feat(router.py): support async image generation on router

litellm/router.py

@@ -7,7 +7,7 @@
 #
 #  Thank you ! We ❤️ you! - Krrish & Ishaan 
 
-import copy
+import copy, httpx
 from datetime import datetime
 from typing import Dict, List, Optional, Union, Literal, Any
 import random, threading, time, traceback, uuid
@@ -18,6 +18,7 @@ import inspect, concurrent
 from openai import AsyncOpenAI
 from collections import defaultdict
 from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
+from litellm.llms.custom_httpx.azure_dall_e_2 import CustomHTTPTransport
 import copy
 class Router:
     """
@@ -166,7 +167,7 @@ class Router:
         self.print_verbose(f"Intialized router with Routing strategy: {self.routing_strategy}\n")
 
     
-    ### COMPLETION + EMBEDDING FUNCTIONS
+    ### COMPLETION, EMBEDDING, IMG GENERATION FUNCTIONS
 
     def completion(self,
                    model: str,
@@ -260,6 +261,50 @@ class Router:
                 self.fail_calls[model_name] +=1
             raise e
        
+    async def aimage_generation(self,
+                               prompt: str, 
+                               model: str,
+                               **kwargs): 
+        try: 
+            kwargs["model"] = model
+            kwargs["prompt"] = prompt
+            kwargs["original_function"] = self._image_generation
+            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+            timeout = kwargs.get("request_timeout", self.timeout)
+            kwargs.setdefault("metadata", {}).update({"model_group": model})
+            response = await self.async_function_with_fallbacks(**kwargs)
+
+            return response
+        except Exception as e: 
+            raise e
+    
+    async def _image_generation(self, 
+                                prompt: str, 
+                                model: str,
+                                **kwargs): 
+        try: 
+            self.print_verbose(f"Inside _image_generation()- model: {model}; kwargs: {kwargs}")
+            deployment = self.get_available_deployment(model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None))
+            kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]})
+            kwargs["model_info"] = deployment.get("model_info", {})
+            data = deployment["litellm_params"].copy()
+            model_name = data["model"]
+            for k, v in self.default_litellm_params.items(): 
+                if k not in kwargs: # prioritize model-specific params > default router params 
+                    kwargs[k] = v
+                elif k == "metadata":
+                    kwargs[k].update(v)
+
+            model_client = self._get_client(deployment=deployment, kwargs=kwargs, client_type="async")
+            self.total_calls[model_name] +=1
+            response = await litellm.aimage_generation(**{**data, "prompt": prompt, "caching": self.cache_responses, "client": model_client, **kwargs})
+            self.success_calls[model_name] +=1
+            return response
+        except Exception as e: 
+            if model_name is not None:
+                self.fail_calls[model_name] +=1
+            raise e
+
     def text_completion(self,
                         model: str,
                         prompt: str,
@@ -1009,14 +1054,16 @@ class Router:
                             azure_endpoint=api_base,
                             api_version=api_version,
                             timeout=timeout,
-                            max_retries=max_retries
+                            max_retries=max_retries,
+                            http_client=httpx.Client(transport=CustomHTTPTransport(),) # type: ignore
                         )
                         model["client"] = openai.AzureOpenAI(
                             api_key=api_key,
                             azure_endpoint=api_base,
                             api_version=api_version,
                             timeout=timeout,
-                            max_retries=max_retries
+                            max_retries=max_retries,
+                            http_client=httpx.Client(transport=CustomHTTPTransport(),) # type: ignore
                         )
                         # streaming clients should have diff timeouts
                         model["stream_async_client"] = openai.AsyncAzureOpenAI(
@@ -1024,7 +1071,7 @@ class Router:
                             azure_endpoint=api_base,
                             api_version=api_version,
                             timeout=stream_timeout,
-                            max_retries=max_retries
+                            max_retries=max_retries,
                         )
 
                         model["stream_client"] = openai.AzureOpenAI(