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import torch
from torch import Tensor, nn
from transformers import PreTrainedModel
from .config import AdapterConfig
class Model(nn.Module):
def __init__(
self,
num_channels: int,
num_filters: int,
window_length: int,
stride: int,
):
super().__init__()
self.stride = stride
padding = window_length // 2 - stride // 2
self.conv = nn.Conv1d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=window_length,
stride=stride,
padding=padding,
padding_mode="reflect",
bias=False,
)
self.decode = nn.ConvTranspose1d(
in_channels=num_filters,
out_channels=num_channels,
kernel_size=window_length,
stride=stride,
padding=padding,
bias=False,
)
def encode(self, x: Tensor) -> Tensor:
return torch.tanh(self.conv(x))
class Adapter(PreTrainedModel):
config_class = AdapterConfig
def __init__(self, config: AdapterConfig):
super().__init__(config)
self.model = Model(
num_channels=2,
num_filters=128,
window_length=128,
stride=64
)
def encode(self, x):
return self.model.encode(x)
def decode(self, x):
return self.model.decode(x)
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