text: # -*- coding: utf-8 -*-
  # Copyright 2022 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, Mapping, Optional, Sequence, Tuple, Type, Union
  import pkg_resources
  
  from google.api_core.client_options import ClientOptions
  from google.api_core import exceptions as core_exceptions
  from google.api_core import gapic_v1
  from google.api_core import retry as retries
  from google.auth import credentials as ga_credentials  # type: ignore
  from google.oauth2 import service_account  # type: ignore
  
  try:
      OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault]
  except AttributeError:  # pragma: NO COVER
      OptionalRetry = Union[retries.Retry, object]  # type: ignore
  
  from google.api_core import operation as gac_operation  # type: ignore
  from google.api_core import operation_async  # type: ignore
  from google.cloud.aiplatform_v1beta1.services.model_service import pagers
  from google.cloud.aiplatform_v1beta1.types import deployed_model_ref
  from google.cloud.aiplatform_v1beta1.types import encryption_spec
  from google.cloud.aiplatform_v1beta1.types import explanation
  from google.cloud.aiplatform_v1beta1.types import model
  from google.cloud.aiplatform_v1beta1.types import model as gca_model
  from google.cloud.aiplatform_v1beta1.types import model_evaluation
  from google.cloud.aiplatform_v1beta1.types import (
      model_evaluation as gca_model_evaluation,
  )
  from google.cloud.aiplatform_v1beta1.types import model_evaluation_slice
  from google.cloud.aiplatform_v1beta1.types import model_service
  from google.cloud.aiplatform_v1beta1.types import operation as gca_operation
  from google.protobuf import empty_pb2  # type: ignore
  from google.protobuf import field_mask_pb2  # type: ignore
  from google.protobuf import struct_pb2  # type: ignore
  from google.protobuf import timestamp_pb2  # type: ignore
  from .transports.base import ModelServiceTransport, DEFAULT_CLIENT_INFO
  from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport
  from .client import ModelServiceClient
  
  
  class ModelServiceAsyncClient:
      """A service for managing Vertex AI's machine learning Models."""
  
      _client: ModelServiceClient
  
      DEFAULT_ENDPOINT = ModelServiceClient.DEFAULT_ENDPOINT
      DEFAULT_MTLS_ENDPOINT = ModelServiceClient.DEFAULT_MTLS_ENDPOINT
  
      endpoint_path = staticmethod(ModelServiceClient.endpoint_path)
      parse_endpoint_path = staticmethod(ModelServiceClient.parse_endpoint_path)
      model_path = staticmethod(ModelServiceClient.model_path)
      parse_model_path = staticmethod(ModelServiceClient.parse_model_path)
      model_evaluation_path = staticmethod(ModelServiceClient.model_evaluation_path)
      parse_model_evaluation_path = staticmethod(
          ModelServiceClient.parse_model_evaluation_path
      )
      model_evaluation_slice_path = staticmethod(
          ModelServiceClient.model_evaluation_slice_path
      )
      parse_model_evaluation_slice_path = staticmethod(
          ModelServiceClient.parse_model_evaluation_slice_path
      )
      training_pipeline_path = staticmethod(ModelServiceClient.training_pipeline_path)
      parse_training_pipeline_path = staticmethod(
          ModelServiceClient.parse_training_pipeline_path
      )
      common_billing_account_path = staticmethod(
          ModelServiceClient.common_billing_account_path
      )
      parse_common_billing_account_path = staticmethod(
          ModelServiceClient.parse_common_billing_account_path
      )
      common_folder_path = staticmethod(ModelServiceClient.common_folder_path)
      parse_common_folder_path = staticmethod(ModelServiceClient.parse_common_folder_path)
      common_organization_path = staticmethod(ModelServiceClient.common_organization_path)
      parse_common_organization_path = staticmethod(
          ModelServiceClient.parse_common_organization_path
      )
      common_project_path = staticmethod(ModelServiceClient.common_project_path)
      parse_common_project_path = staticmethod(
          ModelServiceClient.parse_common_project_path
      )
      common_location_path = staticmethod(ModelServiceClient.common_location_path)
      parse_common_location_path = staticmethod(
          ModelServiceClient.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:
              ModelServiceAsyncClient: The constructed client.
          """
          return ModelServiceClient.from_service_account_info.__func__(ModelServiceAsyncClient, 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:
              ModelServiceAsyncClient: The constructed client.
          """
          return ModelServiceClient.from_service_account_file.__func__(ModelServiceAsyncClient, filename, *args, **kwargs)  # type: ignore
  
      from_service_account_json = from_service_account_file
  
      @classmethod
      def get_mtls_endpoint_and_cert_source(
          cls, client_options: Optional[ClientOptions] = None
      ):
          """Return the API endpoint and client cert source for mutual TLS.
  
          The client cert source is determined in the following order:
          (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
          client cert source is None.
          (2) if `client_options.client_cert_source` is provided, use the provided one; if the
          default client cert source exists, use the default one; otherwise the client cert
          source is None.
  
          The API endpoint is determined in the following order:
          (1) if `client_options.api_endpoint` if provided, use the provided one.
          (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
          default mTLS endpoint; if the environment variabel is "never", use the default API
          endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
          use the default API endpoint.
  
          More details can be found at https://google.aip.dev/auth/4114.
  
          Args:
              client_options (google.api_core.client_options.ClientOptions): Custom options for the
                  client. Only the `api_endpoint` and `client_cert_source` properties may be used
                  in this method.
  
          Returns:
              Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
                  client cert source to use.
  
          Raises:
              google.auth.exceptions.MutualTLSChannelError: If any errors happen.
          """
          return ModelServiceClient.get_mtls_endpoint_and_cert_source(client_options)  # type: ignore
  
      @property
      def transport(self) -> ModelServiceTransport:
          """Returns the transport used by the client instance.
  
          Returns:
              ModelServiceTransport: The transport used by the client instance.
          """
          return self._client.transport
  
      get_transport_class = functools.partial(
          type(ModelServiceClient).get_transport_class, type(ModelServiceClient)
      )
  
      def __init__(
          self,
          *,
          credentials: ga_credentials.Credentials = None,
          transport: Union[str, ModelServiceTransport] = "grpc_asyncio",
          client_options: ClientOptions = None,
          client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
      ) -> None:
          """Instantiates the model 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, ~.ModelServiceTransport]): 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 = ModelServiceClient(
              credentials=credentials,
              transport=transport,
              client_options=client_options,
              client_info=client_info,
          )
  
      async def upload_model(
          self,
          request: Union[model_service.UploadModelRequest, dict] = None,
          *,
          parent: str = None,
          model: gca_model.Model = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> operation_async.AsyncOperation:
          r"""Uploads a Model artifact into Vertex AI.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_upload_model():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  model = aiplatform_v1beta1.Model()
                  model.display_name = "display_name_value"
  
                  request = aiplatform_v1beta1.UploadModelRequest(
                      parent="parent_value",
                      model=model,
                  )
  
                  # Make the request
                  operation = client.upload_model(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = await operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.UploadModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel].
              parent (:class:`str`):
                  Required. The resource name of the Location into which
                  to upload the Model. Format:
                  ``projects/{project}/locations/{location}``
  
                  This corresponds to the ``parent`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              model (:class:`google.cloud.aiplatform_v1beta1.types.Model`):
                  Required. The Model to create.
                  This corresponds to the ``model`` 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_core.operation_async.AsyncOperation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be
                  :class:`google.cloud.aiplatform_v1beta1.types.UploadModelResponse`
                  Response message of
                  [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel]
                  operation.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent, model])
          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 = model_service.UploadModelRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if parent is not None:
              request.parent = parent
          if model is not None:
              request.model = model
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.upload_model,
              default_timeout=5.0,
              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((("parent", request.parent),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Wrap the response in an operation future.
          response = operation_async.from_gapic(
              response,
              self._client._transport.operations_client,
              model_service.UploadModelResponse,
              metadata_type=model_service.UploadModelOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      async def get_model(
          self,
          request: Union[model_service.GetModelRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model.Model:
          r"""Gets a Model.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_get_model():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.GetModelRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  response = await client.get_model(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.GetModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.GetModel][google.cloud.aiplatform.v1beta1.ModelService.GetModel].
              name (:class:`str`):
                  Required. The name of the Model resource. Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``name`` 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_v1beta1.types.Model:
                  A trained machine learning Model.
          """
          # Create or coerce a protobuf request object.
          # Quick 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])
          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 = model_service.GetModelRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.get_model,
              default_timeout=5.0,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def list_models(
          self,
          request: Union[model_service.ListModelsRequest, dict] = None,
          *,
          parent: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelsAsyncPager:
          r"""Lists Models in a Location.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_list_models():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.ListModelsRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  page_result = client.list_models(request=request)
  
                  # Handle the response
                  async for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.ListModelsRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModels][google.cloud.aiplatform.v1beta1.ModelService.ListModels].
              parent (:class:`str`):
                  Required. The resource name of the Location to list the
                  Models from. Format:
                  ``projects/{project}/locations/{location}``
  
                  This corresponds to the ``parent`` 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_v1beta1.services.model_service.pagers.ListModelsAsyncPager:
                  Response message for
                  [ModelService.ListModels][google.cloud.aiplatform.v1beta1.ModelService.ListModels]
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent])
          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 = model_service.ListModelsRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if parent is not None:
              request.parent = parent
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.list_models,
              default_timeout=5.0,
              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((("parent", request.parent),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__aiter__` convenience method.
          response = pagers.ListModelsAsyncPager(
              method=rpc,
              request=request,
              response=response,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def list_model_versions(
          self,
          request: Union[model_service.ListModelVersionsRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelVersionsAsyncPager:
          r"""Lists versions of the specified model.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_list_model_versions():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.ListModelVersionsRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  page_result = client.list_model_versions(request=request)
  
                  # Handle the response
                  async for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.ListModelVersionsRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.ModelService.ListModelVersions].
              name (:class:`str`):
                  Required. The name of the model to
                  list versions for.
  
                  This corresponds to the ``name`` 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_v1beta1.services.model_service.pagers.ListModelVersionsAsyncPager:
                  Response message for
                  [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.ModelService.ListModelVersions]
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick 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])
          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 = model_service.ListModelVersionsRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.list_model_versions,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__aiter__` convenience method.
          response = pagers.ListModelVersionsAsyncPager(
              method=rpc,
              request=request,
              response=response,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def update_model(
          self,
          request: Union[model_service.UpdateModelRequest, dict] = None,
          *,
          model: gca_model.Model = None,
          update_mask: field_mask_pb2.FieldMask = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> gca_model.Model:
          r"""Updates a Model.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_update_model():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  model = aiplatform_v1beta1.Model()
                  model.display_name = "display_name_value"
  
                  request = aiplatform_v1beta1.UpdateModelRequest(
                      model=model,
                  )
  
                  # Make the request
                  response = await client.update_model(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.UpdateModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.UpdateModel][google.cloud.aiplatform.v1beta1.ModelService.UpdateModel].
              model (:class:`google.cloud.aiplatform_v1beta1.types.Model`):
                  Required. The Model which replaces the resource on the
                  server. When Model Versioning is enabled, the model.name
                  will be used to determine whether to update the model or
                  model version.
  
                  1. model.name with the @ value, e.g. models/123@1,
                     refers to a version specific update.
                  2. model.name without the @ value, e.g. models/123,
                     refers to a model update.
                  3. model.name with @-, e.g. models/123@-, refers to a
                     model update.
                  4. Supported model fields: display_name, description;
                     supported version-specific fields:
                     version_description. Labels are supported in both
                     scenarios. Both the model labels and the version
                     labels are merged when a model is returned. When
                     updating labels, if the request is for model-specific
                     update, model label gets updated. Otherwise, version
                     labels get updated.
                  5. A model name or model version name fields update
                     mismatch will cause a precondition error.
                  6. One request cannot update both the model and the
                     version fields. You must update them separately.
  
                  This corresponds to the ``model`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
                  Required. The update mask applies to the resource. For
                  the ``FieldMask`` definition, see
                  [google.protobuf.FieldMask][google.protobuf.FieldMask].
  
                  This corresponds to the ``update_mask`` 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_v1beta1.types.Model:
                  A trained machine learning Model.
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([model, update_mask])
          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 = model_service.UpdateModelRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if model is not None:
              request.model = model
          if update_mask is not None:
              request.update_mask = update_mask
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.update_model,
              default_timeout=5.0,
              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(
                  (("model.name", request.model.name),)
              ),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def delete_model(
          self,
          request: Union[model_service.DeleteModelRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> operation_async.AsyncOperation:
          r"""Deletes a Model.
  
          A model cannot be deleted if any
          [Endpoint][google.cloud.aiplatform.v1beta1.Endpoint] resource
          has a
          [DeployedModel][google.cloud.aiplatform.v1beta1.DeployedModel]
          based on the model in its
          [deployed_models][google.cloud.aiplatform.v1beta1.Endpoint.deployed_models]
          field.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_delete_model():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.DeleteModelRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  operation = client.delete_model(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = await operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.DeleteModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.DeleteModel][google.cloud.aiplatform.v1beta1.ModelService.DeleteModel].
              name (:class:`str`):
                  Required. The name of the Model resource to be deleted.
                  Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``name`` 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_core.operation_async.AsyncOperation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
                     empty messages in your APIs. A typical example is to
                     use it as the request or the response type of an API
                     method. For instance:
  
                        service Foo {
                           rpc Bar(google.protobuf.Empty) returns
                           (google.protobuf.Empty);
  
                        }
  
                     The JSON representation for Empty is empty JSON
                     object {}.
  
          """
          # Create or coerce a protobuf request object.
          # Quick 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])
          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 = model_service.DeleteModelRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.delete_model,
              default_timeout=5.0,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Wrap the response in an operation future.
          response = operation_async.from_gapic(
              response,
              self._client._transport.operations_client,
              empty_pb2.Empty,
              metadata_type=gca_operation.DeleteOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      async def delete_model_version(
          self,
          request: Union[model_service.DeleteModelVersionRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> operation_async.AsyncOperation:
          r"""Deletes a Model version.
  
          Model version can only be deleted if there are no
          [DeployedModels][] created from it. Deleting the only version in
          the Model is not allowed. Use
          [DeleteModel][google.cloud.aiplatform.v1beta1.ModelService.DeleteModel]
          for deleting the Model instead.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_delete_model_version():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.DeleteModelVersionRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  operation = client.delete_model_version(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = await operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.DeleteModelVersionRequest, dict]):
                  The request object. Request message for
                  [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1beta1.ModelService.DeleteModelVersion].
              name (:class:`str`):
                  Required. The name of the model version to be deleted,
                  with a version ID explicitly included.
  
                  Example:
                  ``projects/{project}/locations/{location}/models/{model}@1234``
  
                  This corresponds to the ``name`` 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_core.operation_async.AsyncOperation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
                     empty messages in your APIs. A typical example is to
                     use it as the request or the response type of an API
                     method. For instance:
  
                        service Foo {
                           rpc Bar(google.protobuf.Empty) returns
                           (google.protobuf.Empty);
  
                        }
  
                     The JSON representation for Empty is empty JSON
                     object {}.
  
          """
          # Create or coerce a protobuf request object.
          # Quick 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])
          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 = model_service.DeleteModelVersionRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.delete_model_version,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Wrap the response in an operation future.
          response = operation_async.from_gapic(
              response,
              self._client._transport.operations_client,
              empty_pb2.Empty,
              metadata_type=gca_operation.DeleteOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      async def merge_version_aliases(
          self,
          request: Union[model_service.MergeVersionAliasesRequest, dict] = None,
          *,
          name: str = None,
          version_aliases: Sequence[str] = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model.Model:
          r"""Merges a set of aliases for a Model version.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_merge_version_aliases():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.MergeVersionAliasesRequest(
                      name="name_value",
                      version_aliases=['version_aliases_value_1', 'version_aliases_value_2'],
                  )
  
                  # Make the request
                  response = await client.merge_version_aliases(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.MergeVersionAliasesRequest, dict]):
                  The request object. Request message for
                  [ModelService.MergeVersionAliases][google.cloud.aiplatform.v1beta1.ModelService.MergeVersionAliases].
              name (:class:`str`):
                  Required. The name of the model version to merge
                  aliases, with a version ID explicitly included.
  
                  Example:
                  ``projects/{project}/locations/{location}/models/{model}@1234``
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              version_aliases (:class:`Sequence[str]`):
                  Required. The set of version aliases to merge. The alias
                  should be at most 128 characters, and match
                  ``[a-z][a-z0-9-]{0,126}[a-z-0-9]``. Add the ``-`` prefix
                  to an alias means removing that alias from the version.
                  ``-`` is NOT counted in the 128 characters. Example:
                  ``-golden`` means removing the ``golden`` alias from the
                  version.
  
                  There is NO ordering in aliases, which means
  
                  1) The aliases returned from GetModel API might not have
                     the exactly same order from this MergeVersionAliases
                     API. 2) Adding and deleting the same alias in the
                     request is not recommended, and the 2 operations will
                     be cancelled out.
  
                  This corresponds to the ``version_aliases`` 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_v1beta1.types.Model:
                  A trained machine learning Model.
          """
          # Create or coerce a protobuf request object.
          # Quick 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, version_aliases])
          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 = model_service.MergeVersionAliasesRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
          if version_aliases:
              request.version_aliases.extend(version_aliases)
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.merge_version_aliases,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def export_model(
          self,
          request: Union[model_service.ExportModelRequest, dict] = None,
          *,
          name: str = None,
          output_config: model_service.ExportModelRequest.OutputConfig = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> operation_async.AsyncOperation:
          r"""Exports a trained, exportable Model to a location specified by
          the user. A Model is considered to be exportable if it has at
          least one [supported export
          format][google.cloud.aiplatform.v1beta1.Model.supported_export_formats].
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_export_model():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.ExportModelRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  operation = client.export_model(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = await operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.ExportModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel].
              name (:class:`str`):
                  Required. The resource name of the
                  Model to export.
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              output_config (:class:`google.cloud.aiplatform_v1beta1.types.ExportModelRequest.OutputConfig`):
                  Required. The desired output location
                  and configuration.
  
                  This corresponds to the ``output_config`` 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_core.operation_async.AsyncOperation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be
                  :class:`google.cloud.aiplatform_v1beta1.types.ExportModelResponse`
                  Response message of
                  [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel]
                  operation.
  
          """
          # Create or coerce a protobuf request object.
          # Quick 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, output_config])
          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 = model_service.ExportModelRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
          if output_config is not None:
              request.output_config = output_config
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.export_model,
              default_timeout=5.0,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Wrap the response in an operation future.
          response = operation_async.from_gapic(
              response,
              self._client._transport.operations_client,
              model_service.ExportModelResponse,
              metadata_type=model_service.ExportModelOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      async def import_model_evaluation(
          self,
          request: Union[model_service.ImportModelEvaluationRequest, dict] = None,
          *,
          parent: str = None,
          model_evaluation: gca_model_evaluation.ModelEvaluation = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> gca_model_evaluation.ModelEvaluation:
          r"""Imports an externally generated ModelEvaluation.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_import_model_evaluation():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.ImportModelEvaluationRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  response = await client.import_model_evaluation(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.ImportModelEvaluationRequest, dict]):
                  The request object. Request message for
                  [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1beta1.ModelService.ImportModelEvaluation]
              parent (:class:`str`):
                  Required. The name of the parent model resource. Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``parent`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              model_evaluation (:class:`google.cloud.aiplatform_v1beta1.types.ModelEvaluation`):
                  Required. Model evaluation resource
                  to be imported.
  
                  This corresponds to the ``model_evaluation`` 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_v1beta1.types.ModelEvaluation:
                  A collection of metrics calculated by
                  comparing Model's predictions on all of
                  the test data against annotations from
                  the test data.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent, model_evaluation])
          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 = model_service.ImportModelEvaluationRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if parent is not None:
              request.parent = parent
          if model_evaluation is not None:
              request.model_evaluation = model_evaluation
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.import_model_evaluation,
              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((("parent", request.parent),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def get_model_evaluation(
          self,
          request: Union[model_service.GetModelEvaluationRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model_evaluation.ModelEvaluation:
          r"""Gets a ModelEvaluation.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_get_model_evaluation():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.GetModelEvaluationRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  response = await client.get_model_evaluation(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.GetModelEvaluationRequest, dict]):
                  The request object. Request message for
                  [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1beta1.ModelService.GetModelEvaluation].
              name (:class:`str`):
                  Required. The name of the ModelEvaluation resource.
                  Format:
                  ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}``
  
                  This corresponds to the ``name`` 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_v1beta1.types.ModelEvaluation:
                  A collection of metrics calculated by
                  comparing Model's predictions on all of
                  the test data against annotations from
                  the test data.
  
          """
          # Create or coerce a protobuf request object.
          # Quick 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])
          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 = model_service.GetModelEvaluationRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.get_model_evaluation,
              default_timeout=5.0,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def list_model_evaluations(
          self,
          request: Union[model_service.ListModelEvaluationsRequest, dict] = None,
          *,
          parent: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelEvaluationsAsyncPager:
          r"""Lists ModelEvaluations in a Model.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_list_model_evaluations():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.ListModelEvaluationsRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  page_result = client.list_model_evaluations(request=request)
  
                  # Handle the response
                  async for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.ListModelEvaluationsRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluations].
              parent (:class:`str`):
                  Required. The resource name of the Model to list the
                  ModelEvaluations from. Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``parent`` 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_v1beta1.services.model_service.pagers.ListModelEvaluationsAsyncPager:
                  Response message for
                  [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluations].
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent])
          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 = model_service.ListModelEvaluationsRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if parent is not None:
              request.parent = parent
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.list_model_evaluations,
              default_timeout=5.0,
              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((("parent", request.parent),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__aiter__` convenience method.
          response = pagers.ListModelEvaluationsAsyncPager(
              method=rpc,
              request=request,
              response=response,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def get_model_evaluation_slice(
          self,
          request: Union[model_service.GetModelEvaluationSliceRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model_evaluation_slice.ModelEvaluationSlice:
          r"""Gets a ModelEvaluationSlice.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_get_model_evaluation_slice():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.GetModelEvaluationSliceRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  response = await client.get_model_evaluation_slice(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.GetModelEvaluationSliceRequest, dict]):
                  The request object. Request message for
                  [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1beta1.ModelService.GetModelEvaluationSlice].
              name (:class:`str`):
                  Required. The name of the ModelEvaluationSlice resource.
                  Format:
                  ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}``
  
                  This corresponds to the ``name`` 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_v1beta1.types.ModelEvaluationSlice:
                  A collection of metrics calculated by
                  comparing Model's predictions on a slice
                  of the test data against ground truth
                  annotations.
  
          """
          # Create or coerce a protobuf request object.
          # Quick 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])
          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 = model_service.GetModelEvaluationSliceRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if name is not None:
              request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.get_model_evaluation_slice,
              default_timeout=5.0,
              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((("name", request.name),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      async def list_model_evaluation_slices(
          self,
          request: Union[model_service.ListModelEvaluationSlicesRequest, dict] = None,
          *,
          parent: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelEvaluationSlicesAsyncPager:
          r"""Lists ModelEvaluationSlices in a ModelEvaluation.
  
          .. code-block:: python
  
              from google.cloud import aiplatform_v1beta1
  
              async def sample_list_model_evaluation_slices():
                  # Create a client
                  client = aiplatform_v1beta1.ModelServiceAsyncClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1beta1.ListModelEvaluationSlicesRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  page_result = client.list_model_evaluation_slices(request=request)
  
                  # Handle the response
                  async for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1beta1.types.ListModelEvaluationSlicesRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices].
              parent (:class:`str`):
                  Required. The resource name of the ModelEvaluation to
                  list the ModelEvaluationSlices from. Format:
                  ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}``
  
                  This corresponds to the ``parent`` 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_v1beta1.services.model_service.pagers.ListModelEvaluationSlicesAsyncPager:
                  Response message for
                  [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices].
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent])
          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 = model_service.ListModelEvaluationSlicesRequest(request)
  
          # If we have keyword arguments corresponding to fields on the
          # request, apply these.
          if parent is not None:
              request.parent = parent
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = gapic_v1.method_async.wrap_method(
              self._client._transport.list_model_evaluation_slices,
              default_timeout=5.0,
              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((("parent", request.parent),)),
          )
  
          # Send the request.
          response = await rpc(
              request,
              retry=retry,
              timeout=timeout,
              metadata=metadata,
          )
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__aiter__` convenience method.
          response = pagers.ListModelEvaluationSlicesAsyncPager(
              method=rpc,
              request=request,
              response=response,
              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__ = ("ModelServiceAsyncClient",)
  