Add Image Processors (#19796)

* Add CLIP image processor

* Crop size as dict too

* Update warning

* Actually use logger this time

* Normalize doesn't change dtype of input

* Add perceiver image processor

* Tidy up

* Add DPT image processor

* Add Vilt image processor

* Tidy up

* Add poolformer image processor

* Tidy up

* Add LayoutLM v2 and v3 imsge processors

* Tidy up

* Add Flava image processor

* Tidy up

* Add deit image processor

* Tidy up

* Add ConvNext image processor

* Tidy up

* Add levit image processor

* Add segformer image processor

* Add in post processing

* Fix up

* Add ImageGPT image processor

* Fixup

* Add mobilevit image processor

* Tidy up

* Add postprocessing

* Fixup

* Add VideoMAE image processor

* Tidy up

* Add ImageGPT image processor

* Fixup

* Add ViT image processor

* Tidy up

* Add beit image processor

* Add mobilevit image processor

* Tidy up

* Add postprocessing

* Fixup

* Fix up

* Fix flava and remove tree module

* Fix image classification pipeline failing tests

* Update feature extractor in trainer scripts

* Update pad_if_smaller to accept tuple and int size

* Update for image segmentation pipeline

* Update src/transformers/models/perceiver/image_processing_perceiver.py

Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>

* Update src/transformers/image_processing_utils.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/beit/image_processing_beit.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* PR comments - docstrings; remove accidentally added resize; var names

* Update docstrings

* Add exception if size is not in the right format

* Fix exception check

* Fix up

* Use shortest_edge in tuple in script

Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

tests/models/convnext/test_feature_extraction_convnext.py

@@ -43,12 +43,13 @@ class ConvNextFeatureExtractionTester(unittest.TestCase):
         min_resolution=30,
         max_resolution=400,
         do_resize=True,
-        size=20,
+        size=None,
         crop_pct=0.875,
         do_normalize=True,
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        size = size if size is not None else {"shortest_edge": 20}
         self.parent = parent
         self.batch_size = batch_size
         self.num_channels = num_channels
@@ -113,8 +114,8 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
             (
                 1,
                 self.feature_extract_tester.num_channels,
-                self.feature_extract_tester.size,
-                self.feature_extract_tester.size,
+                self.feature_extract_tester.size["shortest_edge"],
+                self.feature_extract_tester.size["shortest_edge"],
             ),
         )
 
@@ -125,8 +126,8 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
             (
                 self.feature_extract_tester.batch_size,
                 self.feature_extract_tester.num_channels,
-                self.feature_extract_tester.size,
-                self.feature_extract_tester.size,
+                self.feature_extract_tester.size["shortest_edge"],
+                self.feature_extract_tester.size["shortest_edge"],
             ),
         )
 
@@ -145,8 +146,8 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
             (
                 1,
                 self.feature_extract_tester.num_channels,
-                self.feature_extract_tester.size,
-                self.feature_extract_tester.size,
+                self.feature_extract_tester.size["shortest_edge"],
+                self.feature_extract_tester.size["shortest_edge"],
             ),
         )
 
@@ -157,8 +158,8 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
             (
                 self.feature_extract_tester.batch_size,
                 self.feature_extract_tester.num_channels,
-                self.feature_extract_tester.size,
-                self.feature_extract_tester.size,
+                self.feature_extract_tester.size["shortest_edge"],
+                self.feature_extract_tester.size["shortest_edge"],
             ),
         )
 
@@ -177,8 +178,8 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
             (
                 1,
                 self.feature_extract_tester.num_channels,
-                self.feature_extract_tester.size,
-                self.feature_extract_tester.size,
+                self.feature_extract_tester.size["shortest_edge"],
+                self.feature_extract_tester.size["shortest_edge"],
             ),
         )
 
@@ -189,7 +190,7 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
             (
                 self.feature_extract_tester.batch_size,
                 self.feature_extract_tester.num_channels,
-                self.feature_extract_tester.size,
-                self.feature_extract_tester.size,
+                self.feature_extract_tester.size["shortest_edge"],
+                self.feature_extract_tester.size["shortest_edge"],
             ),
         )