Remove repeated prepare_images in processor tests (#33163)

* Remove repeated prepare_images

* Address comments - update docstring; explanatory comment

tests/models/chinese_clip/test_processor_chinese_clip.py

@@ -18,7 +18,6 @@ import shutil
 import tempfile
 import unittest
 
-import numpy as np
 import pytest
 
 from transformers import BertTokenizer, BertTokenizerFast
@@ -26,15 +25,17 @@ from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
 from transformers.testing_utils import require_vision
 from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
 
+from ...test_processing_common import ProcessorTesterMixin
+
 
 if is_vision_available():
-    from PIL import Image
-
     from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
 
 
 @require_vision
-class ChineseCLIPProcessorTest(unittest.TestCase):
+class ChineseCLIPProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = ChineseCLIPProcessor
+
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
 
@@ -76,6 +77,11 @@ class ChineseCLIPProcessorTest(unittest.TestCase):
         with open(self.image_processor_file, "w", encoding="utf-8") as fp:
             json.dump(image_processor_map, fp)
 
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = ChineseCLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)
+        processor.save_pretrained(self.tmpdirname)
+
     def get_tokenizer(self, **kwargs):
         return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
@@ -88,17 +94,6 @@ class ChineseCLIPProcessorTest(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()