# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import math
from dataclasses import dataclass

import numpy as np
import torch
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils import BaseOutput
from diffusers.utils.torch_utils import randn_tensor
from einops import rearrange
from torch import Tensor, nn


class DiagonalGaussianDistribution:
    def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
        if parameters.ndim == 3:
            dim = 2  # (B, L, C)
        elif parameters.ndim == 5 or parameters.ndim == 4:
            dim = 1  # (B, C, T, H ,W) / (B, C, H, W)
        else:
            raise NotImplementedError
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=dim)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(
                self.mean, device=self.parameters.device, dtype=self.parameters.dtype
            )

    def sample(self, generator: torch.Generator | None = None) -> torch.FloatTensor:
        # make sure sample is on the same device as the parameters and has same dtype
        sample = randn_tensor(
            self.mean.shape,
            generator=generator,
            device=self.parameters.device,
            dtype=self.parameters.dtype,
        )
        x = self.mean + self.std * sample
        return x


@dataclass
class DecoderOutput(BaseOutput):
    sample: torch.FloatTensor
    posterior: DiagonalGaussianDistribution | None = None


def swish(x: Tensor) -> Tensor:
    return x * torch.sigmoid(x)


def forward_with_checkpointing(module, *inputs, use_checkpointing=False):
    def create_custom_forward(module):
        def custom_forward(*inputs):
            return module(*inputs)

        return custom_forward

    if use_checkpointing:
        return torch.utils.checkpoint.checkpoint(create_custom_forward(module), *inputs, use_reentrant=False)
    else:
        return module(*inputs)


class Conv3d(nn.Conv3d):
    """
    Perform Conv3d on patches with numerical differences from nn.Conv3d within 1e-5.
    Only symmetric padding is supported.
    """

    def forward(self, input):
        B, C, T, H, W = input.shape
        memory_count = (C * T * H * W) * 2 / 1024**3
        if memory_count > 2:
            n_split = math.ceil(memory_count / 2)
            assert n_split >= 2
            chunks = torch.chunk(input, chunks=n_split, dim=-3)
            padded_chunks = []
            for i in range(len(chunks)):
                if self.padding[0] > 0:
                    padded_chunk = F.pad(
                        chunks[i],
                        (0, 0, 0, 0, self.padding[0], self.padding[0]),
                        mode="constant" if self.padding_mode == "zeros" else self.padding_mode,
                        value=0,
                    )
                    if i > 0:
                        padded_chunk[:, :, : self.padding[0]] = chunks[i - 1][:, :, -self.padding[0] :]
                    if i < len(chunks) - 1:
                        padded_chunk[:, :, -self.padding[0] :] = chunks[i + 1][:, :, : self.padding[0]]
                else:
                    padded_chunk = chunks[i]
                padded_chunks.append(padded_chunk)
            padding_bak = self.padding
            self.padding = (0, self.padding[1], self.padding[2])
            outputs = []
            for i in range(len(padded_chunks)):
                outputs.append(super().forward(padded_chunks[i]))
            self.padding = padding_bak
            return torch.cat(outputs, dim=-3)
        else:
            return super().forward(input)


class AttnBlock(nn.Module):
    """Attention with torch sdpa implementation."""

    def __init__(self, in_channels: int):
        super().__init__()
        self.in_channels = in_channels

        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)

        self.q = Conv3d(in_channels, in_channels, kernel_size=1)
        self.k = Conv3d(in_channels, in_channels, kernel_size=1)
        self.v = Conv3d(in_channels, in_channels, kernel_size=1)
        self.proj_out = Conv3d(in_channels, in_channels, kernel_size=1)

    def attention(self, h_: Tensor) -> Tensor:
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        b, c, f, h, w = q.shape
        q = rearrange(q, "b c f h w -> b 1 (f h w) c").contiguous()
        k = rearrange(k, "b c f h w -> b 1 (f h w) c").contiguous()
        v = rearrange(v, "b c f h w -> b 1 (f h w) c").contiguous()
        h_ = nn.functional.scaled_dot_product_attention(q, k, v)

        return rearrange(h_, "b 1 (f h w) c -> b c f h w", f=f, h=h, w=w, c=c, b=b)

    def forward(self, x: Tensor) -> Tensor:
        return x + self.proj_out(self.attention(x))


class ResnetBlock(nn.Module):
    def __init__(self, in_channels: int, out_channels: int):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels

        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        self.conv1 = Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
        self.conv2 = Conv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        if self.in_channels != self.out_channels:
            self.nin_shortcut = Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        h = x
        h = self.norm1(h)
        h = swish(h)
        h = self.conv1(h)

        h = self.norm2(h)
        h = swish(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            x = self.nin_shortcut(x)
        return x + h


class DownsampleDCAE(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, add_temporal_downsample: bool = True):
        super().__init__()
        factor = 2 * 2 * 2 if add_temporal_downsample else 1 * 2 * 2
        assert out_channels % factor == 0
        self.conv = Conv3d(in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1)

        self.add_temporal_downsample = add_temporal_downsample
        self.group_size = factor * in_channels // out_channels

    def forward(self, x: Tensor):
        r1 = 2 if self.add_temporal_downsample else 1
        h = self.conv(x)
        h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
        shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)

        B, C, T, H, W = shortcut.shape
        shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
        return h + shortcut


class UpsampleDCAE(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, add_temporal_upsample: bool = True):
        super().__init__()
        factor = 2 * 2 * 2 if add_temporal_upsample else 1 * 2 * 2
        self.conv = Conv3d(in_channels, out_channels * factor, kernel_size=3, stride=1, padding=1)

        self.add_temporal_upsample = add_temporal_upsample
        self.repeats = factor * out_channels // in_channels

    def forward(self, x: Tensor):
        r1 = 2 if self.add_temporal_upsample else 1
        h = self.conv(x)
        h = rearrange(h, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
        shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
        shortcut = rearrange(shortcut, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
        return h + shortcut


class Encoder(nn.Module):
    """
    The encoder network of AutoencoderKLConv3D.
    """

    def __init__(
        self,
        in_channels: int,
        z_channels: int,
        block_out_channels: tuple[int, ...],
        num_res_blocks: int,
        ffactor_spatial: int,
        ffactor_temporal: int,
        downsample_match_channel: bool = True,
    ):
        super().__init__()
        assert block_out_channels[-1] % (2 * z_channels) == 0

        self.z_channels = z_channels
        self.block_out_channels = block_out_channels
        self.num_res_blocks = num_res_blocks

        # downsampling
        self.conv_in = Conv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)

        self.down = nn.ModuleList()
        block_in = block_out_channels[0]
        for i_level, ch in enumerate(block_out_channels):
            block = nn.ModuleList()
            block_out = ch
            for _ in range(self.num_res_blocks):
                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                block_in = block_out
            down = nn.Module()
            down.block = block

            add_spatial_downsample = bool(i_level < np.log2(ffactor_spatial))
            add_temporal_downsample = add_spatial_downsample and bool(
                i_level >= np.log2(ffactor_spatial // ffactor_temporal)
            )
            if add_spatial_downsample or add_temporal_downsample:
                assert i_level < len(block_out_channels) - 1
                block_out = block_out_channels[i_level + 1] if downsample_match_channel else block_in
                down.downsample = DownsampleDCAE(block_in, block_out, add_temporal_downsample)
                block_in = block_out
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)

        # end
        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
        self.conv_out = Conv3d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)

        self.gradient_checkpointing = False

    def forward(self, x: Tensor) -> Tensor:
        use_checkpointing = bool(self.training and self.gradient_checkpointing)

        # downsampling
        h = self.conv_in(x)
        for i_level in range(len(self.block_out_channels)):
            for i_block in range(self.num_res_blocks):
                h = forward_with_checkpointing(
                    self.down[i_level].block[i_block], h, use_checkpointing=use_checkpointing
                )
            if hasattr(self.down[i_level], "downsample"):
                h = forward_with_checkpointing(self.down[i_level].downsample, h, use_checkpointing=use_checkpointing)

        # middle
        h = forward_with_checkpointing(self.mid.block_1, h, use_checkpointing=use_checkpointing)
        h = forward_with_checkpointing(self.mid.attn_1, h, use_checkpointing=use_checkpointing)
        h = forward_with_checkpointing(self.mid.block_2, h, use_checkpointing=use_checkpointing)

        # end
        group_size = self.block_out_channels[-1] // (2 * self.z_channels)
        shortcut = rearrange(h, "b (c r) f h w -> b c r f h w", r=group_size).mean(dim=2)
        h = self.norm_out(h)
        h = swish(h)
        h = self.conv_out(h)
        h += shortcut
        return h


class Decoder(nn.Module):
    """
    The decoder network of AutoencoderKLConv3D.
    """

    def __init__(
        self,
        z_channels: int,
        out_channels: int,
        block_out_channels: tuple[int, ...],
        num_res_blocks: int,
        ffactor_spatial: int,
        ffactor_temporal: int,
        upsample_match_channel: bool = True,
    ):
        super().__init__()
        assert block_out_channels[0] % z_channels == 0

        self.z_channels = z_channels
        self.block_out_channels = block_out_channels
        self.num_res_blocks = num_res_blocks

        # z to block_in
        block_in = block_out_channels[0]
        self.conv_in = Conv3d(z_channels, block_in, kernel_size=3, stride=1, padding=1)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)

        # upsampling
        self.up = nn.ModuleList()
        for i_level, ch in enumerate(block_out_channels):
            block = nn.ModuleList()
            block_out = ch
            for _ in range(self.num_res_blocks + 1):
                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                block_in = block_out
            up = nn.Module()
            up.block = block

            add_spatial_upsample = bool(i_level < np.log2(ffactor_spatial))
            add_temporal_upsample = bool(i_level < np.log2(ffactor_temporal))
            if add_spatial_upsample or add_temporal_upsample:
                assert i_level < len(block_out_channels) - 1
                block_out = block_out_channels[i_level + 1] if upsample_match_channel else block_in
                up.upsample = UpsampleDCAE(block_in, block_out, add_temporal_upsample)
                block_in = block_out
            self.up.append(up)

        # end
        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
        self.conv_out = Conv3d(block_in, out_channels, kernel_size=3, stride=1, padding=1)

        self.gradient_checkpointing = False

    def forward(self, z: Tensor) -> Tensor:
        use_checkpointing = bool(self.training and self.gradient_checkpointing)

        # z to block_in
        repeats = self.block_out_channels[0] // (self.z_channels)
        h = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)

        # middle
        h = forward_with_checkpointing(self.mid.block_1, h, use_checkpointing=use_checkpointing)
        h = forward_with_checkpointing(self.mid.attn_1, h, use_checkpointing=use_checkpointing)
        h = forward_with_checkpointing(self.mid.block_2, h, use_checkpointing=use_checkpointing)

        # upsampling
        for i_level in range(len(self.block_out_channels)):
            for i_block in range(self.num_res_blocks + 1):
                h = forward_with_checkpointing(self.up[i_level].block[i_block], h, use_checkpointing=use_checkpointing)
            if hasattr(self.up[i_level], "upsample"):
                h = forward_with_checkpointing(self.up[i_level].upsample, h, use_checkpointing=use_checkpointing)

        # end
        h = self.norm_out(h)
        h = swish(h)
        h = self.conv_out(h)
        return h


class AutoencoderKLConv3D(ModelMixin, ConfigMixin):
    """
    Autoencoder model with KL-regularized latent space based on 3D convolutions.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        latent_channels: int,
        block_out_channels: tuple[int, ...],
        layers_per_block: int,
        ffactor_spatial: int,
        ffactor_temporal: int,
        sample_size: int,
        sample_tsize: int,
        scaling_factor: float | None = None,
        shift_factor: float | None = None,
        downsample_match_channel: bool = True,
        upsample_match_channel: bool = True,
        only_encoder: bool = False,  # only build encoder for saving memory
        only_decoder: bool = False,  # only build decoder for saving memory
    ):
        super().__init__()
        self.ffactor_spatial = ffactor_spatial
        self.ffactor_temporal = ffactor_temporal
        self.scaling_factor = scaling_factor
        self.shift_factor = shift_factor

        # build model
        if not only_decoder:
            self.encoder = Encoder(
                in_channels=in_channels,
                z_channels=latent_channels,
                block_out_channels=block_out_channels,
                num_res_blocks=layers_per_block,
                ffactor_spatial=ffactor_spatial,
                ffactor_temporal=ffactor_temporal,
                downsample_match_channel=downsample_match_channel,
            )
        if not only_encoder:
            self.decoder = Decoder(
                z_channels=latent_channels,
                out_channels=out_channels,
                block_out_channels=list(reversed(block_out_channels)),
                num_res_blocks=layers_per_block,
                ffactor_spatial=ffactor_spatial,
                ffactor_temporal=ffactor_temporal,
                upsample_match_channel=upsample_match_channel,
            )

        # slicing and tiling related
        self.use_slicing = False
        self.slicing_bsz = 1
        self.use_spatial_tiling = False
        self.use_temporal_tiling = False
        self.use_tiling_during_training = False

        # only relevant if vae tiling is enabled
        self.tile_sample_min_size = sample_size
        self.tile_latent_min_size = sample_size // ffactor_spatial
        self.tile_sample_min_tsize = sample_tsize
        self.tile_latent_min_tsize = sample_tsize // ffactor_temporal
        self.tile_overlap_factor = 0.25

        # use torch.compile for faster encode speed
        self.use_compile = False

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (Encoder, Decoder)):
            module.gradient_checkpointing = value

    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int):
        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
        for x in range(blend_extent):
            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
                x / blend_extent
            )
        return b

    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int):
        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
        for y in range(blend_extent):
            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
                y / blend_extent
            )
        return b

    def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int):
        blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
        for x in range(blend_extent):
            b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, x, :, :] * (
                x / blend_extent
            )
        return b

    def spatial_tiled_encode(self, x: torch.Tensor):
        """spatial tailing for frames"""
        B, C, T, H, W = x.shape
        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))  # 256 * (1 - 0.25) = 192
        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)  # 8 * 0.25 = 2
        row_limit = self.tile_latent_min_size - blend_extent  # 8 - 2 = 6

        rows = []
        for i in range(0, H, overlap_size):
            row = []
            for j in range(0, W, overlap_size):
                tile = x[:, :, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                tile = self.encoder(tile)
                row.append(tile)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=-1))
        moments = torch.cat(result_rows, dim=-2)
        return moments

    def temporal_tiled_encode(self, x: torch.Tensor):
        """temporal tailing for frames"""
        B, C, T, H, W = x.shape
        overlap_size = int(self.tile_sample_min_tsize * (1 - self.tile_overlap_factor))  # 64 * (1 - 0.25) = 48
        blend_extent = int(self.tile_latent_min_tsize * self.tile_overlap_factor)  # 8 * 0.25 = 2
        t_limit = self.tile_latent_min_tsize - blend_extent  # 8 - 2 = 6

        row = []
        for i in range(0, T, overlap_size):
            tile = x[:, :, i : i + self.tile_sample_min_tsize, :, :]
            if self.use_spatial_tiling and (
                tile.shape[-1] > self.tile_sample_min_size or tile.shape[-2] > self.tile_sample_min_size
            ):
                tile = self.spatial_tiled_encode(tile)
            else:
                tile = self.encoder(tile)
            row.append(tile)
        result_row = []
        for i, tile in enumerate(row):
            if i > 0:
                tile = self.blend_t(row[i - 1], tile, blend_extent)
            result_row.append(tile[:, :, :t_limit, :, :])
        moments = torch.cat(result_row, dim=-3)
        return moments

    def spatial_tiled_decode(self, z: torch.Tensor):
        """spatial tailing for frames"""
        B, C, T, H, W = z.shape
        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))  # 8 * (1 - 0.25) = 6
        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)  # 256 * 0.25 = 64
        row_limit = self.tile_sample_min_size - blend_extent  # 256 - 64 = 192

        rows = []
        for i in range(0, H, overlap_size):
            row = []
            for j in range(0, W, overlap_size):
                tile = z[:, :, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                decoded = self.decoder(tile)
                row.append(decoded)
            rows.append(row)

        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=-1))
        dec = torch.cat(result_rows, dim=-2)
        return dec

    def temporal_tiled_decode(self, z: torch.Tensor):
        """temporal tailing for frames"""
        B, C, T, H, W = z.shape
        overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor))  # 8 * (1 - 0.25) = 6
        blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor)  # 64 * 0.25 = 16
        t_limit = self.tile_sample_min_tsize - blend_extent  # 64 - 16 = 48
        assert 0 < overlap_size < self.tile_latent_min_tsize

        row = []
        for i in range(0, T, overlap_size):
            tile = z[:, :, i : i + self.tile_latent_min_tsize, :, :]
            if self.use_spatial_tiling and (
                tile.shape[-1] > self.tile_latent_min_size or tile.shape[-2] > self.tile_latent_min_size
            ):
                decoded = self.spatial_tiled_decode(tile)
            else:
                decoded = self.decoder(tile)
            row.append(decoded)

        result_row = []
        for i, tile in enumerate(row):
            if i > 0:
                tile = self.blend_t(row[i - 1], tile, blend_extent)
            result_row.append(tile[:, :, :t_limit, :, :])
        dec = torch.cat(result_row, dim=-3)
        return dec

    def encode(self, x: Tensor, return_dict: bool = True):
        """
        Encodes the input by passing through the encoder network.
        Support slicing and tiling for memory efficiency.
        """

        def _encode(x):
            if self.use_temporal_tiling and x.shape[-3] > self.tile_sample_min_tsize:
                return self.temporal_tiled_encode(x)
            if self.use_spatial_tiling and (
                x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size
            ):
                return self.spatial_tiled_encode(x)

            if self.use_compile:

                @torch.compile
                def encoder(x):
                    return self.encoder(x)

                return encoder(x)
            return self.encoder(x)

        if len(x.shape) != 5:  # (B, C, T, H, W)
            x = x[:, :, None]
        assert len(x.shape) == 5  # (B, C, T, H, W)
        if x.shape[2] == 1:
            x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
        else:
            assert x.shape[2] != self.ffactor_temporal and x.shape[2] % self.ffactor_temporal == 0

        if self.use_slicing and x.shape[0] > 1:
            if self.slicing_bsz == 1:
                encoded_slices = [_encode(x_slice) for x_slice in x.split(1)]
            else:
                sections = [self.slicing_bsz] * (x.shape[0] // self.slicing_bsz)
                if x.shape[0] % self.slicing_bsz != 0:
                    sections.append(x.shape[0] % self.slicing_bsz)
                encoded_slices = [_encode(x_slice) for x_slice in x.split(sections)]
            h = torch.cat(encoded_slices)
        else:
            h = _encode(x)
        posterior = DiagonalGaussianDistribution(h)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=posterior)

    def decode(self, z: Tensor, return_dict: bool = True, generator=None):
        """
        Decodes the input by passing through the decoder network.
        Support slicing and tiling for memory efficiency.
        """

        def _decode(z):
            if self.use_temporal_tiling and z.shape[-3] > self.tile_latent_min_tsize:
                return self.temporal_tiled_decode(z)
            if self.use_spatial_tiling and (
                z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size
            ):
                return self.spatial_tiled_decode(z)
            return self.decoder(z)

        if self.use_slicing and z.shape[0] > 1:
            decoded_slices = [_decode(z_slice) for z_slice in z.split(1)]
            decoded = torch.cat(decoded_slices)
        else:
            decoded = _decode(z)

        if z.shape[-3] == 1:
            decoded = decoded[:, :, -1:]

        if not return_dict:
            return (decoded,)

        return DecoderOutput(sample=decoded)