text: # -*- coding: utf-8 -*-
  #
  # Copyright 2020 Google LLC
  #
  # 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
  #
  #     https://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.
  
  
  from __future__ import absolute_import
  import sys
  
  from google.api_core.protobuf_helpers import get_messages
  
  from google.cloud.automl_v1beta1.proto import annotation_payload_pb2
  from google.cloud.automl_v1beta1.proto import annotation_spec_pb2
  from google.cloud.automl_v1beta1.proto import classification_pb2
  from google.cloud.automl_v1beta1.proto import column_spec_pb2
  from google.cloud.automl_v1beta1.proto import data_items_pb2
  from google.cloud.automl_v1beta1.proto import data_stats_pb2
  from google.cloud.automl_v1beta1.proto import data_types_pb2
  from google.cloud.automl_v1beta1.proto import dataset_pb2
  from google.cloud.automl_v1beta1.proto import detection_pb2
  from google.cloud.automl_v1beta1.proto import geometry_pb2
  from google.cloud.automl_v1beta1.proto import image_pb2
  from google.cloud.automl_v1beta1.proto import io_pb2
  from google.cloud.automl_v1beta1.proto import model_evaluation_pb2
  from google.cloud.automl_v1beta1.proto import model_pb2
  from google.cloud.automl_v1beta1.proto import operations_pb2 as proto_operations_pb2
  from google.cloud.automl_v1beta1.proto import prediction_service_pb2
  from google.cloud.automl_v1beta1.proto import ranges_pb2
  from google.cloud.automl_v1beta1.proto import regression_pb2
  from google.cloud.automl_v1beta1.proto import service_pb2
  from google.cloud.automl_v1beta1.proto import table_spec_pb2
  from google.cloud.automl_v1beta1.proto import tables_pb2
  from google.cloud.automl_v1beta1.proto import temporal_pb2
  from google.cloud.automl_v1beta1.proto import text_extraction_pb2
  from google.cloud.automl_v1beta1.proto import text_pb2
  from google.cloud.automl_v1beta1.proto import text_segment_pb2
  from google.cloud.automl_v1beta1.proto import text_sentiment_pb2
  from google.cloud.automl_v1beta1.proto import translation_pb2
  from google.cloud.automl_v1beta1.proto import video_pb2
  from google.longrunning import operations_pb2 as longrunning_operations_pb2
  from google.protobuf import any_pb2
  from google.protobuf import duration_pb2
  from google.protobuf import field_mask_pb2
  from google.protobuf import struct_pb2
  from google.protobuf import timestamp_pb2
  from google.rpc import status_pb2
  
  
  _shared_modules = [
      longrunning_operations_pb2,
      any_pb2,
      duration_pb2,
      field_mask_pb2,
      struct_pb2,
      timestamp_pb2,
      status_pb2,
  ]
  
  _local_modules = [
      annotation_payload_pb2,
      annotation_spec_pb2,
      classification_pb2,
      column_spec_pb2,
      data_items_pb2,
      data_stats_pb2,
      data_types_pb2,
      dataset_pb2,
      detection_pb2,
      geometry_pb2,
      image_pb2,
      io_pb2,
      model_evaluation_pb2,
      model_pb2,
      proto_operations_pb2,
      prediction_service_pb2,
      ranges_pb2,
      regression_pb2,
      service_pb2,
      table_spec_pb2,
      tables_pb2,
      temporal_pb2,
      text_extraction_pb2,
      text_pb2,
      text_segment_pb2,
      text_sentiment_pb2,
      translation_pb2,
      video_pb2,
  ]
  
  names = []
  
  for module in _shared_modules:  # pragma: NO COVER
      for name, message in get_messages(module).items():
          setattr(sys.modules[__name__], name, message)
          names.append(name)
  for module in _local_modules:
      for name, message in get_messages(module).items():
          message.__module__ = "google.cloud.automl_v1beta1.types"
          setattr(sys.modules[__name__], name, message)
          names.append(name)
  
  
  __all__ = tuple(sorted(names))
  