style: apply black formatting

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -500,9 +500,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         value = _remove_strict_from_schema(value)
 
         for tool in value:
-            openai_function_object: Optional[
-                ChatCompletionToolParamFunctionChunk
-            ] = None
+            openai_function_object: Optional[ChatCompletionToolParamFunctionChunk] = (
+                None
+            )
             if "function" in tool:  # tools list
                 _openai_function_object = ChatCompletionToolParamFunctionChunk(  # type: ignore
                     **tool["function"]
@@ -634,15 +634,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             _tools_list.append(search_tool)
         if googleSearchRetrieval is not None:
             retrieval_tool = Tools()
-            retrieval_tool[
-                VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value
-            ] = googleSearchRetrieval
+            retrieval_tool[VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value] = (
+                googleSearchRetrieval
+            )
             _tools_list.append(retrieval_tool)
         if enterpriseWebSearch is not None:
             enterprise_tool = Tools()
-            enterprise_tool[
-                VertexToolName.ENTERPRISE_WEB_SEARCH.value
-            ] = enterpriseWebSearch
+            enterprise_tool[VertexToolName.ENTERPRISE_WEB_SEARCH.value] = (
+                enterpriseWebSearch
+            )
             _tools_list.append(enterprise_tool)
         if code_execution is not None:
             code_tool = Tools()
@@ -1089,16 +1089,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                         param_description="thinking_budget",
                     )
                     if VertexGeminiConfig._is_gemini_3_or_newer(model):
-                        optional_params[
-                            "thinkingConfig"
-                        ] = VertexGeminiConfig._map_reasoning_effort_to_thinking_level(
-                            effort_value, model
+                        optional_params["thinkingConfig"] = (
+                            VertexGeminiConfig._map_reasoning_effort_to_thinking_level(
+                                effort_value, model
+                            )
                         )
                     else:
-                        optional_params[
-                            "thinkingConfig"
-                        ] = VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(
-                            effort_value, model
+                        optional_params["thinkingConfig"] = (
+                            VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(
+                                effort_value, model
+                            )
                         )
             elif param == "thinking":
                 # Validate no conflict with thinking_level
@@ -1107,11 +1107,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     param_name="thinking",
                     param_description="thinking_budget",
                 )
-                optional_params[
-                    "thinkingConfig"
-                ] = VertexGeminiConfig._map_thinking_param(
-                    cast(AnthropicThinkingParam, value),
-                    model=model,
+                optional_params["thinkingConfig"] = (
+                    VertexGeminiConfig._map_thinking_param(
+                        cast(AnthropicThinkingParam, value),
+                        model=model,
+                    )
                 )
             elif param == "modalities" and isinstance(value, list):
                 response_modalities = self.map_response_modalities(value)
@@ -1533,10 +1533,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                         _tool_response_chunk["provider_specific_fields"] = {  # type: ignore
                             "thought_signature": thought_signature
                         }
-                        _tool_response_chunk[
-                            "id"
-                        ] = _encode_tool_call_id_with_signature(
-                            _tool_response_chunk["id"] or "", thought_signature
+                        _tool_response_chunk["id"] = (
+                            _encode_tool_call_id_with_signature(
+                                _tool_response_chunk["id"] or "", thought_signature
+                            )
                         )
                     _tools.append(_tool_response_chunk)
                 cumulative_tool_call_idx += 1
@@ -2389,28 +2389,28 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             ## ADD METADATA TO RESPONSE ##
 
             setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)
-            model_response._hidden_params[
-                "vertex_ai_grounding_metadata"
-            ] = grounding_metadata
+            model_response._hidden_params["vertex_ai_grounding_metadata"] = (
+                grounding_metadata
+            )
 
             setattr(
                 model_response, "vertex_ai_url_context_metadata", url_context_metadata
             )
 
-            model_response._hidden_params[
-                "vertex_ai_url_context_metadata"
-            ] = url_context_metadata
+            model_response._hidden_params["vertex_ai_url_context_metadata"] = (
+                url_context_metadata
+            )
 
             setattr(model_response, "vertex_ai_safety_results", safety_ratings)
-            model_response._hidden_params[
-                "vertex_ai_safety_results"
-            ] = safety_ratings  # older approach - maintaining to prevent regressions
+            model_response._hidden_params["vertex_ai_safety_results"] = (
+                safety_ratings  # older approach - maintaining to prevent regressions
+            )
 
             ## ADD CITATION METADATA ##
             setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)
-            model_response._hidden_params[
-                "vertex_ai_citation_metadata"
-            ] = citation_metadata  # older approach - maintaining to prevent regressions
+            model_response._hidden_params["vertex_ai_citation_metadata"] = (
+                citation_metadata  # older approach - maintaining to prevent regressions
+            )
 
             ## ADD TRAFFIC TYPE ##
             traffic_type = completion_response.get("usageMetadata", {}).get(

litellm/proxy/management_helpers/audit_logs.py

@@ -51,7 +51,11 @@ def _build_audit_log_payload(
     if request_data.updated_at is not None:
         updated_at = request_data.updated_at.isoformat()
 
-    table_name_str: str = request_data.table_name.value if isinstance(request_data.table_name, LitellmTableNames) else str(request_data.table_name)
+    table_name_str: str = (
+        request_data.table_name.value
+        if isinstance(request_data.table_name, LitellmTableNames)
+        else str(request_data.table_name)
+    )
 
     return StandardAuditLogPayload(
         id=request_data.id,
@@ -89,7 +93,9 @@ async def _dispatch_audit_log_to_callbacks(
 
     for callback in litellm.audit_log_callbacks:
         try:
-            resolved: Optional[CustomLogger] = callback if isinstance(callback, CustomLogger) else None
+            resolved: Optional[CustomLogger] = (
+                callback if isinstance(callback, CustomLogger) else None
+            )
             if isinstance(callback, str):
                 resolved = _resolve_audit_log_callback(callback)
                 if resolved is None:
@@ -138,9 +144,7 @@ async def create_object_audit_log(
         return
 
     _changed_by = (
-        litellm_changed_by
-        or user_api_key_dict.user_id
-        or litellm_proxy_admin_name
+        litellm_changed_by or user_api_key_dict.user_id or litellm_proxy_admin_name
     )
 
     await create_audit_log_for_update(