import json
import datasets

logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """Dataset for relation mapping task (see [paper](https://arxiv.org/abs/2211.15268))."""
_NAME = "scientific_and_creative_analogy"
_VERSION = "0.0.0"
_CITATION = """
@article{czinczoll2022scientific,
  title={Scientific and Creative Analogies in Pretrained Language Models},
  author={Czinczoll, Tamara and Yannakoudakis, Helen and Mishra, Pushkar and Shutova, Ekaterina},
  journal={arXiv preprint arXiv:2211.15268},
  year={2022}
}
"""
_HOME_PAGE = "https://github.com/taczin/SCAN_analogies"
_URLS = {
    str(datasets.Split.TEST): [f'https://huggingface.co/datasets/relbert/{_NAME}/raw/main/data.jsonl']
}


class ScientificAndCreativeAnalogyConfig(datasets.BuilderConfig):
    """BuilderConfig"""

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


class ScientificAndCreativeAnalogy(datasets.GeneratorBasedBuilder):
    """Dataset."""

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

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

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

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "reference": datasets.Sequence(datasets.Value("string")),
                    "source": datasets.Sequence(datasets.Value("string")),
                    "target": datasets.Sequence(datasets.Value("string")),
                    "target_random": datasets.Sequence(datasets.Value("string")),
                }
            ),
            supervised_keys=None,
            homepage=_HOME_PAGE,
            citation=_CITATION,
        )