





from random import shuffle

import tensorflow as tf
from numpy import array





def    __UpperCamelCase      (    a,     a) ->int:
					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__       =       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)):
																				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








from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A_												=     Lock()





def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a,     a) ->Optional[int]:
					global process_lock

					# we perform n swaps since after n swaps we know we are sorted
					# we *could* stop early if we are sorted already, but it takes as long to
					# find out we are sorted as it does to sort the list with this algorithm
					for i in range(0,     10):
										if (i + position) % 2 == 0 and r_send is not None:
															# send your value to your right neighbor
															process_lock.acquire()
															r_send[1].send(a)
															process_lock.release()

															# receive your right neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       rr_cv[0].recv()
															process_lock.release()

															# take the lower value since you are on the left
															lowerCamelCase__       =       min(a,     a)
										elif (i + position) % 2 != 0 and l_send is not None:
															# send your value to your left neighbor
															process_lock.acquire()
															l_send[1].send(a)
															process_lock.release()

															# receive your left neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       lr_cv[0].recv()
															process_lock.release()

															# take the higher value since you are on the right
															lowerCamelCase__       =       max(a,     a)
    # after all swaps are performed, send the values back to main
					result_pipe[1].send(a)





def    __UpperCamelCase      (    a) ->Optional[Any]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					# initialize the list of pipes where the values will be retrieved
					for _ in arr:
										result_pipe.append(Pipe())
					# creates the processes
					# the first and last process only have one neighbor so they are made outside
					# of the loop
					lowerCamelCase__       =       Pipe()
					lowerCamelCase__       =       Pipe()
					process_array_.append(
					    Process(
					        target=a,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),     ))
					lowerCamelCase__       =       temp_rs
					lowerCamelCase__       =       temp_rr

					for i in range(1,     len(a) - 1):
										lowerCamelCase__       =       Pipe()
										lowerCamelCase__       =       Pipe()
										process_array_.append(
										    Process(
										        target=a,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),     ))
										lowerCamelCase__       =       temp_rs
										lowerCamelCase__       =       temp_rr

					process_array_.append(
					    Process(
					        target=a,     args=(
					            len(a) - 1,
					            arr[len(a) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(a) - 1],
					        ),     ))

					# start the processes
					for p in process_array_:
										p.start()

					# wait for the processes to end and write their values to the list
					for p in range(0,     len(a)):
										lowerCamelCase__       =       result_pipe[p][0].recv()
										process_array_[p].join()
					return arr





def    __UpperCamelCase      (    ) ->List[Any]:
					lowerCamelCase__       =       list(range(10,     0,     -1))
					print("Initial List")
					print(*a)
					lowerCamelCase__       =       odd_even_transposition(a)
					print("Sorted List\n")
					print(*a)


if __name__ == "__main__":
						main()

