import torch
from safetensors.torch import load_file

def load_model(path='model.safetensors'):
    return load_file(path)

def xor2(a, b, prefix, w):
    inp = torch.tensor([float(a), float(b)])
    or_out = int((inp @ w[f'{prefix}.or.weight'].T + w[f'{prefix}.or.bias'] >= 0).item())
    nand_out = int((inp @ w[f'{prefix}.nand.weight'].T + w[f'{prefix}.nand.bias'] >= 0).item())
    l1 = torch.tensor([float(or_out), float(nand_out)])
    return int((l1 @ w[f'{prefix}.and.weight'].T + w[f'{prefix}.and.bias'] >= 0).item())

def prefix_xor(x3, x2, x1, x0, w):
    y3 = x3
    y2 = xor2(y3, x2, 'xor2', w)
    y1 = xor2(y2, x1, 'xor1', w)
    y0 = xor2(y1, x0, 'xor0', w)
    return y3, y2, y1, y0

if __name__ == '__main__':
    w = load_model()
    print('Prefix-XOR (running parity):')
    for i in [0b0000, 0b0001, 0b0011, 0b0111, 0b1111, 0b1010]:
        x3, x2, x1, x0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
        y3, y2, y1, y0 = prefix_xor(x3, x2, x1, x0, w)
        print(f'{x3}{x2}{x1}{x0} -> {y3}{y2}{y1}{y0} (parity={y0})')