# coding=utf-8
# 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.
"""datas."""


import csv

import datasets
from datasets.tasks import TextClassification


_CITATION = """\
@inproceedings{Casanueva2020,
    author      = pnr,
    title       = {sentiment},
    year        = {2022},
    month       = {mar},
    note        = {Data available at https://github.com/PnrSvc/dataset},
    url         = {a},
    booktitle   = {a}
}
""" 

_DESCRIPTION = """\
description
"""

_HOMEPAGE = "https://github.com/PnrSvc/dataset"


_TRAIN_DOWNLOAD_URL = (
    "https://github.com/PnrSvc/dataset/blob/main/turkish/train.csv"
)
_TEST_DOWNLOAD_URL = "https://github.com/PnrSvc/dataset/blob/main/turkish/test.csv"


 BUILDER_CONFIGS = [
    datasets.BuilderConfig(name="plain", version=VERSION, description="This loads the dataset in its plain format without any additional data transformations. In case of applying the dataset to a task (e.g. paraphrase classification or generation), some additional data transformations are suggested depending on the task (see'classification', 'classification-context', 'plain-context'  and 'generation' for ready made transformations for paraphrase classification and paraphrase generation)."),
    datasets.BuilderConfig(name="plain-context", version=VERSION, description="This loads the dataset in its plain format without any additional data transformations. In case of applying the dataset to a task (e.g. paraphrase classification or generation), some additional data transformations are suggested depending on the task (see 'classification', 'classification-context', 'plain' and 'generation' for ready made transformations for paraphrase classification and paraphrase generation). Unlike 'plain', this dataset  includes the document contexts, which eats memory, but otherwise it is the same as 'plain'. This is useful for context-based modelling.")
    
       ]

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "label": datasets.Value("string"),
                "target": datasets.features.ClassLabel(
                    names=[
                         "negative",
                         "neutral",
                         "positive"
                    ]
                ),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[TextClassification(text_column="label", label_column="target")],
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        train_path = dl_manager.download_and_extract(_TRAIN_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.TEST, gen_kwargs={"filepath": test_path}),
        ]

    def _generate_examples(self, filepath):
        """Yields examples as (key, example) tuples."""
        with open(filepath, encoding="utf-8") as f:
            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
            # call next to skip header
            next(csv_reader)
            for id_, row in enumerate(csv_reader):
                label, target = row
                yield id_, {"text": label, "label": target}