all fixes to linting

litellm/__init__.py

@@ -1,24 +1,25 @@
 import threading
+from typing import Callable, List, Optional
 
-success_callback = []
-failure_callback = []
+success_callback: List[str] = []
+failure_callback: List[str] = []
 set_verbose = False
 telemetry = True
 max_tokens = 256  # OpenAI Defaults
 retry = True
-api_key = None
-openai_key = None
-azure_key = None
-anthropic_key = None
-replicate_key = None
-cohere_key = None
-openrouter_key = None
-huggingface_key = None
-vertex_project = None
-vertex_location = None
+api_key: Optional[str] = None
+openai_key: Optional[str] = None
+azure_key: Optional[str] = None
+anthropic_key: Optional[str] = None
+replicate_key: Optional[str] = None
+cohere_key: Optional[str] = None
+openrouter_key: Optional[str] = None
+huggingface_key: Optional[str] = None
+vertex_project: Optional[str] = None
+vertex_location: Optional[str] = None
+hugging_api_token: Optional[str] = None
+togetherai_api_key: Optional[str] = None
 caching = False
-hugging_api_token = None
-togetherai_api_key = None
 model_cost = {
     "gpt-3.5-turbo": {
         "max_tokens": 4000,
@@ -223,7 +224,7 @@ from .utils import (
     completion_cost,
     get_litellm_params,
 )
-from .main import *  # Import all the symbols from main.py
+from .main import *  # type: ignore
 from .integrations import *
 from openai.error import (
     AuthenticationError,

litellm/exceptions.py

@@ -8,7 +8,7 @@ from openai.error import (
 )
 
 
-class AuthenticationError(AuthenticationError): # type: ignore
+class AuthenticationError(AuthenticationError):  # type: ignore
     def __init__(self, message, llm_provider):
         self.status_code = 401
         self.message = message
@@ -18,7 +18,7 @@ class AuthenticationError(AuthenticationError): # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class InvalidRequestError(InvalidRequestError): # type: ignore
+class InvalidRequestError(InvalidRequestError):  # type: ignore
     def __init__(self, message, model, llm_provider):
         self.status_code = 400
         self.message = message
@@ -29,7 +29,7 @@ class InvalidRequestError(InvalidRequestError): # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class RateLimitError(RateLimitError): # type: ignore
+class RateLimitError(RateLimitError):  # type: ignore
     def __init__(self, message, llm_provider):
         self.status_code = 429
         self.message = message
@@ -39,7 +39,7 @@ class RateLimitError(RateLimitError): # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class ServiceUnavailableError(ServiceUnavailableError): # type: ignore
+class ServiceUnavailableError(ServiceUnavailableError):  # type: ignore
     def __init__(self, message, llm_provider):
         self.status_code = 500
         self.message = message
@@ -49,7 +49,7 @@ class ServiceUnavailableError(ServiceUnavailableError): # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class OpenAIError(OpenAIError): # type: ignore
+class OpenAIError(OpenAIError):  # type: ignore
     def __init__(self, original_exception):
         self.status_code = original_exception.http_status
         super().__init__(

litellm/llms/anthropic.py

@@ -141,5 +141,7 @@ class AnthropicLLM:
             }
             return model_response
 
-    def embedding(self):  # logic for parsing in - calling - parsing out model embedding calls
+    def embedding(
+        self,
+    ):  # logic for parsing in - calling - parsing out model embedding calls
         pass

litellm/llms/base.py

@@ -5,8 +5,12 @@ class BaseLLM:
     def validate_environment(self):  # set up the environment required to run the model
         pass
 
-    def completion(self):  # logic for parsing in - calling - parsing out model completion calls
+    def completion(
+        self,
+    ):  # logic for parsing in - calling - parsing out model completion calls
         pass
 
-    def embedding(self):  # logic for parsing in - calling - parsing out model embedding calls
+    def embedding(
+        self,
+    ):  # logic for parsing in - calling - parsing out model embedding calls
         pass

litellm/llms/huggingface_restapi.py

@@ -45,10 +45,11 @@ class HuggingfaceRestAPILLM:
         litellm_params=None,
         logger_fn=None,
     ):  # logic for parsing in - calling - parsing out model completion calls
+        completion_url: str = ""
         if custom_api_base:
-            completion_url: Optional[str] = custom_api_base
+            completion_url = custom_api_base
         elif "HF_API_BASE" in os.environ:
-            completion_url = os.getenv("HF_API_BASE")
+            completion_url = os.getenv("HF_API_BASE", "")
         else:
             completion_url = f"https://api-inference.huggingface.co/models/{model}"
         prompt = ""
@@ -137,5 +138,7 @@ class HuggingfaceRestAPILLM:
             return model_response
         pass
 
-    def embedding(self):  # logic for parsing in - calling - parsing out model embedding calls
+    def embedding(
+        self,
+    ):  # logic for parsing in - calling - parsing out model embedding calls
         pass

litellm/main.py

@@ -4,7 +4,7 @@ from functools import partial
 import dotenv, traceback, random, asyncio, time
 from copy import deepcopy
 import litellm
-from litellm import (
+from litellm import (  # type: ignore
     client,
     logging,
     exception_type,
@@ -55,7 +55,7 @@ async def acompletion(*args, **kwargs):
 
 @client
 # @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(2), reraise=True, retry_error_callback=lambda retry_state: setattr(retry_state.outcome, 'retry_variable', litellm.retry)) # retry call, turn this off by setting `litellm.retry = False`
-@timeout(
+@timeout(  # type: ignore
     600
 )  ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
 def completion(
@@ -266,7 +266,7 @@ def completion(
                     or litellm.replicate_key
                 )
                 # set replicate key
-                os.environ["REPLICATE_API_TOKEN"]: str = replicate_key
+                os.environ["REPLICATE_API_TOKEN"] = str(replicate_key)
             prompt = " ".join([message["content"] for message in messages])
             input = {"prompt": prompt}
             if "max_tokens" in optional_params:
@@ -807,7 +807,7 @@ def batch_completion(*args, **kwargs):
 
 ### EMBEDDING ENDPOINTS ####################
 @client
-@timeout(
+@timeout(  # type: ignore
     60
 )  ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
 def embedding(model, input=[], azure=False, force_timeout=60, logger_fn=None):

litellm/tests/test_api_key_param.py

@@ -21,8 +21,8 @@ user_message = "Hello, how are you?"
 messages = [{"content": user_message, "role": "user"}]
 
 ## Test 1: Setting key dynamically
-temp_key = os.environ.get("ANTHROPIC_API_KEY")
-os.environ["ANTHROPIC_API_KEY"]: str = "bad-key"
+temp_key = os.environ.get("ANTHROPIC_API_KEY", "")
+os.environ["ANTHROPIC_API_KEY"] = "bad-key"
 # test on openai completion call
 try:
     response = completion(
@@ -39,7 +39,7 @@ os.environ["ANTHROPIC_API_KEY"] = temp_key
 
 
 ## Test 2: Setting key via __init__ params
-litellm.anthropic_key: str = os.environ.get("ANTHROPIC_API_KEY")
+litellm.anthropic_key = os.environ.get("ANTHROPIC_API_KEY", "")
 os.environ.pop("ANTHROPIC_API_KEY")
 # test on openai completion call
 try:

litellm/tests/test_bad_params.py

@@ -50,4 +50,4 @@ try:
 except:
     print(f"error occurred: {traceback.format_exc()}")
     pass
-os.environ["OPENAI_API_KEY"] = temp_key
+os.environ["OPENAI_API_KEY"] = str(temp_key)