Input data format (#25464)

* Add copied from statements for image processors

* Move out rescale and normalize to base image processor

* Remove rescale and normalize from vit (post rebase)

* Update docstrings and tidy up

* PR comments

* Add input_data_format as preprocess argument

* Resolve tests and tidy up

* Remove num_channels argument

* Update doc strings -> default ints not in code formatting

tests/models/videomae/test_image_processing_videomae.py

@@ -165,6 +165,33 @@ class VideoMAEImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
             tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
         )
 
+    def test_call_numpy_4_channels(self):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random numpy tensors
+        self.image_processor_tester.num_channels = 4
+        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+        for video in video_inputs:
+            self.assertIsInstance(video, list)
+            self.assertIsInstance(video[0], np.ndarray)
+
+        # Test not batched input
+        encoded_videos = image_processing(
+            video_inputs[0], return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
+        ).pixel_values
+        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
+        self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
+
+        # Test batched
+        encoded_videos = image_processing(
+            video_inputs, return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
+        ).pixel_values
+        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
+        self.assertEqual(
+            tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
+        )
+        self.image_processor_tester.num_channels = 3
+
     def test_call_pytorch(self):
         # Initialize image_processing
         image_processing = self.image_processing_class(**self.image_processor_dict)