




snake_case__			:    List[Any]			     =      [0, 2, 4, 6, 8]
snake_case__			:    Union[str, Any]			     =      [1, 3, 5, 7, 9]


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase   ):
  if remaining_length == 0:
    if digits[0] == 0 or digits[-1] == 0:
      return 0

    for i in range(length // 2 - 1 ,  -1 ,  -1   ):
      remainder += digits[i] + digits[length - i - 1]

      if remainder % 2 == 0:
        return 0

      remainder //= 10

    return 1

  if remaining_length == 1:
    if remainder % 2 == 0:
      return 0

    UpperCAmelCase__         = 0
    for digit in range(10   ):
      UpperCAmelCase__         = digit
      result += reversible_numbers(
          0 ,  (remainder + 2 * digit) // 10 ,  __snake_case ,  __snake_case   )
    return result

  UpperCAmelCase__         = 0
  for digita in range(10   ):
    UpperCAmelCase__         = digita

    if (remainder + digita) % 2 == 0:
      UpperCAmelCase__         = ODD_DIGITS
    else:
      UpperCAmelCase__         = EVEN_DIGITS

    for digita in other_parity_digits:
      UpperCAmelCase__         = digita
      result += reversible_numbers(
          remaining_length - 2 ,  (remainder + digita + digita) // 10 ,  __snake_case ,  __snake_case ,  )
  return result


def     lowercase		(  _lowerCAmelCase = 9   ):
  UpperCAmelCase__         = 0
  for length in range(1 ,  max_power + 1   ):
    result += reversible_numbers(__snake_case ,  0 ,  [0] * length ,  __snake_case   )
  return result


if __name__ == "__main__":
   print(F"""{solution() = }""")












import os
import sys

__a:     Union[str, Any]   					=					os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )