



"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class    SCREAMING_SNAKE_CASE__						(				UpperCAmelCase__				):
   __lowerCAmelCase						:     Union[str, Any]		=		'philschmid/bart-large-cnn-samsum'
   __lowerCAmelCase						:     List[str]		=		(
       '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.'
   )
   __lowerCAmelCase						:     List[Any]		=		'summarizer'
   __lowerCAmelCase						:     Optional[Any]		=		AutoTokenizer
   __lowerCAmelCase						:     List[str]		=		AutoModelForSeqaSeqLM

   __lowerCAmelCase						:     int		=		['text']
   __lowerCAmelCase						:     str		=		['text']

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




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





"""simple docstring"""
def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int		):
 return 1 if input_a == input_a else 0

def    _snake_case		(		):
 assert xnor_gate(0    ,	0		) == 1
 assert xnor_gate(0    ,	1		) == 0
 assert xnor_gate(1    ,	0		) == 0
 assert xnor_gate(1    ,	1		) == 1


if __name__ == "__main__":
      print(xnor_gate(0, 0))
      print(xnor_gate(0, 1))
      print(xnor_gate(1, 0))
      print(xnor_gate(1, 1))
