


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase       :      Dict	=  logging.getLogger(__name__)






@dataclass
class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= field(
				    default=0.0    ,		metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}				)
				lowerCAmelCase 		= field(default=__snake_case    ,		metadata={'help': 'Whether to SortishSamler or not.'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}				)
				lowerCAmelCase 		= field(default=__snake_case    ,		metadata={'help': 'whether to use adafactor'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}				)
				lowerCAmelCase 		= field(default=__snake_case    ,		metadata={'help': 'Dropout probability. Goes into model.config.'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Attention dropout probability. Goes into model.config.'}				)
				lowerCAmelCase 		= field(
				    default='linear'    ,		metadata={'help': f"""Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}"""}    ,		)







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



