# Loading script for the PAWS-ca dataset
import json

import datasets


_CITATION = """
"""

_DESCRIPTION = """
The PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.

This dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.

"""

_HOMEPAGE = "https://zenodo.org/record/"

_URL = "https://huggingface.co/datasets/projecte-aina/paws-ca/resolve/main/"
_TRAIN_FILE = "train.json"
_DEV_FILE = "dev_2k.json"
_TEST_FILE = "test_2k.json"


class PAWSXConfig(datasets.BuilderConfig):
    """BuilderConfig for PAWSX-ca."""

    def __init__(self, **kwargs):
        """Constructs a PAWSXConfig.
        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super(PAWSXConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs),


class PAWSX(datasets.GeneratorBasedBuilder):
    """PAWS-ca, a Catalan version of PAWS."""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        PAWSXConfig(
            name="paws-ca",
            description="PAWS-ca dataset",
        ),
    ]


    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("int32"),
                "sentence1": datasets.Value("string"),
                "sentence2": datasets.Value("string"),
                "label": datasets.features.ClassLabel(names=["0", "1"]),
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAIN_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):
        with open(filepath, encoding='utf-8') as f:
            data = json.load(f)
            for i, row in enumerate(data):
                yield i, {
                    'id': row['id'],
                    'sentence1': row['sentence1'],
                    'sentence2': row['sentence2'],
                    'label': row['label'],
                }