"""
Memory Cleanup Module
Adaptive memory cleanup and optimization utilities.
"""
import torch
import gc
import logging
from typing import Optional

logger = logging.getLogger(__name__)

class MemoryCleanup:
    """
    Memory cleanup and optimization utilities.
    """
    def __init__(self, memory_threshold: float = 0.85, cleanup_threshold: float = 0.75):
        self.memory_threshold = memory_threshold
        self.cleanup_threshold = cleanup_threshold
        self.memory_pressure_level = 0
        
        logger.debug("MemoryCleanup initialized")
    
    def check_memory_pressure(self) -> bool:
        """
        Check if memory usage is above threshold.
        
        Returns:
            True if memory pressure is high
        """
        if not torch.cuda.is_available():
            return False
        
        try:
            memory_allocated = torch.cuda.memory_allocated()
            max_memory = torch.cuda.max_memory_allocated()
            
            # Avoid division by zero
            if max_memory == 0:
                return False
            
            memory_ratio = memory_allocated / max_memory
            return memory_ratio > self.memory_threshold
        except Exception:
            return False
    
    def adaptive_cleanup(self, tensor_pool=None) -> None:
        """
        Perform adaptive memory cleanup based on usage patterns.
        
        Args:
            tensor_pool: Optional tensor pool to clean
        """
        if not torch.cuda.is_available():
            return
        
        # Clear unused tensor pools
        if tensor_pool is not None:
            tensor_pool.clear_pool(keep_ratio=0.5)
        
        # Clear cache if memory pressure is high
        if self.check_memory_pressure():
            torch.cuda.empty_cache()
            gc.collect()
            logger.debug("[CLEANUP] Adaptive cleanup performed")
    
    def emergency_cleanup(self, tensor_pool=None) -> None:
        """
        Perform emergency memory cleanup.
        
        Args:
            tensor_pool: Optional tensor pool to clear
        """
        logger.warning("[CLEANUP] Performing emergency memory cleanup")
        
        # Clear tensor pools
        if tensor_pool is not None:
            tensor_pool.clear_all()
        
        # Clear PyTorch cache
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        
        # Force garbage collection
        gc.collect()
        
        logger.info("[CLEANUP] Emergency cleanup completed")
    
    def get_memory_stats(self) -> dict:
        """Get current memory statistics."""
        stats = {
            'memory_pressure_level': self.memory_pressure_level,
            'memory_threshold': self.memory_threshold
        }
        
        if torch.cuda.is_available():
            stats.update({
                'cuda_allocated': torch.cuda.memory_allocated(),
                'cuda_reserved': torch.cuda.memory_reserved(),
                'cuda_max_allocated': torch.cuda.max_memory_allocated(),
                'cuda_max_reserved': torch.cuda.max_memory_reserved()
            })
        
        return stats