""" TweetTopicMultilingual Dataset """
import json
import datasets

logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[TweetTopicMultilingual](TBA)"""
_VERSION = "0.0.3"
_CITATION = """TBA"""
_HOME_PAGE = "https://cardiffnlp.github.io"
_NAME = "tweet_topic_multilingual"
_ROOT_URL = f"https://huggingface.co/datasets/cardiffnlp/{_NAME}/resolve/main/dataset"
_LANGUAGES = ["en", "es", "ja", "gr"]
_CLASS_MAPPING = {
    "en": [
        "Arts & Culture",
        "Business & Entrepreneurs",
        "Celebrity & Pop Culture",
        "Diaries & Daily Life",
        "Family",
        "Fashion & Style",
        "Film, TV & Video",
        "Fitness & Health",
        "Food & Dining",
        "Learning & Educational",
        "News & Social Concern",
        "Relationships",
        "Science & Technology",
        "Youth & Student Life",
        "Music",
        "Gaming",
        "Sports",
        "Travel & Adventure",
        "Other Hobbies"
    ],
    "gr": [
        "Τέχνες & Πολιτισμός",
        "Επιχειρήσεις & Επιχειρηματίες",
        "Διασημότητες & Ποπ κουλτούρα",
        "Ημερολόγια & Καθημερινή ζωή",
        "Οικογένεια",
        "Μόδα & Στυλ",
        "Ταινίες, τηλεόραση & βίντεο",
        "Γυμναστική & Υεία",
        "Φαγητό & Δείπνο",
        "Μάθηση & Εκπαίδευση",
        "Ειδήσεις & Κοινωνία",
        "Σχέσεις",
        "Επιστήμη & Τεχνολογία",
        "Νεανική & Φοιτητική ζωή",
        "Μουσική",
        "Παιχνίδια",
        "Αθλητισμός",
        "Ταξίδια & Περιπέτεια",
        "Άλλα χόμπι"
    ],
    "es": [
        "Arte y cultura",
        "Negocios y emprendedores",
        "Celebridades y cultura pop",
        "Diarios y vida diaria",
        "Familia",
        "Moda y estilo",
        "Cine, televisión y video",
        "Estado físico y salud",
        "Comida y comedor",
        "Aprendizaje y educación",
        "Noticias e interés social",
        "Relaciones",
        "Ciencia y Tecnología",
        "Juventud y Vida Estudiantil",
        "Música",
        "Juegos",
        "Deportes",
        "Viajes y aventuras",
        "Otros pasatiempos"
    ],
    "ja": [
        "アート&カルチャー",
        "ビジネス",
        "芸能",
        "日常",
        "家族",
        "ファッション",
        "映画&ラジオ",
        "フィットネス&健康",
        "料理",
        "教育関連",
        "社会",
        "人間関係",
        "サイエンス",
        "学校",
        "音楽",
        "ゲーム",
        "スポーツ",
        "旅行",
        "その他"
    ]
}

_URL = {}
# plain split
for lan in _LANGUAGES:
    _URL[lan] = {split: f"{_ROOT_URL}/{lan}/{lan}_{split}.jsonl" for split in ["train", "test", "validation"]}
_URL["en_2022"] = {split: f"{_ROOT_URL}/en_2022/{split}.jsonl" for split in ["train", "validation"]}
# cross validation
for lan in _LANGUAGES:
    _URL.update({
        f"{lan}_cross_validation_{n}": {
            split: f"{_ROOT_URL}/{lan}/cross_validation/{lan}_{split}_{n}.jsonl"
            for split in ["train", "test", "validation"]
        } for n in range(5)
    })


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

    def __init__(self, **kwargs):
        """BuilderConfig.

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


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

    BUILDER_CONFIGS = [
        Config(name=i, version=datasets.Version(_VERSION), description=_DESCRIPTION) for i in _URL.keys()
    ]

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URL[self.config.name])
        splits = _URL[self.config.name].keys()
        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[i]}) for i in splits]

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

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "label_name_flatten": datasets.Value("string"),
                    "label": datasets.Sequence(datasets.features.ClassLabel(names=_CLASS_MAPPING["en"])),
                    "label_name": datasets.Sequence(datasets.Value("string"))
                }
            ),
            supervised_keys=None,
            homepage=_HOME_PAGE,
            citation=_CITATION,
        )