






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

import tensorflow as tf
from numpy import array





def  _a(  UpperCamelCase__				:						int,	UpperCamelCase__				:						Any							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Any									=int(UpperCamelCase__							)
						assert noofclusters < len(UpperCamelCase__							)

						# Find out the dimensionality
						SCREAMING_SNAKE_CASE__     :				List[str]									=len(vectors[0]							)

						# Will help select random centroids from among the available vectors
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=list(range(len(UpperCamelCase__							)							)							)
						shuffle(UpperCamelCase__							)

						# 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.

						SCREAMING_SNAKE_CASE__     :				Any									=tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

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

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

												##Now lets construct the node that will compute the mean
												# The placeholder for the input
												SCREAMING_SNAKE_CASE__     :				str									=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
												SCREAMING_SNAKE_CASE__     :				List[str]									=tf.reduce_mean(UpperCamelCase__,	0							)

												##Node for computing Euclidean distances
												# Placeholders for input
												SCREAMING_SNAKE_CASE__     :				Any									=tf.placeholder('''float''',	[dim]							)
												SCREAMING_SNAKE_CASE__     :				List[str]									=tf.placeholder('''float''',	[dim]							)
												SCREAMING_SNAKE_CASE__     :				Any									=tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCamelCase__,	UpperCamelCase__							),	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
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=tf.placeholder('''float''',	[noofclusters]							)
												SCREAMING_SNAKE_CASE__     :				str									=tf.argmin(UpperCamelCase__,	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.
												SCREAMING_SNAKE_CASE__     :				List[str]									=tf.initialize_all_variables()

												# Initialize all variables
												sess.run(UpperCamelCase__							)

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

        # Return centroids and assignments
												SCREAMING_SNAKE_CASE__     :				Tuple									=sess.run(UpperCamelCase__							)
												SCREAMING_SNAKE_CASE__     :				List[Any]									=sess.run(UpperCamelCase__							)
												return centroids, assignments







'''simple docstring'''
import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=inspect.getfile(accelerate.test_utils      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
       SCREAMING_SNAKE_CASE__     :				Dict									=os.path.sep.join(inspect.getfile(self.__class__      ).split(os.path.sep      )[:-1]      )




 @require_tpu
 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Any									=F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
       SCREAMING_SNAKE_CASE__     :				List[str]									=[sys.executable] + distributed_args
       execute_subprocess_async(__lowercase					,      env=os.environ.copy()      )