"""
Memory Pool Management

Advanced memory pool management for efficient allocation and deallocation
"""

import threading

import torch


class DynamicMemoryPool:
    """Dynamic memory pool for efficient tensor allocation"""

    def __init__(self, device: torch.device):
        self.device = device
        self.pool = {}
        self.lock = threading.Lock()

    def get_tensor(self, shape: tuple[int, ...], dtype: torch.dtype) -> torch.Tensor:
        """Get tensor from pool or allocate new one"""
        key = (shape, dtype)
        with self.lock:
            if key in self.pool and self.pool[key]:
                return self.pool[key].pop()
            else:
                return torch.zeros(shape, dtype=dtype, device=self.device)

    def return_tensor(self, tensor: torch.Tensor):
        """Return tensor to pool"""
        key = (tuple(tensor.shape), tensor.dtype)
        with self.lock:
            if key not in self.pool:
                self.pool[key] = []
            self.pool[key].append(tensor)


class MemoryPoolManager:
    """Memory pool manager"""

    def __init__(self):
        self.pools = {}

    def get_pool(self, device: torch.device) -> DynamicMemoryPool:
        """Get memory pool for device"""
        if device not in self.pools:
            self.pools[device] = DynamicMemoryPool(device)
        return self.pools[device]


class SmartMemoryAllocator:
    """Smart memory allocator"""

    def __init__(self):
        self.allocations = {}

    def allocate(self, size: int, device: torch.device) -> torch.Tensor:
        """Smart allocation"""
        return torch.empty(size, device=device)

    def deallocate(self, tensor: torch.Tensor):
        """Smart deallocation"""
        del tensor


class MemoryFragmentationOptimizer:
    """Memory fragmentation optimizer"""

    def __init__(self):
        self.enabled = True

    def optimize(self):
        """Optimize memory fragmentation"""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
