# Copyright 2025 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 __future__ import annotations

from typing import Any

import torch
from torch import nn

from ...utils import deprecate, logging
from ...utils.torch_utils import apply_freeu
from ..attention import Attention
from ..resnet import (
    Downsample2D,
    ResnetBlock2D,
    SpatioTemporalResBlock,
    TemporalConvLayer,
    Upsample2D,
)
from ..transformers.transformer_2d import Transformer2DModel
from ..transformers.transformer_temporal import (
    TransformerSpatioTemporalModel,
    TransformerTemporalModel,
)
from .unet_motion_model import (
    CrossAttnDownBlockMotion,
    CrossAttnUpBlockMotion,
    DownBlockMotion,
    UNetMidBlockCrossAttnMotion,
    UpBlockMotion,
)


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class DownBlockMotion(DownBlockMotion):
    def __init__(self, *args, **kwargs):
        deprecation_message = "Importing `DownBlockMotion` from `diffusers.models.unets.unet_3d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_motion_model import DownBlockMotion` instead."
        deprecate("DownBlockMotion", "1.0.0", deprecation_message)
        super().__init__(*args, **kwargs)


class CrossAttnDownBlockMotion(CrossAttnDownBlockMotion):
    def __init__(self, *args, **kwargs):
        deprecation_message = "Importing `CrossAttnDownBlockMotion` from `diffusers.models.unets.unet_3d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_motion_model import CrossAttnDownBlockMotion` instead."
        deprecate("CrossAttnDownBlockMotion", "1.0.0", deprecation_message)
        super().__init__(*args, **kwargs)


class UpBlockMotion(UpBlockMotion):
    def __init__(self, *args, **kwargs):
        deprecation_message = "Importing `UpBlockMotion` from `diffusers.models.unets.unet_3d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_motion_model import UpBlockMotion` instead."
        deprecate("UpBlockMotion", "1.0.0", deprecation_message)
        super().__init__(*args, **kwargs)


class CrossAttnUpBlockMotion(CrossAttnUpBlockMotion):
    def __init__(self, *args, **kwargs):
        deprecation_message = "Importing `CrossAttnUpBlockMotion` from `diffusers.models.unets.unet_3d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_motion_model import CrossAttnUpBlockMotion` instead."
        deprecate("CrossAttnUpBlockMotion", "1.0.0", deprecation_message)
        super().__init__(*args, **kwargs)


class UNetMidBlockCrossAttnMotion(UNetMidBlockCrossAttnMotion):
    def __init__(self, *args, **kwargs):
        deprecation_message = "Importing `UNetMidBlockCrossAttnMotion` from `diffusers.models.unets.unet_3d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_motion_model import UNetMidBlockCrossAttnMotion` instead."
        deprecate("UNetMidBlockCrossAttnMotion", "1.0.0", deprecation_message)
        super().__init__(*args, **kwargs)


def get_down_block(
    down_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    temb_channels: int,
    add_downsample: bool,
    resnet_eps: float,
    resnet_act_fn: str,
    num_attention_heads: int,
    resnet_groups: int | None = None,
    cross_attention_dim: int | None = None,
    downsample_padding: int | None = None,
    dual_cross_attention: bool = False,
    use_linear_projection: bool = True,
    only_cross_attention: bool = False,
    upcast_attention: bool = False,
    resnet_time_scale_shift: str = "default",
    temporal_num_attention_heads: int = 8,
    temporal_max_seq_length: int = 32,
    transformer_layers_per_block: int | tuple[int] = 1,
    temporal_transformer_layers_per_block: int | tuple[int] = 1,
    dropout: float = 0.0,
) -> "DownBlock3D" | "CrossAttnDownBlock3D" | "DownBlockSpatioTemporal" | "CrossAttnDownBlockSpatioTemporal":
    if down_block_type == "DownBlock3D":
        return DownBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            resnet_time_scale_shift=resnet_time_scale_shift,
            dropout=dropout,
        )
    elif down_block_type == "CrossAttnDownBlock3D":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
        return CrossAttnDownBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            dropout=dropout,
        )
    elif down_block_type == "DownBlockSpatioTemporal":
        # added for SDV
        return DownBlockSpatioTemporal(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
        )
    elif down_block_type == "CrossAttnDownBlockSpatioTemporal":
        # added for SDV
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal")
        return CrossAttnDownBlockSpatioTemporal(
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            add_downsample=add_downsample,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
        )

    raise ValueError(f"{down_block_type} does not exist.")


def get_up_block(
    up_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    prev_output_channel: int,
    temb_channels: int,
    add_upsample: bool,
    resnet_eps: float,
    resnet_act_fn: str,
    num_attention_heads: int,
    resolution_idx: int | None = None,
    resnet_groups: int | None = None,
    cross_attention_dim: int | None = None,
    dual_cross_attention: bool = False,
    use_linear_projection: bool = True,
    only_cross_attention: bool = False,
    upcast_attention: bool = False,
    resnet_time_scale_shift: str = "default",
    temporal_num_attention_heads: int = 8,
    temporal_cross_attention_dim: int | None = None,
    temporal_max_seq_length: int = 32,
    transformer_layers_per_block: int | tuple[int] = 1,
    temporal_transformer_layers_per_block: int | tuple[int] = 1,
    dropout: float = 0.0,
) -> "UpBlock3D" | "CrossAttnUpBlock3D" | "UpBlockSpatioTemporal" | "CrossAttnUpBlockSpatioTemporal":
    if up_block_type == "UpBlock3D":
        return UpBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resolution_idx=resolution_idx,
            dropout=dropout,
        )
    elif up_block_type == "CrossAttnUpBlock3D":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
        return CrossAttnUpBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resolution_idx=resolution_idx,
            dropout=dropout,
        )
    elif up_block_type == "UpBlockSpatioTemporal":
        # added for SDV
        return UpBlockSpatioTemporal(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            resolution_idx=resolution_idx,
            add_upsample=add_upsample,
        )
    elif up_block_type == "CrossAttnUpBlockSpatioTemporal":
        # added for SDV
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal")
        return CrossAttnUpBlockSpatioTemporal(
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            add_upsample=add_upsample,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            resolution_idx=resolution_idx,
        )

    raise ValueError(f"{up_block_type} does not exist.")


class UNetMidBlock3DCrossAttn(nn.Module):
    def __init__(
        self,
        in_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,
        num_attention_heads: int = 1,
        output_scale_factor: float = 1.0,
        cross_attention_dim: int = 1280,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = True,
        upcast_attention: bool = False,
    ):
        super().__init__()

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)

        # there is always at least one resnet
        resnets = [
            ResnetBlock2D(
                in_channels=in_channels,
                out_channels=in_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,
            )
        ]
        temp_convs = [
            TemporalConvLayer(
                in_channels,
                in_channels,
                dropout=0.1,
                norm_num_groups=resnet_groups,
            )
        ]
        attentions = []
        temp_attentions = []

        for _ in range(num_layers):
            attentions.append(
                Transformer2DModel(
                    in_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=in_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    upcast_attention=upcast_attention,
                )
            )
            temp_attentions.append(
                TransformerTemporalModel(
                    in_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=in_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                )
            )
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=in_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,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    in_channels,
                    in_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        num_frames: int = 1,
        cross_attention_kwargs: dict[str, Any] | None = None,
    ) -> torch.Tensor:
        hidden_states = self.resnets[0](hidden_states, temb)
        hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames)
        for attn, temp_attn, resnet, temp_conv in zip(
            self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:]
        ):
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = temp_attn(
                hidden_states,
                num_frames=num_frames,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)

        return hidden_states


class CrossAttnDownBlock3D(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,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        downsample_padding: int = 1,
        add_downsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
    ):
        super().__init__()
        resnets = []
        attentions = []
        temp_attentions = []
        temp_convs = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        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,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )
            attentions.append(
                Transformer2DModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    only_cross_attention=only_cross_attention,
                    upcast_attention=upcast_attention,
                )
            )
            temp_attentions.append(
                TransformerTemporalModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                )
            )
        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)
        self.attentions = nn.ModuleList(attentions)
        self.temp_attentions = nn.ModuleList(temp_attentions)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        num_frames: int = 1,
        cross_attention_kwargs: dict[str, Any] = None,
    ) -> torch.Tensor | tuple[torch.Tensor, ...]:
        # TODO(Patrick, William) - attention mask is not used
        output_states = ()

        for resnet, temp_conv, attn, temp_attn in zip(
            self.resnets, self.temp_convs, self.attentions, self.temp_attentions
        ):
            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = temp_attn(
                hidden_states,
                num_frames=num_frames,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]

            output_states += (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states += (hidden_states,)

        return hidden_states, output_states


class DownBlock3D(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,
        output_scale_factor: float = 1.0,
        add_downsample: bool = True,
        downsample_padding: int = 1,
    ):
        super().__init__()
        resnets = []
        temp_convs = []

        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,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )

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

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor | None = None,
        num_frames: int = 1,
    ) -> torch.Tensor | tuple[torch.Tensor, ...]:
        output_states = ()

        for resnet, temp_conv in zip(self.resnets, self.temp_convs):
            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)

            output_states += (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states += (hidden_states,)

        return hidden_states, output_states


class CrossAttnUpBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: 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,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
        resolution_idx: int | None = None,
    ):
        super().__init__()
        resnets = []
        temp_convs = []
        attentions = []
        temp_attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_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,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )
            attentions.append(
                Transformer2DModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    only_cross_attention=only_cross_attention,
                    upcast_attention=upcast_attention,
                )
            )
            temp_attentions.append(
                TransformerTemporalModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                )
            )
        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)
        self.attentions = nn.ModuleList(attentions)
        self.temp_attentions = nn.ModuleList(temp_attentions)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.Tensor,
        res_hidden_states_tuple: tuple[torch.Tensor, ...],
        temb: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        upsample_size: int | None = None,
        attention_mask: torch.Tensor | None = None,
        num_frames: int = 1,
        cross_attention_kwargs: dict[str, Any] = None,
    ) -> torch.Tensor:
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        # TODO(Patrick, William) - attention mask is not used
        for resnet, temp_conv, attn, temp_attn in zip(
            self.resnets, self.temp_convs, self.attentions, self.temp_attentions
        ):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = temp_attn(
                hidden_states,
                num_frames=num_frames,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states


class UpBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: 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,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        resolution_idx: int | None = None,
    ):
        super().__init__()
        resnets = []
        temp_convs = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_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,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )

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

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.Tensor,
        res_hidden_states_tuple: tuple[torch.Tensor, ...],
        temb: torch.Tensor | None = None,
        upsample_size: int | None = None,
        num_frames: int = 1,
    ) -> torch.Tensor:
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )
        for resnet, temp_conv in zip(self.resnets, self.temp_convs):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states


class MidBlockTemporalDecoder(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        attention_head_dim: int = 512,
        num_layers: int = 1,
        upcast_attention: bool = False,
    ):
        super().__init__()

        resnets = []
        attentions = []
        for i in range(num_layers):
            input_channels = in_channels if i == 0 else out_channels
            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=input_channels,
                    out_channels=out_channels,
                    temb_channels=None,
                    eps=1e-6,
                    temporal_eps=1e-5,
                    merge_factor=0.0,
                    merge_strategy="learned",
                    switch_spatial_to_temporal_mix=True,
                )
            )

        attentions.append(
            Attention(
                query_dim=in_channels,
                heads=in_channels // attention_head_dim,
                dim_head=attention_head_dim,
                eps=1e-6,
                upcast_attention=upcast_attention,
                norm_num_groups=32,
                bias=True,
                residual_connection=True,
            )
        )

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        image_only_indicator: torch.Tensor,
    ):
        hidden_states = self.resnets[0](
            hidden_states,
            image_only_indicator=image_only_indicator,
        )
        for resnet, attn in zip(self.resnets[1:], self.attentions):
            hidden_states = attn(hidden_states)
            hidden_states = resnet(
                hidden_states,
                image_only_indicator=image_only_indicator,
            )

        return hidden_states


class UpBlockTemporalDecoder(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_layers: int = 1,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []
        for i in range(num_layers):
            input_channels = in_channels if i == 0 else out_channels

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=input_channels,
                    out_channels=out_channels,
                    temb_channels=None,
                    eps=1e-6,
                    temporal_eps=1e-5,
                    merge_factor=0.0,
                    merge_strategy="learned",
                    switch_spatial_to_temporal_mix=True,
                )
            )
        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

    def forward(
        self,
        hidden_states: torch.Tensor,
        image_only_indicator: torch.Tensor,
    ) -> torch.Tensor:
        for resnet in self.resnets:
            hidden_states = resnet(
                hidden_states,
                image_only_indicator=image_only_indicator,
            )

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states)

        return hidden_states


class UNetMidBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        num_layers: int = 1,
        transformer_layers_per_block: int | tuple[int] = 1,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
    ):
        super().__init__()

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        # support for variable transformer layers per block
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        # there is always at least one resnet
        resnets = [
            SpatioTemporalResBlock(
                in_channels=in_channels,
                out_channels=in_channels,
                temb_channels=temb_channels,
                eps=1e-5,
            )
        ]
        attentions = []

        for i in range(num_layers):
            attentions.append(
                TransformerSpatioTemporalModel(
                    num_attention_heads,
                    in_channels // num_attention_heads,
                    in_channels=in_channels,
                    num_layers=transformer_layers_per_block[i],
                    cross_attention_dim=cross_attention_dim,
                )
            )

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    temb_channels=temb_channels,
                    eps=1e-5,
                )
            )

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

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        image_only_indicator: torch.Tensor | None = None,
    ) -> torch.Tensor:
        hidden_states = self.resnets[0](
            hidden_states,
            temb,
            image_only_indicator=image_only_indicator,
        )

        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb, image_only_indicator)
            else:
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
                hidden_states = resnet(hidden_states, temb, image_only_indicator=image_only_indicator)

        return hidden_states


class DownBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        num_layers: int = 1,
        add_downsample: bool = True,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=1e-5,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor | None = None,
        image_only_indicator: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, tuple[torch.Tensor, ...]]:
        output_states = ()
        for resnet in self.resnets:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb, image_only_indicator)
            else:
                hidden_states = resnet(hidden_states, temb, image_only_indicator=image_only_indicator)

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


class CrossAttnDownBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        num_layers: int = 1,
        transformer_layers_per_block: int | tuple[int] = 1,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        add_downsample: bool = True,
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=1e-6,
                )
            )
            attentions.append(
                TransformerSpatioTemporalModel(
                    num_attention_heads,
                    out_channels // num_attention_heads,
                    in_channels=out_channels,
                    num_layers=transformer_layers_per_block[i],
                    cross_attention_dim=cross_attention_dim,
                )
            )

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

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=1,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        image_only_indicator: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, tuple[torch.Tensor, ...]]:
        output_states = ()

        blocks = list(zip(self.resnets, self.attentions))
        for resnet, attn in blocks:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb, image_only_indicator)

                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
            else:
                hidden_states = resnet(hidden_states, temb, image_only_indicator=image_only_indicator)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


class UpBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: int,
        out_channels: int,
        temb_channels: int,
        resolution_idx: int | None = None,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.Tensor,
        res_hidden_states_tuple: tuple[torch.Tensor, ...],
        temb: torch.Tensor | None = None,
        image_only_indicator: torch.Tensor | None = None,
        upsample_size: int | None = None,
    ) -> torch.Tensor:
        for resnet in self.resnets:
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb, image_only_indicator)
            else:
                hidden_states = resnet(hidden_states, temb, image_only_indicator=image_only_indicator)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states


class CrossAttnUpBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        resolution_idx: int | None = None,
        num_layers: int = 1,
        transformer_layers_per_block: int | tuple[int] = 1,
        resnet_eps: float = 1e-6,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                )
            )
            attentions.append(
                TransformerSpatioTemporalModel(
                    num_attention_heads,
                    out_channels // num_attention_heads,
                    in_channels=out_channels,
                    num_layers=transformer_layers_per_block[i],
                    cross_attention_dim=cross_attention_dim,
                )
            )

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

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.Tensor,
        res_hidden_states_tuple: tuple[torch.Tensor, ...],
        temb: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        image_only_indicator: torch.Tensor | None = None,
        upsample_size: int | None = None,
    ) -> torch.Tensor:
        for resnet, attn in zip(self.resnets, self.attentions):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb, image_only_indicator)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
            else:
                hidden_states = resnet(hidden_states, temb, image_only_indicator=image_only_indicator)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states
