import datasets
from datasets import load_dataset
from datasets.tasks import Summarization

_DESCRIPTION = """
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split 
of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is 
compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11).
"""
_URL = 'https://huggingface.co/datasets/asahi417/squad_qg/data/processed'
_URLS = {
    'train': ['{}/train{:02d}.jsonl'.format(_URL, i) for i in range(23)],
    'test': ['{}/test{:02d}.jsonl'.format(_URL, i) for i in range(4)],
    'validation': ['{}/dev{:02d}.jsonl'.format(_URL, i) for i in range(4)]
}


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

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


class SquadQG(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "answer": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "sentence": datasets.Value("string"),
                    "passage": datasets.Value("string"),
                    "sentence_answer": datasets.Value("string"),
                    "passage_answer": datasets.Value("string"),
                    "passage_sentence": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            task_templates=[
                Summarization(task='question generation', text_column="passage_answer", summary_column='question')
            ],
            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=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file["validation"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            for _id, i in enumerate(f.read().split('\n')):
                data = json.loads(i)
                yield _id, data