[BETA] Add OpenAI /images/variations + Topaz API support (#7700)

* feat(main.py): initial commit for `/image/variations` endpoint support

* refactor(base_llm/): introduce new base llm base config for image variation endpoints

* refactor(openai/image_variations/transformation.py): implement openai image variation transformation handler

* fix: test

* feat(openai/): working openai `/image/variation` endpoint calls via sdk

* feat(topaz/): topaz sync image variation call support

Addresses https://github.com/BerriAI/litellm/issues/7593

'

* fix(topaz/transformation.py): fix linting errors

* fix(openai/image_variations/handler.py): fix passing json data

* fix(main.py): image_variation/

support async image variation route - `aimage_variation`

* fix(test_get_model_info.py): fix test

* fix: cleanup unused imports

* feat(openai/): add async `/image/variations` endpoint support

* feat(topaz/): support async `/image/variations` calls

* fix: test

* fix(utils.py): fix get_model_info_helper for no model info w/ provider config

handles situation where model info is not known but provider config exists

* test(test_router_fallbacks.py): mark flaky test

* fix: fix unused imports

* test: bump otel load test perf threshold - accounts for current load tests hitting same server

litellm/utils.py

@@ -181,6 +181,9 @@ from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
 from litellm.llms.base_llm.chat.transformation import BaseConfig
 from litellm.llms.base_llm.completion.transformation import BaseTextCompletionConfig
 from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
+from litellm.llms.base_llm.image_variations.transformation import (
+    BaseImageVariationConfig,
+)
 from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
 
 from ._logging import _is_debugging_on, verbose_logger
@@ -924,6 +927,8 @@ def client(original_function):  # noqa: PLR0915
                 return result
             elif "aspeech" in kwargs and kwargs["aspeech"] is True:
                 return result
+            elif asyncio.iscoroutine(result):  # bubble up to relevant async function
+                return result
 
             ### POST-CALL RULES ###
             post_call_processing(
@@ -1954,7 +1959,6 @@ def register_model(model_cost: Union[str, dict]):  # noqa: PLR0915
         ## override / add new keys to the existing model cost dictionary
         updated_dictionary = _update_dictionary(existing_model, value)
         litellm.model_cost.setdefault(model_cost_key, {}).update(updated_dictionary)
-        verbose_logger.debug(f"{model_cost_key} added to model cost map")
         # add new model names to provider lists
         if value.get("litellm_provider") == "openai":
             if key not in litellm.open_ai_chat_completion_models:
@@ -2036,7 +2040,9 @@ def get_litellm_params(
     drop_params: Optional[bool] = None,
     prompt_id: Optional[str] = None,
     prompt_variables: Optional[dict] = None,
-):
+    async_call: Optional[bool] = None,
+    **kwargs,
+) -> dict:
     litellm_params = {
         "acompletion": acompletion,
         "api_key": api_key,
@@ -2072,6 +2078,7 @@ def get_litellm_params(
         "drop_params": drop_params,
         "prompt_id": prompt_id,
         "prompt_variables": prompt_variables,
+        "async_call": async_call,
     }
     return litellm_params
 
@@ -4123,8 +4130,7 @@ def _get_model_info_helper(  # noqa: PLR0915
                         model=model, existing_model_info=_model_info
                     ),
                 )
-                if key is None:
-                    key = "provider_specific_model_info"
+                key = "provider_specific_model_info"
             if _model_info is None or key is None:
                 raise ValueError(
                     "This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
@@ -4230,6 +4236,7 @@ def _get_model_info_helper(  # noqa: PLR0915
                 rpm=_model_info.get("rpm", None),
             )
     except Exception as e:
+        verbose_logger.debug(f"Error getting model info: {e}")
         if "OllamaError" in str(e):
             raise e
         raise Exception(
@@ -6165,11 +6172,26 @@ class ProviderConfigManager:
     ) -> Optional[BaseLLMModelInfo]:
         if LlmProviders.FIREWORKS_AI == provider:
             return litellm.FireworksAIConfig()
+        elif LlmProviders.OPENAI == provider:
+            return litellm.OpenAIGPTConfig()
         elif LlmProviders.LITELLM_PROXY == provider:
             return litellm.LiteLLMProxyChatConfig()
+        elif LlmProviders.TOPAZ == provider:
+            return litellm.TopazModelInfo()
 
         return None
 
+    @staticmethod
+    def get_provider_image_variation_config(
+        model: str,
+        provider: LlmProviders,
+    ) -> Optional[BaseImageVariationConfig]:
+        if LlmProviders.OPENAI == provider:
+            return litellm.OpenAIImageVariationConfig()
+        elif LlmProviders.TOPAZ == provider:
+            return litellm.TopazImageVariationConfig()
+        return None
+
 
 def get_end_user_id_for_cost_tracking(
     litellm_params: dict,