fix: format colqwen_forward.py to pass pre-commit checks

apps/multimodal/vision-based-pdf-multi-vector/colqwen_forward.py

@@ -8,10 +8,9 @@ from pathlib import Path
 # Add the current directory to path to import leann_multi_vector
 sys.path.insert(0, str(Path(__file__).parent))
 
-from PIL import Image
 import torch
-
-from leann_multi_vector import _load_colvision, _embed_images, _ensure_repo_paths_importable
+from leann_multi_vector import _embed_images, _ensure_repo_paths_importable, _load_colvision
+from PIL import Image
 
 # Ensure repo paths are importable
 _ensure_repo_paths_importable(__file__)
@@ -23,7 +22,7 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 def create_test_image():
     """Create a simple test image."""
     # Create a simple RGB image (800x600)
-    img = Image.new('RGB', (800, 600), color='white')
+    img = Image.new("RGB", (800, 600), color="white")
     return img
 
 
@@ -31,22 +30,22 @@ def load_test_image_from_file():
     """Try to load an image from the indexes directory if available."""
     # Try to find an existing image in the indexes directory
     indexes_dir = Path(__file__).parent / "indexes"
-    
+
     # Look for images in common locations
     possible_paths = [
         indexes_dir / "vidore_fastplaid" / "images",
         indexes_dir / "colvision_large.leann.images",
         indexes_dir / "colvision.leann.images",
     ]
-    
+
     for img_dir in possible_paths:
         if img_dir.exists():
             # Find first image file
-            for ext in ['.png', '.jpg', '.jpeg']:
-                for img_file in img_dir.glob(f'*{ext}'):
+            for ext in [".png", ".jpg", ".jpeg"]:
+                for img_file in img_dir.glob(f"*{ext}"):
                     print(f"Loading test image from: {img_file}")
                     return Image.open(img_file)
-    
+
     return None
 
 
@@ -54,7 +53,7 @@ def main():
     print("=" * 60)
     print("Testing ColQwen2 Forward Pass")
     print("=" * 60)
-    
+
     # Step 1: Load or create test image
     print("\n[Step 1] Loading test image...")
     test_image = load_test_image_from_file()
@@ -63,65 +62,67 @@ def main():
         test_image = create_test_image()
     else:
         print(f"✓ Loaded image: {test_image.size} ({test_image.mode})")
-    
+
     # Convert to RGB if needed
-    if test_image.mode != 'RGB':
-        test_image = test_image.convert('RGB')
+    if test_image.mode != "RGB":
+        test_image = test_image.convert("RGB")
         print(f"✓ Converted to RGB: {test_image.size}")
-    
+
     # Step 2: Load model
     print("\n[Step 2] Loading ColQwen2 model...")
     try:
         model_name, model, processor, device_str, device, dtype = _load_colvision("colqwen2")
         print(f"✓ Model loaded: {model_name}")
         print(f"✓ Device: {device_str}, dtype: {dtype}")
-        
+
         # Print model info
-        if hasattr(model, 'device'):
+        if hasattr(model, "device"):
             print(f"✓ Model device: {model.device}")
-        if hasattr(model, 'dtype'):
+        if hasattr(model, "dtype"):
             print(f"✓ Model dtype: {model.dtype}")
-            
+
     except Exception as e:
         print(f"✗ Error loading model: {e}")
         import traceback
+
         traceback.print_exc()
         return
-    
+
     # Step 3: Test forward pass
     print("\n[Step 3] Running forward pass...")
     try:
         # Use the _embed_images function which handles batching and forward pass
         images = [test_image]
         print(f"Processing {len(images)} image(s)...")
-        
+
         doc_vecs = _embed_images(model, processor, images)
-        
-        print(f"✓ Forward pass completed!")
+
+        print("✓ Forward pass completed!")
         print(f"✓ Number of embeddings: {len(doc_vecs)}")
-        
+
         if len(doc_vecs) > 0:
             emb = doc_vecs[0]
             print(f"✓ Embedding shape: {emb.shape}")
             print(f"✓ Embedding dtype: {emb.dtype}")
-            print(f"✓ Embedding stats:")
+            print("✓ Embedding stats:")
             print(f"    - Min: {emb.min().item():.4f}")
             print(f"    - Max: {emb.max().item():.4f}")
             print(f"    - Mean: {emb.mean().item():.4f}")
             print(f"    - Std: {emb.std().item():.4f}")
-            
+
             # Check for NaN or Inf
             if torch.isnan(emb).any():
                 print("⚠ Warning: Embedding contains NaN values!")
             if torch.isinf(emb).any():
                 print("⚠ Warning: Embedding contains Inf values!")
-            
+
     except Exception as e:
         print(f"✗ Error during forward pass: {e}")
         import traceback
+
         traceback.print_exc()
         return
-    
+
     print("\n" + "=" * 60)
     print("Test completed successfully!")
     print("=" * 60)
@@ -129,4 +130,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-