diff --git a/litellm/tests/test_exceptions.py b/litellm/tests/test_exceptions.py
index 526e85289..be1b44a34 100644
--- a/litellm/tests/test_exceptions.py
+++ b/litellm/tests/test_exceptions.py
@@ -1,12 +1,12 @@
-#### What this tests ####
-#    This tests exception mapping -> trigger an exception from an llm provider -> assert if output is of the expected type
+# #### What this tests ####
+# #    This tests exception mapping -> trigger an exception from an llm provider -> assert if output is of the expected type
 
 
-# 5 providers -> OpenAI, Azure, Anthropic, Cohere, Replicate
+# # 5 providers -> OpenAI, Azure, Anthropic, Cohere, Replicate
 
-# 3 main types of exceptions -> - Rate Limit Errors, Context Window Errors, Auth errors (incorrect/rotated key, etc.)
+# # 3 main types of exceptions -> - Rate Limit Errors, Context Window Errors, Auth errors (incorrect/rotated key, etc.)
 
-# Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered
+# # Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered
 
 from openai.error import AuthenticationError, InvalidRequestError, RateLimitError, OpenAIError
 import os 
@@ -19,112 +19,112 @@ from concurrent.futures import ThreadPoolExecutor
 
 models = ["gpt-3.5-turbo", "chatgpt-test", "claude-instant-1", "command-nightly", "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1"]
 
-# Test 1: Rate Limit Errors 
-def test_model(model):
-    try: 
-        sample_text = "how does a court case get to the Supreme Court?" * 50000
-        messages = [{ "content": sample_text,"role": "user"}]
-        azure = False
-        if model == "chatgpt-test":
-            azure = True
-        print(f"model: {model}")
-        response = completion(model=model, messages=messages, azure=azure)
-    except RateLimitError:
-        return True
-    except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
-        return True
-    except Exception as e:
-        print(f"Uncaught Exception {model}: {type(e).__name__} - {e}")
-        pass
-    return False
+# # Test 1: Rate Limit Errors 
+# def test_model(model):
+#     try: 
+#         sample_text = "how does a court case get to the Supreme Court?" * 50000
+#         messages = [{ "content": sample_text,"role": "user"}]
+#         azure = False
+#         if model == "chatgpt-test":
+#             azure = True
+#         print(f"model: {model}")
+#         response = completion(model=model, messages=messages, azure=azure)
+#     except RateLimitError:
+#         return True
+#     except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
+#         return True
+#     except Exception as e:
+#         print(f"Uncaught Exception {model}: {type(e).__name__} - {e}")
+#         pass
+#     return False
 
-# Repeat each model 500 times
-extended_models = [model for model in models for _ in range(250)]
+# # Repeat each model 500 times
+# extended_models = [model for model in models for _ in range(250)]
 
-def worker(model):
-    return test_model(model)
+# def worker(model):
+#     return test_model(model)
 
-# Create a dictionary to store the results
-counts = {True: 0, False: 0}
+# # Create a dictionary to store the results
+# counts = {True: 0, False: 0}
 
-# Use Thread Pool Executor
-with ThreadPoolExecutor(max_workers=500) as executor:
-    # Use map to start the operation in thread pool
-    results = executor.map(worker, extended_models)
+# # Use Thread Pool Executor
+# with ThreadPoolExecutor(max_workers=500) as executor:
+#     # Use map to start the operation in thread pool
+#     results = executor.map(worker, extended_models)
 
-    # Iterate over results and count True/False
-    for result in results:
-        counts[result] += 1
+#     # Iterate over results and count True/False
+#     for result in results:
+#         counts[result] += 1
 
-accuracy_score = counts[True]/(counts[True] + counts[False])
-print(f"accuracy_score: {accuracy_score}")
+# accuracy_score = counts[True]/(counts[True] + counts[False])
+# print(f"accuracy_score: {accuracy_score}")
 
-# Test 2: Context Window Errors 
-print("Testing Context Window Errors")
-def test_model(model): # pass extremely long input
-    sample_text = "how does a court case get to the Supreme Court?" * 100000
-    messages = [{ "content": sample_text,"role": "user"}]
-    try: 
-        azure = False
-        if model == "chatgpt-test":
-            azure = True
-        print(f"model: {model}")
-        response = completion(model=model, messages=messages, azure=azure)
-    except InvalidRequestError:
-        return True
-    except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
-        return True
-    except Exception as e:
-        print(f"Error Type: {type(e).__name__}")
-        print(f"Uncaught Exception - {e}")
-        pass
-    return False
+# # Test 2: Context Window Errors 
+# print("Testing Context Window Errors")
+# def test_model(model): # pass extremely long input
+#     sample_text = "how does a court case get to the Supreme Court?" * 100000
+#     messages = [{ "content": sample_text,"role": "user"}]
+#     try: 
+#         azure = False
+#         if model == "chatgpt-test":
+#             azure = True
+#         print(f"model: {model}")
+#         response = completion(model=model, messages=messages, azure=azure)
+#     except InvalidRequestError:
+#         return True
+#     except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
+#         return True
+#     except Exception as e:
+#         print(f"Error Type: {type(e).__name__}")
+#         print(f"Uncaught Exception - {e}")
+#         pass
+#     return False
 
-## TEST SCORE
-true_val = 0
-for model in models: 
-    if test_model(model=model) == True:
-        true_val += 1
-accuracy_score = true_val/len(models)
-print(f"CTX WINDOW accuracy_score: {accuracy_score}")
+# ## TEST SCORE
+# true_val = 0
+# for model in models: 
+#     if test_model(model=model) == True:
+#         true_val += 1
+# accuracy_score = true_val/len(models)
+# print(f"CTX WINDOW accuracy_score: {accuracy_score}")
 
-# Test 3: InvalidAuth Errors
-def logger_fn(model_call_object: dict):
-    print(f"model call details: {model_call_object}")
+# # Test 3: InvalidAuth Errors
+# def logger_fn(model_call_object: dict):
+#     print(f"model call details: {model_call_object}")
 
 
-def test_model(model): # set the model key to an invalid key, depending on the model 
-    messages = [{ "content": "Hello, how are you?","role": "user"}]
-    try: 
-        azure = False
-        if model == "gpt-3.5-turbo":
-            os.environ["OPENAI_API_KEY"] = "bad-key"
-        elif model == "chatgpt-test":
-            os.environ["AZURE_API_KEY"] = "bad-key"
-            azure = True
-        elif model == "claude-instant-1":
-            os.environ["ANTHROPIC_API_KEY"] = "bad-key"
-        elif model == "command-nightly":
-            os.environ["COHERE_API_KEY"] = "bad-key"
-        elif model == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1":
-            os.environ["REPLICATE_API_KEY"] = "bad-key"
-            os.environ["REPLICATE_API_TOKEN"] = "bad-key"
-        print(f"model: {model}")
-        response = completion(model=model, messages=messages, azure=azure, logger_fn=logger_fn)
-        print(f"response: {response}")
-    except AuthenticationError as e:
-        return True
-    except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
-        return True
-    except Exception as e:
-        print(f"Uncaught Exception - {e}")
-        pass
-    return False
+# def test_model(model): # set the model key to an invalid key, depending on the model 
+#     messages = [{ "content": "Hello, how are you?","role": "user"}]
+#     try: 
+#         azure = False
+#         if model == "gpt-3.5-turbo":
+#             os.environ["OPENAI_API_KEY"] = "bad-key"
+#         elif model == "chatgpt-test":
+#             os.environ["AZURE_API_KEY"] = "bad-key"
+#             azure = True
+#         elif model == "claude-instant-1":
+#             os.environ["ANTHROPIC_API_KEY"] = "bad-key"
+#         elif model == "command-nightly":
+#             os.environ["COHERE_API_KEY"] = "bad-key"
+#         elif model == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1":
+#             os.environ["REPLICATE_API_KEY"] = "bad-key"
+#             os.environ["REPLICATE_API_TOKEN"] = "bad-key"
+#         print(f"model: {model}")
+#         response = completion(model=model, messages=messages, azure=azure, logger_fn=logger_fn)
+#         print(f"response: {response}")
+#     except AuthenticationError as e:
+#         return True
+#     except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
+#         return True
+#     except Exception as e:
+#         print(f"Uncaught Exception - {e}")
+#         pass
+#     return False
 
-## TEST SCORE
-true_val = 0
-for model in models: 
-    if test_model(model=model) == True:
-        true_val += 1
-accuracy_score = true_val/len(models)
-print(f"INVALID AUTH accuracy_score: {accuracy_score}")
\ No newline at end of file
+# ## TEST SCORE
+# true_val = 0
+# for model in models: 
+#     if test_model(model=model) == True:
+#         true_val += 1
+# accuracy_score = true_val/len(models)
+# print(f"INVALID AUTH accuracy_score: {accuracy_score}")