"""
Extract tar shards back to original structure
"""
import argparse
import tarfile
import csv
from pathlib import Path
from tqdm import tqdm
from collections import defaultdict
import shutil

def parse_args():
    parser = argparse.ArgumentParser(
        description="Extract tar shards to recreate dataset"
    )
    parser.add_argument(
        "--sharded-dir",
        type=Path,
        default=".",
        help="Directory containing audio/ and metadata/ folders"
    )
    parser.add_argument(
        "--output-dir",
        type=Path,
        default="extracted",
        help="Output directory for extracted dataset"
    )
    return parser.parse_args()


def main():
    args = parse_args()
    
    audio_dir = args.sharded_dir / "audio"
    metadata_file = args.sharded_dir / "metadata" / "metadata.csv"
    
    if not audio_dir.exists():
        raise FileNotFoundError(f"Audio directory not found: {audio_dir}")
    if not metadata_file.exists():
        raise FileNotFoundError(f"Metadata not found: {metadata_file}")
    
    # Read metadata to understand structure
    print("Reading metadata...")
    with open(metadata_file, 'r', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        rows = list(reader)
    
    # Group by subset and shard
    shards_by_subset = defaultdict(set)
    for row in rows:
        subset = row['subset']
        shard = row['shard']
        shards_by_subset[subset].add(shard)
    
    print(f"Found {len(rows):,} samples across {len(shards_by_subset)} subsets")
    
    
        # Extract all shards
    for subset in sorted(shards_by_subset.keys()):
        print(f"\nExtracting {subset}...")
        subset_output = args.output_dir / subset / "audio"
        subset_output.mkdir(parents=True, exist_ok=True)
        
        shards = sorted(shards_by_subset[subset])
        
        for shard_name in tqdm(shards, desc=f"  {subset}"):
            shard_path = audio_dir / subset / shard_name
            
            if not shard_path.exists():
                print(f"Warning: {shard_path} not found, skipping")
                continue
            
            # Extract tar
            with tarfile.open(shard_path, 'r') as tar:
                tar.extractall(path=subset_output)

    metadata_dir = Path(args.sharded_dir) / "metadata"
    output_dir = Path(args.output_dir)

    # Create the output directory if it does not exist
    output_dir.mkdir(parents=True, exist_ok=True)

    # Copy every CSV file
    for csv_file in metadata_dir.glob("*.csv"):
        destination = output_dir / csv_file.name
        shutil.copy2(csv_file, destination)
    
    print("\n" + "="*60)
    print("Extraction complete!")
    print("="*60)
    print(f"Output directory: {args.output_dir}")
    print(f"\nTo process the dataset:")
    print(f"python process_dataset.py \\")
    print(f"  --taskmaster-root-dir {args.output_dir} \\")
    print(f"  --aug-dataset-save-dir ./processed \\")
    print(f"  --normalize-text")


if __name__ == "__main__":
    main()