"""MTet dataset."""

import json

import datasets


_CITATION = """\
@article{mTet2022,
    author  = {Chinh Ngo, Hieu Tran, Long Phan, Trieu H. Trinh, Hieu Nguyen, Minh Nguyen, Minh-Thang Luong},
    title   = {MTet: Multi-domain Translation for English and Vietnamese},
    journal = {https://github.com/vietai/mTet},
    year    = {2022},
}
"""

_DESCRIPTION = """\
MTet (Multi-domain Translation for English-Vietnamese) dataset contains roughly 4.2 million English-Vietnamese pairs of
texts, ranging across multiple different domains such as medical publications, religious texts, engineering articles,
literature, news, and poems.

This dataset extends our previous SAT (Style Augmented Translation) dataset (v1.0) by adding more high-quality
English-Vietnamese sentence pairs on various domains.
"""

_HOMEPAGE = "https://github.com/vietai/mTet"

_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"

_URLS = {
    "en": "https://storage.googleapis.com/vietai_public/best_vi_translation/v2/train.en",
    "vi": "https://storage.googleapis.com/vietai_public/best_vi_translation/v2/train.vi",
}


class Mtet(datasets.GeneratorBasedBuilder):
    """MTet dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({"translation": datasets.features.Translation(languages=["en", "vi"])}),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_paths = dl_manager.download(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_paths": data_paths,
                },
            ),
        ]

    def _generate_examples(self, data_paths):
        with open(data_paths["en"], encoding="utf-8") as f_en, open(data_paths["vi"], encoding="utf-8") as f_vi:
            for key, (sentence_en, sentence_vi) in enumerate(zip(f_en, f_vi)):
                yield key, {"translation": {"en": sentence_en.strip(), "vi": sentence_vi.strip()}}