





'''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)










'''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}""")



