Python tensorflow_docs.api_generator.generate_lib.DocGenerator() Examples

def main(_):
  private_map = {
      'tfl': ['python'],
      'tfl.aggregation_layer': ['Aggregation'],
      'tfl.categorical_calibration_layer': ['CategoricalCalibration'],
      'tfl.lattice_layer': ['Lattice'],
      'tfl.linear_layer': ['Linear'],
      'tfl.pwl_calibration_layer': ['PWLCalibration'],
      'tfl.parallel_combination_layer': ['ParallelCombination'],
      'tfl.rtl_layer': ['RTL'],
  }
  doc_generator = generate_lib.DocGenerator(
      root_title='TensorFlow Lattice 2.0',
      py_modules=[('tfl', tfl)],
      base_dir=os.path.dirname(tfl.__file__),
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      private_map=private_map,
      callbacks=[local_definitions_filter])

  sys.exit(doc_generator.build(output_dir=FLAGS.output_dir)) 

def main(unused_argv):

    doc_generator = generate_lib.DocGenerator(
        root_title="OpenFermion",
        py_modules=[("openfermion", openfermion)],
        base_dir=os.path.dirname(openfermion.__file__),
        code_url_prefix=FLAGS.code_url_prefix,
        search_hints=FLAGS.search_hints,
        site_path=FLAGS.site_path,
        callbacks=[public_api.local_definitions_filter],
        private_map={
            # Module paths to skip when crawling source code.
            # Example:
            # "cirq.google.engine.client.quantum.QuantumEngineServiceClient":
            # ["enums"]
        })

    doc_generator.build(output_dir=FLAGS.output_dir) 

def main(argv):
    if argv[1:]:
        raise ValueError("Unrecognized arguments: {}".format(argv[1:]))

    if FLAGS.code_url_prefix:
        code_url_prefix = FLAGS.code_url_prefix
    elif FLAGS.git_branch:
        code_url_prefix = CODE_PREFIX_TEMPLATE.format(git_branch=FLAGS.git_branch)
    else:
        code_url_prefix = CODE_PREFIX_TEMPLATE.format(git_branch="master")

    doc_generator = generate_lib.DocGenerator(
        root_title=PROJECT_FULL_NAME,
        py_modules=[(PROJECT_SHORT_NAME, tfa)],
        code_url_prefix=code_url_prefix,
        private_map={"tfa": ["__version__", "utils", "version"]},
        # This callback usually cleans up a lot of aliases caused by internal imports.
        callbacks=[public_api.local_definitions_filter],
        search_hints=FLAGS.search_hints,
        site_path=FLAGS.site_path,
    )

    doc_generator.build(FLAGS.output_dir)

    print("Output docs to: ", FLAGS.output_dir) 

def main(_):
  do_not_generate_docs_for = []

  for blocked_doc in do_not_generate_docs_for:
    doc_controls.do_not_generate_docs(blocked_doc)

  doc_generator = generate_lib.DocGenerator(
      root_title="Neural Structured Learning",
      py_modules=[("nsl", nsl)],
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      # local_definitions_filter ensures that shared modules are only
      # documented in the location that defines them, instead of every location
      # that imports them.
      callbacks=[public_api.local_definitions_filter])
  doc_generator.build(output_dir=FLAGS.output_dir) 

def main(unused_argv):

    doc_generator = generate_lib.DocGenerator(
        root_title="Cirq",
        py_modules=[("cirq", cirq)],
        base_dir=os.path.dirname(cirq.__file__),
        code_url_prefix=FLAGS.code_url_prefix,
        search_hints=FLAGS.search_hints,
        site_path=FLAGS.site_path,
        callbacks=[public_api.local_definitions_filter],
        private_map={
            # Opt to not build docs for these paths for now since they error.
            "cirq.google.engine.client.quantum.QuantumEngineServiceClient":
            ["enums"],
            "cirq.google.engine.client.quantum_v1alpha1.QuantumEngineServiceClient":
            ["enums"]
        })

    doc_generator.build(output_dir=FLAGS.output_dir) 

def main(args):
  if args[1:]:
    raise ValueError('Unrecognized command line args', args[1:])

  for obj in suppress_docs_for:
    doc_controls.do_not_generate_docs(obj)

  doc_generator = generate_lib.DocGenerator(
      root_title='TensorFlow Model Analysis',
      py_modules=[('tfma', tfma)],
      base_dir=os.path.dirname(tfma.__file__),
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      private_map={},
      callbacks=[
          public_api.local_definitions_filter, depth_filter, suppress_docs
      ])

  return doc_generator.build(output_dir=FLAGS.output_dir) 

def main(args):
  if args[1:]:
    raise ValueError('Unrecognized command line args', args[1:])

  for obj in suppress_docs_for:
    doc_controls.do_not_generate_docs(obj)

  doc_generator = generate_lib.DocGenerator(
      root_title='TensorFlow Hub',
      py_modules=[('hub', hub)],
      base_dir=os.path.dirname(hub.__file__),
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      private_map={},
      callbacks=[
          # This filters out objects not defined in the current module or its
          # sub-modules.
          public_api.local_definitions_filter
      ])

  doc_generator.build(output_dir=FLAGS.output_dir) 

def main(args):
  if args[1:]:
    raise ValueError("Unrecognized Command line args", args[1:])

  for obj in supress_docs_for:
    doc_controls.do_not_generate_docs(obj)

  for name, value in inspect.getmembers(tfdv):
    if inspect.ismodule(value):
      doc_controls.do_not_generate_docs(value)

  for name, value in inspect.getmembers(beam.PTransform):
    # This ensures that the methods of PTransform are not documented in any
    # derived classes.
    if name == "__init__":
      continue
    try:
      doc_controls.do_not_doc_inheritable(value)
    except (TypeError, AttributeError):
      pass

  doc_generator = generate_lib.DocGenerator(
      root_title="TensorFlow Data Validation",
      py_modules=[("tfdv", tfdv)],
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      # Use private_map to exclude doc locations by name if excluding by object
      # is insufficient.
      private_map={},
      # local_definitions_filter ensures that shared modules are only
      # documented in the location that defines them, instead of every location
      # that imports them.
      callbacks=[public_api.local_definitions_filter, _filter_class_attributes])

  return doc_generator.build(output_dir=FLAGS.output_dir) 

def main(_):
  doc_generator = generate_lib.DocGenerator(
      root_title="Tensorflow Graphics",
      py_modules=[("tfg", tfg)],
      base_dir=os.path.dirname(tfg.__file__),
      search_hints=FLAGS.search_hints,
      code_url_prefix=FLAGS.code_url_prefix,
      site_path=FLAGS.site_path)

  doc_generator.build(output_dir=FLAGS.output_dir) 

def execute(output_dir, code_url_prefix, search_hints, site_path):
  """Builds API docs for tensorflow_datasets."""
  # Internally, tfds.testing defaults to None. Fill it in here so that we get
  # documentation.
  tfds.testing = testing
  doc_generator = generate_lib.DocGenerator(
      root_title="TensorFlow Datasets",
      py_modules=[("tfds", tfds)],
      base_dir=os.path.dirname(tfds.__file__),
      search_hints=search_hints,
      code_url_prefix=code_url_prefix,
      site_path=site_path)

  doc_generator.build(output_dir)

  new_redirects = []
  for before, after in MOVES:
    old_path = os.path.join(output_dir, before)
    new_path = os.path.join(output_dir, after)
    os.rename(old_path, new_path)

    new_redirects.append({
        "from":
            os.path.join("/datasets/api_docs/python/",
                         os.path.splitext(before)[0]),
        "to":
            os.path.join("/datasets/api_docs/python/",
                         os.path.splitext(after)[0])
    })

  redirect_path = os.path.join(output_dir, "_redirects.yaml")
  with open(redirect_path) as f:
    redirect_content = yaml.load(f)
  redirect_content["redirects"].extend(new_redirects)
  with open(redirect_path, "w") as f:
    yaml.dump(redirect_content, f, default_flow_style=False) 

def main(_):
  # These make up for the empty __init__.py files.
  api_generator.utils.recursive_import(tfx.orchestration)
  api_generator.utils.recursive_import(tfx.components)

  do_not_generate_docs_for = []
  for name in ["utils", "proto", "dependencies", "version"]:
    submodule = getattr(tfx, name, None)
    if submodule is not None:
      do_not_generate_docs_for.append(submodule)

  for obj in do_not_generate_docs_for:
    doc_controls.do_not_generate_docs(obj)

  doc_generator = generate_lib.DocGenerator(
      root_title="TFX",
      py_modules=[("tfx", tfx)],
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      private_map={},
      # local_definitions_filter ensures that shared modules are only
      # documented in the location that defines them, instead of every location
      # that imports them.
      callbacks=[
          api_generator.public_api.local_definitions_filter, ignore_test_objects
      ])
  doc_generator.build(output_dir=FLAGS.output_dir) 

def main(_):
  doc_generator = generate_lib.DocGenerator(
      root_title='TF-Agents',
      py_modules=[('tf_agents', tf_agents)],
      base_dir=os.path.dirname(tf_agents.__file__),
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      private_map={},
      callbacks=[public_api.local_definitions_filter])

  sys.exit(doc_generator.build(output_dir=FLAGS.output_dir)) 

def main(_):
  doc_generator = generate_lib.DocGenerator(
      root_title="TensorFlow/compression",
      py_modules=[("tfc", tfc)],
      base_dir=os.path.dirname(tfc.__file__),
      private_map={
          "tfc.python.ops": ["gen_range_coding_ops", "namespace_helper"],
      },
      code_url_prefix="https://github.com/tensorflow/compression/tree/master/"
                      "tensorflow_compression",
      api_cache=False,
  )
  sys.exit(doc_generator.build(FLAGS.output_dir)) 

def main(unused_argv):
  doc_generator = generate_lib.DocGenerator(
      root_title="TensorFlow Model Optimization",
      py_modules=[("tfmot", tfmot)],
      base_dir=os.path.dirname(tfmot.__file__),
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      # TODO(tfmot): remove this once the next release after 0.3.0 happens.
      # This is needed in the interim because the API docs reflect
      # the latest release and the current release still wildcard imports
      # all of the classes below.
      private_map={
          "tfmot.sparsity.keras": [
              # List of internal classes which get exposed when imported.
              "InputLayer",
              "custom_object_scope",
              "pruning_sched",
              "pruning_wrapper",
              "absolute_import",
              "division",
              "print_function",
              "compat"
          ]
      },
  )

  doc_generator.build(output_dir=FLAGS.output_dir) 

def main(args):
  if args[1:]:
    raise ValueError('Unrecognized Command line args', args[1:])

  tft_out = pathlib.Path(tempfile.mkdtemp())
  doc_generator = generate_lib.DocGenerator(
      root_title='TF-Transform',
      py_modules=[('tft', transform)],
      code_url_prefix=FLAGS.code_url_prefix,
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      callbacks=[public_api.explicit_package_contents_filter])

  doc_generator.build(tft_out)

  doc_controls.do_not_generate_docs(tft_beam.analyzer_impls)

  tft_beam_out = pathlib.Path(tempfile.mkdtemp())
  doc_generator = generate_lib.DocGenerator(
      root_title='TFT-Beam',
      py_modules=[('tft_beam', tft_beam)],
      code_url_prefix=FLAGS.code_url_prefix + '/beam',
      search_hints=FLAGS.search_hints,
      site_path=FLAGS.site_path,
      callbacks=[
          public_api.explicit_package_contents_filter,
          public_api.local_definitions_filter
      ])

  doc_generator.build(tft_beam_out)

  output_dir = pathlib.Path(FLAGS.output_dir)

  def splice(name, tmp_dir):
    shutil.rmtree(output_dir / name, ignore_errors=True)
    shutil.copytree(tmp_dir / name, output_dir / name)
    shutil.copy(tmp_dir / f'{name}.md', output_dir / f'{name}.md')
    try:
      shutil.copy(tmp_dir / '_redirects.yaml',
                  output_dir / name / '_redirects.yaml')
    except FileNotFoundError:
      pass
    shutil.copy(tmp_dir / '_toc.yaml', output_dir / name / '_toc.yaml')

  splice('tft', tft_out)
  splice('tft_beam', tft_beam_out)

  toc_path = output_dir / '_toc.yaml'
  toc_text = yaml.dump(
      {'toc': [
          {'include': f'{FLAGS.site_path}/tft/_toc.yaml'},
          {'break': True},
          {'include': f'{FLAGS.site_path}/tft_beam/_toc.yaml'}]})
  toc_path.write_text(toc_text)