

'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[Any]    )				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =int(_UpperCamelCase    )
     assert noofclusters < len(_UpperCamelCase    )

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

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

     _SCREAMING_SNAKE_CASE							   =tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

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


lowerCamelCase			:				Tuple 			=					{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
