fix typos in the tests directory (#36717)

tests/utils/test_add_new_model_like.py

@@ -436,7 +436,7 @@ NEW_BERT_CONSTANT = "value"
 
             self.init_file(file_name, bert_test)
             duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns)
-            # There should not be a new Copied from statement, the old one should be adapated.
+            # There should not be a new Copied from statement, the old one should be adapted.
             self.check_result(dest_file_name, bert_expected)
 
             self.init_file(file_name, bert_test)

tests/utils/test_image_utils.py

@@ -996,7 +996,7 @@ class UtilFunctionTester(unittest.TestCase):
         image = np.random.randint(0, 256, (3, 32, 64))
         self.assertEqual(get_image_size(image), (32, 64))
 
-        # Test the channel dimension can be overriden
+        # Test the channel dimension can be overridden
         image = np.random.randint(0, 256, (3, 32, 64))
         self.assertEqual(get_image_size(image, channel_dim=ChannelDimension.LAST), (3, 32))
 

tests/utils/test_modeling_rope_utils.py

@@ -411,7 +411,7 @@ class RopeTest(unittest.TestCase):
             self.assertEqual(attention_scale, 1.0)
 
         # Check 2: based on `low_freq_factor` and `high_freq_factor`, the frequencies will be scaled between 1 and
-        # `factor` (similar to yarn). Low frequencies get scaled by `factor`, high frequences see no change, medium
+        # `factor` (similar to yarn). Low frequencies get scaled by `factor`, high frequencies see no change, medium
         # frequencies are scaled by a value in between. Changing `low_freq_factor` and `high_freq_factor` changes what
         # is considered low, medium, and high frequencies.
         factor = 10.0

tests/utils/test_modeling_utils.py

@@ -1686,7 +1686,7 @@ class ModelUtilsTest(TestCasePlus):
     def test_isin_mps_friendly(self):
         """tests that our custom `isin_mps_friendly` matches `torch.isin`"""
         random_ids = torch.randint(0, 100, (100,))
-        # We can match against an interger
+        # We can match against an integer
         random_test_integer = torch.randint(0, 100, (1,)).item()
         self.assertTrue(
             torch.equal(
@@ -1911,7 +1911,7 @@ class ModelUtilsTest(TestCasePlus):
     @require_torch_gpu
     def test_loading_is_fast_on_gpu(self, model_id: str, max_loading_time: float):
         """
-        This test is used to avoid regresion on https://github.com/huggingface/transformers/pull/36380.
+        This test is used to avoid regression on https://github.com/huggingface/transformers/pull/36380.
         10s should be more than enough for both models, and allows for some margin as loading time are quite
         unstable. Before #36380, it used to take more than 40s, so 10s is still reasonable.
         Note that we run this test in a subprocess, to ensure that cuda is not already initialized/warmed-up.