text: # coding=utf-8
  # Copyright 2019 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
  #
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  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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  """The Definite Pronoun Resolution Dataset."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  import tensorflow_datasets.public_api as tfds
  
  _CITATION = """\
  @inproceedings{rahman2012resolving,
    title={Resolving complex cases of definite pronouns: the winograd schema challenge},
    author={Rahman, Altaf and Ng, Vincent},
    booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
    pages={777--789},
    year={2012},
    organization={Association for Computational Linguistics}
  }"""
  
  _DESCRIPTION = """\
  Composed by 30 students from one of the author's undergraduate classes. These
  sentence pairs cover topics ranging from real events (e.g., Iran's plan to
  attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
  Batman) and purely imaginary situations, largely reflecting the pop culture as
  perceived by the American kids born in the early 90s. Each annotated example
  spans four lines: the first line contains the sentence, the second line contains
  the target pronoun, the third line contains the two candidate antecedents, and
  the fourth line contains the correct antecedent. If the target pronoun appears
  more than once in the sentence, its first occurrence is the one to be resolved.
  """
  
  _DATA_URL_PATTERN = 'http://www.hlt.utdallas.edu/~vince/data/emnlp12/{}.c.txt'
  
  
  class DefinitePronounResolution(tfds.core.GeneratorBasedBuilder):
    """The Definite Pronoun Resolution Dataset."""
    BUILDER_CONFIGS = [
        tfds.core.BuilderConfig(
            name='plain_text',
            version=tfds.core.Version(
                '0.0.1', experiments={tfds.core.Experiment.S3: False}),
            supported_versions=[
                tfds.core.Version(
                    '1.0.0',
                    'New split API (https://tensorflow.org/datasets/splits)'),
            ],
            description='Plain text import of the Definite Pronoun Resolution Dataset.',  # pylint: disable=line-too-long
        )
    ]
  
    def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          description=_DESCRIPTION,
          features=tfds.features.FeaturesDict({
              'sentence':
                  tfds.features.Text(),
              'pronoun':
                  tfds.features.Text(),
              'candidates':
                  tfds.features.Sequence(tfds.features.Text(), length=2),
              'label':
                  tfds.features.ClassLabel(num_classes=2),
          }),
          supervised_keys=('sentence', 'label'),
          urls=['http://www.hlt.utdallas.edu/~vince/data/emnlp12/'],
          citation=_CITATION,
      )
  
    def _split_generators(self, dl_manager):
      files = dl_manager.download({
          'train': _DATA_URL_PATTERN.format('train'),
          'test': _DATA_URL_PATTERN.format('test'),
      })
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              num_shards=1,
              gen_kwargs={'filepath': files['test']}),
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              num_shards=1,
              gen_kwargs={'filepath': files['train']}),
      ]
  
    def _generate_examples(self, filepath):
      with tf.io.gfile.GFile(filepath) as f:
        line_num = -1
        while True:
          line_num += 1
          sentence = f.readline().strip()
          pronoun = f.readline().strip()
          candidates = [c.strip() for c in f.readline().strip().split(',')]
          correct = f.readline().strip()
          f.readline()
          if not sentence:
            break
          yield line_num, {
              'sentence': sentence,
              'pronoun': pronoun,
              'candidates': candidates,
              'label': candidates.index(correct),
          }
  