address @zucchini-nlp's comments

src/transformers/models/mgp_str/processing_mgp_str.py

@@ -78,28 +78,35 @@ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
                 FutureWarning,
             )
             feature_extractor = kwargs.pop("feature_extractor")
-        if "char_tokenizer" in kwargs:
-            warnings.warn(
-                "The `char_tokenizer` argument is deprecated and will be removed in future versions, use `tokenizer`"
-                " instead.",
-                FutureWarning,
-            )
-            char_tokenizer = kwargs.pop("char_tokenizer")
 
         image_processor = image_processor if image_processor is not None else feature_extractor
-        tokenizer = tokenizer if tokenizer is not None else char_tokenizer
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`.")
         if tokenizer is None:
             raise ValueError("You need to specify a `tokenizer`.")
 
         self.tokenizer = tokenizer
-        self.char_tokenizer = tokenizer  # For backwards compatibility
         self.bpe_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
         self.wp_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
 
         super().__init__(image_processor, tokenizer)
 
+    @property
+    def char_tokenizer(self):
+        warnings.warn(
+            "The `char_tokenizer` attribute is deprecated and will be removed in future versions, use `tokenizer` instead.",
+            FutureWarning,
+        )
+        return self.tokenizer
+
+    @char_tokenizer.setter
+    def char_tokenizer(self, value):
+        warnings.warn(
+            "The `char_tokenizer` attribute is deprecated and will be removed in future versions, use `tokenizer` instead.",
+            FutureWarning,
+        )
+        self.tokenizer = value
+
     def __call__(
         self,
         text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,

src/transformers/models/tvp/image_processing_tvp.py

@@ -50,7 +50,13 @@
 
 # Copied from transformers.models.vivit.image_processing_vivit.make_batched
 def make_batched(videos) -> List[List[ImageInput]]:
-    if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
+    if isinstance(videos, np.ndarray) and videos.ndim == 5:
+        return videos
+
+    elif isinstance(videos, np.ndarray) and videos.ndim == 4:
+        return [videos]
+
+    elif isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
         return videos
 
     elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
@@ -305,30 +311,20 @@ def _preprocess_image(
         # All transformations expect numpy arrays.
         image = to_numpy_array(image)
 
-        print(f"{image.shape = }")
-
         if do_resize:
             image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
 
-        print(f"{image.shape = }")
-
         if do_center_crop:
             image = self.center_crop(image, size=crop_size, input_data_format=input_data_format)
 
-        print(f"{image.shape = }")
-
         if do_rescale:
             image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
 
-        print(f"{image.shape = }")
-
         if do_normalize:
             image = self.normalize(
                 image=image.astype(np.float32), mean=image_mean, std=image_std, input_data_format=input_data_format
             )
 
-        print(f"{image.shape = }")
-
         if do_pad:
             image = self.pad_image(
                 image=image,
@@ -338,18 +334,12 @@ def _preprocess_image(
                 input_data_format=input_data_format,
             )
 
-        print(f"{image.shape = }")
-
         # the pretrained checkpoints assume images are BGR, not RGB
         if do_flip_channel_order:
             image = flip_channel_order(image=image, input_data_format=input_data_format)
 
-        print(f"{image.shape = }")
-
         image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
 
-        print(f"{image.shape = }")
-
         return image
 
     @filter_out_non_signature_kwargs()

src/transformers/models/videomae/image_processing_videomae.py

@@ -47,8 +47,15 @@
 logger = logging.get_logger(__name__)
 
 
+# Copied from transformers.models.vivit.image_processing_vivit.make_batched
 def make_batched(videos) -> List[List[ImageInput]]:
-    if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
+    if isinstance(videos, np.ndarray) and videos.ndim == 5:
+        return videos
+
+    elif isinstance(videos, np.ndarray) and videos.ndim == 4:
+        return [videos]
+
+    elif isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
         return videos
 
     elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):

src/transformers/models/vivit/image_processing_vivit.py

@@ -51,7 +51,13 @@
 
 
 def make_batched(videos) -> List[List[ImageInput]]:
-    if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
+    if isinstance(videos, np.ndarray) and videos.ndim == 5:
+        return videos
+
+    elif isinstance(videos, np.ndarray) and videos.ndim == 4:
+        return [videos]
+
+    elif isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
         return videos
 
     elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):

tests/models/tvp/test_processor_tvp.py

@@ -2,8 +2,6 @@
 import tempfile
 import unittest
 
-import numpy as np
-
 from transformers import TvpProcessor
 from transformers.testing_utils import require_torch, require_vision
 
@@ -20,10 +18,6 @@ def setUp(self):
         processor = self.processor_class.from_pretrained(self.from_pretrained_id)
         processor.save_pretrained(self.tmpdirname)
 
-    @require_vision
-    def prepare_video_inputs(self):
-        return [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
-
     @require_torch
     @require_vision
     def test_video_processor_defaults_preserved_by_kwargs(self):

tests/models/x_clip/test_processor_x_clip.py

@@ -1,8 +1,6 @@
 import tempfile
 import unittest
 
-import numpy as np
-
 from transformers import XCLIPProcessor
 from transformers.testing_utils import require_torch, require_vision
 
@@ -19,10 +17,6 @@ def setUp(self):
         processor = self.processor_class.from_pretrained(self.from_pretrained_id)
         processor.save_pretrained(self.tmpdirname)
 
-    @require_vision
-    def prepare_video_inputs(self):
-        return [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
-
     @require_torch
     @require_vision
     def test_image_processor_defaults_preserved_by_image_kwargs(self):

tests/test_processing_common.py

@@ -93,7 +93,7 @@ def prepare_image_inputs(self):
 
     @require_vision
     def prepare_video_inputs(self):
-        return [np.random.randint(255, size=(4, 3, 30, 400), dtype=np.uint8)]
+        return np.random.randint(255, size=(1, 4, 3, 30, 400), dtype=np.uint8)
 
     def test_processor_to_json_string(self):
         processor = self.get_processor()