Python google.protobuf.descriptor_pb2.FileDescriptorSet() Examples

The following are 12 code examples of google.protobuf.descriptor_pb2.FileDescriptorSet(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module google.protobuf.descriptor_pb2 , or try the search function

Example #1

Source File: test_descriptor.py From artman with Apache License 2.0

6 votes

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

Example #2

Source File: data.py From ranking with Apache License 2.0

6 votes

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

Example #3

Source File: input_generator.py From lingvo with Apache License 2.0

5 votes

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()

Example #4

Source File: descriptor_set_tasks.py From artman with Apache License 2.0

5 votes

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

Example #5

Source File: analyzers.py From transform with Apache License 2.0

5 votes

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)

Example #6