import csv
import datasets

_DESCRIPTION = "BWNS: The Bahai World News Service dataset."
_HOMEPAGE = "https://news.bahai.org/"
_URLS = {
    "train": "./train.csv",
}


class BwnsDataset(datasets.GeneratorBasedBuilder):
    """BWNS: The Bahai World News Service dataset."""

    BUILDER_CONFIG_CLASS=datasets.BuilderConfig

    VERSION = datasets.Version("1.1.0")
    NAME = "BWNS"

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="original", version=VERSION, description="Original"),
        datasets.BuilderConfig(name="concat", version=VERSION, description="Title and content concatenated"),
    ]

    DEFAULT_CONFIG_NAME = "original"

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "original":
            features = datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "date": datasets.Value("timestamp[s]"),
                    "city": datasets.Value("string"),
                    "region": datasets.Value("string"),
                    "country": datasets.Value("string"),
                    "related_articles": datasets.Sequence(datasets.Value("int32")),
                    "title": datasets.Value("string"),
                    "content": datasets.Value("string"),
                }
            )
        elif self.config.name == "concat":
            features = datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "date": datasets.Value("timestamp[s]"),
                    "city": datasets.Value("string"),
                    "region": datasets.Value("string"),
                    "country": datasets.Value("string"),
                    "related_articles": datasets.Sequence(datasets.Value("int32")),
                    "title": datasets.Value("string"),
                    "title_and_content": datasets.Value("string"),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
        )

    def _split_generators(self, dl_manager):
        data_dirs = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dirs['train'],
                    "split": "train",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for row in reader:
                if self.config.name == 'original':
                    yield row['id'], {
                        "id": int(row['id']),
                        "date": row['date'],
                        "city": row['city'],
                        "region": row['region'],
                        "country": row['country'],
                        "related_articles": [int(x) for x in row['related_articles'][1:-1].split(',') if x],
                        "title": row['title'],
                        "content": row['content'],
                    }
                elif self.config.name == 'concat':
                    yield row['id'], {
                        "id": int(row['id']),
                        "date": row['date'],
                        "city": row['city'],
                        "region": row['region'],
                        "country": row['country'],
                        "related_articles": [int(x) for x in row['related_articles'][1:-1].split(',') if x],
                        "title": row['title'],
                        "title_and_content": f"{row['title']} {row['content']}",
                    }