[pre-commit.ci] auto fixes from pre-commit.com hooks

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tests/utils/ocr_eval.py

@@ -41,14 +41,14 @@ class OCRModelEvaluator:
         Evaluate a model on an OCR dataset.
         """
         # Create output directory if it doesn't exist
-        os.makedirs(output_dir, exist_ok = True)
+        os.makedirs(output_dir, exist_ok=True)
 
         # Initialize results storage
         results = []
 
         # Process each sample in the dataset
         for i, sample in enumerate(
-            tqdm(dataset, desc = "Evaluating OCR performance", disable = not verbose)
+            tqdm(dataset, desc="Evaluating OCR performance", disable=not verbose)
         ):
             try:
                 # Extract components from sample
@@ -187,7 +187,7 @@ class OCRModelEvaluator:
 
         # Preparation for inference using Qwen's specific processing
         text = processor.apply_chat_template(
-            input_messages, tokenize = False, add_generation_prompt = True
+            input_messages, tokenize=False, add_generation_prompt=True
         )
 
         # Process vision info (images/videos) from messages
@@ -195,11 +195,11 @@ class OCRModelEvaluator:
 
         # Create model inputs
         inputs = processor(
-            text = [text],
-            images = image_inputs,
-            videos = video_inputs,
-            padding = True,
-            return_tensors = "pt",
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
         )
         inputs = inputs.to(model.device)
 
@@ -207,10 +207,10 @@ class OCRModelEvaluator:
         with torch.no_grad():
             generated_ids = model.generate(
                 **inputs,
-                max_new_tokens = max_new_tokens,
-                temperature = temperature,
-                min_p = min_p,
-                use_cache = True,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                min_p=min_p,
+                use_cache=True,
             )
 
         # Extract only the generated part (not the input)
@@ -222,8 +222,8 @@ class OCRModelEvaluator:
         # Decode the generated text
         generated_response = processor.batch_decode(
             generated_ids_trimmed,
-            skip_special_tokens = True,
-            clean_up_tokenization_spaces = False,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=False,
         )[0]
 
         return generated_response
@@ -240,7 +240,7 @@ class OCRModelEvaluator:
     ):
         """Save individual sample result to file."""
         output_file = os.path.join(output_dir, f"sample_{sample_idx}.txt")
-        with open(output_file, "w", encoding = "utf-8") as f:
+        with open(output_file, "w", encoding="utf-8") as f:
             f.write(f"Sample {sample_idx}\n")
             f.write(f"Question: {question}\n\n")
             f.write(f"Model output:\n{generated_response.strip()}\n\n")
@@ -268,7 +268,7 @@ class OCRModelEvaluator:
             f.write(f"Average CER: {avg_cer:.4f}\n")
 
         # Save detailed results
-        df.to_csv(os.path.join(output_dir, "detailed_results.csv"), index = False)
+        df.to_csv(os.path.join(output_dir, "detailed_results.csv"), index=False)
 
         if verbose:
             print("\nResults Summary:")
@@ -310,12 +310,12 @@ class OCRModelEvaluator:
 
         # Display the comparison table
         print("\nComparison Table (sorted by WER):")
-        print(comparison_df.to_string(index = False))
+        print(comparison_df.to_string(index=False))
 
         # Save the comparison table
         if save_csv:
             comparison_file = "model_comparison_results.csv"
-            comparison_df.to_csv(comparison_file, index = False)
+            comparison_df.to_csv(comparison_file, index=False)
             print(f"\nComparison table saved to {comparison_file}")
 
         # Generate a bar chart visualization
@@ -326,23 +326,23 @@ class OCRModelEvaluator:
 
     def _create_comparison_plot(self, comparison_df: pd.DataFrame):
         """Create and save comparison plot."""
-        plt.figure(figsize = (12, 6))
+        plt.figure(figsize=(12, 6))
 
         # Plot WER
         plt.subplot(1, 2, 1)
-        plt.bar(comparison_df["Model"], comparison_df["WER"], color = "skyblue")
+        plt.bar(comparison_df["Model"], comparison_df["WER"], color="skyblue")
         plt.title("Word Error Rate Comparison")
         plt.ylabel("WER (lower is better)")
-        plt.ylim(bottom = 0)
-        plt.xticks(rotation = 45, ha = "right")
+        plt.ylim(bottom=0)
+        plt.xticks(rotation=45, ha="right")
 
         # Plot CER
         plt.subplot(1, 2, 2)
-        plt.bar(comparison_df["Model"], comparison_df["CER"], color = "lightgreen")
+        plt.bar(comparison_df["Model"], comparison_df["CER"], color="lightgreen")
         plt.title("Character Error Rate Comparison")
         plt.ylabel("CER (lower is better)")
-        plt.ylim(bottom = 0)
-        plt.xticks(rotation = 45, ha = "right")
+        plt.ylim(bottom=0)
+        plt.xticks(rotation=45, ha="right")
 
         plt.tight_layout()
         plt.savefig("ocr_model_comparison.png")
@@ -360,7 +360,7 @@ class OCRModelEvaluator:
 
 
 def evaluate_ocr_model(
-    model, processor, dataset, output_dir = "ocr_evaluation_results", **kwargs
+    model, processor, dataset, output_dir="ocr_evaluation_results", **kwargs
 ):
     """
     Convenience function that maintains backward compatibility with the original function.