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

from contextlib import nullcontext
from typing import Any

import torch
import torch.distributed as dist
from diffusers.models.autoencoders import AutoencoderKLWan
from diffusers.models.autoencoders.autoencoder_kl_wan import unpatchify
from diffusers.models.autoencoders.vae import DecoderOutput
from vllm.logger import init_logger

from vllm_omni.diffusion.distributed.autoencoders import wan_spatial_shard
from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor import (
    DistributedOperator,
    DistributedVaeMixin,
    GridSpec,
    TileTask,
)
from vllm_omni.platforms import current_omni_platform

logger = init_logger(__name__)


def select_auto_spatial_shard_split_dim(
    z_shape: tuple[int, ...],
    world_size: int,
) -> str | None:
    """Select a profitable Wan spatial-shard axis for one latent shape.

    This selector runs only after Diffusers has already chosen ``tiled_decode``
    for a spatially large request. Splitting the longer eligible axis reduces
    halo traffic for rectangular requests and improves per-rank balance.
    """
    if len(z_shape) != 5 or world_size <= 1:
        return None

    _, _, _, height, width = z_shape
    candidates = [
        (extent, split_dim) for extent, split_dim in ((height, "height"), (width, "width")) if extent >= world_size
    ]
    if not candidates:
        return None
    # Prefer width for square latents: it was modestly faster in the existing
    # four-GPU Wan benchmark and keeps the choice deterministic.
    return max(candidates, key=lambda candidate: (candidate[0], candidate[1] == "width"))[1]


class OmniAutoencoderKLWan(AutoencoderKLWan):
    def _execution_context(self):
        try:
            first_param = next(self.parameters())
        except StopIteration:
            return nullcontext()

        dtype = first_param.dtype
        if dtype not in (torch.float16, torch.bfloat16):
            return nullcontext()

        return current_omni_platform.create_autocast_context(
            device_type=first_param.device.type,
            dtype=dtype,
            enabled=True,
        )

    def encode(self, x: torch.Tensor, return_dict: bool = True):
        with self._execution_context():
            return super().encode(x, return_dict=return_dict)

    def decode(self, z: torch.Tensor, return_dict: bool = True):
        with self._execution_context():
            return super().decode(z, return_dict=return_dict)


class DistributedAutoencoderKLWan(OmniAutoencoderKLWan, DistributedVaeMixin):
    @classmethod
    def from_pretrained(cls, *args: Any, **kwargs: Any):
        model = super().from_pretrained(*args, **kwargs)
        model.init_distributed()
        return model

    def set_parallel_size(self, parallel_size: int, mode: str = "tile") -> None:
        super().set_parallel_size(parallel_size, mode=mode)
        if mode not in {"auto", "spatial_shard_height", "spatial_shard_width"}:
            return

        executor = self.distributed_executor
        world_size = dist.get_world_size(group=executor.group)
        if parallel_size <= 1 or parallel_size != world_size:
            return

        split_dim = "width" if mode == "spatial_shard_width" else "height"
        wan_spatial_shard.install_wan_spatial_shard_decode(self, executor.group, split_dim=split_dim)

    def tile_split(self, z: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
        _, _, num_frames, height, width = z.shape
        sample_height = height * self.spatial_compression_ratio
        sample_width = width * self.spatial_compression_ratio

        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
        tile_sample_stride_height = self.tile_sample_stride_height
        tile_sample_stride_width = self.tile_sample_stride_width
        if self.config.patch_size is not None:
            sample_height = sample_height // self.config.patch_size
            sample_width = sample_width // self.config.patch_size
            tile_sample_stride_height = tile_sample_stride_height // self.config.patch_size
            tile_sample_stride_width = tile_sample_stride_width // self.config.patch_size
            blend_height = self.tile_sample_min_height // self.config.patch_size - tile_sample_stride_height
            blend_width = self.tile_sample_min_width // self.config.patch_size - tile_sample_stride_width
        else:
            blend_height = self.tile_sample_min_height - tile_sample_stride_height
            blend_width = self.tile_sample_min_width - tile_sample_stride_width

        tiletask_list = []
        for i in range(0, height, tile_latent_stride_height):
            for j in range(0, width, tile_latent_stride_width):
                time_list = []
                for k in range(num_frames):
                    self._conv_idx = [0]
                    tile = z[:, :, k : k + 1, i : i + tile_latent_min_height, j : j + tile_latent_min_width]
                    time_list.append(tile)
                tiletask_list.append(
                    TileTask(
                        len(tiletask_list),
                        (i // tile_latent_stride_height, j // tile_latent_stride_width),
                        time_list,
                        workload=time_list[0].shape[3] * time_list[0].shape[4],
                    )
                )
        tile_spec = {
            "sample_height": sample_height,
            "sample_width": sample_width,
            "blend_height": blend_height,
            "blend_width": blend_width,
            "tile_sample_stride_height": tile_sample_stride_height,
            "tile_sample_stride_width": tile_sample_stride_width,
        }
        grid_spec = GridSpec(
            split_dims=(3, 4),
            grid_shape=(tiletask_list[-1].grid_coord[0] + 1, tiletask_list[-1].grid_coord[1] + 1),
            tile_spec=tile_spec,
            output_dtype=self.dtype,
        )
        return tiletask_list, grid_spec

    def tile_exec(self, task: TileTask) -> torch.Tensor:
        """Decode a single latent tile into RGB space."""
        self.clear_cache()
        time = []
        with self._execution_context():
            for k in range(len(task.tensor)):
                self._conv_idx = [0]
                tile = self.post_quant_conv(task.tensor[k])
                decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=(k == 0))
                time.append(decoded)
        result = torch.cat(time, dim=2)
        return result

    def encode_tile_split(self, x: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
        _, _, num_frames, height, width = x.shape
        encode_spatial_compression_ratio = self.spatial_compression_ratio
        # Scale tile parameters for patchified coordinate system
        tile_sample_min_height = self.tile_sample_min_height
        tile_sample_min_width = self.tile_sample_min_width
        tile_sample_stride_height = self.tile_sample_stride_height
        tile_sample_stride_width = self.tile_sample_stride_width
        if self.config.patch_size is not None:
            assert encode_spatial_compression_ratio % self.config.patch_size == 0
            encode_spatial_compression_ratio = self.spatial_compression_ratio // self.config.patch_size
            # When input is patchified, scale tile parameters accordingly
            tile_sample_min_height = tile_sample_min_height // self.config.patch_size
            tile_sample_min_width = tile_sample_min_width // self.config.patch_size
            tile_sample_stride_height = tile_sample_stride_height // self.config.patch_size
            tile_sample_stride_width = tile_sample_stride_width // self.config.patch_size

        latent_height = height // encode_spatial_compression_ratio
        latent_width = width // encode_spatial_compression_ratio

        tile_latent_min_height = tile_sample_min_height // encode_spatial_compression_ratio
        tile_latent_min_width = tile_sample_min_width // encode_spatial_compression_ratio
        tile_latent_stride_height = tile_sample_stride_height // encode_spatial_compression_ratio
        tile_latent_stride_width = tile_sample_stride_width // encode_spatial_compression_ratio

        blend_height = tile_latent_min_height - tile_latent_stride_height
        blend_width = tile_latent_min_width - tile_latent_stride_width

        tiletask_list = []
        temporal_compression = self.config.scale_factor_temporal
        for i in range(0, height, tile_sample_stride_height):
            for j in range(0, width, tile_sample_stride_width):
                time_list = []
                frame_range = 1 + (num_frames - 1) // temporal_compression
                for k in range(frame_range):
                    if k == 0:
                        tile = x[:, :, :1, i : i + tile_sample_min_height, j : j + tile_sample_min_width]
                    else:
                        tile = x[
                            :,
                            :,
                            1 + temporal_compression * (k - 1) : 1 + temporal_compression * k,
                            i : i + tile_sample_min_height,
                            j : j + tile_sample_min_width,
                        ]
                    time_list.append(tile)
                tiletask_list.append(
                    TileTask(
                        len(tiletask_list),
                        (i // tile_sample_stride_height, j // tile_sample_stride_width),
                        time_list,
                        workload=time_list[0].shape[3] * time_list[0].shape[4],
                    )
                )

        grid_spec = GridSpec(
            split_dims=(3, 4),
            grid_shape=(tiletask_list[-1].grid_coord[0] + 1, tiletask_list[-1].grid_coord[1] + 1),
            tile_spec={
                "latent_height": latent_height,
                "latent_width": latent_width,
                "blend_height": blend_height,
                "blend_width": blend_width,
                "tile_latent_stride_height": tile_latent_stride_height,
                "tile_latent_stride_width": tile_latent_stride_width,
            },
            output_dtype=self.dtype,
        )
        return tiletask_list, grid_spec

    def encode_tile_exec(self, task: TileTask) -> torch.Tensor:
        """Encode a single sample tile into latent space."""
        self.clear_cache()
        time = []
        for k, tile in enumerate(task.tensor):
            self._enc_conv_idx = [0]
            encoded = self.encoder(tile, feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
            encoded = self.quant_conv(encoded)
            time.append(encoded)
        result = torch.cat(time, dim=2)
        self.clear_cache()
        return result

    def encode_tile_merge(
        self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid_spec: GridSpec
    ) -> torch.Tensor:
        """Merge encoded tiles into a full latent tensor."""
        grid_h, grid_w = grid_spec.grid_shape
        result_rows = []
        for i in range(grid_h):
            result_row = []
            for j in range(grid_w):
                tile = coord_tensor_map[(i, j)]
                if i > 0:
                    tile = self.blend_v(coord_tensor_map[(i - 1, j)], tile, grid_spec.tile_spec["blend_height"])
                if j > 0:
                    tile = self.blend_h(coord_tensor_map[(i, j - 1)], tile, grid_spec.tile_spec["blend_width"])
                result_row.append(
                    tile[
                        :,
                        :,
                        :,
                        : grid_spec.tile_spec["tile_latent_stride_height"],
                        : grid_spec.tile_spec["tile_latent_stride_width"],
                    ]
                )
            result_rows.append(torch.cat(result_row, dim=-1))

        enc = torch.cat(result_rows, dim=3)[
            :, :, :, : grid_spec.tile_spec["latent_height"], : grid_spec.tile_spec["latent_width"]
        ]
        return enc

    def tile_merge(self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid_spec: GridSpec) -> torch.Tensor:
        """Merge decoded tiles into a full image."""
        grid_h, grid_w = grid_spec.grid_shape
        result_rows = []
        self.clear_cache()
        for i in range(grid_h):
            result_row = []
            for j in range(grid_w):
                tile = coord_tensor_map[(i, j)]
                if i > 0:
                    tile = self.blend_v(coord_tensor_map[(i - 1, j)], tile, grid_spec.tile_spec["blend_height"])
                if j > 0:
                    tile = self.blend_h(coord_tensor_map[(i, j - 1)], tile, grid_spec.tile_spec["blend_width"])
                result_row.append(
                    tile[
                        :,
                        :,
                        :,
                        : grid_spec.tile_spec["tile_sample_stride_height"],
                        : grid_spec.tile_spec["tile_sample_stride_width"],
                    ]
                )
            result_rows.append(torch.cat(result_row, dim=-1))

        dec = torch.cat(result_rows, dim=3)[
            :, :, :, : grid_spec.tile_spec["sample_height"], : grid_spec.tile_spec["sample_width"]
        ]

        if self.config.patch_size is not None:
            dec = unpatchify(dec, patch_size=self.config.patch_size)

        dec = torch.clamp(dec, min=-1.0, max=1.0)
        return dec

    def _spatial_shard_decode_split_dim(
        self,
        z: torch.Tensor | None = None,
        world_size: int | None = None,
    ) -> str | None:
        mode = self.distributed_executor.parallel_mode
        if mode == "spatial_shard_width":
            return "width"
        if mode == "spatial_shard_height":
            return "height"
        if mode == "auto" and z is not None and world_size is not None:
            return select_auto_spatial_shard_split_dim(tuple(z.shape), world_size)
        return None

    def _spatial_shard_decode_enabled(self, z: torch.Tensor) -> bool:
        if self.distributed_executor.parallel_mode == "tile":
            return False
        if z.ndim != 5:
            if self.distributed_executor.parallel_mode != "tile":
                logger.warning("Wan VAE spatial sharded decode expects 5D latent input; falling back to tiled decode.")
            return False
        if not self.is_distributed_enabled():
            return False

        group = self.distributed_executor.group
        world_size = dist.get_world_size(group=group)
        requested_size = int(self.distributed_executor.parallel_size)
        if requested_size != world_size:
            if self.distributed_executor.parallel_mode == "auto":
                logger.debug(
                    "Wan VAE auto decode selected tile mode for a partial DiT group (requested=%s dit_group=%s)",
                    requested_size,
                    world_size,
                )
            elif self._spatial_shard_decode_split_dim() is not None:
                logger.warning(
                    "Wan VAE spatial sharded decode currently requires vae_patch_parallel_size "
                    "to match the DIT group size; falling back to tiled decode. "
                    "requested=%s dit_group=%s split_dim=%s",
                    requested_size,
                    world_size,
                    self._spatial_shard_decode_split_dim(),
                )
            return False
        return self._spatial_shard_decode_split_dim(z, world_size) is not None

    def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
        if not self.is_distributed_enabled():
            return super().tiled_decode(z, return_dict=return_dict)

        if self._spatial_shard_decode_enabled(z):
            world_size = dist.get_world_size(group=self.distributed_executor.group)
            split_dim = self._spatial_shard_decode_split_dim(z, world_size)
            assert split_dim is not None
            logger.debug("Decode running with Wan VAE spatial_shard_%s mode", split_dim)
            return wan_spatial_shard.spatial_shard_decode(
                self,
                z,
                group=self.distributed_executor.group,
                return_dict=return_dict,
                split_dim=split_dim,
            )

        logger.debug("Decode running with distributed executor")
        result = self.distributed_executor.execute(
            z,
            DistributedOperator(split=self.tile_split, exec=self.tile_exec, merge=self.tile_merge),
            broadcast_result=False,
        )
        if not return_dict:
            return (result,)

        return DecoderOutput(sample=result)

    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
        """
        Encode using distributed VAE executor.

        Note: x is already patchified by parent's _encode() before calling this method.
        """
        if not self.is_distributed_enabled():
            return super().tiled_encode(x)

        logger.debug("Encode running with distributed executor")
        self.clear_cache()
        result = self.distributed_executor.execute(
            x,
            DistributedOperator(
                split=self.encode_tile_split,
                exec=self.encode_tile_exec,
                merge=self.encode_tile_merge,
            ),
            broadcast_result=True,
        )
        self.clear_cache()
        return result