

"""simple docstring"""




import functools
import operator

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


__A     :    int    =		logging.get_logger(__name__)

__A     :    List[Any]    =		{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    "hubert"


		def __init__(self :		int     ,     __SCREAMING_SNAKE_CASE :		int=3_2     ,     __SCREAMING_SNAKE_CASE :		List[Any]=7_6_8     ,     __SCREAMING_SNAKE_CASE :		List[Any]=1_2     ,     __SCREAMING_SNAKE_CASE :		Any=1_2     ,     __SCREAMING_SNAKE_CASE :		int=3_0_7_2     ,     __SCREAMING_SNAKE_CASE :		List[Any]="gelu"     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=0.1     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.1     ,     __SCREAMING_SNAKE_CASE :		Tuple=0.1     ,     __SCREAMING_SNAKE_CASE :		List[Any]=True     ,     __SCREAMING_SNAKE_CASE :		int=0.0     ,     __SCREAMING_SNAKE_CASE :		Tuple=0.1     ,     __SCREAMING_SNAKE_CASE :		Dict=0.1     ,     __SCREAMING_SNAKE_CASE :		Dict=0.0_2     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=1E-5     ,     __SCREAMING_SNAKE_CASE :		Optional[int]="group"     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]="gelu"     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,     __SCREAMING_SNAKE_CASE :		str=(5, 2, 2, 2, 2, 2, 2)     ,     __SCREAMING_SNAKE_CASE :		str=(1_0, 3, 3, 3, 3, 2, 2)     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=False     ,     __SCREAMING_SNAKE_CASE :		str=1_2_8     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=1_6     ,     __SCREAMING_SNAKE_CASE :		Any=False     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=True     ,     __SCREAMING_SNAKE_CASE :		Dict=0.0_5     ,     __SCREAMING_SNAKE_CASE :		int=1_0     ,     __SCREAMING_SNAKE_CASE :		Dict=2     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=0.0     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_0     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0     ,     __SCREAMING_SNAKE_CASE :		int="sum"     ,     __SCREAMING_SNAKE_CASE :		Dict=False     ,     __SCREAMING_SNAKE_CASE :		Tuple=False     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=2_5_6     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=0     ,     __SCREAMING_SNAKE_CASE :		Dict=1     ,     __SCREAMING_SNAKE_CASE :		List[str]=2     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE     ,     pad_token_id=__SCREAMING_SNAKE_CASE     ,     bos_token_id=__SCREAMING_SNAKE_CASE     ,     eos_token_id=__SCREAMING_SNAKE_CASE)
							A			=					hidden_size
							A			=					feat_extract_norm
							A			=					feat_extract_activation
							A			=					list(__SCREAMING_SNAKE_CASE)
							A			=					list(__SCREAMING_SNAKE_CASE)
							A			=					list(__SCREAMING_SNAKE_CASE)
							A			=					conv_bias
							A			=					num_conv_pos_embeddings
							A			=					num_conv_pos_embedding_groups
							A			=					len(self.conv_dim)
							A			=					num_hidden_layers
							A			=					intermediate_size
							A			=					hidden_act
							A			=					num_attention_heads
							A			=					hidden_dropout
							A			=					attention_dropout
							A			=					activation_dropout
							A			=					feat_proj_layer_norm
							A			=					feat_proj_dropout
							A			=					final_dropout
							A			=					layerdrop
							A			=					layer_norm_eps
							A			=					initializer_range
							A			=					vocab_size
							A			=					do_stable_layer_norm
							A			=					use_weighted_layer_sum
							A			=					classifier_proj_size

							if (
							    (len(self.conv_stride) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel) != self.num_feat_extract_layers)
							    or (len(self.conv_dim) != self.num_feat_extract_layers)
							):
												raise ValueError(
												    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
												    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
												    F""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
												    F""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							A			=					apply_spec_augment
							A			=					mask_time_prob
							A			=					mask_time_length
							A			=					mask_time_min_masks
							A			=					mask_feature_prob
							A			=					mask_feature_length
							A			=					mask_feature_min_masks

							# ctc loss
							A			=					ctc_loss_reduction
							A			=					ctc_zero_infinity


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							return functools.reduce(operator.mul     ,     self.conv_stride     ,     1)








"""simple docstring"""




def    __SCREAMING_SNAKE_CASE						(						lowercase__ = 1_000      ):
					"""simple docstring"""
					return sum(i for i in range(lowercase__      ) if i % 3 == 0 or i % 5 == 0      )


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





