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
  import numpy as np
  
  FLASH_IS_AVAILABLE = XFORMERS_IS_AVAILBLE = False
  try:
      from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
      FLASH_IS_AVAILABLE = True
  except:
      try:
          import xformers
          import xformers.ops
          XFORMERS_IS_AVAILBLE = True
      except:
          pass
  
  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
  
  def checkpoint(func, inputs, params, flag):
      """
      Evaluate a function without caching intermediate activations, allowing for
      reduced memory at the expense of extra compute in the backward pass.
      :param func: the function to evaluate.
      :param inputs: the argument sequence to pass to `func`.
      :param params: a sequence of parameters `func` depends on but does not
                     explicitly take as arguments.
      :param flag: if False, disable gradient checkpointing.
      """
      if flag:
          args = tuple(inputs) + tuple(params)
          return CheckpointFunction.apply(func, len(inputs), *args)
      else:
          return func(*inputs)
  
  
  class CheckpointFunction(torch.autograd.Function):
      @staticmethod
      def forward(ctx, run_function, length, *args):
          ctx.run_function = run_function
          ctx.input_tensors = list(args[:length])
          ctx.input_params = list(args[length:])
  
          with torch.no_grad():
              output_tensors = ctx.run_function(*ctx.input_tensors)
          return output_tensors
  
      @staticmethod
      def backward(ctx, *output_grads):
          ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
          with torch.enable_grad():
              # Fixes a bug where the first op in run_function modifies the
              # Tensor storage in place, which is not allowed for detach()'d
              # Tensors.
              shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
              output_tensors = ctx.run_function(*shallow_copies)
          input_grads = torch.autograd.grad(
              output_tensors,
              ctx.input_tensors + ctx.input_params,
              output_grads,
              allow_unused=True,
          )
          del ctx.input_tensors
          del ctx.input_params
          del output_tensors
          return (None, None) + input_grads
  
  
  # 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) # [b*h, n, d]
          out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
          return self.to_out(out)
  
  
  class FlashAttention(nn.Module):
      def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
          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.scale = dim_head ** -0.5
          self.heads = heads
          self.dropout = dropout
          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):
          context = default(context, x)
          h = self.heads
          dtype = torch.bfloat16 # torch.half
          q = self.to_q(x).to(dtype)
          k = self.to_k(context).to(dtype)
          v = self.to_v(context).to(dtype)
          q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q, k, v)) # q is [b, 3079, 16, 64]
          out = flash_attn_func(q, k, v, dropout_p=self.dropout, softmax_scale=None, causal=False, window_size=(-1, -1)) # out is same shape to q
          out = rearrange(out, 'b n h d -> b n (h d)', h=h)
          return self.to_out(out.float())
  
  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):
          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.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)
          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 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., 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 = Fp32LayerNorm(dim)
          self.norm2 = Fp32LayerNorm(dim)
          self.norm3 = Fp32LayerNorm(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
  
  ATTENTION_MODES = {
      "softmax": CrossAttention,  # vanilla attention
      "softmax-xformers": MemoryEfficientCrossAttention, 
      "softmax-flash": FlashAttention
  }
  
  def modulate(x, shift, scale):
      return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
  
  
  class Fp32LayerNorm(nn.LayerNorm):
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
      def forward(self, x):
          return super().forward(x.float()).type(x.dtype)
  
  
  class AdaNorm(nn.Module):
      def __init__(self, dim):
          super().__init__()
          self.adaLN_modulation = nn.Sequential(
              nn.SiLU(),
              nn.Linear(dim, 2 * dim, bias=True)
          )
          self.norm = Fp32LayerNorm(dim, elementwise_affine=False, eps=1e-6)
  
      def forward(self, x, c):  # x is fp32, c is fp16
          shift, scale = self.adaLN_modulation(c.float()).chunk(2, dim=1) # bf16
          x = modulate(self.norm(x), shift, scale) # fp32
          return x
  
  
  class BasicTransformerBlockLRM(nn.Module):
      def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, \
                   checkpoint=True):
          super().__init__()
  
          attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
          attn_mode = "softmax-flash" if FLASH_IS_AVAILABLE else attn_mode
          assert attn_mode in ATTENTION_MODES
          attn_cls = ATTENTION_MODES[attn_mode]
  
          self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, \
                                context_dim=context_dim) # cross-attn
          self.attn2 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, \
                                context_dim=None) # self-attn
  
          self.norm1 = Fp32LayerNorm(dim)
          self.norm2 = Fp32LayerNorm(dim)
          self.norm3 = Fp32LayerNorm(dim)
          
          self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
          self.checkpoint = checkpoint
  
      def forward(self, x, context=None, cam_emb=None): # (torch.float32, torch.float32, torch.bfloat16)
          return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
  
  
      def _forward(self, x, context=None, cam_emb=None):
  
          x = self.attn1(self.norm1(x), context=context) + x  # cross-attn
          x = self.attn2(self.norm2(x), context=None) + x # self-attn
          x = self.ff(self.norm3(x)) + x
  
          return x
  
  class ImgToTriplaneTransformer(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, query_dim, n_heads, d_head, depth=1, dropout=0., context_dim=None, triplane_size=64):
          super().__init__()
  
          self.transformer_blocks = nn.ModuleList([
              BasicTransformerBlockLRM(query_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) 
              for d in range(depth)])
          
          self.norm = Fp32LayerNorm(query_dim, eps=1e-6)
          
          self.initialize_weights()
  
      def initialize_weights(self):
          # Initialize transformer layers:
          def _basic_init(module):
              if isinstance(module, nn.Linear):
                  torch.nn.init.xavier_uniform_(module.weight)
                  if module.bias is not None:
                      nn.init.constant_(module.bias, 0)
              elif isinstance(module, nn.LayerNorm):
                  if module.bias is not None:
                      nn.init.constant_(module.bias, 0)
                  if module.weight is not None:
                      nn.init.constant_(module.weight, 1.0)
          self.apply(_basic_init)
  
      def forward(self, x, context=None, cam_emb=None):
          # note: if no context is given, cross-attention defaults to self-attention
          for block in self.transformer_blocks:
              x = block(x, context=context)  
          x = self.norm(x)
          return x
  
  
  
  
  
  