from random import shuffle

import tensorflow as tf
from numpy import array



def A__  (			lowercase:     List[Any],		lowercase:     Optional[int]					)				->		Any:
       A						:     Union[str, Any]         =int(lowercase					)
       assert noofclusters < len(lowercase					)

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

       # Will help select random centroids from among the available vectors
       A						:     List[str]         =list(range(len(lowercase					)					)					)
       shuffle(lowercase					)

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

              ##Variables for cluster assignments of individual vectors(initialized
              ##to 0 at first)
              A						:     int         =[tf.Variable(0					) for i in range(len(lowercase					)					)]
              ##These nodes will assign an assignment Variable the appropriate
              ##value
              A						:     Any         =tf.placeholder('int32'					)
              A						:     Tuple         =[]
              for assignment in assignments:
                     cluster_assigns.append(tf.assign(lowercase,		lowercase					)					)

              ##Now lets construct the node that will compute the mean
              # The placeholder for the input
              A						:     Optional[int]         =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						:     Dict         =tf.reduce_mean(lowercase,		0					)

              ##Node for computing Euclidean distances
              # Placeholders for input
              A						:     Optional[int]         =tf.placeholder('float',		[dim]					)
              A						:     Any         =tf.placeholder('float',		[dim]					)
              A						:     Dict         =tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase,		lowercase					),		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						:     Tuple         =tf.placeholder('float',		[noofclusters]					)
              A						:     Optional[Any]         =tf.argmin(lowercase,		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(lowercase					)

              ##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						:     str         =100
              for _ in range(lowercase					):
                     ##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(lowercase					)					):
                            A						:     List[str]         =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						:     str         =[
                                sess.run(lowercase,		feed_dict={va: vect, va: sess.run(lowercase					)}					)
                                for centroid in centroids
                            ]
                            # Now use the cluster assignment node, with the distances
                            # as the input
                            A						:     str         =sess.run(
                                lowercase,		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(lowercase					):
                            # Collect all the vectors assigned to this cluster
                            A						:     Dict         =[
                                vectors[i]
                                for i in range(len(lowercase					)					)
                                if sess.run(assignments[i]					) == cluster_n
                            ]
                            # Compute new centroid location
                            A						:     int         =sess.run(
                                lowercase,		feed_dict={mean_input: array(lowercase					)}					)
                            # 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(lowercase					)
              A						:     Optional[int]         =sess.run(lowercase					)
              return centroids, assignments







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def A__  (			*lowercase:     Tuple,		lowercase:     Optional[Union[Dict, Any]] = None,		lowercase:     Dict=True,		lowercase:     Any=2					)				->		List[Any]:
       from .. import __version__

       A						:     Optional[Any]         =take_from
       A						:     Union[str, Any]         =()
       if not isinstance(args[0],		lowercase					):
              A						:     List[str]         =(args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(lowercase					).base_version					) >= version.parse(lowercase					):
                     raise ValueError(
                         F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                         F' version {__version__} is >= {version_name}'					)

              A						:     Tuple         =None
              if isinstance(lowercase,		lowercase					) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowercase					),)
                     A						:     Union[str, Any]         =F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(lowercase,		lowercase					):
                     values += (getattr(lowercase,		lowercase					),)
                     A						:     Optional[Any]         =F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     A						:     List[Any]         =F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     A						:     List[Any]         =warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message,		lowercase,		stacklevel=lowercase					)

       if isinstance(lowercase,		lowercase					) and len(lowercase					) > 0:
              A						:     Any         =inspect.getouterframes(inspect.currentframe()					)[1]
              A						:     int         =call_frame.filename
              A						:     int         =call_frame.lineno
              A						:     Optional[int]         =call_frame.function
              A  ,  A						:     int         =next(iter(deprecated_kwargs.items()					)					)
              raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'					)

       if len(lowercase					) == 0:
              return
       elif len(lowercase					) == 1:
              return values[0]
       return values






