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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 parity4(a, b, c, d, weights):
xor_ab = xor2(a, b, 'xor_ab', weights)
xor_cd = xor2(c, d, 'xor_cd', weights)
return xor2(xor_ab, xor_cd, 'xor_final', weights)
if __name__ == '__main__':
w = load_model()
print('parity4 truth table:')
for i in range(16):
a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
print(f' parity({a},{b},{c},{d}) = {parity4(a, b, c, d, w)}')
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