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# Lint as: python3
"""Scientific Papers Dataset."""


import json
import os

import datasets


_CITATION = """
@article{Cohan_2018,
   title={A Discourse-Aware Attention Model for Abstractive Summarization of
            Long Documents},
   url={http://dx.doi.org/10.18653/v1/n18-2097},
   DOI={10.18653/v1/n18-2097},
   journal={Proceedings of the 2018 Conference of the North American Chapter of
          the Association for Computational Linguistics: Human Language
          Technologies, Volume 2 (Short Papers)},
   publisher={Association for Computational Linguistics},
   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},
   year={2018}
}
"""

_DESCRIPTION = """
Scientific papers datasets contains two sets of long and structured documents.
The datasets are obtained from ArXiv and PubMed OpenAccess repositories.

Both "arxiv" and "pubmed" have two features:
  - article: the body of the document, pagragraphs seperated by "/n".
  - abstract: the abstract of the document, pagragraphs seperated by "/n".
  - section_names: titles of sections, seperated by "/n".

"""

_DOCUMENT = "article"
_SUMMARY = "abstract"

_URLS = {
    "arxiv": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip",
    "pubmed": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip",
}


class ScientificPapersConfig(datasets.BuilderConfig):
    """BuilderConfig for Scientific Papers."""

    def __init__(self, filename=None, **kwargs):
        """BuilderConfig for ScientificPapers

        Args:
          filename: filename of different configs for the dataset.
          **kwargs: keyword arguments forwarded to super.
        """
        # 1.1.0 remove sentence breaker <S> and </S> in summary.
        super(ScientificPapersConfig, self).__init__(version=datasets.Version("1.1.1"), **kwargs)
        self.filename = filename


class ScientificPapers(datasets.GeneratorBasedBuilder):
    """Scientific Papers."""

    BUILDER_CONFIGS = [
        ScientificPapersConfig(name="pubmed", description="Documents from PubMed repository."),
        ScientificPapersConfig(name="arxiv", description="Documents from ArXiv repository."),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    _DOCUMENT: datasets.Value("string"),
                    _SUMMARY: datasets.Value("string"),
                    "section_names": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/armancohan/long-summarization",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_paths = dl_manager.download_and_extract(_URLS)
        path = os.path.join(dl_paths[self.config.name], self.config.name + "-dataset")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"path": os.path.join(path, "train.txt")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"path": os.path.join(path, "val.txt")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"path": os.path.join(path, "test.txt")},
            ),
        ]

    def _generate_examples(self, path=None):
        """Yields examples."""
        with open(path, encoding="utf-8") as f:
            for line in f:
                # Possible keys are:
                # "article_id": str
                # "article_text": list[str] article (list of paragraphs).
                # "abstract_text": list[str], abstract (list of paragraphs).
                # "section_names": list[str], list of section names.
                # "sections": list[list[str]], list of sections (list of paragraphs)
                d = json.loads(line)
                summary = "\n".join(d["abstract_text"])
                # In original paper, <S> and </S> are not used in vocab during training
                # or during decoding.
                # https://github.com/armancohan/long-summarization/blob/master/data.py#L27
                summary = summary.replace("<S>", "").replace("</S>", "")
                yield d["article_id"], {
                    _DOCUMENT: "\n".join(d["article_text"]),
                    _SUMMARY: summary,
                    "section_names": "\n".join(d["section_names"]),
                }