





'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array







def  a__    (    lowercase :					Tuple, lowercase :					Tuple				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase       =	int(__a				)
     assert noofclusters < len(__a				)

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

     # Will help select random centroids from among the available vectors
     _UpperCamelCase       =	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.

     _UpperCamelCase       =	tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

          _UpperCamelCase       =	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
          _UpperCamelCase       =	[
              tf.Variable(vectors[vector_indices[i]]				) for i in range(__a				)
          ]
          ##These nodes will assign the centroid Variables the appropriate
          ##values
          _UpperCamelCase       =	tf.placeholder('''float64''', [dim]				)
          _UpperCamelCase       =	[]
          for centroid in centroids:
               cent_assigns.append(tf.assign(__a, __a				)				)

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

          ##Node for computing Euclidean distances
          # Placeholders for input
          _UpperCamelCase       =	tf.placeholder('''float''', [dim]				)
          _UpperCamelCase       =	tf.placeholder('''float''', [dim]				)
          _UpperCamelCase       =	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
          _UpperCamelCase       =	tf.placeholder('''float''', [noofclusters]				)
          _UpperCamelCase       =	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.
          _UpperCamelCase       =	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.
          _UpperCamelCase       =	100
          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				)				):
                    _UpperCamelCase       =	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.
                    _UpperCamelCase       =	[
                        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
                    _UpperCamelCase       =	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
                    _UpperCamelCase       =	[
                        vectors[i]
                        for i in range(len(__a				)				)
                        if sess.run(assignments[i]				) == cluster_n
                    ]
                    # Compute new centroid location
                    _UpperCamelCase       =	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
          _UpperCamelCase       =	sess.run(__a				)
          _UpperCamelCase       =	sess.run(__a				)
          return centroids, assignments










'''simple docstring'''

import random
class     __lowerCAmelCase					:






       """simple docstring"""





       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str					)   ->					tuple[list[int], list[int]]:

            '''simple docstring'''




            _UpperCamelCase       =	[ord(lowerCAmelCase__					) for i in text]
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            for i in plain:
                 _UpperCamelCase       =	random.randint(1	,			300					)
                 _UpperCamelCase       =	(i + k) * k
                 cipher.append(lowerCAmelCase__					)
                 key.append(lowerCAmelCase__					)
            return cipher, key







       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		list[int]	,			lowerCAmelCase__       :		list[int]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[]
            for i in range(len(lowerCAmelCase__					)					):
                 _UpperCamelCase       =	int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(lowerCAmelCase__					)					)
            return "".join(lowerCAmelCase__					)


if __name__ == "__main__":
   lowercase__	,       lowercase__						:      List[str]	=    Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))



