


def 						a__			(				A__ = 6_0_0_8_5_1_4_7_5_1_4_3				):

			try:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					int(A__				)
			except (TypeError, ValueError):
						raise TypeError('Parameter n must be int or castable to int.'				)
			if n <= 0:
						raise ValueError('Parameter n must be greater than or equal to one.'				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					2
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						SCREAMING_SNAKE_CASE_		:     Tuple					=					i
						while n % i == 0:
									SCREAMING_SNAKE_CASE_		:     Tuple					=					n // i
						i += 1
			return int(A__				)


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











'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
