






def 			A__							(	_a  :					str		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    list[list[str]]							       =[[] for _ in range(_a	)]
 snake_case__  :    str							       =key - 1

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

 for position, character in enumerate(_a	):
  snake_case__  :    List[str]							       =position % (lowest * 2)  # puts it in bounds
  snake_case__  :    Optional[int]							       =min(_a		,  lowest * 2 - num	)  # creates zigzag pattern
  temp_grid[num].append(_a	)
 snake_case__  :    Tuple							       =["""""".join(_a	) for row in temp_grid]
 snake_case__  :    Union[str, Any]							       ="""""".join(_a	)

 return output_string







def 			A__							(	_a  :					str		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =[]
 snake_case__  :    Any							       =key - 1

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

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

 snake_case__  :    Optional[int]							       =0
 for row in temp_grid:  # fills in the characters
  snake_case__  :    Tuple							       =input_string[counter : counter + len(_a	)]
  grid.append(list(_a	)	)
  counter += len(_a	)

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







def 			A__							(	_a  :					str	):





 '''simple docstring'''







 snake_case__  :    Any							       ={}
 for key_guess in range(1		,  len(_a	)	):  # tries every key
  snake_case__  :    Optional[Any]							       =decrypt(_a		,  _a	)
 return results


if __name__ == "__main__":
       import doctest

       doctest.testmod()













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





