fix: fixing mypy linting errors and being backwards compatible for azure=true flag

litellm/llms/anthropic.py

@@ -26,7 +26,7 @@ class AnthropicConfig():
     to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
     """
     max_tokens_to_sample: Optional[int]=256 # anthropic requires a default 
-    stop_sequences: Optional[list[str]]=None
+    stop_sequences: Optional[list]=None
     temperature: Optional[int]=None
     top_p: Optional[int]=None
     top_k: Optional[int]=None
@@ -34,7 +34,7 @@ class AnthropicConfig():
 
     def __init__(self, 
                  max_tokens_to_sample: Optional[int]=256, # anthropic requires a default 
-                 stop_sequences: Optional[list[str]]=None,
+                 stop_sequences: Optional[list]=None,
                  temperature: Optional[int]=None,
                  top_p: Optional[int]=None,
                  top_k: Optional[int]=None,

litellm/main.py

@@ -223,7 +223,7 @@ def completion(
     fallbacks = kwargs.get('fallbacks', [])
     ######## end of unpacking kwargs ###########
     openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout"]
-    litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "metadata", "fallbacks"]
+    litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "metadata", "fallbacks", "azure"]
     default_params = openai_params + litellm_params
     non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
     if mock_response:
@@ -239,7 +239,9 @@ def completion(
             ]  # update the model to the actual value if an alias has been passed in
         model_response = ModelResponse()
 
-        if deployment_id != None: # azure llms
+        if kwargs['azure'] == True: # don't remove flag check, to remain backwards compatible for repos like Codium
+            custom_llm_provider="azure"
+        if deployment_id != None: # azure llms 
                 model=deployment_id
                 custom_llm_provider="azure"
         model, custom_llm_provider = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider)

litellm/tests/test_completion.py

@@ -365,7 +365,7 @@ def test_completion_openai():
         litellm.api_key = None
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-test_completion_openai()
+# test_completion_openai()
 
 
 def test_completion_openai_prompt():
@@ -570,17 +570,25 @@ def test_completion_openai_with_more_optional_params():
 
 def test_completion_azure():
     try:
-        print("azure gpt-3.5 test\n\n")
+        litellm.set_verbose=True
+        ## Test azure call
         response = completion(
             model="azure/chatgpt-v-2",
             messages=messages,
+            azure=True
+        )
+        ## Test azure flag for backwards compatibility
+        response = completion(
+            model="chatgpt-v-2",
+            messages=messages,
+            azure=True
         )
         # Add any assertions here to check the response
         print(response)
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
-# test_completion_azure()
+test_completion_azure()
 
 # new azure test for using litellm. vars, 
 # use the following vars in this test and make an azure_api_call