# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""Jigsaw Toxic Comment Challenge dataset"""

import pandas as pd

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """"""

_DESCRIPTION = """\
Jigsaw Toxic Comment Challenge dataset. This dataset was the basis of a Kaggle competition run by Jigsaw
"""

_HOMEPAGE = "https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_URLS = {
    "train": "train.csv",
    "validation": "validation.csv",
    "test": "test.csv",
    "balanced_train": "balanced_train.csv",
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class WikiToxic(datasets.GeneratorBasedBuilder):
    """Jigsaw Toxic Comment Challenge dataset."""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "comment_text": datasets.Value("string"),
                "label": datasets.ClassLabel(names=["non", "tox"])
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            features=features
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files["validation"],
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files["test"],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name="balanced_train",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files["balanced_train"],
                    "split": "balanced_train"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):

        df = pd.read_csv(filepath)

        for index, row in df.iterrows():
            yield index, {
                "id": row["id"],
                "comment_text": row["comment_text"],
                "label": row["label"],
            }