
'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array






def      UpperCamelCase_				(      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_00
		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'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowercase__											=subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowercase__											=(
    subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('utf-8').split()
)

lowercase__											='|'.join(sys.argv[1:])
lowercase__											=re.compile(rF"""^({joined_dirs}).*?\.py$""")

lowercase__											=[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
