import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoConfig

class CustomModel(PreTrainedModel):
    config_class = AutoConfig  # Use AutoConfig to dynamically load the configuration class

    def __init__(self, config):
        super().__init__(config)
        # Implement your model architecture here
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
        try:
            # Load the configuration
            config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
            # Initialize the model with the configuration
            model = cls(config)
            # Load the model weights using the transformers library
            state_dict = torch.load(f"{pretrained_model_name_or_path}/pytorch_model.bin", map_location="cpu")
            model.load_state_dict(state_dict)
            return model
        except Exception as e:
            print(f"Failed to load model from {pretrained_model_name_or_path}. Error: {e}")
            return None