


'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	int(__UpperCAmelCase )
			assert noofclusters < len(__UpperCAmelCase )

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

			# Will help select random centroids from among the available vectors
			__A=	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.

			__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(__UpperCAmelCase )
						]
						##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(__UpperCAmelCase,__UpperCAmelCase ) )

						##Variables for cluster assignments of individual vectors(initialized
						##to 0 at first)
						__A=	[tf.Variable(0 ) for i in range(len(__UpperCAmelCase ) )]
						##These nodes will assign an assignment Variable the appropriate
						##value
						__A=	tf.placeholder('int32' )
						__A=	[]
						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
						__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(__UpperCAmelCase,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(__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
						__A=	tf.placeholder('float',[noofclusters] )
						__A=	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.
						__A=	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.
						__A=	100
						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 ) ):
												__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(__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
												__A=	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
												__A=	[
												    vectors[i]
												    for i in range(len(__UpperCAmelCase ) )
												    if sess.run(assignments[i] ) == cluster_n
												]
												# Compute new centroid location
												__A=	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
						__A=	sess.run(__UpperCAmelCase )
						__A=	sess.run(__UpperCAmelCase )
						return centroids, assignments








'''simple docstring'''

import math

import tensorflow as tf
from packaging import version







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    int      							=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)				,							x.dtype					)					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(math.pi				,							x.dtype					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(__UpperCAmelCase				,							3					))					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)

			return x * tf.tanh(tf.math.softplus(__UpperCAmelCase					)					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[str]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=tf.cast(0.79_7884_5608				,							x.dtype					)

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(1.702				,							x.dtype					)
			return x * tf.math.sigmoid(coeff * x					)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			return tf.clip_by_value(_gelu(__UpperCAmelCase					)				,							-10				,							10					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=-1					)				->					List[str]:





			"""simple docstring"""







			snake_case							,  snake_case:    int      							=tf.split(__UpperCAmelCase				,							2				,							axis=__UpperCAmelCase					)
			return a * tf.math.sigmoid(__UpperCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse('2.4'):







	def a_     (    __UpperCAmelCase					)				->					int:





				"""simple docstring"""







				return tf.keras.activations.gelu(__UpperCAmelCase				,							approximate=__UpperCAmelCase					)

	a							   =				tf.keras.activations.gelu







	a							   =				approximate_gelu_wrap
else:
	a							   =				_gelu
	a							   =				_gelu_new


a							   =				{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)




