






"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCamelCase						:				str             =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				List[Any]             =      [
	    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "XLMForMultipleChoice",
	    "XLMForQuestionAnswering",
	    "XLMForQuestionAnsweringSimple",
	    "XLMForSequenceClassification",
	    "XLMForTokenClassification",
	    "XLMModel",
	    "XLMPreTrainedModel",
	    "XLMWithLMHeadModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Any             =      [
	    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFXLMForMultipleChoice",
	    "TFXLMForQuestionAnsweringSimple",
	    "TFXLMForSequenceClassification",
	    "TFXLMForTokenClassification",
	    "TFXLMMainLayer",
	    "TFXLMModel",
	    "TFXLMPreTrainedModel",
	    "TFXLMWithLMHeadModel",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	UpperCamelCase						:				Optional[Any]             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""


from math import sqrt







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = 0
		for i in range(1 ,			int(sqrt(UpperCamelCase__	) + 1	)	):
				if n % i == 0 and i != sqrt(UpperCamelCase__	):
						total += i + n // i
				elif i == sqrt(UpperCamelCase__	):
						total += i
		return total - n







def  __snake_case       (   UpperCamelCase__ = 10000	)      ->		int:







		"""simple docstring"""





		A					       = sum(
		    i
		    for i in range(1 ,			UpperCamelCase__	)
		    if sum_of_divisors(sum_of_divisors(UpperCamelCase__	)	) == i and sum_of_divisors(UpperCamelCase__	) != i	)
		return total


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))





