
from random import shuffle

import tensorflow as tf
from numpy import array



def 						lowercase_      (       A__			,		A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= int(A__		)
    assert noofclusters < len(A__		)

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

    # Will help select random centroids from among the available vectors
    snake_case								= list(range(len(A__		)		)		)
    shuffle(A__		)

    # 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(A__		)
        ]
        ##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(A__			,		A__		)		)

        ##Variables for cluster assignments of individual vectors(initialized
        ##to 0 at first)
        snake_case								= [tf.Variable(0		) for i in range(len(A__		)		)]
        ##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(A__			,		A__		)		)

        ##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(A__			,		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(A__			,		A__		)			,		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(A__			,		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(A__		)

        ##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(A__		):
            ##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(A__		)		):
                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(A__			,		feed_dict={va: vect, va: sess.run(A__		)}		)
                    for centroid in centroids
                ]
                # Now use the cluster assignment node, with the distances
                # as the input
                snake_case								= sess.run(
                    A__			,		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(A__		):
                # Collect all the vectors assigned to this cluster
                snake_case								= [
                    vectors[i]
                    for i in range(len(A__		)		)
                    if sess.run(assignments[i]		) == cluster_n
                ]
                # Compute new centroid location
                snake_case								= sess.run(
                    A__			,		feed_dict={mean_input: array(A__		)}		)
                # 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(A__		)
        snake_case								= sess.run(A__		)
        return centroids, assignments



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

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

