smol improvements to support more flexible usage (#34857)

* smol improvements to support more flexible usage

* ruff

src/transformers/models/idefics3/image_processing_idefics3.py

@@ -38,6 +38,7 @@
 
 
 logger = logging.get_logger(__name__)
+MAX_IMAGE_SIZE = 4096  # 4k resolution as absolute maximum
 
 
 if is_vision_available():
@@ -116,7 +117,6 @@ def _resize_output_size_scale_below_upper_bound(
 def get_resize_output_image_size(
     image,
     resolution_max_side: int,
-    max_image_size: int = 1820,
     input_data_format: Optional[Union[str, ChannelDimension]] = None,
 ) -> Tuple[int, int]:
     """
@@ -126,24 +126,18 @@ def get_resize_output_image_size(
             Image to resize.
         resolution_max_side (`int`):
             The longest edge of the image will be resized to this value. The shortest edge will be resized to keep the
-            input aspect ratio, with a lower bound of `min_image_size`.
-        max_image_size (`int`, *optional*, defaults to 1820):
-            Maximum image resolution. If the image is larger than this size, the longest edge will be resized to this
-            value, with the shortest edge resized to keep the input aspect ratio, with a lower bound of `min_image_size`.
+            input aspect ratio.
         input_data_format (`ChannelDimension` or `str`):
             The channel dimension format of the input image.
     Returns:
         The output size of the image after resizing.
     """
-    if resolution_max_side > max_image_size:
-        raise ValueError("`resolution_max_side` cannot be larger than `max_image_size`")
-
     height, width = get_image_size(image, channel_dim=input_data_format)
 
     # Find the output size, when rescaling the longest edge to max_len and preserving the aspect ratio
     height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=resolution_max_side)
-    # Find the output size when scaling the image to be below the max_image_size
-    height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=max_image_size)
+    # Find the output size when scaling the image to be below the MAX_IMAGE_SIZE
+    height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=MAX_IMAGE_SIZE)
     return height, width
 
 
@@ -251,7 +245,7 @@ def convert_to_rgb(
     data_format = input_data_format if data_format is None else data_format
 
     mode = "P" if palette is not None else None
-    image = to_pil_image(image, image_mode=mode)
+    image = to_pil_image(image, image_mode=mode, input_data_format=input_data_format)
     if image.mode == "P" and palette is not None:
         image.putpalette(palette)
 
@@ -404,7 +398,7 @@ def resize(
         image_mode = None
         if image.ndim == 2 or image.shape[-1] == 1:
             image_mode = "P"
-        image = to_pil_image(image, image_mode=image_mode)
+        image = to_pil_image(image, image_mode=image_mode, input_data_format=input_data_format)
 
         resized_image = image.resize((size[1], size[0]), resample=resample)
         resized_image = np.array(resized_image)
@@ -754,6 +748,16 @@ def preprocess(
         # All transformations expect numpy arrays.
         images_list = [[to_numpy_array(image) for image in images] for images in images_list]
 
+        # Extra channel dimension for grayscale images
+        if input_data_format in [ChannelDimension.LAST, None]:
+            images_list = [
+                [np.expand_dims(img, axis=-1) if img.ndim == 2 else img for img in images] for images in images_list
+            ]
+        elif input_data_format == ChannelDimension.FIRST:
+            images_list = [
+                [np.expand_dims(img, axis=0) if img.ndim == 2 else img for img in images] for images in images_list
+            ]
+
         if is_scaled_image(images_list[0][0]) and do_rescale:
             logger.warning_once(
                 "It looks like you are trying to rescale already rescaled images. If the input"
@@ -764,18 +768,6 @@ def preprocess(
         if input_data_format is None:
             input_data_format = infer_channel_dimension_format(images_list[0][0], num_channels=(1, 3, 4))
 
-        # Extra channel dimension for grayscale images
-        if input_data_format == ChannelDimension.LAST:
-            images_list = [
-                [np.expand_dims(img, axis=-1) if img.ndim == 2 else img for img in images] for images in images_list
-            ]
-        elif input_data_format == ChannelDimension.FIRST:
-            images_list = [
-                [np.expand_dims(img, axis=0) if img.ndim == 2 else img for img in images] for images in images_list
-            ]
-        else:
-            raise ValueError(f"Invalid channel dimension format {input_data_format}.")
-
         if do_resize:
             images_list = [
                 [