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/blip/test_image_processing_blip.py

@@ -70,7 +70,7 @@ class BlipImageProcessingTester(unittest.TestCase):
         }
 
     def expected_output_image_shape(self, images):
-        return 3, self.size["height"], self.size["width"]
+        return self.num_channels, self.size["height"], self.size["width"]
 
     def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
         return prepare_image_inputs(
@@ -135,3 +135,11 @@ class BlipImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.Tes
     @unittest.skip("BlipImageProcessor does not support 4 channels yet")  # FIXME Amy
     def test_call_pytorch(self):
         return super().test_call_torch()
+
+    @unittest.skip("BLIP doesn't treat 4 channel PIL and numpy consistently yet")  # FIXME Amy
+    def test_call_pil(self):
+        pass
+
+    @unittest.skip("BLIP doesn't treat 4 channel PIL and numpy consistently yet")  # FIXME Amy
+    def test_call_numpy_4_channels(self):
+        pass

tests/models/flava/test_image_processing_flava.py

@@ -337,6 +337,11 @@ class FlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
     def test_call_numpy(self):
         self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
 
+    def test_call_numpy_4_channels(self):
+        self.image_processing_class.num_channels = 4
+        self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
+        self.image_processing_class.num_channels = 3
+
     def test_call_pytorch(self):
         self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})
 

tests/models/imagegpt/test_image_processing_imagegpt.py

@@ -198,6 +198,10 @@ class ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
             tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
         )
 
+    @unittest.skip("ImageGPT assumes clusters for 3 channels")
+    def test_call_numpy_4_channels(self):
+        pass
+
     # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
     def test_call_pytorch(self):
         # Initialize image_processing