content: # Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
  
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import os
  import json
  
  import torch
  import torch.nn as nn
  import torch.utils.checkpoint
  
  from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers import ModelMixin
  from diffusers.utils import BaseOutput, logging
  from diffusers.models.embeddings import TimestepEmbedding, Timesteps
  from .unet_blocks import (
      CrossAttnDownBlock3D,
      CrossAttnUpBlock3D,
      DownBlock3D,
      UNetMidBlock3DCrossAttn,
      UpBlock3D,
      get_down_block,
      get_up_block,
  )
  from .resnet import InflatedConv3d
  
  
  logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  @dataclass
  class UNet3DConditionOutput(BaseOutput):
      sample: torch.FloatTensor
  
  
  class UNet3DConditionModel(ModelMixin, ConfigMixin):
      _supports_gradient_checkpointing = True
  
      @register_to_config
      def __init__(
          self,
          sample_size: Optional[int] = None,
          in_channels: int = 4,
          out_channels: int = 4,
          center_input_sample: bool = False,
          flip_sin_to_cos: bool = True,
          freq_shift: int = 0,
          down_block_types: Tuple[str] = (
              "CrossAttnDownBlock3D",
              "CrossAttnDownBlock3D",
              "CrossAttnDownBlock3D",
              "DownBlock3D",
          ),
          mid_block_type: str = "UNetMidBlock3DCrossAttn",
          up_block_types: Tuple[str] = (
              "UpBlock3D",
              "CrossAttnUpBlock3D",
              "CrossAttnUpBlock3D",
              "CrossAttnUpBlock3D"
          ),
          only_cross_attention: Union[bool, Tuple[bool]] = False,
          block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
          layers_per_block: int = 2,
          downsample_padding: int = 1,
          mid_block_scale_factor: float = 1,
          act_fn: str = "silu",
          norm_num_groups: int = 32,
          norm_eps: float = 1e-5,
          cross_attention_dim: int = 1280,
          attention_head_dim: Union[int, Tuple[int]] = 8,
          dual_cross_attention: bool = False,
          use_linear_projection: bool = False,
          class_embed_type: Optional[str] = None,
          num_class_embeds: Optional[int] = None,
          upcast_attention: bool = False,
          resnet_time_scale_shift: str = "default",
          use_attn_temp: bool = False,
          camera_input_dim: int = 12,
          camera_hidden_dim: int = 320,
          camera_output_dim: int = 1280,
      ):
          super().__init__()
  
          self.sample_size = sample_size
          time_embed_dim = block_out_channels[0] * 4
  
          # input
          self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
  
          # time
          self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
          timestep_input_dim = block_out_channels[0]
  
          self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
  
          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
              self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
          elif class_embed_type == "timestep":
              self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
          elif class_embed_type == "identity":
              self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
          else:
              self.class_embedding = None
  
          # camera metrix
          # def init_linear(l, stddev):
          #     nn.init.normal_(l.weight, std=stddev)
          #     if l.bias is not None:
          #         nn.init.constant_(l.bias, 0.0)
          # self.camera_embedding_1 = nn.Linear(camera_input_dim, camera_hidden_dim)
          # self.camera_embedding_2 = nn.Linear(camera_hidden_dim, camera_output_dim)
          # init_linear(self.camera_embedding_1, 0.25)
          # init_linear(self.camera_embedding_2, 0.25)
  
          self.camera_embedding = nn.Sequential(
              nn.Linear(camera_input_dim, time_embed_dim),
              nn.SiLU(),
              nn.Linear(time_embed_dim, time_embed_dim),
          )
  
          self.down_blocks = nn.ModuleList([])
          self.mid_block = None
          self.up_blocks = nn.ModuleList([])
  
          if isinstance(only_cross_attention, bool):
              only_cross_attention = [only_cross_attention] * len(down_block_types)
  
          if isinstance(attention_head_dim, int):
              attention_head_dim = (attention_head_dim,) * len(down_block_types)
  
          # down
          output_channel = block_out_channels[0]
          for i, down_block_type in enumerate(down_block_types):
              input_channel = output_channel
              output_channel = block_out_channels[i]
              is_final_block = i == len(block_out_channels) - 1
  
              down_block = get_down_block(
                  down_block_type,
                  num_layers=layers_per_block,
                  in_channels=input_channel,
                  out_channels=output_channel,
                  temb_channels=time_embed_dim,
                  add_downsample=not is_final_block,
                  resnet_eps=norm_eps,
                  resnet_act_fn=act_fn,
                  resnet_groups=norm_num_groups,
                  cross_attention_dim=cross_attention_dim,
                  attn_num_head_channels=attention_head_dim[i],
                  downsample_padding=downsample_padding,
                  dual_cross_attention=dual_cross_attention,
                  use_linear_projection=use_linear_projection,
                  only_cross_attention=only_cross_attention[i],
                  upcast_attention=upcast_attention,
                  resnet_time_scale_shift=resnet_time_scale_shift,
                  use_attn_temp=use_attn_temp
              )
              self.down_blocks.append(down_block)
  
          # mid
          if mid_block_type == "UNetMidBlock3DCrossAttn":
              self.mid_block = UNetMidBlock3DCrossAttn(
                  in_channels=block_out_channels[-1],
                  temb_channels=time_embed_dim,
                  resnet_eps=norm_eps,
                  resnet_act_fn=act_fn,
                  output_scale_factor=mid_block_scale_factor,
                  resnet_time_scale_shift=resnet_time_scale_shift,
                  cross_attention_dim=cross_attention_dim,
                  attn_num_head_channels=attention_head_dim[-1],
                  resnet_groups=norm_num_groups,
                  dual_cross_attention=dual_cross_attention,
                  use_linear_projection=use_linear_projection,
                  upcast_attention=upcast_attention,
              )
          else:
              raise ValueError(f"unknown mid_block_type : {mid_block_type}")
  
          # count how many layers upsample the videos
          self.num_upsamplers = 0
  
          # up
          reversed_block_out_channels = list(reversed(block_out_channels))
          reversed_attention_head_dim = list(reversed(attention_head_dim))
          only_cross_attention = list(reversed(only_cross_attention))
          output_channel = reversed_block_out_channels[0]
          for i, up_block_type in enumerate(up_block_types):
              is_final_block = i == len(block_out_channels) - 1
  
              prev_output_channel = output_channel
              output_channel = reversed_block_out_channels[i]
              input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
  
              # add upsample block for all BUT final layer
              if not is_final_block:
                  add_upsample = True
                  self.num_upsamplers += 1
              else:
                  add_upsample = False
  
              up_block = get_up_block(
                  up_block_type,
                  num_layers=layers_per_block + 1,
                  in_channels=input_channel,
                  out_channels=output_channel,
                  prev_output_channel=prev_output_channel,
                  temb_channels=time_embed_dim,
                  add_upsample=add_upsample,
                  resnet_eps=norm_eps,
                  resnet_act_fn=act_fn,
                  resnet_groups=norm_num_groups,
                  cross_attention_dim=cross_attention_dim,
                  attn_num_head_channels=reversed_attention_head_dim[i],
                  dual_cross_attention=dual_cross_attention,
                  use_linear_projection=use_linear_projection,
                  only_cross_attention=only_cross_attention[i],
                  upcast_attention=upcast_attention,
                  resnet_time_scale_shift=resnet_time_scale_shift,
                  use_attn_temp=use_attn_temp,
              )
              self.up_blocks.append(up_block)
              prev_output_channel = output_channel
  
          # out
          self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
          self.conv_act = nn.SiLU()
          self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
  
      def set_attention_slice(self, slice_size):
          r"""
          Enable sliced attention computation.
  
          When this option is enabled, the attention module will split the input tensor in slices, to compute attention
          in several steps. This is useful to save some memory in exchange for a small speed decrease.
  
          Args:
              slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                  When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                  `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
                  provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                  must be a multiple of `slice_size`.
          """
          sliceable_head_dims = []
  
          def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
              if hasattr(module, "set_attention_slice"):
                  sliceable_head_dims.append(module.sliceable_head_dim)
  
              for child in module.children():
                  fn_recursive_retrieve_slicable_dims(child)
  
          # retrieve number of attention layers
          for module in self.children():
              fn_recursive_retrieve_slicable_dims(module)
  
          num_slicable_layers = len(sliceable_head_dims)
  
          if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              slice_size = [dim // 2 for dim in sliceable_head_dims]
          elif slice_size == "max":
              # make smallest slice possible
              slice_size = num_slicable_layers * [1]
  
          slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
  
          if len(slice_size) != len(sliceable_head_dims):
              raise ValueError(
                  f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
                  f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
              )
  
          for i in range(len(slice_size)):
              size = slice_size[i]
              dim = sliceable_head_dims[i]
              if size is not None and size > dim:
                  raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
  
          # Recursively walk through all the children.
          # Any children which exposes the set_attention_slice method
          # gets the message
          def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
              if hasattr(module, "set_attention_slice"):
                  module.set_attention_slice(slice_size.pop())
  
              for child in module.children():
                  fn_recursive_set_attention_slice(child, slice_size)
  
          reversed_slice_size = list(reversed(slice_size))
          for module in self.children():
              fn_recursive_set_attention_slice(module, reversed_slice_size)
  
      def _set_gradient_checkpointing(self, module, value=False):
          if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
              module.gradient_checkpointing = value
  
      def forward(
          self,
          sample: torch.FloatTensor,
          timestep: Union[torch.Tensor, float, int],
          encoder_hidden_states: torch.Tensor,
          camera_matrixs: Optional[torch.Tensor] = None,
          class_labels: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          return_dict: bool = True,
      ) -> Union[UNet3DConditionOutput, Tuple]:
          r"""
          Args:
              sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
              timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
              encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
              return_dict (`bool`, *optional*, defaults to `True`):
                  Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
  
          Returns:
              [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
              [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
              returning a tuple, the first element is the sample tensor.
          """
          # By default samples have to be AT least a multiple of the overall upsampling factor.
          # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
          # However, the upsampling interpolation output size can be forced to fit any upsampling size
          # on the fly if necessary.
          default_overall_up_factor = 2**self.num_upsamplers
  
          # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
          forward_upsample_size = False
          upsample_size = None
  
          if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
              logger.info("Forward upsample size to force interpolation output size.")
              forward_upsample_size = True
  
          # prepare attention_mask
          if attention_mask is not None:
              attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
              attention_mask = attention_mask.unsqueeze(1)
  
          # center input if necessary
          if self.config.center_input_sample:
              sample = 2 * sample - 1.0
          # time
          timesteps = timestep
          if not torch.is_tensor(timesteps):
              # This would be a good case for the `match` statement (Python 3.10+)
              is_mps = sample.device.type == "mps"
              if isinstance(timestep, float):
                  dtype = torch.float32 if is_mps else torch.float64
              else:
                  dtype = torch.int32 if is_mps else torch.int64
              timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
          elif len(timesteps.shape) == 0:
              timesteps = timesteps[None].to(sample.device)
  
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          timesteps = timesteps.expand(sample.shape[0])
  
          t_emb = self.time_proj(timesteps)
  
          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might actually be running in fp16. so we need to cast here.
          # there might be better ways to encapsulate this.
          t_emb = t_emb.to(dtype=self.dtype)
          emb = self.time_embedding(t_emb) #torch.Size([32, 1280])
          emb = torch.unsqueeze(emb, 1)
          if camera_matrixs is not None:
              # came emb
              cam_emb = self.camera_embedding(camera_matrixs)
              # cam_emb = self.camera_embedding_2(cam_emb)
              emb = emb.repeat(1,cam_emb.shape[1],1) #torch.Size([32, 4, 1280])
              emb = emb + cam_emb
          
          # import pdb;pdb.set_trace()
          if self.class_embedding is not None:
              # if class_labels is None:
              #     raise ValueError("class_labels should be provided when num_class_embeds > 0")
              if class_labels is not None:
                  
                  if self.config.class_embed_type == "timestep":
                      class_labels = self.time_proj(class_labels)
  
                  class_emb = self.class_embedding(class_labels)
                  emb = emb + class_emb
  
          # pre-process
          sample = self.conv_in(sample)
  
          # down
          down_block_res_samples = (sample,)
          for downsample_block in self.down_blocks:
              if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                  sample, res_samples = downsample_block(
                      hidden_states=sample,
                      temb=emb,
                      encoder_hidden_states=encoder_hidden_states,
                      attention_mask=attention_mask,
                  )
              else:
                  sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
  
              down_block_res_samples += res_samples
  
          # mid
          sample = self.mid_block(
              sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
          )
  
          # up
          for i, upsample_block in enumerate(self.up_blocks):
              is_final_block = i == len(self.up_blocks) - 1
  
              res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
              down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
  
              # if we have not reached the final block and need to forward the
              # upsample size, we do it here
              if not is_final_block and forward_upsample_size:
                  upsample_size = down_block_res_samples[-1].shape[2:]
  
              if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                  sample = upsample_block(
                      hidden_states=sample,
                      temb=emb,
                      res_hidden_states_tuple=res_samples,
                      encoder_hidden_states=encoder_hidden_states,
                      upsample_size=upsample_size,
                      attention_mask=attention_mask,
                  )
              else:
                  sample = upsample_block(
                      hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
                  )
          # post-process
          sample = self.conv_norm_out(sample)
          sample = self.conv_act(sample)
          sample = self.conv_out(sample)
  
          if not return_dict:
              return (sample,)
  
          return UNet3DConditionOutput(sample=sample)
  
      @classmethod
      def from_pretrained_2d(cls, pretrained_model_path, subfolder=None):
          if subfolder is not None:
              pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
  
          config_file = os.path.join(pretrained_model_path, 'config.json')
          if not os.path.isfile(config_file):
              raise RuntimeError(f"{config_file} does not exist")
          with open(config_file, "r") as f:
              config = json.load(f)
          config["_class_name"] = cls.__name__
          config["down_block_types"] = [
              "CrossAttnDownBlock3D",
              "CrossAttnDownBlock3D",
              "CrossAttnDownBlock3D",
              "DownBlock3D"
          ]
          config["up_block_types"] = [
              "UpBlock3D",
              "CrossAttnUpBlock3D",
              "CrossAttnUpBlock3D",
              "CrossAttnUpBlock3D"
          ]
          
          from diffusers.utils import WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME
          # model = cls.from_config(config)
          # model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
          # if not os.path.isfile(model_file):
          #     raise RuntimeError(f"{model_file} does not exist")
          # state_dict = torch.load(model_file, map_location="cpu")
  
          import safetensors
          model = cls.from_config(config)
          model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
          if not os.path.isfile(model_file):
              model_file = os.path.join(pretrained_model_path, SAFETENSORS_WEIGHTS_NAME)
              if not os.path.isfile(model_file):
                  raise RuntimeError(f"{model_file} does not exist")
              else:
                 state_dict = safetensors.torch.load_file(model_file, device="cpu") 
          else:
              state_dict = torch.load(model_file, map_location="cpu")
              
          for k, v in model.state_dict().items():
              if '_temp.' in k or 'camera_embedding' in k or 'class_embedding' in k:
                  state_dict.update({k: v})
          for k in list(state_dict.keys()):
              if 'camera_embedding_' in k:
                  v = state_dict.pop(k)
          model.load_state_dict(state_dict)
  
          return model