from random import shuffle

import tensorflow as tf
from numpy import array




def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_		:    int         =				int(__lowercase     )
		assert noofclusters < len(__lowercase     )

		# Find out the dimensionality
		A_		:    Optional[int]         =				len(vectors[0]     )

		# Will help select random centroids from among the available vectors
		A_		:    Dict         =				list(range(len(__lowercase     )     )     )
		shuffle(__lowercase     )

		# 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_		:    Dict         =				tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

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

				##Node for computing Euclidean distances
				# Placeholders for input
				A_		:    str         =				tf.placeholder('float'						,[dim]     )
				A_		:    str         =				tf.placeholder('float'						,[dim]     )
				A_		:    Tuple         =				tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowercase						,__lowercase     )						,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_		:    str         =				tf.placeholder('float'						,[noofclusters]     )
				A_		:    Tuple         =				tf.argmin(__lowercase						,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_		:    Union[str, Any]         =				tf.initialize_all_variables()

				# Initialize all variables
				sess.run(__lowercase     )

				##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_		:    Any         =				1_00
				for _ in range(__lowercase     ):
						##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(__lowercase     )     ):
								A_		:    Any         =				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_		:    List[Any]         =				[
								    sess.run(__lowercase						,feed_dict={va: vect, va: sess.run(__lowercase     )}     )
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								A_		:    Optional[Any]         =				sess.run(
								    __lowercase						,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(__lowercase     ):
								# Collect all the vectors assigned to this cluster
								A_		:    Any         =				[
								    vectors[i]
								    for i in range(len(__lowercase     )     )
								    if sess.run(assignments[i]     ) == cluster_n
								]
								# Compute new centroid location
								A_		:    Optional[Any]         =				sess.run(
								    __lowercase						,feed_dict={mean_input: array(__lowercase     )}     )
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n]						,feed_dict={centroid_value: new_location}     )

        # Return centroids and assignments
				A_		:    Tuple         =				sess.run(__lowercase     )
				A_		:    str         =				sess.run(__lowercase     )
				return centroids, assignments







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
	from .pipeline_unclip import UnCLIPPipeline
	from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
	from .text_proj import UnCLIPTextProjModel






