Improve variable names

src/transformers/models/llava/image_processing_llava.py

@@ -177,40 +177,40 @@ def pad_to_square(
         Returns:
             `np.ndarray`: The padded image.
         """
-        h, w = get_image_size(image, input_data_format)
-        c = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
+        height, width = get_image_size(image, input_data_format)
+        num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
 
-        if h == w:
+        if height == width:
             return image
 
-        max_dim = max(h, w)
+        max_dim = max(height, width)
 
         # Ensure background_color is the correct shape
         if isinstance(background_color, (int, float)):
-            background_color = [background_color] * c
-        elif len(background_color) != c:
-            raise ValueError(f"background_color must have {c} elements to match the number of channels")
+            background_color = [background_color] * num_channels
+        elif len(background_color) != num_channels:
+            raise ValueError(f"background_color must have {num_channels} elements to match the number of channels")
 
         if input_data_format == ChannelDimension.FIRST:
-            result = np.zeros((c, max_dim, max_dim), dtype=image.dtype)
+            result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
             for i, color in enumerate(background_color):
                 result[i, :, :] = color
-            if w > h:
-                start = (max_dim - h) // 2
-                result[:, start : start + h, :] = image
+            if width > height:
+                start = (max_dim - height) // 2
+                result[:, start : start + height, :] = image
             else:
-                start = (max_dim - w) // 2
-                result[:, :, start : start + w] = image
+                start = (max_dim - width) // 2
+                result[:, :, start : start + width] = image
         else:
-            result = np.zeros((max_dim, max_dim, c), dtype=image.dtype)
+            result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
             for i, color in enumerate(background_color):
                 result[:, :, i] = color
-            if w > h:
-                start = (max_dim - h) // 2
-                result[start : start + h, :, :] = image
+            if width > height:
+                start = (max_dim - height) // 2
+                result[start : start + height, :, :] = image
             else:
-                start = (max_dim - w) // 2
-                result[:, start : start + w, :] = image
+                start = (max_dim - width) // 2
+                result[:, start : start + width, :] = image
 
         return result