Fix test processing utils

huggingface · Sep 3, 2024 · 146a50a · 146a50a
1 parent 1263a21
commit 146a50a
Showing 1 changed file with 16 additions and 13 deletions.
diff --git a/tests/utils/test_processing_utils.py b/tests/utils/test_processing_utils.py
@@ -16,17 +16,20 @@
 import unittest
 
 import numpy as np
-import PIL
 
-from transformers import is_torch_available
+from transformers import is_torch_available, is_vision_available
 from transformers.processing_utils import _validate_images_text_input_order
-from transformers.testing_utils import require_torch
+from transformers.testing_utils import require_torch, require_vision
 
 
+if is_vision_available():
+    import PIL
+
 if is_torch_available():
     import torch
 
 
+@require_vision
 class ProcessingUtilTester(unittest.TestCase):
     def test_validate_images_text_input_order(self):
         # text string and PIL images inputs
@@ -46,11 +49,11 @@ def test_validate_images_text_input_order(self):
         text = ["text1", "text2"]
         # test correct text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=images, text=text)
-        self.assertIsInstance(valid_images, np.ndarray)
+        self.assertTrue(np.array_equal(valid_images, images))
         self.assertEqual(valid_text, text)
         # test incorrect text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=text, text=images)
-        self.assertIsInstance(valid_images, np.ndarray)
+        self.assertTrue(np.array_equal(valid_images, images))
         self.assertEqual(valid_text, text)
 
         # text nested list of string and list of pil images inputs
@@ -71,23 +74,23 @@ def test_validate_images_text_input_order(self):
         print(type(text))
         # test correct text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=images, text=text)
-        self.assertIsInstance(valid_images[0], np.ndarray)
+        self.assertTrue(np.array_equal(valid_images[0], images[0]))
         self.assertEqual(valid_text, text)
         # test incorrect text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=text, text=images)
-        self.assertIsInstance(valid_images[0], np.ndarray)
+        self.assertTrue(np.array_equal(valid_images[0], images[0]))
         self.assertEqual(valid_text, text)
 
         # list of pretokenized text and nested list of numpy images inputs
         images = [[np.random.rand(224, 224, 3), np.random.rand(224, 224, 3)], [np.random.rand(224, 224, 3)]]
         text = [list(range(10)), list(range(5))]
         # test correct text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=images, text=text)
-        self.assertIsInstance(valid_images[0][0], np.ndarray)
+        self.assertTrue(np.array_equal(valid_images[0][0], images[0][0]))
         self.assertEqual(valid_text, text)
         # test incorrect text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=text, text=images)
-        self.assertIsInstance(valid_images[0][0], np.ndarray)
+        self.assertTrue(np.array_equal(valid_images[0][0], images[0][0]))
         self.assertEqual(valid_text, text)
 
         # nested list of pretokenized text and nested list of PIL images inputs
@@ -142,21 +145,21 @@ def test_validate_images_text_input_order_torch(self):
         text = "text"
         # test correct text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=images, text=text)
-        self.assertIsInstance(valid_images, torch.Tensor)
+        self.assertTrue(torch.equal(valid_images, images))
         self.assertEqual(valid_text, text)
         # test incorrect text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=text, text=images)
-        self.assertIsInstance(valid_images, torch.Tensor)
+        self.assertTrue(torch.equal(valid_images, images))
         self.assertEqual(valid_text, text)
 
         # text list of string and list of torch images inputs
         images = [torch.rand(224, 224, 3), torch.rand(224, 224, 3)]
         text = ["text1", "text2"]
         # test correct text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=images, text=text)
-        self.assertIsInstance(valid_images[0], torch.Tensor)
+        self.assertTrue(torch.equal(valid_images[0], images[0]))
         self.assertEqual(valid_text, text)
         # test incorrect text and images order
         valid_images, valid_text = _validate_images_text_input_order(images=text, text=images)
-        self.assertIsInstance(valid_images[0], torch.Tensor)
+        self.assertTrue(torch.equal(valid_images[0], images[0]))
         self.assertEqual(valid_text, text)