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from transformers import PretrainedConfig
class USADConfig(PretrainedConfig):
model_type = "usad"
def __init__(
self,
encoder_dim: int = 384,
num_layers: int = 12,
attention_type: str = "mhsa",
num_attention_heads: int = 6,
mamba_d_state: int = 16,
mamba_d_conv: int = 4,
mamba_expand: int = 2,
mamba_bidirectional: bool = False,
feed_forward_expansion_factor: int = 4,
conv_expansion_factor: int = 2,
feed_forward_dropout_p: float = 0.1,
attention_dropout_p: float = 0.1,
conv_dropout_p: float = 0.1,
conv_kernel_size: int = 31,
half_step_residual: bool = True,
transformer_style: bool = True,
use_framewise_subsample: bool = True,
use_patchwise_subsample: bool = False,
conv_subsample_channels: int = 64,
conv_subsample_rate: int = 2,
input_dim: int = 128,
input_dropout_p: float = 0.0,
conv_pos: bool = True,
conv_pos_depth: int = 5,
conv_pos_width: int = 95,
conv_pos_groups: int = 16,
subsample_normalization: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.encoder_dim = encoder_dim
self.num_layers = num_layers
self.attention_type = attention_type
self.num_attention_heads = num_attention_heads
self.mamba_d_state = mamba_d_state
self.mamba_d_conv = mamba_d_conv
self.mamba_expand = mamba_expand
self.mamba_bidirectional = mamba_bidirectional
self.feed_forward_expansion_factor = feed_forward_expansion_factor
self.conv_expansion_factor = conv_expansion_factor
self.feed_forward_dropout_p = feed_forward_dropout_p
self.attention_dropout_p = attention_dropout_p
self.conv_dropout_p = conv_dropout_p
self.conv_kernel_size = conv_kernel_size
self.half_step_residual = half_step_residual
self.transformer_style = transformer_style
self.use_framewise_subsample = use_framewise_subsample
self.use_patchwise_subsample = use_patchwise_subsample
self.conv_subsample_channels = conv_subsample_channels
self.conv_subsample_rate = conv_subsample_rate
self.input_dim = input_dim
self.input_dropout_p = input_dropout_p
self.conv_pos = conv_pos
self.conv_pos_depth = conv_pos_depth
self.conv_pos_width = conv_pos_width
self.conv_pos_groups = conv_pos_groups
self.subsample_normalization = subsample_normalization
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