fix: apply Black formatting to 7 files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

litellm/litellm_core_utils/litellm_logging.py

@@ -1686,7 +1686,9 @@ class Logging(LiteLLMLoggingBaseClass):
                 )
         return logging_result
 
-    def _merge_hidden_params_from_response_into_metadata(self, logging_result: Any) -> None:
+    def _merge_hidden_params_from_response_into_metadata(
+        self, logging_result: Any
+    ) -> None:
         """
         Copy response._hidden_params into litellm_params.metadata['hidden_params'].
 
@@ -1704,9 +1706,9 @@ class Logging(LiteLLMLoggingBaseClass):
         self.model_call_details["litellm_params"].setdefault("metadata", {})
         if self.model_call_details["litellm_params"]["metadata"] is None:
             self.model_call_details["litellm_params"]["metadata"] = {}
-        self.model_call_details["litellm_params"]["metadata"]["hidden_params"] = getattr(
-            logging_result, "_hidden_params", {}
-        )
+        self.model_call_details["litellm_params"]["metadata"][
+            "hidden_params"
+        ] = getattr(logging_result, "_hidden_params", {})
 
     def _process_hidden_params_and_response_cost(
         self,

litellm/llms/gemini/image_generation/transformation.py

@@ -98,7 +98,6 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
                 elif modality == "IMAGE":
                     input_tokens_details.image_tokens += token_count
 
-
         return ImageUsage(
             input_tokens=usage_metadata.get("promptTokenCount", 0),
             input_tokens_details=input_tokens_details,

litellm/llms/moonshot/chat/transformation.py

@@ -168,7 +168,9 @@ class MoonshotChatConfig(OpenAIGPTConfig):
             if (
                 msg.get("role") == "assistant"
                 and msg.get("tool_calls")
-                and not msg.get("reasoning_content")  # Check using .get() which works for both dicts and Pydantic models
+                and not msg.get(
+                    "reasoning_content"
+                )  # Check using .get() which works for both dicts and Pydantic models
             ):
                 patched = dict(cast(dict, msg))
                 provider_fields = patched.get("provider_specific_fields") or {}

litellm/llms/ovhcloud/chat/transformation.py

@@ -28,9 +28,7 @@ class OVHCloudChatConfig(OpenAIGPTConfig):
         """
         supports_function_calling: Optional[bool] = None
         try:
-            model_info = _get_model_info_helper(
-                model, custom_llm_provider="ovhcloud"
-            )
+            model_info = _get_model_info_helper(model, custom_llm_provider="ovhcloud")
             supports_function_calling = model_info.get(
                 "supports_function_calling", None
             )

litellm/llms/vertex_ai/gemini/transformation.py

@@ -555,7 +555,9 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                 }
                             }
                             if "thought_signature" in invocation:
-                                tc_part["thoughtSignature"] = invocation["thought_signature"]
+                                tc_part["thoughtSignature"] = invocation[
+                                    "thought_signature"
+                                ]
                             assistant_content.append(tc_part)  # type: ignore
 
                             # Re-inject toolResponse part if response is present
@@ -566,11 +568,11 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                 }
                                 if invocation.get("tool_type"):
                                     tr_dict["toolType"] = invocation["tool_type"]
-                                tr_part: Dict[str, Any] = {
-                                    "toolResponse": tr_dict
-                                }
+                                tr_part: Dict[str, Any] = {"toolResponse": tr_dict}
                                 if "thought_signature" in invocation:
-                                    tr_part["thoughtSignature"] = invocation["thought_signature"]
+                                    tr_part["thoughtSignature"] = invocation[
+                                        "thought_signature"
+                                    ]
                                 assistant_content.append(tr_part)  # type: ignore
 
                 msg_i += 1

litellm/proxy/auth/user_api_key_auth.py

@@ -920,9 +920,9 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                         route=route,
                     )
                 if _end_user_object is not None:
-                    end_user_params["allowed_model_region"] = (
-                        _end_user_object.allowed_model_region
-                    )
+                    end_user_params[
+                        "allowed_model_region"
+                    ] = _end_user_object.allowed_model_region
                     if _end_user_object.litellm_budget_table is not None:
                         _apply_budget_limits_to_end_user_params(
                             end_user_params=end_user_params,
@@ -1499,9 +1499,9 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
 
             if _end_user_object is not None:
                 valid_token_dict.update(end_user_params)
-                valid_token_dict["end_user_object_permission"] = (
-                    _end_user_object.object_permission
-                )
+                valid_token_dict[
+                    "end_user_object_permission"
+                ] = _end_user_object.object_permission
 
         # check if token is from litellm-ui, litellm ui makes keys to allow users to login with sso. These keys can only be used for LiteLLM UI functions
         # sso/login, ui/login, /key functions and /user functions

litellm/proxy/common_request_processing.py

@@ -1004,7 +1004,8 @@ class ProxyBaseLLMRequestProcessing:
                 self.data["deployment"] = llm_router.get_deployment(model_id=model_id)
             asyncio.create_task(
                 proxy_logging_obj.update_request_status(
-                    litellm_call_id=self.data.get("litellm_call_id", ""), status="success"
+                    litellm_call_id=self.data.get("litellm_call_id", ""),
+                    status="success",
                 )
             )
             if self._is_streaming_request(
@@ -1029,11 +1030,13 @@ class ProxyBaseLLMRequestProcessing:
                 )
 
                 # Call response headers hook for streaming success
-                callback_headers = await proxy_logging_obj.post_call_response_headers_hook(
-                    data=self.data,
-                    user_api_key_dict=user_api_key_dict,
-                    response=response,
-                    request_headers=dict(request.headers),
+                callback_headers = (
+                    await proxy_logging_obj.post_call_response_headers_hook(
+                        data=self.data,
+                        user_api_key_dict=user_api_key_dict,
+                        response=response,
+                        request_headers=dict(request.headers),
+                    )
                 )
                 if callback_headers:
                     custom_headers.update(callback_headers)
@@ -1439,7 +1442,8 @@ class ProxyBaseLLMRequestProcessing:
                         )["guardrail_blocked"] = True
         except Exception as e:
             verbose_proxy_logger.exception(
-                "Error in deferred streaming guardrail initialization: %s", e,
+                "Error in deferred streaming guardrail initialization: %s",
+                e,
             )
         finally:
             try:
@@ -1453,7 +1457,8 @@ class ProxyBaseLLMRequestProcessing:
                 )
             except Exception as e:
                 verbose_proxy_logger.exception(
-                    "Error in deferred streaming async logging: %s", e,
+                    "Error in deferred streaming async logging: %s",
+                    e,
                 )
 
             try:
@@ -1466,7 +1471,8 @@ class ProxyBaseLLMRequestProcessing:
                 )
             except Exception as e:
                 verbose_proxy_logger.exception(
-                    "Error in deferred streaming sync logging: %s", e,
+                    "Error in deferred streaming sync logging: %s",
+                    e,
                 )
 
     async def _handle_llm_api_exception(