
from random import shuffle

import tensorflow as tf
from numpy import array




def 	A_ (							snake_case							:   str ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			int(snake_case )
						assert noofclusters < len(snake_case )

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

						# Will help select random centroids from among the available vectors
						__UpperCamelCase       =			list(range(len(snake_case ) ) )
						shuffle(snake_case )

						# 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(snake_case )
												]
												##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(snake_case ,      snake_case ) )

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

												##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(snake_case ,      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(snake_case ,      snake_case ) ,      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(snake_case ,      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(snake_case )

												##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(snake_case ):
																		##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(snake_case ) ):
																								__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(snake_case ,      feed_dict={va: vect, va: sess.run(snake_case )} )
																								    for centroid in centroids
																								]
																								# Now use the cluster assignment node, with the distances
																								# as the input
																								__UpperCamelCase       =			sess.run(
																								    snake_case ,      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(snake_case ):
																								# Collect all the vectors assigned to this cluster
																								__UpperCamelCase       =			[
																								    vectors[i]
																								    for i in range(len(snake_case ) )
																								    if sess.run(assignments[i] ) == cluster_n
																								]
																								# Compute new centroid location
																								__UpperCamelCase       =			sess.run(
																								    snake_case ,      feed_dict={mean_input: array(snake_case )} )
																								# Assign value to appropriate variable
																								sess.run(
																								    cent_assigns[cluster_n] ,      feed_dict={centroid_value: new_location} )

        # Return centroids and assignments
												__UpperCamelCase       =			sess.run(snake_case )
												__UpperCamelCase       =			sess.run(snake_case )
												return centroids, assignments






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax






@require_flax
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  FlaxAutoencoderKL



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			3
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			jax.random.PRNGKey(0			)
													__UpperCamelCase       =			jax.random.uniform(SCREAMING_SNAKE_CASE_     ,	((batch_size, num_channels) + sizes)			)

													return {"sample": image, "prng_key": prng_key}



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{
													    '''block_out_channels''': [32, 64],
													    '''in_channels''': 3,
													    '''out_channels''': 3,
													    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
													    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
													    '''latent_channels''': 4,
													}
													__UpperCamelCase       =			self.dummy_input
													return init_dict, inputs_dict




