
'''simple docstring'''


from random import shuffle

import tensorflow as tf
from numpy import array



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Optional[Any]):
			UpperCamelCase       =  int(_UpperCAmelCase)
			assert noofclusters < len(_UpperCAmelCase)

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

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

			UpperCamelCase       =  tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

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

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

						##Node for computing Euclidean distances
						# Placeholders for input
						UpperCamelCase       =  tf.placeholder('''float''',      [dim])
						UpperCamelCase       =  tf.placeholder('''float''',      [dim])
						UpperCamelCase       =  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
						UpperCamelCase       =  tf.placeholder('''float''',      [noofclusters])
						UpperCamelCase       =  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.
						UpperCamelCase       =  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.
						UpperCamelCase       =  100
						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)):
												UpperCamelCase       =  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.
												UpperCamelCase       =  [
												    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
												UpperCamelCase       =  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
												UpperCamelCase       =  [
												    vectors[i]
												    for i in range(len(_UpperCAmelCase))
												    if sess.run(assignments[i]) == cluster_n
												]
												# Compute new centroid location
												UpperCamelCase       =  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
						UpperCamelCase       =  sess.run(_UpperCAmelCase)
						UpperCamelCase       =  sess.run(_UpperCAmelCase)
						return centroids, assignments







'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case_ :  Any     =					{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					['ElectraTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Tuple     =					[
       'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ElectraForCausalLM',
       'ElectraForMaskedLM',
       'ElectraForMultipleChoice',
       'ElectraForPreTraining',
       'ElectraForQuestionAnswering',
       'ElectraForSequenceClassification',
       'ElectraForTokenClassification',
       'ElectraModel',
       'ElectraPreTrainedModel',
       'load_tf_weights_in_electra',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  int     =					[
       'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFElectraForMaskedLM',
       'TFElectraForMultipleChoice',
       'TFElectraForPreTraining',
       'TFElectraForQuestionAnswering',
       'TFElectraForSequenceClassification',
       'TFElectraForTokenClassification',
       'TFElectraModel',
       'TFElectraPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Optional[int]     =					[
       'FlaxElectraForCausalLM',
       'FlaxElectraForMaskedLM',
       'FlaxElectraForMultipleChoice',
       'FlaxElectraForPreTraining',
       'FlaxElectraForQuestionAnswering',
       'FlaxElectraForSequenceClassification',
       'FlaxElectraForTokenClassification',
       'FlaxElectraModel',
       'FlaxElectraPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  Union[str, Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





