Add support for nested images to LLava and VipLLava (#35558)

* move make_flat_list_of_images and make_batched_videos to image_utils

* remove unnecessary is_vision_available

* move make_nested_list_of_images to image_utils

* fix fast pixtral image processor

* fix import mllama

* fix make_nested_list_of_images

* add tests

* convert 4d arrays/tensors to list

* add test_make_batched_videos

* add support nested batch of videos

* fix image processing qwen2vl

src/transformers/models/aria/image_processing_aria.py

@@ -31,7 +31,7 @@ from ...image_utils import (
     PILImageResampling,
     get_image_size,
     infer_channel_dimension_format,
-    is_valid_image,
+    make_flat_list_of_images,
     to_numpy_array,
     valid_images,
     validate_preprocess_arguments,
@@ -39,29 +39,6 @@ from ...image_utils import (
 from ...utils import TensorType
 
 
-def make_batched_images(images) -> List[List[ImageInput]]:
-    """
-    Accepts images in list or nested list format, and makes a list of images for preprocessing.
-
-    Args:
-        images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
-            The input image.
-
-    Returns:
-        list: A list of images.
-    """
-    if isinstance(images, (list, tuple)) and isinstance(images[0], (list, tuple)) and is_valid_image(images[0][0]):
-        return [img for img_list in images for img in img_list]
-
-    elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
-        return images
-
-    elif is_valid_image(images):
-        return [images]
-
-    raise ValueError(f"Could not make batched video from {images}")
-
-
 def divide_to_patches(image: np.array, patch_size: int, input_data_format) -> List[np.array]:
     """
     Divides an image into patches of a specified size.
@@ -244,7 +221,7 @@ class AriaImageProcessor(BaseImageProcessor):
         if max_image_size not in [490, 980]:
             raise ValueError("max_image_size must be either 490 or 980")
 
-        images = make_batched_images(images)
+        images = make_flat_list_of_images(images)
 
         if not valid_images(images):
             raise ValueError(

src/transformers/models/aria/modular_aria.py

@@ -28,6 +28,7 @@ from ...image_utils import (
     PILImageResampling,
     get_image_size,
     infer_channel_dimension_format,
+    make_flat_list_of_images,
     to_numpy_array,
     valid_images,
     validate_preprocess_arguments,
@@ -58,7 +59,7 @@ from ..llama.modeling_llama import (
     LlamaRMSNorm,
 )
 from ..llava.modeling_llava import LlavaCausalLMOutputWithPast
-from ..llava_next.image_processing_llava_next import divide_to_patches, make_batched_images
+from ..llava_next.image_processing_llava_next import divide_to_patches
 
 
 logger = logging.get_logger(__name__)
@@ -609,7 +610,7 @@ class AriaImageProcessor(BaseImageProcessor):
         if max_image_size not in [490, 980]:
             raise ValueError("max_image_size must be either 490 or 980")
 
-        images = make_batched_images(images)
+        images = make_flat_list_of_images(images)
 
         if not valid_images(images):
             raise ValueError(