





from random import shuffle

import tensorflow as tf
from numpy import array


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any   ,    _lowercase     : Union[str, Any]			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		int(SCREAMING_SNAKE_CASE_			)
		assert noofclusters < len(SCREAMING_SNAKE_CASE_			)

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

		# Will help select random centroids from among the available vectors
		lowercase__					:      Optional[int]      =		list(range(len(SCREAMING_SNAKE_CASE_			)			)			)
		shuffle(SCREAMING_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.

		lowercase__					:      Optional[Any]      =		tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

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

				##Node for computing Euclidean distances
				# Placeholders for input
				lowercase__					:      int      =		tf.placeholder('float'   ,    [dim]			)
				lowercase__					:      Any      =		tf.placeholder('float'   ,    [dim]			)
				lowercase__					:      List[Any]      =		tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(SCREAMING_SNAKE_CASE_   ,    SCREAMING_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
				lowercase__					:      Dict      =		tf.placeholder('float'   ,    [noofclusters]			)
				lowercase__					:      str      =		tf.argmin(SCREAMING_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.
				lowercase__					:      Any      =		tf.initialize_all_variables()

				# Initialize all variables
				sess.run(SCREAMING_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.
				lowercase__					:      Union[str, Any]      =		100
				for _ in range(SCREAMING_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(SCREAMING_SNAKE_CASE_			)			):
								lowercase__					:      Dict      =		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.
								lowercase__					:      Optional[int]      =		[
								    sess.run(SCREAMING_SNAKE_CASE_   ,    feed_dict={va: vect, va: sess.run(SCREAMING_SNAKE_CASE_			)}			)
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								lowercase__					:      str      =		sess.run(
								    SCREAMING_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(SCREAMING_SNAKE_CASE_			):
								# Collect all the vectors assigned to this cluster
								lowercase__					:      Optional[int]      =		[
								    vectors[i]
								    for i in range(len(SCREAMING_SNAKE_CASE_			)			)
								    if sess.run(assignments[i]			) == cluster_n
								]
								# Compute new centroid location
								lowercase__					:      Optional[int]      =		sess.run(
								    SCREAMING_SNAKE_CASE_   ,    feed_dict={mean_input: array(SCREAMING_SNAKE_CASE_			)}			)
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n]   ,    feed_dict={centroid_value: new_location}			)

								# Return centroids and assignments
				lowercase__					:      List[Any]      =		sess.run(SCREAMING_SNAKE_CASE_			)
				lowercase__					:      int      =		sess.run(SCREAMING_SNAKE_CASE_			)
				return centroids, assignments



'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """FocalNetForImageClassification""",
           """FocalNetForMaskedImageModeling""",
           """FocalNetBackbone""",
           """FocalNetModel""",
           """FocalNetPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

