# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Cleaned Dutch split of the mC4 corpus."""


import json
import datasets

logger = datasets.logging.get_logger(__name__)

_HOMEPAGE = "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset"

_CITATION = """
@article{Narayan2018DontGM,
  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.08745}
}
"""

_DESCRIPTION = """
Extreme Summarization (XSum) Dataset.
There are three features:
  - document: Input news article.
  - summary: One sentence summary of the article.
  - id: BBC ID of the article.

"""

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


_DOCUMENT = "document"
_SUMMARY = "summary"
_ID = "id"

_SUPPORTED_VERSIONS = [
    datasets.Version("1.0.0", "Default version."),
]


class XsumDutchConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)


class XsumDutch(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        XsumDutchConfig(
            name=str(version), description=version.description
        )
        for version in _SUPPORTED_VERSIONS
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    _DOCUMENT: datasets.Value("string"),
                    _SUMMARY: datasets.Value("string"),
                    "id": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            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