


from __future__ import annotations



def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[Any]				      =     [0] * max_one

     for i in range(0	,	__UpperCamelCase			):
          a_     :  Union[str, Any]				      =     i

     for i in range(2	,	__UpperCamelCase			):
          if totients[i] == i:
               for j in range(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
                    totients[j] -= totients[j] // i

     return totients



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     return sorted(str(__UpperCamelCase			)			) == sorted(str(__UpperCamelCase			)			)



def 			_a      (    __UpperCamelCase = 1_0_0_0_0_0_0_0			):
     a_     :  List[str]				      =     1  # i
     a_     :  Any				      =     0  # φ(i)
     a_     :  Union[str, Any]				      =     get_totients(max_n + 1			)

     for i in range(2	,	max_n + 1			):
          a_     :  Dict				      =     totients[i]

          if i * min_denominator < min_numerator * t and has_same_digits(__UpperCamelCase	,	__UpperCamelCase			):
               a_     :  List[str]				      =     i
               a_     :  Any				      =     t

     return min_numerator


if __name__ == "__main__":
       print(f'''{solution() = }''')









'''simple docstring'''

from typing import TYPE_CHECKING

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


a__         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTJForCausalLM''',
		    '''GPTJForQuestionAnswering''',
		    '''GPTJForSequenceClassification''',
		    '''GPTJModel''',
		    '''GPTJPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TFGPTJForCausalLM''',
		    '''TFGPTJForQuestionAnswering''',
		    '''TFGPTJForSequenceClassification''',
		    '''TFGPTJModel''',
		    '''TFGPTJPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''FlaxGPTJForCausalLM''',
		    '''FlaxGPTJModel''',
		    '''FlaxGPTJPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gptj import (
				    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTJForCausalLM,
				    GPTJForQuestionAnswering,
				    GPTJForSequenceClassification,
				    GPTJModel,
				    GPTJPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gptj import (
				    TFGPTJForCausalLM,
				    TFGPTJForQuestionAnswering,
				    TFGPTJForSequenceClassification,
				    TFGPTJModel,
				    TFGPTJPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
		import sys

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