"""
Implementation of the 'optimize' command for the Neurenix CLI.

This module provides functionality to optimize a model by adjusting
hyperparameters or applying techniques like quantization.
"""

import os
import json
import argparse
from typing import Dict, Any, Optional, List, Union

import neurenix

def optimize_command(args: argparse.Namespace) -> int:
    """
    Optimize a model by adjusting hyperparameters or applying techniques like quantization.
    
    Args:
        args: Command line arguments
        
    Returns:
        Exit code
    """
    parser = argparse.ArgumentParser(
        description="Optimize a model by adjusting hyperparameters or applying techniques like quantization",
        usage="neurenix optimize [<args>]"
    )
    
    parser.add_argument(
        "--model",
        help="Model file",
        required=True
    )
    
    parser.add_argument(
        "--output",
        help="Output model file",
        default=None
    )
    
    parser.add_argument(
        "--technique",
        help="Optimization technique",
        choices=["quantize", "prune", "distill", "hyperparameter", "auto"],
        default="auto"
    )
    
    parser.add_argument(
        "--quantize",
        help="Quantization precision",
        choices=["int8", "fp16", "fp8"],
        default=None
    )
    
    parser.add_argument(
        "--prune",
        help="Pruning level (0.0 to 1.0)",
        type=float,
        default=None
    )
    
    parser.add_argument(
        "--data",
        help="Calibration data for optimization",
        default=None
    )
    
    parser.add_argument(
        "--config",
        help="Optimization configuration file",
        default=None
    )
    
    parser.add_argument(
        "--device",
        help="Device to use for optimization",
        default="auto"
    )
    
    optimize_args = parser.parse_args(args.args)
    
    if not os.path.exists(optimize_args.model):
        print(f"Error: Model file '{optimize_args.model}' not found.")
        return 1
    
    if not optimize_args.output:
        base_name, ext = os.path.splitext(optimize_args.model)
        
        if optimize_args.technique == "quantize" or optimize_args.quantize:
            precision = optimize_args.quantize or "int8"
            optimize_args.output = f"{base_name}_{precision}{ext}"
        elif optimize_args.technique == "prune":
            level = optimize_args.prune or 0.5
            optimize_args.output = f"{base_name}_pruned_{int(level*100)}{ext}"
        else:
            optimize_args.output = f"{base_name}_optimized{ext}"
    
    try:
        print(f"Loading model from {optimize_args.model}...")
        model = neurenix.load_model(optimize_args.model)
        
        neurenix.set_device(optimize_args.device)
        
        config = {}
        if optimize_args.config and os.path.exists(optimize_args.config):
            with open(optimize_args.config, "r") as f:
                config = json.load(f)
        
        if optimize_args.technique != "auto":
            config["technique"] = optimize_args.technique
        
        if optimize_args.quantize:
            config["quantize"] = optimize_args.quantize
        
        if optimize_args.prune is not None:
            config["prune"] = optimize_args.prune
        
        calibration_data = None
        if optimize_args.data:
            if os.path.exists(optimize_args.data):
                print(f"Loading calibration data from {optimize_args.data}...")
                calibration_data = neurenix.load_dataset(optimize_args.data)
            else:
                print(f"Warning: Calibration data '{optimize_args.data}' not found.")
        
        print(f"Optimizing model using {config.get('technique', 'auto')} technique...")
        
        optimized_model = neurenix.optimize_model(
            model,
            technique=config.get("technique", "auto"),
            calibration_data=calibration_data,
            **config
        )
        
        print(f"Saving optimized model to {optimize_args.output}...")
        neurenix.save_model(optimized_model, optimize_args.output)
        
        if hasattr(optimized_model, "optimization_results"):
            print("\nOptimization Results:")
            for key, value in optimized_model.optimization_results.items():
                print(f"{key}: {value}")
        
        print(f"\nModel successfully optimized and saved to {optimize_args.output}")
        return 0
    except Exception as e:
        print(f"Error optimizing model: {str(e)}")
        return 1
