remove Bert2Rnd test

transformers/tests/modeling_bert_test.py

@@ -29,7 +29,7 @@ if is_torch_available():
     from transformers import (BertConfig, BertModel, BertForMaskedLM,
                               BertForNextSentencePrediction, BertForPreTraining,
                               BertForQuestionAnswering, BertForSequenceClassification,
-                              BertForTokenClassification, BertForMultipleChoice, Bert2Rnd)
+                              BertForTokenClassification, BertForMultipleChoice)
     from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 else:
     pytestmark = pytest.mark.skip("Require Torch")
@@ -255,17 +255,6 @@ class BertModelTest(CommonTestCases.CommonModelTester):
                 [self.batch_size, self.num_choices])
             self.check_loss_output(result)
 
-        def create_and_check_bert2rnd(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
-            config.num_choices = self.num_choices
-            model = Bert2Rnd(config=config)
-            model.eval()
-            bert2rnd_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
-            bert2rnd_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
-            bert2rnd_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
-            _ = model(bert2rnd_inputs_ids,
-                      attention_mask=bert2rnd_input_mask,
-                      token_type_ids=bert2rnd_token_type_ids)
-
         def prepare_config_and_inputs_for_common(self):
             config_and_inputs = self.prepare_config_and_inputs()
             (config, input_ids, token_type_ids, input_mask,