content: # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from dataclasses import dataclass
  from typing import Any, Dict, Optional
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers.models.embeddings import ImagePositionalEmbeddings
  from diffusers.utils import BaseOutput, deprecate
  from diffusers.utils.torch_utils import maybe_allow_in_graph
  from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention
  from diffusers.models.embeddings import PatchEmbed
  from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
  from diffusers.models.modeling_utils import ModelMixin
  from diffusers.utils.import_utils import is_xformers_available
  
  from einops import rearrange
  import pdb
  import random
  import math
  
  
  if is_xformers_available():
      import xformers
      import xformers.ops
  else:
      xformers = None
  
  
  @dataclass
  class TransformerMV2DModelOutput(BaseOutput):
      """
      The output of [`Transformer2DModel`].
  
      Args:
          sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
              The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
              distributions for the unnoised latent pixels.
      """
  
      sample: torch.FloatTensor
  
  
  class TransformerMV2DModel(ModelMixin, ConfigMixin):
      """
      A 2D Transformer model for image-like data.
  
      Parameters:
          num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
          attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
          in_channels (`int`, *optional*):
              The number of channels in the input and output (specify if the input is **continuous**).
          num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
          dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
          cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
          sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
              This is fixed during training since it is used to learn a number of position embeddings.
          num_vector_embeds (`int`, *optional*):
              The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
              Includes the class for the masked latent pixel.
          activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
          num_embeds_ada_norm ( `int`, *optional*):
              The number of diffusion steps used during training. Pass if at least one of the norm_layers is
              `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
              added to the hidden states.
  
              During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
          attention_bias (`bool`, *optional*):
              Configure if the `TransformerBlocks` attention should contain a bias parameter.
      """
  
      @register_to_config
      def __init__(
          self,
          num_attention_heads: int = 16,
          attention_head_dim: int = 88,
          in_channels: Optional[int] = None,
          out_channels: Optional[int] = None,
          num_layers: int = 1,
          dropout: float = 0.0,
          norm_num_groups: int = 32,
          cross_attention_dim: Optional[int] = None,
          attention_bias: bool = False,
          sample_size: Optional[int] = None,
          num_vector_embeds: Optional[int] = None,
          patch_size: Optional[int] = None,
          activation_fn: str = "geglu",
          num_embeds_ada_norm: Optional[int] = None,
          use_linear_projection: bool = False,
          only_cross_attention: bool = False,
          upcast_attention: bool = False,
          norm_type: str = "layer_norm",
          norm_elementwise_affine: bool = True,
          num_views: int = 1,
          cd_attention_last: bool=False,
          cd_attention_mid: bool=False,
          multiview_attention: bool=True,
          sparse_mv_attention: bool = True, # not used
          mvcd_attention: bool=False
      ):
          super().__init__()
          self.use_linear_projection = use_linear_projection
          self.num_attention_heads = num_attention_heads
          self.attention_head_dim = attention_head_dim
          inner_dim = num_attention_heads * attention_head_dim
  
          # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
          # Define whether input is continuous or discrete depending on configuration
          self.is_input_continuous = (in_channels is not None) and (patch_size is None)
          self.is_input_vectorized = num_vector_embeds is not None
          self.is_input_patches = in_channels is not None and patch_size is not None
  
          if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
              deprecation_message = (
                  f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                  " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
                  " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                  " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                  " would be very nice if you could open a Pull request for the `transformer/config.json` file"
              )
              deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
              norm_type = "ada_norm"
  
          if self.is_input_continuous and self.is_input_vectorized:
              raise ValueError(
                  f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                  " sure that either `in_channels` or `num_vector_embeds` is None."
              )
          elif self.is_input_vectorized and self.is_input_patches:
              raise ValueError(
                  f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                  " sure that either `num_vector_embeds` or `num_patches` is None."
              )
          elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
              raise ValueError(
                  f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                  f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
              )
  
          # 2. Define input layers
          if self.is_input_continuous:
              self.in_channels = in_channels
  
              self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
              if use_linear_projection:
                  self.proj_in = LoRACompatibleLinear(in_channels, inner_dim)
              else:
                  self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
          elif self.is_input_vectorized:
              assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
              assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
  
              self.height = sample_size
              self.width = sample_size
              self.num_vector_embeds = num_vector_embeds
              self.num_latent_pixels = self.height * self.width
  
              self.latent_image_embedding = ImagePositionalEmbeddings(
                  num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
              )
          elif self.is_input_patches:
              assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
  
              self.height = sample_size
              self.width = sample_size
  
              self.patch_size = patch_size
              self.pos_embed = PatchEmbed(
                  height=sample_size,
                  width=sample_size,
                  patch_size=patch_size,
                  in_channels=in_channels,
                  embed_dim=inner_dim,
              )
  
          # 3. Define transformers blocks
          self.transformer_blocks = nn.ModuleList(
              [
                  BasicMVTransformerBlock(
                      inner_dim,
                      num_attention_heads,
                      attention_head_dim,
                      dropout=dropout,
                      cross_attention_dim=cross_attention_dim,
                      activation_fn=activation_fn,
                      num_embeds_ada_norm=num_embeds_ada_norm,
                      attention_bias=attention_bias,
                      only_cross_attention=only_cross_attention,
                      upcast_attention=upcast_attention,
                      norm_type=norm_type,
                      norm_elementwise_affine=norm_elementwise_affine,
                      num_views=num_views,
                      cd_attention_last=cd_attention_last,
                      cd_attention_mid=cd_attention_mid,
                      multiview_attention=multiview_attention,
                      mvcd_attention=mvcd_attention
                  )
                  for d in range(num_layers)
              ]
          )
  
          # 4. Define output layers
          self.out_channels = in_channels if out_channels is None else out_channels
          if self.is_input_continuous:
              # TODO: should use out_channels for continuous projections
              if use_linear_projection:
                  self.proj_out = LoRACompatibleLinear(inner_dim, in_channels)
              else:
                  self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
          elif self.is_input_vectorized:
              self.norm_out = nn.LayerNorm(inner_dim)
              self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
          elif self.is_input_patches:
              self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
              self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
              self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          encoder_hidden_states: Optional[torch.Tensor] = None,
          timestep: Optional[torch.LongTensor] = None,
          class_labels: Optional[torch.LongTensor] = None,
          cross_attention_kwargs: Dict[str, Any] = None,
          attention_mask: Optional[torch.Tensor] = None,
          encoder_attention_mask: Optional[torch.Tensor] = None,
          return_dict: bool = True,
      ):
          """
          The [`Transformer2DModel`] forward method.
  
          Args:
              hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                  Input `hidden_states`.
              encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                  Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                  self-attention.
              timestep ( `torch.LongTensor`, *optional*):
                  Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
              class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                  Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                  `AdaLayerZeroNorm`.
              encoder_attention_mask ( `torch.Tensor`, *optional*):
                  Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
  
                      * Mask `(batch, sequence_length)` True = keep, False = discard.
                      * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
  
                  If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                  above. This bias will be added to the cross-attention scores.
              return_dict (`bool`, *optional*, defaults to `True`):
                  Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                  tuple.
  
          Returns:
              If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
              `tuple` where the first element is the sample tensor.
          """
          # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
          #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
          #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
          # expects mask of shape:
          #   [batch, key_tokens]
          # adds singleton query_tokens dimension:
          #   [batch,                    1, key_tokens]
          # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
          #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
          #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
          if attention_mask is not None and attention_mask.ndim == 2:
              # assume that mask is expressed as:
              #   (1 = keep,      0 = discard)
              # convert mask into a bias that can be added to attention scores:
              #       (keep = +0,     discard = -10000.0)
              attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
              attention_mask = attention_mask.unsqueeze(1)
  
          # convert encoder_attention_mask to a bias the same way we do for attention_mask
          if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
              encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
              encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
  
          # 1. Input
          if self.is_input_continuous:
              batch, _, height, width = hidden_states.shape
              residual = hidden_states
  
              hidden_states = self.norm(hidden_states)
              if not self.use_linear_projection:
                  hidden_states = self.proj_in(hidden_states)
                  inner_dim = hidden_states.shape[1]
                  hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
              else:
                  inner_dim = hidden_states.shape[1]
                  hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                  hidden_states = self.proj_in(hidden_states)
          elif self.is_input_vectorized:
              hidden_states = self.latent_image_embedding(hidden_states)
          elif self.is_input_patches:
              hidden_states = self.pos_embed(hidden_states)
  
          # 2. Blocks
          for block in self.transformer_blocks:
              hidden_states = block(
                  hidden_states,
                  attention_mask=attention_mask,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  timestep=timestep,
                  cross_attention_kwargs=cross_attention_kwargs,
                  class_labels=class_labels,
              )
  
          # 3. Output
          if self.is_input_continuous:
              if not self.use_linear_projection:
                  hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                  hidden_states = self.proj_out(hidden_states)
              else:
                  hidden_states = self.proj_out(hidden_states)
                  hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
  
              output = hidden_states + residual
          elif self.is_input_vectorized:
              hidden_states = self.norm_out(hidden_states)
              logits = self.out(hidden_states)
              # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
              logits = logits.permute(0, 2, 1)
  
              # log(p(x_0))
              output = F.log_softmax(logits.double(), dim=1).float()
          elif self.is_input_patches:
              # TODO: cleanup!
              conditioning = self.transformer_blocks[0].norm1.emb(
                  timestep, class_labels, hidden_dtype=hidden_states.dtype
              )
              shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
              hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
              hidden_states = self.proj_out_2(hidden_states)
  
              # unpatchify
              height = width = int(hidden_states.shape[1] ** 0.5)
              hidden_states = hidden_states.reshape(
                  shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
              )
              hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
              output = hidden_states.reshape(
                  shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
              )
  
          if not return_dict:
              return (output,)
  
          return TransformerMV2DModelOutput(sample=output)
  
  
  @maybe_allow_in_graph
  class BasicMVTransformerBlock(nn.Module):
      r"""
      A basic Transformer block.
  
      Parameters:
          dim (`int`): The number of channels in the input and output.
          num_attention_heads (`int`): The number of heads to use for multi-head attention.
          attention_head_dim (`int`): The number of channels in each head.
          dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
          cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
          only_cross_attention (`bool`, *optional*):
              Whether to use only cross-attention layers. In this case two cross attention layers are used.
          double_self_attention (`bool`, *optional*):
              Whether to use two self-attention layers. In this case no cross attention layers are used.
          activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
          num_embeds_ada_norm (:
              obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
          attention_bias (:
              obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
      """
  
      def __init__(
          self,
          dim: int,
          num_attention_heads: int,
          attention_head_dim: int,
          dropout=0.0,
          cross_attention_dim: Optional[int] = None,
          activation_fn: str = "geglu",
          num_embeds_ada_norm: Optional[int] = None,
          attention_bias: bool = False,
          only_cross_attention: bool = False,
          double_self_attention: bool = False,
          upcast_attention: bool = False,
          norm_elementwise_affine: bool = True,
          norm_type: str = "layer_norm",
          final_dropout: bool = False,
          num_views: int = 1,
          cd_attention_last: bool = False,
          cd_attention_mid: bool = False,
          multiview_attention: bool = True,
          mvcd_attention: bool = False,
          rowwise_attention: bool = True
      ):
          super().__init__()
          self.only_cross_attention = only_cross_attention
  
          self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
          self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
  
          if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
              raise ValueError(
                  f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
                  f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
              )
  
          # Define 3 blocks. Each block has its own normalization layer.
          # 1. Self-Attn
          if self.use_ada_layer_norm:
              self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
          elif self.use_ada_layer_norm_zero:
              self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
          else:
              self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
  
          self.multiview_attention = multiview_attention
          self.mvcd_attention = mvcd_attention
          self.rowwise_attention = multiview_attention and rowwise_attention
  
          # rowwise multiview attention
       
          print('INFO: using row wise attention...')
      
          self.attn1 = CustomAttention(
              query_dim=dim,
              heads=num_attention_heads,
              dim_head=attention_head_dim,
              dropout=dropout,
              bias=attention_bias,
              cross_attention_dim=cross_attention_dim if only_cross_attention else None,
              upcast_attention=upcast_attention,
              processor=MVAttnProcessor()
          )
  
          # 2. Cross-Attn
          if cross_attention_dim is not None or double_self_attention:
              # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
              # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
              # the second cross attention block.
              self.norm2 = (
                  AdaLayerNorm(dim, num_embeds_ada_norm)
                  if self.use_ada_layer_norm
                  else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
              )
              self.attn2 = Attention(
                  query_dim=dim,
                  cross_attention_dim=cross_attention_dim if not double_self_attention else None,
                  heads=num_attention_heads,
                  dim_head=attention_head_dim,
                  dropout=dropout,
                  bias=attention_bias,
                  upcast_attention=upcast_attention,
              )  # is self-attn if encoder_hidden_states is none
          else:
              self.norm2 = None
              self.attn2 = None
  
          # 3. Feed-forward
          self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
          self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
  
          # let chunk size default to None
          self._chunk_size = None
          self._chunk_dim = 0
  
          self.num_views = num_views
  
          self.cd_attention_last = cd_attention_last
  
          if self.cd_attention_last:
              # Joint task -Attn
              self.attn_joint = CustomJointAttention(
                  query_dim=dim,
                  heads=num_attention_heads,
                  dim_head=attention_head_dim,
                  dropout=dropout,
                  bias=attention_bias,
                  cross_attention_dim=cross_attention_dim if only_cross_attention else None,
                  upcast_attention=upcast_attention,
                  processor=JointAttnProcessor()
              )
              nn.init.zeros_(self.attn_joint.to_out[0].weight.data)
              self.norm_joint = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
  
  
          self.cd_attention_mid = cd_attention_mid
  
          if self.cd_attention_mid:
              print("joint twice")
              # Joint task -Attn
              self.attn_joint_twice = CustomJointAttention(
                  query_dim=dim,
                  heads=num_attention_heads,
                  dim_head=attention_head_dim,
                  dropout=dropout,
                  bias=attention_bias,
                  cross_attention_dim=cross_attention_dim if only_cross_attention else None,
                  upcast_attention=upcast_attention,
                  processor=JointAttnProcessor()
              )
              nn.init.zeros_(self.attn_joint_twice.to_out[0].weight.data)
              self.norm_joint_twice = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
  
      def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
          # Sets chunk feed-forward
          self._chunk_size = chunk_size
          self._chunk_dim = dim
  
      def forward(
          self,
          hidden_states: torch.FloatTensor,
          attention_mask: Optional[torch.FloatTensor] = None,
          encoder_hidden_states: Optional[torch.FloatTensor] = None,
          encoder_attention_mask: Optional[torch.FloatTensor] = None,
          timestep: Optional[torch.LongTensor] = None,
          cross_attention_kwargs: Dict[str, Any] = None,
          class_labels: Optional[torch.LongTensor] = None,
      ):
          assert attention_mask is None # not supported yet
          # Notice that normalization is always applied before the real computation in the following blocks.
          # 1. Self-Attention
          if self.use_ada_layer_norm:
              norm_hidden_states = self.norm1(hidden_states, timestep)
          elif self.use_ada_layer_norm_zero:
              norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
                  hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
              )
          else:
              norm_hidden_states = self.norm1(hidden_states)
  
          cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
  
          attn_output = self.attn1(
              norm_hidden_states,
              encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
              attention_mask=attention_mask,
              multiview_attention=self.multiview_attention,
              mvcd_attention=self.mvcd_attention,
              num_views=self.num_views,
              **cross_attention_kwargs,
          )
  
          if self.use_ada_layer_norm_zero:
              attn_output = gate_msa.unsqueeze(1) * attn_output
          hidden_states = attn_output + hidden_states
  
          # joint attention twice
          if self.cd_attention_mid:
              norm_hidden_states = (
                  self.norm_joint_twice(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_twice(hidden_states)
              )
              hidden_states = self.attn_joint_twice(norm_hidden_states) + hidden_states
  
          # 2. Cross-Attention
          if self.attn2 is not None:
              norm_hidden_states = (
                  self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
              )
  
              attn_output = self.attn2(
                  norm_hidden_states,
                  encoder_hidden_states=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
                  **cross_attention_kwargs,
              )
              hidden_states = attn_output + hidden_states
  
          # 3. Feed-forward
          norm_hidden_states = self.norm3(hidden_states)
  
          if self.use_ada_layer_norm_zero:
              norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
  
          if self._chunk_size is not None:
              # "feed_forward_chunk_size" can be used to save memory
              if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                  raise ValueError(
                      f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
                  )
  
              num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
              ff_output = torch.cat(
                  [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
                  dim=self._chunk_dim,
              )
          else:
              ff_output = self.ff(norm_hidden_states)
  
          if self.use_ada_layer_norm_zero:
              ff_output = gate_mlp.unsqueeze(1) * ff_output
  
          hidden_states = ff_output + hidden_states
  
          if self.cd_attention_last:
              norm_hidden_states = (
                  self.norm_joint(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint(hidden_states)
              )
              hidden_states = self.attn_joint(norm_hidden_states) + hidden_states
  
          return hidden_states
      
  
  class CustomAttention(Attention):
      def set_use_memory_efficient_attention_xformers(
          self, use_memory_efficient_attention_xformers: bool, *args, **kwargs
      ):
          processor = XFormersMVAttnProcessor()
          self.set_processor(processor)
          # print("using xformers attention processor")
  
  
  class CustomJointAttention(Attention):
      def set_use_memory_efficient_attention_xformers(
          self, use_memory_efficient_attention_xformers: bool, *args, **kwargs
      ):
          processor = XFormersJointAttnProcessor()
          self.set_processor(processor)
          # print("using xformers attention processor")
  
  class MVAttnProcessor:
      r"""
      Default processor for performing attention-related computations.
      """
  
      def __call__(
          self,
          attn: Attention,
          hidden_states,
          encoder_hidden_states=None,
          attention_mask=None,
          temb=None,
          num_views=1,
          multiview_attention=True
      ):
          residual = hidden_states
  
          if attn.spatial_norm is not None:
              hidden_states = attn.spatial_norm(hidden_states, temb)
  
          input_ndim = hidden_states.ndim
  
          if input_ndim == 4:
              batch_size, channel, height, width = hidden_states.shape
              hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
  
          batch_size, sequence_length, _ = (
              hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
          )
          height = int(math.sqrt(sequence_length)) 
          attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
  
          if attn.group_norm is not None:
              hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
  
          query = attn.to_q(hidden_states)
  
          if encoder_hidden_states is None:
              encoder_hidden_states = hidden_states
          elif attn.norm_cross:
              encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
  
          key = attn.to_k(encoder_hidden_states)
          value = attn.to_v(encoder_hidden_states)
  
          # print('query', query.shape, 'key', key.shape, 'value', value.shape)
          #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320])
          # pdb.set_trace()
          # multi-view self-attention
          key = rearrange(key, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) 
          value = rearrange(value, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height)
          query = rearrange(query, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) # torch.Size([192, 384, 320])
  
          query = attn.head_to_batch_dim(query).contiguous()
          key = attn.head_to_batch_dim(key).contiguous()
          value = attn.head_to_batch_dim(value).contiguous()
          
          attention_probs = attn.get_attention_scores(query, key, attention_mask)
          hidden_states = torch.bmm(attention_probs, value)
          hidden_states = attn.batch_to_head_dim(hidden_states)
  
          # linear proj
          hidden_states = attn.to_out[0](hidden_states)
          # dropout
          hidden_states = attn.to_out[1](hidden_states)
  