'''simple docstring'''




from typing import Callable, Optional, Union

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


__snake_case   :      Tuple						    =  logging.get_logger(__name__)

__snake_case   :      List[Any]						    =  {
    'microsoft/prophetnet-large-uncased': (
        'https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'
    ),
}




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Dict    =				'prophetnet'
       __lowercase					:					str    =				['past_key_values']
       __lowercase					:					int    =				{
           'num_attention_heads': 'num_encoder_attention_heads',
       }

       def __init__(      self    ,     _SCREAMING_SNAKE_CASE = 0.1    ,     _SCREAMING_SNAKE_CASE = "gelu"    ,     _SCREAMING_SNAKE_CASE = 3_0522    ,     _SCREAMING_SNAKE_CASE = 1024    ,     _SCREAMING_SNAKE_CASE = 4096    ,     _SCREAMING_SNAKE_CASE = 12    ,     _SCREAMING_SNAKE_CASE = 16    ,     _SCREAMING_SNAKE_CASE = 4096    ,     _SCREAMING_SNAKE_CASE = 12    ,     _SCREAMING_SNAKE_CASE = 16    ,     _SCREAMING_SNAKE_CASE = 0.1    ,     _SCREAMING_SNAKE_CASE = 0.1    ,     _SCREAMING_SNAKE_CASE = 512    ,     _SCREAMING_SNAKE_CASE = 0.02    ,     _SCREAMING_SNAKE_CASE = True    ,     _SCREAMING_SNAKE_CASE = True    ,     _SCREAMING_SNAKE_CASE = 0    ,     _SCREAMING_SNAKE_CASE = 2    ,     _SCREAMING_SNAKE_CASE = 32    ,     _SCREAMING_SNAKE_CASE = 128    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = 0.0    ,     _SCREAMING_SNAKE_CASE = True    ,     _SCREAMING_SNAKE_CASE = 0    ,     _SCREAMING_SNAKE_CASE = 1    ,     _SCREAMING_SNAKE_CASE = 2    ,     **_SCREAMING_SNAKE_CASE    ,     )		->     List[str]:
           A_     =   vocab_size
           A_     =   hidden_size
           A_     =   encoder_ffn_dim
           A_     =   num_encoder_layers
           A_     =   num_encoder_attention_heads
           A_     =   decoder_ffn_dim
           A_     =   num_decoder_layers
           A_     =   num_decoder_attention_heads
           A_     =   max_position_embeddings
           A_     =   init_std  # Normal(0, this parameter)
           A_     =   activation_function

           # parameters for prophetnet
           A_     =   ngram
           A_     =   num_buckets
           A_     =   relative_max_distance
           A_     =   disable_ngram_loss
           A_     =   eps

           # 3 Types of Dropout
           A_     =   attention_dropout
           A_     =   activation_dropout
           A_     =   dropout

           A_     =   use_cache

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

       @property
       def 					__A       (      self )		->     int:
           return self.num_encoder_layers + self.num_decoder_layers

       @num_hidden_layers.setter
       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     List[Any]:
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.''' )







'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  int(input('Please set token:(must be only digits)\n'))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('\n' + ''.join(code))
  print(
      F"""\nYour Token is {token} please write it down.\nIf you want to decode """
      'this message again you should input same digits as token!'
  )






