






"""simple docstring"""


from random import shuffle

import tensorflow as tf
from numpy import array







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Any:







		"""simple docstring"""





		A					       = int(__UpperCamelCase	)
		assert noofclusters < len(__UpperCamelCase	)

		# Find out the dimensionality
		A					       = len(vectors[0]	)

		# Will help select random centroids from among the available vectors
		A					       = list(range(len(__UpperCamelCase	)	)	)
		shuffle(__UpperCamelCase	)

		# GRAPH OF COMPUTATION
		# We initialize a new graph and set it as the default during each run
		# of this algorithm. This ensures that as this function is called
		# multiple times, the default graph doesn't keep getting crowded with
		# unused ops and Variables from previous function calls.

		A					       = tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				A					       = tf.Session()

				##CONSTRUCTING THE ELEMENTS OF COMPUTATION

				##First lets ensure we have a Variable vector for each centroid,
				##initialized to one of the vectors from the available data points
				A					       = [
				    tf.Variable(vectors[vector_indices[i]]	) for i in range(__UpperCamelCase	)
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				A					       = tf.placeholder('float64' ,			[dim]	)
				A					       = []
				for centroid in centroids:
						cent_assigns.append(tf.assign(__UpperCamelCase ,			__UpperCamelCase	)	)

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				A					       = [tf.Variable(0	) for i in range(len(__UpperCamelCase	)	)]
				##These nodes will assign an assignment Variable the appropriate
				##value
				A					       = tf.placeholder('int32'	)
				A					       = []
				for assignment in assignments:
						cluster_assigns.append(tf.assign(__UpperCamelCase ,			__UpperCamelCase	)	)

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				A					       = tf.placeholder('float' ,			[None, dim]	)
				# The Node/op takes the input and computes a mean along the 0th
				# dimension, i.e. the list of input vectors
				A					       = tf.reduce_mean(__UpperCamelCase ,			0	)

				##Node for computing Euclidean distances
				# Placeholders for input
				A					       = tf.placeholder('float' ,			[dim]	)
				A					       = tf.placeholder('float' ,			[dim]	)
				A					       = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__UpperCamelCase ,			__UpperCamelCase	) ,			2	)	)	)

				##This node will figure out which cluster to assign a vector to,
				##based on Euclidean distances of the vector from the centroids.
				# Placeholder for input
				A					       = tf.placeholder('float' ,			[noofclusters]	)
				A					       = tf.argmin(__UpperCamelCase ,			0	)

				##INITIALIZING STATE VARIABLES

				##This will help initialization of all Variables defined with respect
				##to the graph. The Variable-initializer should be defined after
				##all the Variables have been constructed, so that each of them
				##will be included in the initialization.
				A					       = tf.initialize_all_variables()

				# Initialize all variables
				sess.run(__UpperCamelCase	)

				##CLUSTERING ITERATIONS

				# Now perform the Expectation-Maximization steps of K-Means clustering
				# iterations. To keep things simple, we will only do a set number of
				# iterations, instead of using a Stopping Criterion.
				A					       = 100
				for _ in range(__UpperCamelCase	):
						##EXPECTATION STEP
						##Based on the centroid locations till last iteration, compute
						##the _expected_ centroid assignments.
						# Iterate over each vector
						for vector_n in range(len(__UpperCamelCase	)	):
								A					       = vectors[vector_n]
								# Compute Euclidean distance between this vector and each
								# centroid. Remember that this list cannot be named
								#'centroid_distances', since that is the input to the
								# cluster assignment node.
								A					       = [
								    sess.run(__UpperCamelCase ,			feed_dict={va: vect, va: sess.run(__UpperCamelCase	)}	)
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								A					       = sess.run(
								    __UpperCamelCase ,			feed_dict={centroid_distances: distances}	)
								# Now assign the value to the appropriate state variable
								sess.run(
								    cluster_assigns[vector_n] ,			feed_dict={assignment_value: assignment}	)

						##MAXIMIZATION STEP
						# Based on the expected state computed from the Expectation Step,
						# compute the locations of the centroids so as to maximize the
						# overall objective of minimizing within-cluster Sum-of-Squares
						for cluster_n in range(__UpperCamelCase	):
								# Collect all the vectors assigned to this cluster
								A					       = [
								    vectors[i]
								    for i in range(len(__UpperCamelCase	)	)
								    if sess.run(assignments[i]	) == cluster_n
								]
								# Compute new centroid location
								A					       = sess.run(
								    __UpperCamelCase ,			feed_dict={mean_input: array(__UpperCamelCase	)}	)
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n] ,			feed_dict={centroid_value: new_location}	)

        # Return centroids and assignments
				A					       = sess.run(__UpperCamelCase	)
				A					       = sess.run(__UpperCamelCase	)
				return centroids, assignments









"""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())))





