[EncoderDecoder Tests] Improve tests (#4046)

* Hoist bert model tester for patric

* indent

* make tests work

* Update tests/test_modeling_bert.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: sshleifer <sshleifer@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

tests/test_modeling_bert.py

@@ -38,24 +38,7 @@ if is_torch_available():
     from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 
 
-@require_torch
-class BertModelTest(ModelTesterMixin, unittest.TestCase):
-
-    all_model_classes = (
-        (
-            BertModel,
-            BertForMaskedLM,
-            BertForNextSentencePrediction,
-            BertForPreTraining,
-            BertForQuestionAnswering,
-            BertForSequenceClassification,
-            BertForTokenClassification,
-        )
-        if is_torch_available()
-        else ()
-    )
-
-    class BertModelTester(object):
+class BertModelTester:
     def __init__(
         self,
         parent,
@@ -292,10 +275,7 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
         model.to(torch_device)
         model.eval()
         loss, seq_relationship_score = model(
-                input_ids,
-                attention_mask=input_mask,
-                token_type_ids=token_type_ids,
-                next_sentence_label=sequence_labels,
+            input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels,
         )
         result = {
             "loss": loss,
@@ -374,16 +354,12 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
         model = BertForTokenClassification(config=config)
         model.to(torch_device)
         model.eval()
-            loss, logits = model(
-                input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
-            )
+        loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         result = {
             "loss": loss,
             "logits": logits,
         }
-            self.parent.assertListEqual(
-                list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels]
-            )
+        self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels])
         self.check_loss_output(result)
 
     def create_and_check_bert_for_multiple_choice(
@@ -423,8 +399,26 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
         inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict
 
+
+@require_torch
+class BertModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            BertModel,
+            BertForMaskedLM,
+            BertForNextSentencePrediction,
+            BertForPreTraining,
+            BertForQuestionAnswering,
+            BertForSequenceClassification,
+            BertForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+
     def setUp(self):
-        self.model_tester = BertModelTest.BertModelTester(self)
+        self.model_tester = BertModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
 
     def test_config(self):

tests/test_modeling_encoder_decoder.py

@@ -21,7 +21,7 @@ from transformers import is_torch_available
 
 # TODO(PVP): this line reruns all the tests in BertModelTest; not sure whether this can be prevented
 # for now only run module with pytest tests/test_modeling_encoder_decoder.py::EncoderDecoderModelTest
-from .test_modeling_bert import BertModelTest
+from .test_modeling_bert import BertModelTester
 from .utils import require_torch, slow, torch_device
 
 
@@ -34,7 +34,7 @@ if is_torch_available():
 @require_torch
 class EncoderDecoderModelTest(unittest.TestCase):
     def prepare_config_and_inputs_bert(self):
-        bert_model_tester = BertModelTest.BertModelTester(self)
+        bert_model_tester = BertModelTester(self)
         encoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
         decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
         (