[ci] fix 3 remaining slow GPU failures (#4584)

src/transformers/configuration_distilbert.py

@@ -73,10 +73,10 @@ class DistilBertConfig(PretrainedConfig):
                 The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
             qa_dropout (:obj:`float`, optional, defaults to 0.1):
                 The dropout probabilities used in the question answering model
-                :class:`~tranformers.DistilBertForQuestionAnswering`.
+                :class:`~transformers.DistilBertForQuestionAnswering`.
             seq_classif_dropout (:obj:`float`, optional, defaults to 0.2):
                 The dropout probabilities used in the sequence classification model
-                :class:`~tranformers.DistilBertForSequenceClassification`.
+                :class:`~transformers.DistilBertForSequenceClassification`.
 
         Example::
 

src/transformers/modeling_encoder_decoder.py

@@ -125,7 +125,7 @@ class EncoderDecoderModel(PreTrainedModel):
 
         Examples::
 
-            from tranformers import EncoderDecoder
+            from transformers import EncoderDecoder
 
             model = EncoderDecoder.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
         """

tests/test_modeling_bart.py

@@ -240,7 +240,7 @@ class BartTranslationTests(unittest.TestCase):
         with torch.no_grad():
             logits, *other_stuff = model(**self.net_input)
 
-        expected_slice = torch.tensor([9.0078, 10.1113, 14.4787])
+        expected_slice = torch.tensor([9.0078, 10.1113, 14.4787], device=torch_device)
         result_slice = logits[0][0][:3]
         self.assertTrue(torch.allclose(expected_slice, result_slice, atol=TOLERANCE))
 

tests/test_modeling_tf_electra.py

@@ -222,6 +222,6 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
     @slow
     def test_model_from_pretrained(self):
         # for model_name in list(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-        for model_name in ["electra-small-discriminator"]:
+        for model_name in ["google/electra-small-discriminator"]:
             model = TFElectraModel.from_pretrained(model_name)
             self.assertIsNotNone(model)