

def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0
 while n > 0:
  res += n % 10
  n //= 10
 return res


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 return n if n < 10 else n % 10 + sum_of_digits(n // 10	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 return sum(int(_UpperCamelCase	) for c in str(abs(_UpperCamelCase	)	)	)


def        __lowerCAmelCase    (       )		->							None:
 '''simple docstring'''


 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(_UpperCamelCase		:  Callable		,							_UpperCamelCase		:  int	) -> None:
  SCREAMING_SNAKE_CASE								=			f"""{func.__name__}({value})"""
  SCREAMING_SNAKE_CASE								=			timeit(f"""__main__.{call}"""		,							setup='import __main__'	)
  print(f"""{call:56} = {func(_UpperCamelCase	)} -- {timing:.4f} seconds"""	)

 for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
  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()




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

