





'''simple docstring'''




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


_A:					Any							=				logging.get_logger(__name__)

_A:					Dict							=				{
    """facebook/s2t-small-librispeech-asr""": (
        """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Union[str, Any]						   =       """speech_to_text"""
    _A :			str						   =       ["""past_key_values"""]
    _A :			List[Any]						   =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



    def __init__(  self     ,			__A=10_000     ,			__A=12     ,			__A=2_048     ,			__A=4     ,			__A=6     ,			__A=2_048     ,			__A=4     ,			__A=0.0     ,			__A=0.0     ,			__A=True     ,			__A=True     ,			__A="relu"     ,			__A=256     ,			__A=0.1     ,			__A=0.0     ,			__A=0.0     ,			__A=0.0_2     ,			__A=2     ,			__A=True     ,			__A=1     ,			__A=0     ,			__A=2     ,			__A=6_000     ,			__A=1_024     ,			__A=2     ,			__A=(5, 5)     ,			__A=1_024     ,			__A=80     ,			__A=1     ,			**__A     ,			):
     __UpperCAmelCase					=   vocab_size
     __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					=   max_source_positions
     __UpperCAmelCase					=   max_target_positions
     __UpperCAmelCase					=   num_conv_layers
     __UpperCAmelCase					=   list(__A					)
     __UpperCAmelCase					=   conv_channels
     __UpperCAmelCase					=   input_feat_per_channel
     __UpperCAmelCase					=   input_channels

     if len(self.conv_kernel_sizes					) != self.num_conv_layers:
      raise ValueError(
          'Configuration for convolutional module is incorrect. '
          'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
          f'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, '
          f'`config.num_conv_layers = {self.num_conv_layers}`.'					)

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












'''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()





