linting fixes

litellm/__init__.py

@@ -5,7 +5,7 @@ input_callback: List[str] = []
 success_callback: List[str] = []
 failure_callback: List[str] = []
 set_verbose = False
-email = None # for hosted dashboard. Learn more - https://docs.litellm.ai/docs/debugging/hosted_debugging
+email = None  # for hosted dashboard. Learn more - https://docs.litellm.ai/docs/debugging/hosted_debugging
 telemetry = True
 max_tokens = 256  # OpenAI Defaults
 retry = True
@@ -261,7 +261,7 @@ from .utils import (
     get_litellm_params,
     Logging,
     acreate,
-    get_model_list
+    get_model_list,
 )
 from .main import *  # type: ignore
 from .integrations import *

litellm/integrations/litedebugger.py

@@ -4,6 +4,7 @@ import requests, traceback, json, os
 class LiteDebugger:
     user_email = None
     dashboard_url = None
+
     def __init__(self, email=None):
         self.api_url = "https://api.litellm.ai/debugger"
         self.validate_environment(email)
@@ -12,7 +13,7 @@ class LiteDebugger:
     def validate_environment(self, email):
         try:
             self.user_email = os.getenv("LITELLM_EMAIL") or email
-            self.dashboard_url = 'https://admin.litellm.ai/' + self.user_email
+            self.dashboard_url = "https://admin.litellm.ai/" + self.user_email
             print(f"Here's your free Dashboard 👉 {self.dashboard_url}")
             if self.user_email == None:
                 raise Exception(

litellm/main.py

@@ -17,7 +17,7 @@ from litellm.utils import (
     install_and_import,
     CustomStreamWrapper,
     read_config_args,
-    completion_with_fallbacks
+    completion_with_fallbacks,
 )
 from .llms.anthropic import AnthropicLLM
 from .llms.huggingface_restapi import HuggingfaceRestAPILLM
@@ -187,7 +187,7 @@ def completion(
                 response = openai.ChatCompletion.create(
                     engine=model, messages=messages, **optional_params
                 )
-            
+
             ## LOGGING
             logging.post_call(
                 input=messages,

litellm/tests/test_completion.py

@@ -11,6 +11,7 @@ sys.path.insert(
 import pytest
 import litellm
 from litellm import embedding, completion
+
 litellm.debugger = True
 
 # from infisical import InfisicalClient
@@ -349,13 +350,10 @@ def test_petals():
 
 
 def test_completion_with_fallbacks():
-    fallbacks = ['gpt-3.5-turb', 'gpt-3.5-turbo', 'command-nightly']
+    fallbacks = ["gpt-3.5-turb", "gpt-3.5-turbo", "command-nightly"]
     try:
         response = completion(
-            model='bad-model',
+            model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks
-            messages=messages,
-            force_timeout=120,
-            fallbacks=fallbacks
         )
         # Add any assertions here to check the response
         print(response)

litellm/tests/test_get_model_list.py

@@ -8,4 +8,4 @@ from litellm import get_model_list
 
 print(get_model_list())
 print(get_model_list())
-# print(litellm.model_list)
+# print(litellm.model_list)

litellm/utils.py

@@ -288,7 +288,9 @@ def client(original_function):
     ):  # just run once to check if user wants to send their data anywhere - PostHog/Sentry/Slack/etc.
         try:
             global callback_list, add_breadcrumb, user_logger_fn
-            if litellm.email or os.getenv("LITELLM_EMAIL", None) != None: # add to input, success and failure callbacks if user sets debugging to true
+            if (
+                litellm.debugger or os.getenv("LITELLM_EMAIL", None) != None
+            ):  # add to input, success and failure callbacks if user sets debugging to true
                 litellm.input_callback.append("lite_debugger")
                 litellm.success_callback.append("lite_debugger")
                 litellm.failure_callback.append("lite_debugger")
@@ -1020,35 +1022,44 @@ def handle_success(args, kwargs, result, start_time, end_time):
         )
         pass
 
+
 def get_model_list():
     global last_fetched_at
     # if user is using hosted product -> get their updated model list - refresh every 5 minutes
-    user_email = (os.getenv("LITELLM_EMAIL") or litellm.email)
+    user_email = os.getenv("LITELLM_EMAIL") or litellm.email
     if user_email:
         time_delta = 0
         if last_fetched_at != None:
-            current_time = time.time() 
+            current_time = time.time()
             time_delta = current_time - last_fetched_at
         if time_delta > 300 or last_fetched_at == None:
-            # make the api call 
+            # make the api call
             last_fetched_at = time.time()
             print(f"last_fetched_at: {last_fetched_at}")
-            response = requests.get(url="http://api.litellm.ai/get_model_list", headers={"content-type": "application/json"}, data=json.dumps({"user_email": user_email}))
+            response = requests.get(
+                url="http://api.litellm.ai/get_model_list",
+                headers={"content-type": "application/json"},
+                data=json.dumps({"user_email": user_email}),
+            )
             print_verbose(f"get_model_list response: {response.text}")
             data = response.json()
             # update model list
             model_list = data["model_list"]
-            # set environment variables 
+            # set environment variables
             env_dict = data["model_keys"]
             for key, value in env_dict.items():
                 os.environ[key] = value
-            litellm.model_list = model_list # update the user's current litellm model list 
+            litellm.model_list = (
+                model_list  # update the user's current litellm model list
+            )
     # return litellm model list by default
     return litellm.model_list
 
-def acreate(*args, **kwargs): ## Thin client to handle the acreate langchain call
+
+def acreate(*args, **kwargs):  ## Thin client to handle the acreate langchain call
     return litellm.acompletion(*args, **kwargs)
 
+
 def prompt_token_calculator(model, messages):
     # use tiktoken or anthropic's tokenizer depending on the model
     text = " ".join(message["content"] for message in messages)
@@ -1063,6 +1074,7 @@ def prompt_token_calculator(model, messages):
         num_tokens = len(encoding.encode(text))
     return num_tokens
 
+
 def valid_model(model):
     try:
         # for a given model name, check if the user has the right permissions to access the model
@@ -1471,22 +1483,29 @@ def completion_with_fallbacks(**kwargs):
     rate_limited_models = set()
     model_expiration_times = {}
     start_time = time.time()
-    fallbacks = [kwargs['model']] + kwargs['fallbacks']
+    fallbacks = [kwargs["model"]] + kwargs["fallbacks"]
-    del kwargs['fallbacks'] # remove fallbacks so it's not recursive
+    del kwargs["fallbacks"]  # remove fallbacks so it's not recursive
 
     while response == None and time.time() - start_time < 45:
         for model in fallbacks:
-        # loop thru all models
+            # loop thru all models
             try:
-                if model in rate_limited_models: # check if model is currently cooling down
+                if (
-                    if model_expiration_times.get(model) and time.time() >= model_expiration_times[model]:
+                    model in rate_limited_models
-                        rate_limited_models.remove(model) # check if it's been 60s of cool down and remove model
+                ):  # check if model is currently cooling down
+                    if (
+                        model_expiration_times.get(model)
+                        and time.time() >= model_expiration_times[model]
+                    ):
+                        rate_limited_models.remove(
+                            model
+                        )  # check if it's been 60s of cool down and remove model
                     else:
-                        continue # skip model
+                        continue  # skip model
-                
+
                 # delete model from kwargs if it exists
-                if kwargs.get('model'):
+                if kwargs.get("model"):
-                    del kwargs['model']
+                    del kwargs["model"]
 
                 print("making completion call", model)
                 response = litellm.completion(**kwargs, model=model)
@@ -1497,7 +1516,9 @@ def completion_with_fallbacks(**kwargs):
             except Exception as e:
                 print(f"got exception {e} for model {model}")
                 rate_limited_models.add(model)
-                model_expiration_times[model] = time.time() + 60 # cool down this selected model
+                model_expiration_times[model] = (
-                #print(f"rate_limited_models {rate_limited_models}")
+                    time.time() + 60
+                )  # cool down this selected model
+                # print(f"rate_limited_models {rate_limited_models}")
                 pass
-    return response
+    return response