"""Minimal utilities for Bean Vision."""

import logging
import torch
from pathlib import Path
from typing import Union


class ModelError(Exception):
    """Custom exception for model-related errors."""
    pass


def validate_device(device_str: Union[str, torch.device]) -> torch.device:
    """Validate and return torch device."""
    if isinstance(device_str, torch.device):
        return device_str
    
    try:
        device = torch.device(device_str)
        
        # Check if device is available
        if device.type == 'cuda' and not torch.cuda.is_available():
            logging.warning("CUDA requested but not available, falling back to CPU")
            device = torch.device('cpu')
        elif device.type == 'mps' and not torch.backends.mps.is_available():
            logging.warning("MPS requested but not available, falling back to CPU")
            device = torch.device('cpu')
        
        return device
    
    except Exception as e:
        logging.warning(f"Invalid device '{device_str}': {e}. Using CPU")
        return torch.device('cpu')


def safe_load_model_checkpoint(checkpoint_path: Union[str, Path], device: torch.device):
    """Safely load a model checkpoint with proper error handling."""
    checkpoint_path = Path(checkpoint_path)
    
    if not checkpoint_path.exists():
        raise ModelError(f"Checkpoint file not found: {checkpoint_path}")
    
    try:
        checkpoint = torch.load(checkpoint_path, map_location=device)
        return checkpoint
    except Exception as e:
        raise ModelError(f"Failed to load checkpoint: {e}")