style(test_completion.py): fix merge conflict

litellm/llms/sagemaker.py

@@ -1,9 +1,10 @@
-import os
-import json
+import os, types
 from enum import Enum
+import json
 import requests
 import time
-from typing import Callable
+from typing import Callable, Optional
+import litellm
 from litellm.utils import ModelResponse, get_secret
 import sys
 from copy import deepcopy
@@ -16,6 +17,32 @@ class SagemakerError(Exception):
             self.message
         )  # Call the base class constructor with the parameters it needs
 
+class SagemakerConfig(): 
+    """
+    Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb
+    """
+    max_new_tokens: Optional[int]=None
+    top_p: Optional[float]=None
+    temperature: Optional[float]=None
+    return_full_text: Optional[bool]=None
+
+    def __init__(self,
+                 max_new_tokens: Optional[int]=None,
+                 top_p: Optional[float]=None,
+                 temperature: Optional[float]=None,
+                 return_full_text: Optional[bool]=None) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != 'self' and value is not None:
+                setattr(self.__class__, key, value)
+   
+    @classmethod
+    def get_config(cls):
+        return {k: v for k, v in cls.__dict__.items() 
+                if not k.startswith('__') 
+                and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) 
+                and v is not None}
+        
 """
 SAGEMAKER AUTH Keys/Vars
 os.environ['AWS_ACCESS_KEY_ID'] = ""
@@ -47,6 +74,16 @@ def completion(
         region_name=region_name
     )
 
+    # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
+    inference_params = deepcopy(optional_params)
+    inference_params.pop("stream", None)
+
+    ## Load Config
+    config = litellm.SagemakerConfig.get_config() 
+    for k, v in config.items(): 
+        if k not in inference_params: # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
+            inference_params[k] = v
+
     model = model
     prompt = ""
     for message in messages:
@@ -61,9 +98,7 @@ def completion(
                 )
         else:
             prompt += f"{message['content']}"
-    # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
-    inference_params = deepcopy(optional_params)
-    inference_params.pop("stream", None)
+
     data = {
         "inputs": prompt,
         "parameters": inference_params