

def  snake_case_(_UpperCamelCase	)				->      list:



  """simple docstring"""






  _snake_case		=				[]
  _snake_case		=				1

  for i in range(len(A_	)	):
    if i + 1 < len(A_	) and text[i] == text[i + 1]:
      count += 1
    else:
      encoded.append((text[i], count)	)
      _snake_case		=				1

  return encoded



def  snake_case_(_UpperCamelCase	)				->      str:



  """simple docstring"""






  return "".join(char * length for char, length in encoded	)


if __name__ == "__main__":
 from doctest import testmod

 testmod(name='''run_length_encode''', verbose=True)
 testmod(name='''run_length_decode''', verbose=True)





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 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






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


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  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)

