from pandas import read_csv

from datasets import GeneratorBasedBuilder, Value, Version, BuilderConfig, Features, DatasetInfo, SplitGenerator, Split

_DESCRIPTION = '''
This dataset contains anekdotes parsed from a few vk social network communities. The data can be useful for fine-tuning language generation models as well for tasks of automatic humour analysis.
'''

_HOMEPAGE = 'https://huggingface.co/datasets/zeio/baneks'

_LICENSE = 'Apache License Version 2.0'

_URLS = {
    'censored': 'https://huggingface.co/datasets/zeio/baneks/resolve/main/censored.tsv',
    'default': 'https://huggingface.co/datasets/zeio/baneks/resolve/main/default.tsv',
    'inflated': 'https://huggingface.co/datasets/zeio/baneks/resolve/main/inflated.tsv'
}


class Baneks(GeneratorBasedBuilder):

    VERSION = Version('10.10.2023')

    BUILDER_CONFIGS = [
        BuilderConfig(name = 'censored', version = VERSION, description = 'No duplicates - entries with the same text are grouped and aggregated'),
        BuilderConfig(name = 'default', version = VERSION, description = 'Same as "censored", but censored words are replaced with inferred values for their initial form'),
        BuilderConfig(name = 'inflated', version = VERSION, description = 'Each entry corresponds to a post, minimal changes to the source data')
    ]

    DEFAULT_CONFIG_NAME = 'default'

    def _info(self):
        return DatasetInfo(
            description=_DESCRIPTION,
            features = Features({
                'text': Value('string'),
                'published': Value('string'),
                'id': Value('int32'),
                'n-likes': Value('int32'),
                'n-views': Value('int32'),
                'accessed': Value('string'),
                'source': Value('string')
            }),
            homepage=_HOMEPAGE,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):
        name = self.config.name

        url = _URLS[name]
        # path = os.path.join(dl_manager.download_and_extract(url), f'{name}.tsv')

        return [
            SplitGenerator(
                name = Split.TRAIN,
                gen_kwargs = {
                    "path": dl_manager.download_and_extract(url)
                }
            )
        ]

    def _generate_examples(self, path: str):
        for _, row in read_csv(path, sep = '\t').iterrows():
            yield f'{row["id"]:08d}-{row["source"]}', dict(row)