



import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


a_								=   logging.getLogger(__name__)





def 						a__     (						):
   __lowerCamelCase  							=      argparse.ArgumentParser(description='''Train a unigram tokenizer on the wikitext dataset.'''							)
   parser.add_argument(
       '''--dataset_name'''	,type=_UpperCamelCase	,default='''wikitext'''	,help='''Name of the training. Explore datasets at: hf.co/datasets.'''	,)
   parser.add_argument(
       '''--dataset_config'''	,type=_UpperCamelCase	,default='''wikitext-103-raw-v1'''	,help='''Configuration name of the dataset.'''							)
   parser.add_argument(
       '''--batch_size'''	,type=_UpperCamelCase	,default=10_00	,help='''Batch size during training.'''	,)
   parser.add_argument(
       '''--vocab_size'''	,type=_UpperCamelCase	,default=1_00_48	,help='''Size of the desired vocabulary.'''	,)
   parser.add_argument(
       '''--limit'''	,default=_UpperCamelCase	,type=_UpperCamelCase	,help='''Limit the number of shards (used for debugging).'''	,)
   parser.add_argument(
       '''--export_to_hub'''	,action='''store_true'''	,)

   __lowerCamelCase  							=      parser.parse_args()
   return args





def 						a__     (						_UpperCamelCase     :	List[Any]							):
   __lowerCamelCase  							=      datasets.load_dataset(args.dataset_name	,args.dataset_config	,split='''train'''							)

   if args.limit is not None:
      __lowerCamelCase  							=      min(len(_UpperCamelCase							)	,args.limit							)
      __lowerCamelCase  							=      dataset.select(range(_UpperCamelCase							)							)
      logger.info(F"""Limiting the dataset to {args.limit} entries."""							)

   def batch_iterator():
      for i in range(0	,len(_UpperCamelCase							)	,args.batch_size							):
         yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
   __lowerCamelCase  							=      Tokenizer(Unigram()							)
   __lowerCamelCase  							=      normalizers.Sequence([normalizers.Replace('''``'''	,'''"'''							), normalizers.Replace('''\'\''''	,'''"'''							)]							)
   __lowerCamelCase  							=      pre_tokenizers.Metaspace()

   # Prepare the trainer.
   __lowerCamelCase  							=      UnigramTrainer(
       unk_token='''<unk>'''	,special_tokens=['''[CLS]''', '''[SEP]''', '''<unk>''', '''<pad>''', '''[MASK]''']	,vocab_size=args.vocab_size	,)

   logger.info('''Training the tokenizer.'''							)
   tokenizer.train_from_iterator(batch_iterator()	,trainer=_UpperCamelCase							)
   logger.info('''Tokenizer training complete!'''							)

   __lowerCamelCase  							=      tokenizer.token_to_id('''[CLS]'''							)
   __lowerCamelCase  							=      tokenizer.token_to_id('''[SEP]'''							)
   __lowerCamelCase  							=      processors.TemplateProcessing(
       single='''[CLS]:0 $A:0 [SEP]:0'''	,pair='''[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'''	,special_tokens=[
           ('''[CLS]''', cls_token_id),
           ('''[SEP]''', sep_token_id),
       ]	,)
   __lowerCamelCase  							=      decoders.Metaspace()

   if args.export_to_hub:
      logger.info('''Exporting the trained tokenzier to Hub.'''							)
      __lowerCamelCase  							=      AlbertTokenizerFast(tokenizer_object=_UpperCamelCase							)
      new_tokenizer.push_to_hub('''unigram-tokenizer-dataset'''							)


if __name__ == "__main__":
  a_								=   parse_args()
  main(args)











import math



class 	__lowerCAmelCase       :




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      x
    __lowerCamelCase  							=      y
    __lowerCamelCase  							=      step_size
    __lowerCamelCase  							=      function_to_optimize




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.function(self.x		,			self.y			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.step_size
    return [
        SearchProblem(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			self.function			)
        for x, y in (
            (self.x - step_size, self.y - step_size),
            (self.x - step_size, self.y),
            (self.x - step_size, self.y + step_size),
            (self.x, self.y - step_size),
            (self.x, self.y + step_size),
            (self.x + step_size, self.y - step_size),
            (self.x + step_size, self.y),
            (self.x + step_size, self.y + step_size),
        )
    ]




 def __hash__(  self			):



    '''simple docstring'''
    return hash(str(self			)			)




 def __eq__(  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    if isinstance(__UpperCAmelCase		,			__UpperCAmelCase			):
       return hash(str(self			)			) == hash(str(__UpperCAmelCase			)			)
    return False




 def __str__(  self			):



    '''simple docstring'''
    return F"""x: {self.x} y: {self.y}"""





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	bool = True	,_UpperCamelCase     :	float = math.inf	,_UpperCamelCase     :	float = -math.inf	,_UpperCamelCase     :	float = math.inf	,_UpperCamelCase     :	float = -math.inf	,_UpperCamelCase     :	bool = False	,_UpperCamelCase     :	int = 1_00_00	,):
   __lowerCamelCase  							=      search_prob
   __lowerCamelCase  							=      []  # list to store the current score at each iteration
   __lowerCamelCase  							=      0
   __lowerCamelCase  							=      False
   __lowerCamelCase  							=      set()
   while not solution_found and iterations < max_iter:
      visited.add(_UpperCamelCase							)
      iterations += 1
      __lowerCamelCase  							=      current_state.score()
      scores.append(_UpperCamelCase							)
      __lowerCamelCase  							=      current_state.get_neighbors()
      __lowerCamelCase  							=      -math.inf
      __lowerCamelCase  							=      math.inf
      __lowerCamelCase  							=      None  # to hold the next best neighbor
      for neighbor in neighbors:
         if neighbor in visited:
            continue  # do not want to visit the same state again
         if (
             neighbor.x > max_x
             or neighbor.x < min_x
             or neighbor.y > max_y
             or neighbor.y < min_y
         ):
            continue  # neighbor outside our bounds
         __lowerCamelCase  							=      neighbor.score() - current_score
         if find_max:  # finding max
            # going to direction with greatest ascent
            if change > max_change and change > 0:
               __lowerCamelCase  							=      change
               __lowerCamelCase  							=      neighbor
         else:  # finding min
            # to direction with greatest descent
            if change < min_change and change < 0:
               __lowerCamelCase  							=      change
               __lowerCamelCase  							=      neighbor
      if next_state is not None:
         # we found at least one neighbor which improved the current state
         __lowerCamelCase  							=      next_state
      else:
         # since we have no neighbor that improves the solution we stop the search
         __lowerCamelCase  							=      True

   if visualization:
      from matplotlib import pyplot as plt

      plt.plot(range(_UpperCamelCase							)	,_UpperCamelCase							)
      plt.xlabel('''Iterations'''							)
      plt.ylabel('''Function values'''							)
      plt.show()

   return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()





  def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	Union[str, Any]							):
     return (x**2) + (y**2)

  # starting the problem with initial coordinates (3, 4)
  a_								=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(prob, find_max=False)
  print(
      """The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: """
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  a_								=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(
      prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
      f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
  )





  def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	Dict							):
     return (3 * x**2) - (6 * y)

  a_								=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(prob, find_max=True)





  print(
      """The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: """
      f"{local_min.score()}"
  )






