chore(bazel): move rules_gapic up in WORKSPACE loading order

google/cloud/aiplatform_v1/__init__.py

@@ -87,6 +87,8 @@
 from .types.dataset_service import ListDataItemsResponse
 from .types.dataset_service import ListDatasetsRequest
 from .types.dataset_service import ListDatasetsResponse
+from .types.dataset_service import ListSavedQueriesRequest
+from .types.dataset_service import ListSavedQueriesResponse
 from .types.dataset_service import UpdateDatasetRequest
 from .types.deployed_index_ref import DeployedIndexRef
 from .types.deployed_model_ref import DeployedModelRef
@@ -345,6 +347,8 @@
 from .types.model_monitoring import ModelMonitoringObjectiveConfig
 from .types.model_monitoring import SamplingStrategy
 from .types.model_monitoring import ThresholdConfig
+from .types.model_service import BatchImportModelEvaluationSlicesRequest
+from .types.model_service import BatchImportModelEvaluationSlicesResponse
 from .types.model_service import DeleteModelRequest
 from .types.model_service import DeleteModelVersionRequest
 from .types.model_service import ExportModelOperationMetadata
@@ -393,6 +397,7 @@
 from .types.prediction_service import PredictRequest
 from .types.prediction_service import PredictResponse
 from .types.prediction_service import RawPredictRequest
+from .types.saved_query import SavedQuery
 from .types.specialist_pool import SpecialistPool
 from .types.specialist_pool_service import CreateSpecialistPoolOperationMetadata
 from .types.specialist_pool_service import CreateSpecialistPoolRequest
@@ -535,6 +540,8 @@
     "BatchCreateTensorboardTimeSeriesRequest",
     "BatchCreateTensorboardTimeSeriesResponse",
     "BatchDedicatedResources",
+    "BatchImportModelEvaluationSlicesRequest",
+    "BatchImportModelEvaluationSlicesResponse",
     "BatchMigrateResourcesOperationMetadata",
     "BatchMigrateResourcesRequest",
     "BatchMigrateResourcesResponse",
@@ -796,6 +803,8 @@
     "ListOptimalTrialsResponse",
     "ListPipelineJobsRequest",
     "ListPipelineJobsResponse",
+    "ListSavedQueriesRequest",
+    "ListSavedQueriesResponse",
     "ListSpecialistPoolsRequest",
     "ListSpecialistPoolsResponse",
     "ListStudiesRequest",
@@ -886,6 +895,7 @@
     "SampleConfig",
     "SampledShapleyAttribution",
     "SamplingStrategy",
+    "SavedQuery",
     "Scalar",
     "Scheduling",
     "SearchFeaturesRequest",

google/cloud/aiplatform_v1/gapic_metadata.json

@@ -55,6 +55,11 @@
                 "list_datasets"
               ]
             },
+            "ListSavedQueries": {
+              "methods": [
+                "list_saved_queries"
+              ]
+            },
             "UpdateDataset": {
               "methods": [
                 "update_dataset"
@@ -110,6 +115,11 @@
                 "list_datasets"
               ]
             },
+            "ListSavedQueries": {
+              "methods": [
+                "list_saved_queries"
+              ]
+            },
             "UpdateDataset": {
               "methods": [
                 "update_dataset"
@@ -1266,6 +1276,11 @@
         "grpc": {
           "libraryClient": "ModelServiceClient",
           "rpcs": {
+            "BatchImportModelEvaluationSlices": {
+              "methods": [
+                "batch_import_model_evaluation_slices"
+              ]
+            },
             "DeleteModel": {
               "methods": [
                 "delete_model"
@@ -1341,6 +1356,11 @@
         "grpc-async": {
           "libraryClient": "ModelServiceAsyncClient",
           "rpcs": {
+            "BatchImportModelEvaluationSlices": {
+              "methods": [
+                "batch_import_model_evaluation_slices"
+              ]
+            },
             "DeleteModel": {
               "methods": [
                 "delete_model"

google/cloud/aiplatform_v1/services/dataset_service/async_client.py

@@ -42,6 +42,7 @@
 from google.cloud.aiplatform_v1.types import dataset_service
 from google.cloud.aiplatform_v1.types import encryption_spec
 from google.cloud.aiplatform_v1.types import operation as gca_operation
+from google.cloud.aiplatform_v1.types import saved_query
 from google.cloud.location import locations_pb2  # type: ignore
 from google.iam.v1 import iam_policy_pb2  # type: ignore
 from google.iam.v1 import policy_pb2  # type: ignore
@@ -75,6 +76,8 @@ class DatasetServiceAsyncClient:
     parse_data_item_path = staticmethod(DatasetServiceClient.parse_data_item_path)
     dataset_path = staticmethod(DatasetServiceClient.dataset_path)
     parse_dataset_path = staticmethod(DatasetServiceClient.parse_dataset_path)
+    saved_query_path = staticmethod(DatasetServiceClient.saved_query_path)
+    parse_saved_query_path = staticmethod(DatasetServiceClient.parse_saved_query_path)
     common_billing_account_path = staticmethod(
         DatasetServiceClient.common_billing_account_path
     )
@@ -1157,6 +1160,115 @@ async def sample_list_data_items():
         # Done; return the response.
         return response
 
+    async def list_saved_queries(
+        self,
+        request: Union[dataset_service.ListSavedQueriesRequest, dict] = None,
+        *,
+        parent: str = None,
+        retry: OptionalRetry = gapic_v1.method.DEFAULT,
+        timeout: float = None,
+        metadata: Sequence[Tuple[str, str]] = (),
+    ) -> pagers.ListSavedQueriesAsyncPager:
+        r"""Lists SavedQueries in a Dataset.
+
+        .. code-block:: python
+
+            from google.cloud import aiplatform_v1
+
+            async def sample_list_saved_queries():
+                # Create a client
+                client = aiplatform_v1.DatasetServiceAsyncClient()
+
+                # Initialize request argument(s)
+                request = aiplatform_v1.ListSavedQueriesRequest(
+                    parent="parent_value",
+                )
+
+                # Make the request
+                page_result = client.list_saved_queries(request=request)
+
+                # Handle the response
+                async for response in page_result:
+                    print(response)
+
+        Args:
+            request (Union[google.cloud.aiplatform_v1.types.ListSavedQueriesRequest, dict]):
+                The request object. Request message for
+                [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1.DatasetService.ListSavedQueries].
+            parent (:class:`str`):
+                Required. The resource name of the Dataset to list
+                SavedQueries from. Format:
+                ``projects/{project}/locations/{location}/datasets/{dataset}``
+
+                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_v1.services.dataset_service.pagers.ListSavedQueriesAsyncPager:
+                Response message for
+                [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1.DatasetService.ListSavedQueries].
+
+                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 = dataset_service.ListSavedQueriesRequest(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_saved_queries,
+            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,
+        )
+
+        # This method is paged; wrap the response in a pager, which provides
+        # an `__aiter__` convenience method.
+        response = pagers.ListSavedQueriesAsyncPager(
+            method=rpc,
+            request=request,
+            response=response,
+            metadata=metadata,
+        )
+
+        # Done; return the response.
+        return response
+
     async def get_annotation_spec(
         self,
         request: Union[dataset_service.GetAnnotationSpecRequest, dict] = None,

google/cloud/aiplatform_v1/services/dataset_service/client.py

@@ -45,6 +45,7 @@
 from google.cloud.aiplatform_v1.types import dataset_service
 from google.cloud.aiplatform_v1.types import encryption_spec
 from google.cloud.aiplatform_v1.types import operation as gca_operation
+from google.cloud.aiplatform_v1.types import saved_query
 from google.cloud.location import locations_pb2  # type: ignore
 from google.iam.v1 import iam_policy_pb2  # type: ignore
 from google.iam.v1 import policy_pb2  # type: ignore
@@ -277,6 +278,30 @@ def parse_dataset_path(path: str) -> Dict[str, str]:
         )
         return m.groupdict() if m else {}
 
+    @staticmethod
+    def saved_query_path(
+        project: str,
+        location: str,
+        dataset: str,
+        saved_query: str,
+    ) -> str:
+        """Returns a fully-qualified saved_query string."""
+        return "projects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{saved_query}".format(
+            project=project,
+            location=location,
+            dataset=dataset,
+            saved_query=saved_query,
+        )
+
+    @staticmethod
+    def parse_saved_query_path(path: str) -> Dict[str, str]:
+        """Parses a saved_query path into its component segments."""
+        m = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/savedQueries/(?P<saved_query>.+?)$",
+            path,
+        )
+        return m.groupdict() if m else {}
+
     @staticmethod
     def common_billing_account_path(
         billing_account: str,
@@ -517,6 +542,7 @@ def __init__(
                 quota_project_id=client_options.quota_project_id,
                 client_info=client_info,
                 always_use_jwt_access=True,
+                api_audience=client_options.api_audience,
             )
 
     def create_dataset(
@@ -1447,6 +1473,115 @@ def sample_list_data_items():
         # Done; return the response.
         return response
 
+    def list_saved_queries(
+        self,
+        request: Union[dataset_service.ListSavedQueriesRequest, dict] = None,
+        *,
+        parent: str = None,
+        retry: OptionalRetry = gapic_v1.method.DEFAULT,
+        timeout: float = None,
+        metadata: Sequence[Tuple[str, str]] = (),
+    ) -> pagers.ListSavedQueriesPager:
+        r"""Lists SavedQueries in a Dataset.
+
+        .. code-block:: python
+
+            from google.cloud import aiplatform_v1
+
+            def sample_list_saved_queries():
+                # Create a client
+                client = aiplatform_v1.DatasetServiceClient()
+
+                # Initialize request argument(s)
+                request = aiplatform_v1.ListSavedQueriesRequest(
+                    parent="parent_value",
+                )
+
+                # Make the request
+                page_result = client.list_saved_queries(request=request)
+
+                # Handle the response
+                for response in page_result:
+                    print(response)
+
+        Args:
+            request (Union[google.cloud.aiplatform_v1.types.ListSavedQueriesRequest, dict]):
+                The request object. Request message for
+                [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1.DatasetService.ListSavedQueries].
+            parent (str):
+                Required. The resource name of the Dataset to list
+                SavedQueries from. Format:
+                ``projects/{project}/locations/{location}/datasets/{dataset}``
+
+                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_v1.services.dataset_service.pagers.ListSavedQueriesPager:
+                Response message for
+                [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1.DatasetService.ListSavedQueries].
+
+                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."
+            )
+
+        # Minor optimization to avoid making a copy if the user passes
+        # in a dataset_service.ListSavedQueriesRequest.
+        # There's no risk of modifying the input as we've already verified
+        # there are no flattened fields.
+        if not isinstance(request, dataset_service.ListSavedQueriesRequest):
+            request = dataset_service.ListSavedQueriesRequest(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 = self._transport._wrapped_methods[self._transport.list_saved_queries]
+
+        # 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 = rpc(
+            request,
+            retry=retry,
+            timeout=timeout,
+            metadata=metadata,
+        )
+
+        # This method is paged; wrap the response in a pager, which provides
+        # an `__iter__` convenience method.
+        response = pagers.ListSavedQueriesPager(
+            method=rpc,
+            request=request,
+            response=response,
+            metadata=metadata,
+        )
+
+        # Done; return the response.
+        return response
+
     def get_annotation_spec(
         self,
         request: Union[dataset_service.GetAnnotationSpecRequest, dict] = None,