import os
import argparse
import polars as pl
from datasets import load_dataset, Features, Value
from datasets.features import Image

def main():
    parser = argparse.ArgumentParser(
        description="Export the Rare Species dataset into a legacy on-disk folder structure"
    )
    parser.add_argument(
        "--dataset-path", default="dataset",
        help="Directory under which to write the `dataset/...` hierarchy"
    )
    parser.add_argument(
        "--revision", default="main",
        help="Hugging Face dataset revision (branch, tag, or commit SHA)"
    )
    args = parser.parse_args()

    # Read metadata.csv from the remote
    csv_url = (
        f"https://huggingface.co/datasets/imageomics/rare-species/"
        f"resolve/main/metadata.csv?download=true"
    )
    print(f"Loading metadata from {csv_url}")
    df_pl = pl.read_csv(csv_url)
    rel_paths = df_pl["file_name"].to_list()

    # Define schema: file_name as raw bytes (no PIL decode), others as strings
    features = Features({
        "file_name": Image(decode=False),
        **{c: Value("string") for c in df_pl.columns if c != "file_name"}
    })

    # Load the Parquet-backed dataset
    print(f"Loading dataset imageomics/rare-species @ {args.revision}")
    ds = load_dataset(
        "imageomics/rare-species",
        split="train",
        revision=args.revision,
        features=features
    )

    # Export each image's raw bytes under <path>/dataset/...
    print(f"Exporting {len(rel_paths)} images to {args.dataset_path}/")
    for idx, rel in enumerate(rel_paths):
        info = ds[idx]["file_name"]
        img_bytes = info["bytes"]
        dst = os.path.join(args.dataset_path, rel)
        os.makedirs(os.path.dirname(dst), exist_ok=True)
        with open(dst, "wb") as f:
            f.write(img_bytes)

    print(f"Export complete: images written under {args.dataset_path}/dataset")

if __name__ == "__main__":
    main()