




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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowerCamelCase      :  List[str]    						=						logging.getLogger(__name__)
@dataclass
class        __snake_case  (_a		):
				lowerCAmelCase__				=     field(
				    default=0.0						,	metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
				lowerCAmelCase__				=     field(default=_a						,	metadata={"help": "Whether to SortishSamler or not."}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
				lowerCAmelCase__				=     field(default=_a						,	metadata={"help": "whether to use adafactor"}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
				lowerCAmelCase__				=     field(default=_a						,	metadata={"help": "Dropout probability. Goes into model.config."}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	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 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      n ** (1 / 3)
				return (val * val * val) == n


if __name__ == "__main__":
	print(perfect_cube(2_7))
	print(perfect_cube(4))

