content: from inspect import isfunction
  import math
  import torch
  import torch.nn.functional as F
  from torch import nn, einsum
  from einops import rearrange, repeat
  
  from ldm.modules.diffusionmodules.util import checkpoint
  
  
  def exists(val):
      return val is not None
  
  
  def uniq(arr):
      return{el: True for el in arr}.keys()
  
  
  def default(val, d):
      if exists(val):
          return val
      return d() if isfunction(d) else d
  
  
  def max_neg_value(t):
      return -torch.finfo(t.dtype).max
  
  
  def init_(tensor):
      dim = tensor.shape[-1]
      std = 1 / math.sqrt(dim)
      tensor.uniform_(-std, std)
      return tensor
  
  
  # 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)
  
  
  class FeedForward(nn.Module):
      def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
          super().__init__()
          inner_dim = int(dim * mult)
          dim_out = default(dim_out, dim)
          project_in = nn.Sequential(
              nn.Linear(dim, inner_dim),
              nn.GELU()
          ) if not glu else 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)
  
  
  def zero_module(module):
      """
      Zero out the parameters of a module and return it.
      """
      for p in module.parameters():
          p.detach().zero_()
      return module
  
  
  def Normalize(in_channels):
      return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
  
  
  class LinearAttention(nn.Module):
      def __init__(self, dim, heads=4, dim_head=32):
          super().__init__()
          self.heads = heads
          hidden_dim = dim_head * heads
          self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
          self.to_out = nn.Conv2d(hidden_dim, dim, 1)
  
      def forward(self, x):
          b, c, h, w = x.shape
          qkv = self.to_qkv(x)
          q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
          k = k.softmax(dim=-1)  
          context = torch.einsum('bhdn,bhen->bhde', k, v)
          out = torch.einsum('bhde,bhdn->bhen', context, q)
          out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
          return self.to_out(out)
  
  
  class SpatialSelfAttention(nn.Module):
      def __init__(self, in_channels):
          super().__init__()
          self.in_channels = in_channels
  
          self.norm = Normalize(in_channels)
          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)
  
      def forward(self, x):
          h_ = x
          h_ = self.norm(h_)
          q = self.q(h_)
          k = self.k(h_)
          v = self.v(h_)
  
          # compute attention
          b,c,h,w = q.shape
          q = rearrange(q, 'b c h w -> b (h w) c')
          k = rearrange(k, 'b c h w -> b c (h w)')
          w_ = torch.einsum('bij,bjk->bik', q, k)
  
          w_ = w_ * (int(c)**(-0.5))
          w_ = torch.nn.functional.softmax(w_, dim=2)
  
          # attend to values
          v = rearrange(v, 'b c h w -> b c (h w)')
          w_ = rearrange(w_, 'b i j -> b j i')
          h_ = torch.einsum('bij,bjk->bik', v, w_)
          h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
          h_ = self.proj_out(h_)
  
          return x+h_
  
  
  class CrossAttention(nn.Module):
      def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
          super().__init__()
          inner_dim = dim_head * heads
          context_dim = default(context_dim, 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 forward(self, x, context=None, mask=None):
          h = self.heads
  
          q = self.to_q(x)
          context = default(context, x)
          k = self.to_k(context)
          v = self.to_v(context)
  
          q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
  
          sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
  
          if exists(mask):
              mask = rearrange(mask, 'b ... -> b (...)')
              max_neg_value = -torch.finfo(sim.dtype).max
              mask = repeat(mask, 'b j -> (b h) () j', h=h)
              sim.masked_fill_(~mask, max_neg_value)
  
          # attention, what we cannot get enough of
          attn = sim.softmax(dim=-1)
  
          out = einsum('b i j, b j d -> b i d', attn, v)
          out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
          return self.to_out(out)
  
  
  class BasicTransformerBlock(nn.Module):
      def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
                   disable_self_attn=False):
          super().__init__()
          self.disable_self_attn = disable_self_attn
          self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
                                      context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
          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):
          return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
  
      def _forward(self, x, context=None):
          x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
          x = self.attn2(self.norm2(x), context=context) + x
          x = self.ff(self.norm3(x)) + x
          return x
  
  
  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., context_dim=None,
                   disable_self_attn=False):
          super().__init__()
          self.in_channels = in_channels
          inner_dim = n_heads * d_head
          self.norm = Normalize(in_channels)
  
          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,
                                     disable_self_attn=disable_self_attn)
                  for d in range(depth)]
          )
  
          self.proj_out = zero_module(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 = rearrange(x, 'b c h w -> b (h w) c').contiguous()
          for block in self.transformer_blocks:
              x = block(x, context=context)
          x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
          x = self.proj_out(x)
          return x + x_in
  