chore: minimal PEP 8 hygiene (unused imports/vars, trivial fixes)

examples/gradio/gradio_chat.py

@@ -15,25 +15,25 @@
 }
 """.strip()
 
-def chat_with_model(message, history, model_choice, instructions, effort, use_functions, 
+def chat_with_model(message, history, model_choice, instructions, effort, use_functions,
                    function_name, function_description, function_parameters,
                    use_browser_search, temperature, max_output_tokens, debug_mode):
-    
+
     if not message.strip():
         return history, ""
-    
+
     # Append user message and empty assistant placeholder (idiomatic Gradio pattern)
     history = history + [[message, ""]]
-    
+
     # Build messages list from history (excluding the empty assistant placeholder)
     messages = []
-    
+
     # Convert history to messages format (excluding the last empty assistant message)
     for user_msg, assistant_msg in history[:-1]:
         if user_msg:
             messages.append({
                 "type": "message",
-                "role": "user", 
+                "role": "user",
                 "content": [{"type": "input_text", "text": user_msg}]
             })
         if assistant_msg:
@@ -42,14 +42,14 @@ def chat_with_model(message, history, model_choice, instructions, effort, use_fu
                 "role": "assistant",
                 "content": [{"type": "output_text", "text": assistant_msg}]
             })
-    
+
     # Add current user message
     messages.append({
         "type": "message",
         "role": "user",
         "content": [{"type": "input_text", "text": message}]
     })
-    
+
     # Prepare tools
     tools = []
     if use_functions:
@@ -62,18 +62,18 @@ def chat_with_model(message, history, model_choice, instructions, effort, use_fu
             })
         except json.JSONDecodeError:
             pass
-    
+
     if use_browser_search:
         tools.append({"type": "browser_search"})
-    
+
     # Get URL based on model (matching streamlit logic)
     options = ["large", "small"]
-    URL = ("http://localhost:8081/v1/responses" if model_choice == options[1] 
+    url = ("http://localhost:8081/v1/responses" if model_choice == options[1]
            else "http://localhost:8000/v1/responses")
-    
+
     try:
         response = requests.post(
-            URL,
+            url,
             json={
                 "input": messages,
                 "stream": True,
@@ -86,32 +86,31 @@ def chat_with_model(message, history, model_choice, instructions, effort, use_fu
             },
             stream=True,
         )
-        
+
         full_content = ""
-        text_delta = ""
-        current_output_index = 0
+
         in_reasoning = False
-        
+
         for line in response.iter_lines(decode_unicode=True):
             if not line or not line.startswith("data:"):
                 continue
             data_str = line[len("data:"):].strip()
             if not data_str:
                 continue
-            
+
             try:
                 data = json.loads(data_str)
             except Exception:
                 continue
-            
+
             event_type = data.get("type", "")
-            output_index = data.get("output_index", 0)
-            
+
+
             if event_type == "response.output_item.added":
-                current_output_index = output_index
+
                 output_type = data.get("item", {}).get("type", "message")
-                text_delta = ""
-                
+
+
                 if output_type == "reasoning":
                     if not in_reasoning:
                         full_content += "🤔 **Thinking...**\n"
@@ -120,56 +119,56 @@ def chat_with_model(message, history, model_choice, instructions, effort, use_fu
                     if in_reasoning:
                         full_content += "\n\n"
                         in_reasoning = False
-                
+
             elif event_type == "response.reasoning_text.delta":
                 delta = data.get("delta", "")
                 full_content += delta
-                
+
                 # Update last assistant message (idiomatic Gradio pattern)
                 history[-1][1] = full_content
                 yield history, ""
-                
+
             elif event_type == "response.output_text.delta":
                 delta = data.get("delta", "")
                 full_content += delta
-                
-                # Update last assistant message (idiomatic Gradio pattern)  
+
+                # Update last assistant message (idiomatic Gradio pattern)
                 history[-1][1] = full_content
                 yield history, ""
-                
+
             elif event_type == "response.output_item.done":
                 item = data.get("item", {})
                 if item.get("type") == "function_call":
                     function_call_text = f"\n\n🔨 Called `{item.get('name')}`\n**Arguments**\n```json\n{item.get('arguments', '')}\n```"
                     full_content += function_call_text
-                    
+
                     # Update last assistant message (idiomatic Gradio pattern)
                     history[-1][1] = full_content
                     yield history, ""
-                    
+
                 elif item.get("type") == "web_search_call":
                     web_search_text = f"\n\n🌐 **Web Search**\n```json\n{json.dumps(item.get('action', {}), indent=2)}\n```\n✅ Done"
                     full_content += web_search_text
-                    
+
                     # Update last assistant message (idiomatic Gradio pattern)
                     history[-1][1] = full_content
                     yield history, ""
-                    
+
             elif event_type == "response.completed":
                 response_data = data.get("response", {})
                 if debug_mode:
                     debug_info = response_data.get("metadata", {}).get("__debug", "")
                     if debug_info:
                         full_content += f"\n\n**Debug**\n```\n{debug_info}\n```"
-                        
+
                         # Update last assistant message (idiomatic Gradio pattern)
                         history[-1][1] = full_content
                         yield history, ""
                 break
-        
+
         # Return final history and empty string to clear textbox
         return history, ""
-        
+
     except Exception as e:
         error_message = f"❌ Error: {str(e)}"
         history[-1][1] = error_message
@@ -179,69 +178,69 @@ def chat_with_model(message, history, model_choice, instructions, effort, use_fu
 # Create the Gradio interface
 with gr.Blocks(title="💬 Chatbot") as demo:
     gr.Markdown("# 💬 Chatbot")
-    
+
     with gr.Row():
         with gr.Column(scale=3):
             chatbot = gr.Chatbot(height=500)
-            
+
             with gr.Row():
                 msg = gr.Textbox(placeholder="Type a message...", scale=4, show_label=False)
                 send_btn = gr.Button("Send", scale=1)
-            
+
             clear_btn = gr.Button("Clear Chat")
-        
+
         with gr.Column(scale=1):
             model_choice = gr.Radio(["large", "small"], value="small", label="Model")
-            
+
             instructions = gr.Textbox(
-                label="Instructions", 
+                label="Instructions",
                 value="You are a helpful assistant that can answer questions and help with tasks.",
                 lines=3
             )
-            
+
             effort = gr.Radio(["low", "medium", "high"], value="medium", label="Reasoning effort")
-            
+
             gr.Markdown("#### Functions")
             use_functions = gr.Checkbox(label="Use functions", value=False)
-            
+
             with gr.Column(visible=False) as function_group:
                 function_name = gr.Textbox(label="Function name", value="get_weather")
                 function_description = gr.Textbox(
-                    label="Function description", 
+                    label="Function description",
                     value="Get the weather for a given city"
                 )
                 function_parameters = gr.Textbox(
-                    label="Function parameters", 
+                    label="Function parameters",
                     value=DEFAULT_FUNCTION_PROPERTIES,
                     lines=6
                 )
-            
+
             # Conditional browser search (matching Streamlit logic)
             # In Streamlit: if "show_browser" in st.query_params:
             # For Gradio, we'll always show it (simplified)
-            gr.Markdown("#### Built-in Tools") 
+            gr.Markdown("#### Built-in Tools")
             use_browser_search = gr.Checkbox(label="Use browser search", value=False)
-            
+
             temperature = gr.Slider(0.0, 1.0, value=1.0, step=0.01, label="Temperature")
             max_output_tokens = gr.Slider(1000, 20000, value=1024, step=100, label="Max output tokens")
-            
+
             debug_mode = gr.Checkbox(label="Debug mode", value=False)
-    
+
     # Event handlers
     def toggle_function_group(use_funcs):
         return gr.update(visible=use_funcs)
-    
+
     use_functions.change(toggle_function_group, use_functions, function_group)
-    
+
     # Chat functionality
-    inputs = [msg, chatbot, model_choice, instructions, effort, use_functions, 
+    inputs = [msg, chatbot, model_choice, instructions, effort, use_functions,
               function_name, function_description, function_parameters,
               use_browser_search, temperature, max_output_tokens, debug_mode]
-    
+
     msg.submit(chat_with_model, inputs, [chatbot, msg])
     send_btn.click(chat_with_model, inputs, [chatbot, msg])
     clear_btn.click(lambda: [], outputs=chatbot)
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

gpt_oss/evals/aime_eval.py

@@ -44,9 +44,9 @@ def __init__(
         num_examples: int | None = None,  # restrict to a subset of the data for debugging
         n_threads: int = 1,
     ):
-        path1 = f"https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-I.jsonl"
+        path1 = "https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-I.jsonl"
         df1 = pandas.read_json(path1, lines=True)
-        path2 = f"https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-II.jsonl"
+        path2 = "https://huggingface.co/datasets/opencompass/AIME2025/raw/main/aime2025-II.jsonl"
         df2 = pandas.read_json(path2, lines=True)
         examples = [row.to_dict() for _, row in df1.iterrows()] + [row.to_dict() for _, row in df2.iterrows()]
         examples = [{
@@ -94,4 +94,3 @@ def fn(row: dict):
 
         results = report.map_with_progress(fn, self.examples, num_threads=self.n_threads)
         return report.aggregate_results(results)
-

gpt_oss/responses_api/inference/triton.py

@@ -1,12 +1,12 @@
-import datetime
+
 import os
 from typing import Callable
 
 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 import torch
-import torch.distributed as dist
 
-from gpt_oss.triton.model import Cache, ModelConfig, Transformer
+
+from gpt_oss.triton.model import Cache, Transformer
 
 DEFAULT_TEMPERATURE = 0.0
 CONTEXT = 16_384
@@ -73,7 +73,7 @@ def infer_next_token(
         tokens_so_far = lcp(tokens_so_far, tokens)
         for cache in caches:
             cache.truncate(len(tokens_so_far))
-        all_tokens = tokens  # for pdb
+
         tokens = tokens[len(tokens_so_far) :]
 
         if len(tokens) > 1: