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 typing import Optional, Any
  
  from .diffusionmodules.util import checkpoint
  
  
  try:
      import xformers
      import xformers.ops
  
      XFORMERS_IS_AVAILBLE = True
  except:
      XFORMERS_IS_AVAILBLE = False
  
  # CrossAttn precision handling
  import os
  
  _ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
  
  
  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=0.0):
          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 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 MemoryEfficientCrossAttention(nn.Module):
      # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
      def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs):
          super().__init__()
          print(
              f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
              f"{heads} heads."
          )
          inner_dim = dim_head * heads
          context_dim = default(context_dim, query_dim)
  
          self.heads = heads
          self.dim_head = dim_head
          
          self.with_ip = kwargs.get("with_ip", False)
          if self.with_ip and (context_dim is not None):
              self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False)
              self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False)
              self.ip_dim= kwargs.get("ip_dim", 16)
              self.ip_weight = kwargs.get("ip_weight", 1.0)
  
          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)
          )
          self.attention_op: Optional[Any] = None
  
      def forward(self, x, context=None, mask=None):
          q = self.to_q(x)
          
          has_ip = self.with_ip and (context is not None)
          if has_ip:
              # context dim [(b frame_num), (77 + img_token), 1024]
              token_len = context.shape[1]
              context_ip = context[:, -self.ip_dim:, :]
              k_ip = self.to_k_ip(context_ip)
              v_ip = self.to_v_ip(context_ip)
              context = context[:, :(token_len - self.ip_dim), :]
  
          context = default(context, x)
          k = self.to_k(context)
          v = self.to_v(context)
  
          b, _, _ = q.shape
          q, k, v = map(
              lambda t: t.unsqueeze(3)
              .reshape(b, t.shape[1], self.heads, self.dim_head)
              .permute(0, 2, 1, 3)
              .reshape(b * self.heads, t.shape[1], self.dim_head)
              .contiguous(),
              (q, k, v),
          )
  
          # actually compute the attention, what we cannot get enough of
          out = xformers.ops.memory_efficient_attention(
              q, k, v, attn_bias=None, op=self.attention_op
          )
          
          if has_ip:
              k_ip, v_ip = map(
                  lambda t: t.unsqueeze(3)
                  .reshape(b, t.shape[1], self.heads, self.dim_head)
                  .permute(0, 2, 1, 3)
                  .reshape(b * self.heads, t.shape[1], self.dim_head)
                  .contiguous(),
                  (k_ip, v_ip),
              )
              # actually compute the attention, what we cannot get enough of
              out_ip = xformers.ops.memory_efficient_attention(
                  q, k_ip, v_ip, attn_bias=None, op=self.attention_op
              )
              out = out + self.ip_weight * out_ip
              
          if exists(mask):
              raise NotImplementedError
          out = (
              out.unsqueeze(0)
              .reshape(b, self.heads, out.shape[1], self.dim_head)
              .permute(0, 2, 1, 3)
              .reshape(b, out.shape[1], self.heads * self.dim_head)
          )
          return self.to_out(out)
  
  
  class BasicTransformerBlock(nn.Module):
      def __init__(
          self,
          dim,
          n_heads,
          d_head,
          dropout=0.0,
          context_dim=None,
          gated_ff=True,
          checkpoint=True,
          disable_self_attn=False,
          **kwargs
      ):
          super().__init__()
          assert XFORMERS_IS_AVAILBLE, "xformers is not available"
          attn_cls = MemoryEfficientCrossAttention
          self.disable_self_attn = disable_self_attn
          self.attn1 = attn_cls(
              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 = attn_cls(
              query_dim=dim,
              context_dim=context_dim,
              heads=n_heads,
              dim_head=d_head,
              dropout=dropout,
              **kwargs
          )  # 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
      NEW: use_linear for more efficiency instead of the 1x1 convs
      """
  
      def __init__(
          self,
          in_channels,
          n_heads,
          d_head,
          depth=1,
          dropout=0.0,
          context_dim=None,
          disable_self_attn=False,
          use_linear=False,
          use_checkpoint=True,
          **kwargs
      ):
          super().__init__()
          if exists(context_dim) and not isinstance(context_dim, list):
              context_dim = [context_dim]
          self.in_channels = in_channels
          inner_dim = n_heads * d_head
          self.norm = Normalize(in_channels)
          if not use_linear:
              self.proj_in = nn.Conv2d(
                  in_channels, inner_dim, kernel_size=1, stride=1, padding=0
              )
          else:
              self.proj_in = nn.Linear(in_channels, inner_dim)
  
          self.transformer_blocks = nn.ModuleList(
              [
                  BasicTransformerBlock(
                      inner_dim,
                      n_heads,
                      d_head,
                      dropout=dropout,
                      context_dim=context_dim[d],
                      disable_self_attn=disable_self_attn,
                      checkpoint=use_checkpoint,
                      **kwargs
                  )
                  for d in range(depth)
              ]
          )
          if not use_linear:
              self.proj_out = zero_module(
                  nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
              )
          else:
              self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
          self.use_linear = use_linear
  
      def forward(self, x, context=None):
          # note: if no context is given, cross-attention defaults to self-attention
          if not isinstance(context, list):
              context = [context]
          b, c, h, w = x.shape
          x_in = x
          x = self.norm(x)
          if not self.use_linear:
              x = self.proj_in(x)
          x = rearrange(x, "b c h w -> b (h w) c").contiguous()
          if self.use_linear:
              x = self.proj_in(x)
          for i, block in enumerate(self.transformer_blocks):
              x = block(x, context=context[i])
          if self.use_linear:
              x = self.proj_out(x)
          x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
          if not self.use_linear:
              x = self.proj_out(x)
          return x + x_in
  
  
  class BasicTransformerBlock3D(BasicTransformerBlock):
      def forward(self, x, context=None, num_frames=1):
          return checkpoint(
              self._forward, (x, context, num_frames), self.parameters(), self.checkpoint
          )
  
      def _forward(self, x, context=None, num_frames=1):
          x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous()
          x = (
              self.attn1(
                  self.norm1(x), 
                  context=context if self.disable_self_attn else None
              )
              + x
          )
          x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous()
          x = self.attn2(self.norm2(x), context=context) + x
          x = self.ff(self.norm3(x)) + x
          return x
  
  
  class SpatialTransformer3D(nn.Module):
      """3D self-attention"""
  
      def __init__(
          self,
          in_channels,
          n_heads,
          d_head,
          depth=1,
          dropout=0.0,
          context_dim=None,
          disable_self_attn=False,
          use_linear=False,
          use_checkpoint=True,
          **kwargs
      ):
          super().__init__()
          if exists(context_dim) and not isinstance(context_dim, list):
              context_dim = [context_dim]
          self.in_channels = in_channels
          inner_dim = n_heads * d_head
          self.norm = Normalize(in_channels)
          if not use_linear:
              self.proj_in = nn.Conv2d(
                  in_channels, inner_dim, kernel_size=1, stride=1, padding=0
              )
          else:
              self.proj_in = nn.Linear(in_channels, inner_dim)
  
          self.transformer_blocks = nn.ModuleList(
              [
                  BasicTransformerBlock3D(
                      inner_dim,
                      n_heads,
                      d_head,
                      dropout=dropout,
                      context_dim=context_dim[d],
                      disable_self_attn=disable_self_attn,
                      checkpoint=use_checkpoint,
                      **kwargs
                  )
                  for d in range(depth)
              ]
          )
          if not use_linear:
              self.proj_out = zero_module(
                  nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
              )
          else:
              self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
          self.use_linear = use_linear
  
      def forward(self, x, context=None, num_frames=1):
          # note: if no context is given, cross-attention defaults to self-attention
          if not isinstance(context, list):
              context = [context]
          b, c, h, w = x.shape
          x_in = x
          x = self.norm(x)
          if not self.use_linear:
              x = self.proj_in(x)
          x = rearrange(x, "b c h w -> b (h w) c").contiguous()
          if self.use_linear:
              x = self.proj_in(x)
          for i, block in enumerate(self.transformer_blocks):
              x = block(x, context=context[i], num_frames=num_frames)
          if self.use_linear:
              x = self.proj_out(x)
          x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
          if not self.use_linear:
              x = self.proj_out(x)
          return x + x_in
  