import json

import datasets


_CITATION = """\
@article{malhas2020ayatec,
    author = {Malhas, Rana and Elsayed, Tamer},
    title = {AyaTEC: Building a Reusable Verse-Based Test Collection for Arabic Question Answering on the Holy Qur’an},
    year = {2020},
    issue_date = {November 2020},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {19},
    number = {6},
    issn = {2375-4699},
    url = {https://doi.org/10.1145/3400396},
    doi = {10.1145/3400396},
    journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
    month = {oct},
    articleno = {78},
    numpages = {21},
    keywords = {evaluation, Classical Arabic}
}
"""

_DESCRIPTION = """\
The absence of publicly available reusable test collections for Arabic question answering on the Holy Qur’an has \
impeded the possibility of fairly comparing the performance of systems in that domain. In this article, we introduce \
AyaTEC, a reusable test collection for verse-based question answering on the Holy Qur’an, which serves as a common \
experimental testbed for this task. AyaTEC includes 207 questions (with their corresponding 1,762 answers) covering 11 \
topic categories of the Holy Qur’an that target the information needs of both curious and skeptical users. To the best \
of our effort, the answers to the questions (each represented as a sequence of verses) in AyaTEC were exhaustive—that \
is, all qur’anic verses that directly answered the questions were exhaustively extracted and annotated. To facilitate \
the use of AyaTEC in evaluating the systems designed for that task, we propose several evaluation measures to support \
the different types of questions and the nature of verse-based answers while integrating the concept of partial \
matching of answers in the evaluation.
"""

_HOMEPAGE = "https://sites.google.com/view/quran-qa-2022/home"

_LICENSE = "CC-BY-ND 4.0"

_URL = "https://gitlab.com/bigirqu/quranqa/-/raw/main/datasets/"
_URLS = {
    "train": _URL + "qrcd_v1.1_train.jsonl",
    "dev": _URL + "qrcd_v1.1_dev.jsonl",
    "test": _URL + "qrcd_v1.1_test_gold.jsonl",
    "test_noAnswers": _URL + "qrcd_v1.1_test_noAnswers.jsonl",
}


class QuranQAConfig(datasets.BuilderConfig):
    """BuilderConfig for QuranQA."""

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


class QuranQA(datasets.GeneratorBasedBuilder):
    """QuranQA: Qur'anic Reading Comprehension Dataset. Version 1.1.0"""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        QuranQAConfig(name="shared_task", version=VERSION, description="Shared task (LREC 2022)"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "pq_id": datasets.Value("string"),
                    "passage": datasets.Value("string"),
                    "surah": datasets.Value("int8"),
                    "verses": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answers": datasets.features.Sequence(
                        {
                            "text": datasets.Value("string"),
                            "answer_start": datasets.Value("int32"),  # Originally start_char
                        }
                    ),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download(_URLS)

        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"]},
            ),
            datasets.SplitGenerator(
                name="test_noAnswers",
                gen_kwargs={"filepath": downloaded_files["test_noAnswers"]},
            ),
        ]

    def _generate_examples(self, filepath):
        key = 0
        with open(filepath, encoding="utf-8") as f:
            samples = f.readlines()
        samples = [json.loads(s) for s in samples]
        for sample in samples:
            # Remap key names to match HF convention
            sample["answers"] = {
                "text": [answer["text"] for answer in sample["answers"]],
                "answer_start": [answer["start_char"] for answer in sample["answers"]]
            }
            yield key, sample
            key += 1