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
  #
  #     http://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 collections import OrderedDict
  from distutils import util
  import os
  import re
  from typing import Callable, Dict, Optional, Sequence, Tuple, Type, Union
  import pkg_resources
  
  from google.api_core import client_options as client_options_lib  # type: ignore
  from google.api_core import exceptions  # type: ignore
  from google.api_core import gapic_v1  # type: ignore
  from google.api_core import retry as retries  # type: ignore
  from google.auth import credentials  # type: ignore
  from google.auth.transport import mtls  # type: ignore
  from google.auth.transport.grpc import SslCredentials  # type: ignore
  from google.auth.exceptions import MutualTLSChannelError  # type: ignore
  from google.oauth2 import service_account  # type: ignore
  
  from google.api_core import operation  # type: ignore
  from google.api_core import operation_async  # type: ignore
  from google.cloud.automl_v1.types import annotation_payload
  from google.cloud.automl_v1.types import data_items
  from google.cloud.automl_v1.types import io
  from google.cloud.automl_v1.types import operations
  from google.cloud.automl_v1.types import prediction_service
  
  from .transports.base import PredictionServiceTransport, DEFAULT_CLIENT_INFO
  from .transports.grpc import PredictionServiceGrpcTransport
  from .transports.grpc_asyncio import PredictionServiceGrpcAsyncIOTransport
  
  
  class PredictionServiceClientMeta(type):
      """Metaclass for the PredictionService client.
  
      This provides class-level methods for building and retrieving
      support objects (e.g. transport) without polluting the client instance
      objects.
      """
  
      _transport_registry = (
          OrderedDict()
      )  # type: Dict[str, Type[PredictionServiceTransport]]
      _transport_registry["grpc"] = PredictionServiceGrpcTransport
      _transport_registry["grpc_asyncio"] = PredictionServiceGrpcAsyncIOTransport
  
      def get_transport_class(
          cls, label: str = None,
      ) -> Type[PredictionServiceTransport]:
          """Return an appropriate transport class.
  
          Args:
              label: The name of the desired transport. If none is
                  provided, then the first transport in the registry is used.
  
          Returns:
              The transport class to use.
          """
          # If a specific transport is requested, return that one.
          if label:
              return cls._transport_registry[label]
  
          # No transport is requested; return the default (that is, the first one
          # in the dictionary).
          return next(iter(cls._transport_registry.values()))
  
  
  class PredictionServiceClient(metaclass=PredictionServiceClientMeta):
      """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.
      """
  
      @staticmethod
      def _get_default_mtls_endpoint(api_endpoint):
          """Convert api endpoint to mTLS endpoint.
          Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to
          "*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively.
          Args:
              api_endpoint (Optional[str]): the api endpoint to convert.
          Returns:
              str: converted mTLS api endpoint.
          """
          if not api_endpoint:
              return api_endpoint
  
          mtls_endpoint_re = re.compile(
              r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?"
          )
  
          m = mtls_endpoint_re.match(api_endpoint)
          name, mtls, sandbox, googledomain = m.groups()
          if mtls or not googledomain:
              return api_endpoint
  
          if sandbox:
              return api_endpoint.replace(
                  "sandbox.googleapis.com", "mtls.sandbox.googleapis.com"
              )
  
          return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com")
  
      DEFAULT_ENDPOINT = "automl.googleapis.com"
      DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__(  # type: ignore
          DEFAULT_ENDPOINT
      )
  
      @classmethod
      def from_service_account_file(cls, filename: str, *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:
              {@api.name}: 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
  
      def __init__(
          self,
          *,
          credentials: Optional[credentials.Credentials] = None,
          transport: Union[str, PredictionServiceTransport, None] = None,
          client_options: Optional[client_options_lib.ClientOptions] = None,
          client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
      ) -> None:
          """Instantiate the prediction service client.
  
          Args:
              credentials (Optional[google.auth.credentials.Credentials]): The
                  authorization credentials to attach to requests. These
                  credentials identify the application to the service; if none
                  are specified, the client will attempt to ascertain the
                  credentials from the environment.
              transport (Union[str, ~.PredictionServiceTransport]): The
                  transport to use. If set to None, a transport is chosen
                  automatically.
              client_options (client_options_lib.ClientOptions): Custom options for the
                  client. It won't take effect if a ``transport`` instance is provided.
                  (1) The ``api_endpoint`` property can be used to override the
                  default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
                  environment variable can also be used to override the endpoint:
                  "always" (always use the default mTLS endpoint), "never" (always
                  use the default regular endpoint) and "auto" (auto switch to the
                  default mTLS endpoint if client certificate is present, this is
                  the default value). However, the ``api_endpoint`` property takes
                  precedence if provided.
                  (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
                  is "true", then the ``client_cert_source`` property can be used
                  to provide client certificate for mutual TLS transport. If
                  not provided, the default SSL client certificate will be used if
                  present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
                  set, no client certificate will be 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.
  
          Raises:
              google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
                  creation failed for any reason.
          """
          if isinstance(client_options, dict):
              client_options = client_options_lib.from_dict(client_options)
          if client_options is None:
              client_options = client_options_lib.ClientOptions()
  
          # Create SSL credentials for mutual TLS if needed.
          use_client_cert = bool(
              util.strtobool(os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false"))
          )
  
          ssl_credentials = None
          is_mtls = False
          if use_client_cert:
              if client_options.client_cert_source:
                  import grpc  # type: ignore
  
                  cert, key = client_options.client_cert_source()
                  ssl_credentials = grpc.ssl_channel_credentials(
                      certificate_chain=cert, private_key=key
                  )
                  is_mtls = True
              else:
                  creds = SslCredentials()
                  is_mtls = creds.is_mtls
                  ssl_credentials = creds.ssl_credentials if is_mtls else None
  
          # Figure out which api endpoint to use.
          if client_options.api_endpoint is not None:
              api_endpoint = client_options.api_endpoint
          else:
              use_mtls_env = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
              if use_mtls_env == "never":
                  api_endpoint = self.DEFAULT_ENDPOINT
              elif use_mtls_env == "always":
                  api_endpoint = self.DEFAULT_MTLS_ENDPOINT
              elif use_mtls_env == "auto":
                  api_endpoint = (
                      self.DEFAULT_MTLS_ENDPOINT if is_mtls else self.DEFAULT_ENDPOINT
                  )
              else:
                  raise MutualTLSChannelError(
                      "Unsupported GOOGLE_API_USE_MTLS_ENDPOINT value. Accepted values: never, auto, always"
                  )
  
          # Save or instantiate the transport.
          # Ordinarily, we provide the transport, but allowing a custom transport
          # instance provides an extensibility point for unusual situations.
          if isinstance(transport, PredictionServiceTransport):
              # transport is a PredictionServiceTransport instance.
              if credentials or client_options.credentials_file:
                  raise ValueError(
                      "When providing a transport instance, "
                      "provide its credentials directly."
                  )
              if client_options.scopes:
                  raise ValueError(
                      "When providing a transport instance, "
                      "provide its scopes directly."
                  )
              self._transport = transport
          else:
              Transport = type(self).get_transport_class(transport)
              self._transport = Transport(
                  credentials=credentials,
                  credentials_file=client_options.credentials_file,
                  host=api_endpoint,
                  scopes=client_options.scopes,
                  ssl_channel_credentials=ssl_credentials,
                  quota_project_id=client_options.quota_project_id,
                  client_info=client_info,
              )
  
      def predict(
          self,
          request: prediction_service.PredictRequest = None,
          *,
          name: str = None,
          payload: data_items.ExamplePayload = None,
          params: Sequence[prediction_service.PredictRequest.ParamsEntry] = None,
          retry: retries.Retry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> prediction_service.PredictResponse:
          r"""Perform an online prediction. The prediction result is directly
          returned in the response. Available for following ML scenarios,
          and their expected request payloads:
  
          AutoML Vision Classification
  
          -  An image in .JPEG, .GIF or .PNG format, image_bytes up to
             30MB.
  
          AutoML Vision Object Detection
  
          -  An image in .JPEG, .GIF or .PNG format, image_bytes up to
             30MB.
  
          AutoML Natural Language Classification
  
          -  A TextSnippet up to 60,000 characters, UTF-8 encoded or a
             document in .PDF, .TIF or .TIFF format with size upto 2MB.
  
          AutoML Natural Language Entity Extraction
  
          -  A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a
             document in .PDF, .TIF or .TIFF format with size upto 20MB.
  
          AutoML Natural Language Sentiment Analysis
  
          -  A TextSnippet up to 60,000 characters, UTF-8 encoded or a
             document in .PDF, .TIF or .TIFF format with size upto 2MB.
  
          AutoML Translation
  
          -  A TextSnippet up to 25,000 characters, UTF-8 encoded.
  
          AutoML Tables
  
          -  A row with column values matching the columns of the model,
             up to 5MB. Not available for FORECASTING ``prediction_type``.
  
          Args:
              request (:class:`~.prediction_service.PredictRequest`):
                  The request object. Request message for
                  [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
              name (:class:`str`):
                  Required. Name of the model requested
                  to serve the prediction.
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              payload (:class:`~.data_items.ExamplePayload`):
                  Required. Payload to perform a
                  prediction on. The payload must match
                  the problem type that the model was
                  trained to solve.
                  This corresponds to the ``payload`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              params (:class:`Sequence[~.prediction_service.PredictRequest.ParamsEntry]`):
                  Additional domain-specific parameters, any string must
                  be up to 25000 characters long.
  
                  AutoML Vision 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.
  
                  AutoML Vision 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) The maximum number
                  of bounding boxes returned. The default is 100. The
                  number of returned bounding boxes might be limited by
                  the server.
  
                  AutoML Tables
  
                  ``feature_importance`` : (boolean) Whether
  
                  [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
                  is populated in the returned list of
                  [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]
                  objects. The default is false.
                  This corresponds to the ``params`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
  
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              ~.prediction_service.PredictResponse:
                  Response message for
                  [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
  
          """
          # Create or coerce a protobuf request object.
          # Sanity check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name, payload, params])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a prediction_service.PredictRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, prediction_service.PredictRequest):
              request = prediction_service.PredictRequest(request)
  
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
  
              if name is not None:
                  request.name = name
              if payload is not None:
                  request.payload = payload
              if params is not None:
                  request.params = params
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.predict]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Done; return the response.
          return response
  
      def batch_predict(
          self,
          request: prediction_service.BatchPredictRequest = None,
          *,
          name: str = None,
          input_config: io.BatchPredictInputConfig = None,
          output_config: io.BatchPredictOutputConfig = None,
          params: Sequence[prediction_service.BatchPredictRequest.ParamsEntry] = None,
          retry: retries.Retry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> operation.Operation:
          r"""Perform a batch prediction. Unlike the online
          [Predict][google.cloud.automl.v1.PredictionService.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][google.longrunning.Operations.GetOperation]
          method. Once the operation is done,
          [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult]
          is returned in the
          [response][google.longrunning.Operation.response] field.
          Available for following ML scenarios:
  
          -  AutoML Vision Classification
          -  AutoML Vision Object Detection
          -  AutoML Video Intelligence Classification
          -  AutoML Video Intelligence Object Tracking \* AutoML Natural
             Language Classification
          -  AutoML Natural Language Entity Extraction
          -  AutoML Natural Language Sentiment Analysis
          -  AutoML Tables
  
          Args:
              request (:class:`~.prediction_service.BatchPredictRequest`):
                  The request object. Request message for
                  [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
              name (:class:`str`):
                  Required. Name of the model requested
                  to serve the batch prediction.
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              input_config (:class:`~.io.BatchPredictInputConfig`):
                  Required. The input configuration for
                  batch prediction.
                  This corresponds to the ``input_config`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              output_config (:class:`~.io.BatchPredictOutputConfig`):
                  Required. The Configuration
                  specifying where output predictions
                  should be written.
                  This corresponds to the ``output_config`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              params (:class:`Sequence[~.prediction_service.BatchPredictRequest.ParamsEntry]`):
                  Additional domain-specific parameters for the
                  predictions, any string must be up to 25000 characters
                  long.
  
                  AutoML Natural Language 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.
  
                  AutoML Vision 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.
  
                  AutoML Vision 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) The maximum number
                  of bounding boxes returned per image. The default is
                  100, the number of bounding boxes returned might be
                  limited by the server. AutoML Video Intelligence
                  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. The default is false.
  
                  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.
  
                  ``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. The default is false.
  
                  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.
  
                  AutoML Video Intelligence 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) The maximum number
                  of bounding boxes returned per image. The default is
                  100, the number of bounding boxes returned might be
                  limited by the server.
  
                  ``min_bounding_box_size`` : (float) Only bounding boxes
                  with shortest edge at least that long as a relative
                  value of video frame size are returned. Value in 0 to 1
                  range. Default is 0.
                  This corresponds to the ``params`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
  
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              ~.operation.Operation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be
                  :class:``~.prediction_service.BatchPredictResult``:
                  Result of the Batch Predict. This message is returned in
                  [response][google.longrunning.Operation.response] of the
                  operation returned by the
                  [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
  
          """
          # Create or coerce a protobuf request object.
          # Sanity check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name, input_config, output_config, params])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a prediction_service.BatchPredictRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, prediction_service.BatchPredictRequest):
              request = prediction_service.BatchPredictRequest(request)
  
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
  
              if name is not None:
                  request.name = name
              if input_config is not None:
                  request.input_config = input_config
              if output_config is not None:
                  request.output_config = output_config
              if params is not None:
                  request.params = params
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.batch_predict]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Wrap the response in an operation future.
          response = operation.from_gapic(
              response,
              self._transport.operations_client,
              prediction_service.BatchPredictResult,
              metadata_type=operations.OperationMetadata,
          )
  
          # Done; return the response.
          return response
  
  
  try:
      DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
          gapic_version=pkg_resources.get_distribution("google-cloud-automl",).version,
      )
  except pkg_resources.DistributionNotFound:
      DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()
  
  
  __all__ = ("PredictionServiceClient",)
  