text: # Lint as: python3
  # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # 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.
  # ==============================================================================
  r"""Tool to generate api_docs for tensorflow_models/official library.
  
  Example:
  
  $> pip install -U git+https://github.com/tensorflow/docs
  $> python build_docs \
   --output_dir=/tmp/api_docs \
   --project_short_name=tf_nlp.modeling \
   --project_full_name="TensorFlow Official Models - NLP Modeling Library"
  """
  
  import os
  
  from absl import app
  from absl import flags
  from absl import logging
  
  import tensorflow as tf
  from tensorflow_docs.api_generator import doc_controls
  from tensorflow_docs.api_generator import generate_lib
  from tensorflow_docs.api_generator import public_api
  
  from official.nlp import modeling as tf_nlp_modeling
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string('output_dir', None, 'Where to write the resulting docs to.')
  flags.DEFINE_string(
      'code_url_prefix',
      'https://github.com/tensorflow/models/blob/master/official/nlp/modeling/',
      'The url prefix for links to code.')
  
  flags.DEFINE_bool('search_hints', True,
                    'Include metadata search hints in the generated files')
  
  flags.DEFINE_string('site_path', 'tf_nlp_modeling/api_docs/python',
                      'Path prefix in the _toc.yaml')
  
  flags.DEFINE_bool('gen_report', False,
                    'Generate an API report containing the health of the '
                    'docstrings of the public API.')
  flags.DEFINE_string(
      'project_short_name', 'tf_nlp.modeling',
      'The project short name referring to the python module to document.')
  flags.DEFINE_string('project_full_name',
                      'TensorFlow Official Models - NLP Modeling Library',
                      'The main title for the project.')
  
  
  def _hide_module_model_and_layer_methods():
    """Hide methods and properties defined in the base classes of Keras layers.
  
    We hide all methods and properties of the base classes, except:
    - `__init__` is always documented.
    - `call` is always documented, as it can carry important information for
      complex layers.
    """
    module_contents = list(tf.Module.__dict__.items())
    model_contents = list(tf.keras.Model.__dict__.items())
    layer_contents = list(tf.keras.layers.Layer.__dict__.items())
  
    for name, obj in module_contents + layer_contents + model_contents:
      if name == '__init__':
        # Always document __init__.
        continue
  
      if name == 'call':
        # Always document `call`.
        if hasattr(obj, doc_controls._FOR_SUBCLASS_IMPLEMENTERS):  # pylint: disable=protected-access
          delattr(obj, doc_controls._FOR_SUBCLASS_IMPLEMENTERS)  # pylint: disable=protected-access
        continue
  
      # Otherwise, exclude from documentation.
      if isinstance(obj, property):
        obj = obj.fget
  
      if isinstance(obj, (staticmethod, classmethod)):
        obj = obj.__func__
  
      try:
        doc_controls.do_not_doc_in_subclasses(obj)
      except AttributeError:
        pass
  
  
  def gen_api_docs(code_url_prefix, site_path, output_dir, gen_report,
                   project_short_name, project_full_name, search_hints):
    """Generates api docs for the tensorflow docs package."""
    _hide_module_model_and_layer_methods()
  
    doc_generator = generate_lib.DocGenerator(
        root_title=project_full_name,
        py_modules=[(project_short_name, tf_nlp_modeling)],
        base_dir=os.path.dirname(tf_nlp_modeling.__file__),
        code_url_prefix=code_url_prefix,
        search_hints=search_hints,
        site_path=site_path,
        gen_report=gen_report,
        callbacks=[public_api.explicit_package_contents_filter],
    )
  
    doc_generator.build(output_dir)
    logging.info('Output docs to: %s', output_dir)
  
  
  def main(argv):
    if len(argv) > 1:
      raise app.UsageError('Too many command-line arguments.')
  
    gen_api_docs(
        code_url_prefix=FLAGS.code_url_prefix,
        site_path=FLAGS.site_path,
        output_dir=FLAGS.output_dir,
        gen_report=FLAGS.gen_report,
        project_short_name=FLAGS.project_short_name,
        project_full_name=FLAGS.project_full_name,
        search_hints=FLAGS.search_hints)
  
  
  if __name__ == '__main__':
    flags.mark_flag_as_required('output_dir')
    app.run(main)
  