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

logger = datasets.logging.get_logger(__name__)

_CITATION = """

            """

_DESCRIPTION = """
                  Dataset automatically created from Catalan Wikipedia articles and the associated categories.
               """


_URL = "./"
_TRAINING_FILE = "train.json"
_DEV_FILE = "dev.json"


class ca_wiki_tcConfig(datasets.BuilderConfig):
    """ Builder config for the CaWikiTC dataset """

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


class ca_wiki_tc(datasets.GeneratorBasedBuilder):
    """ CaWikiTC Dataset """

    BUILDER_CONFIGS = [
        ca_wiki_tcConfig(
            name="ca-wiki-tc",
            version=datasets.Version("1.0.1"),
            description="CaWikiTC dataset",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel
                        (names=
                    [
                        "Administració",
                        "Aeronàutica",
                        "Agricultura",
                        "Antropologia",
                        "Arqueologia",
                        "Arquitectura",
                        "Art",
                        "Astronomia",
                        "Astronàutica",
                        "Biblioteconomia",
                        "Biotecnologia",
                        "Catàstrofes",
                        "Circ",
                        "Ciència militar",
                        "Ciència-ficció",
                        "Ciències ambientals",
                        "Ciències de la salut",
                        "Ciències polítiques",
                        "Conflictes",
                        "Cronometria",
                        "Cultura popular",
                        "Dansa",
                        "Dret",
                        "Ecologia",
                        "Enginyeria",
                        "Epidèmies",
                        "Esoterisme",
                        "Estris",
                        "Festivals",
                        "Filologia",
                        "Filosofia",
                        "Fiscalitat",
                        "Física",
                        "Geografia",
                        "Geologia",
                        "Gestió",
                        "Heràldica",
                        "Història",
                        "Humor",
                        "Indumentària",
                        "Informàtica",
                        "Jaciments paleontològics",
                        "Jocs",
                        "Lingüística",
                        "Llengües",
                        "Llocs ficticis",
                        "Matemàtiques",
                        "Metodologia",
                        "Mitologia",
                        "Multimèdia",
                        "Museologia",
                        "Nàutica",
                        "Objectes astronòmics",
                        "Pedagogia",
                        "Periodisme",
                        "Protestes",
                        "Pseudociència",
                        "Psicologia",
                        "Química",
                        "Robòtica",
                        "Ràdio",
                        "Seguretat laboral",
                        "Sociologia",
                        "Telecomunicacions",
                        "Televisió",
                        "Teologia",
                        "Ètica",
                    ]
                    ),
                }
            ),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_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"]})
        ]

    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:
            data = json.load(f)
            for id_, article in enumerate(data):
                text = article["text"]
                label = article["label"]
                yield id_, {
                    "text": text,
                    "label": label,
                }