





"""simple docstring"""






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


__UpperCAmelCase						=			logging.get_logger(__name__)

__UpperCAmelCase						=			{
    '''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  ):
				__lowerCamelCase				:	Union[str, Any]				     =				"speech_to_text"
				__lowerCamelCase				:	List[str]				     =				["past_key_values"]
				__lowerCamelCase				:	Union[str, Any]				     =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
				def __init__(		self    :			Tuple	, a_    :			str=1_00_00	, a_    :			Optional[Any]=12	, a_    :			List[Any]=20_48	, a_    :			Any=4	, a_    :			List[Any]=6	, a_    :			Optional[Any]=20_48	, a_    :			int=4	, a_    :			List[str]=0.0	, a_    :			Dict=0.0	, a_    :			List[Any]=True	, a_    :			Tuple=True	, a_    :			Any="relu"	, a_    :			int=2_56	, a_    :			List[str]=0.1	, a_    :			int=0.0	, a_    :			Any=0.0	, a_    :			Optional[int]=0.02	, a_    :			Any=2	, a_    :			Any=True	, a_    :			int=1	, a_    :			Any=0	, a_    :			int=2	, a_    :			Optional[int]=60_00	, a_    :			Union[str, Any]=10_24	, a_    :			str=2	, a_    :			List[Any]=(5, 5)	, a_    :			Union[str, Any]=10_24	, a_    :			str=80	, a_    :			Tuple=1	, **a_    :			str	, )	->	int:



							'''simple docstring'''



							a__  :	List[str]		 =			vocab_size
							a__  :	str		 =			d_model
							a__  :	Dict		 =			encoder_ffn_dim
							a__  :	Union[str, Any]		 =			encoder_layers
							a__  :	Tuple		 =			encoder_attention_heads
							a__  :	Tuple		 =			decoder_ffn_dim
							a__  :	List[str]		 =			decoder_layers
							a__  :	Union[str, Any]		 =			decoder_attention_heads
							a__  :	Optional[int]		 =			dropout
							a__  :	Dict		 =			attention_dropout
							a__  :	Any		 =			activation_dropout
							a__  :	Optional[int]		 =			activation_function
							a__  :	Dict		 =			init_std
							a__  :	List[str]		 =			encoder_layerdrop
							a__  :	List[str]		 =			decoder_layerdrop
							a__  :	int		 =			use_cache
							a__  :	str		 =			encoder_layers
							a__  :	Optional[Any]		 =			scale_embedding  # scale factor will be sqrt(d_model) if True
							a__  :	Union[str, Any]		 =			max_source_positions
							a__  :	Any		 =			max_target_positions
							a__  :	Dict		 =			num_conv_layers
							a__  :	Tuple		 =			list(a_    )
							a__  :	List[Any]		 =			conv_channels
							a__  :	Optional[Any]		 =			input_feat_per_channel
							a__  :	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=a_	, bos_token_id=a_	, eos_token_id=a_	, is_encoder_decoder=a_	, decoder_start_token_id=a_	, **a_	, )






"""simple docstring"""






from __future__ import annotations






def      lowercase__ (					lowerCAmelCase__   :			int			)    ->			list[int]:



			'''simple docstring'''
			a__  :	Tuple		 =			[True] * limit
			a__  :	Tuple		 =			False
			a__  :	int		 =			False
			a__  :	Tuple		 =			True

			for i in range(3							,			int(limit**0.5 + 1			)							,			2			):
						a__  :	int		 =			i * 2
						while index < limit:
									a__  :	Union[str, Any]		 =			False
									a__  :	int		 =			index + i

			a__  :	str		 =			[2]

			for i in range(3							,			lowerCAmelCase__							,			2			):
						if is_prime[i]:
									primes.append(lowerCAmelCase__			)

			return primes






def      lowercase__ (					lowerCAmelCase__   :			int = 1_0_0_0_0_0_0			)    ->			int:



			'''simple docstring'''
			a__  :	List[Any]		 =			prime_sieve(lowerCAmelCase__			)
			a__  :	int		 =			0
			a__  :	str		 =			0

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(i + length							,			len(lowerCAmelCase__			)			):
									a__  :	List[str]		 =			sum(primes[i:j]			)
									if sol >= ceiling:
												break

									if sol in primes:
												a__  :	Tuple		 =			j - i
												a__  :	Union[str, Any]		 =			sol

			return largest


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