more linter nonsense

environments/eval_environments/agieval_eval.py

@@ -36,16 +36,11 @@ import time
 from string import ascii_uppercase
 from typing import Dict, List, Optional, Tuple
 
-import wandb
 from datasets import load_dataset
 from eval_helpers import (
-    build_mcqa_fallback_patterns,
     create_system_content,
     extract_letter_from_answer_tag,
-    extract_thinking_content,
     get_default_thinking_prompt,
-    save_eval_results,
-    validate_thinking_format,
 )
 from pydantic import Field
 from tqdm.asyncio import tqdm_asyncio
@@ -333,12 +328,13 @@ class AGIEvalEnv(BaseEnv):
 
     async def setup(self) -> None:
         """Load the AGIEval dataset and prepare for evaluation."""
-        print(f"\nAGIEval Evaluation Setup (Generative Mode):")
+        print("\nAGIEval Evaluation Setup (Generative Mode):")
         print(f"  Max tokens for reasoning: {self.config.eval_max_tokens}")
         print(f"  Evaluation split: {self.config.eval_split}")
         print(f"  Thinking mode: {self.config.thinking_mode}")
         if self.config.thinking_mode:
-            print(f"  Thinking prompt: {self._get_thinking_prompt()[:100]}...")
+            prompt_preview = self._get_thinking_prompt()[:100]
+            print(f"  Thinking prompt: {prompt_preview}...")
 
         # Determine which subsets to use
         if self.config.subsets:
@@ -379,7 +375,7 @@ class AGIEvalEnv(BaseEnv):
         print(f"\n  Total evaluation items: {len(self.eval_data)}")
 
         # Print subset distribution
-        print(f"\n  Subset distribution:")
+        print("\n  Subset distribution:")
         for subset, count in sorted(subset_counts.items()):
             print(f"    {subset}: {count} questions")
 
@@ -584,7 +580,7 @@ class AGIEvalEnv(BaseEnv):
                             break
                         elif attempt < self.config.max_retries - 1:
                             if self.config.full_debug:
-                                print(f"  Response too short, retrying...")
+                                print("  Response too short, retrying...")
                             await asyncio.sleep(self.config.retry_delay)
 
                 except Exception as e:
@@ -594,15 +590,15 @@ class AGIEvalEnv(BaseEnv):
                     )
                     if hasattr(e, "response"):
                         try:
-                            print(
-                                f"    Response: {e.response.text[:500] if hasattr(e.response, 'text') else e.response}"
-                            )
-                        except:
+                            resp_text = e.response.text[:500] if hasattr(e.response, "text") else str(e.response)
+                            print(f"    Response: {resp_text}")
+                        except Exception:
                             pass
                     if attempt < self.config.max_retries - 1:
                         await asyncio.sleep(self.config.retry_delay)
                     else:
-                        print(f"  Failed after {self.config.max_retries} attempts")
+                        retries = self.config.max_retries
+                        print(f"  Failed after {retries} attempts")
                         return {"is_correct": None, "sample": None}
 
             if not model_response:
@@ -669,9 +665,9 @@ class AGIEvalEnv(BaseEnv):
         """Run AGIEval evaluation."""
         start_time = time.time()
 
-        print(f"\n{'='*60}")
-        print(f"Starting AGIEval Evaluation (Generative/Reasoning Mode)")
-        print(f"{'='*60}")
+        print("\n" + "=" * 60)
+        print("Starting AGIEval Evaluation (Generative/Reasoning Mode)")
+        print("=" * 60)
         print(f"  Total questions: {len(self.all_eval_items)}")
         print(f"  Max tokens (for reasoning): {self.config.eval_max_tokens}")
         print(f"  Thinking mode: {self.config.thinking_mode}")
@@ -782,9 +778,9 @@ class AGIEvalEnv(BaseEnv):
         self.eval_metrics = [(k, v) for k, v in eval_metrics.items()]
 
         # Print summary
-        print(f"\n{'='*60}")
-        print(f"AGIEval Evaluation Results")
-        print(f"{'='*60}")
+        print("\n" + "=" * 60)
+        print("AGIEval Evaluation Results")
+        print("=" * 60)
         print(
             f"Overall Accuracy: {overall_accuracy:.4f} ({total_correct}/{total_count})"
         )
@@ -794,7 +790,7 @@ class AGIEvalEnv(BaseEnv):
             print(f"Format Compliance: {format_compliance_rate:.4f}")
             print(f"Thinking Utilization: {thinking_utilization}/{total_count}")
 
-        print(f"\nSubset Breakdown:")
+        print("\nSubset Breakdown:")
         for subset, stats in sorted(subset_results.items()):
             if stats["total"] > 0:
                 subset_acc = stats["correct"] / stats["total"]
@@ -802,7 +798,7 @@ class AGIEvalEnv(BaseEnv):
                     f"  {subset}: {subset_acc:.4f} ({stats['correct']}/{stats['total']})"
                 )
 
-        print(f"\nExtraction Method Statistics:")
+        print("\nExtraction Method Statistics:")
         for method, stats in sorted(
             extraction_methods.items(), key=lambda x: -x[1]["count"]
         ):
@@ -810,7 +806,7 @@ class AGIEvalEnv(BaseEnv):
                 method_acc = stats["correct"] / stats["count"]
                 print(f"  {method}: {stats['count']} uses, {method_acc:.4f} accuracy")
 
-        print(f"{'='*60}\n")
+        print("=" * 60 + "\n")
 
         # Log evaluation results
         try: