

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		TaConfig.from_json_file(SCREAMING_SNAKE_CASE   )
      print(f"Building PyTorch model from configuration: {config}"   )
      A_							       =		TaForConditionalGeneration(SCREAMING_SNAKE_CASE   )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
      __lowercase				      =						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(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      __lowercase				      =						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








import random

class    _lowercase				:

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    str       ) ->			tuple[list[int], list[int]]:
         """simple docstring"""





         A_							       =		[ord(lowerCamelCase__       ) for i in text]
         A_							       =		[]
         A_							       =		[]
         for i in plain:
               A_							       =		random.randint(1			,						3_0_0       )
               A_							       =		(i + k) * k
               cipher.append(lowerCamelCase__       )
               key.append(lowerCamelCase__       )
         return cipher, key





   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    list[int]			,						lowerCamelCase__	:    list[int]       ) ->			str:
         """simple docstring"""





         A_							       =		[]
         for i in range(len(lowerCamelCase__       )       ):
               A_							       =		int((cipher[i] - (key[i]) ** 2) / key[i]       )
               plain.append(chr(lowerCamelCase__       )       )
         return "".join(lowerCamelCase__       )


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





