

from math import sqrt





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= 0
	for i in range(1			, int(sqrt(lowercase__	) + 1	)	):
		if n % i == 0 and i != sqrt(lowercase__	):
			total += i + n // i
		elif i == sqrt(lowercase__	):
			total += i
	return total - n





def a(lowercase__ = 10000	):





	'''simple docstring'''
	snake_case_      		= sum(
	    i
	    for i in range(1			, lowercase__	)
	    if sum_of_divisors(sum_of_divisors(lowercase__	)	) == i and sum_of_divisors(lowercase__	) != i	)
	return total


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



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A											=     logging.get_logger(__name__)

A											=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """unispeech-sat"""


			def __init__(    self ,				__UpperCamelCase=32 ,				__UpperCamelCase=7_68 ,				__UpperCamelCase=12 ,				__UpperCamelCase=12 ,				__UpperCamelCase=30_72 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase="group" ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,				__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,				__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,				__UpperCamelCase=False ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=16 ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.05 ,				__UpperCamelCase=10 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0 ,				__UpperCamelCase=3_20 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=1_00 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="mean" ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) ,				__UpperCamelCase=(5, 3, 3, 1, 1) ,				__UpperCamelCase=(1, 2, 3, 1, 1) ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=0 ,				__UpperCamelCase=1 ,				__UpperCamelCase=2 ,				__UpperCamelCase=5_04 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase ,				pad_token_id=__UpperCamelCase ,				bos_token_id=__UpperCamelCase ,				eos_token_id=__UpperCamelCase							)
				snake_case_      		= hidden_size
				snake_case_      		= feat_extract_norm
				snake_case_      		= feat_extract_activation
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= conv_bias
				snake_case_      		= num_conv_pos_embeddings
				snake_case_      		= num_conv_pos_embedding_groups
				snake_case_      		= len(self.conv_dim							)
				snake_case_      		= num_hidden_layers
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_dropout
				snake_case_      		= attention_dropout
				snake_case_      		= activation_dropout
				snake_case_      		= feat_proj_dropout
				snake_case_      		= final_dropout
				snake_case_      		= layerdrop
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= vocab_size
				snake_case_      		= num_clusters
				snake_case_      		= do_stable_layer_norm
				snake_case_      		= use_weighted_layer_sum

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
					    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
					    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				snake_case_      		= apply_spec_augment
				snake_case_      		= mask_time_prob
				snake_case_      		= mask_time_length
				snake_case_      		= mask_time_min_masks
				snake_case_      		= mask_feature_prob
				snake_case_      		= mask_feature_length
				snake_case_      		= mask_feature_min_masks

				# parameters for pretraining with codevector quantized representations
				snake_case_      		= num_codevectors_per_group
				snake_case_      		= num_codevector_groups
				snake_case_      		= contrastive_logits_temperature
				snake_case_      		= feat_quantizer_dropout
				snake_case_      		= num_negatives
				snake_case_      		= codevector_dim
				snake_case_      		= proj_codevector_dim
				snake_case_      		= diversity_loss_weight

				# ctc loss
				snake_case_      		= ctc_loss_reduction
				snake_case_      		= ctc_zero_infinity

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				snake_case_      		= classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= xvector_output_dim







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return functools.reduce(operator.mul ,				self.conv_stride ,				1							)
