model.py

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)
    return int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0)

def parity8(bits, weights):
    x01 = xor2(bits[0], bits[1], 'xor_01', weights)
    x23 = xor2(bits[2], bits[3], 'xor_23', weights)
    x45 = xor2(bits[4], bits[5], 'xor_45', weights)
    x67 = xor2(bits[6], bits[7], 'xor_67', weights)
    x0123 = xor2(x01, x23, 'xor_0123', weights)
    x4567 = xor2(x45, x67, 'xor_4567', weights)
    return xor2(x0123, x4567, 'xor_final', weights)

if __name__ == '__main__':
    w = load_model()
    print('parity8 selected outputs:')
    for n_ones in range(9):
        bits = [1 if j < n_ones else 0 for j in range(8)]
        print(f'  {n_ones} ones: {parity8(bits, w)}')