





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



def 				lowerCAmelCase_ (							A_ = 99):
    if not 0 < percent < 1_00:
        raise ValueError("solution() only accepts values from 0 to 100")
    UpperCamelCase__:	Optional[int]					=     0
    UpperCamelCase__:	List[Any]					=     1

    while True:
        if check_bouncy(A_):
            bouncy_num += 1
        if (bouncy_num / num) * 1_00 >= percent:
            return num
        num += 1


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

   testmod()
   print(f"{solution(99)}")








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A__:    Optional[Any]  	=	{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[Any]  	=	[
       '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RobertaPreLayerNormForCausalLM''',
       '''RobertaPreLayerNormForMaskedLM''',
       '''RobertaPreLayerNormForMultipleChoice''',
       '''RobertaPreLayerNormForQuestionAnswering''',
       '''RobertaPreLayerNormForSequenceClassification''',
       '''RobertaPreLayerNormForTokenClassification''',
       '''RobertaPreLayerNormModel''',
       '''RobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple  	=	[
       '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRobertaPreLayerNormForCausalLM''',
       '''TFRobertaPreLayerNormForMaskedLM''',
       '''TFRobertaPreLayerNormForMultipleChoice''',
       '''TFRobertaPreLayerNormForQuestionAnswering''',
       '''TFRobertaPreLayerNormForSequenceClassification''',
       '''TFRobertaPreLayerNormForTokenClassification''',
       '''TFRobertaPreLayerNormMainLayer''',
       '''TFRobertaPreLayerNormModel''',
       '''TFRobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''FlaxRobertaPreLayerNormForCausalLM''',
       '''FlaxRobertaPreLayerNormForMaskedLM''',
       '''FlaxRobertaPreLayerNormForMultipleChoice''',
       '''FlaxRobertaPreLayerNormForQuestionAnswering''',
       '''FlaxRobertaPreLayerNormForSequenceClassification''',
       '''FlaxRobertaPreLayerNormForTokenClassification''',
       '''FlaxRobertaPreLayerNormModel''',
       '''FlaxRobertaPreLayerNormPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roberta_prelayernorm import (
       ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
       RobertaPreLayerNormConfig,
       RobertaPreLayerNormOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaPreLayerNormForCausalLM,
          RobertaPreLayerNormForMaskedLM,
          RobertaPreLayerNormForMultipleChoice,
          RobertaPreLayerNormForQuestionAnswering,
          RobertaPreLayerNormForSequenceClassification,
          RobertaPreLayerNormForTokenClassification,
          RobertaPreLayerNormModel,
          RobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta_prelayernorm import (
          TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaPreLayerNormForCausalLM,
          TFRobertaPreLayerNormForMaskedLM,
          TFRobertaPreLayerNormForMultipleChoice,
          TFRobertaPreLayerNormForQuestionAnswering,
          TFRobertaPreLayerNormForSequenceClassification,
          TFRobertaPreLayerNormForTokenClassification,
          TFRobertaPreLayerNormMainLayer,
          TFRobertaPreLayerNormModel,
          TFRobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormPreTrainedModel,
      )

else:
   import sys

   A__:    Union[str, Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

