code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  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__				: int									=    logging.getLogger(__name__)
  
  
  
  
  def 	__lowerCamelCase (  )      ->Optional[Any]:
  	snake_case__  					=      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'				,							)
  
  	snake_case__  					=      parser.parse_args()
  	return args
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->List[str]:
  	snake_case__  					=      datasets.load_dataset(args.dataset_name				,							args.dataset_config				,							split='train'       )
  
  	if args.limit is not None:
  		snake_case__  					=      min(len(UpperCAmelCase_       )				,							args.limit       )
  		snake_case__  					=      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.
  	snake_case__  					=      Tokenizer(Unigram()       )
  	snake_case__  					=      normalizers.Sequence([normalizers.Replace('``'				,							'"'       ), normalizers.Replace('\'\''				,							'"'       )]       )
  	snake_case__  					=      pre_tokenizers.Metaspace()
  
  	# Prepare the trainer.
  	snake_case__  					=      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!'       )
  
  	snake_case__  					=      tokenizer.token_to_id('[CLS]'       )
  	snake_case__  					=      tokenizer.token_to_id('[SEP]'       )
  	snake_case__  					=      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),
  	    ]				,							)
  	snake_case__  					=      decoders.Metaspace()
  
  	if args.export_to_hub:
  		logger.info('Exporting the trained tokenzier to Hub.'       )
  		snake_case__  					=      AlbertTokenizerFast(tokenizer_object=UpperCAmelCase_       )
  		new_tokenizer.push_to_hub('unigram-tokenizer-dataset'       )
  
  
  if __name__ == "__main__":
  			a__				: List[str]									=    parse_args()
  			main(args)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  	snake_case__  					=      len(UpperCAmelCase_       )
  	for i in range(length - 1       ):
  		snake_case__  					=      False
  		for j in range(length - 1 - i       ):
  			if collection[j] > collection[j + 1]:
  				snake_case__  					=      True
  				snake_case__					,     snake_case__  					=      collection[j + 1], collection[j]
  		if not swapped:
  			break  # Stop iteration if the collection is sorted.
  	return collection
  
  
  if __name__ == "__main__":
  			import doctest
  			import time
  
  			doctest.testmod()
  
  			a__				: Dict									=    input('''Enter numbers separated by a comma:''').strip()
  			a__				: Dict									=    [int(item) for item in user_input.split(''',''')]
  			a__				: Tuple									=    time.process_time()
  			print(*bubble_sort(unsorted), sep=''',''')
  			print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  