'''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=	logging.getLogger(__name__)







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	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=1_0_0_0					,  help='Batch size during training.'					,  )
    parser.add_argument(
        '--vocab_size'					,  type=UpperCAmelCase__					,  default=1_0_0_4_8					,  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'					,  )

    __UpperCamelCase						:						List[Any]		     =	parser.parse_args()
    return args







def 			_UpperCamelCase			(  _a						:						List[Any]      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	datasets.load_dataset(args.dataset_name					,  args.dataset_config					,  split='train'      )

    if args.limit is not None:
        __UpperCamelCase						:						Optional[int]		     =	min(len(UpperCAmelCase__      )					,  args.limit      )
        __UpperCamelCase						:						int		     =	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.
    __UpperCamelCase						:						Optional[Any]		     =	Tokenizer(Unigram()      )
    __UpperCamelCase						:						Dict		     =	normalizers.Sequence([normalizers.Replace('``'					,  '"'      ), normalizers.Replace('\'\''					,  '"'      )]      )
    __UpperCamelCase						:						int		     =	pre_tokenizers.Metaspace()

    # Prepare the trainer.
    __UpperCamelCase						:						List[str]		     =	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!'      )

    __UpperCamelCase						:						Any		     =	tokenizer.token_to_id('[CLS]'      )
    __UpperCamelCase						:						Union[str, Any]		     =	tokenizer.token_to_id('[SEP]'      )
    __UpperCamelCase						:						Tuple		     =	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),
        ]					,  )
    __UpperCamelCase						:						Dict		     =	decoders.Metaspace()

    if args.export_to_hub:
        logger.info('Exporting the trained tokenzier to Hub.'      )
        __UpperCamelCase						:						Optional[Any]		     =	AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__      )
        new_tokenizer.push_to_hub('unigram-tokenizer-dataset'      )


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






import random


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					def is_sorted(UpperCAmelCase__   ):
										for i in range(len(UpperCAmelCase__   ) - 1   ):
															if collection[i] > collection[i + 1]:
																				return False
										return True

					while not is_sorted(UpperCAmelCase__   ):
										random.shuffle(UpperCAmelCase__   )
					return collection


if __name__ == "__main__":
		lowerCamelCase   					=  input('''Enter numbers separated by a comma:\n''').strip()
		lowerCamelCase   					=  [int(item) for item in user_input.split(''',''')]
		print(bogo_sort(unsorted))






