fix: resolve pre-commit issues after rebase

- Remove duplicate test_lmeval_huggingface_model_tier2 function
- Fix bare Exception catch in wait_for_service_account
- Auto-format fixes from ruff

Signed-off-by: Shelton Cyril <sheltoncyril@gmail.com>

Author: sheltoncyril

tests/fixtures/inference.py

@@ -349,23 +349,17 @@ def patched_dsc_garak_kfp(admin_client) -> Generator[DataScienceCluster]:
 
     dsc = get_data_science_cluster(client=admin_client)
     with ResourceEditor(
-            patches={
-                dsc: {
-                    "spec": {
-                        "components": {
-                            "kserve": {
-                                "rawDeploymentServiceConfig": "Headed"
-                            },
-                            "aipipelines": {
-                                "managementState": "Managed"
-                            },
-                            "mlflowoperator": {
-                                "managementState": "Managed"
-                            }
-                        }
+        patches={
+            dsc: {
+                "spec": {
+                    "components": {
+                        "kserve": {"rawDeploymentServiceConfig": "Headed"},
+                        "aipipelines": {"managementState": "Managed"},
+                        "mlflowoperator": {"managementState": "Managed"},
                     }
                 }
             }
-        ):
-            wait_for_dsc_status_ready(dsc_resource=dsc)
-            yield dsc
+        }
+    ):
+        wait_for_dsc_status_ready(dsc_resource=dsc)
+        yield dsc

tests/llama_stack/safety/test_trustyai_fms_provider.py

@@ -8,6 +8,7 @@
 LOGGER = structlog.get_logger(name=__name__)
 SECURE_SHIELD_ID: str = "secure_shield"
 
+
 @pytest.mark.tier1
 @pytest.mark.parametrize(
     "model_namespace, minio_pod, minio_data_connection, "

tests/model_explainability/evalhub/conftest.py

@@ -8,13 +8,11 @@
 from ocp_resources.deployment import Deployment
 from ocp_resources.inference_service import InferenceService
 from ocp_resources.namespace import Namespace
-from ocp_resources.project_project_openshift_io import Project
 from ocp_resources.role import Role
 from ocp_resources.role_binding import RoleBinding
 from ocp_resources.route import Route
 from ocp_resources.secret import Secret
 from ocp_resources.service_account import ServiceAccount
-from ocp_resources.serving_runtime import ServingRuntime
 from pytest_testconfig import config as py_config
 
 from tests.model_explainability.evalhub.constants import (
@@ -203,9 +201,7 @@ def tenant_namespace(
 
 @pytest.fixture(scope="class")
 def evalhub_job_service_account(
-    admin_client: DynamicClient,
-    model_namespace: Namespace,
-    tenant_namespace: Namespace
+    admin_client: DynamicClient, model_namespace: Namespace, tenant_namespace: Namespace
 ) -> ServiceAccount:
     """Wait for the auto-created EvalHub job ServiceAccount in the tenant namespace."""
     sa_name = f"{EVALHUB_JOB_SA_PREFIX}{model_namespace.name}{EVALHUB_JOB_SA_SUFFIX}"
@@ -363,10 +359,12 @@ def dspa_secret_patch(
         "AWS_DEFAULT_REGION": "us-east-1",
     }
 
-    secret.update(resource_dict={
-        "metadata": {"name": secret.name, "namespace": tenant_namespace.name},
-        "stringData": secret_data,
-    })
+    secret.update(
+        resource_dict={
+            "metadata": {"name": secret.name, "namespace": tenant_namespace.name},
+            "stringData": secret_data,
+        }
+    )
     LOGGER.info(f"Patched DSPA S3 secret with additional fields in {tenant_namespace.name}")
     return secret
 
@@ -379,20 +377,21 @@ def dsp_access_for_job_sa(
     tenant_dspa: DataSciencePipelinesApplication,
 ) -> Generator[tuple[Role, RoleBinding, RoleBinding], Any, Any]:
     """Grant the EvalHub job ServiceAccount access to the DSP API and pipeline management."""
-    with Role(
-        client=admin_client,
-        name="evalhub-jobs-dspa-api",
-        namespace=tenant_namespace.name,
-        rules=[
-            {
-                "apiGroups": ["datasciencepipelinesapplications.opendatahub.io"],
-                "resources": ["datasciencepipelinesapplications/api"],
-                "verbs": ["get", "create"],
-            },
-        ],
-        wait_for_resource=True,
-    ) as role:
-        with RoleBinding(
+    with (
+        Role(
+            client=admin_client,
+            name="evalhub-jobs-dspa-api",
+            namespace=tenant_namespace.name,
+            rules=[
+                {
+                    "apiGroups": ["datasciencepipelinesapplications.opendatahub.io"],
+                    "resources": ["datasciencepipelinesapplications/api"],
+                    "verbs": ["get", "create"],
+                },
+            ],
+            wait_for_resource=True,
+        ) as role,
+        RoleBinding(
             client=admin_client,
             name="evalhub-jobs-dspa-api",
             namespace=tenant_namespace.name,
@@ -402,20 +401,20 @@ def dsp_access_for_job_sa(
             subjects_name=evalhub_job_service_account.name,
             subjects_namespace=tenant_namespace.name,
             wait_for_resource=True,
-        ) as api_binding:
-            with RoleBinding(
-                client=admin_client,
-                name="evalhub-jobs-pipeline-management",
-                namespace=tenant_namespace.name,
-                role_ref_kind="Role",
-                role_ref_name="ds-pipeline-dspa",
-                subjects_kind="ServiceAccount",
-                subjects_name=evalhub_job_service_account.name,
-                subjects_namespace=tenant_namespace.name,
-                wait_for_resource=True,
-            ) as pipeline_binding:
-                yield role, api_binding, pipeline_binding
-
+        ) as api_binding,
+        RoleBinding(
+            client=admin_client,
+            name="evalhub-jobs-pipeline-management",
+            namespace=tenant_namespace.name,
+            role_ref_kind="Role",
+            role_ref_name="ds-pipeline-dspa",
+            subjects_kind="ServiceAccount",
+            subjects_name=evalhub_job_service_account.name,
+            subjects_namespace=tenant_namespace.name,
+            wait_for_resource=True,
+        ) as pipeline_binding,
+    ):
+        yield role, api_binding, pipeline_binding
 
 
 # ---------------------------------------------------------------------------
@@ -426,7 +425,4 @@ def dsp_access_for_job_sa(
 @pytest.fixture(scope="class")
 def garak_sim_isvc_url(llm_d_inference_sim_isvc: InferenceService) -> str:
     """Get the internal service URL for the LLM-d inference simulator."""
-    return (
-        f"http://{llm_d_inference_sim_isvc.name}-predictor.{llm_d_inference_sim_isvc.namespace}"
-        f".svc.cluster.local/v1"
-    )
+    return f"http://{llm_d_inference_sim_isvc.name}-predictor.{llm_d_inference_sim_isvc.namespace}.svc.cluster.local/v1"

tests/model_explainability/evalhub/constants.py

@@ -29,4 +29,3 @@
 # Job service account naming
 EVALHUB_JOB_SA_PREFIX: str = "evalhub-"
 EVALHUB_JOB_SA_SUFFIX: str = "-job"
-

tests/model_explainability/evalhub/test_garak.py

@@ -7,13 +7,13 @@
     GARAK_BENCHMARK_ID,
     GARAK_PROVIDER_ID,
 )
-from utilities.constants import LLMdInferenceSimConfig
 from tests.model_explainability.evalhub.utils import (
     submit_garak_job,
     validate_evalhub_health,
     validate_evalhub_providers,
     wait_for_job_completion,
 )
+from utilities.constants import LLMdInferenceSimConfig
 
 
 @pytest.mark.parametrize(

tests/model_explainability/evalhub/utils.py

@@ -214,7 +214,7 @@ def _sa_exists() -> ServiceAccount | None:
             sa = ServiceAccount(client=admin_client, name=sa_name, namespace=namespace)
             if sa.exists:
                 return sa
-        except Exception:
+        except ValueError, AttributeError:
             pass
         return None
 

tests/model_explainability/guardrails/conftest.py

@@ -457,7 +457,7 @@ def minio_deployment_otel(admin_client, model_namespace, minio_pvc_otel):
                 {
                     "name": "minio",
                     "image": "quay.io/minio/minio"
-                             "@sha256:14cea493d9a34af32f524e538b8346cf79f3321eff8e708c1e2960462bd8936e",
+                    "@sha256:14cea493d9a34af32f524e538b8346cf79f3321eff8e708c1e2960462bd8936e",
                     "command": ["/bin/sh", "-c", "mkdir -p /storage/tempo && minio server /storage"],
                     "env": [
                         {"name": "MINIO_ACCESS_KEY", "value": TEMPO},

tests/model_explainability/guardrails/test_guardrails.py

@@ -37,6 +37,7 @@
 
 LOGGER = structlog.get_logger(name=__name__)
 
+
 @pytest.mark.smoke
 @pytest.mark.parametrize(
     "model_namespace, orchestrator_config, guardrails_orchestrator",
@@ -68,6 +69,7 @@ def test_validate_guardrails_orchestrator_images(
     """
     validate_tai_component_images(pod=guardrails_orchestrator_pod, tai_operator_configmap=trustyai_operator_configmap)
 
+
 @pytest.mark.smoke
 @pytest.mark.parametrize(
     "model_namespace, orchestrator_config, guardrails_gateway_config, guardrails_orchestrator",
@@ -209,6 +211,7 @@ def test_guardrails_builtin_detectors_negative_detection(
             model=LLMdInferenceSimConfig.model_name,
         )
 
+
 @pytest.mark.smoke
 @pytest.mark.parametrize(
     "model_namespace, orchestrator_config, guardrails_gateway_config,guardrails_orchestrator",

tests/model_explainability/lm_eval/test_lm_eval.py

@@ -54,23 +54,6 @@ def test_lmeval_huggingface_model(admin_client, model_namespace, lmevaljob_hf_po
     On each test we run a different evaluation task, limiting it to 0.5% of the questions on each eval."""
     validate_lmeval_job_pod_and_logs(lmevaljob_pod=lmevaljob_hf_pod)
 
-@pytest.mark.skip_on_disconnected
-@pytest.mark.tier2
-@pytest.mark.parametrize(
-    "model_namespace, lmevaljob_hf",
-    [
-
-        pytest.param(
-            {"name": "test-lmeval-hf-tier2"},
-            {"task_list": {"taskNames": TIER2_LMEVAL_TASKS}},
-        ),
-    ],
-    indirect=True,
-)
-def test_lmeval_huggingface_model_tier2(admin_client, model_namespace, lmevaljob_hf_pod):
-    """Tests that verify running common evaluations (and a custom one) on a model pulled directly from HuggingFace.
-    On each test we run a different evaluation task, limiting it to 0.5% of the questions on each eval."""
-    validate_lmeval_job_pod_and_logs(lmevaljob_pod=lmevaljob_hf_pod)
 
 @pytest.mark.skip_on_disconnected
 @pytest.mark.tier2
@@ -171,6 +154,7 @@ def test_verify_lmeval_pod_images(lmevaljob_s3_offline_pod, trustyai_operator_co
         pod=lmevaljob_s3_offline_pod, tai_operator_configmap=trustyai_operator_configmap, include_init_containers=True
     )
 
+
 @pytest.mark.tier1
 @pytest.mark.parametrize(
     "model_namespace, oci_registry_pod_with_minio, lmeval_data_downloader_pod, lmevaljob_local_offline_oci",

tests/model_explainability/trustyai_service/service/test_trustyai_service.py

@@ -21,6 +21,7 @@
 )
 from utilities.constants import MinIo
 
+
 @pytest.mark.tier1
 @pytest.mark.parametrize(
     "model_namespace",
@@ -44,6 +45,7 @@ def test_trustyai_service_with_invalid_db_cert(
         label_selector=f"app.kubernetes.io/instance={trustyai_service_with_invalid_db_cert.name}",
     )
 
+
 @pytest.mark.smoke
 @pytest.mark.parametrize(
     "model_namespace, trustyai_service",
@@ -70,6 +72,7 @@ def test_validate_trustyai_service_image(
         trustyai_operator_configmap=trustyai_operator_configmap,
     )
 
+
 @pytest.mark.tier1
 @pytest.mark.parametrize(
     "model_namespace, minio_pod, minio_data_connection, trustyai_service",