





from random import shuffle

import tensorflow as tf
from numpy import array






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						int      		=  int(lowerCamelCase   )
   assert noofclusters < len(lowerCamelCase   )

   # Find out the dimensionality
   a__	:						List[str]      		=  len(vectors[0]   )

   # Will help select random centroids from among the available vectors
   a__	:						List[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.

   a__	:						Any      		=  tf.Graph()

   with graph.as_default():
      # SESSION OF COMPUTATION

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

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

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








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			str			      =						{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						["""DistilBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						[
      """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DistilBertForMaskedLM""",
      """DistilBertForMultipleChoice""",
      """DistilBertForQuestionAnswering""",
      """DistilBertForSequenceClassification""",
      """DistilBertForTokenClassification""",
      """DistilBertModel""",
      """DistilBertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
      """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDistilBertForMaskedLM""",
      """TFDistilBertForMultipleChoice""",
      """TFDistilBertForQuestionAnswering""",
      """TFDistilBertForSequenceClassification""",
      """TFDistilBertForTokenClassification""",
      """TFDistilBertMainLayer""",
      """TFDistilBertModel""",
      """TFDistilBertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Any			      =						[
      """FlaxDistilBertForMaskedLM""",
      """FlaxDistilBertForMultipleChoice""",
      """FlaxDistilBertForQuestionAnswering""",
      """FlaxDistilBertForSequenceClassification""",
      """FlaxDistilBertForTokenClassification""",
      """FlaxDistilBertModel""",
      """FlaxDistilBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

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

