Litellm merge pr (#7161)

* build: merge branch

* test: fix openai naming

* fix(main.py): fix openai renaming

* style: ignore function length for config factory

* fix(sagemaker/): fix routing logic

* fix: fix imports

* fix: fix override

tests/local_testing/test_completion.py

@@ -1606,30 +1606,33 @@ HF Tests we should pass
 #####################################################
 #####################################################
 # Test util to sort models to TGI, conv, None
+from litellm.llms.huggingface.chat.transformation import HuggingfaceChatConfig
+
+
 def test_get_hf_task_for_model():
     model = "glaiveai/glaive-coder-7b"
-    model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
+    model_type, _ = HuggingfaceChatConfig().get_hf_task_for_model(model)
     print(f"model:{model}, model type: {model_type}")
     assert model_type == "text-generation-inference"
 
     model = "meta-llama/Llama-2-7b-hf"
-    model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
+    model_type, _ = HuggingfaceChatConfig().get_hf_task_for_model(model)
     print(f"model:{model}, model type: {model_type}")
     assert model_type == "text-generation-inference"
 
     model = "facebook/blenderbot-400M-distill"
-    model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
+    model_type, _ = HuggingfaceChatConfig().get_hf_task_for_model(model)
     print(f"model:{model}, model type: {model_type}")
     assert model_type == "conversational"
 
     model = "facebook/blenderbot-3B"
-    model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
+    model_type, _ = HuggingfaceChatConfig().get_hf_task_for_model(model)
     print(f"model:{model}, model type: {model_type}")
     assert model_type == "conversational"
 
     # neither Conv or None
     model = "roneneldan/TinyStories-3M"
-    model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
+    model_type, _ = HuggingfaceChatConfig().get_hf_task_for_model(model)
     print(f"model:{model}, model type: {model_type}")
     assert model_type == "text-generation"
 
@@ -1717,14 +1720,17 @@ def tgi_mock_post(url, **kwargs):
 def test_hf_test_completion_tgi():
     litellm.set_verbose = True
     try:
+        client = HTTPHandler()
 
-        with patch("requests.post", side_effect=tgi_mock_post) as mock_client:
+        with patch.object(client, "post", side_effect=tgi_mock_post) as mock_client:
             response = completion(
                 model="huggingface/HuggingFaceH4/zephyr-7b-beta",
                 messages=[{"content": "Hello, how are you?", "role": "user"}],
                 max_tokens=10,
                 wait_for_model=True,
+                client=client,
             )
+            mock_client.assert_called_once()
             # Add any assertions-here to check the response
             print(response)
             assert "options" in mock_client.call_args.kwargs["data"]
@@ -1862,13 +1868,15 @@ def mock_post(url, **kwargs):
 
 def test_hf_classifier_task():
     try:
-        with patch("requests.post", side_effect=mock_post):
+        client = HTTPHandler()
+        with patch.object(client, "post", side_effect=mock_post):
             litellm.set_verbose = True
             user_message = "I like you. I love you"
             messages = [{"content": user_message, "role": "user"}]
             response = completion(
                 model="huggingface/text-classification/shahrukhx01/question-vs-statement-classifier",
                 messages=messages,
+                client=client,
             )
             print(f"response: {response}")
             assert isinstance(response, litellm.ModelResponse)
@@ -3096,19 +3104,20 @@ async def test_completion_replicate_llama3(sync_mode):
             response = completion(
                 model=model_name,
                 messages=messages,
+                max_tokens=10,
             )
         else:
             response = await litellm.acompletion(
                 model=model_name,
                 messages=messages,
+                max_tokens=10,
             )
             print(f"ASYNC REPLICATE RESPONSE - {response}")
-        print(response)
+        print(f"REPLICATE RESPONSE - {response}")
         # Add any assertions here to check the response
         assert isinstance(response, litellm.ModelResponse)
+        assert len(response.choices[0].message.content.strip()) > 0
         response_format_tests(response=response)
-    except litellm.APIError as e:
-        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -3745,22 +3754,6 @@ def test_mistral_anyscale_stream():
 #         pytest.fail(f"Error occurred: {e}")
 
 
-#### Test A121 ###################
-@pytest.mark.skip(reason="Local test")
-def test_completion_ai21():
-    print("running ai21 j2light test")
-    litellm.set_verbose = True
-    model_name = "j2-light"
-    try:
-        response = completion(
-            model=model_name, messages=messages, max_tokens=100, temperature=0.8
-        )
-        # Add any assertions here to check the response
-        print(response)
-    except Exception as e:
-        pytest.fail(f"Error occurred: {e}")
-
-
 # test_completion_ai21()
 # test_completion_ai21()
 ## test deep infra