content: from dataclasses import dataclass
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  from diffusers.models.embeddings import ImagePositionalEmbeddings, PatchEmbed
  
  from CharacterGen.Stage_3D import lrm
  from ...utils.base import BaseModule
  from .attention import (
      BasicTransformerBlock,
      MemoryEfficientAttentionMixin,
  )
  from ...utils.typing import *
  
  
  class Transformer2D(BaseModule, MemoryEfficientAttentionMixin):
      """
      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.
      """
  
      @dataclass
      class Config(BaseModule.Config):
          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
          double_self_attention: bool = False
          upcast_attention: bool = False
          norm_type: str = "layer_norm"
          norm_elementwise_affine: bool = True
          attention_type: str = "default"
          enable_memory_efficient_attention: bool = False
          gradient_checkpointing: bool = False
  
      cfg: Config
  
      def configure(self) -> None:
          super().configure()
  
          self.use_linear_projection = self.cfg.use_linear_projection
          self.num_attention_heads = self.cfg.num_attention_heads
          self.attention_head_dim = self.cfg.attention_head_dim
          inner_dim = self.num_attention_heads * self.attention_head_dim
  
          conv_cls = nn.Conv2d
          linear_cls = nn.Linear
  
          # 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 = (self.cfg.in_channels is not None) and (
              self.cfg.patch_size is None
          )
          self.is_input_vectorized = self.cfg.num_vector_embeds is not None
          self.is_input_patches = (
              self.cfg.in_channels is not None and self.cfg.patch_size is not None
          )
  
          if (
              self.cfg.norm_type == "layer_norm"
              and self.cfg.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"
              )
              lrm.warn(deprecation_message)
              self.cfg.norm_type = "ada_norm"
  
          if self.is_input_continuous and self.is_input_vectorized:
              raise ValueError(
                  f"Cannot define both `in_channels`: {self.cfg.in_channels} and `num_vector_embeds`: {self.cfg.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`: {self.cfg.num_vector_embeds} and `patch_size`: {self.cfg.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`: {self.cfg.in_channels}, `num_vector_embeds`: {self.cfg.num_vector_embeds}, or patch_size:"
                  f" {self.cfg.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 = self.cfg.in_channels
  
              self.norm = torch.nn.GroupNorm(
                  num_groups=self.cfg.norm_num_groups,
                  num_channels=self.cfg.in_channels,
                  eps=1e-6,
                  affine=True,
              )
              if self.cfg.use_linear_projection:
                  self.proj_in = linear_cls(self.cfg.in_channels, inner_dim)
              else:
                  self.proj_in = conv_cls(
                      self.cfg.in_channels, inner_dim, kernel_size=1, stride=1, padding=0
                  )
          elif self.is_input_vectorized:
              assert (
                  self.cfg.sample_size is not None
              ), "Transformer2DModel over discrete input must provide sample_size"
              assert (
                  self.cfg.num_vector_embeds is not None
              ), "Transformer2DModel over discrete input must provide num_embed"
  
              self.height = self.cfg.sample_size
              self.width = self.cfg.sample_size
              self.num_vector_embeds = self.cfg.num_vector_embeds
              self.num_latent_pixels = self.height * self.width
  
              self.latent_image_embedding = ImagePositionalEmbeddings(
                  num_embed=self.cfg.num_vector_embeds,
                  embed_dim=inner_dim,
                  height=self.height,
                  width=self.width,
              )
          elif self.is_input_patches:
              assert (
                  self.cfg.sample_size is not None
              ), "Transformer2DModel over patched input must provide sample_size"
  
              self.height = self.cfg.sample_size
              self.width = self.cfg.sample_size
  
              self.patch_size = self.cfg.patch_size
              self.pos_embed = PatchEmbed(
                  height=self.cfg.sample_size,
                  width=self.cfg.sample_size,
                  patch_size=self.cfg.patch_size,
                  in_channels=self.cfg.in_channels,
                  embed_dim=inner_dim,
              )
  
          # 3. Define transformers blocks
          self.transformer_blocks = nn.ModuleList(
              [
                  BasicTransformerBlock(
                      inner_dim,
                      self.num_attention_heads,
                      self.attention_head_dim,
                      dropout=self.cfg.dropout,
                      cross_attention_dim=self.cfg.cross_attention_dim,
                      activation_fn=self.cfg.activation_fn,
                      num_embeds_ada_norm=self.cfg.num_embeds_ada_norm,
                      attention_bias=self.cfg.attention_bias,
                      only_cross_attention=self.cfg.only_cross_attention,
                      double_self_attention=self.cfg.double_self_attention,
                      upcast_attention=self.cfg.upcast_attention,
                      norm_type=self.cfg.norm_type,
                      norm_elementwise_affine=self.cfg.norm_elementwise_affine,
                      attention_type=self.cfg.attention_type,
                  )
                  for d in range(self.cfg.num_layers)
              ]
          )
  
          # 4. Define output layers
          self.out_channels = (
              self.cfg.in_channels
              if self.cfg.out_channels is None
              else self.cfg.out_channels
          )
          if self.is_input_continuous:
              # TODO: should use out_channels for continuous projections
              if self.cfg.use_linear_projection:
                  self.proj_out = linear_cls(inner_dim, self.cfg.in_channels)
              else:
                  self.proj_out = conv_cls(
                      inner_dim, self.cfg.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, self.cfg.patch_size * self.cfg.patch_size * self.out_channels
              )
  
          self.gradient_checkpointing = self.cfg.gradient_checkpointing
  
          self.set_use_memory_efficient_attention_xformers(
              self.cfg.enable_memory_efficient_attention
          )
  
      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,
      ):
          """
          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`.
              cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                  `self.processor` in
                  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
              attention_mask ( `torch.Tensor`, *optional*):
                  An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                  is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                  negative values to the attention scores corresponding to "discard" tokens.
              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:
              if self.training and self.gradient_checkpointing:
                  hidden_states = torch.utils.checkpoint.checkpoint(
                      block,
                      hidden_states,
                      attention_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      timestep,
                      cross_attention_kwargs,
                      class_labels,
                      use_reentrant=False,
                  )
              else:
                  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,
                  )
              )
  
          return output
  