from __future__ import annotations



def A__  (			lowercase:     str,		lowercase:     Dict,		lowercase:     Optional[Any],		)				->		str:
       if (stress, tangential_force, area).count(0					) != 1:
              raise ValueError('You cannot supply more or less than 2 values'					)
       elif stress < 0:
              raise ValueError('Stress cannot be negative'					)
       elif tangential_force < 0:
              raise ValueError('Tangential Force cannot be negative'					)
       elif area < 0:
              raise ValueError('Area cannot be negative'					)
       elif stress == 0:
              return (
                  "stress",
                  tangential_force / area,
              )
       elif tangential_force == 0:
              return (
                  "tangential_force",
                  stress * area,
              )
       else:
              return (
                  "area",
                  tangential_force / stress,
              )


if __name__ == "__main__":
    import doctest

    doctest.testmod()







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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


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






