# Copyright 2024 The HuggingFace Team. All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import math

from typing import Optional, Tuple, Union

import torch

import torch.nn.functional as F

from torch import nn

from ..activations import get_activation

from ..resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims

class DownResnetBlock1D(nn.Module):

def __init__(

self,

in_channels: int,

out_channels: Optional[int] = None,

num_layers: int = 1,

conv_shortcut: bool = False,

temb_channels: int = 32,

groups: int = 32,

groups_out: Optional[int] = None,

non_linearity: Optional[str] = None,

time_embedding_norm: str = "default",

output_scale_factor: float = 1.0,

add_downsample: bool = True,

):

super().__init__()

self.in_channels = in_channels

out_channels = in_channels if out_channels is None else out_channels

self.out_channels = out_channels

self.use_conv_shortcut = conv_shortcut

self.time_embedding_norm = time_embedding_norm

self.add_downsample = add_downsample

self.output_scale_factor = output_scale_factor

if groups_out is None:

groups_out = groups

# there will always be at least one resnet

resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]

for _ in range(num_layers):

resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))

self.resnets = nn.ModuleList(resnets)

if non_linearity is None:

self.nonlinearity = None

else:

self.nonlinearity = get_activation(non_linearity)

self.downsample = None

if add_downsample:

self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

output_states = ()

hidden_states = self.resnets[0](hidden_states, temb)

for resnet in self.resnets[1:]:

hidden_states = resnet(hidden_states, temb)

output_states += (hidden_states,)

if self.nonlinearity is not None:

hidden_states = self.nonlinearity(hidden_states)

if self.downsample is not None:

hidden_states = self.downsample(hidden_states)

return hidden_states, output_states

class UpResnetBlock1D(nn.Module):

def __init__(

self,

in_channels: int,

out_channels: Optional[int] = None,

num_layers: int = 1,

temb_channels: int = 32,

groups: int = 32,

groups_out: Optional[int] = None,

non_linearity: Optional[str] = None,

time_embedding_norm: str = "default",

output_scale_factor: float = 1.0,

add_upsample: bool = True,

):

super().__init__()

self.in_channels = in_channels

out_channels = in_channels if out_channels is None else out_channels

self.out_channels = out_channels

self.time_embedding_norm = time_embedding_norm

self.add_upsample = add_upsample

self.output_scale_factor = output_scale_factor

if groups_out is None:

groups_out = groups

# there will always be at least one resnet

resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)]

for _ in range(num_layers):

resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))

self.resnets = nn.ModuleList(resnets)

if non_linearity is None:

self.nonlinearity = None

else:

self.nonlinearity = get_activation(non_linearity)

self.upsample = None

if add_upsample:

self.upsample = Upsample1D(out_channels, use_conv_transpose=True)

def forward(

self,

hidden_states: torch.FloatTensor,

res_hidden_states_tuple: Optional[Tuple[torch.FloatTensor, ...]] = None,

temb: Optional[torch.FloatTensor] = None,

) -> torch.FloatTensor:

if res_hidden_states_tuple is not None:

res_hidden_states = res_hidden_states_tuple[-1]

hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1)

hidden_states = self.resnets[0](hidden_states, temb)

for resnet in self.resnets[1:]:

hidden_states = resnet(hidden_states, temb)

if self.nonlinearity is not None:

hidden_states = self.nonlinearity(hidden_states)

if self.upsample is not None:

hidden_states = self.upsample(hidden_states)

return hidden_states

class ValueFunctionMidBlock1D(nn.Module):

def __init__(self, in_channels: int, out_channels: int, embed_dim: int):

super().__init__()

self.in_channels = in_channels

self.out_channels = out_channels

self.embed_dim = embed_dim

self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim)

self.down1 = Downsample1D(out_channels // 2, use_conv=True)

self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)

self.down2 = Downsample1D(out_channels // 4, use_conv=True)

def forward(self, x: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

x = self.res1(x, temb)

x = self.down1(x)

x = self.res2(x, temb)

x = self.down2(x)

return x

class MidResTemporalBlock1D(nn.Module):

def __init__(

self,

in_channels: int,

out_channels: int,

embed_dim: int,

num_layers: int = 1,

add_downsample: bool = False,

add_upsample: bool = False,

non_linearity: Optional[str] = None,

):

super().__init__()

self.in_channels = in_channels

self.out_channels = out_channels

self.add_downsample = add_downsample

# there will always be at least one resnet

resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]

for _ in range(num_layers):

resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))

self.resnets = nn.ModuleList(resnets)

if non_linearity is None:

self.nonlinearity = None

else:

self.nonlinearity = get_activation(non_linearity)

self.upsample = None

if add_upsample:

self.upsample = Downsample1D(out_channels, use_conv=True)

self.downsample = None

if add_downsample:

self.downsample = Downsample1D(out_channels, use_conv=True)

if self.upsample and self.downsample:

raise ValueError("Block cannot downsample and upsample")

def forward(self, hidden_states: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:

hidden_states = self.resnets[0](hidden_states, temb)

for resnet in self.resnets[1:]:

hidden_states = resnet(hidden_states, temb)

if self.upsample:

hidden_states = self.upsample(hidden_states)

if self.downsample:

self.downsample = self.downsample(hidden_states)

return hidden_states

class OutConv1DBlock(nn.Module):

def __init__(self, num_groups_out: int, out_channels: int, embed_dim: int, act_fn: str):

super().__init__()

self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2)

self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)

self.final_conv1d_act = get_activation(act_fn)

self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

hidden_states = self.final_conv1d_1(hidden_states)

hidden_states = rearrange_dims(hidden_states)

hidden_states = self.final_conv1d_gn(hidden_states)

hidden_states = rearrange_dims(hidden_states)

hidden_states = self.final_conv1d_act(hidden_states)

hidden_states = self.final_conv1d_2(hidden_states)

return hidden_states

class OutValueFunctionBlock(nn.Module):

def __init__(self, fc_dim: int, embed_dim: int, act_fn: str = "mish"):

super().__init__()

self.final_block = nn.ModuleList(

[

nn.Linear(fc_dim + embed_dim, fc_dim // 2),

get_activation(act_fn),

nn.Linear(fc_dim // 2, 1),

]

)

def forward(self, hidden_states: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:

hidden_states = hidden_states.view(hidden_states.shape[0], -1)

hidden_states = torch.cat((hidden_states, temb), dim=-1)

for layer in self.final_block:

hidden_states = layer(hidden_states)

return hidden_states

_kernels = {

"linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8],

"cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875],

"lanczos3": [

0.003689131001010537,

0.015056144446134567,

-0.03399861603975296,

-0.066637322306633,

0.13550527393817902,

0.44638532400131226,

0.44638532400131226,

0.13550527393817902,

-0.066637322306633,

-0.03399861603975296,

0.015056144446134567,

0.003689131001010537,

],

}

class Downsample1d(nn.Module):

def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):

super().__init__()

self.pad_mode = pad_mode

kernel_1d = torch.tensor(_kernels[kernel])

self.pad = kernel_1d.shape[0] // 2 - 1

self.register_buffer("kernel", kernel_1d)

def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:

hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode)

weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])

indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)

kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)

weight[indices, indices] = kernel

return F.conv1d(hidden_states, weight, stride=2)

class Upsample1d(nn.Module):

def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):

super().__init__()

self.pad_mode = pad_mode

kernel_1d = torch.tensor(_kernels[kernel]) * 2

self.pad = kernel_1d.shape[0] // 2 - 1

self.register_buffer("kernel", kernel_1d)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode)

weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])

indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)

kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)

weight[indices, indices] = kernel

return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1)

class SelfAttention1d(nn.Module):

def __init__(self, in_channels: int, n_head: int = 1, dropout_rate: float = 0.0):

super().__init__()

self.channels = in_channels

self.group_norm = nn.GroupNorm(1, num_channels=in_channels)

self.num_heads = n_head

self.query = nn.Linear(self.channels, self.channels)

self.key = nn.Linear(self.channels, self.channels)

self.value = nn.Linear(self.channels, self.channels)

self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)

self.dropout = nn.Dropout(dropout_rate, inplace=True)

def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:

new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)

# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)

new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)

return new_projection

def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:

residual = hidden_states

batch, channel_dim, seq = hidden_states.shape

hidden_states = self.group_norm(hidden_states)

hidden_states = hidden_states.transpose(1, 2)

query_proj = self.query(hidden_states)

key_proj = self.key(hidden_states)

value_proj = self.value(hidden_states)

query_states = self.transpose_for_scores(query_proj)

key_states = self.transpose_for_scores(key_proj)

value_states = self.transpose_for_scores(value_proj)

scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))

attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)

attention_probs = torch.softmax(attention_scores, dim=-1)

# compute attention output

hidden_states = torch.matmul(attention_probs, value_states)

hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()

new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)

hidden_states = hidden_states.view(new_hidden_states_shape)

# compute next hidden_states

hidden_states = self.proj_attn(hidden_states)

hidden_states = hidden_states.transpose(1, 2)

hidden_states = self.dropout(hidden_states)

output = hidden_states + residual

return output

class ResConvBlock(nn.Module):

def __init__(self, in_channels: int, mid_channels: int, out_channels: int, is_last: bool = False):

super().__init__()

self.is_last = is_last

self.has_conv_skip = in_channels != out_channels

if self.has_conv_skip:

self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False)

self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2)

self.group_norm_1 = nn.GroupNorm(1, mid_channels)

self.gelu_1 = nn.GELU()

self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2)

if not self.is_last:

self.group_norm_2 = nn.GroupNorm(1, out_channels)

self.gelu_2 = nn.GELU()

def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:

residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states

hidden_states = self.conv_1(hidden_states)

hidden_states = self.group_norm_1(hidden_states)

hidden_states = self.gelu_1(hidden_states)

hidden_states = self.conv_2(hidden_states)

if not self.is_last:

hidden_states = self.group_norm_2(hidden_states)

hidden_states = self.gelu_2(hidden_states)

output = hidden_states + residual

return output

class UNetMidBlock1D(nn.Module):

def __init__(self, mid_channels: int, in_channels: int, out_channels: Optional[int] = None):

super().__init__()

out_channels = in_channels if out_channels is None else out_channels

# there is always at least one resnet

self.down = Downsample1d("cubic")

resnets = [

ResConvBlock(in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels),

]

attentions = [

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(out_channels, out_channels // 32),

]

self.up = Upsample1d(kernel="cubic")

self.attentions = nn.ModuleList(attentions)

self.resnets = nn.ModuleList(resnets)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

hidden_states = self.down(hidden_states)

for attn, resnet in zip(self.attentions, self.resnets):

hidden_states = resnet(hidden_states)

hidden_states = attn(hidden_states)

hidden_states = self.up(hidden_states)

return hidden_states

class AttnDownBlock1D(nn.Module):

def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):

super().__init__()

mid_channels = out_channels if mid_channels is None else mid_channels

self.down = Downsample1d("cubic")

resnets = [

ResConvBlock(in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels),

]

attentions = [

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(out_channels, out_channels // 32),

]

self.attentions = nn.ModuleList(attentions)

self.resnets = nn.ModuleList(resnets)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

hidden_states = self.down(hidden_states)

for resnet, attn in zip(self.resnets, self.attentions):

hidden_states = resnet(hidden_states)

hidden_states = attn(hidden_states)

return hidden_states, (hidden_states,)

class DownBlock1D(nn.Module):

def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):

super().__init__()

mid_channels = out_channels if mid_channels is None else mid_channels

self.down = Downsample1d("cubic")

resnets = [

ResConvBlock(in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels),

]

self.resnets = nn.ModuleList(resnets)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

hidden_states = self.down(hidden_states)

for resnet in self.resnets:

hidden_states = resnet(hidden_states)

return hidden_states, (hidden_states,)

class DownBlock1DNoSkip(nn.Module):

def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):

super().__init__()

mid_channels = out_channels if mid_channels is None else mid_channels

resnets = [

ResConvBlock(in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels),

]

self.resnets = nn.ModuleList(resnets)

def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:

hidden_states = torch.cat([hidden_states, temb], dim=1)

for resnet in self.resnets:

hidden_states = resnet(hidden_states)

return hidden_states, (hidden_states,)

class AttnUpBlock1D(nn.Module):

def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):

super().__init__()

mid_channels = out_channels if mid_channels is None else mid_channels

resnets = [

ResConvBlock(2 * in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels),

]

attentions = [

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(mid_channels, mid_channels // 32),

SelfAttention1d(out_channels, out_channels // 32),

]

self.attentions = nn.ModuleList(attentions)

self.resnets = nn.ModuleList(resnets)

self.up = Upsample1d(kernel="cubic")

def forward(

self,

hidden_states: torch.FloatTensor,

res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],

temb: Optional[torch.FloatTensor] = None,

) -> torch.FloatTensor:

res_hidden_states = res_hidden_states_tuple[-1]

hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

for resnet, attn in zip(self.resnets, self.attentions):

hidden_states = resnet(hidden_states)

hidden_states = attn(hidden_states)

hidden_states = self.up(hidden_states)

return hidden_states

class UpBlock1D(nn.Module):

def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):

super().__init__()

mid_channels = in_channels if mid_channels is None else mid_channels

resnets = [

ResConvBlock(2 * in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels),

]

self.resnets = nn.ModuleList(resnets)

self.up = Upsample1d(kernel="cubic")

def forward(

self,

hidden_states: torch.FloatTensor,

res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],

temb: Optional[torch.FloatTensor] = None,

) -> torch.FloatTensor:

res_hidden_states = res_hidden_states_tuple[-1]

hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

for resnet in self.resnets:

hidden_states = resnet(hidden_states)

hidden_states = self.up(hidden_states)

return hidden_states

class UpBlock1DNoSkip(nn.Module):

def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):

super().__init__()

mid_channels = in_channels if mid_channels is None else mid_channels

resnets = [

ResConvBlock(2 * in_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, mid_channels),

ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),

]

self.resnets = nn.ModuleList(resnets)

def forward(

self,

hidden_states: torch.FloatTensor,

res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],

temb: Optional[torch.FloatTensor] = None,

) -> torch.FloatTensor:

res_hidden_states = res_hidden_states_tuple[-1]

hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

for resnet in self.resnets:

hidden_states = resnet(hidden_states)

return hidden_states

DownBlockType = Union[DownResnetBlock1D, DownBlock1D, AttnDownBlock1D, DownBlock1DNoSkip]

MidBlockType = Union[MidResTemporalBlock1D, ValueFunctionMidBlock1D, UNetMidBlock1D]

OutBlockType = Union[OutConv1DBlock, OutValueFunctionBlock]

UpBlockType = Union[UpResnetBlock1D, UpBlock1D, AttnUpBlock1D, UpBlock1DNoSkip]

def get_down_block(

down_block_type: str,

num_layers: int,

in_channels: int,

out_channels: int,

temb_channels: int,

add_downsample: bool,

) -> DownBlockType:

if down_block_type == "DownResnetBlock1D":

return DownResnetBlock1D(

in_channels=in_channels,

num_layers=num_layers,

out_channels=out_channels,

temb_channels=temb_channels,

add_downsample=add_downsample,

)

elif down_block_type == "DownBlock1D":

return DownBlock1D(out_channels=out_channels, in_channels=in_channels)

elif down_block_type == "AttnDownBlock1D":

return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels)

elif down_block_type == "DownBlock1DNoSkip":

return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels)

raise ValueError(f"{down_block_type} does not exist.")

def get_up_block(

up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool

) -> UpBlockType:

if up_block_type == "UpResnetBlock1D":

return UpResnetBlock1D(

in_channels=in_channels,

num_layers=num_layers,

out_channels=out_channels,

temb_channels=temb_channels,

add_upsample=add_upsample,

)

elif up_block_type == "UpBlock1D":

return UpBlock1D(in_channels=in_channels, out_channels=out_channels)

elif up_block_type == "AttnUpBlock1D":

return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels)

elif up_block_type == "UpBlock1DNoSkip":

return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels)

raise ValueError(f"{up_block_type} does not exist.")

def get_mid_block(

mid_block_type: str,

num_layers: int,

in_channels: int,

mid_channels: int,

out_channels: int,

embed_dim: int,

add_downsample: bool,

) -> MidBlockType:

if mid_block_type == "MidResTemporalBlock1D":

return MidResTemporalBlock1D(

num_layers=num_layers,

in_channels=in_channels,

out_channels=out_channels,

embed_dim=embed_dim,

add_downsample=add_downsample,

)

elif mid_block_type == "ValueFunctionMidBlock1D":

return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim)

elif mid_block_type == "UNetMidBlock1D":

return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels)

raise ValueError(f"{mid_block_type} does not exist.")

def get_out_block(

*, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int

) -> Optional[OutBlockType]:

if out_block_type == "OutConv1DBlock":

return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn)

elif out_block_type == "ValueFunction":

return OutValueFunctionBlock(fc_dim, embed_dim, act_fn)

return None