


"""simple docstring"""







from random import shuffle

import tensorflow as tf
from numpy import array
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=int(a_		)
    assert noofclusters < len(a_		)

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

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

    a					=tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

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

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

        ##Node for computing Euclidean distances
        # Placeholders for input
        a					=tf.placeholder('''float'''	,   [dim]		)
        a					=tf.placeholder('''float'''	,   [dim]		)
        a					=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
        a					=tf.placeholder('''float'''	,   [noofclusters]		)
        a					=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.
        a					=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.
        a					=1_00
        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_		)		):
                a					=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					=[
                    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
                a					=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
                a					=[
                    vectors[i]
                    for i in range(len(a_		)		)
                    if sess.run(assignments[i]		) == cluster_n
                ]
                # Compute new centroid location
                a					=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
        a					=sess.run(a_		)
        a					=sess.run(a_		)
        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,
)


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)