





'''simple docstring'''




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


a__					:			Optional[int]          =     logging.get_logger(__name__)

a__					:			int          =     {
    '''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 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''speech_to_text'''
	__lowerCAmelCase       						=  ['''past_key_values''']
	__lowerCAmelCase       						=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





	def __init__(		self      ,       UpperCamelCase_=1_0000      ,       UpperCamelCase_=12      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=4      ,       UpperCamelCase_=6      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=4      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_="relu"      ,       UpperCamelCase_=256      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=2      ,       UpperCamelCase_=True      ,       UpperCamelCase_=1      ,       UpperCamelCase_=0      ,       UpperCamelCase_=2      ,       UpperCamelCase_=6000      ,       UpperCamelCase_=1024      ,       UpperCamelCase_=2      ,       UpperCamelCase_=(5, 5)      ,       UpperCamelCase_=1024      ,       UpperCamelCase_=80      ,       UpperCamelCase_=1      ,       **UpperCamelCase_      ,       )      ->		int:
		snake_case__	       =   vocab_size
		snake_case__	       =   d_model
		snake_case__	       =   encoder_ffn_dim
		snake_case__	       =   encoder_layers
		snake_case__	       =   encoder_attention_heads
		snake_case__	       =   decoder_ffn_dim
		snake_case__	       =   decoder_layers
		snake_case__	       =   decoder_attention_heads
		snake_case__	       =   dropout
		snake_case__	       =   attention_dropout
		snake_case__	       =   activation_dropout
		snake_case__	       =   activation_function
		snake_case__	       =   init_std
		snake_case__	       =   encoder_layerdrop
		snake_case__	       =   decoder_layerdrop
		snake_case__	       =   use_cache
		snake_case__	       =   encoder_layers
		snake_case__	       =   scale_embedding  # scale factor will be sqrt(d_model) if True
		snake_case__	       =   max_source_positions
		snake_case__	       =   max_target_positions
		snake_case__	       =   num_conv_layers
		snake_case__	       =   list(UpperCamelCase_		)
		snake_case__	       =   conv_channels
		snake_case__	       =   input_feat_per_channel
		snake_case__	       =   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=UpperCamelCase_      ,       bos_token_id=UpperCamelCase_      ,       eos_token_id=UpperCamelCase_      ,       is_encoder_decoder=UpperCamelCase_      ,       decoder_start_token_id=UpperCamelCase_      ,       **UpperCamelCase_      ,       )










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


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



