
'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase			:				Any    		=			{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			['SqueezeBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Optional[Any]    		=			[
          'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SqueezeBertForMaskedLM',
          'SqueezeBertForMultipleChoice',
          'SqueezeBertForQuestionAnswering',
          'SqueezeBertForSequenceClassification',
          'SqueezeBertForTokenClassification',
          'SqueezeBertModel',
          'SqueezeBertModule',
          'SqueezeBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      lowercase			:				Union[str, Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''





def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					abs(UpperCamelCase			)
    lowerCAmelCase__ :  List[Any]							      =					0
    while n > 0:
        res += n % 10
        n //= 10
    return res
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					abs(UpperCamelCase			)
    return n if n < 10 else n % 10 + sum_of_digits(n // 10			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return sum(int(UpperCamelCase			) for c in str(abs(UpperCamelCase			)			)			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(UpperCamelCase							,    UpperCamelCase			) -> None:
        lowerCAmelCase__ :  str							      =					f"""{func.__name__}({value})"""
        lowerCAmelCase__ :  str							      =					timeit(f"""__main__.{call}"""							,    setup="""import __main__"""			)
        print(f"""{call:56} = {func(UpperCamelCase			)} -- {timing:.4f} seconds"""			)

    for value in (262144, 1125899906842624, 1267650600228229401496703205376):
        for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
            benchmark_a_function(UpperCamelCase							,    UpperCamelCase			)
        print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()


