


'''simple docstring'''

import random
import sys

__lowercase:		List[str]    						=  "ABCDEFGHIJKLMNOPQRSTUVWXYZ"



def 							SCREAMING_SNAKE_CASE__(		)		->       None:





   '''simple docstring'''







   UpperCamelCase__			    =    input("Enter message: " )
   UpperCamelCase__			    =    "LFWOAYUISVKMNXPBDCRJTQEGHZ"
   UpperCamelCase__			    =    input("Encrypt/Decrypt [e/d]: " )

   check_valid_key(_SCREAMING_SNAKE_CASE )

   if resp.lower().startswith("e" ):
      UpperCamelCase__			    =    "encrypt"
      UpperCamelCase__			    =    encrypt_message(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE )
   elif resp.lower().startswith("d" ):
      UpperCamelCase__			    =    "decrypt"
      UpperCamelCase__			    =    decrypt_message(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE )

   print(F'\n{mode.title()}ion: \n{translated}' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any )		->       None:





   '''simple docstring'''







   UpperCamelCase__			    =    list(_SCREAMING_SNAKE_CASE )
   UpperCamelCase__			    =    list(_SCREAMING_SNAKE_CASE )
   key_list.sort()
   letters_list.sort()

   if key_list != letters_list:
      sys.exit("Error in the key or symbol set." )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					int )		->       str:





   '''simple docstring'''







   return translate_message(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		"encrypt" )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					List[str] )		->       str:





   '''simple docstring'''







   return translate_message(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		"decrypt" )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Union[str, Any] )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    ""
   UpperCamelCase__			    =    LETTERS
   UpperCamelCase__			    =    key

   if mode == "decrypt":
      UpperCamelCase__    ,		UpperCamelCase__			    =    chars_b, chars_a

   for symbol in message:
      if symbol.upper() in chars_a:
         UpperCamelCase__			    =    chars_a.find(symbol.upper() )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         translated += symbol

   return translated



def 							SCREAMING_SNAKE_CASE__(		)		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    list(_SCREAMING_SNAKE_CASE )
   random.shuffle(_SCREAMING_SNAKE_CASE )
   return "".join(_SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
       main()






import argparse

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


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		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__":
      SCREAMING_SNAKE_CASE_		  =			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."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


