# coding=utf-8

"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""

import json
import datasets

logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
         Mintaka is a complex, natural, and multilingual dataset designed for experimenting with end-to-end
         question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English,
         annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian,
         Japanese, Portuguese, and Spanish for a total of 180,000 samples.
         Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, 
         which were naturally elicited from crowd workers. 
"""

_CITATION = """\
        @inproceedings{sen-etal-2022-mintaka,
            title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
            author = "Sen, Priyanka  and
              Aji, Alham Fikri  and
              Saffari, Amir",
            booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
            month = oct,
            year = "2022",
            address = "Gyeongju, Republic of Korea",
            publisher = "International Committee on Computational Linguistics",
            url = "https://aclanthology.org/2022.coling-1.138",
            pages = "1604--1619"
        }
"""

_LICENSE = """\
Copyright Amazon.com Inc. or its affiliates.
Attribution 4.0 International
"""

_TRAIN_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_train.json"
_DEV_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_dev.json"
_TEST_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_test.json"


_LANGUAGES = ['en', 'ar', 'de', 'ja', 'hi', 'pt', 'es', 'it', 'fr']

_ALL = "all"

class Mintaka(datasets.GeneratorBasedBuilder):
    """Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name = name,
            version = datasets.Version("1.0.0"),
            description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering for {name}",
        ) for name in _LANGUAGES
    ]

    BUILDER_CONFIGS.append(datasets.BuilderConfig(
        name = _ALL,
        version = datasets.Version("1.0.0"),
        description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
    ))

    DEFAULT_CONFIG_NAME = 'en'

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "lang": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answerText": datasets.Value("string"),
                    "category": datasets.Value("string"),
                    "complexityType": datasets.Value("string"),
                    "questionEntity": [{
                        "name": datasets.Value("string"),
                        "entityType": datasets.Value("string"),
                        "label": datasets.Value("string"),
                        "mention": datasets.Value("string"),
                        "span": [datasets.Value("int32")],
                    }],
                    "answerEntity": [{
                        "name": datasets.Value("string"),
                        "label": datasets.Value("string"),
                    }]
                },
            ),
            supervised_keys=None,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "file": dl_manager.download_and_extract(_TRAIN_URL),
                    "lang": self.config.name,
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "file": dl_manager.download_and_extract(_DEV_URL),
                    "lang": self.config.name,
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "file": dl_manager.download_and_extract(_TEST_URL),
                    "lang": self.config.name,
                }
            ),
        ]

    def _generate_examples(self, file, lang):
        if lang == _ALL:
            langs = _LANGUAGES
        else:
            langs = [lang]

        key_ = 0

        logger.info("⏳ Generating examples from = %s", ", ".join(lang))

        with open(file, encoding='utf-8') as json_file:
            data = json.load(json_file)
            for sample in data:
                for lang in langs:
                    questionEntity = [
                        {
                            "name": str(qe["name"]),
                            "entityType": qe["entityType"],
                            "label": qe["label"] if "label" in qe else "",
                            "mention": qe["mention"],
                            "span": qe["span"],
                        } for qe in sample["questionEntity"]
                    ]

                    answers = []
                    if sample['answer']["answerType"] == "entity" and sample['answer']['answer'] is not None:
                        answers = sample['answer']['answer']
                    elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]:
                        answers = sample['answer']['supportingEnt']

                    def get_label(labels, lang):
                        if lang in labels:
                            return labels[lang]
                        if 'en' in labels:
                            return labels['en']
                        return ""

                    answerEntity = [
                        {
                            "name": str(ae["name"]),
                            "label": get_label(ae["label"], lang),
                        } for ae in answers
                    ]

                    yield key_, {
                        "id": sample["id"],
                        "lang": lang,
                        "question": sample["question"] if lang == 'en' else sample['translations'][lang],
                        "answerText": sample["answer"]["mention"],
                        "category": sample["category"],
                        "complexityType": sample["complexityType"],
                        "questionEntity": questionEntity,
                        "answerEntity": answerEntity,

                    }

                    key_ += 1