# 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.


import csv
import json
import os

import datasets


_DESCRIPTION = """\
This new dataset is designed to solve persian long summarization tasks.
"""

_URLS = {
    "train": "https://huggingface.co/datasets/zedfum/long-summarization-persian/blob/main/train.csv",
    "validation": "https://huggingface.co/datasets/zedfum/long-summarization-persian/blob/main/validation.csv",
    "test": "https://huggingface.co/datasets/zedfum/long-summarization-persian/blob/main/test.csv",
}

class NewDataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")


    def _info(self):

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "article": datasets.Value("string"),
                    "summary": datasets.Value("string")
                }
            ),
        )

    def _split_generators(self, dl_manager):
        urls_to_download = _URLS
        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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": downloaded_files["validation"]},
            ),
        ]
    def _generate_examples(self, filepath, split):
        df = pd.read_csv(filepath)
        for idx, example in enumerate(df.itertuples(index=False)):
            yield idx, {"id":example.id,"article": example.article, "summary": example.summary}
                    }