Remove dependency on pytest for running tests (#2055)

* Switch to plain unittest for skipping slow tests.

Add a RUN_SLOW environment variable for running them.

* Switch to plain unittest for PyTorch dependency.

* Switch to plain unittest for TensorFlow dependency.

* Avoid leaking open files in the test suite.

This prevents spurious warnings when running tests.

* Fix unicode warning on Python 2 when running tests.

The warning was:

    UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal

* Support running PyTorch tests on a GPU.

Reverts 27e015bd.

* Tests no longer require pytest.

* Make tests pass on cuda

transformers/tests/modeling_tf_common_test.py

@@ -25,18 +25,17 @@ import unittest
 import uuid
 import tempfile
 
-import pytest
 import sys
 
 from transformers import is_tf_available, is_torch_available
 
+from .utils import require_tf, slow
+
 if is_tf_available():
     import tensorflow as tf
     import numpy as np
     from transformers import TFPreTrainedModel
     # from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 if sys.version_info[0] == 2:
     import cPickle as pickle
@@ -62,6 +61,7 @@ def _config_zero_init(config):
 
 class TFCommonTestCases:
 
+    @require_tf
     class TFCommonModelTester(unittest.TestCase):
 
         model_tester = None
@@ -164,7 +164,7 @@ class TFCommonTestCases:
             for model_class in self.all_model_classes:
                 # Prepare our model
                 model = model_class(config)
-                
+
                 # Let's load it from the disk to be sure we can use pretrained weights
                 with TemporaryDirectory() as tmpdirname:
                     outputs = model(inputs_dict)  # build the model

transformers/tests/modeling_tf_ctrl_test.py

@@ -18,11 +18,11 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 from transformers import CTRLConfig, is_tf_available
 
@@ -30,10 +30,9 @@ if is_tf_available():
     import tensorflow as tf
     from transformers.modeling_tf_ctrl import (TFCTRLModel, TFCTRLLMHeadModel,
                                                 TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
+@require_tf
 class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else ()
@@ -188,7 +187,7 @@ class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_tf_distilbert_test.py

@@ -17,10 +17,10 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import pytest
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 from transformers import DistilBertConfig, is_tf_available
 
@@ -30,10 +30,9 @@ if is_tf_available():
                                                              TFDistilBertForMaskedLM,
                                                              TFDistilBertForQuestionAnswering,
                                                              TFDistilBertForSequenceClassification)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
+@require_tf
 class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering,
@@ -210,7 +209,7 @@ class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs)
 
-    # @pytest.mark.slow
+    # @slow
     # def test_model_from_pretrained(self):
     #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_tf_gpt2_test.py

@@ -18,11 +18,11 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 from transformers import GPT2Config, is_tf_available
 
@@ -31,10 +31,9 @@ if is_tf_available():
     from transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel,
                                                        TFGPT2DoubleHeadsModel,
                                                        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
+@require_tf
 class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel,
@@ -219,7 +218,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_tf_openai_gpt_test.py

@@ -18,11 +18,11 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 from transformers import OpenAIGPTConfig, is_tf_available
 
@@ -31,10 +31,9 @@ if is_tf_available():
     from transformers.modeling_tf_openai import (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
                                                          TFOpenAIGPTDoubleHeadsModel,
                                                          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
+@require_tf
 class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
@@ -218,7 +217,7 @@ class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_tf_roberta_test.py

@@ -18,10 +18,10 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 from transformers import RobertaConfig, is_tf_available
 
@@ -32,10 +32,9 @@ if is_tf_available():
                                                           TFRobertaForSequenceClassification,
                                                           TFRobertaForTokenClassification,
                                                           TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
+@require_tf
 class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFRobertaModel,TFRobertaForMaskedLM,
@@ -191,7 +190,7 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
@@ -203,10 +202,10 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
 
 class TFRobertaModelIntegrationTest(unittest.TestCase):
 
-    @pytest.mark.slow
+    @slow
     def test_inference_masked_lm(self):
         model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
-        
+
         input_ids = tf.constant([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
         output = model(input_ids)[0]
         expected_shape = [1, 11, 50265]
@@ -224,10 +223,10 @@ class TFRobertaModelIntegrationTest(unittest.TestCase):
             numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
         )
 
-    @pytest.mark.slow
+    @slow
     def test_inference_no_head(self):
         model = TFRobertaModel.from_pretrained('roberta-base')
-        
+
         input_ids = tf.constant([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
         output = model(input_ids)[0]
         # compare the actual values for a slice.
@@ -240,10 +239,10 @@ class TFRobertaModelIntegrationTest(unittest.TestCase):
             numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
         )
 
-    @pytest.mark.slow
+    @slow
     def test_inference_classification_head(self):
         model = TFRobertaForSequenceClassification.from_pretrained('roberta-large-mnli')
-        
+
         input_ids = tf.constant([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
         output = model(input_ids)[0]
         expected_shape = [1, 3]

transformers/tests/modeling_tf_transfo_xl_test.py

@@ -19,10 +19,10 @@ from __future__ import print_function
 import unittest
 import random
 import shutil
-import pytest
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 from transformers import TransfoXLConfig, is_tf_available
 
@@ -31,10 +31,9 @@ if is_tf_available():
     from transformers.modeling_tf_transfo_xl import (TFTransfoXLModel,
                                                              TFTransfoXLLMHeadModel,
                                                              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
+@require_tf
 class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else ()
@@ -204,7 +203,7 @@ class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_tf_xlm_test.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 
 from transformers import is_tf_available
 
@@ -29,13 +28,13 @@ if is_tf_available():
                                       TFXLMForSequenceClassification,
                                       TFXLMForQuestionAnsweringSimple,
                                       TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
 
+@require_tf
 class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFXLMModel, TFXLMWithLMHeadModel,
@@ -251,7 +250,7 @@ class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_tf_xlnet_test.py

@@ -21,7 +21,6 @@ import unittest
 import json
 import random
 import shutil
-import pytest
 
 from transformers import XLNetConfig, is_tf_available
 
@@ -33,12 +32,13 @@ if is_tf_available():
                                                         TFXLNetForTokenClassification,
                                                         TFXLNetForQuestionAnsweringSimple,
                                                         TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
-else:
-    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_tf, slow
 
+
+@require_tf
 class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes=(TFXLNetModel, TFXLNetLMHeadModel,
@@ -304,7 +304,7 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
     def test_xlnet_lm_head(self):
         self.model_tester.set_seed()
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) 
+        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
 
     def test_xlnet_sequence_classif(self):
         self.model_tester.set_seed()
@@ -320,7 +320,7 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_transfo_xl_test.py

@@ -19,7 +19,6 @@ from __future__ import print_function
 import unittest
 import random
 import shutil
-import pytest
 
 from transformers import is_torch_available
 
@@ -27,12 +26,13 @@ if is_torch_available():
     import torch
     from transformers import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel)
     from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
-else:
-    pytestmark = pytest.mark.skip("Require Torch")
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_torch, slow, torch_device
 
+
+@require_torch
 class TransfoXLModelTest(CommonTestCases.CommonModelTester):
 
     all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
@@ -111,6 +111,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester):
 
         def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
             model = TransfoXLModel(config)
+            model.to(torch_device)
             model.eval()
 
             hidden_states_1, mems_1 = model(input_ids_1)
@@ -140,6 +141,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester):
 
         def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
             model = TransfoXLLMHeadModel(config)
+            model.to(torch_device)
             model.eval()
 
             lm_logits_1, mems_1 = model(input_ids_1)
@@ -204,7 +206,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester):
         output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
         self.model_tester.check_transfo_xl_lm_head_output(output_result)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_xlm_test.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 
 from transformers import is_torch_available
 
@@ -26,13 +25,13 @@ if is_torch_available():
     from transformers import (XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
                                       XLMForSequenceClassification, XLMForQuestionAnsweringSimple)
     from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
-else:
-    pytestmark = pytest.mark.skip("Require Torch")
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_torch, slow, torch_device
 
 
+@require_torch
 class XLMModelTest(CommonTestCases.CommonModelTester):
 
     all_model_classes = (XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
@@ -148,6 +147,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xlm_model(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
             model = XLMModel(config=config)
+            model.to(torch_device)
             model.eval()
             outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
             outputs = model(input_ids, langs=token_type_ids)
@@ -163,6 +163,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xlm_lm_head(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
             model = XLMWithLMHeadModel(config)
+            model.to(torch_device)
             model.eval()
 
             loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
@@ -182,6 +183,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xlm_simple_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
             model = XLMForQuestionAnsweringSimple(config)
+            model.to(torch_device)
             model.eval()
 
             outputs = model(input_ids)
@@ -206,6 +208,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xlm_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
             model = XLMForQuestionAnswering(config)
+            model.to(torch_device)
             model.eval()
 
             outputs = model(input_ids)
@@ -260,6 +263,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xlm_sequence_classif(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
             model = XLMForSequenceClassification(config)
+            model.to(torch_device)
             model.eval()
 
             (logits,) = model(input_ids)
@@ -312,7 +316,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_xlnet_test.py

@@ -21,7 +21,6 @@ import unittest
 import json
 import random
 import shutil
-import pytest
 
 from transformers import is_torch_available
 
@@ -31,12 +30,13 @@ if is_torch_available():
     from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification,
                               XLNetForTokenClassification, XLNetForQuestionAnswering)
     from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
-else:
-    pytestmark = pytest.mark.skip("Require Torch")
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
+from .utils import require_torch, slow, torch_device
 
+
+@require_torch
 class XLNetModelTest(CommonTestCases.CommonModelTester):
 
     all_model_classes=(XLNetModel, XLNetLMHeadModel, XLNetForTokenClassification,
@@ -100,9 +100,9 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
             input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float()
 
             input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
-            perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float)
+            perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device)
             perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
-            target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float)
+            target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device)
             target_mapping[:, 0, -1] = 1.0  # predict last token
 
             sequence_labels = None
@@ -141,6 +141,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                 target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
             model = XLNetModel(config)
+            model.to(torch_device)
             model.eval()
 
             _, _ = model(input_ids_1, input_mask=input_mask)
@@ -155,6 +156,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
 
             config.mem_len = 0
             model = XLNetModel(config)
+            model.to(torch_device)
             model.eval()
             no_mems_outputs = model(input_ids_1)
             self.parent.assertEqual(len(no_mems_outputs), 1)
@@ -169,6 +171,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xlnet_base_model_with_att_output(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                     target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
             model = XLNetModel(config)
+            model.to(torch_device)
             model.eval()
 
             _, _, attentions = model(input_ids_1, target_mapping=target_mapping)
@@ -181,6 +184,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                 target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
             model = XLNetLMHeadModel(config)
+            model.to(torch_device)
             model.eval()
 
             loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
@@ -221,6 +225,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                 target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
             model = XLNetForQuestionAnswering(config)
+            model.to(torch_device)
             model.eval()
 
             outputs = model(input_ids_1)
@@ -279,6 +284,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xlnet_token_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                 target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
             model = XLNetForTokenClassification(config)
+            model.to(torch_device)
             model.eval()
 
             logits, mems_1 = model(input_ids_1)
@@ -311,6 +317,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                 target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
             model = XLNetForSequenceClassification(config)
+            model.to(torch_device)
             model.eval()
 
             logits, mems_1 = model(input_ids_1)
@@ -362,7 +369,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
     def test_xlnet_lm_head(self):
         self.model_tester.set_seed()
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) 
+        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
 
     def test_xlnet_sequence_classif(self):
         self.model_tester.set_seed()
@@ -379,7 +386,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
 
-    @pytest.mark.slow
+    @slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/optimization_test.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 
 import unittest
 import os
-import pytest
 
 from transformers import is_torch_available
 
@@ -31,10 +30,9 @@ if is_torch_available():
                               get_cosine_schedule_with_warmup,
                               get_cosine_with_hard_restarts_schedule_with_warmup,
                               get_linear_schedule_with_warmup)
-else:
-    pytestmark = pytest.mark.skip("Require Torch")
 
 from .tokenization_tests_commons import TemporaryDirectory
+from .utils import require_torch
 
 
 def unwrap_schedule(scheduler, num_steps=10):
@@ -58,6 +56,7 @@ def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
                 scheduler.load_state_dict(state_dict)
     return lrs
 
+@require_torch
 class OptimizationTest(unittest.TestCase):
 
     def assertListAlmostEqual(self, list1, list2, tol):
@@ -80,6 +79,7 @@ class OptimizationTest(unittest.TestCase):
         self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
 
 
+@require_torch
 class ScheduleInitTest(unittest.TestCase):
     m = torch.nn.Linear(50, 50) if is_torch_available() else None
     optimizer = AdamW(m.parameters(), lr=10.) if is_torch_available() else None

transformers/tests/tokenization_auto_test.py

@@ -18,15 +18,16 @@ from __future__ import print_function
 
 import unittest
 import shutil
-import pytest
 import logging
 
 from transformers import AutoTokenizer, BertTokenizer, AutoTokenizer, GPT2Tokenizer
 from transformers import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
 
+from .utils import slow
+
 
 class AutoTokenizerTest(unittest.TestCase):
-    @pytest.mark.slow
+    @slow
     def test_tokenizer_from_pretrained(self):
         logging.basicConfig(level=logging.INFO)
         for model_name in list(BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys())[:1]:

transformers/tests/tokenization_bert_test.py

@@ -16,7 +16,6 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import os
 import unittest
-import pytest
 from io import open
 
 from transformers.tokenization_bert import (BasicTokenizer,
@@ -26,6 +25,7 @@ from transformers.tokenization_bert import (BasicTokenizer,
                                                     _is_whitespace, VOCAB_FILES_NAMES)
 
 from .tokenization_tests_commons import CommonTestCases
+from .utils import slow
 
 class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
 
@@ -126,7 +126,7 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
         self.assertFalse(_is_punctuation(u"A"))
         self.assertFalse(_is_punctuation(u" "))
 
-    @pytest.mark.slow
+    @slow
     def test_sequence_builders(self):
         tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
 

transformers/tests/tokenization_distilbert_test.py

@@ -16,13 +16,13 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import os
 import unittest
-import pytest
 from io import open
 
 from transformers.tokenization_distilbert import (DistilBertTokenizer)
 
 from .tokenization_tests_commons import CommonTestCases
 from .tokenization_bert_test import BertTokenizationTest
+from .utils import slow
 
 class DistilBertTokenizationTest(BertTokenizationTest):
 
@@ -31,7 +31,7 @@ class DistilBertTokenizationTest(BertTokenizationTest):
     def get_tokenizer(self, **kwargs):
         return DistilBertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
-    @pytest.mark.slow
+    @slow
     def test_sequence_builders(self):
         tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
 

transformers/tests/tokenization_roberta_test.py

@@ -17,11 +17,11 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 import os
 import json
 import unittest
-import pytest
 from io import open
 
 from transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES
 from .tokenization_tests_commons import CommonTestCases
+from .utils import slow
 
 
 class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
@@ -79,7 +79,7 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
             [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
         )
 
-    @pytest.mark.slow
+    @slow
     def test_sequence_builders(self):
         tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
 

transformers/tests/tokenization_tests_commons.py

@@ -102,9 +102,11 @@ class CommonTestCases:
             with TemporaryDirectory() as tmpdirname:
 
                 filename = os.path.join(tmpdirname, u"tokenizer.bin")
-                pickle.dump(tokenizer, open(filename, "wb"))
+                with open(filename, "wb") as handle:
+                    pickle.dump(tokenizer, handle)
 
-                tokenizer_new = pickle.load(open(filename, "rb"))
+                with open(filename, "rb") as handle:
+                    tokenizer_new = pickle.load(handle)
 
             subwords_loaded = tokenizer_new.tokenize(text)
 

transformers/tests/tokenization_transfo_xl_test.py

@@ -16,7 +16,6 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import os
 import unittest
-import pytest
 from io import open
 
 from transformers import is_torch_available
@@ -24,11 +23,12 @@ from transformers import is_torch_available
 if is_torch_available():
     import torch
     from transformers.tokenization_transfo_xl import TransfoXLTokenizer, VOCAB_FILES_NAMES
-else:
-    pytestmark = pytest.mark.skip("Require Torch")  # TODO: untangle Transfo-XL tokenizer from torch.load and torch.save
 
 from .tokenization_tests_commons import CommonTestCases
+from .utils import require_torch
 
+
+@require_torch
 class TransfoXLTokenizationTest(CommonTestCases.CommonTokenizerTester):
 
     tokenizer_class = TransfoXLTokenizer if is_torch_available() else None

transformers/tests/tokenization_utils_test.py

@@ -18,13 +18,14 @@ from __future__ import print_function
 
 import unittest
 import six
-import pytest
 
 from transformers import PreTrainedTokenizer
 from transformers.tokenization_gpt2 import GPT2Tokenizer
 
+from .utils import slow
+
 class TokenizerUtilsTest(unittest.TestCase):
-    @pytest.mark.slow
+
     def check_tokenizer_from_pretrained(self, tokenizer_class):
         s3_models = list(tokenizer_class.max_model_input_sizes.keys())
         for model_name in s3_models[:1]:
@@ -41,6 +42,7 @@ class TokenizerUtilsTest(unittest.TestCase):
                 special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
                 self.assertIsInstance(special_tok_id, int)
 
+    @slow
     def test_pretrained_tokenizers(self):
         self.check_tokenizer_from_pretrained(GPT2Tokenizer)