from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
import torch

def load_local_model(model_path: str, device: int = -1, token: str = None):
    """
    Load a Hugging Face model (CPU by default) with optional token for private repos.

    Args:
        model_path (str): Hugging Face repo ID or local path.
        device (int): -1 for CPU, >=0 for GPU index.
        token (str): HF token for private models.

    Returns:
        model, tokenizer
    """
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token=token)
    except Exception as e:
        raise RuntimeError(f"Failed to load tokenizer: {e}")

    try:
        config = AutoConfig.from_pretrained(model_path, use_auth_token=token)
        model = AutoModelForCausalLM.from_pretrained(
            model_path, config=config, use_auth_token=token
        )

        # Device mapping
        if device >= 0 and torch.cuda.is_available():
            model.to(f"cuda:{device}")
        else:
            model.to("cpu")
    except Exception as e:
        raise RuntimeError(f"Failed to load model: {e}")

    return model, tokenizer