import torch
from safetensors.torch import load_file

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

def xor2(a, b, prefix, w):
    or_out = int(a * w[f'{prefix}.or.weight'][0] + b * w[f'{prefix}.or.weight'][1] + w[f'{prefix}.or.bias'] >= 0)
    nand_out = int(a * w[f'{prefix}.nand.weight'][0] + b * w[f'{prefix}.nand.weight'][1] + w[f'{prefix}.nand.bias'] >= 0)
    and_out = int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0)
    return and_out

def parity3(a, b, c, weights):
    xor_ab = xor2(a, b, 'xor1', weights)
    return xor2(xor_ab, c, 'xor2', weights)

if __name__ == '__main__':
    w = load_model()
    print('parity3 truth table:')
    for i in range(8):
        a, b, c = (i >> 2) & 1, (i >> 1) & 1, i & 1
        print(f'  parity({a},{b},{c}) = {parity3(a, b, c, w)}')