






'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       bool:
							if not isinstance(UpperCamelCase				,			UpperCamelCase      ):
														raise ValueError("""check_bouncy() accepts only integer arguments"""      )
							lowerCamelCase__					:	Any         =       str(UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       """""".join(sorted(UpperCamelCase      )      )
							return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase = 99      )      ->       int:
							if not 0 < percent < 100:
														raise ValueError("""solution() only accepts values from 0 to 100"""      )
							lowerCamelCase__					:	Union[str, Any]         =       0
							lowerCamelCase__					:	Tuple         =       1

							while True:
														if check_bouncy(UpperCamelCase      ):
																					bouncy_num += 1
														if (bouncy_num / num) * 100 >= percent:
																					return num
														num += 1


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			print(F'{solution(99)}')









'''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__)

