# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "datasets>=3.2.0",
# ]
# ///
"""
This script is used to create the data for the AI-aktindsigt project.

This derived the data from a .json.gz file.
"""

from pathlib import Path
from typing import cast

from datasets import Dataset, load_dataset

source = "ai-aktindsigt"


def convert_sample(example):
    # {'text': 'Vallensbæk Stationstorv 100 2665 Vallensbæk Strand Telefon: +45 4797 4000',
    # 'id': '0_03fe7662f6d37df0ffbf5013907414f935350db9931043891a95ed830965a507a7bcb4df93741429bdfa4958cf25f6c273aa73146f2be80948f767eb5fa04645',
    # 'source': 'AI-aktindsigt',
    # 'added': '2024-04-16T12:35:52.000Z',
    # 'metadata': {'url': 'https://vallensbaek.dk/', 'kommune': 'vallensbaek', 'sentence': 1,
    # 'ppl_score': [634.6341],
    # 'sha512': '03fe7662f6d37df0ffbf5013907414f935350db9931043891a95ed830965a507a7bcb4df93741429bdfa4958cf25f6c273aa73146f2be80948f767eb5fa04645'}
    # }

    new_example = dict(
        text_new=example["text"],
        source=source,
        domain="Web",
        license="Apache-2.0",
        added="2025-03-24",
        created="2010-01-01, 2024-03-18",  # Start date is approximate guess end date is the date of the last update
        metadata={"source-pretty": "AI Aktindsigt"},
    )

    return new_example


def main():
    data_path = Path(
        "/work/dfm-data/pre-training/ai_aktindsigt/documents/ai_aktindsigt.jsonl.gz"
    )
    ds = load_dataset("json", data_files=data_path.as_posix(), split="train")

    ds = cast(Dataset, ds)

    ds = ds.map(convert_sample, remove_columns=ds.column_names)
    ds = ds.rename_columns({"text_new": "text"})
    ds = ds.add_column("id", [f"{source}_{i}" for i in range(len(ds))])  # type: ignore
    ds = ds.select_columns(
        ["text", "source", "id", "added", "created", "license", "domain", "metadata"]
    )

    save_path = Path(__file__).parent / f"{source}.parquet"
    ds.to_parquet(save_path)


if __name__ == "__main__":
    main()