

from __future__ import annotations

from fractions import Fraction




def 			UpperCAmelCase__				(			_A      : int	,      _A      : int							):





  '''simple docstring'''







  return (
      num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
  )




def 			UpperCAmelCase__				(			_A      : int							):





  '''simple docstring'''







  a__  =[]
  a__  =11
  a__  =int('''1''' + '''0''' * digit_len							)
  for num in range(_A	,      _A							):
    while den <= 99:
      if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
        if is_digit_cancelling(_A	,      _A							):
          solutions.append(F"""{num}/{den}"""							)
      den += 1
    num += 1
    a__  =10
  return solutions




def 			UpperCAmelCase__				(			_A      : int = 2							):





  '''simple docstring'''







  a__  =1.0
  for fraction in fraction_list(_A							):
    a__  =Fraction(_A							)
    result *= frac.denominator / frac.numerator
  return int(_A							)


if __name__ == "__main__":
       print(solution())



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase           =					{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''XLNetForMultipleChoice''',
           '''XLNetForQuestionAnswering''',
           '''XLNetForQuestionAnsweringSimple''',
           '''XLNetForSequenceClassification''',
           '''XLNetForTokenClassification''',
           '''XLNetLMHeadModel''',
           '''XLNetModel''',
           '''XLNetPreTrainedModel''',
           '''load_tf_weights_in_xlnet''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFXLNetForMultipleChoice''',
           '''TFXLNetForQuestionAnsweringSimple''',
           '''TFXLNetForSequenceClassification''',
           '''TFXLNetForTokenClassification''',
           '''TFXLNetLMHeadModel''',
           '''TFXLNetMainLayer''',
           '''TFXLNetModel''',
           '''TFXLNetPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet import XLNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet_fast import XLNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlnet import (
                  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLNetForMultipleChoice,
                  XLNetForQuestionAnswering,
                  XLNetForQuestionAnsweringSimple,
                  XLNetForSequenceClassification,
                  XLNetForTokenClassification,
                  XLNetLMHeadModel,
                  XLNetModel,
                  XLNetPreTrainedModel,
                  load_tf_weights_in_xlnet,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlnet import (
                  TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLNetForMultipleChoice,
                  TFXLNetForQuestionAnsweringSimple,
                  TFXLNetForSequenceClassification,
                  TFXLNetForTokenClassification,
                  TFXLNetLMHeadModel,
                  TFXLNetMainLayer,
                  TFXLNetModel,
                  TFXLNetPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
