Litellm remove circular imports (#7232)

* fix(utils.py): initial commit to remove circular imports - moves llmproviders to utils.py

* fix(router.py): fix 'litellm.EmbeddingResponse' import from router.py

'

* refactor: fix litellm.ModelResponse import on pass through endpoints

* refactor(litellm_logging.py): fix circular import for custom callbacks literal

* fix(factory.py): fix circular imports inside prompt factory

* fix(cost_calculator.py): fix circular import for 'litellm.Usage'

* fix(proxy_server.py): fix potential circular import with `litellm.Router'

* fix(proxy/utils.py): fix potential circular import in `litellm.Router`

* fix: remove circular imports in 'auth_checks' and 'guardrails/'

* fix(prompt_injection_detection.py): fix router impor t

* fix(vertex_passthrough_logging_handler.py): fix potential circular imports in vertex pass through

* fix(anthropic_pass_through_logging_handler.py): fix potential circular imports

* fix(slack_alerting.py-+-ollama_chat.py): fix modelresponse import

* fix(base.py): fix potential circular import

* fix(handler.py): fix potential circular ref in codestral + cohere handler's

* fix(azure.py): fix potential circular imports

* fix(gpt_transformation.py): fix modelresponse import

* fix(litellm_logging.py): add logging base class - simplify typing

makes it easy for other files to type check the logging obj without introducing circular imports

* fix(azure_ai/embed): fix potential circular import on handler.py

* fix(databricks/): fix potential circular imports in databricks/

* fix(vertex_ai/): fix potential circular imports on vertex ai embeddings

* fix(vertex_ai/image_gen): fix import

* fix(watsonx-+-bedrock): cleanup imports

* refactor(anthropic-pass-through-+-petals): cleanup imports

* refactor(huggingface/): cleanup imports

* fix(ollama-+-clarifai): cleanup circular imports

* fix(openai_like/): fix impor t

* fix(openai_like/): fix embedding handler

cleanup imports

* refactor(openai.py): cleanup imports

* fix(sagemaker/transformation.py): fix import

* ci(config.yml): add circular import test to ci/cd

litellm/llms/huggingface/chat/handler.py

@@ -24,6 +24,7 @@ import requests
 
 import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     HTTPHandler,
@@ -36,8 +37,9 @@ from litellm.llms.huggingface.chat.transformation import (
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.completion import ChatCompletionMessageToolCallParam
 from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import EmbeddingResponse
 from litellm.types.utils import Logprobs as TextCompletionLogprobs
-from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
+from litellm.types.utils import ModelResponse, Usage
 
 from ...base import BaseLLM
 from ..common_utils import HuggingfaceError, hf_task_list, hf_tasks
@@ -453,11 +455,11 @@ class Huggingface(BaseLLM):
     def _process_embedding_response(
         self,
         embeddings: dict,
-        model_response: litellm.EmbeddingResponse,
+        model_response: EmbeddingResponse,
         model: str,
         input: List,
         encoding: Any,
-    ) -> litellm.EmbeddingResponse:
+    ) -> EmbeddingResponse:
         output_data = []
         if "similarities" in embeddings:
             for idx, embedding in embeddings["similarities"]:
@@ -583,7 +585,7 @@ class Huggingface(BaseLLM):
         self,
         model: str,
         input: list,
-        model_response: litellm.EmbeddingResponse,
+        model_response: EmbeddingResponse,
         optional_params: dict,
         logging_obj: LiteLLMLoggingObj,
         encoding: Callable,
@@ -593,7 +595,7 @@ class Huggingface(BaseLLM):
         aembedding: Optional[bool] = None,
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
         headers={},
-    ) -> litellm.EmbeddingResponse:
+    ) -> EmbeddingResponse:
         super().embedding()
         headers = hf_chat_config.validate_environment(
             api_key=api_key,