

"""simple docstring"""




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


_SCREAMING_SNAKE_CASE					:  int             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Dict             =      {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "speech_to_text_2"
  a									= ["past_key_values"]
  a									= {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		Dict=1_0000     ,       __lowerCamelCase						:		Optional[Any]=6     ,       __lowerCamelCase						:		Optional[Any]=2048     ,       __lowerCamelCase						:		Optional[int]=4     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Optional[Any]="relu"     ,       __lowerCamelCase						:		Dict=256     ,       __lowerCamelCase						:		Any=0.1     ,       __lowerCamelCase						:		List[str]=0.0     ,       __lowerCamelCase						:		List[str]=0.0     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Dict=2     ,       __lowerCamelCase						:		Optional[int]=True     ,       __lowerCamelCase						:		Optional[Any]=1     ,       __lowerCamelCase						:		Optional[int]=0     ,       __lowerCamelCase						:		List[str]=2     ,       __lowerCamelCase						:		Optional[Any]=1024     ,       **__lowerCamelCase						:		Dict     ,       )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   decoder_ffn_dim
       SCREAMING_SNAKE_CASE__   =   decoder_layers
       SCREAMING_SNAKE_CASE__   =   decoder_attention_heads
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   attention_dropout
       SCREAMING_SNAKE_CASE__   =   activation_dropout
       SCREAMING_SNAKE_CASE__   =   activation_function
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   decoder_layerdrop
       SCREAMING_SNAKE_CASE__   =   use_cache
       SCREAMING_SNAKE_CASE__   =   decoder_layers
       SCREAMING_SNAKE_CASE__   =   scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE__   =   max_target_positions

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





def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if divisor % 5 == 0 or divisor % 2 == 0:
          return 0
     SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   1
     while repunit:
          SCREAMING_SNAKE_CASE__   =   (10 * repunit + 1) % divisor
          repunit_index += 1
     return repunit_index
def     UpperCAmelCase_       (						_A = 1_00_00_00    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   limit - 1
     if divisor % 2 == 0:
          divisor += 1
     while least_divisible_repunit(_A    ) <= limit:
          divisor += 2
     return divisor


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


