# Loading script for the VilaQuAD dataset.

import json

import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """

               Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021). 

               VilaQuAD: an extractive QA dataset for catalan, from Vilaweb newswire text

               [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4562337
               
            """

_DESCRIPTION = """

                This dataset contains 2095 of Catalan language news articles along with 1 to 5 questions referring to each fragment (or context).
                
                VilaQuad articles are extracted from the daily Vilaweb (www.vilaweb.cat) and used under CC-by-nc-sa-nd (https://creativecommons.org/licenses/by-nc-nd/3.0/deed.ca) licence. 
                
                This dataset can be used to build extractive-QA and Language Models.

                Funded by the Generalitat de Catalunya, Departament de Polítiques Digitals i Administració Pública (AINA),

                MT4ALL and Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).

               """

_HOMEPAGE = """https://doi.org/10.5281/zenodo.4562337"""

_URL = "https://huggingface.co/datasets/projecte-aina/vilaquad/resolve/main/"

_TRAINING_FILE = "train.json"

_DEV_FILE = "dev.json"

_TEST_FILE = "test.json"

class VilaQuADConfig(datasets.BuilderConfig):

    """ Builder config for the VilaQuAD dataset """

    def __init__(self, **kwargs):

        """BuilderConfig for VilaQuAD.

        Args:

          **kwargs: keyword arguments forwarded to super.

        """

        super(VilaQuADConfig, self).__init__(**kwargs)

class VilaQuAD(datasets.GeneratorBasedBuilder):

    """VilaQuAD Dataset."""

    BUILDER_CONFIGS = [

        VilaQuADConfig(

            name="VilaQuAD",

            version=datasets.Version("1.0.1"),

            description="VilaQuAD dataset",

        ),

    ]

    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": [

                        {

                            "text": datasets.Value("string"),

                            "answer_start": datasets.Value("int32"),

                        }

                    ],

                }

            ),

            # No default supervised_keys (as we have to pass both question

            # and context as input).

            supervised_keys=None,

            homepage=_HOMEPAGE,

            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):

        """This function returns the examples in the raw (text) form."""

        logger.info("generating examples from = %s", filepath)

        with open(filepath, encoding="utf-8") as f:

            vilaquad = json.load(f, encoding="utf-8")

            for article in vilaquad["data"]:

                title = article.get("title", "").strip()

                for paragraph in article["paragraphs"]:

                    context = paragraph["context"].strip()

                    for qa in paragraph["qas"]:

                        question = qa["question"].strip()

                        id_ = qa["id"]

                        #answer_starts = [answer["answer_start"] for answer in qa["answers"]]

                        #answers = [answer["text"].strip() for answer in qa["answers"]]
                        

                        # Features currently used are "context", "question", and "answers".

                        # Others are extracted here for the ease of future expansions.
                        text = qa["answers"][0]["text"]
                        answer_start = qa["answers"][0]["answer_start"]

                        yield id_, {

                            "title": title,

                            "context": context,

                            "question": question,

                            "id": id_,

                            "answers": [{"text": text, "answer_start": answer_start}]

                        }