




'''simple docstring'''




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


_A:					Tuple							=				logging.get_logger(__name__)

_A:					List[str]							=				{
    "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			int						   =       'nllb-moe'
    _A :			Optional[int]						   =       ['past_key_values']
    _A :			Union[str, Any]						   =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



    def __init__(  self     ,			__A=128_112     ,			__A=1_024     ,			__A=12     ,			__A=4_096     ,			__A=16     ,			__A=12     ,			__A=4_096     ,			__A=16     ,			__A=0.0_5     ,			__A=0.0_5     ,			__A=True     ,			__A=True     ,			__A="relu"     ,			__A=1_024     ,			__A=0.1     ,			__A=0.1     ,			__A=0.0     ,			__A=0.0_2     ,			__A=2     ,			__A=True     ,			__A=False     ,			__A="float32"     ,			__A=False     ,			__A=128     ,			__A=64     ,			__A=4     ,			__A=4     ,			__A=0.0_0_1     ,			__A=0.0_0_1     ,			__A="all"     ,			__A=False     ,			__A=False     ,			__A=1.0     ,			__A=0.2     ,			__A=1     ,			__A=0     ,			__A=2     ,			__A=False     ,			**__A     ,			):
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   max_position_embeddings
     __UpperCAmelCase					=   d_model
     __UpperCAmelCase					=   encoder_ffn_dim
     __UpperCAmelCase					=   encoder_layers
     __UpperCAmelCase					=   encoder_attention_heads
     __UpperCAmelCase					=   decoder_ffn_dim
     __UpperCAmelCase					=   decoder_layers
     __UpperCAmelCase					=   decoder_attention_heads
     __UpperCAmelCase					=   dropout
     __UpperCAmelCase					=   attention_dropout
     __UpperCAmelCase					=   activation_dropout
     __UpperCAmelCase					=   activation_function
     __UpperCAmelCase					=   init_std
     __UpperCAmelCase					=   encoder_layerdrop
     __UpperCAmelCase					=   decoder_layerdrop
     __UpperCAmelCase					=   use_cache
     __UpperCAmelCase					=   encoder_layers
     __UpperCAmelCase					=   scale_embedding  # scale factor will be sqrt(d_model) if True
     __UpperCAmelCase					=   router_z_loss_coef
     __UpperCAmelCase					=   router_aux_loss_coef
     __UpperCAmelCase					=   decoder_sparse_step
     __UpperCAmelCase					=   encoder_sparse_step
     __UpperCAmelCase					=   num_experts
     __UpperCAmelCase					=   expert_capacity
     __UpperCAmelCase					=   router_bias
     if router_dtype not in ["float32", "float16", "bfloat16"]:
      raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'					)
     __UpperCAmelCase					=   router_dtype

     __UpperCAmelCase					=   router_ignore_padding_tokens
     __UpperCAmelCase					=   batch_prioritized_routing
     __UpperCAmelCase					=   second_expert_policy
     __UpperCAmelCase					=   normalize_router_prob_before_dropping
     __UpperCAmelCase					=   moe_eval_capacity_token_fraction
     __UpperCAmelCase					=   moe_token_dropout
     __UpperCAmelCase					=   output_router_logits
     super().__init__(
         pad_token_id=_lowercase     ,			bos_token_id=_lowercase     ,			eos_token_id=_lowercase     ,			is_encoder_decoder=_lowercase     ,			decoder_start_token_id=_lowercase     ,			**_lowercase     ,			)












'''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()





