chore: Upgrade black version to 25.1.0 for Vertex SDK

.flake8

@@ -16,7 +16,7 @@
 [flake8]
 # TODO(https://github.com/googleapis/gapic-generator-python/issues/2333):
 # Resolve flake8 lint issues
-ignore = E203, E231, E266, E501, W503
+ignore = E203, E231, E266, E501, W503, E704
 exclude =
   # TODO(https://github.com/googleapis/gapic-generator-python/issues/2333):
   # Ensure that generated code passes flake8 lint

google/cloud/aiplatform/_streaming_prediction.py

@@ -130,7 +130,9 @@ async def predict_stream_of_tensor_lists_from_single_tensor_list_async(
         inputs=tensor_list,
         parameters=parameters_tensor,
     )
-    async for response in await prediction_service_async_client.server_streaming_predict(
+    async for (
+        response
+    ) in await prediction_service_async_client.server_streaming_predict(
         request=request
     ):
         yield response.outputs
@@ -183,7 +185,9 @@ async def predict_stream_of_dict_lists_from_single_dict_list_async(
     """
     tensor_list = [value_to_tensor(d) for d in dict_list]
     parameters_tensor = value_to_tensor(parameters) if parameters else None
-    async for tensor_list in predict_stream_of_tensor_lists_from_single_tensor_list_async(
+    async for (
+        tensor_list
+    ) in predict_stream_of_tensor_lists_from_single_tensor_list_async(
         prediction_service_async_client=prediction_service_async_client,
         endpoint_name=endpoint_name,
         tensor_list=tensor_list,

google/cloud/aiplatform/base.py

@@ -639,7 +639,6 @@ def _get_and_validate_project_location(
         project: Optional[str] = None,
         location: Optional[str] = None,
     ) -> Tuple[str, str]:
-
         """Validate the project and location for the resource.
 
         Args:

google/cloud/aiplatform/datasets/__init__.py

@@ -16,9 +16,13 @@
 #
 
 from google.cloud.aiplatform.datasets.dataset import _Dataset
-from google.cloud.aiplatform.datasets.column_names_dataset import _ColumnNamesDataset
+from google.cloud.aiplatform.datasets.column_names_dataset import (
+    _ColumnNamesDataset,
+)
 from google.cloud.aiplatform.datasets.tabular_dataset import TabularDataset
-from google.cloud.aiplatform.datasets.time_series_dataset import TimeSeriesDataset
+from google.cloud.aiplatform.datasets.time_series_dataset import (
+    TimeSeriesDataset,
+)
 from google.cloud.aiplatform.datasets.image_dataset import ImageDataset
 from google.cloud.aiplatform.datasets.text_dataset import TextDataset
 from google.cloud.aiplatform.datasets.video_dataset import VideoDataset

google/cloud/aiplatform/docker_utils/build.py

@@ -223,7 +223,7 @@ def _prepare_exposed_ports(exposed_ports: Optional[List[int]] = None) -> str:
 
 
 def _prepare_environment_variables(
-    environment_variables: Optional[Dict[str, str]] = None
+    environment_variables: Optional[Dict[str, str]] = None,
 ) -> str:
     """Returns the Dockerfile entries required to set environment variables in containers.
 

google/cloud/aiplatform/docker_utils/run.py

@@ -30,7 +30,9 @@
     )
 
 from google.cloud.aiplatform.constants import prediction
-from google.cloud.aiplatform.docker_utils.utils import DEFAULT_MOUNTED_MODEL_DIRECTORY
+from google.cloud.aiplatform.docker_utils.utils import (
+    DEFAULT_MOUNTED_MODEL_DIRECTORY,
+)
 from google.cloud.aiplatform.utils import prediction_utils
 
 _logger = logging.getLogger(__name__)

google/cloud/aiplatform/explain/lit.py

@@ -311,7 +311,9 @@ def _set_up_attribution_explainer(
         """
         try:
             import explainable_ai_sdk
-            from explainable_ai_sdk.metadata.tf.v2 import SavedModelMetadataBuilder
+            from google3.third_party.explainable_ai_sdk.sdk.metadata.tf.v2.saved_model_metadata_builder import (
+                SavedModelMetadataBuilder,
+            )
         except ImportError:
             logging.info(
                 "Skipping explanations because the Explainable AI SDK is not installed."

google/cloud/aiplatform/explain/metadata/tf/v1/saved_model_metadata_builder.py

@@ -126,7 +126,7 @@ def get_metadata_protobuf(self) -> explanation_metadata.ExplanationMetadata:
 
 
 def _create_input_metadata_from_signature(
-    signature_inputs: Dict[str, "tf.Tensor"]  # noqa: F821
+    signature_inputs: Dict[str, "tf.Tensor"],  # noqa: F821
 ) -> Dict[str, explanation_metadata.ExplanationMetadata.InputMetadata]:
     """Creates InputMetadata from signature inputs.
 

google/cloud/aiplatform/explain/metadata/tf/v2/saved_model_metadata_builder.py

@@ -75,9 +75,7 @@ def __init__(
             signature_name
         )
 
-    def _infer_metadata_entries_from_model(
-        self, signature_name: str
-    ) -> Tuple[
+    def _infer_metadata_entries_from_model(self, signature_name: str) -> Tuple[
         Dict[str, explanation_metadata.ExplanationMetadata.InputMetadata],
         Dict[str, explanation_metadata.ExplanationMetadata.OutputMetadata],
     ]:
@@ -107,10 +105,10 @@ def _infer_metadata_entries_from_model(
         output_mds = {}
         for name in output_sig:
             if not self._explain_output or self._explain_output[0] == name:
-                output_mds[
-                    name
-                ] = explanation_metadata.ExplanationMetadata.OutputMetadata(
-                    output_tensor_name=name,
+                output_mds[name] = (
+                    explanation_metadata.ExplanationMetadata.OutputMetadata(
+                        output_tensor_name=name,
+                    )
                 )
                 break
         else:

google/cloud/aiplatform/featurestore/_entity_type.py

@@ -116,11 +116,11 @@ def __init__(
         )
         self._gca_resource = self._get_gca_resource(
             resource_name=entity_type_name,
-            parent_resource_name_fields={
-                featurestore.Featurestore._resource_noun: featurestore_id
-            }
-            if featurestore_id
-            else featurestore_id,
+            parent_resource_name_fields=(
+                {featurestore.Featurestore._resource_noun: featurestore_id}
+                if featurestore_id
+                else featurestore_id
+            ),
         )
 
         self._featurestore_online_client = self._instantiate_featurestore_online_client(
@@ -1842,7 +1842,7 @@ def _apply_feature_timestamp(
 
     @staticmethod
     def _is_timestamp(
-        timestamp: Union[datetime.datetime, timestamp_pb2.Timestamp]
+        timestamp: Union[datetime.datetime, timestamp_pb2.Timestamp],
     ) -> bool:
         return isinstance(timestamp, datetime.datetime) or isinstance(
             timestamp, timestamp_pb2.Timestamp

google/cloud/aiplatform/featurestore/feature.py

@@ -114,12 +114,14 @@ def __init__(
         )
         self._gca_resource = self._get_gca_resource(
             resource_name=feature_name,
-            parent_resource_name_fields={
-                featurestore.Featurestore._resource_noun: featurestore_id,
-                featurestore.EntityType._resource_noun: entity_type_id,
-            }
-            if featurestore_id
-            else featurestore_id,
+            parent_resource_name_fields=(
+                {
+                    featurestore.Featurestore._resource_noun: featurestore_id,
+                    featurestore.EntityType._resource_noun: entity_type_id,
+                }
+                if featurestore_id
+                else featurestore_id
+            ),
         )
 
     def _get_featurestore_name(self) -> str:
@@ -338,11 +340,11 @@ def list(
                 resource_noun=featurestore.EntityType._resource_noun,
                 parse_resource_name_method=featurestore.EntityType._parse_resource_name,
                 format_resource_name_method=featurestore.EntityType._format_resource_name,
-                parent_resource_name_fields={
-                    featurestore.Featurestore._resource_noun: featurestore_id
-                }
-                if featurestore_id
-                else featurestore_id,
+                parent_resource_name_fields=(
+                    {featurestore.Featurestore._resource_noun: featurestore_id}
+                    if featurestore_id
+                    else featurestore_id
+                ),
                 project=project,
                 location=location,
                 resource_id_validator=featurestore.EntityType._resource_id_validator,
@@ -585,11 +587,11 @@ def create(
             resource_noun=featurestore.EntityType._resource_noun,
             parse_resource_name_method=featurestore.EntityType._parse_resource_name,
             format_resource_name_method=featurestore.EntityType._format_resource_name,
-            parent_resource_name_fields={
-                featurestore.Featurestore._resource_noun: featurestore_id
-            }
-            if featurestore_id
-            else featurestore_id,
+            parent_resource_name_fields=(
+                {featurestore.Featurestore._resource_noun: featurestore_id}
+                if featurestore_id
+                else featurestore_id
+            ),
             project=project,
             location=location,
             resource_id_validator=featurestore.EntityType._resource_id_validator,

google/cloud/aiplatform/gapic/schema/__init__.py

@@ -18,12 +18,20 @@
 from google.cloud.aiplatform.v1.schema import predict
 from google.cloud.aiplatform.v1.schema import trainingjob
 from google.cloud.aiplatform.v1beta1.schema import predict as predict_v1beta1
-from google.cloud.aiplatform.v1beta1.schema import predict as trainingjob_v1beta1
+from google.cloud.aiplatform.v1beta1.schema import (
+    predict as trainingjob_v1beta1,
+)
 
 # import the v1 submodules for enhancement
-from google.cloud.aiplatform.v1.schema.predict.instance_v1 import types as instance
-from google.cloud.aiplatform.v1.schema.predict.params_v1 import types as params
-from google.cloud.aiplatform.v1.schema.predict.prediction_v1 import types as prediction
+from google.cloud.aiplatform.v1.schema.predict.instance_v1 import (
+    types as instance,
+)
+from google.cloud.aiplatform.v1.schema.predict.params_v1 import (
+    types as params,
+)
+from google.cloud.aiplatform.v1.schema.predict.prediction_v1 import (
+    types as prediction,
+)
 from google.cloud.aiplatform.v1.schema.trainingjob.definition_v1 import (
     types as definition,
 )

google/cloud/aiplatform/hyperparameter_tuning.py

@@ -140,7 +140,7 @@ def _to_parameter_spec(
         """Converts this parameter to ParameterSpec."""
         conditions = []
         if self.conditional_parameter_spec is not None:
-            for (conditional_param_id, spec) in self.conditional_parameter_spec.items():
+            for conditional_param_id, spec in self.conditional_parameter_spec.items():
                 condition = (
                     gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec()
                 )
@@ -178,7 +178,7 @@ def _to_parameter_spec_v1beta1(
         """Converts this parameter to ParameterSpec."""
         conditions = []
         if self.conditional_parameter_spec is not None:
-            for (conditional_param_id, spec) in self.conditional_parameter_spec.items():
+            for conditional_param_id, spec in self.conditional_parameter_spec.items():
                 condition = (
                     gca_study_compat_v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec()
                 )

google/cloud/aiplatform/jobs.py

@@ -1325,12 +1325,16 @@ def _submit_impl(
                 model_monitoring_alert_config._config_for_bp = True
             gapic_mm_config = gca_model_monitoring_v1beta1.ModelMonitoringConfig(
                 objective_configs=[model_monitoring_objective_config.as_proto()],
-                alert_config=model_monitoring_alert_config.as_proto()
-                if model_monitoring_alert_config is not None
-                else None,
-                analysis_instance_schema_uri=analysis_instance_schema_uri
-                if analysis_instance_schema_uri is not None
-                else None,
+                alert_config=(
+                    model_monitoring_alert_config.as_proto()
+                    if model_monitoring_alert_config is not None
+                    else None
+                ),
+                analysis_instance_schema_uri=(
+                    analysis_instance_schema_uri
+                    if analysis_instance_schema_uri is not None
+                    else None
+                ),
             )
             gapic_batch_prediction_job.model_monitoring_config = gapic_mm_config
 
@@ -3297,7 +3301,7 @@ def _parse_configs(
                     + "]. Note that deployed model IDs are different from the uploaded model's ID"
                 )
                 raise ValueError(error_string)
-            for (deployed_model, objective_config) in objective_configs.items():
+            for deployed_model, objective_config in objective_configs.items():
                 if (
                     deployed_model not in xai_enabled
                     and objective_config.explanation_config is not None

google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py

@@ -831,9 +831,9 @@ def _instantiate_private_match_service_stub(
         if ip_address not in self._match_grpc_stub_cache:
             # Set up channel and stub
             channel = grpc.insecure_channel("{}:10000".format(ip_address))
-            self._match_grpc_stub_cache[
-                ip_address
-            ] = match_service_pb2_grpc.MatchServiceStub(channel)
+            self._match_grpc_stub_cache[ip_address] = (
+                match_service_pb2_grpc.MatchServiceStub(channel)
+            )
         return self._match_grpc_stub_cache[ip_address]
 
     @property
@@ -1863,9 +1863,9 @@ def find_neighbors(
                 MatchNeighbor(
                     id=neighbor.datapoint.datapoint_id,
                     distance=neighbor.distance,
-                    sparse_distance=neighbor.sparse_distance
-                    if neighbor.sparse_distance
-                    else None,
+                    sparse_distance=(
+                        neighbor.sparse_distance if neighbor.sparse_distance else None
+                    ),
                 ).from_index_datapoint(index_datapoint=neighbor.datapoint)
                 for neighbor in embedding_neighbors.neighbors
             ]
@@ -2163,17 +2163,21 @@ def match(
                     approx_num_neighbors=approx_num_neighbors,
                     fraction_leaf_nodes_to_search_override=fraction_leaf_nodes_to_search_override,
                     numeric_restricts=numeric_restricts,
-                    sparse_embedding=match_service_pb2.SparseEmbedding(
-                        float_val=query.sparse_embedding_values,
-                        dimension=query.sparse_embedding_dimensions,
-                    )
-                    if query_is_hybrid
-                    else None,
-                    rrf=match_service_pb2.MatchRequest.RRF(
-                        alpha=query.rrf_ranking_alpha,
-                    )
-                    if query_is_hybrid and query.rrf_ranking_alpha
-                    else None,
+                    sparse_embedding=(
+                        match_service_pb2.SparseEmbedding(
+                            float_val=query.sparse_embedding_values,
+                            dimension=query.sparse_embedding_dimensions,
+                        )
+                        if query_is_hybrid
+                        else None
+                    ),
+                    rrf=(
+                        match_service_pb2.MatchRequest.RRF(
+                            alpha=query.rrf_ranking_alpha,
+                        )
+                        if query_is_hybrid and query.rrf_ranking_alpha
+                        else None
+                    ),
                 )
                 requests.append(request)
         else:
@@ -2199,9 +2203,9 @@ def match(
                 match_neighbors_id_map[neighbor.id] = MatchNeighbor(
                     id=neighbor.id,
                     distance=neighbor.distance,
-                    sparse_distance=neighbor.sparse_distance
-                    if neighbor.sparse_distance
-                    else None,
+                    sparse_distance=(
+                        neighbor.sparse_distance if neighbor.sparse_distance else None
+                    ),
                 )
             for embedding in resp.embeddings:
                 if embedding.id in match_neighbors_id_map:

google/cloud/aiplatform/metadata/_models.py

@@ -538,7 +538,7 @@ def save_model(
 
 
 def load_model(
-    model: Union[str, google_artifact_schema.ExperimentModel]
+    model: Union[str, google_artifact_schema.ExperimentModel],
 ) -> Union["sklearn.base.BaseEstimator", "xgb.Booster", "tf.Module"]:  # noqa: F821
     """Retrieves the original ML model from an ExperimentModel resource.
 
@@ -853,7 +853,7 @@ def register_model(
 
 
 def get_experiment_model_info(
-    model: Union[str, google_artifact_schema.ExperimentModel]
+    model: Union[str, google_artifact_schema.ExperimentModel],
 ) -> Dict[str, Any]:
     """Get the model's info from an experiment model artifact.
 

google/cloud/aiplatform/metadata/execution.py

@@ -324,9 +324,9 @@ def _add_artifact(
         events = [
             gca_event.Event(
                 artifact=artifact_resource_name,
-                type_=gca_event.Event.Type.INPUT
-                if input
-                else gca_event.Event.Type.OUTPUT,
+                type_=(
+                    gca_event.Event.Type.INPUT if input else gca_event.Event.Type.OUTPUT
+                ),
             )
             for artifact_resource_name in artifact_resource_names
         ]

google/cloud/aiplatform/metadata/metadata_store.py

@@ -24,7 +24,9 @@
 from google.cloud.aiplatform import base, initializer
 from google.cloud.aiplatform import compat
 from google.cloud.aiplatform import utils
-from google.cloud.aiplatform.compat.types import metadata_store as gca_metadata_store
+from google.cloud.aiplatform.compat.types import (
+    metadata_store as gca_metadata_store,
+)
 from google.cloud.aiplatform.constants import base as base_constants