diff --git a/tests/models/mobilenet_v2/test_image_processing_mobilenet_v2.py b/tests/models/mobilenet_v2/test_image_processing_mobilenet_v2.py
index 7027a0b77a3c..3be6613e0c79 100644
--- a/tests/models/mobilenet_v2/test_image_processing_mobilenet_v2.py
+++ b/tests/models/mobilenet_v2/test_image_processing_mobilenet_v2.py
@@ -16,26 +16,16 @@
 import unittest
 
 import requests
+import torch
 from datasets import load_dataset
+from PIL import Image
 
+from transformers import MobileNetV2ImageProcessor, MobileNetV2ImageProcessorFast
 from transformers.testing_utils import require_torch, require_vision
-from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
 
 from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
 
 
-if is_torch_available():
-    import torch
-
-if is_vision_available():
-    from PIL import Image
-
-    from transformers import MobileNetV2ImageProcessor
-
-    if is_torchvision_available():
-        from transformers import MobileNetV2ImageProcessorFast
-
-
 class MobileNetV2ImageProcessingTester:
     def __init__(
         self,
@@ -103,9 +93,6 @@ def prepare_semantic_batch_inputs():
 @require_torch
 @require_vision
 class MobileNetV2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
-    image_processing_class = MobileNetV2ImageProcessor if is_vision_available() else None
-    fast_image_processing_class = MobileNetV2ImageProcessorFast if is_torchvision_available() else None
-
     def setUp(self):
         super().setUp()
         self.image_processor_tester = MobileNetV2ImageProcessingTester(self)
@@ -115,7 +102,7 @@ def image_processor_dict(self):
         return self.image_processor_tester.prepare_image_processor_dict()
 
     def test_image_processor_properties(self):
-        for image_processing_class in self.image_processor_list:
+        for image_processing_class in [MobileNetV2ImageProcessor, MobileNetV2ImageProcessorFast]:
             image_processor = image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(image_processor, "do_resize"))
             self.assertTrue(hasattr(image_processor, "size"))
@@ -124,7 +111,7 @@ def test_image_processor_properties(self):
             self.assertTrue(hasattr(image_processor, "do_reduce_labels"))
 
     def test_image_processor_from_dict_with_kwargs(self):
-        for image_processing_class in self.image_processor_list:
+        for image_processing_class in [MobileNetV2ImageProcessor, MobileNetV2ImageProcessorFast]:
             image_processor = image_processing_class.from_dict(self.image_processor_dict)
             self.assertEqual(image_processor.size, {"shortest_edge": 20})
             self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
@@ -139,7 +126,7 @@ def test_image_processor_from_dict_with_kwargs(self):
 
     def test_call_segmentation_maps(self):
         # Initialize image_processing
-        for image_processing_class in self.image_processor_list:
+        for image_processing_class in [MobileNetV2ImageProcessor, MobileNetV2ImageProcessorFast]:
             image_processing = image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
@@ -246,7 +233,7 @@ def test_call_segmentation_maps(self):
 
     def test_reduce_labels(self):
         # Initialize image_processing
-        for image_processing_class in self.image_processor_list:
+        for image_processing_class in [MobileNetV2ImageProcessor, MobileNetV2ImageProcessorFast]:
             image_processing = image_processing_class(**self.image_processor_dict)
 
             # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
@@ -260,19 +247,12 @@ def test_reduce_labels(self):
             self.assertTrue(encoding["labels"].min().item() >= 0)
             self.assertTrue(encoding["labels"].max().item() <= 255)
 
-    def test_slow_fast_equivalence(self):
-        if not self.test_slow_image_processor or not self.test_fast_image_processor:
-            self.skipTest(reason="Skipping slow/fast equivalence test")
-
-        if self.image_processing_class is None or self.fast_image_processing_class is None:
-            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
-
         # Test with single image
         dummy_image = Image.open(
             requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
         )
-        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
-        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+        image_processor_slow = MobileNetV2ImageProcessor(**self.image_processor_dict)
+        image_processor_fast = MobileNetV2ImageProcessorFast(**self.image_processor_dict)
 
         encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
         encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")