
from dataclasses import dataclass
from math import prod
from typing import Dict, Optional, Tuple, Union

import torch
import torch.distributed as dist
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config

from fastvideo.utils.parallel_states import nccl_info

try:
    # This diffusers is modified and packed in the mirror.
    from diffusers.loaders import FromOriginalVAEMixin
except ImportError:
    # Use this to be compatible with the original diffusers.
    from diffusers.loaders.single_file_model import (
        FromOriginalModelMixin as FromOriginalVAEMixin, )

from diffusers.models.attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, Attention,
    AttentionProcessor, AttnAddedKVProcessor, AttnProcessor)
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils.accelerate_utils import apply_forward_hook

from .vae import (BaseOutput, DecoderCausal3D, DecoderOutput,
                  DiagonalGaussianDistribution, EncoderCausal3D)


@dataclass
class DecoderOutput2(BaseOutput):
    sample: torch.FloatTensor
    posterior: Optional[DiagonalGaussianDistribution] = None


class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
    r"""
    A VAE model with KL loss for encoding images/videos into latents and decoding latent representations into images/videos.

    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 = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str] = ("DownEncoderBlockCausal3D", ),
        up_block_types: Tuple[str] = ("UpDecoderBlockCausal3D", ),
        block_out_channels: Tuple[int] = (64, ),
        layers_per_block: int = 1,
        act_fn: str = "silu",
        latent_channels: int = 4,
        norm_num_groups: int = 32,
        sample_size: int = 32,
        sample_tsize: int = 64,
        scaling_factor: float = 0.18215,
        force_upcast: float = True,
        spatial_compression_ratio: int = 8,
        time_compression_ratio: int = 4,
        mid_block_add_attention: bool = True,
    ):
        super().__init__()

        self.time_compression_ratio = time_compression_ratio

        self.encoder = EncoderCausal3D(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            double_z=True,
            time_compression_ratio=time_compression_ratio,
            spatial_compression_ratio=spatial_compression_ratio,
            mid_block_add_attention=mid_block_add_attention,
        )

        self.decoder = DecoderCausal3D(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            time_compression_ratio=time_compression_ratio,
            spatial_compression_ratio=spatial_compression_ratio,
            mid_block_add_attention=mid_block_add_attention,
        )

        self.quant_conv = nn.Conv3d(2 * latent_channels,
                                    2 * latent_channels,
                                    kernel_size=1)
        self.post_quant_conv = nn.Conv3d(latent_channels,
                                         latent_channels,
                                         kernel_size=1)

        self.use_slicing = False
        self.use_spatial_tiling = False
        self.use_temporal_tiling = False
        self.use_parallel = False

        # only relevant if vae tiling is enabled
        self.tile_sample_min_tsize = sample_tsize
        self.tile_latent_min_tsize = sample_tsize // time_compression_ratio

        self.tile_sample_min_size = self.config.sample_size
        sample_size = (self.config.sample_size[0] if isinstance(
            self.config.sample_size,
            (list, tuple)) else self.config.sample_size)
        self.tile_latent_min_size = int(
            sample_size / (2**(len(self.config.block_out_channels) - 1)))
        self.tile_overlap_factor = 0.25

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (EncoderCausal3D, DecoderCausal3D)):
            module.gradient_checkpointing = value

    def enable_temporal_tiling(self, use_tiling: bool = True):
        self.use_temporal_tiling = use_tiling

    def disable_temporal_tiling(self):
        self.enable_temporal_tiling(False)

    def enable_spatial_tiling(self, use_tiling: bool = True):
        self.use_spatial_tiling = use_tiling

    def disable_spatial_tiling(self):
        self.enable_spatial_tiling(False)

    def enable_tiling(self, use_tiling: bool = True):
        r"""
        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
        processing larger videos.
        """
        self.enable_spatial_tiling(use_tiling)
        self.enable_temporal_tiling(use_tiling)

    def disable_tiling(self):
        r"""
        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
        decoding in one step.
        """
        self.disable_spatial_tiling()
        self.disable_temporal_tiling()

    def enable_parallel(self):
        r"""
        Enable sequence parallelism for the model. This will allow the vae to decode (with tiling) in parallel.
        """
        self.use_parallel = True

    def enable_slicing(self):
        r"""
        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.use_slicing = True

    def disable_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
        decoding in one step.
        """
        self.use_slicing = False

    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(
            name: str,
            module: torch.nn.Module,
            processors: Dict[str, AttentionProcessor],
        ):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(
                    return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child,
                                            processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(
        self,
        processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]],
        _remove_lora=False,
    ):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module,
                                        processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor, _remove_lora=_remove_lora)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"),
                                         _remove_lora=_remove_lora)

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child,
                                            processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS
               for proc in self.attn_processors.values()):
            processor = AttnAddedKVProcessor()
        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS
                 for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor, _remove_lora=True)

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

        Args:
            x (`torch.FloatTensor`): Input batch of images/videos.
            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 images/videos. If `return_dict` is True, a
                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
        """
        assert len(x.shape) == 5, "The input tensor should have 5 dimensions."

        if self.use_temporal_tiling and x.shape[2] > self.tile_sample_min_tsize:
            return self.temporal_tiled_encode(x, return_dict=return_dict)

        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, return_dict=return_dict)

        if self.use_slicing and x.shape[0] > 1:
            encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]
            h = torch.cat(encoded_slices)
        else:
            h = self.encoder(x)

        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)

        if not return_dict:
            return (posterior, )

        return AutoencoderKLOutput(latent_dist=posterior)

    def _decode(
            self,
            z: torch.FloatTensor,
            return_dict: bool = True
    ) -> Union[DecoderOutput, torch.FloatTensor]:
        assert len(z.shape) == 5, "The input tensor should have 5 dimensions."

        if self.use_parallel:
            return self.parallel_tiled_decode(z, return_dict=return_dict)

        if self.use_temporal_tiling and z.shape[2] > self.tile_latent_min_tsize:
            return self.temporal_tiled_decode(z, return_dict=return_dict)

        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_dict=return_dict)

        z = self.post_quant_conv(z)
        dec = self.decoder(z)

        if not return_dict:
            return (dec, )

        return DecoderOutput(sample=dec)

    @apply_forward_hook
    def decode(self,
               z: torch.FloatTensor,
               return_dict: bool = True,
               generator=None) -> Union[DecoderOutput, torch.FloatTensor]:
        """
        Decode a batch of images/videos.

        Args:
            z (`torch.FloatTensor`): 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.

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

        if not return_dict:
            return (decoded, )

        return DecoderOutput(sample=decoded)

    def blend_v(self, a: torch.Tensor, b: torch.Tensor,
                blend_extent: int) -> torch.Tensor:
        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_h(self, a: torch.Tensor, b: torch.Tensor,
                blend_extent: int) -> torch.Tensor:
        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_t(self, a: torch.Tensor, b: torch.Tensor,
                blend_extent: int) -> torch.Tensor:
        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.FloatTensor,
        return_dict: bool = True,
        return_moments: bool = False,
    ) -> AutoencoderKLOutput:
        r"""Encode a batch of images/videos using a tiled encoder.

        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
        steps. This is useful to keep memory use constant regardless of image/videos size. The end result of tiled encoding is
        different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
        tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
        output, but they should be much less noticeable.

        Args:
            x (`torch.FloatTensor`): Input batch of images/videos.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

        Returns:
            [`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
                If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
                `tuple` is returned.
        """
        overlap_size = int(self.tile_sample_min_size *
                           (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_latent_min_size *
                           self.tile_overlap_factor)
        row_limit = self.tile_latent_min_size - blend_extent

        # Split video into tiles and encode them separately.
        rows = []
        for i in range(0, x.shape[-2], overlap_size):
            row = []
            for j in range(0, x.shape[-1], overlap_size):
                tile = x[:, :, :, i:i + self.tile_sample_min_size,
                         j:j + self.tile_sample_min_size, ]
                tile = self.encoder(tile)
                tile = self.quant_conv(tile)
                row.append(tile)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result 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)
        if return_moments:
            return moments

        posterior = DiagonalGaussianDistribution(moments)
        if not return_dict:
            return (posterior, )

        return AutoencoderKLOutput(latent_dist=posterior)

    def spatial_tiled_decode(self,
                             z: torch.FloatTensor,
                             return_dict: bool = True
                             ) -> Union[DecoderOutput, torch.FloatTensor]:
        r"""
        Decode a batch of images/videos using a tiled decoder.

        Args:
            z (`torch.FloatTensor`): Input batch of latent vectors.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not 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.
        """
        overlap_size = int(self.tile_latent_min_size *
                           (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_sample_min_size *
                           self.tile_overlap_factor)
        row_limit = self.tile_sample_min_size - blend_extent

        # Split z into overlapping tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        rows = []
        for i in range(0, z.shape[-2], overlap_size):
            row = []
            for j in range(0, z.shape[-1], overlap_size):
                tile = z[:, :, :, i:i + self.tile_latent_min_size,
                         j:j + self.tile_latent_min_size, ]
                tile = self.post_quant_conv(tile)
                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):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result 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)
        if not return_dict:
            return (dec, )

        return DecoderOutput(sample=dec)

    def temporal_tiled_encode(self,
                              x: torch.FloatTensor,
                              return_dict: bool = True) -> AutoencoderKLOutput:

        B, C, T, H, W = x.shape
        overlap_size = int(self.tile_sample_min_tsize *
                           (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_latent_min_tsize *
                           self.tile_overlap_factor)
        t_limit = self.tile_latent_min_tsize - blend_extent

        # Split the video into tiles and encode them separately.
        row = []
        for i in range(0, T, overlap_size):
            tile = x[:, :, i:i + self.tile_sample_min_tsize + 1, :, :]
            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, return_moments=True)
            else:
                tile = self.encoder(tile)
                tile = self.quant_conv(tile)
            if i > 0:
                tile = tile[:, :, 1:, :, :]
            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, :, :])
            else:
                result_row.append(tile[:, :, :t_limit + 1, :, :])

        moments = torch.cat(result_row, dim=2)
        posterior = DiagonalGaussianDistribution(moments)

        if not return_dict:
            return (posterior, )

        return AutoencoderKLOutput(latent_dist=posterior)

    def temporal_tiled_decode(self,
                              z: torch.FloatTensor,
                              return_dict: bool = True
                              ) -> Union[DecoderOutput, torch.FloatTensor]:
        # Split z into overlapping tiles and decode them separately.

        B, C, T, H, W = z.shape
        overlap_size = int(self.tile_latent_min_tsize *
                           (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_sample_min_tsize *
                           self.tile_overlap_factor)
        t_limit = self.tile_sample_min_tsize - blend_extent

        row = []
        for i in range(0, T, overlap_size):
            tile = z[:, :, i:i + self.tile_latent_min_tsize + 1, :, :]
            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,
                                                    return_dict=True).sample
            else:
                tile = self.post_quant_conv(tile)
                decoded = self.decoder(tile)
            if i > 0:
                decoded = decoded[:, :, 1:, :, :]
            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, :, :])
            else:
                result_row.append(tile[:, :, :t_limit + 1, :, :])

        dec = torch.cat(result_row, dim=2)
        if not return_dict:
            return (dec, )

        return DecoderOutput(sample=dec)

    def _parallel_data_generator(self, gathered_results,
                                 gathered_dim_metadata):
        global_idx = 0
        for i, per_rank_metadata in enumerate(gathered_dim_metadata):
            _start_shape = 0
            for shape in per_rank_metadata:
                mul_shape = prod(shape)
                yield (gathered_results[i, _start_shape:_start_shape +
                                        mul_shape].reshape(shape), global_idx)
                _start_shape += mul_shape
                global_idx += 1

    def parallel_tiled_decode(self,
                              z: torch.FloatTensor,
                              return_dict: bool = True
                              ) -> Union[DecoderOutput, torch.FloatTensor]:
        """
        Parallel version of tiled_decode that distributes both temporal and spatial computation across GPUs
        """
        world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group
        B, C, T, H, W = z.shape

        # Calculate parameters
        t_overlap_size = int(self.tile_latent_min_tsize *
                             (1 - self.tile_overlap_factor))
        t_blend_extent = int(self.tile_sample_min_tsize *
                             self.tile_overlap_factor)
        t_limit = self.tile_sample_min_tsize - t_blend_extent

        s_overlap_size = int(self.tile_latent_min_size *
                             (1 - self.tile_overlap_factor))
        s_blend_extent = int(self.tile_sample_min_size *
                             self.tile_overlap_factor)
        s_row_limit = self.tile_sample_min_size - s_blend_extent

        # Calculate tile dimensions
        num_t_tiles = (T + t_overlap_size - 1) // t_overlap_size
        num_h_tiles = (H + s_overlap_size - 1) // s_overlap_size
        num_w_tiles = (W + s_overlap_size - 1) // s_overlap_size
        total_spatial_tiles = num_h_tiles * num_w_tiles
        total_tiles = num_t_tiles * total_spatial_tiles

        # Calculate tiles per rank and padding
        tiles_per_rank = (total_tiles + world_size - 1) // world_size
        start_tile_idx = rank * tiles_per_rank
        end_tile_idx = min((rank + 1) * tiles_per_rank, total_tiles)

        local_results = []
        local_dim_metadata = []
        # Process assigned tiles
        for local_idx, global_idx in enumerate(
                range(start_tile_idx, end_tile_idx)):
            # Convert flat index to 3D indices
            t_idx = global_idx // total_spatial_tiles
            spatial_idx = global_idx % total_spatial_tiles
            h_idx = spatial_idx // num_w_tiles
            w_idx = spatial_idx % num_w_tiles

            # Calculate positions
            t_start = t_idx * t_overlap_size
            h_start = h_idx * s_overlap_size
            w_start = w_idx * s_overlap_size

            # Extract and process tile
            tile = z[:, :, t_start:t_start + self.tile_latent_min_tsize + 1,
                     h_start:h_start + self.tile_latent_min_size,
                     w_start:w_start + self.tile_latent_min_size]

            # Process tile
            tile = self.post_quant_conv(tile)
            decoded = self.decoder(tile)

            if t_start > 0:
                decoded = decoded[:, :, 1:, :, :]

            # Store metadata
            shape = decoded.shape
            # Store decoded data (flattened)
            decoded_flat = decoded.reshape(-1)
            local_results.append(decoded_flat)
            local_dim_metadata.append(shape)

        results = torch.cat(local_results, dim=0).contiguous()
        del local_results
        torch.cuda.empty_cache()
        # first gather size to pad the results
        local_size = torch.tensor([results.size(0)],
                                  device=results.device,
                                  dtype=torch.int64)
        all_sizes = [
            torch.zeros(1, device=results.device, dtype=torch.int64)
            for _ in range(world_size)
        ]
        dist.all_gather(all_sizes, local_size)
        max_size = max(size.item() for size in all_sizes)
        padded_results = torch.zeros(max_size, device=results.device)
        padded_results[:results.size(0)] = results
        del results
        torch.cuda.empty_cache()
        # Gather all results
        gathered_dim_metadata = [None] * world_size
        gathered_results = torch.zeros_like(padded_results).repeat(
            world_size, *[1] * len(padded_results.shape)
        ).contiguous(
        )  # use contiguous to make sure it won't copy data in the following operations
        dist.all_gather_into_tensor(gathered_results, padded_results)
        dist.all_gather_object(gathered_dim_metadata, local_dim_metadata)
        # Process gathered results
        data = [[[[] for _ in range(num_w_tiles)] for _ in range(num_h_tiles)]
                for _ in range(num_t_tiles)]
        for current_data, global_idx in self._parallel_data_generator(
                gathered_results, gathered_dim_metadata):
            t_idx = global_idx // total_spatial_tiles
            spatial_idx = global_idx % total_spatial_tiles
            h_idx = spatial_idx // num_w_tiles
            w_idx = spatial_idx % num_w_tiles
            data[t_idx][h_idx][w_idx] = current_data
        # Merge results
        result_slices = []
        last_slice_data = None
        for i, tem_data in enumerate(data):
            slice_data = self._merge_spatial_tiles(tem_data, s_blend_extent,
                                                   s_row_limit)
            if i > 0:
                slice_data = self.blend_t(last_slice_data, slice_data,
                                          t_blend_extent)
                result_slices.append(slice_data[:, :, :t_limit, :, :])
            else:
                result_slices.append(slice_data[:, :, :t_limit + 1, :, :])
            last_slice_data = slice_data
        dec = torch.cat(result_slices, dim=2)

        if not return_dict:
            return (dec, )
        return DecoderOutput(sample=dec)

    def _merge_spatial_tiles(self, spatial_rows, blend_extent, row_limit):
        """Helper function to merge spatial tiles with blending"""
        result_rows = []
        for i, row in enumerate(spatial_rows):
            result_row = []
            for j, tile in enumerate(row):
                if i > 0:
                    tile = self.blend_v(spatial_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))
        return torch.cat(result_rows, dim=-2)

    def forward(
        self,
        sample: torch.FloatTensor,
        sample_posterior: bool = False,
        return_dict: bool = True,
        return_posterior: bool = False,
        generator: Optional[torch.Generator] = None,
    ) -> Union[DecoderOutput2, torch.FloatTensor]:
        r"""
        Args:
            sample (`torch.FloatTensor`): Input sample.
            sample_posterior (`bool`, *optional*, defaults to `False`):
                Whether to sample from the posterior.
            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).sample

        if not return_dict:
            if return_posterior:
                return (dec, posterior)
            else:
                return (dec, )
        if return_posterior:
            return DecoderOutput2(sample=dec, posterior=posterior)
        else:
            return DecoderOutput2(sample=dec)

    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
        key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError(
                    "`fuse_qkv_projections()` is not supported for models having added KV projections."
                )

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)
