# 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.
"""Cleaned Dutch split of the mC4 corpus."""


import json
import datasets

logger = datasets.logging.get_logger(__name__)

_HOMEPAGE = "https://github.com/abisee/cnn-dailymail"

_DESCRIPTION = """\
CNN/DailyMail non-anonymized summarization dataset, translated to Dutch with ccmatrix.
There are two features:
  - article: text of news article, used as the document to be summarized
  - highlights: joined text of highlights with <s> and </s> around each
    highlight, which is the target summary
"""

_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0"

_DATA_URL_NL = "https://huggingface.co/datasets/yhavinga/cnn_dailymail_dutch/resolve/main/{config}/{split}.json.gz"

# 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}
}
"""

_HIGHLIGHTS = "highlights"
_ARTICLE = "article"

_SUPPORTED_VERSIONS = [
    # Using cased version.
    datasets.Version("3.0.0", "Using cased version."),
]


class CnnDailymailDutchConfig(datasets.BuilderConfig):
    """BuilderConfig for CnnDailymail Dutch."""

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


class CnnDailymailDutch(datasets.GeneratorBasedBuilder):
    """CNN/DailyMail non-anonymized summarization dataset in Dutch."""

    BUILDER_CONFIGS = [
        CnnDailymailDutchConfig(
            name=str(version), description=version.description
        )
        for version in _SUPPORTED_VERSIONS
    ]

    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=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        result = [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(
                        _DATA_URL_NL.format(split=str(split), config=str(self.config.name))
                    )
                },
            )
            for split in [
                datasets.Split.TRAIN,
                datasets.Split.VALIDATION,
                datasets.Split.TEST,
            ]
        ]
        return result

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form by iterating on all the files."""
        logger.info(f"Generating examples from {filepath}")

        with open(filepath, "r") as file:
            for _id, line in enumerate(file):
                example = json.loads(line)
                yield _id, example