# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
from typing import Optional

import torch
import torch.cuda.amp as amp
import torch.nn as nn
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin

from diffusion.model.nets.basic_modules import GLUMBConvTemp, Mlp
from diffusion.utils.logger import get_logger

from .attention import flash_attention

__all__ = ["WanModel"]


class AttentionHook:
    output = None

    def __init__(self, device):
        self.device = device

    def __call__(self, attn_output):
        self.output = attn_output
        self.output.to(self.device)

    def clear(self):
        self.output = None


def cosine_similarity(x, y, dim=1):
    x_norm = F.normalize(x, p=2, dim=dim)
    y_norm = F.normalize(y, p=2, dim=dim)
    return torch.sum(x_norm * y_norm, dim=dim)


class BlockHook:
    x_in = None
    x_self_attn = None
    x_cross_attn = None
    x_ffn = None

    def __init__(self, device, detach=True, score_only="cos"):
        self.device = device
        self.detach = detach if not score_only else True  # if score_only, always detach
        self.score_only = score_only

    def __call__(self, x_in, x_self_attn, x_cross_attn, x_ffn):
        # input shape is B,L,C
        if self.score_only == "cos":
            # make sure all feats are not none
            assert x_in is not None
            assert x_self_attn is not None
            assert x_cross_attn is not None
            assert x_ffn is not None
            # detach and float all feats
            x_in = x_in.detach().float()
            x_self_attn = x_self_attn.detach().float()
            x_cross_attn = x_cross_attn.detach().float()
            x_ffn = x_ffn.detach().float()

            # compute cosine similarity
            self.x_in = None
            self.x_self_attn = cosine_similarity(x_in, x_self_attn, dim=-1).to(self.device)
            self.x_cross_attn = cosine_similarity(x_self_attn, x_cross_attn, dim=-1).to(self.device)
            self.x_ffn = cosine_similarity(x_cross_attn, x_ffn, dim=-1).to(self.device)
        elif self.score_only == "l2":
            # make sure all feats are not none
            assert x_in is not None
            assert x_self_attn is not None
            assert x_cross_attn is not None
            assert x_ffn is not None
            # detach and float all feats
            x_in = x_in.detach().float()
            x_self_attn = x_self_attn.detach().float()
            x_cross_attn = x_cross_attn.detach().float()
            x_ffn = x_ffn.detach().float()

            self.x_in = None
            self.x_self_attn = F.mse_loss(x_in, x_self_attn, reduction="none").mean(dim=-1).to(self.device)
            self.x_cross_attn = F.mse_loss(x_self_attn, x_cross_attn, reduction="none").mean(dim=-1).to(self.device)
            self.x_ffn = F.mse_loss(x_cross_attn, x_ffn, reduction="none").mean(dim=-1).to(self.device)  # B,L
        else:
            self.x_in = x_in.to(self.device) if x_in is not None else None
            self.x_self_attn = x_self_attn.to(self.device) if x_self_attn is not None else None
            self.x_cross_attn = x_cross_attn.to(self.device) if x_cross_attn is not None else None
            self.x_ffn = x_ffn.to(self.device) if x_ffn is not None else None
            if self.detach:
                self.x_in = self.x_in.detach() if self.x_in is not None else None
                self.x_self_attn = self.x_self_attn.detach() if self.x_self_attn is not None else None
                self.x_cross_attn = self.x_cross_attn.detach() if self.x_cross_attn is not None else None
                self.x_ffn = self.x_ffn.detach() if self.x_ffn is not None else None

    def clear(self):
        self.x_in = None
        self.x_self_attn = None
        self.x_cross_attn = None
        self.x_ffn = None

    def get_output(self):
        return {
            "x_in": self.x_in,
            "x_self_attn": self.x_self_attn,
            "x_cross_attn": self.x_cross_attn,
            "x_ffn": self.x_ffn,
        }


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 rope_apply(x, grid_sizes, freqs):
    n, c = x.size(2), x.size(3) // 2

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

    # loop over samples
    output = []
    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
        seq_len = f * h * w

        # precompute multipliers
        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2))
        freqs_i = torch.cat(
            [
                freqs[0][: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)

        # apply rotary embedding
        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
        x_i = torch.cat([x_i, x[i, seq_len:]])

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


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()).type_as(x) * self.weight

    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, x):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
        """
        return super().forward(x).type_as(x)


class WanSelfAttention(nn.Module):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, **kwargs):
        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.qkv_store_buffer = None

    def forward(self, x, seq_lens, grid_sizes, freqs, block_mask=None):
        r"""
        Args:
            x(Tensor): Shape [B, L, num_heads, C / num_heads]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
        # print(f"In Attention, x dtype {x.dtype}")
        x_dtype = x.dtype

        # 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)

        q = rope_apply(q, grid_sizes, freqs)
        k = rope_apply(k, grid_sizes, freqs)
        if self.qkv_store_buffer is not None:
            self.qkv_store_buffer["q"] = q[1].cpu()  # b, n, h, h_d
            self.qkv_store_buffer["k"] = k[1].cpu()  # b, n, h, h_d
            self.qkv_store_buffer["v"] = v[1].cpu()  # b, n, h, h_d

        x = flash_attention(
            q=q,
            k=k,
            v=v,
            k_lens=seq_lens,
            window_size=self.window_size,
        )

        x = x.flatten(2).to(x_dtype)
        x = self.o(x)
        return x


class WanLinearAttention(WanSelfAttention):
    PAD_VAL = 1

    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, **kwargs):
        super().__init__(dim, num_heads, window_size, qk_norm, eps, **kwargs)
        self.kernel_func = nn.ReLU(inplace=False)
        self.fp32_attention = True
        self.qkv_store_buffer = None
        self.rope_after = kwargs.get("rope_after", False)
        self.power = kwargs.get("power", 1.0)

    @torch.autocast(device_type="cuda", enabled=False)
    def attn_matmul(self, q, k, v: torch.Tensor) -> torch.Tensor:
        v = F.pad(v.float(), (0, 0, 0, 1), mode="constant", value=self.PAD_VAL)
        vk = torch.matmul(v, k)
        out = torch.matmul(vk, q)  # b, h, h_d, n

        norm_out = out[:, :, :-1] / (out[:, :, -1:] + self.eps)

        return norm_out

    def forward(self, x, seq_lens, grid_sizes, freqs):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
        x_dtype = x.dtype

        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)  # B, seq, num_heads, head_dim

        # save before rope
        if self.qkv_store_buffer is not None:
            # qkv store buffer shoud be dict
            self.qkv_store_buffer["q"] = q[1].cpu()  # b, n, h, h_d
            self.qkv_store_buffer["k"] = k[1].cpu()  # b, n, h, h_d
            self.qkv_store_buffer["v"] = v[1].cpu()  # b, n, h, h_d

        power = self.power
        rope_after = self.rope_after
        if rope_after:
            # apply kernel function
            q = self.kernel_func(q)  # B, h, h_d, N
            k = self.kernel_func(k)

            # power qk
            if power != 1.0:
                q_norm = q.norm(dim=-1, keepdim=True)
                k_norm = k.norm(dim=-1, keepdim=True)
                q = q**power
                k = k**power
                q = (q / (q.norm(dim=-1, keepdim=True) + 1e-6)) * q_norm
                k = (k / (k.norm(dim=-1, keepdim=True) + 1e-6)) * k_norm

            # apply rope after kernel function
            q_rope = rope_apply(q, grid_sizes, freqs)
            k_rope = rope_apply(k, grid_sizes, freqs)

            with torch.autocast(device_type="cuda", enabled=False):
                q_rope = q_rope.permute(0, 2, 1, 3).contiguous()  # B, seq, num_heads, head_dim -> B, h, seq, h_d
                k_rope = k_rope.permute(0, 2, 1, 3).contiguous()  # B, seq, num_heads, head_dim -> B, h, seq, h_d

                q = q.permute(0, 2, 1, 3).contiguous()  # B, seq, num_heads, head_dim -> B, h, seq, h_d
                k = k.permute(0, 2, 1, 3).contiguous()  # B, seq, num_heads, head_dim -> B, h, seq, h_d
                v = v.permute(0, 2, 1, 3).contiguous()  # B, seq, num_heads, head_dim -> B, h, seq, h_d

                z = 1 / (q @ k.mean(dim=-2, keepdim=True).transpose(-2, -1) + 1e-6)

                kv = (k_rope.transpose(-2, -1)) @ v.float() / s
                x = q_rope @ kv

                x = x * z

        else:
            # apply rope before kernel function
            q = rope_apply(q, grid_sizes, freqs)
            k = rope_apply(k, grid_sizes, freqs)

            # apply kernel function
            q = self.kernel_func(q)  # B, h, h_d, N
            k = self.kernel_func(k)

            # power qk
            if power != 1.0:
                q_norm = q.norm(dim=-1, keepdim=True)
                k_norm = k.norm(dim=-1, keepdim=True)
                q = q**power
                k = k**power
                q = (q / (q.norm(dim=-1, keepdim=True) + 1e-6)) * q_norm
                k = (k / (k.norm(dim=-1, keepdim=True) + 1e-6)) * k_norm

            x = self.attn_matmul(q.permute(0, 2, 3, 1), k.permute(0, 2, 1, 3), v.permute(0, 2, 3, 1))

        x = x.view(b, n * d, s).permute(0, 2, 1).to(x_dtype)  # B, C, N -> B, N, C
        x = self.o(x)
        return x


class STConv(nn.Module):
    def __init__(self, in_dim, out_dim, kernel_size=3, stride=1, padding=1):
        super().__init__()
        self.spatial_conv = nn.Conv2d(in_dim, out_dim, kernel_size, stride, padding, groups=in_dim)
        self.temporal_conv = nn.Conv1d(in_dim, out_dim, kernel_size, stride, padding, groups=in_dim)

    def forward(self, x):
        B, C, T, H, W = x.shape
        x = x.permute(0, 2, 1, 3, 4).reshape(B * T, C, H, W)
        x = self.spatial_conv(x)
        x = x.reshape(B, T, C, H, W).permute(0, 3, 4, 2, 1).reshape(B * H * W, C, T)  # B, T, C, H, W -> B*H*W, C, T
        x = self.temporal_conv(x)
        x = x.reshape(B, H, W, C, T).permute(0, 3, 4, 1, 2).reshape(B, C, T, H, W)  # B*H*W, C, T -> B, C, T, H, W
        return x


class MLLALinearAttention(WanLinearAttention):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, **kwargs):
        super().__init__(dim, num_heads, window_size, qk_norm, eps, **kwargs)
        self.kernel_func = nn.ReLU(inplace=False)
        self.fp32_attention = True
        self.qkv_store_buffer = None
        self.st_conv = STConv(dim, dim)
        self.act = nn.SiLU(inplace=False)

    def forward(self, x, seq_lens, grid_sizes, freqs):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """

        F, H, W = grid_sizes[0]  # grid size should be the same for all the samples
        B, L, C = x.shape

        x = x.reshape(B, F, H, W, C).permute(0, 4, 1, 2, 3)  # B, C, F, H, W
        x = self.act(self.st_conv(x)).permute(0, 2, 3, 4, 1).reshape(B, L, C)

        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
        x_dtype = x.dtype

        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)  # B, seq, num_heads, head_dim
        q = rope_apply(q, grid_sizes, freqs)
        k = rope_apply(k, grid_sizes, freqs)

        # apply kernel function
        q = self.kernel_func(q)  # B, h, h_d, N
        k = self.kernel_func(k)
        # attn matmul input: q: b, h, h_d, n
        # k: b, h, n, h_d
        # v: b, h, h_d, n
        # output: b, h, n, h_d
        x = self.attn_matmul(q.permute(0, 2, 3, 1), k.permute(0, 2, 1, 3), v.permute(0, 2, 3, 1))
        x = x.view(b, n * d, s).permute(0, 2, 1).to(x_dtype)  # B, C, N -> B, N, C

        x = self.o(x)  # B, L, C

        return x


class MLLALePEAttention(WanLinearAttention):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, **kwargs):
        super().__init__(dim, num_heads, window_size, qk_norm, eps, **kwargs)
        self.kernel_func = nn.ELU(inplace=False)
        self.fp32_attention = True
        self.qkv_store_buffer = None
        self.st_conv = STConv(dim, dim)
        self.act = nn.SiLU(inplace=False)
        self.lepe_conv = STConv(dim, dim)

    def forward(self, x, seq_lens, grid_sizes, freqs):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """

        F, H, W = grid_sizes[0]  # grid size should be the same for all the samples
        B, L, C = x.shape

        x = x.reshape(B, F, H, W, C).permute(0, 4, 1, 2, 3)  # B, C, F, H, W
        x = self.act(self.st_conv(x)).permute(0, 2, 3, 4, 1).reshape(B, L, C)

        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
        x_dtype = x.dtype

        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)  # B, seq, num_heads, head_dim

        # apply kernel function before rope
        q = self.kernel_func(q) + 1
        k = self.kernel_func(k) + 1  # elu + 1

        # apply rope
        q = rope_apply(q, grid_sizes, freqs)
        k = rope_apply(k, grid_sizes, freqs)

        # attn matmul input: q: b, h, h_d, n
        # k: b, h, n, h_d
        # v: b, h, h_d, n
        # output: b, h, n, h_d
        x = self.attn_matmul(q.permute(0, 2, 3, 1), k.permute(0, 2, 1, 3), v.permute(0, 2, 3, 1))
        x = x.view(b, n * d, s).permute(0, 2, 1).to(x_dtype)  # B, C, N -> B, N, C
        # LePE conv for v
        v = v.reshape(B, F, H, W, C).permute(0, 4, 1, 2, 3)  # B, C, F, H, W
        lepe_v = self.lepe_conv(v).permute(0, 2, 3, 4, 1).reshape(B, L, C)

        x = self.o(x + lepe_v)  # B, L, C

        return x


class WanT2VCrossAttention(WanSelfAttention):
    def forward(self, x, context, context_lens):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
        """
        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)

        # compute attention
        x = flash_attention(q, k, v, k_lens=context_lens)

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


class WanI2VCrossAttention(WanSelfAttention):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
        super().__init__(dim, num_heads, window_size, qk_norm, eps)

        self.k_img = nn.Linear(dim, dim)
        self.v_img = nn.Linear(dim, dim)
        # self.alpha = nn.Parameter(torch.zeros((1, )))
        self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

    def forward(self, x, context, context_lens):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
        """
        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)
        img_x = flash_attention(q, k_img, v_img, k_lens=None)
        # compute attention
        x = flash_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


WAN_CROSSATTENTION_CLASSES = {
    "t2v_cross_attn": WanT2VCrossAttention,
    "i2v_cross_attn": WanI2VCrossAttention,
}

WAN_SELFATTENTION_CLASSES = {
    "flash": WanSelfAttention,
    "linear": WanLinearAttention,
    "mllalinear": MLLALinearAttention,
    "mllalepe": MLLALePEAttention,
    "bsa": WanSelfAttention,
}


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,
        self_attn_type="flash",
        rope_after=False,
        power=1.0,
        ffn_type="mlp",
        mlp_acts=("silu", "silu", None),
    ):
        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
        self.attn_hook: Optional[AttentionHook] = None
        self.block_hook: Optional[BlockHook] = None

        # layers
        self.norm1 = WanLayerNorm(dim, eps)
        self.self_attn = WAN_SELFATTENTION_CLASSES[self_attn_type](
            dim, num_heads, window_size, qk_norm, eps, rope_after=rope_after, power=power
        )
        self.self_attn_type = self_attn_type
        self.norm3 = WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
        self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](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))
        if ffn_type == "mlp":
            self.skip_ffn = None
        elif ffn_type == "GLUMBConvTemp":
            self.skip_ffn = GLUMBConvTemp(
                in_features=dim,
                hidden_features=ffn_dim,
                use_bias=(True, True, False),
                norm=(None, None, None),
                act=mlp_acts,
                t_kernel_size=3,
            )
            nn.init.zeros_(self.skip_ffn.t_conv.weight)
            nn.init.zeros_(self.skip_ffn.point_conv.conv.weight)

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

    def forward(
        self,
        x,
        e,
        seq_lens,
        grid_sizes,
        freqs,
        context,
        context_lens,
        block_mask=None,
    ):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            e(Tensor): Shape [B, 6, C]
            seq_lens(Tensor): Shape [B], length of each sequence in batch
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        assert e.dtype == torch.float32
        with amp.autocast(dtype=torch.float32):
            e = (self.modulation + e).chunk(6, dim=1)
        assert e[0].dtype == torch.float32

        intermediate_feats = {
            "x_in": x,
            "x_self_attn": None,
            "x_cross_attn": None,
            "x_ffn": None,
        }
        # self-attention
        x_dtype = x.dtype
        x_sa_in = (self.norm1(x).float() * (1 + e[1]) + e[0]).to(x_dtype)
        self_attn_kwargs = dict(
            x=x_sa_in,
            seq_lens=seq_lens,
            grid_sizes=grid_sizes,
            freqs=freqs,
        )

        y = self.self_attn(**self_attn_kwargs)

        # Call hook if registered
        if self.attn_hook is not None:
            self.attn_hook(y)

        with amp.autocast(dtype=torch.float32):
            x = x + y * e[2]

        intermediate_feats["x_self_attn"] = x

        # cross-attention
        x = x.to(x_dtype)
        x_ca_in = self.norm3(x)
        x = x + self.cross_attn(x_ca_in, context, context_lens)

        intermediate_feats["x_cross_attn"] = x

        # ffn
        ffn_in = (self.norm2(x).float() * (1 + e[4]) + e[3]).to(x_dtype)
        y = self.ffn(ffn_in)
        if self.skip_ffn is not None:
            y_skip = self.skip_ffn(ffn_in, HW=grid_sizes[0])  # grid_sizes[0] is three values for T,H,W
            y = y + y_skip

        with amp.autocast(dtype=torch.float32):
            x = x + y * e[5]

        intermediate_feats["x_ffn"] = x
        if self.block_hook is not None:
            self.block_hook(**intermediate_feats)

        del intermediate_feats

        return x.to(x_dtype)


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]
        """
        x_type = x.dtype
        assert e.dtype == torch.float32
        with amp.autocast(dtype=torch.float32):
            e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
            x = self.head(self.norm(x) * (1 + e[1]) + e[0])
        return x.to(x_type)


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 WanModel(ModelMixin, ConfigMixin):
    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"]
    ignore_for_config = ["cross_attn_norm", "qk_norm", "text_dim", "window_size"]
    _no_split_modules = ["WanAttentionBlock"]

    @register_to_config
    def __init__(
        self,
        model_type="t2v",
        patch_size=(1, 2, 2),
        text_len=512,
        in_dim=16,
        dim=2048,
        ffn_dim=8192,
        freq_dim=256,
        image_dim=1280,
        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,
    ):
        r"""
        Initialize the diffusion model backbone.

        Args:
            model_type (`str`, *optional*, defaults to 't2v'):
                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
            text_len (`int`, *optional*, defaults to 512):
                Fixed length for text embeddings
            in_dim (`int`, *optional*, defaults to 16):
                Input video channels (C_in)
            dim (`int`, *optional*, defaults to 2048):
                Hidden dimension of the transformer
            ffn_dim (`int`, *optional*, defaults to 8192):
                Intermediate dimension in feed-forward network
            freq_dim (`int`, *optional*, defaults to 256):
                Dimension for sinusoidal time embeddings
            text_dim (`int`, *optional*, defaults to 4096):
                Input dimension for text embeddings
            out_dim (`int`, *optional*, defaults to 16):
                Output video channels (C_out)
            num_heads (`int`, *optional*, defaults to 16):
                Number of attention heads
            num_layers (`int`, *optional*, defaults to 32):
                Number of transformer blocks
            window_size (`tuple`, *optional*, defaults to (-1, -1)):
                Window size for local attention (-1 indicates global attention)
            qk_norm (`bool`, *optional*, defaults to True):
                Enable query/key normalization
            cross_attn_norm (`bool`, *optional*, defaults to False):
                Enable cross-attention normalization
            eps (`float`, *optional*, defaults to 1e-6):
                Epsilon value for normalization layers
        """

        super().__init__()

        assert model_type in ["t2v", "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.enable_autocast = True

        # 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 = "t2v_cross_attn" if model_type == "t2v" else "i2v_cross_attn"
        self.blocks = nn.ModuleList(
            [
                WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps)
                for _ in range(num_layers)
            ]
        )

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

        # buffers (don't use register_buffer otherwise dtype will be changed in to())
        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(image_dim, dim)

        # initialize weights
        self.init_weights()
        self.lr_scale = None

    def forward(self, x, timestep, context, seq_len, clip_fea=None, y=None, **kwargs):
        r"""
        Forward pass through the diffusion model

        Args:
            x (List[Tensor]):
                List of input video tensors, each with shape [C_in, F, H, W]
            t (Tensor):
                Diffusion timesteps tensor of shape [B]
            context (List[Tensor]):
                List of text embeddings each with shape [L, C]
            seq_len (`int`):
                Maximum sequence length for positional encoding
            clip_fea (Tensor, *optional*):
                CLIP image features for image-to-video mode
            y (List[Tensor], *optional*):
                Conditional video inputs for image-to-video mode, same shape as x

        Returns:
            List[Tensor]:
                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
        """
        x = [_x.to(self.dtype) for _x in x]
        context = [_c.to(self.dtype) for _c in context]
        t = timestep
        # params
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.freqs.to(device)

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

        # 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)
        assert seq_lens.max() <= seq_len
        x = torch.cat([torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in 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

        # context
        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])
        )
        if clip_fea is not None:
            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
            context = torch.concat([context_clip, context], dim=1)

        # arguments
        kwargs = dict(
            e=e0,
            seq_lens=seq_lens,
            grid_sizes=grid_sizes,
            freqs=self.freqs,
            context=context.to(self.dtype),
            context_lens=context_lens,
        )

        for i, block in enumerate(self.blocks):
            if self.gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(block, x, **kwargs, use_reentrant=False)
            else:
                x = block(x, **kwargs)

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

        # unpatchify
        x = self.unpatchify(x, grid_sizes)
        return torch.stack(x, dim=0)  # .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=0.02)
        for m in self.time_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=0.02)

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

        # init zero for v_img in each block
        for block in self.blocks:
            if isinstance(block.cross_attn, WanI2VCrossAttention):
                nn.init.zeros_(block.cross_attn.v_img.weight)
                nn.init.zeros_(block.cross_attn.v_img.bias)

    def load_model_ckpt(self, pretrained_model_path, init_patch_embedding=False, verbose=True, enable_lora=False):
        if enable_lora:
            return self.load_base_model_peft_ckpt(pretrained_model_path, init_patch_embedding, verbose)

        logger = get_logger(__name__)
        logger.info(f"======> Loading pretrained model {pretrained_model_path} with missing keys <=======")
        if pretrained_model_path.endswith(".safetensors"):
            import safetensors

            pretrained_model_state_dict = safetensors.torch.load_file(pretrained_model_path, device="cpu")
        elif pretrained_model_path.endswith(".safetensors.index.json"):
            import json
            import os

            import safetensors

            index = json.load(open(pretrained_model_path))["weight_map"]
            safetensors_list = set(index.values())
            logger.info(f"======> Loading safetensors {safetensors_list} <=======")
            pretrained_model_state_dict = {}
            for safetensors_path in safetensors_list:
                pretrained_model_state_dict.update(
                    safetensors.torch.load_file(
                        os.path.join(os.path.dirname(pretrained_model_path), safetensors_path), device="cpu"
                    )
                )

        else:
            pretrained_model_state_dict = torch.load(pretrained_model_path)
        if "state_dict" in pretrained_model_state_dict:
            pretrained_model_state_dict = pretrained_model_state_dict["state_dict"]
        cur_state_dict = self.state_dict()
        new_state_dict = {}
        ## load multiview from temporal layer
        non_matched_keys = []
        for k, cur_v in cur_state_dict.items():
            new_state_dict[k] = cur_v
            if k not in pretrained_model_state_dict:
                non_matched_keys.append(k)
                continue
            elif cur_v.shape != pretrained_model_state_dict[k].shape:
                non_matched_keys.append(k)
                continue
            else:
                new_state_dict[k] = pretrained_model_state_dict[k]
        if "patch_embedding.weight" in non_matched_keys:
            # remove patch embedding bias
            non_matched_keys.append("patch_embedding.bias")
            new_state_dict["patch_embedding.bias"] = cur_state_dict["patch_embedding.bias"]
        if init_patch_embedding:
            logger.info("======> init patch embedding <=======")
            non_matched_keys.append("patch_embedding.weight")
            new_state_dict["patch_embedding.weight"] = cur_state_dict["patch_embedding.weight"]
            non_matched_keys.append("patch_embedding.bias")
            new_state_dict["patch_embedding.bias"] = cur_state_dict["patch_embedding.bias"]
            # init head
            non_matched_keys.append("head.head.weight")
            new_state_dict["head.head.weight"] = cur_state_dict["head.head.weight"]
            non_matched_keys.append("head.head.bias")
            new_state_dict["head.head.bias"] = cur_state_dict["head.head.bias"]

        if verbose:
            for nmk in non_matched_keys:
                logger.warning(f"Non matched key: {nmk}")

        self.load_state_dict(new_state_dict)

    def load_state_dict(self, state_dict, strict=True):
        """Load model with optimizations"""
        from tqdm import tqdm

        # Convert and move to device in chunks
        logger = get_logger(__name__)

        chunk_size = 100  # Process 100  parameters at a time
        logger.info(f"Loading model state dict with chunk size {chunk_size}")
        param_items = list(state_dict.items())
        missing_keys = set(self.state_dict().keys()) - set(state_dict.keys())
        unexpected_keys = set(state_dict.keys()) - set(self.state_dict().keys())
        for i in tqdm(range(0, len(param_items), chunk_size)):
            chunk = param_items[i : i + chunk_size]

            # Process chunk
            for name, tensor in chunk:
                try:
                    self.get_parameter(name)
                    exists = True
                except:
                    exists = False
                if exists:
                    # get device and dtype of the parameter
                    param_device = self.get_parameter(name).device
                    param_dtype = self.get_parameter(name).dtype
                    # Move to device with optimal settings
                    tensor = tensor.to(device=param_device, dtype=param_dtype, non_blocking=True)
                    # Set parameter data
                    self.get_parameter(name).data = tensor
                else:

                    if strict:
                        raise ValueError(f"Parameter {name} not found in model")

        return missing_keys, unexpected_keys

    def register_attn_hook(self, layers=None, device="cpu"):
        for i, block in enumerate(self.blocks):
            if layers is None or i in layers:
                block.attn_hook = AttentionHook(device)

    def get_attn_output(self):
        attn_outputs = {}
        for i, block in enumerate(self.blocks):
            if block.attn_hook is not None:
                attn_outputs[i] = block.attn_hook.output
                block.attn_hook.clear()
        return attn_outputs

    def register_block_hook(self, layers=None, device="cpu", detach=True, score_only=False):
        for i, block in enumerate(self.blocks):
            if layers is None or i in layers:
                block.block_hook = BlockHook(device, detach, score_only)

    def get_block_output(self):
        block_outputs = {}
        for i, block in enumerate(self.blocks):
            if block.block_hook is not None:
                block_outputs[i] = block.block_hook.get_output()
                block.block_hook.clear()
        return block_outputs


class WanLinearAttentionModel(WanModel):
    ignore_for_config = ["cross_attn_norm", "qk_norm", "text_dim", "window_size"]

    @register_to_config
    def __init__(
        self,
        model_type="t2v",
        patch_size=(1, 2, 2),
        text_len=512,
        in_dim=16,
        dim=2048,
        ffn_dim=8192,
        freq_dim=256,
        image_dim=1280,
        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,
        linear_attn_idx=None,
        attn_type="flash",  # flash, linear, mllalinear
        ffn_type="mlp",
        rope_after=False,
        power=1.0,
    ):
        super().__init__(
            model_type=model_type,
            patch_size=patch_size,
            text_len=text_len,
            in_dim=in_dim,
            dim=dim,
            ffn_dim=ffn_dim,
            freq_dim=freq_dim,
            image_dim=image_dim,
            text_dim=text_dim,
            out_dim=out_dim,
            num_heads=num_heads,
            num_layers=num_layers,
            window_size=window_size,
            qk_norm=qk_norm,
            cross_attn_norm=cross_attn_norm,
            eps=eps,
        )

        # blocks
        cross_attn_type = "t2v_cross_attn" if model_type == "t2v" else "i2v_cross_attn"
        self_attn_types = ["flash"] * num_layers
        ffn_types = ["mlp"] * num_layers
        if linear_attn_idx is not None:
            for la_idx in linear_attn_idx:
                self_attn_types[la_idx] = attn_type
                ffn_types[la_idx] = ffn_type

        self.self_attn_types = self_attn_types
        self.repo_after = rope_after
        self.power = power

        self.blocks = nn.ModuleList(
            [
                WanAttentionBlock(
                    cross_attn_type,
                    dim,
                    ffn_dim,
                    num_heads,
                    window_size,
                    qk_norm,
                    cross_attn_norm,
                    eps,
                    self_attn_types[i],
                    rope_after,
                    power,
                    ffn_types[i],
                )
                for i in range(num_layers)
            ]
        )

    def forward(self, x, timestep, context, seq_len, clip_fea=None, y=None, block_mask=None, **kwargs):
        r"""
        Forward pass through the diffusion model
        Same as WanModel, but save qkv for linear attention

        Args:
            x (List[Tensor]):
                List of input video tensors, each with shape [C_in, F, H, W]
            t (Tensor):
                Diffusion timesteps tensor of shape [B]
            context (List[Tensor]):
                List of text embeddings each with shape [L, C]
            seq_len (`int`):
                Maximum sequence length for positional encoding
            clip_fea (Tensor, *optional*):
                CLIP image features for image-to-video mode
            y (List[Tensor], *optional*):
                Conditional video inputs for image-to-video mode, same shape as x

        Returns:
            List[Tensor]:
                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
        """
        x = [_x.to(self.dtype) for _x in x]
        context = [_c.to(self.dtype) for _c in context]
        t = timestep
        self.inference_timestep = int(t[-1].item())
        if not self.training and self.inference_timestep >= 850:
            # NOTE: hard code now. Keep the first several steps using dense attention
            pass

        if self.model_type == "i2v":
            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)

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

        # 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)
        assert seq_lens.max() <= seq_len
        x = torch.cat([torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in 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

        # context
        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])
        )
        if clip_fea is not None:
            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
            context = torch.concat([context_clip, context], dim=1)

        for i, block in enumerate(self.blocks):

            # arguments
            kwargs = dict(
                e=e0,
                seq_lens=seq_lens,
                grid_sizes=grid_sizes,
                freqs=self.freqs,
                context=context.to(self.dtype),
                context_lens=context_lens,
                block_mask=None,
            )

            if self.gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(block, x, **kwargs, use_reentrant=False)
            else:
                x = block(x, **kwargs)

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

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

        return torch.stack(x, dim=0)  # .float()


def init_model_configs(model_cfg, vae_cfg):
    # 1.3B T2V
    model_name = model_cfg.model
    if "1300M" in model_name or "1.3B" in model_name:
        basic_config = {
            "model_type": "t2v",
            "dim": 1536,
            "eps": 1e-06,
            "ffn_dim": 8960,
            "freq_dim": 256,
            "in_dim": 16,
            "num_heads": 12,
            "num_layers": 30,
            "out_dim": 16,
            "text_len": 512,
            "patch_size": (1, 2, 2),
        }

    elif "14B" in model_name:  # default is T2V
        basic_config = {
            "model_type": "t2v",
            "dim": 5120,
            "eps": 1e-06,
            "ffn_dim": 13824,
            "freq_dim": 256,
            "in_dim": 16,
            "num_heads": 40,
            "num_layers": 40,
            "out_dim": 16,
            "text_len": 512,
            "patch_size": (1, 2, 2),
        }
    else:
        raise ValueError(f"Model {model_name} not found")

    # update basic config with specific model config
    # now all I2V are use cross attention
    if "i2v" in model_name.lower():
        basic_config["model_type"] = "i2v"
        in_dim = vae_cfg.vae_latent_dim * 2
        if model_cfg.mask is not None:
            in_dim += 4
    else:
        in_dim = vae_cfg.vae_latent_dim

    basic_config["in_dim"] = in_dim
    basic_config["out_dim"] = vae_cfg.vae_latent_dim
    basic_config["patch_size"] = model_cfg.patch_size
    basic_config["linear_attn_idx"] = model_cfg.linear_attn_idx
    basic_config["attn_type"] = model_cfg.self_attn_type
    basic_config["rope_after"] = model_cfg.rope_after
    basic_config["power"] = model_cfg.power
    basic_config["ffn_type"] = model_cfg.ffn_type

    return basic_config