


from __future__ import annotations



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		bool:
    if len(_SCREAMING_SNAKE_CASE				) == 0:
        return False
    _lowercase :							int   	=	len(_SCREAMING_SNAKE_CASE				) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]							,				_SCREAMING_SNAKE_CASE				)
    else:
        return binary_search(a_list[midpoint + 1 :]							,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   List[str]							      =				input("Enter numbers separated by comma:\n").strip()
 SCREAMING_SNAKE_CASE :   Tuple							      =				[int(item.strip()) for item in user_input.split(",")]
 SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				int(input("Enter the number to be found in the list:\n").strip())
 SCREAMING_SNAKE_CASE :   int							      =				'' if binary_search(sequence, target) else 'not '
 print(F"{target} was {not_str}found in {sequence}")






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


