'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=int(a__      )
						assert noofclusters < len(a__      )

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

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

						_lowerCAmelCase										=tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

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

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

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






'''simple docstring'''

lowercase_     		= {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_     		= {value: key for key, value in MORSE_CODE_DICT.items()}



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return " ".join(MORSE_CODE_DICT[char] for char in message.upper()      )



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return "".join(REVERSE_DICT[char] for char in message.split()      )



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										='Morse code here!'
						print(a__      )
						_lowerCAmelCase										=encrypt(a__      )
						print(a__      )
						_lowerCAmelCase										=decrypt(a__      )
						print(a__      )


if __name__ == "__main__":
							main()





