


'''simple docstring'''



from random import shuffle

import tensorflow as tf
from numpy import array



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Union[str, Any]				)	->						List[str]:
     __lowerCamelCase  :      str														=						int(UpperCAmelCase_				)
     assert noofclusters < len(UpperCAmelCase_				)

     # Find out the dimensionality
     __lowerCamelCase  :      Any														=						len(vectors[0]				)

     # Will help select random centroids from among the available vectors
     __lowerCamelCase  :      Union[str, Any]														=						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.

     __lowerCamelCase  :      List[Any]														=						tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

          ##Node for computing Euclidean distances
          # Placeholders for input
          __lowerCamelCase  :      List[str]														=						tf.placeholder('float' ,       [dim]				)
          __lowerCamelCase  :      List[Any]														=						tf.placeholder('float' ,       [dim]				)
          __lowerCamelCase  :      Dict														=						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
          __lowerCamelCase  :      List[Any]														=						tf.placeholder('float' ,       [noofclusters]				)
          __lowerCamelCase  :      Tuple														=						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.
          __lowerCamelCase  :      Any														=						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.
          __lowerCamelCase  :      List[Any]														=						1_00
          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_				)				):
                    __lowerCamelCase  :      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.
                    __lowerCamelCase  :      List[str]														=						[
                        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
                    __lowerCamelCase  :      Dict														=						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
                    __lowerCamelCase  :      Optional[Any]														=						[
                        vectors[i]
                        for i in range(len(UpperCAmelCase_				)				)
                        if sess.run(assignments[i]				) == cluster_n
                    ]
                    # Compute new centroid location
                    __lowerCamelCase  :      Tuple														=						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
          __lowerCamelCase  :      str														=						sess.run(UpperCAmelCase_				)
          __lowerCamelCase  :      Any														=						sess.run(UpperCAmelCase_				)
          return centroids, assignments










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





