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"""Introduction to the Yoruba GV NER dataset: A Yoruba Global Voices (News) Named Entity Recognition Dataset"""


import datasets


logger = datasets.logging.get_logger(__name__)


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{alabi-etal-2020-massive,
    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Yorùbá} 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",
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The Yoruba GV NER dataset is a labeled dataset for named entity recognition in Yoruba. The texts were obtained from
Yoruba Global Voices News articles https://yo.globalvoices.org/ . We concentrate on
four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].

The Yoruba GV NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and
there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second
is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase
of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words
have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.

For more details, see https://www.aclweb.org/anthology/2020.lrec-1.335/
"""

_URL = "https://github.com/ajesujoba/YorubaTwi-Embedding/raw/master/Yoruba/Yoruba-NER/"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "valid.tsv"
_TEST_FILE = "test.tsv"


class YorubaGvNerConfig(datasets.BuilderConfig):
    """BuilderConfig for YorubaGvNer"""

    def __init__(self, **kwargs):
        """BuilderConfig for YorubaGvNer.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(YorubaGvNerConfig, self).__init__(**kwargs)


class YorubaGvNer(datasets.GeneratorBasedBuilder):
    """Yoruba GV NER dataset."""

    BUILDER_CONFIGS = [
        YorubaGvNerConfig(
            name="yoruba_gv_ner", version=datasets.Version("1.0.0"), description="Yoruba GV NER dataset"
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-PER",
                                "I-PER",
                                "B-ORG",
                                "I-ORG",
                                "B-LOC",
                                "I-LOC",
                                "B-DATE",
                                "I-DATE",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                line = line.strip()
                if line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # yoruba_gv_ner tokens are tab separated
                    splits = line.strip().split("\t")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }