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

import torch
import torch.nn as nn
import torch.utils.checkpoint
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from einops import repeat

from .attention import flash_attention

__all__ = ['WanModel']


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

        # Match the original Wan implementation exactly.  RoPE is evaluated in
        # fp64/complex128 and cast back only after the rotation; changing this
        # globally also changes the causal student's pretrained forward.
        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).type_as(x)


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)

    def reset_parameters(self):
        # Required by FSDP's meta-device materialisation path; matches __init__.
        nn.init.ones_(self.weight)


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):
        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()

    def project_kv(self, x, grid_sizes, freqs, token_indices=None):
        """Project the exact K/V used by attention, optionally retaining tokens.

        This is also used by the last encoder block's KV-only fast path, where Q,
        attention output, cross-attention and FFN are unnecessary.
        """
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
        k = self.norm_k(self.k(x)).view(b, s, n, d)
        v = self.v(x).view(b, s, n, d)
        k = rope_apply(k, grid_sizes, freqs)
        if token_indices is not None:
            indices = token_indices.to(k.device)
            k = k.index_select(1, indices)
            v = v.index_select(1, indices)
        return k, v

    def forward(
        self,
        x,
        seq_lens,
        grid_sizes,
        freqs,
        return_kv=False,
        kv_token_indices=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

        # 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)
        # These are the tensors that are actually consumed by attention.  Keeping
        # K after RoPE (rather than the pre-RoPE projection) makes the extracted
        # feature identical to a real self-attention KV cache.
        k_attn = rope_apply(k, grid_sizes, freqs)

        x = flash_attention(
            q=rope_apply(q, grid_sizes, freqs),
            k=k_attn,
            v=v,
            k_lens=seq_lens,
            window_size=self.window_size)

        # output
        x = x.flatten(2)
        x = self.o(x)
        if return_kv:
            # Attention needs full K/V, but downstream MMD does not. Slice here,
            # before returning to the block's cross-attention/FFN, so full K/V do
            # not stay alive for the remainder of a checkpointed block.
            if kv_token_indices is not None:
                indices = kv_token_indices.to(k_attn.device)
                k_attn = k_attn.index_select(1, indices)
                v = v.index_select(1, indices)
            return x, k_attn, v
        return x


class WanT2VCrossAttention(WanSelfAttention):

    def forward(self, x, context, context_lens, crossattn_cache=None):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
            crossattn_cache (List[dict], *optional*): Contains the cached key and value tensors for context embedding.
        """
        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)

        if crossattn_cache is not None:
            if not crossattn_cache["is_init"]:
                crossattn_cache["is_init"] = True
                k = self.norm_k(self.k(context)).view(b, -1, n, d)
                v = self.v(context).view(b, -1, n, d)
                crossattn_cache["k"] = k
                crossattn_cache["v"] = v
            else:
                k = crossattn_cache["k"]
                v = crossattn_cache["v"]
        else:
            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 WanGanCrossAttention(WanSelfAttention):

    def forward(self, x, context, crossattn_cache=None):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
            crossattn_cache (List[dict], *optional*): Contains the cached key and value tensors for context embedding.
        """
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
        qq = self.norm_q(self.q(context)).view(b, 1, -1, d)

        kk = self.norm_k(self.k(x)).view(b, -1, n, d)
        vv = self.v(x).view(b, -1, n, d)

        # compute attention
        x = flash_attention(qq, kk, vv)

        # 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,
}


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):
        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 = 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))

        # 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,
        return_kv=False,
        kv_token_indices=None,
        kv_only=False,
    ):
        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

        modulated_x = self.norm1(x) * (1 + e[1]) + e[0]
        if kv_only:
            if not return_kv:
                raise ValueError("kv_only requires return_kv=True")
            return self.self_attn.project_kv(
                modulated_x, grid_sizes, freqs, token_indices=kv_token_indices)

        # self-attention
        attn_out = self.self_attn(
            modulated_x, seq_lens, grid_sizes, freqs,
            return_kv=return_kv, kv_token_indices=kv_token_indices)
        if return_kv:
            y, k, v = attn_out
        else:
            y = attn_out
        # with amp.autocast(dtype=torch.float32):
        x = x + y * e[2]

        # cross-attention & ffn function
        def cross_attn_ffn(x, context, context_lens, e):
            x = x + self.cross_attn(self.norm3(x), context, context_lens)
            y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
            # with amp.autocast(dtype=torch.float32):
            x = x + y * e[5]
            return x

        x = cross_attn_ffn(x, context, context_lens, e)
        if return_kv:
            return x, k, v
        return x


class GanAttentionBlock(nn.Module):

    def __init__(self,
                 dim=1536,
                 ffn_dim=8192,
                 num_heads=12,
                 window_size=(-1, -1),
                 qk_norm=True,
                 cross_attn_norm=True,
                 eps=1e-6):
        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.norm2 = WanLayerNorm(dim, eps)
        self.ffn = nn.Sequential(
            nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
            nn.Linear(ffn_dim, dim))

        self.cross_attn = WanGanCrossAttention(dim, num_heads,
                                               (-1, -1),
                                               qk_norm,
                                               eps)

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

    def forward(
        self,
        x,
        context,
        # seq_lens,
        # grid_sizes,
        # freqs,
        # context,
        # context_lens,
    ):
        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

        # # self-attention
        # y = self.self_attn(
        #     self.norm1(x) * (1 + e[1]) + e[0], seq_lens, grid_sizes,
        #     freqs)
        # # with amp.autocast(dtype=torch.float32):
        # x = x + y * e[2]

        # cross-attention & ffn function
        def cross_attn_ffn(x, context):
            token = context + self.cross_attn(self.norm3(x), context)
            y = self.ffn(self.norm2(token)) + token  # * (1 + e[4]) + e[3])
            # with amp.autocast(dtype=torch.float32):
            # x = x + y * e[5]
            return y

        x = cross_attn_ffn(x, context)
        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 + 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 RegisterTokens(nn.Module):
    def __init__(self, num_registers: int, dim: int):
        super().__init__()
        self.register_tokens = nn.Parameter(torch.randn(num_registers, dim) * 0.02)
        self.rms_norm = WanRMSNorm(dim, eps=1e-6)

    def forward(self):
        return self.rms_norm(self.register_tokens)

    def reset_parameters(self):
        nn.init.normal_(self.register_tokens, std=0.02)


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'
    ]
    _no_split_modules = ['WanAttentionBlock']
    _supports_gradient_checkpointing = True

    @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,
                 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.local_attn_size = 21

        # 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(1280, dim)

        # initialize weights
        self.init_weights()

        self.gradient_checkpointing = False

    def _set_gradient_checkpointing(
        self,
        module=None,
        value=False,
        enable=None,
        gradient_checkpointing_func=None,
    ):
        self.gradient_checkpointing = value if enable is None else enable

    def forward(
        self,
        *args,
        **kwargs
    ):
        # if kwargs.get('classify_mode', False) is True:
        # kwargs.pop('classify_mode')
        # return self._forward_classify(*args, **kwargs)
        # else:
        return self._forward(*args, **kwargs)

    def _forward(
        self,
        x,
        t,
        context,
        seq_len,
        classify_mode=False,
        concat_time_embeddings=False,
        register_tokens=None,
        cls_pred_branch=None,
        gan_ca_blocks=None,
        clip_fea=None,
        y=None,
        kv_feature_layers=None,
        kv_token_indices=None,
        latent_feature_layer=None,
        latent_token_indices=None,
    ):
        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]
        """
        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
        if kv_feature_layers is not None and latent_feature_layer is not None:
            raise ValueError(
                "KV and post-block latent feature extraction are mutually exclusive")
        if kv_token_indices is not None and kv_feature_layers is None:
            raise ValueError(
                "kv_token_indices requires kv_feature_layers")
        if latent_token_indices is not None and latent_feature_layer is None:
            raise ValueError(
                "latent_token_indices requires latent_feature_layer")

        def validate_token_indices(name, indices):
            if indices is None:
                return
            if not isinstance(indices, torch.Tensor):
                raise ValueError(f"{name} must be a torch.Tensor")
            if indices.ndim != 1 or indices.dtype != torch.long:
                raise ValueError(f"{name} must be a 1-D torch.long tensor")
            if indices.numel() == 0:
                raise ValueError(f"{name} cannot be empty")
            min_index = int(indices.min().item())
            max_index = int(indices.max().item())
            if min_index < 0 or max_index >= int(seq_lens.min().item()):
                raise ValueError(
                    f"{name} range [{min_index}, {max_index}] exceeds "
                    f"the shortest valid token sequence ({int(seq_lens.min().item())})")

        if kv_feature_layers is not None:
            validate_token_indices("kv_token_indices", kv_token_indices)
        if latent_feature_layer is not None:
            if (
                isinstance(latent_feature_layer, bool)
                or not isinstance(latent_feature_layer, int)
            ):
                raise ValueError("latent_feature_layer must be an integer block index")
            if latent_feature_layer < 0 or latent_feature_layer >= len(self.blocks):
                raise ValueError(
                    f"latent_feature_layer={latent_feature_layer} outside "
                    f"[0, {len(self.blocks) - 1}]")
            validate_token_indices("latent_token_indices", latent_token_indices)
        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).type_as(x))
        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,
            context_lens=context_lens)

        def create_custom_forward(module):
            def custom_forward(*inputs, **kwargs):
                return module(*inputs, **kwargs)
            return custom_forward

        # Frozen-Wan post-block latent encoder path.  Unlike the final KV-only
        # shortcut below, the requested block runs in full (self-attention,
        # cross-attention, FFN and all residual updates).  The diffusion head
        # and all later blocks are intentionally skipped.
        if latent_feature_layer is not None:
            for ii, block in enumerate(self.blocks):
                if torch.is_grad_enabled() and self.gradient_checkpointing:
                    x = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(block),
                        x, **kwargs,
                        use_reentrant=False,
                    )
                else:
                    x = block(x, **kwargs)
                if ii == latent_feature_layer:
                    if x.ndim != 3 or x.shape[-1] != self.dim:
                        raise RuntimeError(
                            "post-block latent must have shape [batch, tokens, dim]")
                    if latent_token_indices is not None:
                        indices = latent_token_indices.to(device=x.device)
                        x = x.index_select(1, indices)
                    return x
            raise RuntimeError(
                "latent feature block was not reached despite validated index")

        # Frozen-Wan feature-encoder path.  Only the prefix ending at the last
        # requested block is evaluated.  K/V are concatenated along the feature
        # dimension and remain a [B, tokens, features] matrix; no global pooling
        # or vector reshape is applied.
        if kv_feature_layers is not None:
            capture = sorted({int(i) for i in kv_feature_layers})
            if not capture:
                raise ValueError("kv_feature_layers must contain at least one block index")
            if capture[0] < 0 or capture[-1] >= len(self.blocks):
                raise ValueError(
                    f"kv_feature_layers={capture} outside [0, {len(self.blocks) - 1}]")
            captured = []
            capture_set = set(capture)
            last_capture = capture[-1]
            for ii, block in enumerate(self.blocks):
                if ii > last_capture:
                    break
                block_kwargs = dict(kwargs)
                block_kwargs["return_kv"] = ii in capture_set
                block_kwargs["kv_token_indices"] = (
                    kv_token_indices if ii in capture_set else None)
                # The last requested feature only needs this block's modulated
                # K/V projections. No later block consumes its full output.
                block_kwargs["kv_only"] = ii == last_capture
                if torch.is_grad_enabled() and self.gradient_checkpointing:
                    out = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(block),
                        x, **block_kwargs,
                        use_reentrant=False,
                    )
                else:
                    out = block(x, **block_kwargs)
                if ii == last_capture:
                    k, v = out
                    kv = torch.cat([k.flatten(2), v.flatten(2)], dim=-1)
                    captured.append(kv)
                    break
                if ii in capture_set:
                    x, k, v = out
                    # [B,L,H,Dh] -> [B,L,2*D].
                    kv = torch.cat([k.flatten(2), v.flatten(2)], dim=-1)
                    captured.append(kv)
                else:
                    x = out
            return torch.cat(captured, dim=-1)

        # TODO: Tune the number of blocks for feature extraction
        final_x = None
        if classify_mode:
            assert register_tokens is not None
            assert gan_ca_blocks is not None
            assert cls_pred_branch is not None

            final_x = []
            registers = repeat(register_tokens(), "n d -> b n d", b=x.shape[0])
            # x = torch.cat([registers, x], dim=1)

        gan_idx = 0
        for ii, block in enumerate(self.blocks):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    x, **kwargs,
                    use_reentrant=False,
                )
            else:
                x = block(x, **kwargs)

            if classify_mode and ii in [13, 21, 29]:
                gan_token = registers[:, gan_idx: gan_idx + 1]
                final_x.append(gan_ca_blocks[gan_idx](x, gan_token))
                gan_idx += 1

        if classify_mode:
            final_x = torch.cat(final_x, dim=1)
            if concat_time_embeddings:
                final_x = cls_pred_branch(torch.cat([final_x, 10 * e[:, None, :]], dim=1).view(final_x.shape[0], -1))
            else:
                final_x = cls_pred_branch(final_x.view(final_x.shape[0], -1))

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

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

        if classify_mode:
            return torch.stack(x), final_x

        return torch.stack(x)

    def _forward_classify(
        self,
        x,
        t,
        context,
        seq_len,
        register_tokens,
        cls_pred_branch,
        clip_fea=None,
        y=None,
    ):
        r"""
        Feature extraction 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 video features with original input shapes [C_block, F, H / 8, W / 8]
        """
        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).type_as(x))
        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,
            context_lens=context_lens)

        def create_custom_forward(module):
            def custom_forward(*inputs, **kwargs):
                return module(*inputs, **kwargs)
            return custom_forward

        # TODO: Tune the number of blocks for feature extraction
        for block in self.blocks[:16]:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    x, **kwargs,
                    use_reentrant=False,
                )
            else:
                x = block(x, **kwargs)

        # unpatchify
        x = self.unpatchify(x, grid_sizes, c=self.dim // 4)
        return torch.stack(x)

    def unpatchify(self, x, grid_sizes, c=None):
        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 if c is None else c
        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)