import os
import json
import random
import json
import os
import numpy as np
from pathlib import Path
from typing import Iterable, Union, Any
import pickle
from .examples import get_examples
import random

def set_seed(seed: int = 42) -> None:
    np.random.seed(seed)
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    print(f"Random seed set as {seed}")


def load_jsonl(file: Union[str, Path]) -> Iterable[Any]:
    with open(file, "r", encoding="utf-8") as f:
        for line in f:
            try:
                yield json.loads(line)
            except:
                print("Error in loading:", line)
                exit()


def save_jsonl(samples, save_path):
    # ensure path
    folder = os.path.dirname(save_path)
    os.makedirs(folder, exist_ok=True)

    with open(save_path, "w", encoding="utf-8") as f:
        for sample in samples:
            f.write(json.dumps(sample, ensure_ascii=False) + "\n")
    print("Saved to", save_path)


def lower_keys(example):
    new_example = {}
    for key, value in example.items():
        if key != key.lower():
            new_key = key.lower()
            new_example[new_key] = value
        else:
            new_example[key] = value
    return new_example


EXAMPLES = get_examples()


def load_prompt(data_name, prompt_type, num_shots):
    if not num_shots:
        return []

    if data_name in ["gsm_hard", "svamp", "tabmwp", "asdiv", "mawps"]:
        data_name = "gsm8k"
    if data_name in ["math_oai", "hungarian_exam", "math-oai", "aime24", "amc23", "aimo2"]:
        data_name = "math"
    if data_name in ["sat_math"]:
        data_name = "mmlu_stem"
    if data_name in [
        "gaokao2024_I",
        "gaokao2024_II",
        "gaokao_math_qa",
        "gaokao2024_mix",
        "cn_middle_school",
    ]:
        data_name = "gaokao"

    if prompt_type in ["tool-integrated"]:
        prompt_type = "tora"
    if prompt_type in ["router"]:
        data_name = "router"

    return EXAMPLES[data_name][:num_shots]


PROMPT_TEMPLATES = {
    "direct": ("Question: {input}\nAnswer: ", "{output}", "\n\n"),
    "cot": ("Question: {input}\nAnswer: ", "{output}", "\n\n\n"),
    "pal": ("Question: {input}\n\n", "{output}", "\n---\n"),
    "tool-integrated": ("Question: {input}\n\nSolution:\n", "{output}", "\n---\n"),
    "self-instruct": ("<|user|>\n{input}\n<|assistant|>\n", "{output}", "\n"),
    "tora": ("<|user|>\n{input}\n<|assistant|>\n", "{output}", "\n"),
    "wizard_zs": (
        "### Instruction:\n{input}\n\n### Response: Let's think step by step.",
        "{output}",
        "\n\n\n",
    ),
    "platypus_fs": (
        "### Instruction:\n{input}\n\n### Response:\n",
        "{output}",
        "\n\n\n",
    ),
    "deepseek-math": (
        "User: {input}\nPlease reason step by step, "
        "and put your final answer within \\boxed{{}}.\n\nAssistant:",
        "{output}",
        "\n\n\n",
    ),
    "kpmath": (
        "User: Please reason step by step and put your final answer at the end "
        'with "The answer is: ".\n\n{input}\n\nAssistant:',
        "{output}",
    ),
    "jiuzhang": (
        "## Question\n{input}\n\n## Solution\n",
        "{output}",
        "\n\n\n",
    ),
    "jiuzhang_tora": (
        "## Question\n{input}\n\n## Code Solution\n",
        "{output}",
        "\n\n\n",
    ),
    "jiuzhang_nl": (
        "## Question\n{input}\n\n## Natural Language Solution\n",
        "{output}",
        "\n\n\n",
    ),
    "mmiqc": (
        'Please solve the following problem and put your answer at the end with "The answer is: ".\n\n{input}\n\n',
        "{output}",
        "\n\n\n",
    ),
    "abel": (
        "Question:\n{input}\nAnswer:\nLet's think step by step.\n",
        "{output}",
        "\n\n",
    ),
    "shepherd": ("{input}\n", "{output}", "\n\n\n"),
    "qwen-boxed": (
        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
        "<|im_start|>user\n{input}\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|im_end|>\n"
        "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "qwen25-math-cot": (
        "<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|im_end|>\n"
        "<|im_start|>user\n{input}<|im_end|>\n"
        "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "router": (
        "<|im_start|>system\nYou are a math expert. You are given a question and a list of steps solved by a small language model, the model is completing the solution step by step and will show you the intermediate solution at some random step. You need to determine if its better to use a bigger model to solve the last step. If you think a bigger model can better solve the last step, you need to return True. If you think bigger language model would also have done the same in the last step, you need to return False.<|im_end|>\n"
        "<|im_start|>user\n{input}",
        "<|im_end|>\n<|im_start|>assistant\n Answer:",
        "\n\n",
    ),
    "qwen25-math-cot-en": (
        "<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{{}}. You must answer in English.<|im_end|>\n"
        "<|im_start|>user\n{input}<|im_end|>\n"
        "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "mathstral": (
        "{input}\nPlease reason step by step, and put your final answer within \\boxed{{}}.",
        "{output}",
        "\n\n",
    ),
    "internlm-math-fs": ("Question:{input}\nAnswer:", "{output}", "\n"),
    "internlm-math-chat": (
        "<|im_start|>user\n{input}<|im_end|>\n" "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "mistral": (
        "[INST] {input}[/INST]",
        "{output}",
        "\n\n",
    ),
    "numina": ("### Problem: {input}\n### Solution:", " {output}", "\n\n"),
    "llama3": (
        "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
        "Please reason step by step, and put your final answer within \\boxed{{}}.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
        "{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
        "{output}",
        "\n\n",
    ),
    "deepseek-r1": (
        "<｜begin▁of▁sentence｜><｜User｜>{input}\n"
        "Please reason step by step, and put your final answer within \\boxed{{}}.<｜Assistant｜>",
        "{output}",
        "\n\n",
    ),
    "deepseek-r1-system": (
        "<｜begin▁of▁sentence｜>Please reason step by step, and put your final answer within \\boxed{{}}."
        "<｜User｜>{input}<｜Assistant｜>",
        "{output}",
        "\n\n",
    ),
}

def construct_router_prompt(example, data_name, demos, args):
    prompt_temp = PROMPT_TEMPLATES['router']
    splitter = prompt_temp[2]
    input_template, output_template, splitter = (
        prompt_temp[0],
        prompt_temp[1],
        prompt_temp[2],
    )

    
    
    demo_prompt = splitter.join([q for q in demos])
    full_prompt = demo_prompt + splitter + 'Question: ' + example["question"]
    full_prompt = input_template.format(input=full_prompt)

    return full_prompt.strip(" "), output_template

def construct_prompt(example, data_name, args):
    if args.adapt_few_shot and data_name in [
        "gaokao2024_I",
        "gaokao2024_II",
        "gaokao_math_qa",
        "gaokao2024_mix",
        "cn_middle_school",
    ]:
        demos = load_prompt(data_name, args.prompt_type, 5)
    else:
        demos = load_prompt(data_name, args.prompt_type, args.num_shots)
    prompt_type = args.prompt_type
    if prompt_type == "platypus_fs":
        prompt_type = "cot"
    if prompt_type == "tool-integrated":
        prompt_type = "tora"

    prompt_temp = PROMPT_TEMPLATES[args.prompt_type]

    splitter = prompt_temp[2]
    input_template, output_template, splitter = (
        prompt_temp[0],
        prompt_temp[1],
        prompt_temp[2],
    )
    if args.prompt_type == "qwen25-math-cot":
        # Hotfix to support putting all demos into a single turn
        demo_prompt = splitter.join([q + "\n" + a for q, a in demos])
    else:
        demo_prompt = splitter.join(
            [
                input_template.format(input=q) + output_template.format(output=a)
                for q, a in demos
            ]
        )
    context = input_template.format(input=example["question"])
    if len(demo_prompt) == 0 or (
        args.adapt_few_shot and example["gt_ans"] not in ["A", "B", "C", "D", "E"]
    ):
        full_prompt = context
    else:
        if args.prompt_type == "qwen25-math-cot":
            # Hotfix to supportting put all demos into a single turn
            full_prompt = demo_prompt + splitter + example["question"]
            full_prompt = input_template.format(input=full_prompt)
        else:
            full_prompt = demo_prompt + splitter + context

    if args.prompt_type == "platypus_fs":
        full_prompt_temp = (
            "Below is an instruction that describes a task. "
            "Write a response that appropriately completes the request.\n\n"
            "### Instruction:\n{instruction}\n\n### Response:\n"
        )
        full_prompt = full_prompt_temp.format(instruction=full_prompt)

    if prompt_type == "tora":
        full_prompt = (
            """Integrate step-by-step reasoning and Python code to solve math problems using the following guidelines:

- Analyze the question and write functions to solve the problem; the function should not take any arguments.
- Present the final result in LaTeX using a `\boxed{}` without any units.
- Utilize the `pi` symbol and `Rational`` from Sympy for $\pi$ and fractions, and simplify all fractions and square roots without converting them to decimal values.

Here are some examples you may refer to:

---

"""
            + full_prompt
        )
    return full_prompt.strip(" ")  # important!


key_map = {
    "gt": "Ground Truth",
    "pred": "Prediction",
    "gt_cot": "Reference CoT",
    "score": "Score",
}


def show_sample(sample, print_all_preds=False):
    print("==" * 20)
    for key in ["idx", "type", "level", "dataset"]:
        if key in sample:
            # capitalize
            print("{}: {}".format(key[0].upper() + key[1:], sample[key]))
    print("Question:", repr(sample["question"]))
    if "code" in sample:
        if print_all_preds:
            for code in sample["code"]:
                print("-" * 20)
                print("code:", code)
            print("Execution:", sample["report"])
        else:
            print("Solution:\n", sample["code"][0])
            print("Execution:", sample["report"][0])
    if "pred" in sample:
        print("Prediction:", repr(sample["pred"][0]))
    for key in ["gt", "score", "unit", "gt_cot"]:
        if key in sample:
            _key = key_map.get(key, key)
            print("{}: {}".format(_key, repr(sample[key])))
    print()
