# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import copy
import math
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from xfuser.core.distributed import (
    get_sequence_parallel_rank,
    get_sequence_parallel_world_size,
    get_sp_group,
)

from einops import rearrange
from diffusers import ModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin
from xfuser.core.long_ctx_attention import xFuserLongContextAttention

from .attention import flash_attention, sdpa_attention, flex_attention
from fp8_gemm import FP8Linear
import logging

logger = logging.getLogger(__name__)

try:
    from sageattention import sageattn

    USE_SAGEATTN = True
    logger.info("Using sageattn")
except Exception:
    USE_SAGEATTN = False
from yunchang.kernels import AttnType

__all__ = ['WanModel']
_SAGE_ATTN_TYPES_BY_DEVICE: dict[int, object | None] = {}
_LOGGED_SAGE_ATTN_TYPES: set[str] = set()


def _get_cuda_device_index(device) -> int | None:
    if isinstance(device, torch.device):
        if device.type != "cuda":
            return None
        return torch.cuda.current_device() if device.index is None else device.index
    if isinstance(device, str):
        parsed = torch.device(device)
        if parsed.type != "cuda":
            return None
        return torch.cuda.current_device() if parsed.index is None else parsed.index
    return int(device)


def _get_sage_attn_type_for_device(device):
    if not torch.cuda.is_available():
        return None
    device_index = _get_cuda_device_index(device)
    if device_index is None:
        return None
    if device_index in _SAGE_ATTN_TYPES_BY_DEVICE:
        return _SAGE_ATTN_TYPES_BY_DEVICE[device_index]

    major, _ = torch.cuda.get_device_capability(device_index)
    attn_type = None
    if major == 9 and hasattr(AttnType, "SAGE_FP8_SM90"):
        attn_type = AttnType.SAGE_FP8_SM90
    elif major == 12:
        if hasattr(AttnType, "SAGE_FP8_SM120"):
            attn_type = AttnType.SAGE_FP8_SM120
        elif hasattr(AttnType, "SAGE_FP8"):
            attn_type = AttnType.SAGE_FP8

    _SAGE_ATTN_TYPES_BY_DEVICE[device_index] = attn_type
    if attn_type is not None:
        attn_type_name = str(attn_type)
        if attn_type_name not in _LOGGED_SAGE_ATTN_TYPES:
            logger.info("Using sageattn %s", attn_type)
            _LOGGED_SAGE_ATTN_TYPES.add(attn_type_name)
    return attn_type


def sinusoidal_embedding_1d(dim, position):
    # preprocess
    assert dim % 2 == 0
    half = dim // 2
    position = position.type(torch.float64)

    # calculation
    sinusoid = torch.outer(
        position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
    return x


# @amp.autocast(enabled=False)
def rope_params(max_seq_len, dim, theta=10000):
    assert dim % 2 == 0
    freqs = torch.outer(
        torch.arange(max_seq_len),
        1.0 / torch.pow(theta,
                        torch.arange(0, dim, 2).to(torch.float64).div(dim)))
    freqs = torch.polar(torch.ones_like(freqs), freqs)
    return freqs


# @amp.autocast(enabled=False)
def causal_rope_apply(x, grid_sizes, freqs, sp_size, sp_rank, start_frame=0, _f=None):
    s, n, c = x.size(1), x.size(2), x.size(3) // 2

    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)

    output = []
    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
        f = _f if _f else f
        seq_len = f * h * w

        x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
            s, n, -1, 2))
        freqs_i = torch.cat([
            freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1),
            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
        ],
            dim=-1).reshape(seq_len, 1, -1)
        s_per_rank = s
        freqs_i = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
        freqs_i = freqs_i.to(device=x_i.device)
        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)

        output.append(x_i)
    return torch.stack(output)  # .float()


def rope_apply(x, grid_sizes, freqs, f_list=[], rope_list=[]):
    s, n, c = x.size(1), x.size(2), x.size(3) // 2

    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)

    output = []
    for f_l, r_l in zip(f_list, rope_list):
        start_f, end_f = f_l
        start_r, end_r = r_l
        f = end_f - start_f
        _, h, w = grid_sizes.tolist()[0]
        seq_len = (end_f - start_f) * h * w
        x_i = torch.view_as_complex(
            x[0, start_f * h * w:end_f * h * w].to(torch.float64) \
                .reshape(seq_len, n, -1, 2)
        )
        freqs_i = torch.cat([
            freqs[0][start_r:end_r].view(f, 1, 1, -1).expand(f, h, w, -1),
            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
        ],
            dim=-1).reshape(seq_len, 1, -1)
        freqs_i = freqs_i.to(device=x_i.device)
        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
        output.append(x_i)
    return torch.concat(output, dim=0).unsqueeze(0)


class WanRMSNorm(nn.Module):

    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
        """
        return self._norm(x.float()).to(dtype=x.dtype) * self.weight.to(dtype=x.dtype)

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)


class WanLayerNorm(nn.LayerNorm):

    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        origin_dtype = inputs.dtype
        out = F.layer_norm(
            inputs.float(),
            self.normalized_shape,
            None if self.weight is None else self.weight.float(),
            None if self.bias is None else self.bias.float(),
            self.eps
        ).to(origin_dtype)
        return out


class WanSelfAttention(nn.Module):

    def __init__(self,
                 dim,
                 num_heads,
                 window_size=(-1, -1),
                 qk_norm=True,
                 eps=1e-6):
        assert dim % num_heads == 0
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.eps = eps

        # layers
        self.q = nn.Linear(dim, dim)
        self.k = nn.Linear(dim, dim)
        self.v = nn.Linear(dim, dim)
        self.o = nn.Linear(dim, dim)
        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
        self.attn_mask = None
        self.memory_proj_k = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False)
        self.memory_proj_v = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False)

    def post_init(self, device):
        self.memory_proj_k = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False).to(
            device, dtype=torch.bfloat16)
        self.memory_proj_v = nn.Conv1d(self.dim, self.dim, kernel_size=5, stride=5, groups=self.dim, bias=False).to(
            device, dtype=torch.bfloat16)
        nn.init.constant_(self.memory_proj_k.weight, 1.0 / 5.0)
        nn.init.constant_(self.memory_proj_v.weight, 1.0 / 5.0)

    # @torch.compiler.disable
    def k_compress(self, k, n_frame=5):
        B, N, H, C = k.shape
        assert N % n_frame == 0
        T = N // n_frame
        k = k.view(B, N, H * C).transpose(1, 2)
        k = self.memory_proj_k(k)
        k = k.view(B, H, C, T).permute(0, 3, 1, 2)
        return k

    # @torch.compiler.disable
    def v_compress(self, v, n_frame=5):
        B, N, H, C = v.shape
        assert N % n_frame == 0
        T = N // n_frame
        v = v.view(B, N, H * C).transpose(1, 2)
        v = self.memory_proj_k(v)
        v = v.view(B, H, C, T).permute(0, 3, 1, 2)
        return v

    def kv_mean(self, kv, n_frame=5):
        B, N, H, C = kv.shape
        assert N % n_frame == 0
        T = N // n_frame
        kv = kv.view(B, T, n_frame, H, C).mean(dim=2)
        return kv

    def init_kvidx(self, frame_len, world_size):
        self.frame_seqlen = frame_len
        self.kv_idx0 = torch.tensor(list(range(6 * frame_len // world_size)),
                                    device=f'cuda:{int(os.getenv("RANK", 0))}')
        self.kv_idx2 = torch.tensor(list(range(14 * frame_len // world_size)),
                                    device=f'cuda:{int(os.getenv("RANK", 0))}')

    def _move_kv_cache_to_device(self, kv_cache, device):
        kv_cache["k"] = kv_cache["k"].to(device=device, non_blocking=True)
        kv_cache["v"] = kv_cache["v"].to(device=device, non_blocking=True)
        if kv_cache.get("k_scale") is not None:
            kv_cache["k_scale"] = kv_cache["k_scale"].to(device=device, non_blocking=True)
        if kv_cache.get("v_scale") is not None:
            kv_cache["v_scale"] = kv_cache["v_scale"].to(device=device, non_blocking=True)

    def _quantize_kv_tensor(self, kv):
        fp8_max = torch.finfo(torch.float8_e4m3fn).max
        scale = kv.detach().abs().amax(dim=-1, keepdim=True).to(torch.float32)
        scale = torch.clamp(scale / fp8_max, min=1e-12)
        q_kv = (kv / scale.to(dtype=kv.dtype)).to(torch.float8_e4m3fn)
        return q_kv.contiguous(), scale.contiguous()

    def _dequantize_kv_tensor(self, q_kv, scale, dtype):
        return q_kv.to(dtype=dtype) * scale.to(device=q_kv.device, dtype=dtype)

    def _load_kv_cache(self, kv_cache, device, dtype):
        if kv_cache["offload_cache"]:
            self._move_kv_cache_to_device(kv_cache, device)

        if kv_cache.get("fp8_kv_cache", False):
            k_cache = self._dequantize_kv_tensor(kv_cache["k"], kv_cache["k_scale"], dtype)
            v_cache = self._dequantize_kv_tensor(kv_cache["v"], kv_cache["v_scale"], dtype)
        else:
            if kv_cache["k"].dtype != dtype:
                kv_cache["k"] = kv_cache["k"].to(dtype=dtype)
            if kv_cache["v"].dtype != dtype:
                kv_cache["v"] = kv_cache["v"].to(dtype=dtype)
            k_cache = kv_cache["k"]
            v_cache = kv_cache["v"]
        return k_cache, v_cache

    def _store_kv_cache(self, kv_cache, k_cache, v_cache):
        if kv_cache.get("fp8_kv_cache", False):
            kv_cache["k"], kv_cache["k_scale"] = self._quantize_kv_tensor(k_cache)
            kv_cache["v"], kv_cache["v_scale"] = self._quantize_kv_tensor(v_cache)
        else:
            kv_cache["k"] = k_cache
            kv_cache["v"] = v_cache

        if kv_cache["offload_cache"]:
            self._move_kv_cache_to_device(kv_cache, 'cpu')

    def forward(self, x, seq_lens, grid_sizes, freqs, sp_size, sp_rank, kv_cache={}, start_idx=None, end_idx=None,
                update_cache=False):
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim

        # query, key, value function
        def qkv_fn(x):
            q = self.norm_q(self.q(x)).view(b, s, n, d)
            k = self.norm_k(self.k(x)).view(b, s, n, d)
            v = self.v(x).view(b, s, n, d)
            return q, k, v

        q, k, v = qkv_fn(x)
        k_cache, v_cache = self._load_kv_cache(kv_cache, f'cuda:{int(os.getenv("RANK", 0))}', torch.bfloat16)
        # print('----q.shape, k.shape, v.shape:', q.shape, k.shape, v.shape)
        frame_seqlen = self.frame_seqlen

        if update_cache:
            if kv_cache["mean_memory"]:
                k_compress, v_compress = self.kv_mean, self.kv_mean
            else:
                k_compress, v_compress = self.k_compress, self.v_compress
            if sp_rank == 1:
                k_cache[:, : 1 * frame_seqlen].copy_(k_compress(k_cache[:, : 5 * frame_seqlen]))
                v_cache[:, : 1 * frame_seqlen].copy_(v_compress(v_cache[:, : 5 * frame_seqlen]))

                k_cache[:, 1 * frame_seqlen: 3 * frame_seqlen].copy_(k_cache[:, 5 * frame_seqlen: 7 * frame_seqlen])
                v_cache[:, 1 * frame_seqlen: 3 * frame_seqlen].copy_(v_cache[:, 5 * frame_seqlen: 7 * frame_seqlen])
            elif sp_rank == 0:
                k_cache[:, 2 * frame_seqlen: 3 * frame_seqlen, ...].copy_(
                    k_compress(k_cache[:, 2 * frame_seqlen: 7 * frame_seqlen]))
                v_cache[:, 2 * frame_seqlen: 3 * frame_seqlen, ...].copy_(
                    v_compress(v_cache[:, 2 * frame_seqlen: 7 * frame_seqlen]))
                pass

        if start_idx != 0:
            k_cache[:, 3 * frame_seqlen:] = k
            v_cache[:, 3 * frame_seqlen:] = v
        else:
            k_cache[:, : 3 * frame_seqlen] = k
            v_cache[:, : 3 * frame_seqlen] = v

        kv_idx = self.kv_idx0 if end_idx == 6 * frame_seqlen else \
            self.kv_idx2 if end_idx == 14 * frame_seqlen else -1

        rope_list = [[0 + 3 * sp_rank, 3 + 3 * sp_rank]] if end_idx == 6 * frame_seqlen else \
            [[0 + 3 * sp_rank, 3 + 3 * sp_rank], [6 + 4 * sp_rank, 10 + 4 * sp_rank]]
        f_list = [[0, 3]] if end_idx == 6 * frame_seqlen else \
            [[0, 3], [3, 7]] if end_idx == 14 * frame_seqlen else \
                [[0, 3], [3, 7]] if end_idx == 22 * frame_seqlen else -1

        sage_attn_type = _get_sage_attn_type_for_device(q.device)
        attn_layer = xFuserLongContextAttention(attn_type=sage_attn_type) \
            if sage_attn_type is not None else xFuserLongContextAttention()

        x = attn_layer(
            None,
            query=causal_rope_apply(q, grid_sizes, freqs, sp_size, sp_rank,
                                    start_frame=0 if end_idx == 6 * frame_seqlen else 6).type_as(v),
            key=rope_apply(kv_cache["k"][:, kv_idx], grid_sizes, freqs, f_list=f_list, rope_list=rope_list).type_as(v),
            value=kv_cache["v"][:, kv_idx],
            window_size=self.window_size
        )

        self._store_kv_cache(kv_cache, k_cache, v_cache)

        # output
        x = x.flatten(2)
        x = self.o(x)
        return x, None


class WanI2VCrossAttention(nn.Module):

    def __init__(self,
                 dim,
                 num_heads,
                 window_size=(-1, -1),
                 qk_norm=True,
                 eps=1e-6):
        assert dim % num_heads == 0
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.eps = eps

        # layers
        self.q = nn.Linear(dim, dim)
        self.k = nn.Linear(dim, dim)
        self.v = nn.Linear(dim, dim)
        self.o = nn.Linear(dim, dim)
        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

        self.k_img = nn.Linear(dim, dim)
        self.v_img = nn.Linear(dim, dim)
        self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

    def forward(self, x, context, context_lens, cross_kv_cache={}):
        context_img = context[:, :257]
        context = context[:, 257:]
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
        q = self.norm_q(self.q(x)).view(b, -1, n, d)
        k = self.norm_k(self.k(context)).view(b, -1, n, d)
        v = self.v(context).view(b, -1, n, d)
        k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
        v_img = self.v_img(context_img).view(b, -1, n, d)

        if USE_SAGEATTN:
            img_x = sageattn(q, k_img, v_img, tensor_layout='NHD')
            x = sageattn(q, k, v, tensor_layout='NHD')
        else:
            img_x = sdpa_attention(q, k_img, v_img, k_lens=None)
            x = sdpa_attention(q, k, v, k_lens=context_lens)

        # output
        x = x.flatten(2)
        img_x = img_x.flatten(2)
        x = x + img_x
        x = self.o(x)
        return x


class SingleStreamAttention(nn.Module):
    def __init__(
            self,
            dim: int,
            encoder_hidden_states_dim: int,
            num_heads: int,
            qkv_bias: bool,
            qk_norm: bool,
            norm_layer: nn.Module,
            attn_drop: float = 0.0,
            proj_drop: float = 0.0,
            eps: float = 1e-6,
    ) -> None:
        super().__init__()
        assert dim % num_heads == 0, "dim should be divisible by num_heads"
        self.dim = dim
        self.encoder_hidden_states_dim = encoder_hidden_states_dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        self.qk_norm = qk_norm

        self.q_linear = nn.Linear(dim, dim, bias=qkv_bias)

        self.q_norm = norm_layer(self.head_dim, eps=eps) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim, eps=eps) if qk_norm else nn.Identity()

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.kv_linear = nn.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias)

        self.add_q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.add_k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()

        self.q_buf = None  # torch.empty((B, H, Lpad, D), device=x.device, dtype=x.dtype)

    def forward(
        self,
        x,
        encoder_hidden_states,
        sp_size,
        sp_rank,
        shape=None,
        start_f=0,
        frame_seqlen=None,
    ) -> torch.Tensor:
        encoder_hidden_states = encoder_hidden_states.squeeze(0)
        if frame_seqlen is None:
            if shape is None:
                raise ValueError("Either frame_seqlen or shape must be provided.")
            frame_seqlen = int(shape[1]) * int(shape[2])

        batch_size, seq_tokens, channels = x.shape
        num_frames = seq_tokens // frame_seqlen
        x = x.reshape(batch_size, num_frames, frame_seqlen, channels)
        x = x.reshape(batch_size * num_frames, frame_seqlen, channels)

        # get q for hidden_state
        B, N, C = x.shape  # [f, N_h*N_w, dim]
        q = self.q_linear(x)
        q_shape = (B, N, self.num_heads, self.head_dim)
        q = q.view(q_shape).permute((0, 2, 1, 3))  # B H N K = [f, 40, N_h*N_w, head_dim]

        if self.qk_norm:
            q = self.q_norm(q)

        # get kv from encoder_hidden_states
        B_e, N_a, _ = encoder_hidden_states.shape  # [21, 32, 768]
        encoder_kv = self.kv_linear(encoder_hidden_states)
        encoder_kv_shape = (B_e, N_a, 2, self.num_heads, self.head_dim)  # [21, 32, 2, 40, 128]
        encoder_kv = encoder_kv.view(encoder_kv_shape)[start_f + sp_rank * B:start_f + (sp_rank + 1) * B].permute(
            (2, 0, 3, 1, 4))  # [2, B, 40, 32, 128]
        encoder_k, encoder_v = encoder_kv.unbind(0)  # [B, 40, 32, 128]

        if self.qk_norm:
            encoder_k = self.add_k_norm(encoder_k)

        if USE_SAGEATTN:
            x = sageattn(q, encoder_k, encoder_v, tensor_layout='HND')
        else:
            x = torch.nn.functional.scaled_dot_product_attention(
                q, encoder_k, encoder_v, attn_mask=None, is_causal=False, dropout_p=0.0)  # [f, 40, N_h*N_w, head_dim]

        # linear transform
        x_output_shape = (B, N, C)
        x = x.transpose(1, 2)
        x = x.reshape(x_output_shape)  # [f, N_h*N_w, 40*head_dim]
        x = self.proj(x)
        x = self.proj_drop(x)

        x = x.reshape(batch_size, num_frames, frame_seqlen, C)
        x = x.reshape(batch_size, num_frames * frame_seqlen, C)

        return x


class WanAttentionBlock(nn.Module):

    def __init__(self,
                 cross_attn_type,
                 dim,
                 ffn_dim,
                 num_heads,
                 window_size=(-1, -1),
                 qk_norm=True,
                 cross_attn_norm=False,
                 eps=1e-6,
                 output_dim=768,
                 norm_input_visual=True):
        super().__init__()
        self.dim = dim
        self.ffn_dim = ffn_dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.cross_attn_norm = cross_attn_norm
        self.eps = eps

        # layers
        self.norm1 = WanLayerNorm(dim, eps)
        self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
        self.norm3 = WanLayerNorm(
            dim, eps,
            elementwise_affine=True) if cross_attn_norm else nn.Identity()
        self.cross_attn = WanI2VCrossAttention(dim,
                                               num_heads,
                                               (-1, -1),
                                               qk_norm,
                                               eps)
        self.norm2 = WanLayerNorm(dim, eps)
        self.ffn = nn.Sequential(
            nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
            nn.Linear(ffn_dim, dim))

        # modulation
        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim ** 0.5)

        # init audio module
        self.audio_cross_attn = SingleStreamAttention(
            dim=dim,
            encoder_hidden_states_dim=output_dim,
            num_heads=num_heads,
            qk_norm=False,
            qkv_bias=True,
            eps=eps,
            norm_layer=WanRMSNorm,
        )
        self.norm_x = WanLayerNorm(dim, eps, elementwise_affine=True) if norm_input_visual else nn.Identity()

    def forward(
            self,
            x,
            e,
            seq_lens,
            grid_sizes,
            freqs,
            context,
            context_lens,
            kv_cache={},
            start_idx=None,
            end_idx=None,
            update_cache=False,
            cross_kv_cache={},
            audio_embedding=None,
            ref_target_masks=None,
            human_num=None,
            skip_audio=False,
    ):

        dtype = x.dtype
        # assert e.dtype == torch.float32
        if len(e.shape) == 3:
            # with amp.autocast(dtype=torch.float32):
            e = (self.modulation.to(e.device) + e).chunk(6, dim=1)
        else:
            # with amp.autocast(dtype=torch.float32):
            e = (self.modulation.unsqueeze(-2).to(e.device) + e)[0].chunk(6, dim=0)
        # assert e[0].dtype == torch.float32

        sp_size = get_sequence_parallel_world_size()
        sp_rank = get_sequence_parallel_rank()

        # self-attention
        y, x_ref_attn_map = self.self_attn(
            (self.norm1(x).float() * (1 + e[1]) + e[0]).type_as(x), seq_lens, grid_sizes,
            freqs, sp_size, sp_rank, kv_cache=kv_cache, start_idx=start_idx, end_idx=end_idx,
            update_cache=update_cache,
        )
        # with amp.autocast(dtype=torch.float32):
        x = x + y * e[2]

        x = x.to(dtype)

        # cross-attention of text
        x = x + self.cross_attn(self.norm3(x), context, context_lens, cross_kv_cache=cross_kv_cache)

        # cross attn of audio
        if not skip_audio:
            frame_seqlen = self.self_attn.frame_seqlen
            start_f = start_idx // frame_seqlen
            x_a = self.audio_cross_attn(self.norm_x(x), audio_embedding,
                                        sp_size, sp_rank, frame_seqlen=frame_seqlen, start_f=start_f)
            if start_f == 0 and sp_rank == 0:
                x_a[:, :frame_seqlen] = 0
            x = x + x_a

        y = self.ffn((self.norm2(x).float() * (1 + e[4]) + e[3]).to(dtype))
        # with amp.autocast(dtype=torch.float32):
        x = x + y * e[5]
        x = x.to(dtype)

        return x


class Head(nn.Module):

    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
        super().__init__()
        self.dim = dim
        self.out_dim = out_dim
        self.patch_size = patch_size
        self.eps = eps

        # layers
        out_dim = math.prod(patch_size) * out_dim
        self.norm = WanLayerNorm(dim, eps)
        self.head = nn.Linear(dim, out_dim)

        # modulation
        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim ** 0.5)

    def forward(self, x, e):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            e(Tensor): Shape [B, C]
        """
        # assert e.dtype == torch.float32
        # with amp.autocast(dtype=torch.float32):
        e = (self.modulation.to(e.device) + e.unsqueeze(1)).chunk(2, dim=1)
        x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
        return x


class MLPProj(torch.nn.Module):

    def __init__(self, in_dim, out_dim):
        super().__init__()

        self.proj = torch.nn.Sequential(
            torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim),
            torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim),
            torch.nn.LayerNorm(out_dim))

    def forward(self, image_embeds):
        clip_extra_context_tokens = self.proj(image_embeds)
        return clip_extra_context_tokens


class AudioProjModel(ModelMixin, ConfigMixin):
    def __init__(
            self,
            seq_len=5,
            seq_len_vf=12,
            blocks=12,
            channels=768,
            intermediate_dim=512,
            output_dim=768,
            context_tokens=32,
            norm_output_audio=False,
    ):
        super().__init__()

        self.seq_len = seq_len
        self.blocks = blocks
        self.channels = channels
        self.input_dim = seq_len * blocks * channels
        self.input_dim_vf = seq_len_vf * blocks * channels
        self.intermediate_dim = intermediate_dim
        self.context_tokens = context_tokens
        self.output_dim = output_dim

        # define multiple linear layers
        self.proj1 = nn.Linear(self.input_dim, intermediate_dim)
        self.proj1_vf = nn.Linear(self.input_dim_vf, intermediate_dim)
        self.proj2 = nn.Linear(intermediate_dim, intermediate_dim)
        self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim)
        self.norm = nn.LayerNorm(output_dim) if norm_output_audio else nn.Identity()

    def forward(self, audio_embeds, audio_embeds_vf):
        video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]
        B, _, _, S, C = audio_embeds.shape

        # process audio of first frame
        audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
        batch_size, window_size, blocks, channels = audio_embeds.shape
        audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)

        # process audio of latter frame
        audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c")
        batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape
        audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf)

        # first projection
        audio_embeds = torch.relu(self.proj1(audio_embeds))
        audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf))
        audio_embeds = rearrange(audio_embeds, "(bz f) c -> bz f c", bz=B)
        audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B)
        audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1)
        batch_size_c, N_t, C_a = audio_embeds_c.shape
        audio_embeds_c = audio_embeds_c.view(batch_size_c * N_t, C_a)

        # second projection
        audio_embeds_c = torch.relu(self.proj2(audio_embeds_c))

        context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c * N_t, self.context_tokens, self.output_dim)

        # normalization and reshape
        # with amp.autocast(dtype=torch.float32):
        context_tokens = self.norm(context_tokens)
        context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)

        return context_tokens


from torch.utils.checkpoint import checkpoint


class WanBlockOffloadManager:
    def __init__(self, blocks, onload_device, offload_device='cpu'):
        self.blocks = blocks
        self.onload_device = torch.device(onload_device)
        self.offload_device = torch.device(offload_device)
        self.prefetch_stream = torch.cuda.Stream(device=self.onload_device)
        self.compute_slot = 0
        self.prefetch_slot = 1
        self.pending_slots = set()
        self.slot_block_indices = [None, None]
        self.cuda_blocks = nn.ModuleList([
            copy.deepcopy(self.blocks[0]).to(self.onload_device),
            copy.deepcopy(self.blocks[0]).to(self.onload_device),
        ])

        for block in self.blocks:
            block.to(self.offload_device)
            self._pin_module_memory(block)

    def _copy_tensor(self, dst, src):
        dst.copy_(src, non_blocking=True)

    def _pin_tensor(self, tensor):
        if tensor is None or tensor.device.type != 'cpu' or tensor.is_pinned():
            return tensor
        return tensor.pin_memory()

    def _pin_module_memory(self, module):
        for name, param in module.named_parameters(recurse=False):
            if param is not None:
                param.data = self._pin_tensor(param.data)

        for name, buffer in module.named_buffers(recurse=False):
            if buffer is not None:
                module._buffers[name] = self._pin_tensor(buffer)

        if isinstance(module, FP8Linear):
            module._fp16_weight_cpu = self._pin_tensor(module._fp16_weight_cpu)
            module._fp16_bias_cpu = self._pin_tensor(module._fp16_bias_cpu)

        for child in module.children():
            self._pin_module_memory(child)

    def _copy_fp8_linear(self, dst_module, src_module):
        if dst_module.linear is not None and src_module.linear is not None:
            self._copy_module_state(dst_module.linear, src_module.linear)

        if dst_module.bias is not None and src_module.bias is not None:
            self._copy_tensor(dst_module.bias.data, src_module.bias.data)

        dst_module._fp16_weight_cpu = src_module._fp16_weight_cpu
        dst_module._fp16_bias_cpu = src_module._fp16_bias_cpu

        if src_module._fp8_weight is None or src_module._fp8_weight_scale is None:
            dst_module._fp8_weight = None
            dst_module._fp8_weight_scale = None
            dst_module._weight_cache_device = None
            if dst_module._fp16_weight_cpu is not None:
                dst_module.materialize_fp8_weight(self.onload_device)
        else:
            if dst_module._fp8_weight is None or dst_module._fp8_weight.shape != src_module._fp8_weight.shape:
                dst_module._fp8_weight = src_module._fp8_weight.to(device=self.onload_device, non_blocking=True)
            else:
                self._copy_tensor(dst_module._fp8_weight, src_module._fp8_weight)

            if dst_module._fp8_weight_scale is None or dst_module._fp8_weight_scale.shape != src_module._fp8_weight_scale.shape:
                dst_module._fp8_weight_scale = src_module._fp8_weight_scale.to(device=self.onload_device,
                                                                               non_blocking=True)
            else:
                self._copy_tensor(dst_module._fp8_weight_scale, src_module._fp8_weight_scale)
            dst_module._weight_cache_device = dst_module._cached_fp8_device()

        dst_module._last_weight_version = src_module._last_weight_version

    def _copy_module_state(self, dst_module, src_module):
        if isinstance(dst_module, FP8Linear) and isinstance(src_module, FP8Linear):
            self._copy_fp8_linear(dst_module, src_module)
            return

        dst_params = dict(dst_module.named_parameters(recurse=False))
        src_params = dict(src_module.named_parameters(recurse=False))
        for name, dst_param in dst_params.items():
            src_param = src_params.get(name)
            if src_param is not None:
                self._copy_tensor(dst_param.data, src_param.data)

        dst_buffers = dict(dst_module.named_buffers(recurse=False))
        src_buffers = dict(src_module.named_buffers(recurse=False))
        for name, dst_buffer in dst_buffers.items():
            src_buffer = src_buffers.get(name)
            if src_buffer is not None:
                self._copy_tensor(dst_buffer, src_buffer)

        dst_children = dict(dst_module.named_children())
        src_children = dict(src_module.named_children())
        for name, dst_child in dst_children.items():
            src_child = src_children.get(name)
            if src_child is not None:
                self._copy_module_state(dst_child, src_child)

    def _load_slot(self, slot_idx, block_idx, async_transfer=False):
        def copy_block():
            self._copy_module_state(self.cuda_blocks[slot_idx], self.blocks[block_idx])
            self.slot_block_indices[slot_idx] = block_idx

        if async_transfer:
            with torch.cuda.stream(self.prefetch_stream):
                copy_block()
            self.pending_slots.add(slot_idx)
        else:
            copy_block()
            self.pending_slots.discard(slot_idx)

    def _wait_slot(self, slot_idx):
        if slot_idx in self.pending_slots:
            torch.cuda.current_stream(device=self.onload_device).wait_stream(self.prefetch_stream)
            self.pending_slots.discard(slot_idx)

    def get_block(self, block_idx):
        if self.slot_block_indices[self.compute_slot] == block_idx:
            self._wait_slot(self.compute_slot)
        elif self.slot_block_indices[self.prefetch_slot] == block_idx:
            self._wait_slot(self.prefetch_slot)
            self.compute_slot, self.prefetch_slot = self.prefetch_slot, self.compute_slot
        else:
            self._load_slot(self.compute_slot, block_idx, async_transfer=False)

        next_idx = block_idx + 1
        if next_idx < len(self.blocks) and self.slot_block_indices[self.prefetch_slot] != next_idx:
            # We are about to overwrite self.prefetch_slot on the prefetch stream.
            # Must ensure the compute stream has finished using it from previous steps.
            self.prefetch_stream.wait_stream(torch.cuda.current_stream(device=self.onload_device))
            self._load_slot(self.prefetch_slot, next_idx, async_transfer=True)

        return self.cuda_blocks[self.compute_slot]

    def unload_all(self):
        torch.cuda.current_stream(device=self.onload_device).wait_stream(self.prefetch_stream)
        self.pending_slots.clear()
        self.slot_block_indices = [None, None]


class WanModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    r"""
    Wan diffusion backbone supporting both text-to-video and image-to-video.
    """

    ignore_for_config = [
        'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size'
    ]
    _no_split_modules = ['WanAttentionBlock']

    @register_to_config
    def __init__(self,
                 model_type='i2v',
                 patch_size=(1, 2, 2),
                 text_len=512,
                 in_dim=16,
                 dim=2048,
                 ffn_dim=8192,
                 freq_dim=256,
                 text_dim=4096,
                 out_dim=16,
                 num_heads=16,
                 num_layers=32,
                 window_size=(-1, -1),
                 qk_norm=True,
                 cross_attn_norm=True,
                 eps=1e-6,
                 # audio params
                 audio_window=5,
                 intermediate_dim=512,
                 output_dim=768,
                 context_tokens=32,
                 vae_scale=4,
                 norm_input_visual=True,
                 norm_output_audio=True,
                 weight_init=True):
        super().__init__()

        assert model_type == 'i2v', 'MultiTalk model requires your model_type is i2v.'
        self.model_type = model_type

        self.patch_size = patch_size
        self.text_len = text_len
        self.in_dim = in_dim
        self.dim = dim
        self.ffn_dim = ffn_dim
        self.freq_dim = freq_dim
        self.text_dim = text_dim
        self.out_dim = out_dim
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.cross_attn_norm = cross_attn_norm
        self.eps = eps
        self.gradient_checkpointing = False

        self.norm_output_audio = norm_output_audio
        self.audio_window = audio_window
        self.intermediate_dim = intermediate_dim
        self.vae_scale = vae_scale

        self.return_layers_cosine = False
        self.cos_sims = []
        self.skip_layer = []
        self.block_offload_manager = None
        self.block_offload_enabled = False

        # embeddings
        self.patch_embedding = nn.Conv3d(
            in_dim, dim, kernel_size=patch_size, stride=patch_size)
        self.text_embedding = nn.Sequential(
            nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
            nn.Linear(dim, dim))

        self.time_embedding = nn.Sequential(
            nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))

        # blocks
        cross_attn_type = 'i2v_cross_attn'
        self.blocks = nn.ModuleList([
            WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
                              window_size, qk_norm, cross_attn_norm, eps,
                              output_dim=output_dim, norm_input_visual=norm_input_visual)
            for _ in range(num_layers)
        ])

        # head
        self.head = Head(dim, out_dim, patch_size, eps)

        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
        d = dim // num_heads
        self.freqs = torch.cat([
            rope_params(1024, d - 4 * (d // 6)),
            rope_params(1024, 2 * (d // 6)),
            rope_params(1024, 2 * (d // 6))
        ],
            dim=1)

        if model_type == 'i2v':
            self.img_emb = MLPProj(1280, dim)
        else:
            raise NotImplementedError('Not supported model type.')

        # init audio adapter
        self.audio_proj = AudioProjModel(
            seq_len=audio_window,
            seq_len_vf=audio_window + vae_scale - 1,
            intermediate_dim=intermediate_dim,
            output_dim=output_dim,
            context_tokens=context_tokens,
            norm_output_audio=norm_output_audio,
        )

        # initialize weights
        if weight_init:
            self.init_weights()

    def init_freqs(self):
        d = self.dim // self.num_heads
        self.freqs = torch.cat([
            rope_params(1024, d - 4 * (d // 6)),
            rope_params(1024, 2 * (d // 6)),
            rope_params(1024, 2 * (d // 6))
        ],
            dim=1)

    def enable_block_offload(self, onload_device=None, offload_device='cpu'):
        if onload_device is None:
            onload_device = self.patch_embedding.weight.device
        onload_device = torch.device(onload_device)
        if onload_device.type != 'cuda':
            raise ValueError("WanModel block offload requires a CUDA onload device.")

        self.block_offload_manager = WanBlockOffloadManager(
            self.blocks,
            onload_device=onload_device,
            offload_device=offload_device,
        )
        self.block_offload_enabled = True
        torch.cuda.empty_cache()
        return self

    def forward(
            self,
            x,
            t,
            context,
            clip_fea=None,
            y=None,
            audio=None,
            ref_target_masks=None,
            kv_cache={},
            start_idx=None,
            end_idx=None,
            cross_kv_cache={},
            update_cache=False,
            skip_audio=False,
    ):
        assert clip_fea is not None and y is not None
        # params
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.freqs.to(device)

        _, T, H, W = x[0].shape
        N_t = T // self.patch_size[0]
        N_h = H // self.patch_size[1]
        N_w = W // self.patch_size[2]

        if y is not None:
            x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
        x[0] = x[0].to(context[0].dtype)

        # embeddings
        x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
        grid_sizes = torch.stack(
            [torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
        x = [u.flatten(2).transpose(1, 2) for u in x]
        seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
        x = torch.cat(x)

        # time embeddings
        # with amp.autocast(dtype=torch.float32):
        e = self.time_embedding(
            sinusoidal_embedding_1d(self.freq_dim, t).float())
        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
        # assert e.dtype == torch.float32 and e0.dtype == torch.float32

        # text embedding
        context_lens = None
        context = self.text_embedding(
            torch.stack([
                torch.cat(
                    [u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
                for u in context
            ]))

        # clip embedding
        if clip_fea is not None:
            context_clip = self.img_emb(clip_fea)
            context = torch.concat([context_clip, context], dim=1).to(x.dtype)

        audio_cond = audio.to(device=x.device, dtype=x.dtype)
        first_frame_audio_emb_s = audio_cond[:, :1, ...]
        latter_frame_audio_emb = audio_cond[:, 1:, ...]
        latter_frame_audio_emb = rearrange(latter_frame_audio_emb, "b (n_t n) w s c -> b n_t n w s c", n=self.vae_scale)
        middle_index = self.audio_window // 2
        latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index + 1, ...]
        latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
        latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...]
        latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
        latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index + 1, ...]
        latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
        latter_frame_audio_emb_s = torch.concat(
            [latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2)
        audio_embedding = self.audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s)
        human_num = len(audio_embedding)
        audio_embedding = torch.concat(audio_embedding.split(1), dim=2).to(x.dtype)

        # convert ref_target_masks to token_ref_target_masks
        if ref_target_masks is not None:
            ref_target_masks = ref_target_masks.unsqueeze(0)  # .to(torch.float32)
            token_ref_target_masks = nn.functional.interpolate(ref_target_masks, size=(N_h, N_w), mode='nearest')
            token_ref_target_masks = token_ref_target_masks.squeeze(0)
            token_ref_target_masks = (token_ref_target_masks > 0)
            token_ref_target_masks = token_ref_target_masks.view(token_ref_target_masks.shape[0], -1)
            token_ref_target_masks = token_ref_target_masks.to(x.dtype)

        # Context Parallel
        x = torch.chunk(
            x, get_sequence_parallel_world_size(),
            dim=1)[get_sequence_parallel_rank()]

        # arguments
        kwargs = dict(
            e=e0,
            seq_lens=seq_lens,
            grid_sizes=grid_sizes,
            freqs=self.freqs,
            context=context,
            context_lens=context_lens,
            audio_embedding=audio_embedding,
            ref_target_masks=token_ref_target_masks,
            human_num=human_num,
            start_idx=start_idx,
            end_idx=end_idx,
            update_cache=update_cache,
        )

        block_offload_manager = self.block_offload_manager if self.block_offload_enabled else None
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            for block_index, block in enumerate(self.blocks):
                if block_offload_manager is not None:
                    block = block_offload_manager.get_block(block_index)
                if kv_cache.get(block_index) is None: kv_cache[block_index] = {}
                if cross_kv_cache.get(block_index) is None: cross_kv_cache[block_index] = {}
                x = checkpoint(
                    block, x, kv_cache=kv_cache[block_index], cross_kv_cache=cross_kv_cache[block_index],
                    skip_audio=skip_audio, use_reentrant=False, **kwargs
                )
        else:
            for block_index, block in enumerate(self.blocks):
                if block_offload_manager is not None:
                    block = block_offload_manager.get_block(block_index)
                if kv_cache.get(block_index) is None: kv_cache[block_index] = {}
                if cross_kv_cache.get(block_index) is None: cross_kv_cache[block_index] = {}
                x = block(x, kv_cache=kv_cache[block_index], cross_kv_cache=cross_kv_cache[block_index],
                          skip_audio=skip_audio, **kwargs)

        # head
        x = self.head(x, e)

        # Context Parallel
        x = get_sp_group().all_gather(x, dim=1)

        # unpatchify
        x = self.unpatchify(x, grid_sizes)

        return torch.stack(x)  # .float()

    def unpatchify(self, x, grid_sizes):
        r"""
        Reconstruct video tensors from patch embeddings.

        Args:
            x (List[Tensor]):
                List of patchified features, each with shape [L, C_out * prod(patch_size)]
            grid_sizes (Tensor):
                Original spatial-temporal grid dimensions before patching,
                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)

        Returns:
            List[Tensor]:
                Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
        """

        c = self.out_dim
        out = []
        for u, v in zip(x, grid_sizes.tolist()):
            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
            u = torch.einsum('fhwpqrc->cfphqwr', u)
            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
            out.append(u)
        return out

    def init_weights(self):
        r"""
        Initialize model parameters using Xavier initialization.
        """

        # basic init
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

        # init embeddings
        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
        for m in self.text_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=.02)
        for m in self.time_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=.02)

        # init output layer
        nn.init.zeros_(self.head.head.weight)