

"""simple docstring"""


from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class     snake_case	(				lowerCamelCase_  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  """philschmid/bart-large-cnn-samsum"""
				SCREAMING_SNAKE_CASE_   :      int         =  (
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  """summarizer"""
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  AutoTokenizer
				SCREAMING_SNAKE_CASE_   :      str         =  AutoModelForSeqaSeqLM

				SCREAMING_SNAKE_CASE_   :      int         =  ["""text"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  ["""text"""]

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Tuple)->   List[str]:



						'''simple docstring'''


						return self.pre_processor(lowerCAmelCase__     ,  return_tensors="pt"     ,  truncation=lowerCAmelCase__)

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Optional[int])->   int:



						'''simple docstring'''


						return self.model.generate(**lowerCAmelCase__)[0]




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str)->   Optional[Any]:



						'''simple docstring'''


						return self.pre_processor.decode(lowerCAmelCase__     ,  skip_special_tokens=lowerCAmelCase__     ,  clean_up_tokenization_spaces=lowerCAmelCase__)






"""simple docstring"""




def     lowercase			(SCREAMING_SNAKE_CASE_		:				int = 10_00  )   ->						int:
						SCREAMING_SNAKE_CASE													=					2**power
						SCREAMING_SNAKE_CASE													=					0
						while n:
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					r + n % 10, n // 10
						return r


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))


