from functools import wraps
import torch
from huggingface_hub import HfApi
import os
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class DeviceManager:
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(DeviceManager, cls).__new__(cls)
            cls._instance._initialized = False
        return cls._instance
    
    def __init__(self):
        if self._initialized:
            return
            
        self._initialized = True
        self._current_device = None
        self._zero_gpu_available = None
        
    def check_zero_gpu_availability(self):
        try:
            api = HfApi()
            # 檢查環境變數或其他方式確認是否在 Spaces 環境
            if 'SPACE_ID' in os.environ:
                # 這裡可以添加更多具體的 ZeroGPU 可用性檢查
                self._zero_gpu_available = True
                return True
        except Exception as e:
            logger.warning(f"Error checking ZeroGPU availability: {e}")
        
        self._zero_gpu_available = False
        return False
    
    def get_optimal_device(self):
        if self._current_device is None:
            if self.check_zero_gpu_availability():
                self._current_device = torch.device('cuda')
                logger.info("Using ZeroGPU")
            else:
                self._current_device = torch.device('cpu')
                logger.info("Using CPU")
        return self._current_device
    
    def move_to_device(self, tensor_or_model):
        device = self.get_optimal_device()
        if hasattr(tensor_or_model, 'to'):
            return tensor_or_model.to(device)
        return tensor_or_model

def device_handler(func):
    """Decorator for handling device placement"""
    @wraps(func)
    async def wrapper(*args, **kwargs):
        device_mgr = DeviceManager()
        
        # 處理輸入參數的設備轉換
        def process_arg(arg):
            if torch.is_tensor(arg) or hasattr(arg, 'to'):
                return device_mgr.move_to_device(arg)
            return arg
        
        processed_args = [process_arg(arg) for arg in args]
        processed_kwargs = {k: process_arg(v) for k, v in kwargs.items()}
        
        try:
            result = await func(*processed_args, **processed_kwargs)
            
            # 處理輸出結果的設備轉換
            if torch.is_tensor(result):
                return device_mgr.move_to_device(result)
            elif isinstance(result, tuple):
                return tuple(device_mgr.move_to_device(r) if torch.is_tensor(r) else r for r in result)
            return result
            
        except RuntimeError as e:
            if "out of memory" in str(e):
                logger.warning("GPU memory exceeded, falling back to CPU")
                device_mgr._current_device = torch.device('cpu')
                return await wrapper(*args, **kwargs)
            raise e
            
    return wrapper