\" -> \")\n        self.print_list_recurive(nodo.next)\n        \n    def buscar_recursivamente(self, nodo, value):\n        if nodo == None:\n            return False\n        if nodo.value == value: #4 == 7\n            return True\n        \n        return self.buscar_recursivamente(nodo.next, value)\n\n    def delete_value(self, value):\n        if self.head == None:\n            print(\"La lista no contiene ning\u00f9n nodo\")\n            return\n        \n        if self.head.value == value:\n            self.head = self.head.next\n\n        current = self.head\n        while current.next is not None:\n            if current.next.value == value:\n                current.next = current.next.next\n                return\n            current = current.next\n\n    def insertar_valor(self, valor, index):\n        new_node = Node(valor)\n\n        if index == 0:\n            new_node.next = self.head\n            self.head = new_node\n            return\n\n        contador = 0\n        current = self.head\n        while current.next != None and contador < index:\n            current = current.next\n            contador += 1\n\n        if current.next == None:\n            print(\"No es posible ingresa un nuevo nodo con un nuevo valor, puesto que la lista es m\u00e0s peque\u00f1a\")\n            return\n\n        new_node.next = current.next\n        current.next = new_node\n        print(\"nodo con su valor agregado correctamente a la lista\")\n        return\n\n    def largo(self):\n        if self.head == None:\n            print(\"La lista esta vacia, no hay nodos\")\n            return\n        \n        contador = 0\n        current = self.head\n        while current.next != None:\n            current = current.next\n            contador += 1\n        print(f\"El largo de la lista es: {contador}\")\n        return\n\n    def largo_recursivo(self, nodo):\n        if nodo == None:\n            return 0\n        \n        return 1 + self.largo_recursivo(nodo.next) # 1 + (1 + (1 + (1 +(1 + (1 + (0))))))\n\n    def encontrar_mayor(self):\n        if self.head == None:\n            return None\n\n        mayor = self.head.value\n\n        current = self.head.next\n\n        while current != None:\n            if current.value > mayor:\n                mayor = current.value\n\n            current = current.next\n\n        return mayor\n   \n    def encontrar_mayor_recurisvo(self, nodo):\n        if nodo == None:\n            return float('-inf')\n\n        mayor = self.encontrar_mayor_recurisvo(nodo.next)\n\n        return max(nodo.value, mayor)\n\ndef test_append():\n    # Test case 1: Append to an empty list\n    list1 = LinkedList()\n    list2 = LinkedList()\n    list1.append(1)\n    list2.append_new_implementation(1)\n    assert list1.head.value == list2.head.value\n\n    # Test case 2: Append multiple elements\n    list1.append(2)\n    list1.append(3)\n    list2.append_new_implementation(2)\n    list2.append_new_implementation(3)\n    current1 = list1.head\n    current2 = list2.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\n    # Test case 3: Append to a list with existing elements\n    list1.append(4)\n    list2.append_new_implementation(4)\n    current1 = list1.head\n    current2 = list2.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\n    # Test case 4: Append None\n    list1.append(None)\n    list2.append_new_implementation(None)\n    current1 = list1.head\n    current2 = list2.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\n    # Test case 5: Append duplicate values\n    list1.append(4)\n    list2.append_new_implementation(4)\n    current1 = list1.head\n    current2 = list2.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\n    # Test case 6: Append different data types\n    list1.append(\"string\")\n    list2.append_new_implementation(\"string\")\n    list1.append(5.5)\n    list2.append_new_implementation(5.5)\n    current1 = list1.head\n    current2 = list2.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\n    # Test case 7: Append to a list with a single element\n    list3 = LinkedList()\n    list4 = LinkedList()\n    list3.append(10)\n    list4.append_new_implementation(10)\n    list3.append(20)\n    list4.append_new_implementation(20)\n    current1 = list3.head\n    current2 = list4.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\n    # Test case 8: Append large numbers\n    large_number = 10**18\n    list1.append(large_number)\n    list2.append_new_implementation(large_number)\n    current1 = list1.head\n    current2 = list2.head\n    while current1 is not None and current2 is not None:\n        assert current1.value == current2.value\n        current1 = current1.next\n        current2 = current2.next\n    assert current1 is None and current2 is None\n\nif __name__ == \"__main__\":\n    test_append()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function and the revised function both perform the same task of appending a new node with a given value to the end of a linked list. The logic in both functions is identical: they create a new node, check if the list is empty, and if not, traverse to the end of the list to append the new node. The revised function is part of a class `LinkedList`, which is consistent with the context of the original function. The additional methods and test cases in the revised code do not affect the functionality of the `append` method. Therefore, the functionality of the revised function is the same as the original function.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n\n- CONDITION 1: The `append` function modifies the state of the `LinkedList` object by adding a new node to the list. It does not return a value but modifies the input argument (the list itself), satisfying this condition.\n\n- CONDITION 2: The test cases check the state of the `LinkedList` objects by comparing the values of nodes in the list. They do not rely on printed or logged outputs, satisfying this condition.\n\n- CONDITION 3: The test cases compare the state of two `LinkedList` objects, one using `append` and the other using `append_new_implementation`. This ensures that `append_new_implementation` must have the exact same functionality as `append` to pass all tests, satisfying this condition.\n\n- CONDITION 4: The test cases use assertions to compare the values of nodes in the lists, which is reasonable given that `append` modifies the list's state. There are no assertions comparing return values, as `append` does not return anything, satisfying this condition.\n\n- CONDITION 5: The test cases cover various scenarios, including appending to an empty list, appending multiple elements, appending `None`, appending duplicate values, appending different data types, appending to a list with a single element, and appending large numbers. These scenarios are non-trivial and comprehensive, satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    },
    {
        "func_name": "LinkedList.buscar_recursivamente",
        "idx": "1037",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/linked_list_ayudantia1.py",
        "orig_func": "def buscar_recursivamente(self, nodo, value):\n    if nodo == None:\n        return False\n    if nodo.value == value:\n        return True\n    return self.buscar_recursivamente(nodo.next, value)",
        "orig_context": "```python\n## LinkedList/linked_list_ayudantia1.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n    \n        current = self.head\n        while current.next != None:\n            current = current.next\n        \n        current.next = new_node\n\n\n    def append_recursive(self, nodo, value ):\n        if nodo == None:\n            return Node(value)\n        \n        nodo.next = self.append_recursive(nodo.next, value)\n        return nodo\n\n    def print_list(self):\n        if self.head is None:\n            print(\"La lista est\u00e1 vac\u00eda\")\n            return\n\n        current = self.head\n        while current is not None:\n            print(current.value, end=\" -> \")\n            current = current.next\n        print(\"None\")\n\n\n    def print_list_recurive(self, nodo):\n        if nodo == None:\n            print(\"None:\")\n            return\n        print(nodo.value, end=\" -> \")\n        self.print_list_recurive(nodo.next)\n        \n        \n    def buscar_recursivamente(self, nodo, value):\n        if nodo == None:\n            return False\n        if nodo.value == value: #4 == 7\n            return True\n        \n        return self.buscar_recursivamente(nodo.next, value)\n\n\n    def delete_value(self, value):\n        if self.head == None:\n            print(\"La lista no contiene ning\u00f9n nodo\")\n            return\n        \n        if self.head.value == value:\n            self.head = self.head.next\n\n        current = self.head\n        while current.next is not None:\n            if current.next.value == value:\n                current.next = current.next.next\n                return\n            current = current.next\n\n\n    def insertar_valor(self, valor, index):\n        new_node = Node(valor)\n\n        if index == 0:\n            new_node.next = self.head\n            self.head = new_node\n            return\n\n        contador = 0\n        current = self.head\n        while current.next != None and contador < index:\n            current = current.next\n            contador += 1\n\n        if current.next == None:\n            print(\"No es posible ingresa un nuevo nodo con un nuevo valor, puesto que la lista es m\u00e0s peque\u00f1a\")\n            return\n\n\n        new_node.next = current.next\n        current.next = new_node\n        print(\"nodo con su valor agregado correctamente a la lista\")\n        return\n\n    def largo(self):\n        if self.head == None:\n            print(\"La lista esta vacia, no hay nodos\")\n            return\n        \n        contador = 0\n        current = self.head\n        while current.next != None:\n            current = current.next\n            contador += 1\n        print(f\"El largo de la lista es: {contador}\")\n        return\n\n\n    def largo_recursivo(self, nodo):\n        if nodo == None:\n            return 0\n        \n        return 1 + self.largo_recursivo(nodo.next) # 1 + (1 + (1 + (1 +(1 + (1 + (0))))))\n\n\n    def encontrar_mayor(self):\n        if self.head == None:\n            return None\n\n        mayor = self.head.value\n\n        current = self.head.next\n\n        while current != None:\n            if current.value > mayor:\n                mayor = current.value\n\n            current = current.next\n\n        return mayor\n   \n    def encontrar_mayor_recurisvo(self, nodo):\n        if nodo == None:\n            return float('-inf')\n\n        mayor = self.encontrar_mayor_recurisvo(nodo.next)\n\n        return max(nodo.value, mayor)\n\n```\n\n\n",
        "eval_script": "## LinkedList/linked_list_ayudantia1.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n    \n        current = self.head\n        while current.next != None:\n            current = current.next\n        \n        current.next = new_node\n\n\n    def append_recursive(self, nodo, value ):\n        if nodo == None:\n            return Node(value)\n        \n        nodo.next = self.append_recursive(nodo.next, value)\n        return nodo\n\n    def print_list(self):\n        if self.head is None:\n            print(\"La lista est\u00e1 vac\u00eda\")\n            return\n\n        current = self.head\n        while current is not None:\n            print(current.value, end=\" -> \")\n            current = current.next\n        print(\"None\")\n\n\n    def print_list_recurive(self, nodo):\n        if nodo == None:\n            print(\"None:\")\n            return\n        print(nodo.value, end=\" -> \")\n        self.print_list_recurive(nodo.next)\n        \n    def buscar_recursivamente(self, nodo, value):\n        if nodo == None:\n            return False\n        if nodo.value == value: #4 == 7\n            return True\n        \n        return self.buscar_recursivamente(nodo.next, value)\n\n    # New wrapper method to call buscar_recursivamente directly\n    def buscar(self, value):\n        return self.buscar_recursivamente(self.head, value)\n\n    def delete_value(self, value):\n        if self.head == None:\n            print(\"La lista no contiene ning\u00f9n nodo\")\n            return\n        \n        if self.head.value == value:\n            self.head = self.head.next\n\n        current = self.head\n        while current.next is not None:\n            if current.next.value == value:\n                current.next = current.next.next\n                return\n            current = current.next\n\n\n    def insertar_valor(self, valor, index):\n        new_node = Node(valor)\n\n        if index == 0:\n            new_node.next = self.head\n            self.head = new_node\n            return\n\n        contador = 0\n        current = self.head\n        while current.next != None and contador < index:\n            current = current.next\n            contador += 1\n\n        if current.next == None:\n            print(\"No es posible ingresa un nuevo nodo con un nuevo valor, puesto que la lista es m\u00e0s peque\u00f1a\")\n            return\n\n\n        new_node.next = current.next\n        current.next = new_node\n        print(\"nodo con su valor agregado correctamente a la lista\")\n        return\n\n    def largo(self):\n        if self.head == None:\n            print(\"La lista esta vacia, no hay nodos\")\n            return\n        \n        contador = 0\n        current = self.head\n        while current.next != None:\n            current = current.next\n            contador += 1\n        print(f\"El largo de la lista es: {contador}\")\n        return\n\n\n    def largo_recursivo(self, nodo):\n        if nodo == None:\n            return 0\n        \n        return 1 + self.largo_recursivo(nodo.next) # 1 + (1 + (1 + (1 +(1 + (1 + (0))))))\n\n\n    def encontrar_mayor(self):\n        if self.head == None:\n            return None\n\n        mayor = self.head.value\n\n        current = self.head.next\n\n        while current != None:\n            if current.value > mayor:\n                mayor = current.value\n\n            current = current.next\n\n        return mayor\n   \n    def encontrar_mayor_recurisvo(self, nodo):\n        if nodo == None:\n            return float('-inf')\n\n        mayor = self.encontrar_mayor_recurisvo(nodo.next)\n\n        return max(nodo.value, mayor)\n\n\ndef test_buscar_recursivamente():\n    ll = LinkedList()\n    ll.append(1)\n    ll.append(2)\n    ll.append(3)\n\n    # Test case 1: Value exists in the list\n    assert ll.buscar_recursivamente(ll.head, 2) == ll.buscar_recursivamente_new_implementation(ll.head, 2)\n\n    # Test case 2: Value does not exist in the list\n    assert ll.buscar_recursivamente(ll.head, 4) == ll.buscar_recursivamente_new_implementation(ll.head, 4)\n\n    # Test case 3: Empty list\n    empty_ll = LinkedList()\n    assert empty_ll.buscar_recursivamente(empty_ll.head, 1) == empty_ll.buscar_recursivamente_new_implementation(empty_ll.head, 1)\n\n    # Test case 4: Single element list where the element is the search value\n    single_ll = LinkedList()\n    single_ll.append(5)\n    assert single_ll.buscar_recursivamente(single_ll.head, 5) == single_ll.buscar_recursivamente_new_implementation(single_ll.head, 5)\n\n    # Test case 5: Single element list where the element is not the search value\n    assert single_ll.buscar_recursivamente(single_ll.head, 6) == single_ll.buscar_recursivamente_new_implementation(single_ll.head, 6)\n\n    # Test case 6: Multiple occurrences of the search value\n    ll_multiple = LinkedList()\n    ll_multiple.append(7)\n    ll_multiple.append(8)\n    ll_multiple.append(7)\n    assert ll_multiple.buscar_recursivamente(ll_multiple.head, 7) == ll_multiple.buscar_recursivamente_new_implementation(ll_multiple.head, 7)\n\n    # Test case 7: Search value is at the head of the list\n    assert ll.buscar_recursivamente(ll.head, 1) == ll.buscar_recursivamente_new_implementation(ll.head, 1)\n\n    # Test case 8: Search value is at the tail of the list\n    assert ll.buscar_recursivamente(ll.head, 3) == ll.buscar_recursivamente_new_implementation(ll.head, 3)\n\n    # Test case 9: Non-integer values (e.g., strings)\n    ll_strings = LinkedList()\n    ll_strings.append(\"a\")\n    ll_strings.append(\"b\")\n    ll_strings.append(\"c\")\n    assert ll_strings.buscar_recursivamente(ll_strings.head, \"b\") == ll_strings.buscar_recursivamente_new_implementation(ll_strings.head, \"b\")\n    assert ll_strings.buscar_recursivamente(ll_strings.head, \"d\") == ll_strings.buscar_recursivamente_new_implementation(ll_strings.head, \"d\")\n\n    # Test case 10: Non-integer values (e.g., floats)\n    ll_floats = LinkedList()\n    ll_floats.append(1.1)\n    ll_floats.append(2.2)\n    ll_floats.append(3.3)\n    assert ll_floats.buscar_recursivamente(ll_floats.head, 2.2) == ll_floats.buscar_recursivamente_new_implementation(ll_floats.head, 2.2)\n    assert ll_floats.buscar_recursivamente(ll_floats.head, 4.4) == ll_floats.buscar_recursivamente_new_implementation(ll_floats.head, 4.4)\n\nif __name__ == \"__main__\":\n    test_buscar_recursivamente()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The original function `buscar_recursivamente` is a recursive function that searches for a value in a linked list starting from a given node. It returns `True` if the value is found and `False` if it reaches the end of the list without finding the value. The revised function in the provided code is identical in terms of logic and functionality. Both functions check if the current node is `None` (base case), and if the node's value matches the target value. If neither condition is met, they recursively call themselves with the next node. The revised function is encapsulated within a class, but this does not change its functionality. The wrapper method `buscar` is an addition that allows the function to be called starting from the head of the list, but it does not alter the core functionality of `buscar_recursivamente`.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `buscar_recursivamente` function returns a boolean value indicating whether a value exists in the linked list, satisfying this condition.\n- [CONDITION 2] The test cases use assertions to check return values, not printed or logged contents, satisfying this condition.\n- [CONDITION 3] The test cases compare the outputs of `buscar_recursivamente` and `buscar_recursivamente_new_implementation` for various scenarios, ensuring that both implementations must have the same functionality to pass, satisfying this condition.\n- [CONDITION 4] The test cases use assertions to compare the return values of both implementations, which is reasonable given that the function returns a boolean, satisfying this condition.\n- [CONDITION 5] The test cases cover a variety of scenarios, including lists with multiple elements, empty lists, single-element lists, and lists with non-integer values, making them non-trivial and satisfying this condition.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    },
    {
        "func_name": "LinkedList.encontrar_mayor",
        "idx": "1041",
        "repo_name": "sergiocayuqueo___DataStrutureAndAlgorithms",
        "func_path": "LinkedList/linked_list_ayudantia1.py",
        "orig_func": "def encontrar_mayor(self):\n    if self.head == None:\n        return None\n    mayor = self.head.value\n    current = self.head.next\n    while current != None:\n        if current.value > mayor:\n            mayor = current.value\n        current = current.next\n    return mayor",
        "orig_context": "```python\n## LinkedList/linked_list_ayudantia1.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n    \n        current = self.head\n        while current.next != None:\n            current = current.next\n        \n        current.next = new_node\n\n\n    def append_recursive(self, nodo, value ):\n        if nodo == None:\n            return Node(value)\n        \n        nodo.next = self.append_recursive(nodo.next, value)\n        return nodo\n\n    def print_list(self):\n        if self.head is None:\n            print(\"La lista est\u00e1 vac\u00eda\")\n            return\n\n        current = self.head\n        while current is not None:\n            print(current.value, end=\" -> \")\n            current = current.next\n        print(\"None\")\n\n\n    def print_list_recurive(self, nodo):\n        if nodo == None:\n            print(\"None:\")\n            return\n        print(nodo.value, end=\" -> \")\n        self.print_list_recurive(nodo.next)\n        \n        \n    def buscar_recursivamente(self, nodo, value):\n        if nodo == None:\n            return False\n        if nodo.value == value: #4 == 7\n            return True\n        \n        return self.buscar_recursivamente(nodo.next, value)\n\n\n    def delete_value(self, value):\n        if self.head == None:\n            print(\"La lista no contiene ning\u00f9n nodo\")\n            return\n        \n        if self.head.value == value:\n            self.head = self.head.next\n\n        current = self.head\n        while current.next is not None:\n            if current.next.value == value:\n                current.next = current.next.next\n                return\n            current = current.next\n\n\n    def insertar_valor(self, valor, index):\n        new_node = Node(valor)\n\n        if index == 0:\n            new_node.next = self.head\n            self.head = new_node\n            return\n\n        contador = 0\n        current = self.head\n        while current.next != None and contador < index:\n            current = current.next\n            contador += 1\n\n        if current.next == None:\n            print(\"No es posible ingresa un nuevo nodo con un nuevo valor, puesto que la lista es m\u00e0s peque\u00f1a\")\n            return\n\n\n        new_node.next = current.next\n        current.next = new_node\n        print(\"nodo con su valor agregado correctamente a la lista\")\n        return\n\n    def largo(self):\n        if self.head == None:\n            print(\"La lista esta vacia, no hay nodos\")\n            return\n        \n        contador = 0\n        current = self.head\n        while current.next != None:\n            current = current.next\n            contador += 1\n        print(f\"El largo de la lista es: {contador}\")\n        return\n\n\n    def largo_recursivo(self, nodo):\n        if nodo == None:\n            return 0\n        \n        return 1 + self.largo_recursivo(nodo.next) # 1 + (1 + (1 + (1 +(1 + (1 + (0))))))\n\n\n    def encontrar_mayor(self):\n        if self.head == None:\n            return None\n\n        mayor = self.head.value\n\n        current = self.head.next\n\n        while current != None:\n            if current.value > mayor:\n                mayor = current.value\n\n            current = current.next\n\n        return mayor\n   \n    def encontrar_mayor_recurisvo(self, nodo):\n        if nodo == None:\n            return float('-inf')\n\n        mayor = self.encontrar_mayor_recurisvo(nodo.next)\n\n        return max(nodo.value, mayor)\n\n```\n\n\n",
        "eval_script": "## LinkedList/linked_list_ayudantia1.py\nclass Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append(self, value):\n        new_node = Node(value)\n\n        if self.head == None:\n            self.head = new_node\n            return\n    \n        current = self.head\n        while current.next != None:\n            current = current.next\n        \n        current.next = new_node\n\n\n    def append_recursive(self, nodo, value ):\n        if nodo == None:\n            return Node(value)\n        \n        nodo.next = self.append_recursive(nodo.next, value)\n        return nodo\n\n    def print_list(self):\n        if self.head is None:\n            print(\"La lista est\u00e1 vac\u00eda\")\n            return\n\n        current = self.head\n        while current is not None:\n            print(current.value, end=\" -> \")\n            current = current.next\n        print(\"None\")\n\n\n    def print_list_recurive(self, nodo):\n        if nodo == None:\n            print(\"None:\")\n            return\n        print(nodo.value, end=\" -> \")\n        self.print_list_recurive(nodo.next)\n        \n        \n    def buscar_recursivamente(self, nodo, value):\n        if nodo == None:\n            return False\n        if nodo.value == value: #4 == 7\n            return True\n        \n        return self.buscar_recursivamente(nodo.next, value)\n\n\n    def delete_value(self, value):\n        if self.head == None:\n            print(\"La lista no contiene ning\u00f9n nodo\")\n            return\n        \n        if self.head.value == value:\n            self.head = self.head.next\n\n        current = self.head\n        while current.next is not None:\n            if current.next.value == value:\n                current.next = current.next.next\n                return\n            current = current.next\n\n\n    def insertar_valor(self, valor, index):\n        new_node = Node(valor)\n\n        if index == 0:\n            new_node.next = self.head\n            self.head = new_node\n            return\n\n        contador = 0\n        current = self.head\n        while current.next != None and contador < index:\n            current = current.next\n            contador += 1\n\n        if current.next == None:\n            print(\"No es posible ingresa un nuevo nodo con un nuevo valor, puesto que la lista es m\u00e0s peque\u00f1a\")\n            return\n\n\n        new_node.next = current.next\n        current.next = new_node\n        print(\"nodo con su valor agregado correctamente a la lista\")\n        return\n\n    def largo(self):\n        if self.head == None:\n            print(\"La lista esta vacia, no hay nodos\")\n            return\n        \n        contador = 0\n        current = self.head\n        while current.next != None:\n            current = current.next\n            contador += 1\n        print(f\"El largo de la lista es: {contador}\")\n        return\n\n\n    def largo_recursivo(self, nodo):\n        if nodo == None:\n            return 0\n        \n        return 1 + self.largo_recursivo(nodo.next) # 1 + (1 + (1 + (1 +(1 + (1 + (0))))))\n\n\n    def encontrar_mayor(self):\n        if self.head == None:\n            return None\n\n        mayor = self.head.value\n\n        current = self.head.next\n\n        while current != None:\n            if current.value > mayor:\n                mayor = current.value\n\n            current = current.next\n\n        return mayor\n   \n    def encontrar_mayor_recurisvo(self, nodo):\n        if nodo == None:\n            return float('-inf')\n\n        mayor = self.encontrar_mayor_recurisvo(nodo.next)\n\n        return max(nodo.value, mayor)\n\n\ndef test_encontrar_mayor():\n    # Test case 1: Empty list\n    ll = LinkedList()\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 2: List with one element\n    ll.append(10)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 3: List with multiple elements\n    ll.append(5)\n    ll.append(20)\n    ll.append(15)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 4: List with negative numbers\n    ll = LinkedList()\n    ll.append(-10)\n    ll.append(-20)\n    ll.append(-5)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 5: List with all elements equal\n    ll = LinkedList()\n    ll.append(7)\n    ll.append(7)\n    ll.append(7)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 6: List with duplicates\n    ll = LinkedList()\n    ll.append(3)\n    ll.append(3)\n    ll.append(5)\n    ll.append(5)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 7: List with large numbers\n    ll = LinkedList()\n    ll.append(1000000)\n    ll.append(500000)\n    ll.append(1000001)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\n    # Test case 8: List with mixed positive and negative numbers\n    ll = LinkedList()\n    ll.append(-1)\n    ll.append(0)\n    ll.append(1)\n    ll.append(-2)\n    assert ll.encontrar_mayor() == ll.encontrar_mayor_new_implementation()\n\nif __name__ == \"__main__\":\n    test_encontrar_mayor()",
        "coverage_rate": 1.0,
        "coverage_report": "Name                               Stmts   Miss Branch BrPart  Cover\n--------------------------------------------------------------------\n/home/user/tmp/execution_test.py       1      0      0      0   100%\n--------------------------------------------------------------------\nTOTAL                                  1      0      0      0   100%\n",
        "sandbox_ast_check": true,
        "sandbox_functionality_check": {
            "reasoning": "REASONING: The REVISED FUNCTION `encontrar_mayor` in the `LinkedList` class is identical to the ORIGINAL FUNCTION. Both functions perform the same task: they iterate through the linked list to find and return the maximum value. The logic and structure of the code are the same, with no changes in functionality or implementation details. The test cases provided in the code also verify that the function behaves as expected in various scenarios, confirming that the functionality is preserved.",
            "answer": "same"
        },
        "test_correctness_check": {
            "reasoning": "REASONING: \n- [CONDITION 1] The `encontrar_mayor` function returns the maximum value in the linked list, satisfying the condition of having return values.\n- [CONDITION 2] The test cases use assertions to compare the return values of `encontrar_mayor` and `encontrar_mayor_new_implementation`, not printed or logged outputs.\n- [CONDITION 3] The test cases are designed to check if both implementations return the same result for various scenarios, ensuring that `encontrar_mayor_new_implementation` must have the same functionality as `encontrar_mayor` to pass all tests.\n- [CONDITION 4] The test cases use assertions to compare the return values, which is appropriate since `encontrar_mayor` returns a value.\n- [CONDITION 5] The test cases cover a range of scenarios, including empty lists, lists with one element, multiple elements, negative numbers, duplicates, large numbers, and mixed positive and negative numbers, making them non-trivial.",
            "answer": "yes"
        },
        "commit_id": "ef5a33737d8c7aec5ca00092d33ac4f2ea0945c8"
    }
]