


def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		str:
    _lowercase :							list[list[str]]   	=	[[] for _ in range(lowerCamelCase_				)]
    _lowercase :							Optional[int]   	=	key - 1

    if key <= 0:
        raise ValueError('Height of grid can\'t be 0 or negative'				)
    if key == 1 or len(lowerCamelCase_				) <= key:
        return input_string

    for position, character in enumerate(lowerCamelCase_				):
        _lowercase :							Dict   	=	position % (lowest * 2)  # puts it in bounds
        _lowercase :							Dict   	=	min(lowerCamelCase_							,				lowest * 2 - num				)  # creates zigzag pattern
        temp_grid[num].append(lowerCamelCase_				)
    _lowercase :							Tuple   	=	[''.join(lowerCamelCase_				) for row in temp_grid]
    _lowercase :							List[str]   	=	''.join(lowerCamelCase_				)

    return output_string



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		str:
    _lowercase :							Union[str, Any]   	=	[]
    _lowercase :							str   	=	key - 1

    if key <= 0:
        raise ValueError('Height of grid can\'t be 0 or negative'				)
    if key == 1:
        return input_string

    _lowercase :							list[list[str]]   	=	[[] for _ in range(lowerCamelCase_				)]  # generates template
    for position in range(len(lowerCamelCase_				)				):
        _lowercase :							Optional[Any]   	=	position % (lowest * 2)  # puts it in bounds
        _lowercase :							Any   	=	min(lowerCamelCase_							,				lowest * 2 - num				)  # creates zigzag pattern
        temp_grid[num].append('*'				)

    _lowercase :							Dict   	=	0
    for row in temp_grid:  # fills in the characters
        _lowercase :							int   	=	input_string[counter : counter + len(lowerCamelCase_				)]
        grid.append(list(lowerCamelCase_				)				)
        counter += len(lowerCamelCase_				)

    _lowercase :							List[str]   	=	''  # reads as zigzag
    for position in range(len(lowerCamelCase_				)				):
        _lowercase :							Union[str, Any]   	=	position % (lowest * 2)  # puts it in bounds
        _lowercase :							Dict   	=	min(lowerCamelCase_							,				lowest * 2 - num				)  # creates zigzag pattern
        output_string += grid[num][0]
        grid[num].pop(0				)
    return output_string



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict[int, str]:
    _lowercase :							int   	=	{}
    for key_guess in range(1							,				len(lowerCamelCase_				)				):  # tries every key
        _lowercase :							str   	=	decrypt(lowerCamelCase_							,				lowerCamelCase_				)
    return results


if __name__ == "__main__":
 import doctest

 doctest.testmod()






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)


