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# Lint as: python3
"""Gigaword summarization dataset."""


import os

import datasets


_CITATION = """
@article{graff2003english,
  title={English gigaword},
  author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
  journal={Linguistic Data Consortium, Philadelphia},
  volume={4},
  number={1},
  pages={34},
  year={2003}
}

@article{Rush_2015,
   title={A Neural Attention Model for Abstractive Sentence Summarization},
   url={http://dx.doi.org/10.18653/v1/D15-1044},
   DOI={10.18653/v1/d15-1044},
   journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
   publisher={Association for Computational Linguistics},
   author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
   year={2015}
}
"""

_DESCRIPTION = """
Headline-generation on a corpus of article pairs from Gigaword consisting of
around 4 million articles. Use the 'org_data' provided by
https://github.com/microsoft/unilm/ which is identical to
https://github.com/harvardnlp/sent-summary but with better format.

There are two features:
  - document: article.
  - summary: headline.

"""

# Source: https://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV
_URL = "data/ggw_data.zip"

_DOCUMENT = "document"
_SUMMARY = "summary"


class Gigaword(datasets.GeneratorBasedBuilder):
    """Gigaword summarization dataset."""

    # 1.0.0 contains a bug that uses validation data as training data.
    # 1.1.0 Update to the correct train, validation and test data.
    # 1.2.0 Replace <unk> with <UNK> in train/val to be consistent with test.
    VERSION = datasets.Version("1.2.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
            supervised_keys=(_DOCUMENT, _SUMMARY),
            homepage="https://github.com/harvardnlp/sent-summary",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_path = dl_manager.download_and_extract(_URL)
        pattern = os.path.join(dl_path, "org_data", "%s.%s.txt")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "src_path": pattern % ("train", "src"),
                    "tgt_path": pattern % ("train", "tgt"),
                    "replace_unk": True,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "src_path": pattern % ("dev", "src"),
                    "tgt_path": pattern % ("dev", "tgt"),
                    "replace_unk": True,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "src_path": pattern % ("test", "src"),
                    "tgt_path": pattern % ("test", "tgt"),
                    "replace_unk": False,
                },
            ),
        ]

    def _generate_examples(self, src_path=None, tgt_path=None, replace_unk=None):
        """Yields examples."""
        with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
            for i, (doc_text, sum_text) in enumerate(zip(f_d, f_s)):
                if replace_unk:
                    yield i, {
                        _DOCUMENT: doc_text.strip().replace("<unk>", "UNK"),
                        _SUMMARY: sum_text.strip().replace("<unk>", "UNK"),
                    }
                else:
                    yield i, {_DOCUMENT: doc_text.strip(), _SUMMARY: sum_text.strip()}