





import itertools
from pathlib import Path




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	[tuple(UpperCamelCase_	) for login in logins]

       UpperCamelCase_ 							=	{char for login in split_logins for char in login}

       for permutation in itertools.permutations(UpperCamelCase_	):
              UpperCamelCase_ 							=	True
              for login in logins:
                     if not (
                         permutation.index(login[0]	)
                         < permutation.index(login[1]	)
                         < permutation.index(login[2]	)
                     ):
                            UpperCamelCase_ 							=	False
                            break

              if satisfied:
                     return int("".join(UpperCamelCase_	)	)

       raise Exception("Unable to find the secret passcode"	)




def 			lowerCAmelCase_			(				UpperCamelCase_ = "keylog.txt"	)  ->		int:
       UpperCamelCase_ 							=	Path(UpperCamelCase_	).parent.joinpath(UpperCamelCase_	).read_text().splitlines()

       return find_secret_passcode(UpperCamelCase_	)


if __name__ == "__main__":
     print(f'''{solution() = }''')




"""simple docstring"""
from typing import Callable, Optional, Union

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


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
