A=None
e_sd_pt="ko.pth"
d_sd_pt="ok.pth"
import torch as t, torch.nn as nn, torch.nn.functional as F
def C(n_in, n_out, **kwargs):
    return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
class Clamp(nn.Module):
    def forward(self, x):
        return t.tanh(x / 3) * 3
class B(nn.Module):
    def __init__(self, n_in, n_out):
        super().__init__()
        self.conv = nn.Sequential(C(n_in, n_out), nn.ReLU(), C(n_out, n_out), nn.ReLU(), C(n_out, n_out))
        self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
        self.fuse = nn.ReLU()
    def forward(self, x):
        return self.fuse(self.conv(x) + self.skip(x))
def E(latent_channels=4):
    return nn.Sequential(
        C(3, 64), B(64, 64),
        C(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64),
        C(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64),
        C(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64),
        C(64, latent_channels),
    )
def D(latent_channels=16):
    return nn.Sequential(
        Clamp(), 
        C(latent_channels, 48),nn.ReLU(),B(48, 48), B(48, 48),
        nn.Upsample(scale_factor=2), C(48, 48, bias=False),B(48, 48), B(48, 48), 
        nn.Upsample(scale_factor=2), C(48, 48, bias=False),B(48, 48),
        nn.Upsample(scale_factor=2), C(48, 48, bias=False),B(48, 48), 
        C(48, 3), 
    )    
class M(nn.Module):
    lm, ls = 3, 0.5
    def __init__(s, ep="encoder.pth", dp="decoder.pth", lc=None):
        super().__init__()
        if lc is None: lc = s.glc(str(ep))
        s.e, s.d = E(lc), D(lc)
        def f(sd, mod, pfx):
            f_sd = {k.strip(pfx): v for k, v in sd.items() if k.strip(pfx) in mod.state_dict() and v.size() == mod.state_dict()[k.strip(pfx)].size()}
            mod.load_state_dict(f_sd, strict=False)
        if ep: f(t.load(ep, map_location="cpu", weights_only=True), s.e, "encoder.")
        if dp: f(t.load(dp, map_location="cpu", weights_only=True), s.d, "decoder.")
        s.e.requires_grad_(False)
        s.d.requires_grad_(False)
    def glc(s, ep): return 16 if "taef1" in ep or "taesd3" in ep else 4
    @staticmethod
    def sl(x): return x.div(2 * M.lm).add(M.ls).clamp(0, 1)
    @staticmethod
    def ul(x): return x.sub(M.ls).mul(2 * M.lm)
    def forward(s, x, rl=False):
        l, o = s.e(x), s.d(s.e(x))
        return (o.clamp(0, 1), l) if rl else o.clamp(0, 1)
def filter_state_dict(model, name):
    state_dict = t.load(e_sd_pt if name=="E" else d_sd_pt, map_location="cpu", weights_only=True)
    prefix = 'encoder.' if name=="E" else 'decoder.'
    return {k.strip(prefix): v for k, v in state_dict.items() if k.strip(prefix) in model.state_dict() and v.size() == model.state_dict()[k.strip(prefix)].size()}
def _load(model, name, dtype=t.bfloat16):
    model = E(16) if name=="E" else D(16)
    model.load_state_dict(filter_state_dict(model, name), strict=False)
    model.requires_grad_(False).to(dtype=dtype)
    return model