fix: fix unit test, black-check, pylint errors

Author: Roja Reddy Sareddy

src/sagemaker/huggingface/model.py

@@ -298,8 +298,8 @@ def deploy(
             explainer_config (sagemaker.explainer.ExplainerConfig): Specifies online explainability
                 configuration for use with Amazon SageMaker Clarify. (default: None)
             update_endpoint (Optional[bool]): Flag to update the model in an existing Amazon SageMaker endpoint.
-                If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
-                corresponding to the previous EndpointConfig. Default: False
+                If True, this will deploy a new EndpointConfig to an already existing endpoint and
+                delete resources corresponding to the previous EndpointConfig. Default: False
         Raises:
              ValueError: If arguments combination check failed in these circumstances:
                 - If no role is specified or

src/sagemaker/model.py

@@ -1631,7 +1631,9 @@ def deploy(
         # Support multiple models on same endpoint
         if endpoint_type == EndpointType.INFERENCE_COMPONENT_BASED:
             if update_endpoint:
-                raise ValueError("Currently update_endpoint is supported for single model endpoints")
+                raise ValueError(
+                    "Currently update_endpoint is supported for single model endpoints"
+                )
             if endpoint_name:
                 self.endpoint_name = endpoint_name
             else:

src/sagemaker/serve/builder/model_builder.py

@@ -1663,7 +1663,9 @@ def deploy(
 
         if isinstance(inference_config, ResourceRequirements):
             if update_endpoint:
-                raise ValueError("Currently update_endpoint is supported for single model endpoints")
+                raise ValueError(
+                    "Currently update_endpoint is supported for single model endpoints"
+                )
             # Multi Model and MultiContainer endpoints with Inference Component
             return self.built_model.deploy(
                 instance_type=self.instance_type,

src/sagemaker/session.py

@@ -4540,8 +4540,8 @@ def create_endpoint_config(
                 empty object passed through, will use pre-defined values in
                 ``ServerlessInferenceConfig`` class to deploy serverless endpoint. Deploy an
                 instance based endpoint if it's None. (default: None).
-            routing_config (Optional[Dict[str, Any]): Settings the control how the endpoint routes incoming
-                traffic to the instances that the endpoint hosts.
+            routing_config (Optional[Dict[str, Any]): Settings the control how the endpoint routes
+                incoming traffic to the instances that the endpoint hosts.
                 Currently, support dictionary key ``RoutingStrategy``.
 
                 .. code:: python

tests/unit/sagemaker/jumpstart/model/test_model.py

@@ -794,7 +794,7 @@ def test_jumpstart_model_kwargs_match_parent_class(self):
         and reach out to JumpStart team."""
 
         init_args_to_skip: Set[str] = set(["model_reference_arn"])
-        deploy_args_to_skip: Set[str] = set(["kwargs", "model_reference_arn"])
+        deploy_args_to_skip: Set[str] = set(["kwargs", "model_reference_arn", "update_endpoint"])
         deploy_args_removed_at_deploy_time: Set[str] = set(["model_access_configs"])
 
         parent_class_init = Model.__init__

tests/unit/sagemaker/model/test_deploy.py

@@ -1134,13 +1134,12 @@ def test_deploy_with_update_endpoint(production_variant, name_from_base, sagemak
 
     # Test update_endpoint with async inference config
     async_inference_config = AsyncInferenceConfig(
-        output_path="s3://bucket/output",
-        failure_path="s3://bucket/failure"
+        output_path="s3://bucket/output", failure_path="s3://bucket/failure"
     )
     async_inference_config_dict = {
         "OutputConfig": {
             "S3OutputPath": "s3://bucket/output",
-            "S3FailurePath": "s3://bucket/failure"
+            "S3FailurePath": "s3://bucket/failure",
         },
     }
     model.deploy(
@@ -1182,7 +1181,9 @@ def test_deploy_with_update_endpoint_inference_component(production_variant, sag
     )
 
     # Test that updating endpoint with inference component raises error
-    with pytest.raises(ValueError, match="Currently update_endpoint is supported for single model endpoints"):
+    with pytest.raises(
+        ValueError, match="Currently update_endpoint is supported for single model endpoints"
+    ):
         model.deploy(
             endpoint_name="test-endpoint",
             instance_type=INSTANCE_TYPE,

tests/unit/sagemaker/serve/builder/test_model_builder.py

@@ -4138,9 +4138,7 @@ def test_neuron_configurations_rule_set(self):
         {
             "input_args": {
                 "inference_config": BatchTransformInferenceConfig(
-                    instance_count=1,
-                    instance_type="ml.m5.large",
-                    output_path="op-path"
+                    instance_count=1, instance_type="ml.m5.large", output_path="op-path"
                 )
             },
             "call_params": {
@@ -4194,9 +4192,9 @@ def test_deploy_multi_model_update_error():
     )
     setattr(model_builder, "built_model", MagicMock())
 
-    with pytest.raises(ValueError, match="Currently update_endpoint is supported for single model endpoints"):
+    with pytest.raises(
+        ValueError, match="Currently update_endpoint is supported for single model endpoints"
+    ):
         model_builder.deploy(
-            endpoint_name="test",
-            inference_config=RESOURCE_REQUIREMENTS,
-            update_endpoint=True
+            endpoint_name="test", inference_config=RESOURCE_REQUIREMENTS, update_endpoint=True
         )