import math

import torch

import torch.nn.functional as F

from torch import nn

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.

Uses three q, k, v linear layers to compute attention

"""

def __init__(

self,

channels,

num_head_channels=None,

num_groups=32,

rescale_output_factor=1.0,

eps=1e-5,

):

super().__init__()

self.channels = channels

self.num_heads = channels // num_head_channels if num_head_channels is not None else 1

self.num_head_size = num_head_channels

self.group_norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=eps, affine=True)

# define q,k,v as linear layers

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

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

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

self.rescale_output_factor = rescale_output_factor

self.proj_attn = nn.Linear(channels, channels, 1)

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):

residual = hidden_states

batch, channel, height, width = hidden_states.shape

# norm

hidden_states = self.group_norm(hidden_states)

hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)

# proj to q, k, v

query_proj = self.query(hidden_states)

key_proj = self.key(hidden_states)

value_proj = self.value(hidden_states)

# transpose

query_states = self.transpose_for_scores(query_proj)

key_states = self.transpose_for_scores(key_proj)

value_states = self.transpose_for_scores(value_proj)

# get scores

scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))

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

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

# compute attention output

context_states = torch.matmul(attention_probs, value_states)

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

new_context_states_shape = context_states.size()[:-2] + (self.channels,)

context_states = context_states.view(new_context_states_shape)

# compute next hidden_states

hidden_states = self.proj_attn(context_states)

hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)

# res connect and rescale

hidden_states = (hidden_states + residual) / self.rescale_output_factor

return hidden_states

class SpatialTransformer(nn.Module):

"""

Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply

standard transformer action. Finally, reshape to image

"""

def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None):

super().__init__()

self.n_heads = n_heads

self.d_head = d_head

self.in_channels = in_channels

inner_dim = n_heads * d_head

self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)

self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)

self.transformer_blocks = nn.ModuleList(

[

BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)

for d in range(depth)

]

)

self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)

def forward(self, x, context=None):

# note: if no context is given, cross-attention defaults to self-attention

b, c, h, w = x.shape

x_in = x

x = self.norm(x)

x = self.proj_in(x)

x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)

for block in self.transformer_blocks:

x = block(x, context=context)

x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)

x = self.proj_out(x)

return x + x_in

class BasicTransformerBlock(nn.Module):

def __init__(self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True):

super().__init__()

self.attn1 = CrossAttention(

query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout

) # is a self-attention

self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)

self.attn2 = CrossAttention(

query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout

) # is self-attn if context is none

self.norm1 = nn.LayerNorm(dim)

self.norm2 = nn.LayerNorm(dim)

self.norm3 = nn.LayerNorm(dim)

self.checkpoint = checkpoint

def forward(self, x, context=None):

x = self.attn1(self.norm1(x)) + x

x = self.attn2(self.norm2(x), context=context) + x

x = self.ff(self.norm3(x)) + x

return x

class CrossAttention(nn.Module):

def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):

super().__init__()

inner_dim = dim_head * heads

context_dim = context_dim if context_dim is not None else query_dim

self.scale = dim_head**-0.5

self.heads = heads

self.to_q = nn.Linear(query_dim, inner_dim, bias=False)

self.to_k = nn.Linear(context_dim, inner_dim, bias=False)

self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))

def reshape_heads_to_batch_dim(self, tensor):

batch_size, seq_len, dim = tensor.shape

head_size = self.heads

tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)

tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)

return tensor

def reshape_batch_dim_to_heads(self, tensor):

batch_size, seq_len, dim = tensor.shape

head_size = self.heads

tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)

tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)

return tensor

def forward(self, x, context=None, mask=None):

batch_size, sequence_length, dim = x.shape

h = self.heads

q = self.to_q(x)

context = context if context is not None else x

k = self.to_k(context)

v = self.to_v(context)

q = self.reshape_heads_to_batch_dim(q)

k = self.reshape_heads_to_batch_dim(k)

v = self.reshape_heads_to_batch_dim(v)

sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale

if mask is not None:

mask = mask.reshape(batch_size, -1)

max_neg_value = -torch.finfo(sim.dtype).max

mask = mask[:, None, :].repeat(h, 1, 1)

sim.masked_fill_(~mask, max_neg_value)

# attention, what we cannot get enough of

attn = sim.softmax(dim=-1)

out = torch.einsum("b i j, b j d -> b i d", attn, v)

out = self.reshape_batch_dim_to_heads(out)

return self.to_out(out)

class FeedForward(nn.Module):

def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):

super().__init__()

inner_dim = int(dim * mult)

dim_out = dim_out if dim_out is not None else dim

project_in = GEGLU(dim, inner_dim)

self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))

def forward(self, x):

return self.net(x)

# feedforward

class GEGLU(nn.Module):

def __init__(self, dim_in, dim_out):

super().__init__()

self.proj = nn.Linear(dim_in, dim_out * 2)

def forward(self, x):

x, gate = self.proj(x).chunk(2, dim=-1)

return x * F.gelu(gate)