



"""simple docstring"""






from random import shuffle

import tensorflow as tf
from numpy import array




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				List[Any]:
  _snake_case   	=      int(lowerCAmelCase_	)
  assert noofclusters < len(lowerCAmelCase_	)

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

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

  _snake_case   	=      tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

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

    ##Node for computing Euclidean distances
    # Placeholders for input
    _snake_case   	=      tf.placeholder('''float'''  ,						[dim]	)
    _snake_case   	=      tf.placeholder('''float'''  ,						[dim]	)
    _snake_case   	=      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
    _snake_case   	=      tf.placeholder('''float'''  ,						[noofclusters]	)
    _snake_case   	=      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.
    _snake_case   	=      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.
    _snake_case   	=      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_	)	):
        _snake_case   	=      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.
        _snake_case   	=      [
            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
        _snake_case   	=      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
        _snake_case   	=      [
            vectors[i]
            for i in range(len(lowerCAmelCase_	)	)
            if sess.run(assignments[i]	) == cluster_n
        ]
        # Compute new centroid location
        _snake_case   	=      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
    _snake_case   	=      sess.run(lowerCAmelCase_	)
    _snake_case   	=      sess.run(lowerCAmelCase_	)
    return centroids, assignments










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__          =  {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ClapModel",
     "ClapPreTrainedModel",
     "ClapTextModel",
     "ClapTextModelWithProjection",
     "ClapAudioModel",
     "ClapAudioModelWithProjection",
 ]
 SCREAMING_SNAKE_CASE__          =  ["ClapFeatureExtractor"]

if TYPE_CHECKING:
 from .configuration_clap import (
     CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
     ClapAudioConfig,
     ClapConfig,
     ClapTextConfig,
 )
 from .processing_clap import ClapProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clap import ClapFeatureExtractor
  from .modeling_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioModel,
      ClapAudioModelWithProjection,
      ClapModel,
      ClapPreTrainedModel,
      ClapTextModel,
      ClapTextModelWithProjection,
  )


else:
 import sys

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




