




import random
class 			_UpperCamelCase      :
      @staticmethod
      def 	UpperCAmelCase_						(			lowerCamelCase   :str      )     ->			tuple[list[int], list[int]]:
       UpperCAmelCase__							 =						[ord(lowerCamelCase      ) for i in text]
       UpperCAmelCase__							 =						[]
       UpperCAmelCase__							 =						[]
       for i in plain:
        UpperCAmelCase__							 =						random.randint(1 ,      300      )
        UpperCAmelCase__							 =						(i + k) * k
        cipher.append(lowerCamelCase      )
        key.append(lowerCamelCase      )
       return cipher, key







      @staticmethod
      def 	UpperCAmelCase_						(			lowerCamelCase   :list[int] ,      lowerCamelCase   :list[int]      )     ->			str:
       UpperCAmelCase__							 =						[]
       for i in range(len(lowerCamelCase      )      ):
        UpperCAmelCase__							 =						int((cipher[i] - (key[i]) ** 2) / key[i]      )
        plain.append(chr(lowerCamelCase      )      )
       return "".join(lowerCamelCase      )


if __name__ == "__main__":
  _lowerCAmelCase,					_lowerCAmelCase						:   Tuple		=   Onepad().encrypt("Hello")
  print(c, k)
  print(Onepad().decrypt(c, k))











import argparse

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


logging.set_verbosity_info()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						BigBirdConfig.from_json_file(_lowerCAmelCase	)
 print(F'''Building PyTorch model from configuration: {config}'''	)

 if is_trivia_qa:
  UpperCAmelCase__							 =						BigBirdForQuestionAnswering(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						BigBirdForPreTraining(_lowerCAmelCase	)

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

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   List[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						:   Dict		=   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
  )





