def create_and_check_for_question_answering(
     self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 ):
     model = LongformerForQuestionAnswering(config=config)
     model.to(torch_device)
     model.eval()
     result = model(
         input_ids,
         attention_mask=input_mask,
         global_attention_mask=input_mask,
         token_type_ids=token_type_ids,
         start_positions=sequence_labels,
         end_positions=sequence_labels,
     )
     self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
     self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
 def create_and_check_longformer_for_question_answering(
         self, config, input_ids, token_type_ids, input_mask,
         sequence_labels, token_labels, choice_labels):
     model = LongformerForQuestionAnswering(config=config)
     model.to(torch_device)
     model.eval()
     result = model(
         input_ids,
         attention_mask=input_mask,
         global_attention_mask=input_mask,
         token_type_ids=token_type_ids,
         start_positions=sequence_labels,
         end_positions=sequence_labels,
     )
     self.parent.assertListEqual(list(result["start_logits"].size()),
                                 [self.batch_size, self.seq_length])
     self.parent.assertListEqual(list(result["end_logits"].size()),
                                 [self.batch_size, self.seq_length])
     self.check_loss_output(result)