Update kwargs validation for `preprocess` with decorator (#32024)

* BLIP preprocess

* BIT preprocess

* BRIDGETOWER preprocess

* CHAMELEON preprocess

* CHINESE_CLIP preprocess

* CONVNEXT preprocess

* DEIT preprocess

* DONUT preprocess

* DPT preprocess

* FLAVA preprocess

* EFFICIENTNET preprocess

* FUYU preprocess

* GLPN preprocess

* IMAGEGPT preprocess

* INTRUCTBLIPVIDEO preprocess

* VIVIT preprocess

* ZOEDEPTH preprocess

* VITMATTE preprocess

* VIT preprocess

* VILT preprocess

* VIDEOMAE preprocess

* VIDEOLLAVA

* TVP processing

* TVP fixup

* SWIN2SR preprocess

* SIGLIP preprocess

* SAM preprocess

* RT-DETR preprocess

* PVT preprocess

* POOLFORMER preprocess

* PERCEIVER preprocess

* OWLVIT preprocess

* OWLV2 preprocess

* NOUGAT preprocess

* MOBILEVIT preprocess

* MOBILENETV2 preprocess

* MOBILENETV1 preprocess

* LEVIT preprocess

* LAYOUTLMV2 preprocess

* LAYOUTLMV3 preprocess

* Add test

* Update tests

tests/models/blip/test_image_processing_blip.py

@@ -43,6 +43,7 @@ class BlipImageProcessingTester(unittest.TestCase):
         image_std=[0.26862954, 0.26130258, 0.27577711],
         do_convert_rgb=True,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 20, "width": 20}
         self.parent = parent
         self.batch_size = batch_size

tests/models/bridgetower/test_image_processing_bridgetower.py

@@ -50,6 +50,7 @@ class BridgeTowerImageProcessingTester(unittest.TestCase):
         max_resolution=400,
         num_channels=3,
     ):
+        super().__init__()
         self.parent = parent
         self.do_resize = do_resize
         self.size = size if size is not None else {"shortest_edge": 288}

tests/models/chameleon/test_image_processing_chameleon.py

@@ -50,6 +50,7 @@ class ChameleonImageProcessingTester(unittest.TestCase):
         image_std=[1.0, 1.0, 1.0],
         do_convert_rgb=True,
     ):
+        super().__init__()
         size = size if size is not None else {"shortest_edge": 18}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
         self.parent = parent

tests/models/chinese_clip/test_image_processing_chinese_clip.py

@@ -44,6 +44,7 @@ class ChineseCLIPImageProcessingTester(unittest.TestCase):
         image_std=[0.26862954, 0.26130258, 0.27577711],
         do_convert_rgb=True,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 224, "width": 224}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
         self.parent = parent

tests/models/convnext/test_image_processing_convnext.py

@@ -42,6 +42,7 @@ class ConvNextImageProcessingTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         size = size if size is not None else {"shortest_edge": 20}
         self.parent = parent
         self.batch_size = batch_size

tests/models/deit/test_image_processing_deit.py

@@ -43,6 +43,7 @@ class DeiTImageProcessingTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         size = size if size is not None else {"height": 20, "width": 20}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
 

tests/models/donut/test_image_processing_donut.py

@@ -51,6 +51,7 @@ class DonutImageProcessingTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         self.parent = parent
         self.batch_size = batch_size
         self.num_channels = num_channels

tests/models/dpt/test_image_processing_dpt.py

@@ -43,6 +43,7 @@ class DPTImageProcessingTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         size = size if size is not None else {"height": 18, "width": 18}
         self.parent = parent
         self.batch_size = batch_size

tests/models/efficientnet/test_image_processing_efficientnet.py

@@ -43,6 +43,7 @@ class EfficientNetImageProcessorTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         size = size if size is not None else {"height": 18, "width": 18}
         self.parent = parent
         self.batch_size = batch_size

tests/models/flava/test_image_processing_flava.py

@@ -76,6 +76,7 @@ class FlavaImageProcessingTester(unittest.TestCase):
         codebook_image_mean=FLAVA_CODEBOOK_MEAN,
         codebook_image_std=FLAVA_CODEBOOK_STD,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 224, "width": 224}
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
         codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}

tests/models/glpn/test_image_processing_glpn.py

@@ -46,6 +46,7 @@ class GLPNImageProcessingTester(unittest.TestCase):
         size_divisor=32,
         do_rescale=True,
     ):
+        super().__init__()
         self.parent = parent
         self.batch_size = batch_size
         self.num_channels = num_channels

tests/models/imagegpt/test_image_processing_imagegpt.py

@@ -51,6 +51,7 @@ class ImageGPTImageProcessingTester(unittest.TestCase):
         size=None,
         do_normalize=True,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 18, "width": 18}
         self.parent = parent
         self.batch_size = batch_size

tests/models/instructblipvideo/test_image_processing_instrictblipvideo.py

@@ -50,6 +50,7 @@ class InstructBlipVideoProcessingTester(unittest.TestCase):
         do_convert_rgb=True,
         frames=4,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 18, "width": 18}
         self.parent = parent
         self.batch_size = batch_size

tests/models/layoutlmv2/test_image_processing_layoutlmv2.py

@@ -41,6 +41,7 @@ class LayoutLMv2ImageProcessingTester(unittest.TestCase):
         size=None,
         apply_ocr=True,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 18, "width": 18}
         self.parent = parent
         self.batch_size = batch_size

tests/models/layoutlmv3/test_image_processing_layoutlmv3.py

@@ -41,6 +41,7 @@ class LayoutLMv3ImageProcessingTester(unittest.TestCase):
         size=None,
         apply_ocr=True,
     ):
+        super().__init__()
         size = size if size is not None else {"height": 18, "width": 18}
         self.parent = parent
         self.batch_size = batch_size

tests/models/levit/test_image_processing_levit.py

@@ -43,6 +43,7 @@ class LevitImageProcessingTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         size = size if size is not None else {"shortest_edge": 18}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
         self.parent = parent

tests/models/mobilenet_v1/test_image_processing_mobilenet_v1.py

@@ -40,6 +40,7 @@ class MobileNetV1ImageProcessingTester(unittest.TestCase):
         do_center_crop=True,
         crop_size=None,
     ):
+        super().__init__()
         size = size if size is not None else {"shortest_edge": 20}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
         self.parent = parent

tests/models/mobilenet_v2/test_image_processing_mobilenet_v2.py

@@ -40,6 +40,7 @@ class MobileNetV2ImageProcessingTester(unittest.TestCase):
         do_center_crop=True,
         crop_size=None,
     ):
+        super().__init__()
         size = size if size is not None else {"shortest_edge": 20}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
         self.parent = parent

tests/models/mobilevit/test_image_processing_mobilevit.py

@@ -48,6 +48,7 @@ class MobileViTImageProcessingTester(unittest.TestCase):
         crop_size=None,
         do_flip_channel_order=True,
     ):
+        super().__init__()
         size = size if size is not None else {"shortest_edge": 20}
         crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
         self.parent = parent

tests/models/nougat/test_image_processing_nougat.py

@@ -53,6 +53,7 @@ class NougatImageProcessingTester(unittest.TestCase):
         image_mean=[0.5, 0.5, 0.5],
         image_std=[0.5, 0.5, 0.5],
     ):
+        super().__init__()
         size = size if size is not None else {"height": 20, "width": 20}
         self.parent = parent
         self.batch_size = batch_size