"""simple docstring"""



from random import shuffle

import tensorflow as tf
from numpy import array



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  int(a							)
       assert noofclusters < len(a							)

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

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

       # 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(a							)
              ]
              ##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(a ,  a							)							)

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

              ##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(a ,  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(a ,  a							) ,  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(a ,  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(a							)

              ##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     				=  1_0_0
              for _ in range(a							):
                     ##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(a							)							):
                            __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(a ,  feed_dict={va: vect, va: sess.run(a							)}							)
                                for centroid in centroids
                            ]
                            # Now use the cluster assignment node, with the distances
                            # as the input
                            __a     				=  sess.run(
                                a ,  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(a							):
                            # Collect all the vectors assigned to this cluster
                            __a     				=  [
                                vectors[i]
                                for i in range(len(a							)							)
                                if sess.run(assignments[i]							) == cluster_n
                            ]
                            # Compute new centroid location
                            __a     				=  sess.run(
                                a ,  feed_dict={mean_input: array(a							)}							)
                            # Assign value to appropriate variable
                            sess.run(
                                cent_assigns[cluster_n] ,  feed_dict={centroid_value: new_location}							)

        # Return centroids and assignments
              __a     				=  sess.run(a							)
              __a     				=  sess.run(a							)
              return centroids, assignments

"""simple docstring"""



import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE__:List[str]			=	3



def 					_lowerCamelCase(					a							):
       print("Generating primitive root of p"							)
       while True:
              __a     				=  random.randrange(3 ,  a							)
              if pow(a ,  2 ,  a							) == 1:
                     continue
              if pow(a ,  a ,  a							) == 1:
                     continue
              return g



def 					_lowerCamelCase(					a							):
       print("Generating prime p..."							)
       __a     				=  rabin_miller.generate_large_prime(a							)  # select large prime number.
       __a     				=  primitive_root(a							)  # one primitive root on modulo p.
       __a     				=  random.randrange(3 ,  a							)  # private_key -> have to be greater than 2 for safety.
       __a     				=  cryptomath.find_mod_inverse(pow(a ,  a ,  a							) ,  a							)

       __a     				=  (key_size, e_a, e_a, p)
       __a     				=  (key_size, d)

       return public_key, private_key



def 					_lowerCamelCase(					a ,  a							):
       if os.path.exists(F"{name}_pubkey.txt"							) or os.path.exists(F"{name}_privkey.txt"							):
              print("\nWARNING:"							)
              print(
                  F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                  "Use a different name or delete these files and re-run this program."							)
              sys.exit()

       __a				, __a     				=  generate_key(a							)
       print(F"\nWriting public key to file {name}_pubkey.txt..."							)
       with open(F"{name}_pubkey.txt" ,  "w"							) as fo:
              fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"							)

       print(F"Writing private key to file {name}_privkey.txt..."							)
       with open(F"{name}_privkey.txt" ,  "w"							) as fo:
              fo.write(F"{private_key[0]},{private_key[1]}"							)



def 					_lowerCamelCase(					):
       print("Making key files..."							)
       make_key_files("elgamal" ,  2_0_4_8							)
       print("Key files generation successful"							)


if __name__ == "__main__":
   main()
