fix: linting fixes

litellm/litellm_core_utils/litellm_logging.py

@@ -426,13 +426,22 @@ def post_call(
             self.model_call_details["additional_args"] = additional_args
             self.model_call_details["log_event_type"] = "post_api_call"
 
-            verbose_logger.debug(
-                "RAW RESPONSE:\n{}\n\n".format(
-                    self.model_call_details.get(
-                        "original_response", self.model_call_details
+            if json_logs:
+                verbose_logger.debug(
+                    "RAW RESPONSE:\n{}\n\n".format(
+                        self.model_call_details.get(
+                            "original_response", self.model_call_details
+                        )
+                    ),
+                )
+            else:
+                print_verbose(
+                    "RAW RESPONSE:\n{}\n\n".format(
+                        self.model_call_details.get(
+                            "original_response", self.model_call_details
+                        )
                     )
-                ),
-            )
+                )
             if self.logger_fn and callable(self.logger_fn):
                 try:
                     self.logger_fn(

litellm/llms/anthropic.py

@@ -601,13 +601,16 @@ def completion(
             optional_params["tools"] = anthropic_tools
 
         stream = optional_params.pop("stream", None)
+        is_vertex_request: bool = optional_params.pop("is_vertex_request", False)
 
         data = {
-            "model": model,
             "messages": messages,
             **optional_params,
         }
 
+        if is_vertex_request is False:
+            data["model"] = model
+
         ## LOGGING
         logging_obj.pre_call(
             input=messages,

litellm/llms/vertex_ai_anthropic.py

@@ -15,6 +15,7 @@
 import litellm
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.anthropic import AnthropicMessagesToolChoice
 from litellm.types.utils import ResponseFormatChunk
 from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
 
@@ -121,6 +122,17 @@ def map_openai_params(self, non_default_params: dict, optional_params: dict):
                 optional_params["max_tokens"] = value
             if param == "tools":
                 optional_params["tools"] = value
+            if param == "tool_choice":
+                _tool_choice: Optional[AnthropicMessagesToolChoice] = None
+                if value == "auto":
+                    _tool_choice = {"type": "auto"}
+                elif value == "required":
+                    _tool_choice = {"type": "any"}
+                elif isinstance(value, dict):
+                    _tool_choice = {"type": "tool", "name": value["function"]["name"]}
+
+                if _tool_choice is not None:
+                    optional_params["tool_choice"] = _tool_choice
             if param == "stream":
                 optional_params["stream"] = value
             if param == "stop":
@@ -177,17 +189,29 @@ def get_vertex_client(
         _credentials, cred_project_id = VertexLLM().load_auth(
             credentials=vertex_credentials, project_id=vertex_project
         )
+
         vertex_ai_client = AnthropicVertex(
             project_id=vertex_project or cred_project_id,
             region=vertex_location or "us-central1",
             access_token=_credentials.token,
         )
+        access_token = _credentials.token
     else:
         vertex_ai_client = client
+        access_token = client.access_token
 
     return vertex_ai_client, access_token
 
 
+def create_vertex_anthropic_url(
+    vertex_location: str, vertex_project: str, model: str, stream: bool
+) -> str:
+    if stream is True:
+        return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:streamRawPredict"
+    else:
+        return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:rawPredict"
+
+
 def completion(
     model: str,
     messages: list,
@@ -196,6 +220,8 @@ def completion(
     encoding,
     logging_obj,
     optional_params: dict,
+    custom_prompt_dict: dict,
+    headers: Optional[dict],
     vertex_project=None,
     vertex_location=None,
     vertex_credentials=None,
@@ -207,6 +233,9 @@ def completion(
     try:
         import vertexai
         from anthropic import AnthropicVertex
+
+        from litellm.llms.anthropic import AnthropicChatCompletion
+        from litellm.llms.vertex_httpx import VertexLLM
     except:
         raise VertexAIError(
             status_code=400,
@@ -222,13 +251,14 @@ def completion(
         )
     try:
 
-        vertex_ai_client, access_token = get_vertex_client(
-            client=client,
-            vertex_project=vertex_project,
-            vertex_location=vertex_location,
-            vertex_credentials=vertex_credentials,
+        vertex_httpx_logic = VertexLLM()
+
+        access_token, project_id = vertex_httpx_logic._ensure_access_token(
+            credentials=vertex_credentials, project_id=vertex_project
         )
 
+        anthropic_chat_completions = AnthropicChatCompletion()
+
         ## Load Config
         config = litellm.VertexAIAnthropicConfig.get_config()
         for k, v in config.items():

litellm/main.py

@@ -2008,6 +2008,8 @@ def completion(
                     vertex_credentials=vertex_credentials,
                     logging_obj=logging,
                     acompletion=acompletion,
+                    headers=headers,
+                    custom_prompt_dict=custom_prompt_dict,
                 )
             else:
                 model_response = vertex_ai.completion(

litellm/tests/test_amazing_vertex_completion.py

@@ -640,11 +640,13 @@ def test_gemini_pro_vision_base64():
             pytest.fail(f"An exception occurred - {str(e)}")
 
 
-@pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
-@pytest.mark.parametrize("provider", ["vertex_ai_beta"])  # "vertex_ai",
+# @pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
+@pytest.mark.parametrize(
+    "model", ["vertex_ai_beta/gemini-1.5-pro", "vertex_ai/claude-3-sonnet@20240229"]
+)  # "vertex_ai",
 @pytest.mark.parametrize("sync_mode", [True])  # "vertex_ai",
 @pytest.mark.asyncio
-async def test_gemini_pro_function_calling_httpx(provider, sync_mode):
+async def test_gemini_pro_function_calling_httpx(model, sync_mode):
     try:
         load_vertex_ai_credentials()
         litellm.set_verbose = True
@@ -682,7 +684,7 @@ async def test_gemini_pro_function_calling_httpx(provider, sync_mode):
         ]
 
         data = {
-            "model": "{}/gemini-1.5-pro".format(provider),
+            "model": model,
             "messages": messages,
             "tools": tools,
             "tool_choice": "required",