@@ -0,0 +1,289 @@
+import math
+
+import torch
+from torch import nn
+
+
+# unet_grad_tts.py
+# TODO(Patrick) - weird linear attention layer. Check with: https://github.com/huawei-noah/Speech-Backbones/issues/15
+class LinearAttention(torch.nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super(LinearAttention, self).__init__()
+        self.heads = heads
+        self.dim_head = dim_head
+        hidden_dim = dim_head * heads
+        self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = torch.nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = (
+            qkv.reshape(b, 3, self.heads, self.dim_head, h, w)
+            .permute(1, 0, 2, 3, 4, 5)
+            .reshape(3, b, self.heads, self.dim_head, -1)
+        )
+        k = k.softmax(dim=-1)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        out = out.reshape(b, self.heads, self.dim_head, h, w).reshape(b, self.heads * self.dim_head, h, w)
+        return self.to_out(out)
+
+
+# the main attention block that is used for all models
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        num_groups=32,
+        use_checkpoint=False,
+        encoder_channels=None,
+        use_new_attention_order=False,  # TODO(Patrick) -> is never used, maybe delete?
+        overwrite_qkv=False,
+        overwrite_linear=False,
+        rescale_output_factor=1.0,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+
+        self.use_checkpoint = use_checkpoint
+        self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-5, affine=True)
+        self.qkv = nn.Conv1d(channels, channels * 3, 1)
+        self.n_heads = self.num_heads
+        self.rescale_output_factor = rescale_output_factor
+
+        if encoder_channels is not None:
+            self.encoder_kv = nn.Conv1d(encoder_channels, channels * 2, 1)
+
+        self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
+
+        self.overwrite_qkv = overwrite_qkv
+        if overwrite_qkv:
+            in_channels = channels
+            self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-6)
+            self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+            self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+            self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+            self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+        self.overwrite_linear = overwrite_linear
+        if self.overwrite_linear:
+            num_groups = min(channels // 4, 32)
+            self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-6)
+            self.NIN_0 = NIN(channels, channels)
+            self.NIN_1 = NIN(channels, channels)
+            self.NIN_2 = NIN(channels, channels)
+            self.NIN_3 = NIN(channels, channels)
+
+            self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=channels, eps=1e-6)
+
+        self.is_overwritten = False
+
+    def set_weights(self, module):
+        if self.overwrite_qkv:
+            qkv_weight = torch.cat([module.q.weight.data, module.k.weight.data, module.v.weight.data], dim=0)[
+                :, :, :, 0
+            ]
+            qkv_bias = torch.cat([module.q.bias.data, module.k.bias.data, module.v.bias.data], dim=0)
+
+            self.qkv.weight.data = qkv_weight
+            self.qkv.bias.data = qkv_bias
+
+            proj_out = zero_module(nn.Conv1d(self.channels, self.channels, 1))
+            proj_out.weight.data = module.proj_out.weight.data[:, :, :, 0]
+            proj_out.bias.data = module.proj_out.bias.data
+
+            self.proj_out = proj_out
+        elif self.overwrite_linear:
+            self.qkv.weight.data = torch.concat(
+                [self.NIN_0.W.data.T, self.NIN_1.W.data.T, self.NIN_2.W.data.T], dim=0
+            )[:, :, None]
+            self.qkv.bias.data = torch.concat([self.NIN_0.b.data, self.NIN_1.b.data, self.NIN_2.b.data], dim=0)
+
+            self.proj_out.weight.data = self.NIN_3.W.data.T[:, :, None]
+            self.proj_out.bias.data = self.NIN_3.b.data
+
+            self.norm.weight.data = self.GroupNorm_0.weight.data
+            self.norm.bias.data = self.GroupNorm_0.bias.data
+
+    def forward(self, x, encoder_out=None):
+        if (self.overwrite_qkv or self.overwrite_linear) and not self.is_overwritten:
+            self.set_weights(self)
+            self.is_overwritten = True
+
+        b, c, *spatial = x.shape
+        hid_states = self.norm(x).view(b, c, -1)
+
+        qkv = self.qkv(hid_states)
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+
+        if encoder_out is not None:
+            encoder_kv = self.encoder_kv(encoder_out)
+            assert encoder_kv.shape[1] == self.n_heads * ch * 2
+            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
+            k = torch.cat([ek, k], dim=-1)
+            v = torch.cat([ev, v], dim=-1)
+
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+
+        a = torch.einsum("bts,bcs->bct", weight, v)
+        h = a.reshape(bs, -1, length)
+
+        h = self.proj_out(h)
+        h = h.reshape(b, c, *spatial)
+
+        result = x + h
+
+        result = result / self.rescale_output_factor
+
+        return result
+
+
+# unet_score_estimation.py
+# class AttnBlockpp(nn.Module):
+#    """Channel-wise self-attention block. Modified from DDPM."""
+#
+#    def __init__(
+#        self,
+#        channels,
+#        skip_rescale=False,
+#        init_scale=0.0,
+#        num_heads=1,
+#        num_head_channels=-1,
+#        use_checkpoint=False,
+#        encoder_channels=None,
+#        use_new_attention_order=False,  # TODO(Patrick) -> is never used, maybe delete?
+#        overwrite_qkv=False,
+#        overwrite_from_grad_tts=False,
+#    ):
+#        super().__init__()
+#        num_groups = min(channels // 4, 32)
+#        self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=channels, eps=1e-6)
+#        self.NIN_0 = NIN(channels, channels)
+#        self.NIN_1 = NIN(channels, channels)
+#        self.NIN_2 = NIN(channels, channels)
+#        self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
+#        self.skip_rescale = skip_rescale
+#
+#        self.channels = channels
+#        if num_head_channels == -1:
+#            self.num_heads = num_heads
+#        else:
+#            assert (
+#                channels % num_head_channels == 0
+#            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+#            self.num_heads = channels // num_head_channels
+#
+#        self.use_checkpoint = use_checkpoint
+#        self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-6)
+#        self.qkv = nn.Conv1d(channels, channels * 3, 1)
+#        self.n_heads = self.num_heads
+#
+#        self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
+#
+#        self.is_weight_set = False
+#
+#    def set_weights(self):
+#        self.qkv.weight.data = torch.concat([self.NIN_0.W.data.T, self.NIN_1.W.data.T, self.NIN_2.W.data.T], dim=0)[:, :, None]
+#        self.qkv.bias.data = torch.concat([self.NIN_0.b.data, self.NIN_1.b.data, self.NIN_2.b.data], dim=0)
+#
+#        self.proj_out.weight.data = self.NIN_3.W.data.T[:, :, None]
+#        self.proj_out.bias.data = self.NIN_3.b.data
+#
+#        self.norm.weight.data = self.GroupNorm_0.weight.data
+#        self.norm.bias.data = self.GroupNorm_0.bias.data
+#
+#    def forward(self, x):
+#        if not self.is_weight_set:
+#            self.set_weights()
+#            self.is_weight_set = True
+#
+#        B, C, H, W = x.shape
+#        h = self.GroupNorm_0(x)
+#        q = self.NIN_0(h)
+#        k = self.NIN_1(h)
+#        v = self.NIN_2(h)
+#
+#        w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
+#        w = torch.reshape(w, (B, H, W, H * W))
+#        w = F.softmax(w, dim=-1)
+#        w = torch.reshape(w, (B, H, W, H, W))
+#        h = torch.einsum("bhwij,bcij->bchw", w, v)
+#        h = self.NIN_3(h)
+#
+#        if not self.skip_rescale:
+#            result = x + h
+#        else:
+#            result = (x + h) / np.sqrt(2.0)
+#
+#        result = self.forward_2(x)
+#
+#        return result
+#
+#    def forward_2(self, x, encoder_out=None):
+#        b, c, *spatial = x.shape
+#        hid_states = self.norm(x).view(b, c, -1)
+#
+#        qkv = self.qkv(hid_states)
+#        bs, width, length = qkv.shape
+#        assert width % (3 * self.n_heads) == 0
+#        ch = width // (3 * self.n_heads)
+#        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+#
+#        if encoder_out is not None:
+#            encoder_kv = self.encoder_kv(encoder_out)
+#            assert encoder_kv.shape[1] == self.n_heads * ch * 2
+#            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
+#            k = torch.cat([ek, k], dim=-1)
+#            v = torch.cat([ev, v], dim=-1)
+#
+#        scale = 1 / math.sqrt(math.sqrt(ch))
+#        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
+#        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+#
+#        a = torch.einsum("bts,bcs->bct", weight, v)
+#        h = a.reshape(bs, -1, length)
+#
+#        h = self.proj_out(h)
+#        h = h.reshape(b, c, *spatial)
+#
+#        return (x + h) / np.sqrt(2.0)
+
+
+# TODO(Patrick) - this can and should be removed
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+# TODO(Patrick) - remove once all weights have been converted -> not needed anymore then
+class NIN(nn.Module):
+    def __init__(self, in_dim, num_units, init_scale=0.1):
+        super().__init__()
+        self.W = nn.Parameter(torch.zeros(in_dim, num_units), requires_grad=True)
+        self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)