fix tests

pytorch_transformers/__init__.py

@@ -56,8 +56,6 @@ from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CO
 
 # Modeling
 if is_torch_available():
-    logger.info("PyTorch version {} available.".format(torch.__version__))
-
     from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
     from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
                                 AutoModelWithLMHead)
@@ -96,8 +94,6 @@ if is_torch_available():
 
 # TensorFlow
 if is_tf_available():
-    logger.info("TensorFlow version {} available.".format(tf.__version__))
-
     from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
     from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
                                    TFAutoModelWithLMHead)

pytorch_transformers/file_utils.py

@@ -23,16 +23,20 @@ from botocore.exceptions import ClientError
 import requests
 from tqdm import tqdm
 
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
 try:
     import tensorflow as tf
     assert int(tf.__version__[0]) >= 2
     _tf_available = True  # pylint: disable=invalid-name
+    logger.info("TensorFlow version {} available.".format(tf.__version__))
 except (ImportError, AssertionError):
     _tf_available = False  # pylint: disable=invalid-name
 
 try:
     import torch
     _torch_available = True  # pylint: disable=invalid-name
+    logger.info("PyTorch version {} available.".format(torch.__version__))
 except ImportError:
     _torch_available = False  # pylint: disable=invalid-name
 
@@ -67,8 +71,6 @@ TF2_WEIGHTS_NAME = 'tf_model.h5'
 TF_WEIGHTS_NAME = 'model.ckpt'
 CONFIG_NAME = "config.json"
 
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
 def is_torch_available():
     return _torch_available
 

pytorch_transformers/tokenization_utils.py

@@ -27,7 +27,7 @@ from .file_utils import cached_path, is_tf_available, is_torch_available
 
 if is_tf_available():
     import tensorflow as tf
-if is_torch_available()
+if is_torch_available():
     import torch
 
 logger = logging.getLogger(__name__)