# 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
"""CiteSum dataset"""

import hashlib
import os

import datasets


logger = datasets.logging.get_logger(__name__)


_HOMEPAGE = "https://github.com/morningmoni/CiteSum"

_DESCRIPTION = """\
Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation
CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation.
CiteSum is around 30 times larger than the previous human-curated dataset SciTLDR.
"""

# The second citation introduces the source data, while the first
# introduces the specific form (non-anonymized) we use here.
_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2205.06207,
  doi = {10.48550/ARXIV.2205.06207},
  url = {https://arxiv.org/abs/2205.06207},
  author = {Mao, Yuning and Zhong, Ming and Han, Jiawei},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

"""

_DOWNLOAD_URL = "https://drive.google.com/file/d/1ndHCREXGSPnDUNllladh9qCtayqbXAfJ"


class CiteSumConfig(datasets.BuilderConfig):
    """BuilderConfig for CiteSum."""

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


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

    BUILDER_CONFIGS = [CiteSumConfig(name="citesum", description="Plain text")]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "src": datasets.Value("string"),
                    "tgt": datasets.Value("string"),
                    "paper_id": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "discipline": {
                        "venue": datasets.Value("string"),
                        "journal": datasets.Value("string"),
                        "mag_field_of_study": datasets.features.Sequence(
                            datasets.Value("string")
                        ),
                    },
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_paths = dl_manager.download(_DOWNLOAD_URL)
        return [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "urls_file": dl_paths[split],
                    "files_per_archive": [
                        dl_manager.iter_archive(dl_paths["cnn_stories"]),
                        dl_manager.iter_archive(dl_paths["dm_stories"]),
                    ],
                },
            )
            for split in [
                datasets.Split.TRAIN,
                datasets.Split.VALIDATION,
                datasets.Split.TEST,
            ]
        ]

    def _generate_examples(self, urls_file, files_per_archive):
        urls = _get_url_hashes(urls_file)
        idx = 0
        for files in files_per_archive:
            for path, file in files:
                hash_from_path = _get_hash_from_path(path)
                if hash_from_path in urls:
                    article, highlights = _get_art_abs(file, self.config.version)
                    if not article or not highlights:
                        continue
                    yield idx, {
                        _ARTICLE: article,
                        _HIGHLIGHTS: highlights,
                        "id": hash_from_path,
                    }
                    idx += 1