Python descriptor_pb2.FileDescriptorSet方法代碼示例

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def execute(self, descriptor_set):
        desc_set = desc.FileDescriptorSet()
        with open(descriptor_set, 'rb') as f:
            desc_set.ParseFromString(f.read())

        for file_descriptor_proto in desc_set.file:
            if not file_descriptor_proto.source_code_info:
                continue

            for location in file_descriptor_proto.source_code_info.location:
                location.leading_comments = md2rst(location.leading_comments)
                location.trailing_comments = md2rst(location.trailing_comments)
                detached = [md2rst(c) for c in location.leading_detached_comments]
                del location.leading_detached_comments[:]
                location.leading_detached_comments.extend(detached)

        desc_file, desc_ext = os.path.splitext(descriptor_set)
        new_descriptor_set = desc_file + '_updated_py_docs' + desc_ext

        with open(new_descriptor_set, mode='wb') as f:
            f.write(desc_set.SerializeToString())

        return new_descriptor_set 

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def test_valid_rst(self):
        descriptor_set = 'test/tasks/data/test_descriptor/descriptor_set'
        task = descriptor_set_tasks.PythonDocsConvertionTask()
        updated_descriptor = task.execute(descriptor_set)

        desc_set = desc.FileDescriptorSet()
        with open(updated_descriptor, 'rb') as f:
            desc_set.ParseFromString(f.read())

        lint_errors = []
        comment_count = 0
        for comment in gather_comments_from_descriptor_set(desc_set):
            lint_errors.extend(restructuredtext_lint.lint(comment))
            comment_count += 1

        print(lint_errors)
        assert len(lint_errors) == 0
        assert comment_count == 1913 

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def _get_descriptor_set():
  """Returns a FileDescriptorSet proto to be used by tf.io.decode_proto."""
  proto = pb.FileDescriptorSet()

  # The FileDescriptor for tensorflow.ranking.internal.ExampleListWithContext.
  file_proto = proto.file.add(
      name=_FILE_NAME, package=_PACKAGE, syntax="proto3")
  message_proto = file_proto.message_type.add(name=_MESSAGE_NAME)
  message_proto.field.add(
      name=_EXAMPLES_FIELD_NAME,
      number=1,
      type=pb.FieldDescriptorProto.TYPE_BYTES,
      label=pb.FieldDescriptorProto.LABEL_REPEATED)
  message_proto.field.add(
      name=_CONTEXT_FIELD_NAME,
      number=2,
      type=pb.FieldDescriptorProto.TYPE_BYTES)

  return proto 

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def _GetDescriptorSetForTextInput():
  """Returns a string for tf.io.decode_proto's descriptor_source."""
  file_descriptor_set = descriptor_pb2.FileDescriptorSet()
  text_input_pb2.DESCRIPTOR.CopyToProto(file_descriptor_set.file.add())
  return b'bytes://' + file_descriptor_set.SerializeToString() 

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def _maybe_annotate_vocab_metadata(vocab_filename, unfiltered_vocabulary_size):
  """Annotates a bucketized tensor with the boundaries that were applied.

  Creates a deferred annotation for the specified tensor.

  Args:
    vocab_filename: The name of the vocabulary.
    unfiltered_vocabulary_size: A tf.int32 tensor containing the unfiltered
      vocab size.
  """
  if not common.IS_ANNOTATIONS_PB_AVAILABLE:
    return

  from tensorflow_transform import annotations_pb2  # pylint: disable=g-import-not-at-top
  message_type = annotations_pb2.VocabularyMetadata.DESCRIPTOR.full_name
  unfiltered_vocabulary_size = tf.expand_dims(unfiltered_vocabulary_size, 0)
  file_name = tf.convert_to_tensor([vocab_filename])
  descriptor_source = descriptor_pb2.FileDescriptorSet()
  annotations_pb2.VocabularyMetadata.DESCRIPTOR.file.CopyToProto(
      descriptor_source.file.add())
  descriptor_source_str = b'bytes://' + descriptor_source.SerializeToString()
  message_proto = tf_utils._encode_proto(  # pylint: disable=protected-access
      {
          'unfiltered_vocabulary_size': unfiltered_vocabulary_size,
          'file_name': file_name,
      }, message_type, descriptor_source=descriptor_source_str)
  assert message_proto.shape == [1]
  message_proto = message_proto[0]

  # Note: we annotate globally here (tied to a vocabulary by filename) rather
  # than attaching to a tensor, because this annotation is tied to an analysis
  # output not a final tensor produced by a mapper.
  type_url = os.path.join(common.ANNOTATION_PREFIX_URL, message_type)
  schema_inference.annotate(type_url, message_proto) 

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def test(self):
    serv = service.Discovery()
    serv.add_service(TestService.DESCRIPTION)
    res = serv.Describe(None, None)
    self.assertEquals(res.services, ['discovery_test.TestService'])

    for f in res.description.file:
      self.assertTrue(f.HasField('source_code_info'))
      f.ClearField('source_code_info')

    expected_description = descriptor_pb2.FileDescriptorSet()
    text_format.Merge(EXPECTED_DESCRIPTION, expected_description)
    self.assertEqual(expected_description, res.description) 

# 需要導入模塊: from google.protobuf import descriptor_pb2 [as 別名]
# 或者: from google.protobuf.descriptor_pb2 import FileDescriptorSet [as 別名]
def load_meta_descriptor(self):
        """
        Load the protobuf version of descriptor.proto to use it in
        decoding protobuf paths.
        """
        fpath = os.path.abspath(os.path.join(os.path.dirname(__file__),
                                             'descriptor.desc'))
        with open(fpath, 'r') as f:
            blob = f.read()
        proto = descriptor_pb2.FileDescriptorSet()
        proto.ParseFromString(blob)
        assert len(proto.file) == 1
        return proto.file[0]