import os
import textwrap
from textwrap import TextWrapper
import datasets
import pyarrow.parquet as pq

_URLS = {
    "original_text": "./original_text",
    "unlabeled_sentences": "./unlabeled_sentences"
}

_metadata = {
    "citation": """\
    @InProceedings{
        huggingface:dataset,
        title = {Paraguay Legislation Dataset},
        author={Peres, Fernando; Costa, Victor},
        year={2023}
    }
    """,

    "description": "Dataset for researching.",

    "homepage": "https://www.leyes.com.py/",

    "license": "apache-2.0",
}


class TestBuilder(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="raw_text",
            version=VERSION,
            description="desc raw text",
        ),

        datasets.BuilderConfig(
            name="unlabeled_sentences",
            version=VERSION,
            description="desc unlabeled",
        ),
    ]

    def _info(self):

        features = None
        if self.config.name == "raw_text":

            features = datasets.Features(
                {
                    "id": datasets.Value(dtype="int64"),
                    "text": datasets.Value(dtype="string"),
                }
            )

        if self.config.name == "unlabeled_sentences":
            features = features = datasets.Features(
                {
                    "id": datasets.Value(dtype="int64"),
                    "text_2": datasets.Value(dtype="string"),
                    "note": datasets.Value(dtype="string"),
                }
            )

        return datasets.DatasetInfo(
            builder_name=self.config.name,
            description="description xxxxxxxxxxxxxxx",
            features=features,
            homepage=_metadata["homepage"],
            license=_metadata["license"],
            citation=_metadata["citation"],
        )

    def _split_generators(self, dl_manager):
        urls_to_download = _URLS[self.config.name]

        filepaths = dl_manager.download_and_extract(urls_to_download)

        return datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={"filepath": filepaths},
        )

    def _generate_examples(self, filepath):
        pq_table = pq.read_table(filepath)
        for i in range(len(pq_table)):
            yield i, {
                col_name: pq_table[col_name][i].as_py()
                for col_name in pq_table.column_names
            }