Remove unused functions in image_transforms.py

src/transformers/image_transforms.py

@@ -821,12 +821,6 @@ def split_to_tiles(images: "torch.Tensor", num_tiles_height: int, num_tiles_widt
     return image
 
 
-def _cast_tensor_to_float(x):
-    if x.is_floating_point():
-        return x
-    return x.float()
-
-
 def _group_images_by_shape(nested_images, *paired_inputs, is_nested: bool = False):
     """Helper function to flatten a single level of nested image and batch structures and group by shape."""
     grouped_images = defaultdict(list)
@@ -843,7 +837,7 @@ def _group_images_by_shape(nested_images, *paired_inputs, is_nested: bool = Fals
     for i, (sublist, *paired_sublists) in enumerate(zip(normalized_images, *normalized_paired)):
         for j, (image, *paired_values) in enumerate(zip(sublist, *paired_sublists)):
             key = (i, j) if is_nested else j
-            shape = image.shape[1:]
+            shape = image.shape
 
             # Add to grouped structures
             grouped_images[shape].append(image)

src/transformers/models/vitmatte/image_processing_vitmatte_fast.py

@@ -152,7 +152,8 @@ def _preprocess(
         processed_images_grouped = {}
         for shape in grouped_images:
             stacked_images = grouped_images[shape]
-            stacked_trimaps = grouped_trimaps[shape]
+            trimaps_shape = torch.Size([1, *shape[1:]]) # Trimaps have single channel
+            stacked_trimaps = grouped_trimaps[trimaps_shape]
             # Fused rescale and normalize
             stacked_images = self.rescale_and_normalize(
                 stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std