

# USER: 
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Help me modify this Python code"                                                                                                           │
│    # --- 3. LLM Interface ---                                                                                                            │
│    class LLMInterface:                                                                                                                   │
│        def __init__(self, model_name, device):                                                                                           │
│            print(f"Loading model: {model_name} on {device}...")                                                                          │
│            self.tokenizer = AutoTokenizer.from_pretrained(model_name)                                                                    │
│            self.model = AutoModelForCausalLM.from_pretrained(                                                                            │
│                model_name,                                                                                                               │
│                dtype="auto",                                                                                                             │
│                device_map="auto"                                                                                                         │
│            ).eval()                                                                                                                      │
│            self.device = self.model.device                                                                                               │
│            print("Model loaded successfully.")                                                                                           │
│                                                                                                                                          │
│        def get_vector(self, text: str) -> np.ndarray:                                                                                    │
│            with torch.no_grad():                                                                                                         │
│                inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device)                       │
│                outputs = self.model(**inputs, output_hidden_states=True)                                                                 │
│                vector = outputs.hidden_states[-1][0, -1, :].cpu().to(torch.float32).numpy()                                              │
│                del inputs, outputs                                                                                                       │
│                return vector                                                                                                             │
│                                                                                                                                          │
│        def generate_chat_completion(self, messages: list, max_length: int, temperature: float = 0.7, thinking=True) -> str:              │
│            text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=thinking)     │
│            model_inputs = self.tokenizer([text], return_tensors="pt").to(self.device)                                                    │
│            generated_ids = self.model.generate(                                                                                          │
│                model_inputs.input_ids,                                                                                                   │
│                attention_mask=model_inputs.attention_mask,                                                                               │
│                max_new_tokens=max_length,                                                                                                │
│                do_sample=True, top_p=0.9, temperature=temperature,                                                                       │
│                pad_token_id=self.tokenizer.eos_token_id                                                                                  │
│            )                                                                                                                             │
│            response = self.tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[-1]:], skip_special_tokens=True)[0]      │
│            del text, model_inputs, generated_ids                                                                                         │
│            return response                                                                                                               │
│                                                                                                                                          │
│        def get_prob_and_vector(self, text: str) -> tuple[float, np.ndarray]:                                                             │
│            with torch.no_grad():                                                                                                         │
│                try:                                                                                                                      │
│                    inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device)                   │
│                    outputs = self.model(**inputs, labels=inputs.input_ids, output_hidden_states=True)                                    │
│                    log_prob = -outputs.loss.item()                                                                                       │
│                    vector = outputs.hidden_states[-1][0, -1, :].cpu().to(torch.float32).numpy()                                          │
│                    del inputs, outputs                                                                                                   │
│                    return log_prob, vector                                                                                               │
│                except Exception as e:                                                                                                    │
│                    print(f"Error in get_prob_and_vector: {e}")                                                                           │
│                    return 0.0, None                                                                                                      │
│    "Please add features: Separate counting of input and output tokens, accumulated across multiple conversations. It should be possible to actively call reset to clear, or call attributes to read the current accumulated tokens" │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Okay, I can add the token counting functionality to your LLMInterface class.

  Here's the plan:
   1. Initialize _total_input_tokens and _total_output_tokens in the __init__ method.
   2. Update _total_input_tokens in get_vector and get_prob_and_vector based on the length of the input input_ids.
   3. Update both _total_input_tokens and _total_output_tokens in generate_chat_completion. Input tokens will be model_inputs.input_ids.shape[-1]
      and output tokens will be generated_ids.shape[-1] - model_inputs.input_ids.shape[-1].
   4. Add a reset_token_counts method to clear the accumulated token counts.
   5. Add @property decorators for total_input_tokens and total_output_tokens to allow reading the current counts.

  Here's the modified code:

 ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ x  Edit {"file_path":"${PWD}/paper/llm_judge.py","new_string":"# --- 3. LLM Interface ---\nclass LLMInterface…  │
 │                                                                                                                                                   │
│    Failed to edit, 0 occurrences found for old_string in ${PWD}/paper/llm_judge.py. No edits made.              │
 │    The exact text in old_string was not found. Ensure you're not escaping content incorrectly and check whitespace, indentation, and              │
 │    context. Use read_file tool to verify.                                                                                                         │
 ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ The previous attempt to modify the file failed because the old_string didn't match exactly. I need to read the file first to get the precise
  content and then construct the old_string and new_string accurately.

I will now read the file ${PWD}/paper/llm_judge.py.
 ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  ReadFile llm_judge.py                                                                                                                          │
 ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ My apologies. I made an incorrect assumption. The LLMInterface class is not present in the llm_judge.py file that I just read.

  Please tell me the absolute path to the Python file where the LLMInterface class is defined so I can apply the requested changes.


# USER: 

╭────────────────────────────────────────────────────╮

│  > You only need to print out the modified code, do not need to read and write files  │
╰────────────────────────────────────────────────────╯

# AGENT: 


✦ Okay, here is the modified Python code, which includes the functionality to count the number of input and output tokens, as well as attributes to reset the count and read the current accumulated tokens:
```

    1 # --- 3. LLM Interface ---

    2 class LLMInterface:

    3     def __init__(self, model_name, device):

    4         print(f"Loading model: {model_name} on {device}...")

    5         self.tokenizer = AutoTokenizer.from_pretrained(model_name)

    6         self.model = AutoModelForCausalLM.from_pretrained(

    7             model_name,

    8             dtype="auto",

    9             device_map="auto"

   10         ).eval()

   11         self.device = self.model.device

   12         print("Model loaded successfully.")

   13         self._total_input_tokens = 0

   14         self._total_output_tokens = 0

   15 

   16     @property

   17     def total_input_tokens(self) -> int:

   18         """Returns the total number of input tokens accumulated."""

   19         return self._total_input_tokens

   20 

   21     @property

   22     def total_output_tokens(self) -> int:

   23         """Returns the total number of output tokens accumulated."""

   24         return self._total_output_tokens

   25 

   26     def reset_token_counts(self):

   27         """Resets the accumulated input and output token counts to zero."""

   28         self._total_input_tokens = 0

   29         self._total_output_tokens = 0

   30         print("Token counts reset.")

   31 

   32     def get_vector(self, text: str) -> np.ndarray:

   33         with torch.no_grad():

   34             inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device)

   35             self._total_input_tokens += inputs.input_ids.shape[-1] # Accumulate input tokens

   36             outputs = self.model(**inputs, output_hidden_states=True)

   37             vector = outputs.hidden_states[-1][0, -1, :].cpu().to(torch.float32).numpy()

   38             del inputs, outputs

   39             return vector

   40 

   41     def generate_chat_completion(self, messages: list, max_length: int, temperature: float = 0.7, thinking=True) -> str:

   42         text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=thinking)

   43         model_inputs = self.tokenizer([text], return_tensors="pt").to(self.device)

   44         self._total_input_tokens += model_inputs.input_ids.shape[-1] # Accumulate input tokens for generation

   45         generated_ids = self.model.generate(

   46             model_inputs.input_ids,

   47             attention_mask=model_inputs.attention_mask,

   48             max_new_tokens=max_length,

   49             do_sample=True, top_p=0.9, temperature=temperature,

   50             pad_token_id=self.tokenizer.eos_token_id

   51         )

   52         output_tokens = generated_ids.shape[-1] - model_inputs.input_ids.shape[-1]

   53         self._total_output_tokens += output_tokens # Accumulate output tokens

   54         response = self.tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[-1]:], skip_special_tokens=True)[0]

   55         del text, model_inputs, generated_ids

   56         return response

   57 

   58     def get_prob_and_vector(self, text: str) -> tuple[float, np.ndarray]:

   59         with torch.no_grad():

   60             try:

   61                 inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device)

   62                 self._total_input_tokens += inputs.input_ids.shape[-1] # Accumulate input tokens

   63                 outputs = self.model(**inputs, labels=inputs.input_ids, output_hidden_states=True)

   64                 log_prob = -outputs.loss.item()

   65                 vector = outputs.hidden_states[-1][0, -1, :].cpu().to(torch.float32).numpy()

   66                 del inputs, outputs

   67                 return log_prob, vector

   68             except Exception as e:

   69                 print(f"Error in get_prob_and_vector: {e}")

   70                 return 0.0, None
```

# USER: 

╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Also help me add the same function to this one:                                                                                           │
│    class TreeOfThoughts:                                                                                                      │
│        """Adapted Tree of Thoughts class for local model execution."""                                                        │
│        def __init__(                                                                                                          │
│                self,                                                                                                          │
│                model: AutoModelForCausalLM,                                                                                   │
│                tokenizer: AutoTokenizer,                                                                                      │
│                input_seq: str,                                                                                                │
│                n_steps: int,                                                                                                  │
│                get_thought_gen_prompt: Callable,                                                                              │
│                get_state_eval_prompt: Callable,                                                                               │
│                heuristic_calculator: Callable,                                                                                │
│                n_evals: int,                                                                                                  │
│                breadth_limit: int,                                                                                            │
│        ):                                                                                                                     │
│            self.model = model                                                                                                 │
│            self.tokenizer = tokenizer                                                                                         │
│            self.input_seq = input_seq                                                                                         │
│            self.root = TreeNode(state='', thought='')                                                                         │
│            self.n_steps = n_steps                                                                                             │
│            self.get_thought_gen_prompt = get_thought_gen_prompt                                                               │
│            self.get_state_eval_prompt = get_state_eval_prompt                                                                 │
│            self.heuristic_calculator = heuristic_calculator                                                                   │
│            self.n_evals = n_evals                                                                                             │
│            self.breadth_limit = breadth_limit                                                                                 │
│                                                                                                                               │
│        def chat_completions(                                                                                                  │
│                self,                                                                                                          │
│                prompt: str,                                                                                                   │
│                n: int = 1,                                                                                                    │
│        ) -> List[str]:                                                                                                        │
│            """Custom chat completion function using the local transformers model."""                                          │
│            messages = [{'role': "user", 'content': prompt}]                                                                   │
│            text_prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)             │
│            model_inputs = self.tokenizer([text_prompt], return_tensors="pt").to(self.model.device)                            │
│                                                                                                                               │
│            generated_ids = self.model.generate(                                                                               │
│                model_inputs.input_ids,                                                                                        │
│                attention_mask=model_inputs.attention_mask,                                                                    │
│                max_new_tokens=Config.MAX_NEW_TOKENS,                                                                          │
│                do_sample=True,                                                                                                │
│                temperature=Config.TEMPERATURE,                                                                                │
│                top_p=Config.TOP_P,                                                                                            │
│                pad_token_id=self.tokenizer.eos_token_id,                                                                      │
│                num_return_sequences=n                                                                                         │
│            )                                                                                                                  │
│                                                                                                                               │
│            return self.tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[-1]:], skip_special_tokens=True)  │
│                                                                                                                               │
│        def thought_generator(self, state: str) -> List[str]:                                                                  │
│            prompt = self.get_thought_gen_prompt(self.input_seq, state)                                                        │
│            # We use a 'propose' strategy: one call returns multiple thoughts                                                  │
│            response = self.chat_completions(prompt, n=1)[0]                                                                   │
│            # Thoughts are separated by a specific delimiter                                                                   │
│            thoughts = [t.strip() for t in response.split('---') if t.strip()]                                                 │
│            return thoughts                                                                                                    │
│                                                                                                                               │
│        def state_evaluator(self, state: str) -> float:                                                                        │
│            prompt = self.get_state_eval_prompt(self.input_seq, state)                                                         │
│            state_evals = self.chat_completions(prompt, n=self.n_evals)                                                        │
│            value = self.heuristic_calculator(state, state_evals)                                                              │
│            return value                                                                                                       │
│                                                                                                                               │
│        def bfs(self, verbose: bool = True) -> str:                                                                            │
│            """Performs a Breadth-First Search on the thought tree."""                                                         │
│            queue = deque()                                                                                                    │
│            queue.append(self.root)                                                                                            │
│                                                                                                                               │
│            for step in range(1, self.n_steps + 1):                                                                            │
│                if verbose: print(f"\nStep {step}/{self.n_steps}...")                                                          │
│                                                                                                                               │
│                level_nodes = list(queue)                                                                                      │
│                queue.clear()                                                                                                  │
│                                                                                                                               │
│                for node in level_nodes:                                                                                       │
│                    thoughts = self.thought_generator(state=node.state)                                                        │
│                    for thought in thoughts:                                                                                   │
│                        updated_state = (node.state + "\n\n" + thought).strip()                                                │
│                        child = TreeNode(state=updated_state, thought=thought)                                                 │
│                        node.children.append(child)                                                                            │
│                        queue.append(child)                                                                                    │
│                                                                                                                               │
│                if not queue: break                                                                                            │
│                                                                                                                               │
│                if verbose: print(f"  Generated {len(queue)} new states. Evaluating...")                                       │
│                for i, node in enumerate(queue):                                                                               │
│                    node.value = self.state_evaluator(state=node.state)                                                        │
│                    if verbose: print(f"    State {i+1}/{len(queue)} evaluated with score: {node.value:.2f}")                  │
│                                                                                                                               │
│                # Pruning                                                                                                      │
│                sorted_nodes = sorted(queue, key=lambda n: n.value, reverse=True)                                              │
│                limit = 1 if step == self.n_steps else self.breadth_limit                                                      │
│                top_nodes = sorted_nodes[:limit]                                                                               │
│                                                                                                                               │
│                queue = deque(top_nodes)                                                                                       │
│                if verbose: print(f"  Pruned to {len(queue)} best states.")                                                    │
│                                                                                                                               │
│            if not queue:                                                                                                      │
│                return "ToT search failed to produce a result."                                                                │
│                                                                                                                               │
│            return queue[0].state                                                                                              │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Okay, here is the modified code for the TreeOfThoughts class, which also includes functionality to count the number of input and output tokens, as well as attributes to reset the count and read the current cumulative token count:
     1 class TreeOfThoughts:
     2     """Adapted Tree of Thoughts class for local model execution."""
     3     def __init__(
     4             self,
     5             model: AutoModelForCausalLM,
     6             tokenizer: AutoTokenizer,
     7             input_seq: str,
     8             n_steps: int,
     9             get_thought_gen_prompt: Callable,
    10             get_state_eval_prompt: Callable,
    11             heuristic_calculator: Callable,
    12             n_evals: int,
    13             breadth_limit: int,
    14     ):
    15         self.model = model
    16         self.tokenizer = tokenizer
    17         self.input_seq = input_seq
    18         self.root = TreeNode(state='', thought='')
    19         self.n_steps = n_steps
    20         self.get_thought_gen_prompt = get_thought_gen_prompt
    21         self.get_state_eval_prompt = get_state_eval_prompt
    22         self.heuristic_calculator = heuristic_calculator
    23         self.n_evals = n_evals
    24         self.breadth_limit = breadth_limit
    25         self._total_input_tokens = 0  # Initialize total input tokens
    26         self._total_output_tokens = 0 # Initialize total output tokens
    27 
    28     @property
    29     def total_input_tokens(self) -> int:
    30         """Returns the total number of input tokens accumulated by this TreeOfThoughts instance."""
    31         return self._total_input_tokens
    32 
    33     @property
    34     def total_output_tokens(self) -> int:
    35         """Returns the total number of output tokens accumulated by this TreeOfThoughts instance."""
    36         return self._total_output_tokens
    37 
    38     def reset_token_counts(self):
    39         """Resets the accumulated input and output token counts for this TreeOfThoughts instance to zero."""
    40         self._total_input_tokens = 0
    41         self._total_output_tokens = 0
    42         print("Token counts reset for TreeOfThoughts instance.")
    43 
    44     def chat_completions(
    45             self,
    46             prompt: str,
    47             n: int = 1,
    48     ) -> List[str]:
    49         """Custom chat completion function using the local transformers model."""
    50         messages = [{'role': "user", 'content': prompt}]
    51         text_prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    52         model_inputs = self.tokenizer([text_prompt], return_tensors="pt").to(self.model.device)
    53 
    54         self._total_input_tokens += model_inputs.input_ids.shape[-1] # Accumulate input tokens
    55 
    56         generated_ids = self.model.generate(
    57             model_inputs.input_ids,
    58             attention_mask=model_inputs.attention_mask,
    59             max_new_tokens=Config.MAX_NEW_TOKENS,
    60             do_sample=True,
    61             temperature=Config.TEMPERATURE,
    62             top_p=Config.TOP_P,
    63             pad_token_id=self.tokenizer.eos_token_id,
    64             num_return_sequences=n
    65         )
    66 
    67         # Calculate output tokens: total generated tokens minus input tokens, multiplied by num_return_sequences
    68         output_tokens_per_sequence = generated_ids.shape[-1] - model_inputs.input_ids.shape[-1]
    69         self._total_output_tokens += (output_tokens_per_sequence * n) # Accumulate output tokens
    70 
    71         return self.tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[-1]:], skip_special_tokens=True)
    72 
    73     def thought_generator(self, state: str) -> List[str]:
    74         prompt = self.get_thought_gen_prompt(self.input_seq, state)
    75         # We use a 'propose' strategy: one call returns multiple thoughts
    76         response = self.chat_completions(prompt, n=1)[0]
    77         # Thoughts are separated by a specific delimiter
    78         thoughts = [t.strip() for t in response.split('---') if t.strip()]
    79         return thoughts
    80 
    81     def state_evaluator(self, state: str) -> float:
    82         prompt = self.get_state_eval_prompt(self.input_seq, state)
    83         state_evals = self.chat_completions(prompt, n=self.n_evals)
    84         value = self.heuristic_calculator(state, state_evals)
    85         return value
    86 
    87     def bfs(self, verbose: bool = True) -> str:
    88         """Performs a Breadth-First Search on the thought tree."""
    89         queue = deque()
    90         queue.append(self.root)
    91 
    92         for step in range(1, self.n_steps + 1):
    93             if verbose: print(f"\nStep {step}/{self.n_steps}...")
    94 
    95             level_nodes = list(queue)
    96             queue.clear()
    97 
    98             for node in level_nodes:
    99                 thoughts = self.thought_generator(state=node.state)
   100                 for thought in thoughts:
   101                     updated_state = (node.state + "\n\n" + thought).strip()
   102                     child = TreeNode(state=updated_state, thought=thought)
   103                     node.children.append(child)
   104                     queue.append(child)
   105 
   106             if not queue: break
   107 
   108             if verbose: print(f"  Generated {len(queue)} new states. Evaluating...")
   109             for i, node in enumerate(queue):
   110                 node.value = self.state_evaluator(state=node.state)
   111                 if verbose: print(f"    State {i+1}/{len(queue)} evaluated with score: {node.value:.2f}")
   112 
   113             # Pruning
   114             sorted_nodes = sorted(queue, key=lambda n: n.value, reverse=True)
   115             limit = 1 if step == self.n_steps else self.breadth_limit
   116             top_nodes = sorted_nodes[:limit]
   117 
   118             queue = deque(top_nodes)
   119             if verbose: print(f"  Pruned to {len(queue)} best states.")
   120 
   121         if not queue:
   122             return "ToT search failed to produce a result."
   123 
   124         return queue[0].state

