





from random import shuffle

import tensorflow as tf
from numpy import array







def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					int(_lowerCamelCase     )
						assert noofclusters < len(_lowerCamelCase     )

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

						# Will help select random centroids from among the available vectors
						SCREAMING_SNAKE_CASE__     :				Optional[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[Any]   =					tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

												SCREAMING_SNAKE_CASE__     :				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
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[
												    tf.Variable(vectors[vector_indices[i]]     ) for i in range(_lowerCamelCase     )
												]
												##These nodes will assign the centroid Variables the appropriate
												##values
												SCREAMING_SNAKE_CASE__     :				List[str]   =					tf.placeholder("float64"			,   [dim]     )
												SCREAMING_SNAKE_CASE__     :				Any   =					[]
												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__     :				Dict   =					[tf.Variable(0     ) for i in range(len(_lowerCamelCase     )     )]
												##These nodes will assign an assignment Variable the appropriate
												##value
												SCREAMING_SNAKE_CASE__     :				str   =					tf.placeholder("int32"     )
												SCREAMING_SNAKE_CASE__     :				Tuple   =					[]
												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__     :				Tuple   =					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[Any]   =					tf.reduce_mean(_lowerCamelCase			,   0     )

												##Node for computing Euclidean distances
												# Placeholders for input
												SCREAMING_SNAKE_CASE__     :				List[str]   =					tf.placeholder("float"			,   [dim]     )
												SCREAMING_SNAKE_CASE__     :				str   =					tf.placeholder("float"			,   [dim]     )
												SCREAMING_SNAKE_CASE__     :				Union[str, 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__     :				Optional[int]   =					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__     :				Any   =					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__     :				Union[str, Any]   =					100
												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__     :				int   =					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__     :				Optional[int]   =					[
																								    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__     :				Tuple   =					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__     :				Union[str, Any]   =					[
																								    vectors[i]
																								    for i in range(len(_lowerCamelCase     )     )
																								    if sess.run(assignments[i]     ) == cluster_n
																								]
																								# Compute new centroid location
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					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[Any]   =					sess.run(_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					sess.run(_lowerCamelCase     )
												return centroids, assignments






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__lowercase       :List[Any]       =  "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
				pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
				pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
				if pkg in deps:
								if pkg == "tokenizers":
												# must be loaded here, or else tqdm check may fail
												from .utils import is_tokenizers_available

												if not is_tokenizers_available():
																continue  # not required, check version only if installed

								require_version_core(deps[pkg])
				else:
								raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Optional[Any]=None     ):





						'''simple docstring'''


						require_version(deps[pkg]			,   _lowerCamelCase     )