chore(deps): bump black from 22.3.0 to 24.3.0 in /requirements/extras

requirements/extras/test_requirements.txt

@@ -9,7 +9,7 @@ coverage>=5.2, <6.2
 mock==4.0.3
 contextlib2==21.6.0
 awslogs==0.14.0
-black==22.3.0
+black==24.3.0
 stopit==1.1.2
 # Update tox.ini to have correct version of airflow constraints file
 apache-airflow==2.8.4

src/sagemaker/automl/automl.py

@@ -930,9 +930,9 @@ def _load_config(cls, inputs, auto_ml, expand_role=True, validate_uri=True):
 
         auto_ml_model_deploy_config = {}
         if auto_ml.auto_generate_endpoint_name is not None:
-            auto_ml_model_deploy_config[
-                "AutoGenerateEndpointName"
-            ] = auto_ml.auto_generate_endpoint_name
+            auto_ml_model_deploy_config["AutoGenerateEndpointName"] = (
+                auto_ml.auto_generate_endpoint_name
+            )
         if not auto_ml.auto_generate_endpoint_name and auto_ml.endpoint_name is not None:
             auto_ml_model_deploy_config["EndpointName"] = auto_ml.endpoint_name
 
@@ -1034,9 +1034,9 @@ def _prepare_auto_ml_stop_condition(
         if max_candidates is not None:
             stopping_condition["MaxCandidates"] = max_candidates
         if max_runtime_per_training_job_in_seconds is not None:
-            stopping_condition[
-                "MaxRuntimePerTrainingJobInSeconds"
-            ] = max_runtime_per_training_job_in_seconds
+            stopping_condition["MaxRuntimePerTrainingJobInSeconds"] = (
+                max_runtime_per_training_job_in_seconds
+            )
         if total_job_runtime_in_seconds is not None:
             stopping_condition["MaxAutoMLJobRuntimeInSeconds"] = total_job_runtime_in_seconds
 

src/sagemaker/automl/automlv2.py

@@ -1446,9 +1446,9 @@ def _load_config(cls, inputs, auto_ml, expand_role=True):
 
         auto_ml_model_deploy_config = {}
         if auto_ml.auto_generate_endpoint_name is not None:
-            auto_ml_model_deploy_config[
-                "AutoGenerateEndpointName"
-            ] = auto_ml.auto_generate_endpoint_name
+            auto_ml_model_deploy_config["AutoGenerateEndpointName"] = (
+                auto_ml.auto_generate_endpoint_name
+            )
         if not auto_ml.auto_generate_endpoint_name and auto_ml.endpoint_name is not None:
             auto_ml_model_deploy_config["EndpointName"] = auto_ml.endpoint_name
 

src/sagemaker/collection.py

@@ -377,9 +377,11 @@ def _convert_group_resource_response(
                     {
                         "Name": collection_name,
                         "Arn": collection_arn,
-                        "Type": resource_group["Identifier"]["ResourceType"]
-                        if is_model_group
-                        else "Collection",
+                        "Type": (
+                            resource_group["Identifier"]["ResourceType"]
+                            if is_model_group
+                            else "Collection"
+                        ),
                     }
                 )
         return collection_details

src/sagemaker/debugger/profiler_config.py

@@ -162,19 +162,19 @@ def _to_request_dict(self):
         profiler_config_request["DisableProfiler"] = self.disable_profiler
 
         if self.system_monitor_interval_millis is not None:
-            profiler_config_request[
-                "ProfilingIntervalInMilliseconds"
-            ] = self.system_monitor_interval_millis
+            profiler_config_request["ProfilingIntervalInMilliseconds"] = (
+                self.system_monitor_interval_millis
+            )
 
         if self.framework_profile_params is not None:
-            profiler_config_request[
-                "ProfilingParameters"
-            ] = self.framework_profile_params.profiling_parameters
+            profiler_config_request["ProfilingParameters"] = (
+                self.framework_profile_params.profiling_parameters
+            )
 
         if self.profile_params is not None:
-            profiler_config_request[
-                "ProfilingParameters"
-            ] = self.profile_params.profiling_parameters
+            profiler_config_request["ProfilingParameters"] = (
+                self.profile_params.profiling_parameters
+            )
 
         return profiler_config_request
 

src/sagemaker/djl_inference/model.py

@@ -213,9 +213,7 @@ def _create_estimator(
     vpc_config: Optional[
         Dict[
             str,
-            List[
-                str,
-            ],
+            List[str],
         ]
     ] = None,
     volume_kms_key=None,
@@ -820,9 +818,9 @@ def _get_container_env(self):
             logger.warning("Ignoring invalid container log level: %s", self.container_log_level)
             return self.env
 
-        self.env[
-            "SERVING_OPTS"
-        ] = f'"-Dai.djl.logging.level={_LOG_LEVEL_MAP[self.container_log_level]}"'
+        self.env["SERVING_OPTS"] = (
+            f'"-Dai.djl.logging.level={_LOG_LEVEL_MAP[self.container_log_level]}"'
+        )
         return self.env
 
 

src/sagemaker/estimator.py

@@ -2539,9 +2539,9 @@ def _get_train_args(cls, estimator, inputs, experiment_config):
         # which is parsed in execution time
         # This does not check config because the EstimatorBase constuctor already did that check
         if estimator.encrypt_inter_container_traffic:
-            train_args[
-                "encrypt_inter_container_traffic"
-            ] = estimator.encrypt_inter_container_traffic
+            train_args["encrypt_inter_container_traffic"] = (
+                estimator.encrypt_inter_container_traffic
+            )
 
         if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
             train_args["algorithm_arn"] = estimator.algorithm_arn
@@ -2556,9 +2556,9 @@ def _get_train_args(cls, estimator, inputs, experiment_config):
             train_args["debugger_hook_config"] = estimator.debugger_hook_config._to_request_dict()
 
         if estimator.tensorboard_output_config:
-            train_args[
-                "tensorboard_output_config"
-            ] = estimator.tensorboard_output_config._to_request_dict()
+            train_args["tensorboard_output_config"] = (
+                estimator.tensorboard_output_config._to_request_dict()
+            )
 
         cls._add_spot_checkpoint_args(local_mode, estimator, train_args)
 

src/sagemaker/experiments/run.py

@@ -220,9 +220,9 @@ def __init__(
             trial_component_name=self._trial_component.trial_component_name,
             sagemaker_session=sagemaker_session,
             artifact_bucket=artifact_bucket,
-            artifact_prefix=_DEFAULT_ARTIFACT_PREFIX
-            if artifact_prefix is None
-            else artifact_prefix,
+            artifact_prefix=(
+                _DEFAULT_ARTIFACT_PREFIX if artifact_prefix is None else artifact_prefix
+            ),
         )
         self._lineage_artifact_tracker = _LineageArtifactTracker(
             trial_component_arn=self._trial_component.trial_component_arn,

src/sagemaker/explainer/explainer_config.py

@@ -37,8 +37,8 @@ def _to_request_dict(self):
         request_dict = {}
 
         if self.clarify_explainer_config:
-            request_dict[
-                "ClarifyExplainerConfig"
-            ] = self.clarify_explainer_config._to_request_dict()
+            request_dict["ClarifyExplainerConfig"] = (
+                self.clarify_explainer_config._to_request_dict()
+            )
 
         return request_dict

src/sagemaker/feature_store/feature_processor/lineage/_feature_processor_lineage.py

@@ -115,19 +115,19 @@ class FeatureProcessorLineageHandler:
 
     def create_lineage(self, tags: Optional[List[Dict[str, str]]] = None) -> None:
         """Create and Update Feature Processor Lineage"""
-        input_feature_group_contexts: List[
-            FeatureGroupContexts
-        ] = self._retrieve_input_feature_group_contexts()
+        input_feature_group_contexts: List[FeatureGroupContexts] = (
+            self._retrieve_input_feature_group_contexts()
+        )
         output_feature_group_contexts: FeatureGroupContexts = (
             self._retrieve_output_feature_group_contexts()
         )
         input_raw_data_artifacts: List[Artifact] = self._retrieve_input_raw_data_artifacts()
-        transformation_code_artifact: Optional[
-            Artifact
-        ] = S3LineageEntityHandler.create_transformation_code_artifact(
-            transformation_code=self.transformation_code,
-            pipeline_last_update_time=self.pipeline[LAST_MODIFIED_TIME].strftime("%s"),
-            sagemaker_session=self.sagemaker_session,
+        transformation_code_artifact: Optional[Artifact] = (
+            S3LineageEntityHandler.create_transformation_code_artifact(
+                transformation_code=self.transformation_code,
+                pipeline_last_update_time=self.pipeline[LAST_MODIFIED_TIME].strftime("%s"),
+                sagemaker_session=self.sagemaker_session,
+            )
         )
         if transformation_code_artifact is not None:
             logger.info("Created Transformation Code Artifact: %s", transformation_code_artifact)
@@ -362,40 +362,40 @@ def _update_pipeline_lineage(
         current_pipeline_version_context: Context = self._get_pipeline_version_context(
             last_update_time=pipeline_context.properties[LAST_UPDATE_TIME]
         )
-        upstream_feature_group_associations: Iterator[
-            AssociationSummary
-        ] = LineageAssociationHandler.list_upstream_associations(
-            # pylint: disable=no-member
-            entity_arn=current_pipeline_version_context.context_arn,
-            source_type=FEATURE_GROUP_PIPELINE_VERSION_CONTEXT_TYPE,
-            sagemaker_session=self.sagemaker_session,
+        upstream_feature_group_associations: Iterator[AssociationSummary] = (
+            LineageAssociationHandler.list_upstream_associations(
+                # pylint: disable=no-member
+                entity_arn=current_pipeline_version_context.context_arn,
+                source_type=FEATURE_GROUP_PIPELINE_VERSION_CONTEXT_TYPE,
+                sagemaker_session=self.sagemaker_session,
+            )
         )
 
-        upstream_raw_data_associations: Iterator[
-            AssociationSummary
-        ] = LineageAssociationHandler.list_upstream_associations(
-            # pylint: disable=no-member
-            entity_arn=current_pipeline_version_context.context_arn,
-            source_type=DATA_SET,
-            sagemaker_session=self.sagemaker_session,
+        upstream_raw_data_associations: Iterator[AssociationSummary] = (
+            LineageAssociationHandler.list_upstream_associations(
+                # pylint: disable=no-member
+                entity_arn=current_pipeline_version_context.context_arn,
+                source_type=DATA_SET,
+                sagemaker_session=self.sagemaker_session,
+            )
         )
 
-        upstream_transformation_code: Iterator[
-            AssociationSummary
-        ] = LineageAssociationHandler.list_upstream_associations(
-            # pylint: disable=no-member
-            entity_arn=current_pipeline_version_context.context_arn,
-            source_type=TRANSFORMATION_CODE,
-            sagemaker_session=self.sagemaker_session,
+        upstream_transformation_code: Iterator[AssociationSummary] = (
+            LineageAssociationHandler.list_upstream_associations(
+                # pylint: disable=no-member
+                entity_arn=current_pipeline_version_context.context_arn,
+                source_type=TRANSFORMATION_CODE,
+                sagemaker_session=self.sagemaker_session,
+            )
         )
 
-        downstream_feature_group_associations: Iterator[
-            AssociationSummary
-        ] = LineageAssociationHandler.list_downstream_associations(
-            # pylint: disable=no-member
-            entity_arn=current_pipeline_version_context.context_arn,
-            destination_type=FEATURE_GROUP_PIPELINE_VERSION_CONTEXT_TYPE,
-            sagemaker_session=self.sagemaker_session,
+        downstream_feature_group_associations: Iterator[AssociationSummary] = (
+            LineageAssociationHandler.list_downstream_associations(
+                # pylint: disable=no-member
+                entity_arn=current_pipeline_version_context.context_arn,
+                destination_type=FEATURE_GROUP_PIPELINE_VERSION_CONTEXT_TYPE,
+                sagemaker_session=self.sagemaker_session,
+            )
         )
 
         is_upstream_feature_group_equal: bool = self._compare_upstream_feature_groups(
@@ -598,9 +598,9 @@ def _update_last_transformation_code(
             last_transformation_code_artifact.properties["state"]
             == TRANSFORMATION_CODE_STATUS_ACTIVE
         ):
-            last_transformation_code_artifact.properties[
-                "state"
-            ] = TRANSFORMATION_CODE_STATUS_INACTIVE
+            last_transformation_code_artifact.properties["state"] = (
+                TRANSFORMATION_CODE_STATUS_INACTIVE
+            )
             last_transformation_code_artifact.properties["exclusive_end_date"] = self.pipeline[
                 LAST_MODIFIED_TIME
             ].strftime("%s")

src/sagemaker/feature_store/feature_processor/lineage/_s3_lineage_entity_handler.py

@@ -172,9 +172,9 @@ def retrieve_pipeline_schedule_artifact(
                 sagemaker_session=sagemaker_session,
             )
             pipeline_schedule_artifact.properties["pipeline_name"] = pipeline_schedule.pipeline_name
-            pipeline_schedule_artifact.properties[
-                "schedule_expression"
-            ] = pipeline_schedule.schedule_expression
+            pipeline_schedule_artifact.properties["schedule_expression"] = (
+                pipeline_schedule.schedule_expression
+            )
             pipeline_schedule_artifact.properties["state"] = pipeline_schedule.state
             pipeline_schedule_artifact.properties["start_date"] = pipeline_schedule.start_date
             pipeline_schedule_artifact.save()