class AttnDownBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        attention_head_dim: int = 1,
        output_scale_factor: float = 1.0,
        downsample_padding: int = 1,
        downsample_type: str = "conv",
    ):
        super().__init__()
        resnets = []
        attentions = []
        self.downsample_type = downsample_type

        if attention_head_dim is None:
            logger.warning(
                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
            )
            attention_head_dim = out_channels

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            attentions.append(
                Attention(
                    out_channels,
                    heads=out_channels // attention_head_dim,
                    dim_head=attention_head_dim,
                    rescale_output_factor=output_scale_factor,
                    eps=resnet_eps,
                    norm_num_groups=resnet_groups,
                    residual_connection=True,
                    bias=True,
                    upcast_softmax=True,
                    _from_deprecated_attn_block=True,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if downsample_type == "conv":
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                    )
                ]
            )
        elif downsample_type == "resnet":
            self.downsamplers = nn.ModuleList(
                [
                    ResnetBlock2D(
                        in_channels=out_channels,
                        out_channels=out_channels,
                        temb_channels=temb_channels,
                        eps=resnet_eps,
                        groups=resnet_groups,
                        dropout=dropout,
                        time_embedding_norm=resnet_time_scale_shift,
                        non_linearity=resnet_act_fn,
                        output_scale_factor=output_scale_factor,
                        pre_norm=resnet_pre_norm,
                        down=True,
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: Optional[torch.Tensor] = None,
        upsample_size: Optional[int] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:
        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
        if cross_attention_kwargs.get("scale", None) is not None:
            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")

        output_states = ()

        for resnet, attn in zip(self.resnets, self.attentions):
            if torch.is_grad_enabled() and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
                hidden_states = attn(hidden_states, **cross_attention_kwargs)
                output_states = output_states + (hidden_states,)
            else:
                hidden_states = resnet(hidden_states, temb)
                hidden_states = attn(hidden_states, **cross_attention_kwargs)
                output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                if self.downsample_type == "resnet":
                    hidden_states = downsampler(hidden_states, temb=temb)
                else:
                    hidden_states = downsampler(hidden_states)

            output_states += (hidden_states,)

        return hidden_states, output_states
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py