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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""ElkarHizketak: Conversational Question Answering dataset in Basque"""

import json

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{otegi-etal-2020-conversational,
    title = "{Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for {B}asque}",
    author = "Otegi, Arantxa  and
      Agirre, Aitor  and
      Campos, Jon Ander  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    year = "2020",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.55",
    pages = "436--442",
    ISBN = "979-10-95546-34-4",
}
"""

_DESCRIPTION = """
ElkarHizketak is a low resource conversational Question Answering
(QA) dataset in Basque created by Basque speaker volunteers. The
dataset contains close to 400 dialogues and more than 1600 question
and answers, and its small size presents a realistic low-resource
scenario for conversational QA systems. The dataset is built on top of
Wikipedia sections about popular people and organizations. The
dialogues involve two crowd workers: (1) a student ask questions after
reading a small introduction about the person, but without seeing the
section text; and (2) a teacher answers the questions selecting a span
of text of the section.  """

_HOMEPAGE = "http://ixa.si.ehu.es/node/12934"

_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)"

_URLs = {
    "train": "http://ixa2.si.ehu.es/convai/elkarhizketak-v1.0/elkarhizketak-train-v1.0.json",
    "validation": "http://ixa2.si.ehu.es/convai/elkarhizketak-v1.0/elkarhizketak-dev-v1.0.json",
    "test": "http://ixa2.si.ehu.es/convai/elkarhizketak-v1.0/elkarhizketak-test-v1.0.json",
}


class Elkarhizketak(datasets.GeneratorBasedBuilder):
    """ElkarHizketak: Conversational Question Answering dataset in Basque. Version 1.0."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="plain_text",
            description="Plain text",
            version=VERSION,
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "dialogue_id": datasets.Value("string"),
                    "wikipedia_page_title": datasets.Value("string"),
                    "background": datasets.Value("string"),
                    "section_title": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "turn_id": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "yesno": datasets.ClassLabel(names=["y", "n", "x"]),
                    "answers": datasets.Sequence(
                        {
                            "text": datasets.Value("string"),
                            "answer_start": datasets.Value("int32"),
                            "input_text": datasets.Value("string"),
                        }
                    ),
                    "orig_answer": {
                        "text": datasets.Value("string"),
                        "answer_start": datasets.Value("int32"),
                    },
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_dir["validation"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": data_dir["test"],
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        logger.info("generating examples from = %s", filepath)

        key = 0
        with open(filepath, encoding="utf-8") as f:
            elkarhizketak = json.load(f)
            for section in elkarhizketak["data"]:
                wiki_page_title = section.get("title", "").strip()
                background = section.get("background", "").strip()
                section_title = section.get("section_title", "").strip()
                for dialogue in section["paragraphs"]:
                    context = dialogue["context"].strip()
                    dialogue_id = dialogue["id"]
                    for qa in dialogue["qas"]:
                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
                        answers = [answer["text"].strip() for answer in qa["answers"]]
                        input_texts = [answer["input_text"].strip() for answer in qa["answers"]]
                        yield key, {
                            "wikipedia_page_title": wiki_page_title,
                            "background": background,
                            "section_title": section_title,
                            "context": context,
                            "dialogue_id": dialogue_id,
                            "question": qa["question"],
                            "turn_id": qa["id"],
                            "yesno": qa["yesno"],
                            "answers": {
                                "answer_start": answer_starts,
                                "text": answers,
                                "input_text": input_texts,
                            },
                            "orig_answer": {
                                "answer_start": qa["orig_answer"]["answer_start"],
                                "text": qa["orig_answer"]["text"],
                            },
                        }
                        key += 1