"""English review multi-classification dataset."""


import csv
import datasets
from datasets.tasks import TextClassification

_CITATION = """\
----EnglishNLPDataset----
"""

_DESCRIPTION = """\
The dataset, prepared in English, includes 10.000 tests, 10.000 validations and 80000 train data.
The data is composed of customer comments and created from e-commerce sites.
"""


_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/BihterDass/EnglishTextClassificationDataset/main/train.csv"
_VALIDATION_DOWNLOAD_URL ="https://raw.githubusercontent.com/BihterDass/EnglishTextClassificationDataset/main/dev.csv"
_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/BihterDass/EnglishTextClassificationDataset/main/test.csv"

class EnglishNLPDatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for EnglishNLPDataset Config"""

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

class EnglishNLPDataset(datasets.GeneratorBasedBuilder):
    """EnglishNLPDataset Classification dataset."""
    BUILDER_CONFIGS = [
       EnglishNLPDatasetConfig(
            name="EnglishData",
            version=datasets.Version("1.0.0"),
            description="EnglishNLPDataset: It is a classification study that will contribute to natural language processing operations.",
        ),
    ]
    def _info(self):
      
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.ClassLabel(names=["neg", "nor","pos"]),
                }
            ),
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage="https://github.com/BihterDass/EnglishTextClassificationDataset",
            citation=_CITATION,
	    task_templates=[TextClassification(text_column="text", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
        validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(
                csv_file,
                delimiter=",",
                quoting=csv.QUOTE_ALL,
                skipinitialspace=True,
            )
            for id_, row in enumerate(csv_reader):
                (
                    text,
                    label,
                ) = row

                yield id_, {
                    "text": text,
                    "label": int(label),
                }