import torch
import torch.nn as nn
import einops

from torch.nn import functional as F
from torch.jit import Final
from timm.layers import use_fused_attn
from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, get_act_layer

__all__ = ['SVDNoiseUnet', 'SVDNoiseUnet_Concise']

class Attention(nn.Module):
    fused_attn: Final[bool]

    def __init__(
            self,
            dim: int,
            num_heads: int = 8,
            qkv_bias: bool = False,
            qk_norm: bool = False,
            attn_drop: float = 0.,
            proj_drop: float = 0.,
            norm_layer: nn.Module = nn.LayerNorm,
    ) -> None:
        super().__init__()
        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        self.fused_attn = use_fused_attn()

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)
        q, k = self.q_norm(q), self.k_norm(k)

        if self.fused_attn:
            x = F.scaled_dot_product_attention(
                q, k, v,
                dropout_p=self.attn_drop.p if self.training else 0.,
            )
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class SVDNoiseUnet(nn.Module):
    def __init__(self, in_channels=4, out_channels=4, resolution=128): # resolution = size // 8
        super(SVDNoiseUnet, self).__init__()

        _in = int(resolution * in_channels // 2)
        _out = int(resolution * out_channels // 2)
        self.mlp1 = nn.Sequential(
            nn.Linear(_in, 64),
            nn.ReLU(inplace=True),
            nn.Linear(64, _out),
        )
        self.mlp2 = nn.Sequential(
            nn.Linear(_in, 64),
            nn.ReLU(inplace=True),
            nn.Linear(64, _out),
        )

        self.mlp3 = nn.Sequential(
            nn.Linear(_in, _out),
        )

        self.attention = Attention(_out)

        self.bn = nn.BatchNorm2d(_out)

        self.mlp4 =  nn.Sequential(
            nn.Linear(_out, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, _out),
        )

    def forward(self, x, residual=False):
        b, c, h, w = x.shape
        x = einops.rearrange(x, "b (a c)h w ->b (a h)(c w)", a=2,c=2) # x -> [1, 256, 256]
        U, s, V = torch.linalg.svd(x) # U->[b 256 256], s-> [b 256], V->[b 256 256]
        U_T = U.permute(0, 2, 1)
        out = self.mlp1(U_T) + self.mlp2(V) + self.mlp3(s).unsqueeze(1) # s -> [b, 1, 256]  => [b, 256, 256]
        out = self.attention(out).mean(1)
        out = self.mlp4(out) + s
        pred = U @ torch.diag_embed(out) @ V
        return einops.rearrange(pred, "b (a h)(c w) -> b (a c) h w", a=2,c=2)
    

class SVDNoiseUnet_Concise(nn.Module):
    def __init__(self, in_channels=4, out_channels=4, resolution=128):
        super(SVDNoiseUnet_Concise, self).__init__()