# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and Ilya Gusev
#
# 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.

# Lint as: python3
"""Gazeta: Dataset for Automatic Summarization of Russian News"""


import json
import os

import datasets


_CITATION = """
@InProceedings{10.1007/978-3-030-59082-6_9,
    author="Gusev, Ilya",
    editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia",
    title="Dataset for Automatic Summarization of Russian News",
    booktitle="Artificial Intelligence and Natural Language",
    year="2020",
    publisher="Springer International Publishing",
    address="Cham",
    pages="122--134",
    isbn="978-3-030-59082-6"
}
"""

_DESCRIPTION = "Dataset for automatic summarization of Russian news"
_HOMEPAGE = "https://github.com/IlyaGusev/gazeta"
_URLS = {
    "train": "gazeta_train.jsonl",
    "val": "gazeta_val.jsonl",
    "test": "gazeta_test.jsonl"
}
_DOCUMENT = "text"
_SUMMARY = "summary"


class GazetaDataset(datasets.GeneratorBasedBuilder):
    """Gazeta Dataset"""

    VERSION = datasets.Version("2.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="default", version=VERSION, description=""),
    ]

    DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        features = datasets.Features(
            {
                _DOCUMENT: datasets.Value("string"),
                _SUMMARY: datasets.Value("string"),
                "title": datasets.Value("string"),
                "date": datasets.Value("string"),
                "url": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=(_DOCUMENT, _SUMMARY),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)
        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["val"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                yield id_, data