"""
Tensor Pool Module
Unified tensor pooling system for memory efficiency.
"""
import torch
import logging
from typing import Dict, Tuple, List
from collections import defaultdict

logger = logging.getLogger(__name__)

class TensorPool:
    """
    Unified tensor pool for efficient memory management.
    """
    def __init__(self, max_pool_size: int = 50, max_tensor_size: int = 1000000):
        self.max_pool_size = max_pool_size
        self.max_tensor_size = max_tensor_size
        self.pools = defaultdict(list)
        self.usage_stats = defaultdict(int)
        self.operation_count = 0
        
        logger.debug("TensorPool initialized")
    
    def get_tensor(self, shape: Tuple[int, ...], dtype: torch.dtype = torch.float32,
                   requires_grad: bool = False, device: torch.device = None) -> torch.Tensor:
        """
        Get tensor from pool or create new one.
        
        Args:
            shape: Tensor shape
            dtype: Tensor data type
            requires_grad: Whether tensor requires gradients
            device: Device to create tensor on
            
        Returns:
            Tensor from pool or newly created tensor
        """
        self.operation_count += 1
        key = (shape, dtype, requires_grad)
        
        # Try to get tensor from pool
        if key in self.pools and self.pools[key]:
            tensor = self.pools[key].pop()
            tensor.zero_()  # Clear tensor
            self.usage_stats[key] += 1
            return tensor.to(device) if device else tensor
        
        # Create new tensor
        if device is None:
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        tensor = torch.zeros(shape, dtype=dtype, device=device, requires_grad=requires_grad)
        self.usage_stats[key] += 1
        
        return tensor
    
    def return_tensor(self, tensor: torch.Tensor) -> None:
        """
        Return tensor to pool for reuse.
        
        Args:
            tensor: Tensor to return to pool
        """
        if tensor is None or not isinstance(tensor, torch.Tensor):
            return
        
        # Don't pool very large tensors
        if tensor.numel() > self.max_tensor_size:
            return
        
        key = (tuple(tensor.shape), tensor.dtype, tensor.requires_grad)
        
        # Only pool if we have space
        if len(self.pools[key]) < self.max_pool_size:
            tensor.detach_()
            self.pools[key].append(tensor)
    
    def clear_pool(self, keep_ratio: float = 0.5) -> None:
        """
        Clear tensor pool, keeping a percentage.
        
        Args:
            keep_ratio: Ratio of pool to keep (0.0 to 1.0)
        """
        for key, pool in self.pools.items():
            if len(pool) > self.max_pool_size * keep_ratio:
                excess = len(pool) - int(self.max_pool_size * keep_ratio)
                for _ in range(excess):
                    if pool:
                        pool.pop()
    
    def clear_all(self) -> None:
        """Clear all tensor pools."""
        self.pools.clear()
        self.usage_stats.clear()
        logger.debug("TensorPool cleared")
    
    def get_stats(self) -> Dict:
        """Get pool statistics."""
        return {
            'pools': {str(k): len(v) for k, v in self.pools.items()},
            'usage_stats': dict(self.usage_stats),
            'operation_count': self.operation_count
        }