



"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:





     '''simple docstring'''

     for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
          yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]


# Configuration
_lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
_lowerCAmelCase       :      Dict							=							parser.parse_args()

# Base tokenizer
_lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())

# Load dataset
_lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
_lowerCAmelCase       :      List[Any]							=							iter(dataset)


# Training and saving
_lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)





"""simple docstring"""




def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:





     '''simple docstring'''

     if right <= left:
          return
     _UpperCAmelCase    :			Any              =					left
     _UpperCAmelCase    :			Tuple              =					right
     _UpperCAmelCase    :			List[Any]              =					sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
     quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:





     '''simple docstring'''

     if left < right:
          _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
          quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					sorting[right]
     _UpperCAmelCase    :			Any              =					left
     for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          if sorting[i] < pivot:
               _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
               store_index += 1
     _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
     return store_index

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__       ) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

 _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
 quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
 print(unsorted)
