import torch
from safetensors.torch import load_file, save_file
import logging
import asyncio

# Logging setup
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

# Define model checkpoint path
MODEL_CHECKPOINT = "model-3-of-10.safetensors"

# Detect GPU availability
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load model with efficient memory management
async def load_model(filepath: str) -> dict:
    """Asynchronously loads a model from a safetensors file."""
    try:
        logging.info(f"Loading model from {filepath} on {DEVICE}...")
        model_data = load_file(filepath, device=DEVICE)
        logging.info(f"Model {filepath} successfully loaded.")
        return model_data
    except Exception as e:
        logging.error(f"Error loading model: {str(e)}")
        raise RuntimeError(f"Error loading model: {str(e)}")

# Save model with optimized storage format
async def save_model(filepath: str, model_tensors: dict):
    """Asynchronously saves a model to a safetensors file."""
    try:
        logging.info(f"Saving model to {filepath}...")
        save_file(model_tensors, filepath)
        logging.info(f"Model saved at {filepath}")
    except Exception as e:
        logging.error(f"Error saving model: {str(e)}")
        raise RuntimeError(f"Error saving model: {str(e)}")

# Dynamically generate layers for efficient scaling
def initialize_model(layers: list = [4096, 8192, 16384], dtype: torch.dtype = torch.float16) -> dict:
    """Initializes a model with random tensors for each layer."""
    model_tensors = {}
    for i, size in enumerate(layers):
        layer_name = f"layer_{i+1}"
        logging.info(f"Initializing {layer_name} with size {size}x{size} on {DEVICE}...")
        model_tensors[layer_name] = torch.randn(size, size, dtype=dtype, device=DEVICE)
    
    torch.cuda.empty_cache()  # Free unused memory
    logging.info("Model initialization completed.")
    return model_tensors

# Main execution
async def main():
    model_data = initialize_model()

    # Save the model for deployment
    await save_model(MODEL_CHECKPOINT, model_data)

    # Load the model for verification
    loaded_model_data = await load_model(MODEL_CHECKPOINT)

    # Verify loaded tensors match saved tensors
    for key in model_data:
        if not torch.allclose(model_data[key], loaded_model_data[key], atol=1e-5):
            logging.warning(f"Tensor mismatch in {key}!")
        else:
            logging.info(f"Tensor {key} verified successfully.")

# Run asynchronously
asyncio.run(main())