





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__lowerCamelCase			:	Any					    =	{
    """debug""": logging.DEBUG,
    """info""": logging.INFO,
    """warning""": logging.WARNING,
    """error""": logging.ERROR,
    """critical""": logging.CRITICAL,
}

__lowerCamelCase			:	int					    =	logging.WARNING



def 						A_							(	)		->					Union[str, Any]:
 UpperCamelCase      :   str			      =  os.getenv("DATASETS_VERBOSITY"				,  _lowerCAmelCase	)
 if env_level_str:
  if env_level_str in log_levels:
   return log_levels[env_level_str]
  else:
   logging.getLogger().warning(
       F"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
       F"""has to be one of: { ", ".join(log_levels.keys()	) }"""	)
 return _default_log_level



def 						A_							(	)		->					str:
 return __name__.split("."	)[0]



def 						A_							(	)		->					logging.Logger:
 return logging.getLogger(_get_library_name()	)



def 						A_							(	)		->					None:
 # Apply our default configuration to the library root logger.
 UpperCamelCase      :   str			      =  _get_library_root_logger()
 library_root_logger.setLevel(_get_default_logging_level()	)



def 						A_							(	)		->					None:
 UpperCamelCase      :   Dict			      =  _get_library_root_logger()
 library_root_logger.setLevel(logging.NOTSET	)



def 						A_							(	_lowerCAmelCase = None	)		->					logging.Logger:
 if name is None:
  UpperCamelCase      :   List[Any]			      =  _get_library_name()
 return logging.getLogger(_lowerCAmelCase	)



def 						A_							(	)		->					int:
 return _get_library_root_logger().getEffectiveLevel()



def 						A_							(	_lowerCAmelCase	)		->					None:
 _get_library_root_logger().setLevel(_lowerCAmelCase	)



def 						A_							(	)		->					List[str]:
 return set_verbosity(_lowerCAmelCase	)



def 						A_							(	)		->					Optional[Any]:
 return set_verbosity(_lowerCAmelCase	)



def 						A_							(	)		->					Union[str, Any]:
 return set_verbosity(_lowerCAmelCase	)



def 						A_							(	)		->					Optional[Any]:
 return set_verbosity(_lowerCAmelCase	)



def 						A_							(	)		->					None:
 UpperCamelCase      :   Optional[int]			      =  False



def 						A_							(	)		->					None:
 UpperCamelCase      :   Tuple			      =  True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()



class    A__  :





      def __init__(					self			,				*A_			,				**A_  ):  # pylint: disable=unused-argument
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  args[0] if args else None





      def __iter__(					self  ):
       '''simple docstring'''




       return iter(self._iterator  )





      def __getattr__(					self			,				A_  ):
       '''simple docstring'''




       def empty_fn(*A_			,				**A_  ):  # pylint: disable=unused-argument
        return

       return empty_fn





      def __enter__(					self  ):
       '''simple docstring'''




       return self





      def __exit__(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       return


__lowerCamelCase			:	Optional[Any]					    =	True



class    A__  :





      def __call__(					self			,				*A_			,				A_=False			,				**A_  ):
       '''simple docstring'''




       if _tqdm_active and not disable:
        return tqdm_lib.tqdm(*A_			,				**A_  )
       else:
        return EmptyTqdm(*A_			,				**A_  )





      def 			__UpperCamelCase(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  None
       if _tqdm_active:
        return tqdm_lib.tqdm.set_lock(*A_			,				**A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       if _tqdm_active:
        return tqdm_lib.tqdm.get_lock()


__lowerCamelCase			:	List[Any]					    =	_tqdm_cls()



def 						A_							(	)		->					bool:
 global _tqdm_active
 return bool(_tqdm_active	)



def 						A_							(	)		->					Dict:
 global _tqdm_active
 UpperCamelCase      :   Optional[Any]			      =  True



def 						A_							(	)		->					Any:
 global _tqdm_active
 UpperCamelCase      :   Optional[Any]			      =  False







from random import shuffle

import tensorflow as tf
from numpy import array



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[Any]:
 UpperCamelCase      :   List[Any]			      =  int(_lowerCAmelCase	)
 assert noofclusters < len(_lowerCAmelCase	)

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

 # Will help select random centroids from among the available vectors
 UpperCamelCase      :   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.

 UpperCamelCase      :   Any			      =  tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

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

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

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