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  # --------
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  # Modified 2024 by the Tripo AI and Stability AI Team.
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  # Copyright (c) 2024 Tripo AI & Stability AI
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  # SOFTWARE.
  
  from dataclasses import dataclass
  from typing import Optional
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from ...utils import BaseModule
  from .basic_transformer_block import BasicTransformerBlock
  
  
  class Transformer1D(BaseModule):
      @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
          activation_fn: str = "geglu"
          only_cross_attention: bool = False
          double_self_attention: bool = False
          upcast_attention: bool = False
          norm_type: str = "layer_norm"
          norm_elementwise_affine: bool = True
          gradient_checkpointing: bool = False
  
      cfg: Config
  
      def configure(self) -> None:
          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
  
          linear_cls = nn.Linear
  
          # 2. Define input layers
          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,
          )
          self.proj_in = linear_cls(self.cfg.in_channels, 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,
                      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,
                  )
                  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
          )
  
          self.proj_out = linear_cls(inner_dim, self.cfg.in_channels)
  
          self.gradient_checkpointing = self.cfg.gradient_checkpointing
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          encoder_hidden_states: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          encoder_attention_mask: Optional[torch.Tensor] = None,
      ):
          """
          The [`Transformer1DModel`] 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.
              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.
  
          Returns:
              torch.FloatTensor
          """
          # 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
          batch, _, seq_len = hidden_states.shape
          residual = hidden_states
  
          hidden_states = self.norm(hidden_states)
          inner_dim = hidden_states.shape[1]
          hidden_states = hidden_states.permute(0, 2, 1).reshape(
              batch, seq_len, inner_dim
          )
          hidden_states = self.proj_in(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,
                      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,
                  )
  
          # 3. Output
          hidden_states = self.proj_out(hidden_states)
          hidden_states = (
              hidden_states.reshape(batch, seq_len, inner_dim)
              .permute(0, 2, 1)
              .contiguous()
          )
  
          output = hidden_states + residual
  
          return output
  