import argparse
import tempfile
import polars as pl
from pathlib import Path

from pipeline.local_temp import local_tmp_dir
from pipeline.utils import download, extract_zip

URL = "https://www.arcgis.com/sharing/rest/content/items/36b718ad00de49afb9ad364f8b815b9e/data"


def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
    # Classification code columns (ruc21ind, oac11ind, imd20ind) look numeric
    # in early rows but contain string codes like "UN1" (Unclassified) later
    # on. Force them to String to avoid mid-stream dtype inference failures.
    # Note: NSPL renames these year suffixes as new releases roll in (e.g.
    # Feb 2026 bumped oac from oac21ind → oac11ind, imd from imd19ind →
    # imd20ind), so keep this dict in sync with the current CSV headers —
    # polars silently ignores overrides for missing columns, masking drift.
    df = pl.scan_csv(
        data_path / "Data/NSPL_FEB_2026_UK.csv",
        try_parse_dates=True,
        schema_overrides={
            "ruc21ind": pl.String,
            "oac11ind": pl.String,
            "imd20ind": pl.String,
        },
    )
    print(f"Columns: {df.collect_schema().names()}")
    parquet_path.parent.mkdir(parents=True, exist_ok=True)
    df.sink_parquet(parquet_path, compression="zstd")
    print(f"Saved to {parquet_path}")


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Download and convert ArcGIS postcode data"
    )
    parser.add_argument(
        "--output", type=Path, required=True, help="Output parquet file path"
    )
    args = parser.parse_args()

    with tempfile.TemporaryDirectory(dir=local_tmp_dir()) as cache_dir:
        download_path = Path(cache_dir) / "arcgis_data.zip"
        extract_path = Path(cache_dir) / "arcgis_extracted"

        download(URL, download_path)
        extract_zip(download_path, extract_path)
        convert_to_parquet(extract_path, args.output)


if __name__ == "__main__":
    main()