"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  bool:
   if not isinstance(__UpperCAmelCase       ,    __UpperCAmelCase ):
      raise ValueError('''check_bouncy() accepts only integer arguments''' )
   lowercase__:   str					=      str(__UpperCAmelCase )
   lowercase__:   Tuple					=      ''''''.join(sorted(__UpperCAmelCase ) )
   return sorted_str_n != str_n and sorted_str_n[::-1] != str_n



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 9_9 )  ->  int:
   if not 0 < percent < 1_0_0:
      raise ValueError('''solution() only accepts values from 0 to 100''' )
   lowercase__:   Union[str, Any]					=      0
   lowercase__:   Tuple					=      1

   while True:
      if check_bouncy(__UpperCAmelCase ):
         bouncy_num += 1
      if (bouncy_num / num) * 1_0_0 >= percent:
         return num
      num += 1


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

       testmod()
       print(f'''{solution(9_9)}''')

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
