






'''simple docstring'''







from __future__ import annotations





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       tuple[str, float]:
							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()









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

