""" python -c "from datasets import load_dataset;load_dataset('.')" """
import json
from itertools import chain
import datasets

logger = datasets.logging.get_logger(__name__)
_VERSION = "5.0.0"
_NAME = "qg_koquad"
_CITATION = """
@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
"""
_DESCRIPTION = """[KorQuAD](https://huggingface.co/datasets/squad_kor_v1) dataset for question generation (QG) task."""
_URL = 'https://huggingface.co/datasets/lmqg/qg_koquad/raw/main/data/processed'
_URLS = {
    str(datasets.Split.TEST): [f'{_URL}/test{i:02d}.jsonl' for i in range(9)],
    str(datasets.Split.TRAIN): [f'{_URL}/train{i:02d}.jsonl' for i in range(78)],
    str(datasets.Split.VALIDATION): [f'{_URL}/validation{i:02d}.jsonl' for i in range(9)],
}


class QGKoQuADConfig(datasets.BuilderConfig):
    """BuilderConfig for SquadQG"""

    def __init__(self, **kwargs):
        """BuilderConfig for SquadQG.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(QGKoQuADConfig, self).__init__(**kwargs)


class QGKoQuAD(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        QGKoQuADConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "answer": datasets.Value("string"), "paragraph_question": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "sentence": datasets.Value("string"),
                    "paragraph": datasets.Value("string"),
                    "sentence_answer": datasets.Value("string"),
                    "paragraph_answer": datasets.Value("string"),
                    "paragraph_sentence": datasets.Value("string"),
                    "paragraph_id": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/asahi417/lm-question-generation"
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URLS)
        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]

    def _generate_examples(self, filepaths):
        _key = 0
        for filepath in filepaths:
            logger.info("generating examples from = %s", filepath)
            with open(filepath, encoding="utf-8") as f:
                _list = f.read().split('\n')
                if _list[-1] == '':
                    _list = _list[:-1]
                for i in _list:
                    data = json.loads(i)
                    yield _key, data
                    _key += 1