


'''simple docstring'''






from random import shuffle

import tensorflow as tf
from numpy import array






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:Optional[int]						)				->					List[Any]:




     '''simple docstring'''



     _a      =							int(lowerCAmelCase__						)
     assert noofclusters < len(lowerCAmelCase__						)

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

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

     _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(lowerCAmelCase__						)
          ]
          ##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(lowerCAmelCase__			,  lowerCAmelCase__						)						)

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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

