




from random import shuffle

import tensorflow as tf
from numpy import array



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  lowercase    :int      	=						int(lowerCamelCase   )
  assert noofclusters < len(lowerCamelCase   )

  # Find out the dimensionality
  lowercase    :Dict      	=						len(vectors[0]   )

  # Will help select random centroids from among the available vectors
  lowercase    :str      	=						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.

  lowercase    :int      	=						tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    lowercase    :Union[str, Any]      	=						[tf.Variable(0   ) for i in range(len(lowerCamelCase   )   )]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    lowercase    :Optional[Any]      	=						tf.placeholder("int32"   )
    lowercase    :int      	=						[]
    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
    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    :str      	=						tf.reduce_mean(lowerCamelCase,			0   )

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









import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment




@require_huggingface_suite
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						inspect.getfile(accelerate.test_utils				)
         lowercase    :List[Any]      	=						os.path.sep.join(
             mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

         from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

         lowercase    :str      	=						test_metrics






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         debug_launcher(self.test_metrics.main       ,					num_processes=1				)






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         debug_launcher(self.test_metrics.main				)






       @require_single_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         self.test_metrics.main()






       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         print(F"Found {torch.cuda.device_count()} devices."				)
         lowercase    :str      	=						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
         with patch_environment(omp_num_threads=1				):
           execute_subprocess_async(_lowerCAmelCase       ,					env=os.environ.copy()				)



