# Copyright 2025 The Wan Team and 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 typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint

from ...configuration_utils import ConfigMixin, register_to_config
from ...utils import logging
from ...utils.accelerate_utils import apply_forward_hook
from ..activations import get_activation
from ..modeling_outputs import AutoencoderKLOutput
from ..modeling_utils import ModelMixin
from .vae import DecoderOutput, DiagonalGaussianDistribution


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

CACHE_T = 2


class WanCausalConv3d(nn.Conv3d):
    r"""
    A custom 3D causal convolution layer with feature caching support.

    This layer extends the standard Conv3D layer by ensuring causality in the time dimension and handling feature
    caching for efficient inference.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int, int]],
        stride: Union[int, Tuple[int, int, int]] = 1,
        padding: Union[int, Tuple[int, int, int]] = 0,
    ) -> None:
        super().__init__(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
        )

        # Set up causal padding
        self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0)
        self.padding = (0, 0, 0)

    def forward(self, x, cache_x=None):
        padding = list(self._padding)
        if cache_x is not None and self._padding[4] > 0:
            cache_x = cache_x.to(x.device)
            x = torch.cat([cache_x, x], dim=2)
            padding[4] -= cache_x.shape[2]
        x = F.pad(x, padding)
        return super().forward(x)


class WanRMS_norm(nn.Module):
    r"""
    A custom RMS normalization layer.

    Args:
        dim (int): The number of dimensions to normalize over.
        channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
            Default is True.
        images (bool, optional): Whether the input represents image data. Default is True.
        bias (bool, optional): Whether to include a learnable bias term. Default is False.
    """

    def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
        super().__init__()
        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
        shape = (dim, *broadcastable_dims) if channel_first else (dim,)

        self.channel_first = channel_first
        self.scale = dim**0.5
        self.gamma = nn.Parameter(torch.ones(shape))
        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0

    def forward(self, x):
        return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias


class WanUpsample(nn.Upsample):
    r"""
    Perform upsampling while ensuring the output tensor has the same data type as the input.

    Args:
        x (torch.Tensor): Input tensor to be upsampled.

    Returns:
        torch.Tensor: Upsampled tensor with the same data type as the input.
    """

    def forward(self, x):
        return super().forward(x.float()).type_as(x)


class WanResample(nn.Module):
    r"""
    A custom resampling module for 2D and 3D data.

    Args:
        dim (int): The number of input/output channels.
        mode (str): The resampling mode. Must be one of:
            - 'none': No resampling (identity operation).
            - 'upsample2d': 2D upsampling with nearest-exact interpolation and convolution.
            - 'upsample3d': 3D upsampling with nearest-exact interpolation, convolution, and causal 3D convolution.
            - 'downsample2d': 2D downsampling with zero-padding and convolution.
            - 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution.
    """

    def __init__(self, dim: int, mode: str) -> None:
        super().__init__()
        self.dim = dim
        self.mode = mode

        # layers
        if mode == "upsample2d":
            self.resample = nn.Sequential(
                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
            )
        elif mode == "upsample3d":
            self.resample = nn.Sequential(
                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
            )
            self.time_conv = WanCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))

        elif mode == "downsample2d":
            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
        elif mode == "downsample3d":
            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
            self.time_conv = WanCausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))

        else:
            self.resample = nn.Identity()

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        b, c, t, h, w = x.size()
        if self.mode == "upsample3d":
            if feat_cache is not None:
                idx = feat_idx[0]
                if feat_cache[idx] is None:
                    feat_cache[idx] = "Rep"
                    feat_idx[0] += 1
                else:
                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep":
                        # cache last frame of last two chunk
                        cache_x = torch.cat(
                            [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2
                        )
                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep":
                        cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2)
                    if feat_cache[idx] == "Rep":
                        x = self.time_conv(x)
                    else:
                        x = self.time_conv(x, feat_cache[idx])
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1

                    x = x.reshape(b, 2, c, t, h, w)
                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
                    x = x.reshape(b, c, t * 2, h, w)
        t = x.shape[2]
        x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
        x = self.resample(x)
        x = x.view(b, t, x.size(1), x.size(2), x.size(3)).permute(0, 2, 1, 3, 4)

        if self.mode == "downsample3d":
            if feat_cache is not None:
                idx = feat_idx[0]
                if feat_cache[idx] is None:
                    feat_cache[idx] = x.clone()
                    feat_idx[0] += 1
                else:
                    cache_x = x[:, :, -1:, :, :].clone()
                    x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1
        return x


class WanResidualBlock(nn.Module):
    r"""
    A custom residual block module.

    Args:
        in_dim (int): Number of input channels.
        out_dim (int): Number of output channels.
        dropout (float, optional): Dropout rate for the dropout layer. Default is 0.0.
        non_linearity (str, optional): Type of non-linearity to use. Default is "silu".
    """

    def __init__(
        self,
        in_dim: int,
        out_dim: int,
        dropout: float = 0.0,
        non_linearity: str = "silu",
    ) -> None:
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.nonlinearity = get_activation(non_linearity)

        # layers
        self.norm1 = WanRMS_norm(in_dim, images=False)
        self.conv1 = WanCausalConv3d(in_dim, out_dim, 3, padding=1)
        self.norm2 = WanRMS_norm(out_dim, images=False)
        self.dropout = nn.Dropout(dropout)
        self.conv2 = WanCausalConv3d(out_dim, out_dim, 3, padding=1)
        self.conv_shortcut = WanCausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        # Apply shortcut connection
        h = self.conv_shortcut(x)

        # First normalization and activation
        x = self.norm1(x)
        x = self.nonlinearity(x)

        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)

            x = self.conv1(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv1(x)

        # Second normalization and activation
        x = self.norm2(x)
        x = self.nonlinearity(x)

        # Dropout
        x = self.dropout(x)

        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)

            x = self.conv2(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv2(x)

        # Add residual connection
        return x + h


class WanAttentionBlock(nn.Module):
    r"""
    Causal self-attention with a single head.

    Args:
        dim (int): The number of channels in the input tensor.
    """

    def __init__(self, dim):
        super().__init__()
        self.dim = dim

        # layers
        self.norm = WanRMS_norm(dim)
        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
        self.proj = nn.Conv2d(dim, dim, 1)

    def forward(self, x):
        identity = x
        batch_size, channels, time, height, width = x.size()

        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * time, channels, height, width)
        x = self.norm(x)

        # compute query, key, value
        qkv = self.to_qkv(x)
        qkv = qkv.reshape(batch_size * time, 1, channels * 3, -1)
        qkv = qkv.permute(0, 1, 3, 2).contiguous()
        q, k, v = qkv.chunk(3, dim=-1)

        # apply attention
        x = F.scaled_dot_product_attention(q, k, v)

        x = x.squeeze(1).permute(0, 2, 1).reshape(batch_size * time, channels, height, width)

        # output projection
        x = self.proj(x)

        # Reshape back: [(b*t), c, h, w] -> [b, c, t, h, w]
        x = x.view(batch_size, time, channels, height, width)
        x = x.permute(0, 2, 1, 3, 4)

        return x + identity


class WanMidBlock(nn.Module):
    """
    Middle block for WanVAE encoder and decoder.

    Args:
        dim (int): Number of input/output channels.
        dropout (float): Dropout rate.
        non_linearity (str): Type of non-linearity to use.
    """

    def __init__(self, dim: int, dropout: float = 0.0, non_linearity: str = "silu", num_layers: int = 1):
        super().__init__()
        self.dim = dim

        # Create the components
        resnets = [WanResidualBlock(dim, dim, dropout, non_linearity)]
        attentions = []
        for _ in range(num_layers):
            attentions.append(WanAttentionBlock(dim))
            resnets.append(WanResidualBlock(dim, dim, dropout, non_linearity))
        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        # First residual block
        x = self.resnets[0](x, feat_cache, feat_idx)

        # Process through attention and residual blocks
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if attn is not None:
                x = attn(x)

            x = resnet(x, feat_cache, feat_idx)

        return x


class WanEncoder3d(nn.Module):
    r"""
    A 3D encoder module.

    Args:
        dim (int): The base number of channels in the first layer.
        z_dim (int): The dimensionality of the latent space.
        dim_mult (list of int): Multipliers for the number of channels in each block.
        num_res_blocks (int): Number of residual blocks in each block.
        attn_scales (list of float): Scales at which to apply attention mechanisms.
        temperal_downsample (list of bool): Whether to downsample temporally in each block.
        dropout (float): Dropout rate for the dropout layers.
        non_linearity (str): Type of non-linearity to use.
    """

    def __init__(
        self,
        dim=128,
        z_dim=4,
        dim_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_scales=[],
        temperal_downsample=[True, True, False],
        dropout=0.0,
        non_linearity: str = "silu",
    ):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_downsample = temperal_downsample
        self.nonlinearity = get_activation(non_linearity)

        # dimensions
        dims = [dim * u for u in [1] + dim_mult]
        scale = 1.0

        # init block
        self.conv_in = WanCausalConv3d(3, dims[0], 3, padding=1)

        # downsample blocks
        self.down_blocks = nn.ModuleList([])
        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
            # residual (+attention) blocks
            for _ in range(num_res_blocks):
                self.down_blocks.append(WanResidualBlock(in_dim, out_dim, dropout))
                if scale in attn_scales:
                    self.down_blocks.append(WanAttentionBlock(out_dim))
                in_dim = out_dim

            # downsample block
            if i != len(dim_mult) - 1:
                mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
                self.down_blocks.append(WanResample(out_dim, mode=mode))
                scale /= 2.0

        # middle blocks
        self.mid_block = WanMidBlock(out_dim, dropout, non_linearity, num_layers=1)

        # output blocks
        self.norm_out = WanRMS_norm(out_dim, images=False)
        self.conv_out = WanCausalConv3d(out_dim, z_dim, 3, padding=1)

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_in(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_in(x)

        ## downsamples
        for layer in self.down_blocks:
            if feat_cache is not None:
                x = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## middle
        x = self.mid_block(x, feat_cache, feat_idx)

        ## head
        x = self.norm_out(x)
        x = self.nonlinearity(x)
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_out(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_out(x)
        return x


class WanUpBlock(nn.Module):
    """
    A block that handles upsampling for the WanVAE decoder.

    Args:
        in_dim (int): Input dimension
        out_dim (int): Output dimension
        num_res_blocks (int): Number of residual blocks
        dropout (float): Dropout rate
        upsample_mode (str, optional): Mode for upsampling ('upsample2d' or 'upsample3d')
        non_linearity (str): Type of non-linearity to use
    """

    def __init__(
        self,
        in_dim: int,
        out_dim: int,
        num_res_blocks: int,
        dropout: float = 0.0,
        upsample_mode: Optional[str] = None,
        non_linearity: str = "silu",
    ):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim

        # Create layers list
        resnets = []
        # Add residual blocks and attention if needed
        current_dim = in_dim
        for _ in range(num_res_blocks + 1):
            resnets.append(WanResidualBlock(current_dim, out_dim, dropout, non_linearity))
            current_dim = out_dim

        self.resnets = nn.ModuleList(resnets)

        # Add upsampling layer if needed
        self.upsamplers = None
        if upsample_mode is not None:
            self.upsamplers = nn.ModuleList([WanResample(out_dim, mode=upsample_mode)])

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        """
        Forward pass through the upsampling block.

        Args:
            x (torch.Tensor): Input tensor
            feat_cache (list, optional): Feature cache for causal convolutions
            feat_idx (list, optional): Feature index for cache management

        Returns:
            torch.Tensor: Output tensor
        """
        for resnet in self.resnets:
            if feat_cache is not None:
                x = resnet(x, feat_cache, feat_idx)
            else:
                x = resnet(x)

        if self.upsamplers is not None:
            if feat_cache is not None:
                x = self.upsamplers[0](x, feat_cache, feat_idx)
            else:
                x = self.upsamplers[0](x)
        return x


class WanDecoder3d(nn.Module):
    r"""
    A 3D decoder module.

    Args:
        dim (int): The base number of channels in the first layer.
        z_dim (int): The dimensionality of the latent space.
        dim_mult (list of int): Multipliers for the number of channels in each block.
        num_res_blocks (int): Number of residual blocks in each block.
        attn_scales (list of float): Scales at which to apply attention mechanisms.
        temperal_upsample (list of bool): Whether to upsample temporally in each block.
        dropout (float): Dropout rate for the dropout layers.
        non_linearity (str): Type of non-linearity to use.
    """

    def __init__(
        self,
        dim=128,
        z_dim=4,
        dim_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_scales=[],
        temperal_upsample=[False, True, True],
        dropout=0.0,
        non_linearity: str = "silu",
    ):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_upsample = temperal_upsample

        self.nonlinearity = get_activation(non_linearity)

        # dimensions
        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
        scale = 1.0 / 2 ** (len(dim_mult) - 2)

        # init block
        self.conv_in = WanCausalConv3d(z_dim, dims[0], 3, padding=1)

        # middle blocks
        self.mid_block = WanMidBlock(dims[0], dropout, non_linearity, num_layers=1)

        # upsample blocks
        self.up_blocks = nn.ModuleList([])
        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
            # residual (+attention) blocks
            if i > 0:
                in_dim = in_dim // 2

            # Determine if we need upsampling
            upsample_mode = None
            if i != len(dim_mult) - 1:
                upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d"

            # Create and add the upsampling block
            up_block = WanUpBlock(
                in_dim=in_dim,
                out_dim=out_dim,
                num_res_blocks=num_res_blocks,
                dropout=dropout,
                upsample_mode=upsample_mode,
                non_linearity=non_linearity,
            )
            self.up_blocks.append(up_block)

            # Update scale for next iteration
            if upsample_mode is not None:
                scale *= 2.0

        # output blocks
        self.norm_out = WanRMS_norm(out_dim, images=False)
        self.conv_out = WanCausalConv3d(out_dim, 3, 3, padding=1)

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        ## conv1
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_in(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_in(x)

        ## middle
        x = self.mid_block(x, feat_cache, feat_idx)

        ## upsamples
        for up_block in self.up_blocks:
            x = up_block(x, feat_cache, feat_idx)

        ## head
        x = self.norm_out(x)
        x = self.nonlinearity(x)
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_out(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_out(x)
        return x


class AutoencoderKLWan(ModelMixin, ConfigMixin):
    r"""
    A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
    Introduced in [Wan 2.1].

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).
    """

    _supports_gradient_checkpointing = False

    @register_to_config
    def __init__(
        self,
        base_dim: int = 96,
        z_dim: int = 16,
        dim_mult: Tuple[int] = [1, 2, 4, 4],
        num_res_blocks: int = 2,
        attn_scales: List[float] = [],
        temperal_downsample: List[bool] = [False, True, True],
        dropout: float = 0.0,
        latents_mean: List[float] = [
            -0.7571,
            -0.7089,
            -0.9113,
            0.1075,
            -0.1745,
            0.9653,
            -0.1517,
            1.5508,
            0.4134,
            -0.0715,
            0.5517,
            -0.3632,
            -0.1922,
            -0.9497,
            0.2503,
            -0.2921,
        ],
        latents_std: List[float] = [
            2.8184,
            1.4541,
            2.3275,
            2.6558,
            1.2196,
            1.7708,
            2.6052,
            2.0743,
            3.2687,
            2.1526,
            2.8652,
            1.5579,
            1.6382,
            1.1253,
            2.8251,
            1.9160,
        ],
    ) -> None:
        super().__init__()

        # Store normalization parameters as tensors
        self.mean = torch.tensor(latents_mean)
        self.std = torch.tensor(latents_std)
        self.scale = torch.stack([self.mean, 1.0 / self.std])  # Shape: [2, C]

        self.z_dim = z_dim
        self.temperal_downsample = temperal_downsample
        self.temperal_upsample = temperal_downsample[::-1]

        self.encoder = WanEncoder3d(
            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
        )
        self.quant_conv = WanCausalConv3d(z_dim * 2, z_dim * 2, 1)
        self.post_quant_conv = WanCausalConv3d(z_dim, z_dim, 1)

        self.decoder = WanDecoder3d(
            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
        )

    def clear_cache(self):
        def _count_conv3d(model):
            count = 0
            for m in model.modules():
                if isinstance(m, WanCausalConv3d):
                    count += 1
            return count

        self._conv_num = _count_conv3d(self.decoder)
        self._conv_idx = [0]
        self._feat_map = [None] * self._conv_num
        # cache encode
        self._enc_conv_num = _count_conv3d(self.encoder)
        self._enc_conv_idx = [0]
        self._enc_feat_map = [None] * self._enc_conv_num

    def _encode(self, x: torch.Tensor) -> torch.Tensor:
        scale = self.scale.type_as(x)
        self.clear_cache()
        ## cache
        t = x.shape[2]
        iter_ = 1 + (t - 1) // 4
        for i in range(iter_):
            self._enc_conv_idx = [0]
            if i == 0:
                out = self.encoder(x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
            else:
                out_ = self.encoder(
                    x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i, :, :],
                    feat_cache=self._enc_feat_map,
                    feat_idx=self._enc_conv_idx,
                )
                out = torch.cat([out, out_], 2)

        enc = self.quant_conv(out)
        mu, logvar = enc[:, : self.z_dim, :, :, :], enc[:, self.z_dim :, :, :, :]
        mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
        logvar = (logvar - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
        enc = torch.cat([mu, logvar], dim=1)
        self.clear_cache()
        return enc

    @apply_forward_hook
    def encode(
        self, x: torch.Tensor, return_dict: bool = True
    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
        r"""
        Encode a batch of images into latents.

        Args:
            x (`torch.Tensor`): Input batch of images.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

        Returns:
                The latent representations of the encoded videos. If `return_dict` is True, a
                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
        """
        h = self._encode(x)
        posterior = DiagonalGaussianDistribution(h)
        if not return_dict:
            return (posterior,)
        return AutoencoderKLOutput(latent_dist=posterior)

    def _decode(self, z: torch.Tensor, scale, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
        self.clear_cache()
        # z: [b,c,t,h,w]
        z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)

        iter_ = z.shape[2]
        x = self.post_quant_conv(z)
        for i in range(iter_):
            self._conv_idx = [0]
            if i == 0:
                out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
            else:
                out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
                out = torch.cat([out, out_], 2)

        out = torch.clamp(out, min=-1.0, max=1.0)
        self.clear_cache()
        if not return_dict:
            return (out,)

        return DecoderOutput(sample=out)

    @apply_forward_hook
    def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
        r"""
        Decode a batch of images.

        Args:
            z (`torch.Tensor`): Input batch of latent vectors.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.

        Returns:
            [`~models.vae.DecoderOutput`] or `tuple`:
                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                returned.
        """
        scale = self.scale.type_as(z)
        decoded = self._decode(z, scale).sample
        if not return_dict:
            return (decoded,)

        return DecoderOutput(sample=decoded)

    def forward(
        self,
        sample: torch.Tensor,
        sample_posterior: bool = False,
        return_dict: bool = True,
        generator: Optional[torch.Generator] = None,
    ) -> Union[DecoderOutput, torch.Tensor]:
        """
        Args:
            sample (`torch.Tensor`): Input sample.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
        """
        x = sample
        posterior = self.encode(x).latent_dist
        if sample_posterior:
            z = posterior.sample(generator=generator)
        else:
            z = posterior.mode()
        dec = self.decode(z, return_dict=return_dict)
        return dec
