





'''simple docstring'''




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_					,			UpperCAmelCase_							):



    @register_to_config
    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A = False     ,			):
     super().__init__()

     __UpperCAmelCase					=   nn.Embedding(__A     ,			__A					)

     __UpperCAmelCase					=   nn.Embedding(__A     ,			__A					)
     __UpperCAmelCase					=   False

     __UpperCAmelCase					=   nn.Dropout(p=__A					)

     __UpperCAmelCase					=   TaConfig(
         vocab_size=__A     ,			d_model=__A     ,			num_heads=__A     ,			d_kv=__A     ,			d_ff=__A     ,			dropout_rate=__A     ,			feed_forward_proj=__A     ,			is_decoder=__A     ,			is_encoder_decoder=__A     ,			)

     __UpperCAmelCase					=   nn.ModuleList()
     for lyr_num in range(__A					):
      __UpperCAmelCase					=   TaBlock(__A					)
      self.encoders.append(__A					)

     __UpperCAmelCase					=   TaLayerNorm(__A					)
     __UpperCAmelCase					=   nn.Dropout(p=__A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.token_embedder(__A					)

     __UpperCAmelCase					=   encoder_input_tokens.shape[1]
     __UpperCAmelCase					=   torch.arange(__A     ,			device=encoder_input_tokens.device					)
     x += self.position_encoding(__A					)

     __UpperCAmelCase					=   self.dropout_pre(__A					)

     # inverted the attention mask
     __UpperCAmelCase					=   encoder_input_tokens.size()
     __UpperCAmelCase					=   self.get_extended_attention_mask(__A     ,			__A					)

     for lyr in self.encoders:
      __UpperCAmelCase					=   lyr(__A     ,			__A					)[0]
     __UpperCAmelCase					=   self.layer_norm(__A					)

     return self.dropout_post(__A					), encoder_inputs_mask












'''simple docstring'''




from string import ascii_uppercase

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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   len(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 while True:
  if x == i:
   __UpperCAmelCase					=   0
  if len(_lowerCAmelCase    ) == len(_lowerCAmelCase    ):
   break
  key += key[i]
  i += 1
 return key







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in message:
  if letter == " ":
   cipher_text += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] - dicta[key_new[i]]) % 26
   i += 1
   cipher_text += dicta[x]
 return cipher_text







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in cipher_text:
  if letter == " ":
   or_txt += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] + dicta[key_new[i]] + 26) % 26
   i += 1
   or_txt += dicta[x]
 return or_txt







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   'THE GERMAN ATTACK'
 __UpperCAmelCase					=   'SECRET'
 __UpperCAmelCase					=   generate_key(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   cipher_text(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print(F'Encrypted Text = {s}'    )
 print(F'Original Text = {original_text(_lowerCAmelCase       ,    _lowerCAmelCase    )}'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





