from string import ascii_uppercase

_lowercase							:      str      							={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase							:      List[Any]      							=dict(enumerate(ascii_uppercase))



def A__  (			lowercase:     str,		lowercase:     str					)				->		str:
       A						:     Optional[Any]         =len(lowercase					)
       A						:     int         =0
       while True:
              if x == i:
                     A						:     Optional[int]         =0
              if len(lowercase					) == len(lowercase					):
                     break
              key += key[i]
              i += 1
       return key



def A__  (			lowercase:     str,		lowercase:     str					)				->		str:
       A						:     List[str]         =''
       A						:     str         =0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     A						:     Optional[int]         =(dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text



def A__  (			lowercase:     str,		lowercase:     str					)				->		str:
       A						:     Tuple         =''
       A						:     List[str]         =0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     A						:     Optional[int]         =(dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt



def A__  (			)				->		None:
       A						:     str         ='THE GERMAN ATTACK'
       A						:     Optional[int]         ='SECRET'
       A						:     Optional[int]         =generate_key(lowercase,		lowercase					)
       A						:     List[Any]         =cipher_text(lowercase,		lowercase					)
       print(F'Encrypted Text = {s}'					)
       print(F'Original Text = {original_text(lowercase,		lowercase					)}'					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






