diff --git a/@ b/@
deleted file mode 100644
index 72548e44..00000000
--- a/@
+++ /dev/null
@@ -1,1040 +0,0 @@
-import string
-from abc import abstractmethod
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def conv_transpose_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.ConvTranspose1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.ConvTranspose2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.ConvTranspose3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def Normalize(in_channels, num_groups=32, eps=1e-6):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
-
-
-def nonlinearity(x, swish=1.0):
-    # swish
-    if swish == 1.0:
-        return F.silu(x)
-    else:
-        return x * F.sigmoid(x * float(swish))
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
-    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv=False, use_conv_transpose=False, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        self.use_conv_transpose = use_conv_transpose
-
-        if use_conv_transpose:
-            self.conv = conv_transpose_nd(dims, channels, self.out_channels, 4, 2, 1)
-        elif use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.use_conv_transpose:
-            return self.conv(x)
-
-        if self.dims == 3:
-            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-        else:
-            x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-
-        if self.use_conv:
-            x = self.conv(x)
-
-        return x
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
-    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv=False, dims=2, out_channels=None, padding=1, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        self.padding = padding
-        stride = 2 if dims != 3 else (1, 2, 2)
-        self.name = name
-
-        if use_conv:
-            conv = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
-        else:
-            assert self.channels == self.out_channels
-            conv = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-        if name == "conv":
-            self.conv = conv
-        else:
-            self.op = conv
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.use_conv and self.padding == 0 and self.dims == 2:
-            pad = (0, 1, 0, 1)
-            x = F.pad(x, pad, mode="constant", value=0)
-
-        if self.name == "conv":
-            return self.conv(x)
-        else:
-            return self.op(x)
-
-
-# class UNetUpsample(nn.Module):
-#    def __init__(self, in_channels, with_conv):
-#        super().__init__()
-#        self.with_conv = with_conv
-#        if self.with_conv:
-#            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-#
-#    def forward(self, x):
-#        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-#        if self.with_conv:
-#            x = self.conv(x)
-#        return x
-#
-#
-# class GlideUpsample(nn.Module):
-#    """
-# An upsampling layer with an optional convolution. # # :param channels: channels in the inputs and outputs. :param
-# use_conv: a bool determining if a convolution is # applied. :param dims: determines if the signal is 1D, 2D, or 3D. If
-# 3D, then # upsampling occurs in the inner-two dimensions. #"""
-#
-#    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-#        super().__init__()
-#        self.channels = channels
-#        self.out_channels = out_channels or channels
-#        self.use_conv = use_conv
-#        self.dims = dims
-#        if use_conv:
-#            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-#
-#    def forward(self, x):
-#        assert x.shape[1] == self.channels
-#        if self.dims == 3:
-#            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-#        else:
-#            x = F.interpolate(x, scale_factor=2, mode="nearest")
-#        if self.use_conv:
-#            x = self.conv(x)
-#        return x
-#
-#
-# class LDMUpsample(nn.Module):
-#    """
-# An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param #
-# use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. # If
-# 3D, then # upsampling occurs in the inner-two dimensions. #"""
-#
-#    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-#        super().__init__()
-#        self.channels = channels
-#        self.out_channels = out_channels or channels
-#        self.use_conv = use_conv
-#        self.dims = dims
-#        if use_conv:
-#            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-#
-#    def forward(self, x):
-#        assert x.shape[1] == self.channels
-#        if self.dims == 3:
-#            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-#        else:
-#            x = F.interpolate(x, scale_factor=2, mode="nearest")
-#        if self.use_conv:
-#            x = self.conv(x)
-#        return x
-#
-#
-# class GradTTSUpsample(torch.nn.Module):
-#    def __init__(self, dim):
-#        super(Upsample, self).__init__()
-#        self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
-#
-#    def forward(self, x):
-#        return self.conv(x)
-#
-#
-# TODO (patil-suraj): needs test
-# class Upsample1d(nn.Module):
-#    def __init__(self, dim):
-#        super().__init__()
-#        self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1)
-#
-#    def forward(self, x):
-#        return self.conv(x)
-
-
-# RESNETS
-
-# unet_glide.py & unet_ldm.py
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-
-    :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param
-    use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing
-    on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for
-    downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels, swish=1.0),
-            nn.Identity(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, use_conv=False, dims=dims)
-            self.x_upd = Upsample(channels, use_conv=False, dims=dims)
-        elif down:
-            self.h_upd = Downsample(channels, use_conv=False, dims=dims, padding=1, name="op")
-            self.x_upd = Downsample(channels, use_conv=False, dims=dims, padding=1, name="op")
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
-            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
-            nn.Dropout(p=dropout),
-            zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-
-        :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = torch.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-# unet.py
-class OLD_ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512):
-        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.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x + h
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512, groups=32, pre_norm=True, eps=1e-6):
-        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.pre_norm = pre_norm
-
-        if self.pre_norm:
-            self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
-        else:
-            self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
-
-        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
-        self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.nonlinearity = nonlinearity
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-#        num_groups = 8
-#        self.pre_norm = False
-#        eps = 1e-5
-#        self.nonlinearity = Mish()
-
-    def forward(self, x, temb, mask=None):
-        if mask is None:
-            mask = torch.ones_like(x)
-
-        h = x
-
-        h = h * mask
-        if self.pre_norm:
-            h = self.norm1(h)
-            h = self.nonlinearity(h)
-
-        h = self.conv1(h)
-
-        if not self.pre_norm:
-            h = self.norm1(h)
-            h = self.nonlinearity(h)
-            h = h * mask
-
-        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
-
-        if self.pre_norm:
-            h = self.norm2(h)
-            h = self.nonlinearity(h)
-
-        h = h * mask
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if not self.pre_norm:
-            h = self.norm2(h)
-            h = self.nonlinearity(h)
-            h = h * mask
-
-        x = x * mask
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x + h
-
-
-# unet_grad_tts.py
-class ResnetBlockGradTTS(torch.nn.Module):
-    def __init__(self, dim, dim_out, time_emb_dim, groups=8, eps=1e-6, overwrite=True, conv_shortcut=False, pre_norm=True):
-        super(ResnetBlockGradTTS, self).__init__()
-        self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
-        self.pre_norm = pre_norm
-
-        self.block1 = Block(dim, dim_out, groups=groups)
-        self.block2 = Block(dim_out, dim_out, groups=groups)
-        if dim != dim_out:
-            self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
-        else:
-            self.res_conv = torch.nn.Identity()
-
-        self.overwrite = overwrite
-        if self.overwrite:
-            in_channels = dim
-            out_channels = dim_out
-            temb_channels = time_emb_dim
-
-            # To set via init
-            self.pre_norm = False
-            eps = 1e-5
-
-            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
-
-            if self.pre_norm:
-                self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
-            else:
-                self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
-
-            self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
-            self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
-            dropout = 0.0
-            self.dropout = torch.nn.Dropout(dropout)
-            self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-            if self.in_channels != self.out_channels:
-                if self.use_conv_shortcut:
-                    self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-                else:
-                    self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-            self.nonlinearity = Mish()
-
-        self.is_overwritten = False
-
-    def set_weights(self):
-        self.conv1.weight.data = self.block1.block[0].weight.data
-        self.conv1.bias.data = self.block1.block[0].bias.data
-        self.norm1.weight.data = self.block1.block[1].weight.data
-        self.norm1.bias.data = self.block1.block[1].bias.data
-
-        self.conv2.weight.data = self.block2.block[0].weight.data
-        self.conv2.bias.data = self.block2.block[0].bias.data
-        self.norm2.weight.data = self.block2.block[1].weight.data
-        self.norm2.bias.data = self.block2.block[1].bias.data
-
-        self.temb_proj.weight.data = self.mlp[1].weight.data
-        self.temb_proj.bias.data = self.mlp[1].bias.data
-
-        if self.in_channels != self.out_channels:
-            self.nin_shortcut.weight.data = self.res_conv.weight.data
-            self.nin_shortcut.bias.data = self.res_conv.bias.data
-
-    def forward(self, x, mask, time_emb):
-        h = self.block1(x, mask)
-        h += self.mlp(time_emb).unsqueeze(-1).unsqueeze(-1)
-        h = self.block2(h, mask)
-        output = h + self.res_conv(x * mask)
-
-        output_2 = self.forward_2(x, time_emb, mask=mask)
-        return output
-
-    def forward_2(self, x, temb, mask=None):
-        if not self.is_overwritten:
-            self.set_weights()
-            self.is_overwritten = True
-
-        if mask is None:
-            mask = torch.ones_like(x)
-
-        h = x
-
-        h = h * mask
-        if self.pre_norm:
-            h = self.norm1(h)
-            h = self.nonlinearity(h)
-
-        h = self.conv1(h)
-
-        if not self.pre_norm:
-            h = self.norm1(h)
-            h = self.nonlinearity(h)
-        h = h * mask
-
-        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
-
-        h = h * mask
-        if self.pre_norm:
-            h = self.norm2(h)
-            h = self.nonlinearity(h)
-
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if not self.pre_norm:
-            h = self.norm2(h)
-            h = self.nonlinearity(h)
-        h = h * mask
-
-        x = x * mask
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x + h
-
-
-class Block(torch.nn.Module):
-    def __init__(self, dim, dim_out, groups=8):
-        super(Block, self).__init__()
-        self.block = torch.nn.Sequential(
-            torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
-        )
-
-    def forward(self, x, mask):
-        output = self.block(x * mask)
-        return output * mask
-
-
-# unet_score_estimation.py
-class ResnetBlockBigGANpp(nn.Module):
-    def __init__(
-        self,
-        act,
-        in_ch,
-        out_ch=None,
-        temb_dim=None,
-        up=False,
-        down=False,
-        dropout=0.1,
-        fir=False,
-        fir_kernel=(1, 3, 3, 1),
-        skip_rescale=True,
-        init_scale=0.0,
-    ):
-        super().__init__()
-
-        out_ch = out_ch if out_ch else in_ch
-        self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
-        self.up = up
-        self.down = down
-        self.fir = fir
-        self.fir_kernel = fir_kernel
-
-        self.Conv_0 = conv3x3(in_ch, out_ch)
-        if temb_dim is not None:
-            self.Dense_0 = nn.Linear(temb_dim, out_ch)
-            self.Dense_0.weight.data = default_init()(self.Dense_0.weight.shape)
-            nn.init.zeros_(self.Dense_0.bias)
-
-        self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
-        self.Dropout_0 = nn.Dropout(dropout)
-        self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
-        if in_ch != out_ch or up or down:
-            self.Conv_2 = conv1x1(in_ch, out_ch)
-
-        self.skip_rescale = skip_rescale
-        self.act = act
-        self.in_ch = in_ch
-        self.out_ch = out_ch
-
-    def forward(self, x, temb=None):
-        h = self.act(self.GroupNorm_0(x))
-
-        if self.up:
-            if self.fir:
-                h = upsample_2d(h, self.fir_kernel, factor=2)
-                x = upsample_2d(x, self.fir_kernel, factor=2)
-            else:
-                h = naive_upsample_2d(h, factor=2)
-                x = naive_upsample_2d(x, factor=2)
-        elif self.down:
-            if self.fir:
-                h = downsample_2d(h, self.fir_kernel, factor=2)
-                x = downsample_2d(x, self.fir_kernel, factor=2)
-            else:
-                h = naive_downsample_2d(h, factor=2)
-                x = naive_downsample_2d(x, factor=2)
-
-        h = self.Conv_0(h)
-        # Add bias to each feature map conditioned on the time embedding
-        if temb is not None:
-            h += self.Dense_0(self.act(temb))[:, :, None, None]
-        h = self.act(self.GroupNorm_1(h))
-        h = self.Dropout_0(h)
-        h = self.Conv_1(h)
-
-        if self.in_ch != self.out_ch or self.up or self.down:
-            x = self.Conv_2(x)
-
-        if not self.skip_rescale:
-            return x + h
-        else:
-            return (x + h) / np.sqrt(2.0)
-
-
-# unet_score_estimation.py
-class ResnetBlockDDPMpp(nn.Module):
-    """ResBlock adapted from DDPM."""
-
-    def __init__(
-        self,
-        act,
-        in_ch,
-        out_ch=None,
-        temb_dim=None,
-        conv_shortcut=False,
-        dropout=0.1,
-        skip_rescale=False,
-        init_scale=0.0,
-    ):
-        super().__init__()
-        out_ch = out_ch if out_ch else in_ch
-        self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
-        self.Conv_0 = conv3x3(in_ch, out_ch)
-        if temb_dim is not None:
-            self.Dense_0 = nn.Linear(temb_dim, out_ch)
-            self.Dense_0.weight.data = default_init()(self.Dense_0.weight.data.shape)
-            nn.init.zeros_(self.Dense_0.bias)
-        self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
-        self.Dropout_0 = nn.Dropout(dropout)
-        self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
-        if in_ch != out_ch:
-            if conv_shortcut:
-                self.Conv_2 = conv3x3(in_ch, out_ch)
-            else:
-                self.NIN_0 = NIN(in_ch, out_ch)
-
-        self.skip_rescale = skip_rescale
-        self.act = act
-        self.out_ch = out_ch
-        self.conv_shortcut = conv_shortcut
-
-    def forward(self, x, temb=None):
-        h = self.act(self.GroupNorm_0(x))
-        h = self.Conv_0(h)
-        if temb is not None:
-            h += self.Dense_0(self.act(temb))[:, :, None, None]
-        h = self.act(self.GroupNorm_1(h))
-        h = self.Dropout_0(h)
-        h = self.Conv_1(h)
-        if x.shape[1] != self.out_ch:
-            if self.conv_shortcut:
-                x = self.Conv_2(x)
-            else:
-                x = self.NIN_0(x)
-        if not self.skip_rescale:
-            return x + h
-        else:
-            return (x + h) / np.sqrt(2.0)
-
-
-# unet_rl.py
-class ResidualTemporalBlock(nn.Module):
-    def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
-        super().__init__()
-
-        self.blocks = nn.ModuleList(
-            [
-                Conv1dBlock(inp_channels, out_channels, kernel_size),
-                Conv1dBlock(out_channels, out_channels, kernel_size),
-            ]
-        )
-
-        self.time_mlp = nn.Sequential(
-            nn.Mish(),
-            nn.Linear(embed_dim, out_channels),
-            RearrangeDim(),
-            #            Rearrange("batch t -> batch t 1"),
-        )
-
-        self.residual_conv = (
-            nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
-        )
-
-    def forward(self, x, t):
-        """
-        x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
-        out_channels x horizon ]
-        """
-        out = self.blocks[0](x) + self.time_mlp(t)
-        out = self.blocks[1](out)
-        return out + self.residual_conv(x)
-
-
-# HELPER Modules
-
-
-def normalization(channels, swish=0.0):
-    """
-    Make a standard normalization layer, with an optional swish activation.
-
-    :param channels: number of input channels. :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
-        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
-        self.swish = swish
-
-    def forward(self, x):
-        y = super().forward(x.float()).to(x.dtype)
-        if self.swish == 1.0:
-            y = F.silu(y)
-        elif self.swish:
-            y = y * F.sigmoid(y * float(self.swish))
-        return y
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-class Mish(torch.nn.Module):
-    def forward(self, x):
-        return x * torch.tanh(torch.nn.functional.softplus(x))
-
-
-class Conv1dBlock(nn.Module):
-    """
-    Conv1d --> GroupNorm --> Mish
-    """
-
-    def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
-        super().__init__()
-
-        self.block = nn.Sequential(
-            nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
-            RearrangeDim(),
-            #            Rearrange("batch channels horizon -> batch channels 1 horizon"),
-            nn.GroupNorm(n_groups, out_channels),
-            RearrangeDim(),
-            #            Rearrange("batch channels 1 horizon -> batch channels horizon"),
-            nn.Mish(),
-        )
-
-    def forward(self, x):
-        return self.block(x)
-
-
-class RearrangeDim(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, tensor):
-        if len(tensor.shape) == 2:
-            return tensor[:, :, None]
-        if len(tensor.shape) == 3:
-            return tensor[:, :, None, :]
-        elif len(tensor.shape) == 4:
-            return tensor[:, :, 0, :]
-        else:
-            raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
-
-
-def conv1x1(in_planes, out_planes, stride=1, bias=True, init_scale=1.0, padding=0):
-    """1x1 convolution with DDPM initialization."""
-    conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=padding, bias=bias)
-    conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
-    nn.init.zeros_(conv.bias)
-    return conv
-
-
-def conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1, init_scale=1.0, padding=1):
-    """3x3 convolution with DDPM initialization."""
-    conv = nn.Conv2d(
-        in_planes, out_planes, kernel_size=3, stride=stride, padding=padding, dilation=dilation, bias=bias
-    )
-    conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
-    nn.init.zeros_(conv.bias)
-    return conv
-
-
-def default_init(scale=1.0):
-    """The same initialization used in DDPM."""
-    scale = 1e-10 if scale == 0 else scale
-    return variance_scaling(scale, "fan_avg", "uniform")
-
-
-def variance_scaling(scale, mode, distribution, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
-    """Ported from JAX."""
-
-    def _compute_fans(shape, in_axis=1, out_axis=0):
-        receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis]
-        fan_in = shape[in_axis] * receptive_field_size
-        fan_out = shape[out_axis] * receptive_field_size
-        return fan_in, fan_out
-
-    def init(shape, dtype=dtype, device=device):
-        fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
-        if mode == "fan_in":
-            denominator = fan_in
-        elif mode == "fan_out":
-            denominator = fan_out
-        elif mode == "fan_avg":
-            denominator = (fan_in + fan_out) / 2
-        else:
-            raise ValueError("invalid mode for variance scaling initializer: {}".format(mode))
-        variance = scale / denominator
-        if distribution == "normal":
-            return torch.randn(*shape, dtype=dtype, device=device) * np.sqrt(variance)
-        elif distribution == "uniform":
-            return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
-        else:
-            raise ValueError("invalid distribution for variance scaling initializer")
-
-    return init
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
-
-
-def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
-
-
-def upsample_2d(x, k=None, factor=2, gain=1):
-    r"""Upsample a batch of 2D images with the given filter.
-
-    Args:
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
-    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
-    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
-    multiple of the upsampling factor.
-        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
-          C]`.
-        k: FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
-        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]`
-    """
-    assert isinstance(factor, int) and factor >= 1
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * (gain * (factor**2))
-    p = k.shape[0] - factor
-    return upfirdn2d(x, torch.tensor(k, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))
-
-
-def downsample_2d(x, k=None, factor=2, gain=1):
-    r"""Downsample a batch of 2D images with the given filter.
-
-    Args:
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
-    given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
-    specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
-    shape is a multiple of the downsampling factor.
-        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
-          C]`.
-        k: FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to average pooling.
-        factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]`
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * gain
-    p = k.shape[0] - factor
-    return upfirdn2d(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
-
-
-def naive_upsample_2d(x, factor=2):
-    _N, C, H, W = x.shape
-    x = torch.reshape(x, (-1, C, H, 1, W, 1))
-    x = x.repeat(1, 1, 1, factor, 1, factor)
-    return torch.reshape(x, (-1, C, H * factor, W * factor))
-
-
-def naive_downsample_2d(x, factor=2):
-    _N, C, H, W = x.shape
-    x = torch.reshape(x, (-1, C, H // factor, factor, W // factor, factor))
-    return torch.mean(x, dim=(3, 5))
-
-
-class NIN(nn.Module):
-    def __init__(self, in_dim, num_units, init_scale=0.1):
-        super().__init__()
-        self.W = nn.Parameter(default_init(scale=init_scale)((in_dim, num_units)), requires_grad=True)
-        self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)
-
-    def forward(self, x):
-        x = x.permute(0, 2, 3, 1)
-        y = contract_inner(x, self.W) + self.b
-        return y.permute(0, 3, 1, 2)
-
-
-def _setup_kernel(k):
-    k = np.asarray(k, dtype=np.float32)
-    if k.ndim == 1:
-        k = np.outer(k, k)
-    k /= np.sum(k)
-    assert k.ndim == 2
-    assert k.shape[0] == k.shape[1]
-    return k
-
-
-def contract_inner(x, y):
-    """tensordot(x, y, 1)."""
-    x_chars = list(string.ascii_lowercase[: len(x.shape)])
-    y_chars = list(string.ascii_lowercase[len(x.shape) : len(y.shape) + len(x.shape)])
-    y_chars[0] = x_chars[-1]  # first axis of y and last of x get summed
-    out_chars = x_chars[:-1] + y_chars[1:]
-    return _einsum(x_chars, y_chars, out_chars, x, y)
-
-
-def _einsum(a, b, c, x, y):
-    einsum_str = "{},{}->{}".format("".join(a), "".join(b), "".join(c))
-    return torch.einsum(einsum_str, x, y)