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
  import functools
  import re
  from typing import Dict, Sequence, Tuple, Type, Union
  import pkg_resources
  
  import google.api_core.client_options as ClientOptions # type: ignore
  from google.api_core import exceptions as core_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 as ga_credentials   # type: ignore
  from google.oauth2 import service_account              # type: ignore
  
  from google.api import httpbody_pb2  # type: ignore
  from google.cloud.aiplatform_v1.types import explanation
  from google.cloud.aiplatform_v1.types import prediction_service
  from google.protobuf import any_pb2  # type: ignore
  from google.protobuf import struct_pb2  # type: ignore
  from .transports.base import PredictionServiceTransport, DEFAULT_CLIENT_INFO
  from .transports.grpc_asyncio import PredictionServiceGrpcAsyncIOTransport
  from .client import PredictionServiceClient
  
  
  class PredictionServiceAsyncClient:
      """A service for online predictions and explanations."""
  
      _client: PredictionServiceClient
  
      DEFAULT_ENDPOINT = PredictionServiceClient.DEFAULT_ENDPOINT
      DEFAULT_MTLS_ENDPOINT = PredictionServiceClient.DEFAULT_MTLS_ENDPOINT
  
      endpoint_path = staticmethod(PredictionServiceClient.endpoint_path)
      parse_endpoint_path = staticmethod(PredictionServiceClient.parse_endpoint_path)
      model_path = staticmethod(PredictionServiceClient.model_path)
      parse_model_path = staticmethod(PredictionServiceClient.parse_model_path)
      common_billing_account_path = staticmethod(PredictionServiceClient.common_billing_account_path)
      parse_common_billing_account_path = staticmethod(PredictionServiceClient.parse_common_billing_account_path)
      common_folder_path = staticmethod(PredictionServiceClient.common_folder_path)
      parse_common_folder_path = staticmethod(PredictionServiceClient.parse_common_folder_path)
      common_organization_path = staticmethod(PredictionServiceClient.common_organization_path)
      parse_common_organization_path = staticmethod(PredictionServiceClient.parse_common_organization_path)
      common_project_path = staticmethod(PredictionServiceClient.common_project_path)
      parse_common_project_path = staticmethod(PredictionServiceClient.parse_common_project_path)
      common_location_path = staticmethod(PredictionServiceClient.common_location_path)
      parse_common_location_path = staticmethod(PredictionServiceClient.parse_common_location_path)
  
      @classmethod
      def from_service_account_info(cls, info: dict, *args, **kwargs):
          """Creates an instance of this client using the provided credentials
              info.
  
          Args:
              info (dict): The service account private key info.
              args: Additional arguments to pass to the constructor.
              kwargs: Additional arguments to pass to the constructor.
  
          Returns:
              PredictionServiceAsyncClient: The constructed client.
          """
          return PredictionServiceClient.from_service_account_info.__func__(PredictionServiceAsyncClient, info, *args, **kwargs)  # type: ignore
  
      @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:
              PredictionServiceAsyncClient: The constructed client.
          """
          return PredictionServiceClient.from_service_account_file.__func__(PredictionServiceAsyncClient, filename, *args, **kwargs)  # type: ignore
  
      from_service_account_json = from_service_account_file
  
      @property
      def transport(self) -> PredictionServiceTransport:
          """Returns the transport used by the client instance.
  
          Returns:
              PredictionServiceTransport: The transport used by the client instance.
          """
          return self._client.transport
  
      get_transport_class = functools.partial(type(PredictionServiceClient).get_transport_class, type(PredictionServiceClient))
  
      def __init__(self, *,
              credentials: ga_credentials.Credentials = None,
              transport: Union[str, PredictionServiceTransport] = "grpc_asyncio",
              client_options: ClientOptions = None,
              client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
              ) -> None:
          """Instantiates 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 (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.
  
          Raises:
              google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport
                  creation failed for any reason.
          """
          self._client = PredictionServiceClient(
              credentials=credentials,
              transport=transport,
              client_options=client_options,
              client_info=client_info,
  
          )
  
      async def predict(self,
              request: prediction_service.PredictRequest = None,
              *,
              endpoint: str = None,
              instances: Sequence[struct_pb2.Value] = None,
              parameters: struct_pb2.Value = None,
              retry: retries.Retry = gapic_v1.method.DEFAULT,
              timeout: float = None,
              metadata: Sequence[Tuple[str, str]] = (),
              ) -> prediction_service.PredictResponse:
          r"""Perform an online prediction.
  
          Args:
              request (:class:`google.cloud.aiplatform_v1.types.PredictRequest`):
                  The request object. Request message for
                  [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict].
              endpoint (:class:`str`):
                  Required. The name of the Endpoint requested to serve
                  the prediction. Format:
                  ``projects/{project}/locations/{location}/endpoints/{endpoint}``
  
                  This corresponds to the ``endpoint`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              instances (:class:`Sequence[google.protobuf.struct_pb2.Value]`):
                  Required. The instances that are the input to the
                  prediction call. A DeployedModel may have an upper limit
                  on the number of instances it supports per request, and
                  when it is exceeded the prediction call errors in case
                  of AutoML Models, or, in case of customer created
                  Models, the behaviour is as documented by that Model.
                  The schema of any single instance may be specified via
                  Endpoint's DeployedModels'
                  [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
                  [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
                  [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
  
                  This corresponds to the ``instances`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              parameters (:class:`google.protobuf.struct_pb2.Value`):
                  The parameters that govern the prediction. The schema of
                  the parameters may be specified via Endpoint's
                  DeployedModels' [Model's
                  ][google.cloud.aiplatform.v1.DeployedModel.model]
                  [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
                  [parameters_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].
  
                  This corresponds to the ``parameters`` 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:
              google.cloud.aiplatform_v1.types.PredictResponse:
                  Response message for
                  [PredictionService.Predict][google.cloud.aiplatform.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([endpoint, instances, parameters])
          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.")
  
          request = prediction_service.PredictRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if endpoint is not None:
              request.endpoint = endpoint
          if parameters is not None:
              request.parameters = parameters
          if instances:
              request.instances.extend(instances)
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.predict,
              default_timeout=None,
              client_info=DEFAULT_CLIENT_INFO,
          )
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((
                  ("endpoint", request.endpoint),
              )),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def raw_predict(self,
              request: prediction_service.RawPredictRequest = None,
              *,
              endpoint: str = None,
              http_body: httpbody_pb2.HttpBody = None,
              retry: retries.Retry = gapic_v1.method.DEFAULT,
              timeout: float = None,
              metadata: Sequence[Tuple[str, str]] = (),
              ) -> httpbody_pb2.HttpBody:
          r"""Perform an online prediction with an arbitrary HTTP payload.
  
          The response includes the following HTTP headers:
  
          -  ``X-Vertex-AI-Endpoint-Id``: ID of the
             [Endpoint][google.cloud.aiplatform.v1.Endpoint] that served
             this prediction.
  
          -  ``X-Vertex-AI-Deployed-Model-Id``: ID of the Endpoint's
             [DeployedModel][google.cloud.aiplatform.v1.DeployedModel]
             that served this prediction.
  
          Args:
              request (:class:`google.cloud.aiplatform_v1.types.RawPredictRequest`):
                  The request object. Request message for
                  [PredictionService.RawPredict][google.cloud.aiplatform.v1.PredictionService.RawPredict].
              endpoint (:class:`str`):
                  Required. The name of the Endpoint requested to serve
                  the prediction. Format:
                  ``projects/{project}/locations/{location}/endpoints/{endpoint}``
  
                  This corresponds to the ``endpoint`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              http_body (:class:`google.api.httpbody_pb2.HttpBody`):
                  The prediction input. Supports HTTP headers and
                  arbitrary data payload.
  
                  A
                  [DeployedModel][google.cloud.aiplatform.v1.DeployedModel]
                  may have an upper limit on the number of instances it
                  supports per request. When this limit it is exceeded for
                  an AutoML model, the
                  [RawPredict][google.cloud.aiplatform.v1.PredictionService.RawPredict]
                  method returns an error. When this limit is exceeded for
                  a custom-trained model, the behavior varies depending on
                  the model.
  
                  You can specify the schema for each instance in the
                  [predict_schemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
                  field when you create a
                  [Model][google.cloud.aiplatform.v1.Model]. This schema
                  applies when you deploy the ``Model`` as a
                  ``DeployedModel`` to an
                  [Endpoint][google.cloud.aiplatform.v1.Endpoint] and use
                  the ``RawPredict`` method.
  
                  This corresponds to the ``http_body`` 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:
              google.api.httpbody_pb2.HttpBody:
                  Message that represents an arbitrary HTTP body. It should only be used for
                     payload formats that can't be represented as JSON,
                     such as raw binary or an HTML page.
  
                     This message can be used both in streaming and
                     non-streaming API methods in the request as well as
                     the response.
  
                     It can be used as a top-level request field, which is
                     convenient if one wants to extract parameters from
                     either the URL or HTTP template into the request
                     fields and also want access to the raw HTTP body.
  
                     Example:
  
                        message GetResourceRequest {
                           // A unique request id. string request_id = 1;
  
                           // The raw HTTP body is bound to this field.
                           google.api.HttpBody http_body = 2;
  
                        }
  
                        service ResourceService {
                           rpc GetResource(GetResourceRequest)
                              returns (google.api.HttpBody);
  
                           rpc UpdateResource(google.api.HttpBody)
                              returns (google.protobuf.Empty);
  
                        }
  
                     Example with streaming methods:
  
                        service CaldavService {
                           rpc GetCalendar(stream google.api.HttpBody)
                              returns (stream google.api.HttpBody);
  
                           rpc UpdateCalendar(stream google.api.HttpBody)
                              returns (stream google.api.HttpBody);
  
                        }
  
                     Use of this type only changes how the request and
                     response bodies are handled, all other features will
                     continue to work unchanged.
  
          """
          # 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([endpoint, http_body])
          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.")
  
          request = prediction_service.RawPredictRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if endpoint is not None:
              request.endpoint = endpoint
          if http_body is not None:
              request.http_body = http_body
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.raw_predict,
              default_timeout=None,
              client_info=DEFAULT_CLIENT_INFO,
          )
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((
                  ("endpoint", request.endpoint),
              )),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def explain(self,
              request: prediction_service.ExplainRequest = None,
              *,
              endpoint: str = None,
              instances: Sequence[struct_pb2.Value] = None,
              parameters: struct_pb2.Value = None,
              deployed_model_id: str = None,
              retry: retries.Retry = gapic_v1.method.DEFAULT,
              timeout: float = None,
              metadata: Sequence[Tuple[str, str]] = (),
              ) -> prediction_service.ExplainResponse:
          r"""Perform an online explanation.
  
          If
          [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id]
          is specified, the corresponding DeployModel must have
          [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
          populated. If
          [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id]
          is not specified, all DeployedModels must have
          [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
          populated. Only deployed AutoML tabular Models have
          explanation_spec.
  
          Args:
              request (:class:`google.cloud.aiplatform_v1.types.ExplainRequest`):
                  The request object. Request message for
                  [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
              endpoint (:class:`str`):
                  Required. The name of the Endpoint requested to serve
                  the explanation. Format:
                  ``projects/{project}/locations/{location}/endpoints/{endpoint}``
  
                  This corresponds to the ``endpoint`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              instances (:class:`Sequence[google.protobuf.struct_pb2.Value]`):
                  Required. The instances that are the input to the
                  explanation call. A DeployedModel may have an upper
                  limit on the number of instances it supports per
                  request, and when it is exceeded the explanation call
                  errors in case of AutoML Models, or, in case of customer
                  created Models, the behaviour is as documented by that
                  Model. The schema of any single instance may be
                  specified via Endpoint's DeployedModels'
                  [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
                  [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
                  [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
  
                  This corresponds to the ``instances`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              parameters (:class:`google.protobuf.struct_pb2.Value`):
                  The parameters that govern the prediction. The schema of
                  the parameters may be specified via Endpoint's
                  DeployedModels' [Model's
                  ][google.cloud.aiplatform.v1.DeployedModel.model]
                  [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
                  [parameters_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].
  
                  This corresponds to the ``parameters`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              deployed_model_id (:class:`str`):
                  If specified, this ExplainRequest will be served by the
                  chosen DeployedModel, overriding
                  [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split].
  
                  This corresponds to the ``deployed_model_id`` 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:
              google.cloud.aiplatform_v1.types.ExplainResponse:
                  Response message for
                  [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
  
          """
          # 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([endpoint, instances, parameters, deployed_model_id])
          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.")
  
          request = prediction_service.ExplainRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if endpoint is not None:
              request.endpoint = endpoint
          if parameters is not None:
              request.parameters = parameters
          if deployed_model_id is not None:
              request.deployed_model_id = deployed_model_id
          if instances:
              request.instances.extend(instances)
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.explain,
              default_timeout=None,
              client_info=DEFAULT_CLIENT_INFO,
          )
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((
                  ("endpoint", request.endpoint),
              )),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def __aenter__(self):
          return self
  
      async def __aexit__(self, exc_type, exc, tb):
          await self.transport.close()
  
  try:
      DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
          gapic_version=pkg_resources.get_distribution(
              "google-cloud-aiplatform",
          ).version,
      )
  except pkg_resources.DistributionNotFound:
      DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()
  
  
  __all__ = (
      "PredictionServiceAsyncClient",
  )
  