# coding=utf-8
# Copyright 2020 The TensorFlow 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.

"""XSum dataset."""

import json
import os

from absl import logging
import tensorflow.compat.v2 as tf
import tensorflow_datasets.public_api as tfds

_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 two features:
  - document: Input news article.
  - summary: One sentence summary of the article.

This data need to manaully downloaded and extracted as described in
https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/README.md.
The folder 'xsum-extracts-from-downloads' need to be compressed as
'xsum-extracts-from-downloads.tar.gz' and put in manually downloaded folder.
"""

_URL = "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"

_DOCUMENT = "document"
_SUMMARY = "summary"

_REMOVE_LINES = set([
    "Share this with\n", "Email\n", "Facebook\n", "Messenger\n", "Twitter\n",
    "Pinterest\n", "WhatsApp\n", "Linkedin\n", "LinkedIn\n", "Copy this link\n",
    "These are external links and will open in a new window\n"
])


class Xsum(tfds.core.GeneratorBasedBuilder):
  """Extreme Summarization (XSum) Dataset."""

  # Version 1.1.0 removes web contents.
  VERSION = tfds.core.Version("1.1.0")
  SUPPORTED_VERSIONS = [tfds.core.Version("1.0.0", "Dataset without cleaning.")]

  MANUAL_DOWNLOAD_INSTRUCTIONS = """\
  Detailed download instructions (which require running a custom script) are
  here:
  https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/README.md#running-the-download-and-extraction-scripts
  Afterwards, please put xsum-extracts-from-downloads.tar.gz file in the manual_dir.
  """

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            _DOCUMENT: tfds.features.Text(),
            _SUMMARY: tfds.features.Text(),
        }),
        supervised_keys=(_DOCUMENT, _SUMMARY),
        homepage=
        "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset",
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    dl_path = dl_manager.download(_URL)
    with tf.io.gfile.GFile(dl_path, "r") as json_file:
      split_ids = json.load(json_file)
    folder_name = "xsum-extracts-from-downloads"
    extract_path = os.path.join(
        dl_manager.extract(
            os.path.join(dl_manager.manual_dir, folder_name + ".tar.gz")),
        folder_name)
    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={
                "split_ids": split_ids["train"],
                "path": extract_path,
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs={
                "split_ids": split_ids["validation"],
                "path": extract_path,
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST,
            gen_kwargs={
                "split_ids": split_ids["test"],
                "path": extract_path,
            },
        ),
    ]

  def _generate_examples(self, split_ids=None, path=None):
    """Yields examples."""
    missing = 0
    total_num = len(split_ids)
    for i in split_ids:
      filename = os.path.join(path, i + ".data")
      if tf.io.gfile.exists(filename):
        with tf.io.gfile.GFile(filename) as f:
          text = "".join([
              line for line in f.readlines()
              if line not in _REMOVE_LINES and line.strip()
          ])
          # Each file follows below format:
          # [XSUM]URL[XSUM]
          # http://somelink
          #
          # [XSUM]INTRODUCTION[XSUM]
          # some intro
          #
          # [XSUM]RESTBODY[XSUM]
          # text line.
          # another text line.
          # "another text line."
          segs = text.split("[XSUM]")
          yield i, {_DOCUMENT: segs[6].strip(), _SUMMARY: segs[4].strip()}
      else:
        missing += 1
        logging.info("id %s missing.", i)
    if missing:
      logging.warning("%d out of %d examples are missing.", missing, total_num)

@@ -2148,17 +2148,53 @@
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+)%5D%0A  RELEASE_NOTES = %7B%0A      %221.0.0%22:
  %22Datase
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 eaning.%22
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Add release notes for xsum