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

This module provides functionality to export a trained model to a specific format.
"""

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

import neurenix

def export_command(args: argparse.Namespace) -> int:
    """
    Export a trained model to a specific format.
    
    Args:
        args: Command line arguments
        
    Returns:
        Exit code
    """
    parser = argparse.ArgumentParser(
        description="Export a trained model to a specific format",
        usage="neurenix export [<args>]"
    )
    
    parser.add_argument(
        "--model",
        help="Model file",
        required=True
    )
    
    parser.add_argument(
        "--format",
        help="Export format",
        choices=["onnx", "torchscript", "tensorflow", "tflite", "wasm", "c"],
        required=True
    )
    
    parser.add_argument(
        "--output",
        help="Output file",
        default=None
    )
    
    parser.add_argument(
        "--optimize",
        help="Optimize the exported model",
        action="store_true"
    )
    
    parser.add_argument(
        "--quantize",
        help="Quantize the exported model",
        choices=["int8", "fp16", "none"],
        default="none"
    )
    
    export_args = parser.parse_args(args.args)
    
    if not os.path.exists(export_args.model):
        print(f"Error: Model file '{export_args.model}' not found.")
        return 1
    
    if not export_args.output:
        base_name = os.path.splitext(export_args.model)[0]
        
        if export_args.format == "onnx":
            export_args.output = f"{base_name}.onnx"
        elif export_args.format == "torchscript":
            export_args.output = f"{base_name}.pt"
        elif export_args.format == "tensorflow":
            export_args.output = f"{base_name}_tf"
        elif export_args.format == "tflite":
            export_args.output = f"{base_name}.tflite"
        elif export_args.format == "wasm":
            export_args.output = f"{base_name}.wasm"
        elif export_args.format == "c":
            export_args.output = f"{base_name}.c"
    
    try:
        print(f"Loading model from {export_args.model}...")
        model = neurenix.load_model(export_args.model)
        
        print(f"Exporting model to {export_args.format} format...")
        
        export_options = {
            "optimize": export_args.optimize,
            "quantize": export_args.quantize if export_args.quantize != "none" else None
        }
        
        neurenix.export_model(
            model,
            export_args.output,
            format=export_args.format,
            **export_options
        )
        
        print(f"Model exported to {export_args.output}")
        return 0
    except Exception as e:
        print(f"Error exporting model: {str(e)}")
        return 1
