Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.

TASK : 






Drawback: below code very basic logic , but task our visualization model every neuron in that which is contribution to generate next token , when user give input ,

you use play butoom cocepts , highlighted with different or which neuron getting actiavted, contributioning




import sys
from typing import Any, Dict, List, Optional, Tuple

import torch
from torch.nn import Module
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    PreTrainedModel,
    PreTrainedTokenizer,
)
import plotly.graph_objs as go
from plotly.subplots import make_subplots


def load_model_and_tokenizer(
    model_name: str
) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
    """
    Load the pre-trained model and tokenizer.

    Args:
        model_name (str): Name of the pre-trained model.

    Returns:
        Tuple containing the model and tokenizer.
    """
    try:
        model = AutoModelForCausalLM.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        return model, tokenizer
    except Exception as e:
        print(f"Error loading model or tokenizer: {e}", file=sys.stderr)
        raise


def get_neuron_parameters(
    model: PreTrainedModel, recurse: bool = False
) -> Dict[str, Dict[str, Any]]:
    """
    Retrieve model parameters with their metadata.

    Args:
        model (PreTrainedModel): The pre-trained model.
        recurse (bool): Whether to recurse through submodules.

    Returns:
        Dictionary mapping parameter names to their metadata.
    """
    parameters = {}
    try:
        for name, param in model.named_parameters(recurse=recurse):
            shape = tuple(param.shape)
            trainable = param.requires_grad
            num_params = param.numel()
            parameters[name] = {
                "shape": shape,
                "trainable": trainable,
                "num_params": num_params,
            }
        return parameters
    except Exception as e:
        print(f"Error retrieving parameters: {e}", file=sys.stderr)
        raise


def compute_neuron_contributions(
    model: PreTrainedModel, tokenizer: PreTrainedTokenizer, input_text: str
) -> Optional[Dict[str, torch.Tensor]]:
    """
    Compute neuron contributions to each token.

    Args:
        model (PreTrainedModel): The pre-trained model.
        tokenizer (PreTrainedTokenizer): The tokenizer.
        input_text (str): The input string.

    Returns:
        Dictionary mapping layer names to their neuron activations.
    """
    try:
        inputs = tokenizer(input_text, return_tensors="pt")
        outputs = model(**inputs, output_hidden_states=True)
        hidden_states = outputs.hidden_states  # Tuple of (layers + embedding)

        # Example: Aggregate neuron activations per layer
        contributions = {}
        for idx, layer_hidden in enumerate(hidden_states):
            layer_name = f"layer_{idx}"
            # Mean activation per neuron across tokens and batch
            mean_activation = layer_hidden.mean(dim=(0, 1))
            contributions[layer_name] = mean_activation.detach().cpu()
        return contributions
    except Exception as e:
        print(f"Error computing neuron contributions: {e}", file=sys.stderr)
        return None


def visualize_contributions(
    contributions: Dict[str, torch.Tensor], input_text: str
) -> None:
    """
    Visualize neuron contributions using Plotly.

    Args:
        contributions (Dict[str, torch.Tensor]): Neuron contributions per layer.
        input_text (str): The input string.
    """
    try:
        layers = list(contributions.keys())
        num_layers = len(layers)
        fig = make_subplots(rows=num_layers, cols=1, subplot_titles=layers)

        for idx, layer in enumerate(layers, start=1):
            data = contributions[layer].numpy()
            fig.add_trace(
                go.Bar(
                    x=list(range(len(data))),
                    y=data,
                    marker=dict(color=data, colorscale='Viridis'),
                    showlegend=False,
                ),
                row=idx,
                col=1,
            )

        fig.update_layout(
            title=f'Neuron Contributions for Input: "{input_text}"',
            height=300 * num_layers,
            showlegend=False,
        )
        fig.show()
    except Exception as e:
        print(f"Error during visualization: {e}", file=sys.stderr)
        raise


def main(input_text: str, model_name: str = "gpt2") -> None:
    """
    Main function to execute the workflow.

    Args:
        input_text (str): The input string to process.
        model_name (str): The name of the pre-trained model.
    """
    try:
        model, tokenizer = load_model_and_tokenizer(model_name)
        params = get_neuron_parameters(model)
        print(f"Loaded model '{model_name}' with {len(params)} parameters.")

        contributions = compute_neuron_contributions(model, tokenizer, input_text)
        if contributions is None:
            print("Failed to compute neuron contributions.", file=sys.stderr)
            return

        visualize_contributions(contributions, input_text)
    except Exception as e:
        print(f"An error occurred in the main workflow: {e}", file=sys.stderr)


if __name__ == "__main__":
    # Example usage:
    example_input = "once up on a time"
    main(example_input)