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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""The Yoruba Text C3 dataset."""


import datasets


_DESCRIPTION = """\
Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the
YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
"""

_HOMEPAGE = "https://github.com/ajesujoba/YorubaTwi-Embedding/"

_CITATION = """\
@inproceedings{alabi-etal-2020-massive,
    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
    author = "Alabi, Jesujoba  and
      Amponsah-Kaakyire, Kwabena  and
      Adelani, David  and
      Espa{\\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
    pages = "2754--2762",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
"""

URL = "data/yo_C3_large_clean_plus_noisy.txt.gz"


class YorubaTextC3(datasets.GeneratorBasedBuilder):
    """Yoruba Text C3 dataset."""
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        filepath = dl_manager.download_and_extract(URL)

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

    def _generate_examples(self, filepath):
        with open(filepath, mode="r", encoding="utf-8") as f:
            lines = f.read().splitlines()
            for id, line in enumerate(lines):
                yield id, {"text": line.strip()}