Lint

extensions/openai/completions.py

@@ -1,14 +1,14 @@
 import copy
-import time
 import json
+import time
 from collections import deque
 
 import tiktoken
+from pydantic import ValidationError
 
 from extensions.openai.errors import InvalidRequestError
-from extensions.openai.utils import debug_msg, getToolCallId, parseToolCall
 from extensions.openai.typing import ToolDefinition
-from pydantic import ValidationError
+from extensions.openai.utils import debug_msg, getToolCallId, parseToolCall
 from modules import shared
 from modules.chat import (
     generate_chat_prompt,
@@ -141,7 +141,7 @@ def chat_completions_common(body: dict, is_legacy: bool = False, stream=False, p
 
     tools = None
     if 'tools' in body and body['tools'] is not None and isinstance(body['tools'], list) and len(body['tools']) > 0:
-        tools = validateTools(body['tools']) # raises InvalidRequestError if validation fails
+        tools = validateTools(body['tools'])  # raises InvalidRequestError if validation fails
 
     messages = body['messages']
     for m in messages:

extensions/openai/utils.py

@@ -1,9 +1,9 @@
 import base64
-import os
-import time
 import json
+import os
 import random
 import re
+import time
 import traceback
 from typing import Callable, Optional
 
@@ -91,7 +91,7 @@ def parseToolCall(answer: str, tool_names: list[str]):
         return matches
 
     # Define the regex pattern to find the JSON content wrapped in <function>, <tools>, <tool_call>, and other tags observed from various models
-    patterns = [ r"(```[^\n]*)\n(.*?)```", r"<([^>]+)>(.*?)</\1>" ]
+    patterns = [r"(```[^\n]*)\n(.*?)```", r"<([^>]+)>(.*?)</\1>"]
 
     for pattern in patterns:
         for match in re.finditer(pattern, answer, re.DOTALL):

extensions/superboogav2/chromadb.py

@@ -1,10 +1,11 @@
 import math
 import random
 import threading
-import torch
+
 import chromadb
 import numpy as np
 import posthog
+import torch
 from chromadb.config import Settings
 from chromadb.utils import embedding_functions
 

modules/tensorrt_llm.py

@@ -1,15 +1,15 @@
 from pathlib import Path
 
-import torch
-
 import tensorrt_llm
+import torch
+from tensorrt_llm.runtime import ModelRunner, ModelRunnerCpp
+
 from modules import shared
 from modules.logging_colors import logger
 from modules.text_generation import (
     get_max_prompt_length,
     get_reply_from_output_ids
 )
-from tensorrt_llm.runtime import ModelRunner, ModelRunnerCpp
 
 
 class TensorRTLLMModel: