# Loading script for the ReviewsFinder dataset.


import json
import csv

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """ """


_DESCRIPTION = """ GuiaCat is a dataset consisting of 5.750 restaurant reviews in Catalan, with 5 associated scores and a label of sentiment. The data was provided by GuiaCat and curated by the BSC. """


_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/Parafraseja/ """



_URL = "https://huggingface.co/datasets/projecte-aina/Parafraseja/resolve/main/"
_TRAINING_FILE = "train.csv"
_DEV_FILE = "dev.csv"
_TEST_FILE = "test.csv"


class GuiaCatConfig(datasets.BuilderConfig):
    """ Builder config for the reviews_finder dataset """

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


class GuiaCat(datasets.GeneratorBasedBuilder):
    """ GuiaCat Dataset """


    BUILDER_CONFIGS = [
        GuiaCatConfig(
            name="GuiaCat",
            version=datasets.Version("1.0.0"),
            description="GuiaCat dataset",
        ),
    ]


    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel
                        (names=
                    [
                        "molt bo",
                        "bo",
                        "regular",
                        "dolent",
                        "molt dolent"
                    ]
                    ),
                }
            ),
            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) as f:
            read = csv.reader(f)
            data = [item for item in read]
            text = ""
            label = ""
            for id_, article in enumerate(data): 
                text = article[5]
                label = article[6]
                yield id_, {
                    "text": text,
                    "label": label,
                }