





from typing import TYPE_CHECKING

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


_UpperCamelCase    =	{'''configuration_regnet''': ['''REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RegNetConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''REGNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RegNetForImageClassification''',
         '''RegNetModel''',
         '''RegNetPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFRegNetForImageClassification''',
         '''TFRegNetModel''',
         '''TFRegNetPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''FlaxRegNetForImageClassification''',
         '''FlaxRegNetModel''',
         '''FlaxRegNetPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_regnet import (
              REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              RegNetForImageClassification,
              RegNetModel,
              RegNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_regnet import (
              TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRegNetForImageClassification,
              TFRegNetModel,
              TFRegNetPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_regnet import (
              FlaxRegNetForImageClassification,
              FlaxRegNetModel,
              FlaxRegNetPreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure)








def  lowerCAmelCase__(		lowercase    :      int = 10**6			)	->      int:

  if n <= 0:
    raise ValueError("Please enter an integer greater than 0"			)

  __snake_case				:			List[Any]            =    list(range(n + 1			)			)
  for number in range(2				,       n + 1			):
    if phi[number] == number:
      phi[number] -= 1
      for multiple in range(number * 2				,       n + 1				,       lowercase			):
        __snake_case				:			Optional[int]            =    (phi[multiple] // number) * (number - 1)

  __snake_case				:			List[Any]            =    1
  for number in range(1				,       n + 1			):
    if (answer / phi[answer]) < (number / phi[number]):
      __snake_case				:			List[str]            =    number

  return answer


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

