




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        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







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


			def __init__( self     ,		lowerCamelCase__=10_000     ,		lowerCamelCase__=12     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=4     ,		lowerCamelCase__=6     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=4     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__="relu"     ,		lowerCamelCase__=256     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=2     ,		lowerCamelCase__=True     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0     ,		lowerCamelCase__=2     ,		lowerCamelCase__=6_000     ,		lowerCamelCase__=1_024     ,		lowerCamelCase__=2     ,		lowerCamelCase__=(5, 5)     ,		lowerCamelCase__=1_024     ,		lowerCamelCase__=80     ,		lowerCamelCase__=1     ,		**lowerCamelCase__     ,		)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       d_model
							__lowerCamelCase									=       encoder_ffn_dim
							__lowerCamelCase									=       encoder_layers
							__lowerCamelCase									=       encoder_attention_heads
							__lowerCamelCase									=       decoder_ffn_dim
							__lowerCamelCase									=       decoder_layers
							__lowerCamelCase									=       decoder_attention_heads
							__lowerCamelCase									=       dropout
							__lowerCamelCase									=       attention_dropout
							__lowerCamelCase									=       activation_dropout
							__lowerCamelCase									=       activation_function
							__lowerCamelCase									=       init_std
							__lowerCamelCase									=       encoder_layerdrop
							__lowerCamelCase									=       decoder_layerdrop
							__lowerCamelCase									=       use_cache
							__lowerCamelCase									=       encoder_layers
							__lowerCamelCase									=       scale_embedding  # scale factor will be sqrt(d_model) if True
							__lowerCamelCase									=       max_source_positions
							__lowerCamelCase									=       max_target_positions
							__lowerCamelCase									=       num_conv_layers
							__lowerCamelCase									=       list(lowerCamelCase__				)
							__lowerCamelCase									=       conv_channels
							__lowerCamelCase									=       input_feat_per_channel
							__lowerCamelCase									=       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=lowerCamelCase__     ,		bos_token_id=lowerCamelCase__     ,		eos_token_id=lowerCamelCase__     ,		is_encoder_decoder=lowerCamelCase__     ,		decoder_start_token_id=lowerCamelCase__     ,		**lowerCamelCase__     ,		)








def      lowerCamelCase_						(			)	->			List[str]:





				"""simple docstring"""


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


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


