
'''simple docstring'''


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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__lowercase							:   Tuple					=    logging.getLogger(__name__)

@dataclass
class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Optional[float]	      =			field(
			    default=0.0      ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}    )
			lowerCamelCase			:		bool	      =			field(default=_lowercase      ,     metadata={"help": "Whether to SortishSamler or not."}    )
			lowerCamelCase			:		bool	      =			field(
			    default=_lowercase      ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
			lowerCamelCase			:		bool	      =			field(default=_lowercase      ,     metadata={"help": "whether to use adafactor"}    )
			lowerCamelCase			:		Optional[float]	      =			field(
			    default=_lowercase      ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}    )
			lowerCamelCase			:		Optional[float]	      =			field(
			    default=_lowercase      ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}    )
			lowerCamelCase			:		Optional[float]	      =			field(default=_lowercase      ,     metadata={"help": "Dropout probability. Goes into model.config."}    )
			lowerCamelCase			:		Optional[float]	      =			field(
			    default=_lowercase      ,     metadata={"help": "Attention dropout probability. Goes into model.config."}    )
			lowerCamelCase			:		Optional[str]	      =			field(
			    default="linear"      ,     metadata={"help": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()    )}'''}      ,     )




'''simple docstring'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							dict[str, float]:
					'''simple docstring'''

					if (voltage, current, resistance).count(0				) != 1:
										raise ValueError('''One and only one argument must be 0'''				)
					if resistance < 0:
										raise ValueError('''Resistance cannot be negative'''				)
					if voltage == 0:
										return {"voltage": float(current * resistance				)}
					elif current == 0:
										return {"current": voltage / resistance}
					elif resistance == 0:
										return {"resistance": voltage / current}
					else:
										raise ValueError('''Exactly one argument must be 0'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


