import math

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

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):
    # x: [b, n, d]
    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()


@amp.autocast(enabled=False)
def rope_apply_ar(x, grid_sizes, freqs, ar_window_f, n_latent_f):
    """
    freqs: list of 3 tensors, freq_f, freq_h, freq_w
     of shape [1024, 22], [1024, 21], [1024, 21] for head_dim=64.
    Does not support variable length.
    # TODO! make this faster! precompute the freqs!
    """
    # x: [b, n, d]
    n, c = x.size(2), x.size(3) // 2

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

    total_f = n_latent_f * 2 - ar_window_f

    latent_f = n_latent_f

    num_chunks_per_video = latent_f // ar_window_f

    freq_f = freqs[0][:latent_f]  # [latent_f, 22]
    interleave_freq_f = freq_f.repeat(2, 1)  # [2 * latent_f, 22]

    for i in range(num_chunks_per_video):
        start = i * ar_window_f * 2
        end = start + ar_window_f

        interleave_freq_f[start:end] = freq_f[i * ar_window_f : (i + 1) * ar_window_f]
        interleave_freq_f[start + ar_window_f : end + ar_window_f] = freq_f[
            i * ar_window_f : (i + 1) * ar_window_f
        ]

    interleave_freq_f = interleave_freq_f[:total_f]
    interleave_freq_f = interleave_freq_f.view(total_f, 1, 1, -1)

    # loop over samples
    output = []
    for i, (_, h, w) in enumerate(grid_sizes.tolist()):
        seq_len = total_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(
            [
                interleave_freq_f.expand(total_f, h, w, -1),
                freqs[1][:h].view(1, h, 1, -1).expand(total_f, h, w, -1),
                freqs[2][:w].view(1, 1, w, -1).expand(total_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()


def precompute_freqs_time_for_repeat(
    head_dim, freqs, ar_window_f, n_latent_f, num_repeat=1
):
    """
    freqs: list of 3 tensors, freq_f, freq_h, freq_w
     of shape [1024, 22], [1024, 21], [1024, 21] for head_dim=64.
    """
    c = head_dim // 2
    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)

    total_f = n_latent_f * (1 + num_repeat) - ar_window_f

    num_chunks_per_video = n_latent_f // ar_window_f

    freq_f = freqs[0][:n_latent_f]  # [latent_f, 22]

    interleave_freq_f = freq_f.repeat(
        1 + num_repeat, 1
    )  # [(num_repeat + 1) * latent_f, 22]

    for i in range(num_chunks_per_video):
        # start noise, then start clean.  Then repeat.
        start = i * ar_window_f * (num_repeat + 1)
        end_noise = start + num_repeat * ar_window_f
        start_clean = end_noise
        end_clean = start_clean + ar_window_f

        interleave_freq_f[start:end_noise] = freq_f[
            i * ar_window_f : (i + 1) * ar_window_f
        ].repeat(num_repeat, 1)
        interleave_freq_f[start_clean:end_clean] = freq_f[
            i * ar_window_f : (i + 1) * ar_window_f
        ]
        # print("debug interleave_freq_f", interleave_freq_f.shape, start, end_noise, start_clean, end_clean, "num chunks", num_chunks_per_video)

    # drop the last clean chunk. # [total_f, c//3]
    interleave_freq_f = interleave_freq_f[:total_f]

    return interleave_freq_f


@amp.autocast(enabled=False)
def rope_apply_ar_with_repeat(
    x, grid_sizes, freqs, ar_window_f, n_latent_f, interleave_freqs_time, num_repeat=1
):
    """
    freqs: list of 3 tensors, freq_f, freq_h, freq_w
     of shape [1024, 22], [1024, 21], [1024, 21] for head_dim=64.
    Does not support variable length.
    # TODO!
    # Also, make it faster!
    """
    # x: [b, n, d]
    n, c = x.size(2), x.size(3) // 2

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

    total_f = n_latent_f * (1 + num_repeat) - ar_window_f

    interleave_freq_f = interleave_freqs_time.view(total_f, 1, 1, -1)

    # loop over samples
    output = []
    for i, (_, h, w) in enumerate(grid_sizes.tolist()):
        seq_len = total_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(
            [
                interleave_freq_f.expand(total_f, h, w, -1),
                freqs[1][:h].view(1, h, 1, -1).expand(total_f, h, w, -1),
                freqs[2][:w].view(1, 1, w, -1).expand(total_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()


@amp.autocast(enabled=False)
def rope_apply_ar_inference(x, grid_sizes, freqs, ar_window_f, f_offset, interleave):
    """
    grid_sizes: list of 3 ints, F, H, W
        where F is the number of latent frames for each video in the batch.
    freqs: list of 3 tensors, freq_f, freq_h, freq_w
     of shape [1024, 22], [1024, 21], [1024, 21] for head_dim=64.
    f_offset: int, the offset of the first frame in the batch.
    interleave: bool, whether to interleave the frequency.
    Does not support variable length.
    """
    # x: [b, n, d]
    n, c = x.size(2), x.size(3) // 2

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

    total_f = grid_sizes[0][0]
    latent_f = total_f
    if interleave:
        total_f = total_f * 2

    n_chunks = 1

    if interleave:
        interleave_freq_f = freqs[0][
            f_offset - latent_f : f_offset + latent_f
        ]  # [2 * latent_f, 22]
    else:
        interleave_freq_f = freqs[0][f_offset : f_offset + latent_f]  # [latent_f, 22]

    interleave_freq_f = interleave_freq_f[:total_f]
    interleave_freq_f = interleave_freq_f.view(total_f, 1, 1, -1)
    # if interleave:
    #     print("debug interleave_freq_f", interleave_freq_f.shape, ar_window_f, f_offset, interleave)

    # loop over samples
    output = []
    for i, (_, h, w) in enumerate(grid_sizes.tolist()):
        seq_len = total_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(
            [
                interleave_freq_f.expand(total_f, h, w, -1),
                freqs[1][:h].view(1, h, 1, -1).expand(total_f, h, w, -1),
                freqs[2][:w].view(1, 1, w, -1).expand(total_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.float()).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 forward(self, x, seq_lens, grid_sizes, freqs):
        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

        # [b, s, n_h, d]
        q, k, v = qkv_fn(x)

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

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


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,
}


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,
    ):
        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).float() * (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, context_lens, e):
            x = x + self.cross_attn(self.norm3(x), context, context_lens)
            y = self.ffn(self.norm2(x).float() * (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)
        extra_info = None
        return x, extra_info


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 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

        # 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  # might not use it.

    def _set_gradient_checkpointing(self, module, value=False):
        self.gradient_checkpointing = value

    def forward(
        self,
        x,
        t,
        context,
        seq_len,
        clip_fea=None,
        y=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
        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,
            context_lens=context_lens,
        )

        extra_info_list = []
        for block in self.blocks:
            x, extra_info = block(x, **kwargs)
            extra_info_list.append(extra_info)

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

        # unpatchify
        x = self.unpatchify(x, grid_sizes)
        return [u.float() for u in x], extra_info_list

    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)
