"""
ChromaDCT Memory Optimization Integration
Automatically applies memory optimizations to ChromaDCT models during loading
"""

import torch
from typing import Optional

# Import the optimization components
try:
    from myerflow.src.models.chroma.memory_optimization_helper import (
        apply_memory_optimization, 
        should_use_optimized_offloading,
        print_memory_optimization_info
    )
    OPTIMIZATION_AVAILABLE = True
except ImportError:
    print("Warning: ChromaDCT optimization components not found")
    OPTIMIZATION_AVAILABLE = False


def patch_chromadct_model_loading():
    """
    Patch the model loading system to automatically apply ChromaDCT optimizations
    """
    if not OPTIMIZATION_AVAILABLE:
        return
    
    # This would be called during model initialization
    # For now, this serves as a reference implementation
    pass


def optimize_model_if_needed(model, device: torch.device, strategy: Optional[str] = None):
    """
    Apply optimization to model if it's a ChromaDCT model
    
    Args:
        model: Model to potentially optimize
        device: Target device
        strategy: Offloading strategy (auto-detect if None)
        
    Returns:
        Optimized model or original model
    """
    if not OPTIMIZATION_AVAILABLE:
        return model
    
    return apply_memory_optimization(model, device, strategy)


# Example usage for manual optimization:
def example_optimize_chromadct():
    """
    Example of how to manually apply ChromaDCT optimization
    """
    print("ChromaDCT Memory Optimization Example")
    print("=====================================")
    
    # This would typically be called during model loading:
    # 
    # # Load your ChromaDCT model
    # model = load_chromadct_model()  
    # device = torch.device('cuda')
    # 
    # # Apply optimization
    # optimized_model = optimize_model_if_needed(model, device, strategy='balanced')
    # 
    # # Print optimization status
    # print_memory_optimization_info(optimized_model)
    # 
    # # Use optimized model for inference
    # result = optimized_model(img, img_ids, txt, txt_ids, txt_mask, timesteps, guidance)
    # 
    # # Print performance stats
    # if hasattr(optimized_model, 'print_performance_summary'):
    #     optimized_model.print_performance_summary()
    
    print("See chromadct_optimization_integration.py for implementation details")


if __name__ == "__main__":
    example_optimize_chromadct()