# 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 long_summarization_persianConfig(datasets.BuilderConfig):

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

class long_summarization_persian(datasets.GeneratorBasedBuilder):

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

    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):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            data = json.load(f)
            for id_, article in enumerate(data):
                article = article["article"]
                summary = article["summary"]
                yield id_, {
                    "article": article,
                    "summary": summary,
                }