# Loading script for the WikiCAT dataset.
import json
import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """

            """

_DESCRIPTION = """
                   WikiCAT: Text Classification English dataset from the Viquipedia

               """

_HOMEPAGE = """   """

# TODO: upload datasets to github
_URL = "https://huggingface.co/datasets/crodri/wikicat_en/resolve/main/"
_TRAINING_FILE = "hftrain_en.json"
_DEV_FILE = "hfeval_en.json"
#_TEST_FILE = "test.json"


class wikicat_enConfig(datasets.BuilderConfig):
    """ Builder config for the Topicat dataset """

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


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

    BUILDER_CONFIGS = [
        wikicat_enConfig(
            name="wikicat_en",
            version=datasets.Version("1.1.0"),
            description="wikicat_en",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel
                        (names= ['Health', 'Law', 'Entertainment', 'Religion', 'Business', 'Science', 'Engineering', 'Nature', 'Philosophy', 'Economy', 'Sports', 'Technology', 'Government', 'Mathematics', 'Military', 'Humanities', 'Music', 'Politics', 'History']
                    ),
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
#            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
#            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            wikicat_en = json.load(f)
            for id_, article in enumerate(wikicat_en["data"]):
                text = article["sentence"]
                label = article["label"]
                yield id_, {
                    "text": text,
                    "label": label,
                }