



'''simple docstring'''




from random import shuffle

import tensorflow as tf
from numpy import array


def        lowerCAmelCase_     (			_lowerCamelCase:     Any ,   _lowerCamelCase:     int      ):

		'''simple docstring'''





		__SCREAMING_SNAKE_CASE :	Any					 =	int(_lowerCamelCase      )
		assert noofclusters < len(_lowerCamelCase      )

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

		# Will help select random centroids from among the available vectors
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	list(range(len(_lowerCamelCase      )      )      )
		shuffle(_lowerCamelCase      )

		# 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 :	List[str]					 =	tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				__SCREAMING_SNAKE_CASE :	Tuple					 =	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(_lowerCamelCase      )
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				__SCREAMING_SNAKE_CASE :	str					 =	tf.placeholder("""float64""" ,   [dim]      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[]
				for centroid in centroids:
						cent_assigns.append(tf.assign(_lowerCamelCase ,   _lowerCamelCase      )      )

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[tf.Variable(0      ) for i in range(len(_lowerCamelCase      )      )]
				##These nodes will assign an assignment Variable the appropriate
				##value
				__SCREAMING_SNAKE_CASE :	List[str]					 =	tf.placeholder("""int32"""      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[]
				for assignment in assignments:
						cluster_assigns.append(tf.assign(_lowerCamelCase ,   _lowerCamelCase      )      )

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

				##Node for computing Euclidean distances
				# Placeholders for input
				__SCREAMING_SNAKE_CASE :	str					 =	tf.placeholder("""float""" ,   [dim]      )
				__SCREAMING_SNAKE_CASE :	Any					 =	tf.placeholder("""float""" ,   [dim]      )
				__SCREAMING_SNAKE_CASE :	Any					 =	tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase ,   _lowerCamelCase      ) ,   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 :	Union[str, Any]					 =	tf.placeholder("""float""" ,   [noofclusters]      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	tf.argmin(_lowerCamelCase ,   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 :	int					 =	tf.initialize_all_variables()

				# Initialize all variables
				sess.run(_lowerCamelCase      )

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

        # Return centroids and assignments
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	sess.run(_lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	Dict					 =	sess.run(_lowerCamelCase      )
				return centroids, assignments




'''simple docstring'''




from ..utils import DummyObject, requires_backends






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	['''torch''', '''transformers''', '''onnx''']

			def __init__(							self  :				Optional[int]							,  *lowerCAmelCase__  :				Optional[Any]							,  **lowerCAmelCase__  :				int		):





					"""simple docstring"""






					requires_backends(self							,  ["""torch""", """transformers""", """onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				Tuple							,  *lowerCAmelCase__  :				Union[str, Any]							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				str							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Dict    					=	['''torch''', '''transformers''', '''onnx''']

			def __init__(							self  :				str							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					requires_backends(self							,  ["""torch""", """transformers""", """onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				Optional[Any]							,  *lowerCAmelCase__  :				Dict							,  **lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				str							,  *lowerCAmelCase__  :				List[str]							,  **lowerCAmelCase__  :				str		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Tuple    					=	['''torch''', '''transformers''', '''onnx''']

			def __init__(							self  :				str							,  *lowerCAmelCase__  :				List[str]							,  **lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					requires_backends(self							,  ["""torch""", """transformers""", """onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				Any							,  *lowerCAmelCase__  :				Tuple							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				List[str]							,  *lowerCAmelCase__  :				Union[str, Any]							,  **lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	['''torch''', '''transformers''', '''onnx''']

			def __init__(							self  :				Optional[int]							,  *lowerCAmelCase__  :				Dict							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					requires_backends(self							,  ["""torch""", """transformers""", """onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				Any							,  *lowerCAmelCase__  :				List[Any]							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				List[str]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	['''torch''', '''transformers''', '''onnx''']

			def __init__(							self  :				Optional[int]							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					requires_backends(self							,  ["""torch""", """transformers""", """onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				List[str]							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				int		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				str							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	['''torch''', '''transformers''', '''onnx''']

			def __init__(							self  :				Optional[int]							,  *lowerCAmelCase__  :				Tuple							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					requires_backends(self							,  ["""torch""", """transformers""", """onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				Tuple							,  *lowerCAmelCase__  :				Dict							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				List[str]							,  *lowerCAmelCase__  :				Dict							,  **lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					requires_backends(cls							,  ["""torch""", """transformers""", """onnx"""]		)