



"""simple docstring"""
from random import shuffle

import tensorflow as tf
from numpy import array


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Union[str, Any]:







  '''simple docstring'''


  lowercase_								= int(__lowerCAmelCase							)
  assert noofclusters < len(__lowerCAmelCase							)

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

  # Will help select random centroids from among the available vectors
  lowercase_								= 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_								= tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

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

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






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Any			= {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= ["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Dict			= ["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		int			= [
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= [
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure)

