test: fix linting issues

litellm/llms/azure.py

@@ -147,7 +147,7 @@ class AzureChatCompletion(BaseLLM):
                 if optional_params.get("stream", False):
                     return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
                 else:
-                    return self.acompletion(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
+                    return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response)
             elif "stream" in optional_params and optional_params["stream"] == True:
                 response = self._client_session.post(
                     url=api_base,

litellm/llms/openai.py

@@ -222,7 +222,7 @@ class OpenAIChatCompletion(BaseLLM):
                         if optional_params.get("stream", False):
                             return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
                         else:
-                            return self.acompletion(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
+                            return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response)
                     elif "stream" in optional_params and optional_params["stream"] == True:
                         response = self._client_session.post(
                             url=api_base,
@@ -276,11 +276,9 @@ class OpenAIChatCompletion(BaseLLM):
                 raise OpenAIError(status_code=500, message=traceback.format_exc())
     
     async def acompletion(self, 
-                          logging_obj,
                           api_base: str, 
                           data: dict, headers: dict, 
-                          model_response: ModelResponse, 
-                          model: str): 
+                          model_response: ModelResponse): 
         async with aiohttp.ClientSession() as session:
             async with session.post(api_base, json=data, headers=headers) as response:
                 response_json = await response.json()

litellm/main.py

@@ -77,7 +77,7 @@ openai_text_completions = OpenAITextCompletion()
 azure_chat_completions = AzureChatCompletion()
 ####### COMPLETION ENDPOINTS ################
 
-async def acompletion(model: str, messages: List = [], *args, **kwargs):
+async def acompletion(*args, **kwargs):
     """
     Asynchronously executes a litellm.completion() call for any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly)
 
@@ -117,7 +117,8 @@ async def acompletion(model: str, messages: List = [], *args, **kwargs):
         - If `stream` is True, the function returns an async generator that yields completion lines.
     """
     loop = asyncio.get_event_loop()
-
+    model = args[0] if len(args) > 0 else kwargs["model"]
+    messages = args[1] if len(args) > 1 else kwargs["messages"]
     ### INITIALIZE LOGGING OBJECT ### 
     kwargs["litellm_call_id"] = str(uuid.uuid4())
     start_time = datetime.datetime.now()

litellm/proxy/proxy_server.py

@@ -199,7 +199,7 @@ def save_params_to_config(data: dict):
 
 def load_router_config(router: Optional[litellm.Router], config_file_path: str):
     config = {}
-    server_settings  = {} 
+    server_settings: dict = {} 
     try: 
         if os.path.exists(config_file_path):
             with open(config_file_path, 'r') as file: