


from random import shuffle

import tensorflow as tf
from numpy import array
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Dict:







 """simple docstring"""


 _lowercase							      =int(__snake_case						)
 assert noofclusters < len(__snake_case						)

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

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

 # 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.

 _lowercase							      =tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

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

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

  ##Now lets construct the node that will compute the mean
  # The placeholder for the input
  _lowercase							      =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
  _lowercase							      =tf.reduce_mean(__snake_case     ,						0						)

  ##Node for computing Euclidean distances
  # Placeholders for input
  _lowercase							      =tf.placeholder('''float'''     ,						[dim]						)
  _lowercase							      =tf.placeholder('''float'''     ,						[dim]						)
  _lowercase							      =tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__snake_case     ,						__snake_case						)     ,						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
  _lowercase							      =tf.placeholder('''float'''     ,						[noofclusters]						)
  _lowercase							      =tf.argmin(__snake_case     ,						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.
  _lowercase							      =tf.initialize_all_variables()

  # Initialize all variables
  sess.run(__snake_case						)

  ##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.
  _lowercase							      =100
  for _ in range(__snake_case						):
   ##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(__snake_case						)						):
    _lowercase							      =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.
    _lowercase							      =[
        sess.run(__snake_case     ,						feed_dict={va: vect, va: sess.run(__snake_case						)}						)
        for centroid in centroids
    ]
    # Now use the cluster assignment node, with the distances
    # as the input
    _lowercase							      =sess.run(
        __snake_case     ,						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(__snake_case						):
    # Collect all the vectors assigned to this cluster
    _lowercase							      =[
        vectors[i]
        for i in range(len(__snake_case						)						)
        if sess.run(assignments[i]						) == cluster_n
    ]
    # Compute new centroid location
    _lowercase							      =sess.run(
        __snake_case     ,						feed_dict={mean_input: array(__snake_case						)}						)
    # Assign value to appropriate variable
    sess.run(
        cent_assigns[cluster_n]     ,						feed_dict={centroid_value: new_location}						)

        # Return centroids and assignments
  _lowercase							      =sess.run(__snake_case						)
  _lowercase							      =sess.run(__snake_case						)
  return centroids, assignments









"""simple docstring"""





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class      a						(					__snake_case		):






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str = "▁"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<unk>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "</s>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<pad>"       ,						)     ->		Optional[int]:
      lowerCamelCase_         =						{
          'pad': {'id': 0, 'token': pad_token},
          'eos': {'id': 1, 'token': eos_token},
          'unk': {'id': 2, 'token': unk_token},
      }

      lowerCamelCase_         =						[None] * len(self.special_tokens					)
      for token_dict in self.special_tokens.values():
        lowerCamelCase_         =						token_dict['token']

      lowerCamelCase_         =						Tokenizer(Unigram()					)

      lowerCamelCase_         =						normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(' {2,}'					)       ,						' '					),
              normalizers.Lowercase(),
          ]					)
      lowerCamelCase_         =						pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Digits(individual_digits=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Punctuation(),
          ]					)
      lowerCamelCase_         =						decoders.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						TemplateProcessing(
          single=F'''$A {self.special_tokens["eos"]["token"]}'''       ,						special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]       ,						)

      lowerCamelCase_         =						{
          'model': 'SentencePieceUnigram',
          'replacement': replacement,
          'add_prefix_space': add_prefix_space,
      }

      super().__init__(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		Tuple:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						[files]
      self._tokenizer.train(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[Iterator[str], Iterator[Iterator[str]]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		str:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      self._tokenizer.train_from_iterator(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Dict:
      lowerCamelCase_         =						json.loads(self._tokenizer.to_str()					)

      lowerCamelCase_         =						self.special_tokens['unk']['id']

      lowerCamelCase_         =						Tokenizer.from_str(json.dumps(__SCREAMING_SNAKE_CASE					)					)




