Fix failing tests

litellm/integrations/rubrik.py

@@ -319,12 +319,8 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
                         "content": system_prompt_msg_list,
                     }
                     if isinstance(standard_logging_payload["messages"], list):
-                        standard_logging_payload["messages"].insert(
-                            0, system_scaffold
-                        )
-                    elif isinstance(
-                        standard_logging_payload["messages"], (dict, str)
-                    ):
+                        standard_logging_payload["messages"].insert(0, system_scaffold)
+                    elif isinstance(standard_logging_payload["messages"], (dict, str)):
                         standard_logging_payload["messages"] = [
                             system_scaffold,
                             standard_logging_payload["messages"],
@@ -354,14 +350,10 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
                 exc_info=True,
             )
 
-    async def async_log_success_event(
-        self, kwargs, response_obj, start_time, end_time
-    ):
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
         await self._enqueue_log_event(kwargs, "success")
 
-    async def async_log_failure_event(
-        self, kwargs, response_obj, start_time, end_time
-    ):
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
         await self._enqueue_log_event(kwargs, "failure")
 
     # -- Batch logging ---------------------------------------------------------

litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py

@@ -170,7 +170,9 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
         anthropic_request.pop("model", None)
         anthropic_request.pop("stream", None)
         anthropic_request.pop("output_format", None)
-        if not _supports_factory(model=model, custom_llm_provider=None, key="supports_output_config"):
+        if not _supports_factory(
+            model=model, custom_llm_provider=None, key="supports_output_config"
+        ):
             anthropic_request.pop("output_config", None)
         if "anthropic_version" not in anthropic_request:
             anthropic_request["anthropic_version"] = self.anthropic_version

litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py

@@ -515,7 +515,9 @@ class AmazonAnthropicClaudeMessagesConfig(
         # 5b. Bedrock Invoke supports output_config (effort) for Claude 4.6+ models,
         # but older models do not — strip it to avoid request rejection.
         # Ref: https://github.com/BerriAI/litellm/issues/22797
-        if not _supports_factory(model=model, custom_llm_provider=None, key="supports_output_config"):
+        if not _supports_factory(
+            model=model, custom_llm_provider=None, key="supports_output_config"
+        ):
             anthropic_messages_request.pop("output_config", None)
 
         # 5a. Remove `custom` field from tools (Bedrock doesn't support it)

tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py

@@ -118,7 +118,7 @@ async def test_vertex_ai_gpt_oss_simple_request():
             "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
         ) as mock_http_handler,
         patch(
-            "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "pathrise-convert-1606954137718"),
         ),
         patch.dict(
@@ -217,7 +217,7 @@ async def test_vertex_ai_gpt_oss_reasoning_effort():
             "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
         ) as mock_http_handler,
         patch(
-            "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "pathrise-convert-1606954137718"),
         ),
         patch.dict(

tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/qwen/test_vertex_ai_qwen_global_endpoint.py

@@ -7,7 +7,6 @@ These tests verify that:
 3. The completion() and responses() API work with Qwen models
 """
 
-import json
 import os
 import sys
 from unittest.mock import MagicMock, patch, AsyncMock
@@ -179,7 +178,7 @@ async def test_vertex_ai_qwen_global_endpoint_url():
             "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
         ) as mock_http_handler,
         patch(
-            "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "test-project"),
         ),
         patch.dict(