Traceback (most recent call last):

 "c:\Python27\Scripts\behave-script.py", line 9, in <module>
load_entry_point('behave==1.2.4', 'console_scripts', 'behave')()

 "c:\Python27\lib\site-packages\behave__main__.py", line 111, in main
failed = runner.run()

 "c:\Python27\lib\site-packages\behave\runner.py", line 659, in run
return self.run_with_paths()

 "c:\Python27\lib\site-packages\behave\runner.py", line 680, in run_with_paths
return self.run_model()

 "c:\Python27\lib\site-packages\behave\runner.py", line 481, in run_model
failed = feature.run(self)

 "c:\Python27\lib\site-packages\behave\model.py", line 461, in run
failed = scenario.run(runner)

 "c:\Python27\lib\site-packages\behave\model.py", line 784, in run
if not step.run(runner):

 "c:\Python27\lib\site-packages\behave\model.py", line 1218, in run
formatter.result(self)

 "c:\Python27\lib\site-packages\behave\formatter\pretty.py", line 160, in result
self.stream.write(indent(result.error_message.strip(), u'
'))

 "c:\Python27\lib\site-packages\behave\textutil.py", line 29, in indent
return newline.join([prefix + unicode(line) for line in lines])UnicodeDecodeError: 'ascii' codec can't decode byte 0x82 in position 11: ordinal not in range(128)
Traceback (most recent call last):


 "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap

fn(i, *args)


 "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 321, in distributed_main

main(args, init_distributed=True)


 "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 89, in main

train(args, trainer, task, epoch_itr)


 "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 152, in train

log_output = trainer.train_step(samples)


 "/home/dh/swapnil/fairseq/fairseq/trainer.py", line 327, in train_step

sample, self.model, self.criterion, self.optimizer, ignore_grad


 "/home/dh/swapnil/fairseq/fairseq/tasks/fairseq_task.py", line 251, in train_step

loss, sample_size, logging_output = criterion(model, sample)


 "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)


 "/home/dh/swapnil/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py", line 57, in forward

net_output = model(**sample['net_input'])


 "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)


 "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 442, in forward

output = self.module(*inputs[0], **kwargs[0])


 "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)


 "/home/dh/swapnil/fairseq/fairseq/models/fairseq_model.py", line 385, in forward

return self.decoder(src_tokens, **kwargs)


 "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)
TypeError: forward() got multiple values for argument 'prev_output_tokens'
