import datasets

_LANGUAGE_PAIRS = {
    "en_de": "data/en_de.parquet",
    "en_es": "data/en_es.parquet",
    "en_fr": "data/en_fr.parquet",
    "en_it": "data/en_it.parquet",
    "en_ko": "data/en_ko.parquet",
    "en_nl": "data/en_nl.parquet",
    "en_pt": "data/en_pt.parquet",
    "en_ru": "data/en_ru.parquet",
    "en_zh": "data/en_zh.parquet",
}

VERSION = datasets.Version("1.0.0")


class Spite(datasets.GeneratorBasedBuilder):
    VERSION = VERSION

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=lang_pair,
            version=VERSION,
            description=f"Training data for language pair: {lang_pair}",
        )
        for lang_pair in _LANGUAGE_PAIRS
    ]

    DEFAULT_CONFIG_NAME = "en_de"

    def _info(self):
        return datasets.DatasetInfo(
            description="Pseudolabeled speech translation dataset",
            features=datasets.Features({
                "src": datasets.Value("string"),
                "mt": datasets.Value("string"),
                'cometqe_22': datasets.Value("float64"),
                'xcomet_xl': datasets.Value("float64"),
                'blaser2_src': datasets.Value("float64"),
                'audio_length': datasets.Value("float64"),
                "example_id": datasets.Value("string"),
                "index": datasets.Value("int64")
                # add more columns if needed
            }),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        parquet_path = dl_manager.download(_LANGUAGE_PAIRS[self.config.name])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": parquet_path},
            )
        ]

    def _generate_examples(self, filepath):
        import pyarrow.parquet as pq

        table = pq.read_table(filepath)
        for idx, row in enumerate(table.to_pylist()):
            yield idx, row