Remove repeated prepare_images in processor tests (#33163)

* Remove repeated prepare_images

* Address comments - update docstring; explanatory comment

tests/models/flava/test_processor_flava.py

@@ -19,7 +19,6 @@ import shutil
 import tempfile
 import unittest
 
-import numpy as np
 import pytest
 
 from transformers import BertTokenizer, BertTokenizerFast
@@ -27,10 +26,10 @@ from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
 from transformers.testing_utils import require_vision
 from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
 
+from ...test_processing_common import ProcessorTesterMixin
+
 
 if is_vision_available():
-    from PIL import Image
-
     from transformers import FlavaImageProcessor, FlavaProcessor
     from transformers.models.flava.image_processing_flava import (
         FLAVA_CODEBOOK_MEAN,
@@ -41,7 +40,9 @@ if is_vision_available():
 
 
 @require_vision
-class FlavaProcessorTest(unittest.TestCase):
+class FlavaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = FlavaProcessor
+
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
 
@@ -91,17 +92,6 @@ class FlavaProcessorTest(unittest.TestCase):
     def tearDown(self):
         shutil.rmtree(self.tmpdirname)
 
-    def prepare_image_inputs(self):
-        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
-        or a list of PyTorch tensors if one specifies torchify=True.
-        """
-
-        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
-
-        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
-
-        return image_inputs
-
     def test_save_load_pretrained_default(self):
         tokenizer_slow = self.get_tokenizer()
         tokenizer_fast = self.get_rust_tokenizer()