#!/usr/bin/env python3
"""
Example script to convert fashion datasets to parquet format for LookBench
This is a template for creating dataset converters for various fashion datasets
"""

import os
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from pathlib import Path
from typing import List, Dict, Any
import argparse


def convert_to_parquet(
    image_paths: List[str],
    labels: List[int],
    output_path: str,
    metadata: Dict[str, Any] = None
):
    """
    Convert image paths and labels to parquet format

    Args:
        image_paths: List of image file paths
        labels: List of corresponding labels
        output_path: Output parquet file path
        metadata: Optional metadata to include
    """
    # Create dataframe
    df = pd.DataFrame({
        'image': image_paths,
        'label': labels
    })

    # Add metadata if provided
    if metadata:
        for key, value in metadata.items():
            df[key] = value

    # Save to parquet
    output_dir = os.path.dirname(output_path)
    if output_dir:
        os.makedirs(output_dir, exist_ok=True)

    df.to_parquet(output_path, engine='pyarrow', index=False)
    print(f"Saved {len(df)} samples to {output_path}")


def example_fashion200k_converter(data_root: str, output_root: str):
    """
    Example converter for Fashion200K dataset
    
    Args:
        data_root: Root directory containing Fashion200K data
        output_root: Output directory for parquet files
    """
    print("Converting Fashion200K dataset...")

    # Example: Create query and gallery splits
    # In a real implementation, you would:
    # 1. Read the actual dataset structure
    # 2. Parse image paths and labels
    # 3. Split into query and gallery sets

    # Example query data
    query_images = [
        "query/image_001.jpg",
        "query/image_002.jpg",
        # ... more images
    ]
    query_labels = [0, 1]  # Corresponding labels

    # Example gallery data
    gallery_images = [
        "gallery/image_001.jpg",
        "gallery/image_002.jpg",
        # ... more images
    ]
    gallery_labels = [0, 1]  # Corresponding labels

    # Convert to parquet
    os.makedirs(output_root, exist_ok=True)
    
    convert_to_parquet(
        query_images,
        query_labels,
        os.path.join(output_root, "query", "query.parquet")
    )
    
    convert_to_parquet(
        gallery_images,
        gallery_labels,
        os.path.join(output_root, "gallery", "gallery.parquet")
    )

    print(f"Fashion200K conversion complete. Output: {output_root}")


def example_deepfashion_converter(data_root: str, output_root: str):
    """
    Example converter for DeepFashion dataset
    
    Args:
        data_root: Root directory containing DeepFashion data
        output_root: Output directory for parquet files
    """
    print("Converting DeepFashion dataset...")
    
    # Similar structure to Fashion200K converter
    # Implement based on DeepFashion dataset structure
    
    print(f"DeepFashion conversion complete. Output: {output_root}")


def main():
    """Main entry point"""
    parser = argparse.ArgumentParser(
        description="Convert fashion datasets to parquet format"
    )
    parser.add_argument(
        "--dataset",
        type=str,
        required=True,
        choices=["fashion200k", "deepfashion", "deepfashion2", "product10k"],
        help="Dataset type to convert"
    )
    parser.add_argument(
        "--data_root",
        type=str,
        required=True,
        help="Root directory containing the dataset"
    )
    parser.add_argument(
        "--output_root",
        type=str,
        required=True,
        help="Output directory for parquet files"
    )
    
    args = parser.parse_args()

    # Call appropriate converter
    if args.dataset == "fashion200k":
        example_fashion200k_converter(args.data_root, args.output_root)
    elif args.dataset == "deepfashion":
        example_deepfashion_converter(args.data_root, args.output_root)
    else:
        print(f"Converter for {args.dataset} not implemented yet")


if __name__ == "__main__":
    main()

