add warning if neither pt nor tf are found

examples/run_tf_glue.py

@@ -15,12 +15,12 @@ valid_dataset = valid_dataset.batch(64)
 
 # Compile tf.keras model for training
 learning_rate = tf.keras.optimizers.schedules.PolynomialDecay(2e-5, 345, end_learning_rate=0)
+optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08, clipnorm=1.0)
 loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-tf_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08, clipnorm=1.0),
-              loss=loss, metrics=['sparse_categorical_accuracy'])
+tf_model.compile(optimizer=optimizer, loss=loss, metrics=['sparse_categorical_accuracy'])
 
 # Train and evaluate using tf.keras.Model.fit()
-tf_model.fit(train_dataset, epochs=3, steps_per_epoch=115, validation_data=valid_dataset, validation_steps=7)
+tf_model.fit(train_dataset, epochs=1, steps_per_epoch=115, validation_data=valid_dataset, validation_steps=7)
 
 # Save the model and load it in PyTorch
 tf_model.save_pretrained('./runs/')

pytorch_transformers/__init__.py

@@ -157,3 +157,8 @@ if is_tf_available() and is_torch_available():
                                             load_tf2_checkpoint_in_pytorch_model,
                                             load_tf2_weights_in_pytorch_model,
                                             load_tf2_model_in_pytorch_model)
+
+if not is_tf_available() and not is_torch_available():
+    logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
+                   "Models won't be available and only tokenizers, configuration"
+                   "and file/data utilities can be used.")