'''simple docstring'''




from __future__ import annotations





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Optional[Any]  )      -> list[int]:
    A_      =  [0] * max_one

    for i in range(0,  __lowerCAmelCase  ):
        A_      =  i

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

    return totients





def 	_UpperCAmelCase       (				_UpperCamelCase       :      List[Any],  _UpperCamelCase       :      Optional[Any]  )      -> bool:
    return sorted(str(__lowerCAmelCase  )  ) == sorted(str(__lowerCAmelCase  )  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Union[str, Any] = 10_00_00_00  )      -> int:
    A_      =  1  # i
    A_      =  0  # φ(i)
    A_      =  get_totients(max_n + 1  )

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

        if i * min_denominator < min_numerator * t and has_same_digits(__lowerCAmelCase,  __lowerCAmelCase  ):
            A_      =  i
            A_      =  t

    return min_numerator


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







from typing import TYPE_CHECKING

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


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

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''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

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