# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
"""CNN/Dailymail Dutch summarization dataset."""


import csv

import datasets


_DESCRIPTION = """\
    This dataset is the CNN/Dailymail dataset translated to Dutch.
    This is the original dataset:
    ```
    load_dataset("cnn_dailymail", '3.0.0')
    ```
    And this is the HuggingFace translation pipeline:
    ```
    pipeline(
        task='translation_en_to_nl',
        model='Helsinki-NLP/opus-mt-en-nl',
        tokenizer='Helsinki-NLP/opus-mt-en-nl')
    ```
"""

# The second citation introduces the source data, while the first
# introduces the specific form (non-anonymized) we use here.
_CITATION = """\
@article{DBLP:journals/corr/SeeLM17,
  author    = {Abigail See and
               Peter J. Liu and
               Christopher D. Manning},
  title     = {Get To The Point: Summarization with Pointer-Generator Networks},
  journal   = {CoRR},
  volume    = {abs/1704.04368},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.04368},
  archivePrefix = {arXiv},
  eprint    = {1704.04368},
  timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/SeeLM17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{hermann2015teaching,
  title={Teaching machines to read and comprehend},
  author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
  booktitle={Advances in neural information processing systems},
  pages={1693--1701},
  year={2015}
}
"""


_TRAIN_DOWNLOAD_URLS = [
    "https://huggingface.co/datasets/ml6team/cnn_dailymail_nl/resolve/main/cnn_dailymail_nl_train_000000000000.csv.gz",
    "https://huggingface.co/datasets/ml6team/cnn_dailymail_nl/resolve/main/cnn_dailymail_nl_train_000000000001.csv.gz",
    "https://huggingface.co/datasets/ml6team/cnn_dailymail_nl/resolve/main/cnn_dailymail_nl_train_000000000002.csv.gz",
]
_VALIDATION_DOWNLOAD_URL = "https://huggingface.co/datasets/ml6team/cnn_dailymail_nl/resolve/main/cnn_dailymail_nl_validation.csv.gz"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/ml6team/cnn_dailymail_nl/resolve/main/cnn_dailymail_nl_test.csv.gz"


_ID = "id"
_HIGHLIGHTS = "highlights"
_ARTICLE = "article"


class CnnDailymailNl(datasets.GeneratorBasedBuilder):
    """CNN/Dailymail Dutch summarization dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    _ARTICLE: datasets.Value("string"),
                    _HIGHLIGHTS: datasets.Value("string"),
                    _ID: datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://huggingface.co/datasets/ml6team/cnn_dailymail_nl",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_paths = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URLS)
        validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_paths}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepaths": [validation_path]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepaths": [test_path]}
            ),
        ]

    def _generate_examples(self, filepaths):
        """Generate Dutch CNN/Dailymail examples."""
        for filepath in filepaths:  # training data is divided over multiple shards
            with open(filepath, encoding="utf-8") as csv_file:
                csv_reader = csv.reader(
                    csv_file,
                    quotechar='"',
                    delimiter=",",
                    quoting=csv.QUOTE_ALL,
                    skipinitialspace=True,
                )
                next(csv_reader)  # skip header
                for row in csv_reader:
                    article_id, article, highlights = row
                    yield article_id, {
                        _ARTICLE: article,
                        _HIGHLIGHTS: highlights,
                        _ID: article_id,
                    }