



"""simple docstring"""




_UpperCamelCase:				Optional[Any]    							=  {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      return c.isdigit()


def   lowercase__     (			_UpperCAmelCase				)     ->			int:






      '''simple docstring'''


      lowercase  :      Dict					=   []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(_UpperCAmelCase				):
                  stack.append(int(_UpperCAmelCase				)				)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowercase  :      Any					=   stack.pop()
                  lowercase  :      Dict					=   stack.pop()
                  stack.append(calc[c](_UpperCAmelCase	,      _UpperCAmelCase				)				)

      return stack.pop()


# Driver code
if __name__ == "__main__":
     _UpperCamelCase:				int    							=  '+ 9 * 2 6'
     print(evaluate(test_expression))

     _UpperCamelCase:				str    							=  '/ * 10 2 + 4 1 '
     print(evaluate(test_expression))





"""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   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


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


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

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

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  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)
