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.
  
  """Accesses the google.cloud.automl.v1beta1 PredictionService API."""
  
  import pkg_resources
  import warnings
  
  from google.oauth2 import service_account
  import google.api_core.client_options
  import google.api_core.gapic_v1.client_info
  import google.api_core.gapic_v1.config
  import google.api_core.gapic_v1.method
  import google.api_core.gapic_v1.routing_header
  import google.api_core.grpc_helpers
  import google.api_core.operation
  import google.api_core.operations_v1
  import google.api_core.path_template
  import grpc
  
  from google.cloud.automl_v1beta1.gapic import enums
  from google.cloud.automl_v1beta1.gapic import prediction_service_client_config
  from google.cloud.automl_v1beta1.gapic.transports import (
      prediction_service_grpc_transport,
  )
  from google.cloud.automl_v1beta1.proto import annotation_spec_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 dataset_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 prediction_service_pb2_grpc
  from google.cloud.automl_v1beta1.proto import service_pb2
  from google.cloud.automl_v1beta1.proto import service_pb2_grpc
  from google.cloud.automl_v1beta1.proto import table_spec_pb2
  from google.longrunning import operations_pb2 as longrunning_operations_pb2
  from google.protobuf import empty_pb2
  from google.protobuf import field_mask_pb2
  
  
  _GAPIC_LIBRARY_VERSION = pkg_resources.get_distribution("google-cloud-automl").version
  
  
  class PredictionServiceClient(object):
      """
      AutoML Prediction API.
  
      On any input that is documented to expect a string parameter in
      snake\_case or kebab-case, either of those cases is accepted.
      """
  
      SERVICE_ADDRESS = "automl.googleapis.com:443"
      """The default address of the service."""
  
      # The name of the interface for this client. This is the key used to
      # find the method configuration in the client_config dictionary.
      _INTERFACE_NAME = "google.cloud.automl.v1beta1.PredictionService"
  
      @classmethod
      def from_service_account_file(cls, filename, *args, **kwargs):
          """Creates an instance of this client using the provided credentials
          file.
  
          Args:
              filename (str): The path to the service account private key json
                  file.
              args: Additional arguments to pass to the constructor.
              kwargs: Additional arguments to pass to the constructor.
  
          Returns:
              PredictionServiceClient: The constructed client.
          """
          credentials = service_account.Credentials.from_service_account_file(filename)
          kwargs["credentials"] = credentials
          return cls(*args, **kwargs)
  
      from_service_account_json = from_service_account_file
  
      @classmethod
      def model_path(cls, project, location, model):
          """Return a fully-qualified model string."""
          return google.api_core.path_template.expand(
              "projects/{project}/locations/{location}/models/{model}",
              project=project,
              location=location,
              model=model,
          )
  
      def __init__(
          self,
          transport=None,
          channel=None,
          credentials=None,
          client_config=None,
          client_info=None,
          client_options=None,
      ):
          """Constructor.
  
          Args:
              transport (Union[~.PredictionServiceGrpcTransport,
                      Callable[[~.Credentials, type], ~.PredictionServiceGrpcTransport]): A transport
                  instance, responsible for actually making the API calls.
                  The default transport uses the gRPC protocol.
                  This argument may also be a callable which returns a
                  transport instance. Callables will be sent the credentials
                  as the first argument and the default transport class as
                  the second argument.
              channel (grpc.Channel): DEPRECATED. A ``Channel`` instance
                  through which to make calls. This argument is mutually exclusive
                  with ``credentials``; providing both will raise an exception.
              credentials (google.auth.credentials.Credentials): The
                  authorization credentials to attach to requests. These
                  credentials identify this application to the service. If none
                  are specified, the client will attempt to ascertain the
                  credentials from the environment.
                  This argument is mutually exclusive with providing a
                  transport instance to ``transport``; doing so will raise
                  an exception.
              client_config (dict): DEPRECATED. A dictionary of call options for
                  each method. If not specified, the default configuration is used.
              client_info (google.api_core.gapic_v1.client_info.ClientInfo):
                  The client info used to send a user-agent string along with
                  API requests. If ``None``, then default info will be used.
                  Generally, you only need to set this if you're developing
                  your own client library.
              client_options (Union[dict, google.api_core.client_options.ClientOptions]):
                  Client options used to set user options on the client. API Endpoint
                  should be set through client_options.
          """
          # Raise deprecation warnings for things we want to go away.
          if client_config is not None:
              warnings.warn(
                  "The `client_config` argument is deprecated.",
                  PendingDeprecationWarning,
                  stacklevel=2,
              )
          else:
              client_config = prediction_service_client_config.config
  
          if channel:
              warnings.warn(
                  "The `channel` argument is deprecated; use " "`transport` instead.",
                  PendingDeprecationWarning,
                  stacklevel=2,
              )
  
          api_endpoint = self.SERVICE_ADDRESS
          if client_options:
              if type(client_options) == dict:
                  client_options = google.api_core.client_options.from_dict(
                      client_options
                  )
              if client_options.api_endpoint:
                  api_endpoint = client_options.api_endpoint
  
          # Instantiate the transport.
          # The transport is responsible for handling serialization and
          # deserialization and actually sending data to the service.
          if transport:
              if callable(transport):
                  self.transport = transport(
                      credentials=credentials,
                      default_class=prediction_service_grpc_transport.PredictionServiceGrpcTransport,
                      address=api_endpoint,
                  )
              else:
                  if credentials:
                      raise ValueError(
                          "Received both a transport instance and "
                          "credentials; these are mutually exclusive."
                      )
                  self.transport = transport
          else:
              self.transport = prediction_service_grpc_transport.PredictionServiceGrpcTransport(
                  address=api_endpoint, channel=channel, credentials=credentials
              )
  
          if client_info is None:
              client_info = google.api_core.gapic_v1.client_info.ClientInfo(
                  gapic_version=_GAPIC_LIBRARY_VERSION
              )
          else:
              client_info.gapic_version = _GAPIC_LIBRARY_VERSION
          self._client_info = client_info
  
          # Parse out the default settings for retry and timeout for each RPC
          # from the client configuration.
          # (Ordinarily, these are the defaults specified in the `*_config.py`
          # file next to this one.)
          self._method_configs = google.api_core.gapic_v1.config.parse_method_configs(
              client_config["interfaces"][self._INTERFACE_NAME]
          )
  
          # Save a dictionary of cached API call functions.
          # These are the actual callables which invoke the proper
          # transport methods, wrapped with `wrap_method` to add retry,
          # timeout, and the like.
          self._inner_api_calls = {}
  
      # Service calls
      def predict(
          self,
          name,
          payload,
          params=None,
          retry=google.api_core.gapic_v1.method.DEFAULT,
          timeout=google.api_core.gapic_v1.method.DEFAULT,
          metadata=None,
      ):
          """
          Perform an online prediction. The prediction result will be directly
          returned in the response. Available for following ML problems, and their
          expected request payloads:
  
          -  Image Classification - Image in .JPEG, .GIF or .PNG format,
             image\_bytes up to 30MB.
          -  Image Object Detection - Image in .JPEG, .GIF or .PNG format,
             image\_bytes up to 30MB.
          -  Text Classification - TextSnippet, content up to 60,000 characters,
             UTF-8 encoded.
          -  Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8
             NFC encoded.
          -  Translation - TextSnippet, content up to 25,000 characters, UTF-8
             encoded.
          -  Tables - Row, with column values matching the columns of the model,
             up to 5MB. Not available for FORECASTING
  
          ``prediction_type``.
  
          -  Text Sentiment - TextSnippet, content up 500 characters, UTF-8
             encoded.
  
          Example:
              >>> from google.cloud import automl_v1beta1
              >>>
              >>> client = automl_v1beta1.PredictionServiceClient()
              >>>
              >>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')
              >>>
              >>> # TODO: Initialize `payload`:
              >>> payload = {}
              >>>
              >>> response = client.predict(name, payload)
  
          Args:
              name (str): Name of the model requested to serve the prediction.
              payload (Union[dict, ~google.cloud.automl_v1beta1.types.ExamplePayload]): Required. Payload to perform a prediction on. The payload must match the
                  problem type that the model was trained to solve.
  
                  If a dict is provided, it must be of the same form as the protobuf
                  message :class:`~google.cloud.automl_v1beta1.types.ExamplePayload`
              params (dict[str -> str]): Additional domain-specific parameters, any string must be up to 25000
                  characters long.
  
                  -  For Image Classification:
  
                     ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model
                     makes predictions for an image, it will only produce results that
                     have at least this confidence score. The default is 0.5.
  
                  -  For Image Object Detection: ``score_threshold`` - (float) When Model
                     detects objects on the image, it will only produce bounding boxes
                     which have at least this confidence score. Value in 0 to 1 range,
                     default is 0.5. ``max_bounding_box_count`` - (int64) No more than
                     this number of bounding boxes will be returned in the response.
                     Default is 100, the requested value may be limited by server.
  
                  -  For Tables: ``feature_importance`` - (boolean) Whether
  
                  [feature\_importance][[google.cloud.automl.v1beta1.TablesModelColumnInfo.feature\_importance]
                  should be populated in the returned
  
                  [TablesAnnotation(-s)][[google.cloud.automl.v1beta1.TablesAnnotation].
                  The default is false.
              retry (Optional[google.api_core.retry.Retry]):  A retry object used
                  to retry requests. If ``None`` is specified, requests will
                  be retried using a default configuration.
              timeout (Optional[float]): The amount of time, in seconds, to wait
                  for the request to complete. Note that if ``retry`` is
                  specified, the timeout applies to each individual attempt.
              metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
                  that is provided to the method.
  
          Returns:
              A :class:`~google.cloud.automl_v1beta1.types.PredictResponse` instance.
  
          Raises:
              google.api_core.exceptions.GoogleAPICallError: If the request
                      failed for any reason.
              google.api_core.exceptions.RetryError: If the request failed due
                      to a retryable error and retry attempts failed.
              ValueError: If the parameters are invalid.
          """
          # Wrap the transport method to add retry and timeout logic.
          if "predict" not in self._inner_api_calls:
              self._inner_api_calls[
                  "predict"
              ] = google.api_core.gapic_v1.method.wrap_method(
                  self.transport.predict,
                  default_retry=self._method_configs["Predict"].retry,
                  default_timeout=self._method_configs["Predict"].timeout,
                  client_info=self._client_info,
              )
  
          request = prediction_service_pb2.PredictRequest(
              name=name, payload=payload, params=params
          )
          if metadata is None:
              metadata = []
          metadata = list(metadata)
          try:
              routing_header = [("name", name)]
          except AttributeError:
              pass
          else:
              routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(
                  routing_header
              )
              metadata.append(routing_metadata)
  
          return self._inner_api_calls["predict"](
              request, retry=retry, timeout=timeout, metadata=metadata
          )
  
      def batch_predict(
          self,
          name,
          input_config,
          output_config,
          params=None,
          retry=google.api_core.gapic_v1.method.DEFAULT,
          timeout=google.api_core.gapic_v1.method.DEFAULT,
          metadata=None,
      ):
          """
          Perform a batch prediction. Unlike the online ``Predict``, batch
          prediction result won't be immediately available in the response.
          Instead, a long running operation object is returned. User can poll the
          operation result via ``GetOperation`` method. Once the operation is
          done, ``BatchPredictResult`` is returned in the ``response`` field.
          Available for following ML problems:
  
          -  Image Classification
          -  Image Object Detection
          -  Video Classification
          -  Video Object Tracking \* Text Extraction
          -  Tables
  
          Example:
              >>> from google.cloud import automl_v1beta1
              >>>
              >>> client = automl_v1beta1.PredictionServiceClient()
              >>>
              >>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')
              >>>
              >>> # TODO: Initialize `input_config`:
              >>> input_config = {}
              >>>
              >>> # TODO: Initialize `output_config`:
              >>> output_config = {}
              >>>
              >>> response = client.batch_predict(name, input_config, output_config)
              >>>
              >>> def callback(operation_future):
              ...     # Handle result.
              ...     result = operation_future.result()
              >>>
              >>> response.add_done_callback(callback)
              >>>
              >>> # Handle metadata.
              >>> metadata = response.metadata()
  
          Args:
              name (str): Name of the model requested to serve the batch prediction.
              input_config (Union[dict, ~google.cloud.automl_v1beta1.types.BatchPredictInputConfig]): Required. The input configuration for batch prediction.
  
                  If a dict is provided, it must be of the same form as the protobuf
                  message :class:`~google.cloud.automl_v1beta1.types.BatchPredictInputConfig`
              output_config (Union[dict, ~google.cloud.automl_v1beta1.types.BatchPredictOutputConfig]): Required. The Configuration specifying where output predictions should
                  be written.
  
                  If a dict is provided, it must be of the same form as the protobuf
                  message :class:`~google.cloud.automl_v1beta1.types.BatchPredictOutputConfig`
              params (dict[str -> str]): Additional domain-specific parameters for the predictions, any string
                  must be up to 25000 characters long.
  
                  -  For Text Classification:
  
                     ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model
                     makes predictions for a text snippet, it will only produce results
                     that have at least this confidence score. The default is 0.5.
  
                  -  For Image Classification:
  
                     ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model
                     makes predictions for an image, it will only produce results that
                     have at least this confidence score. The default is 0.5.
  
                  -  For Image Object Detection:
  
                     ``score_threshold`` - (float) When Model detects objects on the
                     image, it will only produce bounding boxes which have at least this
                     confidence score. Value in 0 to 1 range, default is 0.5.
                     ``max_bounding_box_count`` - (int64) No more than this number of
                     bounding boxes will be produced per image. Default is 100, the
                     requested value may be limited by server.
  
                  -  For Video Classification : ``score_threshold`` - (float) A value from
                     0.0 to 1.0. When the model makes predictions for a video, it will
                     only produce results that have at least this confidence score. The
                     default is 0.5. ``segment_classification`` - (boolean) Set to true to
                     request segment-level classification. AutoML Video Intelligence
                     returns labels and their confidence scores for the entire segment of
                     the video that user specified in the request configuration. The
                     default is "true". ``shot_classification`` - (boolean) Set to true to
                     request shot-level classification. AutoML Video Intelligence
                     determines the boundaries for each camera shot in the entire segment
                     of the video that user specified in the request configuration. AutoML
                     Video Intelligence then returns labels and their confidence scores
                     for each detected shot, along with the start and end time of the
                     shot. WARNING: Model evaluation is not done for this classification
                     type, the quality of it depends on training data, but there are no
                     metrics provided to describe that quality. The default is "false".
                     ``1s_interval_classification`` - (boolean) Set to true to request
                     classification for a video at one-second intervals. AutoML Video
                     Intelligence returns labels and their confidence scores for each
                     second of the entire segment of the video that user specified in the
                     request configuration. WARNING: Model evaluation is not done for this
                     classification type, the quality of it depends on training data, but
                     there are no metrics provided to describe that quality. The default
                     is "false".
  
                  -  For Video Object Tracking: ``score_threshold`` - (float) When Model
                     detects objects on video frames, it will only produce bounding boxes
                     which have at least this confidence score. Value in 0 to 1 range,
                     default is 0.5. ``max_bounding_box_count`` - (int64) No more than
                     this number of bounding boxes will be returned per frame. Default is
                     100, the requested value may be limited by server.
                     ``min_bounding_box_size`` - (float) Only bounding boxes with shortest
                     edge at least that long as a relative value of video frame size will
                     be returned. Value in 0 to 1 range. Default is 0.
              retry (Optional[google.api_core.retry.Retry]):  A retry object used
                  to retry requests. If ``None`` is specified, requests will
                  be retried using a default configuration.
              timeout (Optional[float]): The amount of time, in seconds, to wait
                  for the request to complete. Note that if ``retry`` is
                  specified, the timeout applies to each individual attempt.
              metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
                  that is provided to the method.
  
          Returns:
              A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance.
  
          Raises:
              google.api_core.exceptions.GoogleAPICallError: If the request
                      failed for any reason.
              google.api_core.exceptions.RetryError: If the request failed due
                      to a retryable error and retry attempts failed.
              ValueError: If the parameters are invalid.
          """
          # Wrap the transport method to add retry and timeout logic.
          if "batch_predict" not in self._inner_api_calls:
              self._inner_api_calls[
                  "batch_predict"
              ] = google.api_core.gapic_v1.method.wrap_method(
                  self.transport.batch_predict,
                  default_retry=self._method_configs["BatchPredict"].retry,
                  default_timeout=self._method_configs["BatchPredict"].timeout,
                  client_info=self._client_info,
              )
  
          request = prediction_service_pb2.BatchPredictRequest(
              name=name,
              input_config=input_config,
              output_config=output_config,
              params=params,
          )
          if metadata is None:
              metadata = []
          metadata = list(metadata)
          try:
              routing_header = [("name", name)]
          except AttributeError:
              pass
          else:
              routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(
                  routing_header
              )
              metadata.append(routing_metadata)
  
          operation = self._inner_api_calls["batch_predict"](
              request, retry=retry, timeout=timeout, metadata=metadata
          )
          return google.api_core.operation.from_gapic(
              operation,
              self.transport._operations_client,
              prediction_service_pb2.BatchPredictResult,
              metadata_type=proto_operations_pb2.OperationMetadata,
          )
  