Move tests/utils.py -> transformers/testing_utils.py (#5350)

tests/test_tokenization_transfo_xl.py

@@ -18,9 +18,9 @@ import os
 import unittest
 
 from transformers import is_torch_available
+from transformers.testing_utils import require_torch
 
 from .test_tokenization_common import TokenizerTesterMixin
-from .utils import require_torch
 
 
 if is_torch_available():

tests/test_tokenization_utils.py

@@ -17,10 +17,9 @@ import unittest
 from typing import Callable, Optional
 
 from transformers import BatchEncoding, BertTokenizer, BertTokenizerFast, PreTrainedTokenizer, TensorType
+from transformers.testing_utils import require_tf, require_torch, slow
 from transformers.tokenization_gpt2 import GPT2Tokenizer
 
-from .utils import require_tf, require_torch, slow
-
 
 class TokenizerUtilsTest(unittest.TestCase):
     def check_tokenizer_from_pretrained(self, tokenizer_class):

tests/test_tokenization_xlm.py

@@ -18,10 +18,10 @@ import json
 import os
 import unittest
 
+from transformers.testing_utils import slow
 from transformers.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
 
 from .test_tokenization_common import TokenizerTesterMixin
-from .utils import slow
 
 
 class XLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):

tests/test_tokenization_xlm_roberta.py

@@ -18,10 +18,10 @@ import os
 import unittest
 
 from transformers.file_utils import cached_property
+from transformers.testing_utils import slow
 from transformers.tokenization_xlm_roberta import SPIECE_UNDERLINE, XLMRobertaTokenizer
 
 from .test_tokenization_common import TokenizerTesterMixin
-from .utils import slow
 
 
 SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")

tests/test_tokenization_xlnet.py

@@ -17,10 +17,10 @@
 import os
 import unittest
 
+from transformers.testing_utils import slow
 from transformers.tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
 
 from .test_tokenization_common import TokenizerTesterMixin
-from .utils import slow
 
 
 SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")

tests/test_trainer.py

 import unittest
 
 from transformers import AutoTokenizer, TrainingArguments, is_torch_available
-
-from .utils import require_torch
+from transformers.testing_utils import require_torch
 
 
 if is_torch_available():