# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
from torch import nn
import torch
from typing import Tuple, Optional
from einops import rearrange
import torch.nn.functional as F
import math
from shared.attention import pay_attention

MEMORY_LAYOUT = {
    "flash": (
        lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]),
        lambda x: x,
    ),
    "torch": (
        lambda x: x.transpose(1, 2),
        lambda x: x.transpose(1, 2),
    ),
    "vanilla": (
        lambda x: x.transpose(1, 2),
        lambda x: x.transpose(1, 2),
    ),
}


def attention(
    q,
    k,
    v,
    mode="torch",
    drop_rate=0,
    attn_mask=None,
    causal=False,
    max_seqlen_q=None,
    batch_size=1,
):
    """
    Perform QKV self attention.

    Args:
        q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads.
        k (torch.Tensor): Key tensor with shape [b, s1, a, d]
        v (torch.Tensor): Value tensor with shape [b, s1, a, d]
        mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'.
        drop_rate (float): Dropout rate in attention map. (default: 0)
        attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla).
            (default: None)
        causal (bool): Whether to use causal attention. (default: False)
        cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
            used to index into q.
        cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
            used to index into kv.
        max_seqlen_q (int): The maximum sequence length in the batch of q.
        max_seqlen_kv (int): The maximum sequence length in the batch of k and v.

    Returns:
        torch.Tensor: Output tensor after self attention with shape [b, s, ad]
    """
    pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode]

    if mode == "torch":
        if attn_mask is not None and attn_mask.dtype != torch.bool:
            attn_mask = attn_mask.to(q.dtype)
        x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal)

    elif mode == "flash":
        x = flash_attn_func(
            q,
            k,
            v,
        )
        x = x.view(batch_size, max_seqlen_q, x.shape[-2], x.shape[-1])  # reshape x to [b, s, a, d]
    elif mode == "vanilla":
        scale_factor = 1 / math.sqrt(q.size(-1))

        b, a, s, _ = q.shape
        s1 = k.size(2)
        attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device)
        if causal:
            # Only applied to self attention
            assert attn_mask is None, "Causal mask and attn_mask cannot be used together"
            temp_mask = torch.ones(b, a, s, s, dtype=torch.bool, device=q.device).tril(diagonal=0)
            attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
            attn_bias.to(q.dtype)

        if attn_mask is not None:
            if attn_mask.dtype == torch.bool:
                attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
            else:
                attn_bias += attn_mask

        attn = (q @ k.transpose(-2, -1)) * scale_factor
        attn += attn_bias
        attn = attn.softmax(dim=-1)
        attn = torch.dropout(attn, p=drop_rate, train=True)
        x = attn @ v
    else:
        raise NotImplementedError(f"Unsupported attention mode: {mode}")

    x = post_attn_layout(x)
    b, s, a, d = x.shape
    out = x.reshape(b, s, -1)
    return out


class CausalConv1d(nn.Module):

    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", **kwargs):
        super().__init__()

        self.pad_mode = pad_mode
        padding = (kernel_size - 1, 0)  # T
        self.time_causal_padding = padding

        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)

    def forward(self, x):
        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
        return self.conv(x)



class FaceEncoder(nn.Module):
    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None):
        factory_kwargs = {"dtype": dtype, "device": device}
        super().__init__()

        self.num_heads = num_heads
        self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1)
        self.norm1 = nn.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
        self.act = nn.SiLU()
        self.conv2 = CausalConv1d(1024, 1024, 3, stride=2)
        self.conv3 = CausalConv1d(1024, 1024, 3, stride=2)

        self.out_proj = nn.Linear(1024, hidden_dim)
        self.norm1 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)

        self.norm2 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)

        self.norm3 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)

        self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim))

    def forward(self, x):
        
        x = rearrange(x, "b t c -> b c t")
        b, c, t = x.shape

        x = self.conv1_local(x)
        x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
        
        x = self.norm1(x)
        x = self.act(x)
        x = rearrange(x, "b t c -> b c t")
        x = self.conv2(x)
        x = rearrange(x, "b c t -> b t c")
        x = self.norm2(x)
        x = self.act(x)
        x = rearrange(x, "b t c -> b c t")
        x = self.conv3(x)
        x = rearrange(x, "b c t -> b t c")
        x = self.norm3(x)
        x = self.act(x)
        x = self.out_proj(x)
        x = rearrange(x, "(b n) t c -> b t n c", b=b)
        padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1)
        x = torch.cat([x, padding], dim=-2)
        x_local = x.clone()

        return x_local



class RMSNorm(nn.Module):
    def __init__(
        self,
        dim: int,
        elementwise_affine=True,
        eps: float = 1e-6,
        device=None,
        dtype=None,
    ):
        """
        Initialize the RMSNorm normalization layer.

        Args:
            dim (int): The dimension of the input tensor.
            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.

        Attributes:
            eps (float): A small value added to the denominator for numerical stability.
            weight (nn.Parameter): Learnable scaling parameter.

        """
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        self.eps = eps
        if elementwise_affine:
            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))

    def _norm(self, x):
        """
        Apply the RMSNorm normalization to the input tensor.

        Args:
            x (torch.Tensor): The input tensor.

        Returns:
            torch.Tensor: The normalized tensor.

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

    def forward(self, x):
        """
        Forward pass through the RMSNorm layer.

        Args:
            x (torch.Tensor): The input tensor.

        Returns:
            torch.Tensor: The output tensor after applying RMSNorm.

        """
        output = self._norm(x.float()).type_as(x)
        if hasattr(self, "weight"):
            output = output * self.weight
        return output


def get_norm_layer(norm_layer):
    """
    Get the normalization layer.

    Args:
        norm_layer (str): The type of normalization layer.

    Returns:
        norm_layer (nn.Module): The normalization layer.
    """
    if norm_layer == "layer":
        return nn.LayerNorm
    elif norm_layer == "rms":
        return RMSNorm
    else:
        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")


class FaceAdapter(nn.Module):
    def __init__(
        self,
        hidden_dim: int,
        heads_num: int,
        qk_norm: bool = True,
        qk_norm_type: str = "rms",
        num_adapter_layers: int = 1,
        dtype=None,
        device=None,
    ):

        factory_kwargs = {"dtype": dtype, "device": device}
        super().__init__()
        self.hidden_size = hidden_dim
        self.heads_num = heads_num
        self.fuser_blocks = nn.ModuleList(
            [
                FaceBlock(
                    self.hidden_size,
                    self.heads_num,
                    qk_norm=qk_norm,
                    qk_norm_type=qk_norm_type,
                    **factory_kwargs,
                )
                for _ in range(num_adapter_layers)
            ]
        )

    def forward(
        self,
        x: torch.Tensor,
        motion_embed: torch.Tensor,
        idx: int,
        freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
        freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
    ) -> torch.Tensor:

        return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)



class FaceBlock(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        heads_num: int,
        qk_norm: bool = True,
        qk_norm_type: str = "rms",
        qk_scale: float = None,
        dtype: Optional[torch.dtype] = None,
        device: Optional[torch.device] = None,
    ):
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()

        self.deterministic = False
        self.hidden_size = hidden_size
        self.heads_num = heads_num
        head_dim = hidden_size // heads_num
        self.scale = qk_scale or head_dim**-0.5
       
        self.linear1_kv = nn.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
        self.linear1_q = nn.Linear(hidden_size, hidden_size, **factory_kwargs)

        self.linear2 = nn.Linear(hidden_size, hidden_size, **factory_kwargs)

        qk_norm_layer = get_norm_layer(qk_norm_type)
        self.q_norm = (
            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
        )
        self.k_norm = (
            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
        )

        self.pre_norm_feat = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)

        self.pre_norm_motion = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)

    def forward(
        self,
        x: torch.Tensor,
        motion_vec: torch.Tensor,
        motion_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        
        B, T, N, C = motion_vec.shape
        T_comp = T

        x_motion = self.pre_norm_motion(motion_vec)
        x_feat = self.pre_norm_feat(x)

        kv = self.linear1_kv(x_motion)
        q = self.linear1_q(x_feat)

        k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
        q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)

        # Apply QK-Norm if needed.
        q = self.q_norm(q).to(v)
        k = self.k_norm(k).to(v)

        k = rearrange(k, "B L N H D -> (B L) N H D")  
        v = rearrange(v, "B L N H D -> (B L) N H D") 

        q = rearrange(q, "B (L S) H D -> (B L) S H D", L=T_comp)  
        # Compute attention.
    # Size([batches, tokens, heads, head_features])
        qkv_list = [q, k, v]
        del q,k,v
        attn = pay_attention(qkv_list)
        # attn = attention(
        #     q,
        #     k,
        #     v,
        #     max_seqlen_q=q.shape[1],
        #     batch_size=q.shape[0],
        # )

        attn = attn.reshape(*attn.shape[:2], -1)
        attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp)

        output = self.linear2(attn)

        if motion_mask is not None:
            output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)

        return output