




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


__a							     =					logging.get_logger(__name__)

__a							     =					{
    '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     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''speech_to_text'''
    lowerCAmelCase         = ['''past_key_values''']
    lowerCAmelCase         = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





    def __init__( self  ,_SCREAMING_SNAKE_CASE=10_000  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=6  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE="relu"  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=6_000  ,_SCREAMING_SNAKE_CASE=1_024  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=(5, 5)  ,_SCREAMING_SNAKE_CASE=1_024  ,_SCREAMING_SNAKE_CASE=80  ,_SCREAMING_SNAKE_CASE=1  ,**_SCREAMING_SNAKE_CASE  ,)					->						Union[str, Any]:
         UpperCAmelCase_     :				Dict  =     vocab_size
         UpperCAmelCase_     :				Any  =     d_model
         UpperCAmelCase_     :				Union[str, Any]  =     encoder_ffn_dim
         UpperCAmelCase_     :				List[str]  =     encoder_layers
         UpperCAmelCase_     :				Tuple  =     encoder_attention_heads
         UpperCAmelCase_     :				List[str]  =     decoder_ffn_dim
         UpperCAmelCase_     :				int  =     decoder_layers
         UpperCAmelCase_     :				int  =     decoder_attention_heads
         UpperCAmelCase_     :				Tuple  =     dropout
         UpperCAmelCase_     :				Any  =     attention_dropout
         UpperCAmelCase_     :				Dict  =     activation_dropout
         UpperCAmelCase_     :				Optional[Any]  =     activation_function
         UpperCAmelCase_     :				int  =     init_std
         UpperCAmelCase_     :				Dict  =     encoder_layerdrop
         UpperCAmelCase_     :				List[str]  =     decoder_layerdrop
         UpperCAmelCase_     :				Optional[int]  =     use_cache
         UpperCAmelCase_     :				List[str]  =     encoder_layers
         UpperCAmelCase_     :				Optional[int]  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase_     :				str  =     max_source_positions
         UpperCAmelCase_     :				Any  =     max_target_positions
         UpperCAmelCase_     :				Optional[int]  =     num_conv_layers
         UpperCAmelCase_     :				Optional[int]  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				str  =     conv_channels
         UpperCAmelCase_     :				List[Any]  =     input_feat_per_channel
         UpperCAmelCase_     :				List[str]  =     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=_SCREAMING_SNAKE_CASE  ,bos_token_id=_SCREAMING_SNAKE_CASE  ,eos_token_id=_SCREAMING_SNAKE_CASE  ,is_encoder_decoder=_SCREAMING_SNAKE_CASE  ,decoder_start_token_id=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)





def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for i in range(1					,		1001 ):
          total += i**i
     return str(_lowercase )[-10:]


if __name__ == "__main__":
     print(solution())