"""TiQuAD: Tigrinya Question-Answering Dataset."""

import json

import datasets


_HOMEPAGE = "https://github.com/fgaim/tiquad"

_DESCRIPTION = """\
TiQuAD is a manually annotated extractive Question-Answering (QA) dataset for the Tigrinya language.
"""

_CITATION = """\
@inproceedings{gaim-etal-2023-tiquad,
    title = "{Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya}",
    author = "Fitsum Gaim and Wonsuk Yang and Hancheol Park and Jong C. Park",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.661",
    pages = "11857--11870",
}
"""

_LICENSE = "Creative Commons Attribution-ShareAlike 4.0"

_DATA_PATHS = {
    "train": "data/TiQuAD-v1-train.json",
    "dev": "data/TiQuAD-v1-dev.json",
}


class TiQuADConfig(datasets.BuilderConfig):
    """BuilderConfig for TiQuAD"""

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


class TiQuAD(datasets.GeneratorBasedBuilder):
    """TiQuAD dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answers": datasets.features.Sequence(
                        {
                            "text": datasets.Value("string"),
                            "answer_start": datasets.Value("int32"),
                        }
                    ),
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_files = dl_manager.download_and_extract(_DATA_PATHS)

        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"]},
            )
        ]

    def _generate_examples(self, filepath):
        """Yields TiQuAD examples."""
        with open(filepath, encoding="utf-8") as fin:
            _quad = json.load(fin)
            for example in _quad["data"]:
                title = example.get("title", "")
                for paragraph in example["paragraphs"]:
                    context = paragraph["context"]
                    for qa in paragraph["qas"]:
                        _id = qa["id"]
                        answers = [
                            {
                                "text": answer["text"],
                                "answer_start": answer["answer_start"],
                            }
                            for answer in qa["answers"]
                        ]
                        yield (
                            _id,
                            {
                                "id": _id,
                                "title": title,
                                "context": context,
                                "question": qa["question"],
                                "answers": answers,
                            },
                        )