





"""simple docstring"""





def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							str		):

 '''simple docstring'''






 if not isinstance(lowercase_ ,      lowercase_		):
  raise TypeError('''plaintext must be a string'''		)
 if not isinstance(lowercase_ ,      lowercase_		):
  raise TypeError('''key must be a string'''		)

 if not plaintext:
  raise ValueError('''plaintext is empty'''		)
 if not key:
  raise ValueError('''key is empty'''		)

 key += plaintext
 __SCREAMING_SNAKE_CASE :			str												=       plaintext.lower()
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       key.lower()
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       0
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       0
 __SCREAMING_SNAKE_CASE :			Dict												=       ''''''
 while plaintext_iterator < len(lowercase_		):
  if (
      ord(plaintext[plaintext_iterator]		) < 97
      or ord(plaintext[plaintext_iterator]		) > 122
  ):
   ciphertext += plaintext[plaintext_iterator]
   plaintext_iterator += 1
  elif ord(key[key_iterator]		) < 97 or ord(key[key_iterator]		) > 122:
   key_iterator += 1
  else:
   ciphertext += chr(
       (
           (ord(plaintext[plaintext_iterator]		) - 97 + ord(key[key_iterator]		))
           - 97
       )
       % 26
       + 97		)
   key_iterator += 1
   plaintext_iterator += 1
 return ciphertext




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							str		):

 '''simple docstring'''






 if not isinstance(lowercase_ ,      lowercase_		):
  raise TypeError('''ciphertext must be a string'''		)
 if not isinstance(lowercase_ ,      lowercase_		):
  raise TypeError('''key must be a string'''		)

 if not ciphertext:
  raise ValueError('''ciphertext is empty'''		)
 if not key:
  raise ValueError('''key is empty'''		)

 __SCREAMING_SNAKE_CASE :			str												=       key.lower()
 __SCREAMING_SNAKE_CASE :			Dict												=       0
 __SCREAMING_SNAKE_CASE :			List[str]												=       0
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       ''''''
 while ciphertext_iterator < len(lowercase_		):
  if (
      ord(ciphertext[ciphertext_iterator]		) < 97
      or ord(ciphertext[ciphertext_iterator]		) > 122
  ):
   plaintext += ciphertext[ciphertext_iterator]
  else:
   plaintext += chr(
       (ord(ciphertext[ciphertext_iterator]		) - ord(key[key_iterator]		)) % 26
       + 97		)
   key += chr(
       (ord(ciphertext[ciphertext_iterator]		) - ord(key[key_iterator]		)) % 26
       + 97		)
   key_iterator += 1
  ciphertext_iterator += 1
 return plaintext


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _lowerCamelCase       =	int(input('''Type 1 to encrypt or 2 to decrypt:'''))
    if operation == 1:
        _lowerCamelCase       =	input('''Typeplaintext to be encrypted:\n''')
        _lowerCamelCase       =	input('''Type the key:\n''')
        print(encrypt(plaintext, key))
    elif operation == 2:
        _lowerCamelCase       =	input('''Type the ciphertext to be decrypted:\n''')
        _lowerCamelCase       =	input('''Type the key:\n''')
        print(decrypt(ciphertext, key))
    decrypt('''jsqqs avvwo''', '''coffee''')








"""simple docstring"""





import argparse

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


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

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

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


if __name__ == "__main__":
    _lowerCamelCase       =	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.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

