import torch
from safetensors.torch import load_file

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

def prefix_or(x3, x2, x1, x0, w):
    inp = torch.tensor([float(x3), float(x2), float(x1), float(x0)])
    y3 = int((inp @ w['y3.weight'].T + w['y3.bias'] >= 0).item())
    y2 = int((inp @ w['y2.weight'].T + w['y2.bias'] >= 0).item())
    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
    return y3, y2, y1, y0

if __name__ == '__main__':
    w = load_model()
    print('Prefix-OR selected tests:')
    for i in [0b0000, 0b0001, 0b0010, 0b0100, 0b1000, 0b1111]:
        x3, x2, x1, x0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
        y3, y2, y1, y0 = prefix_or(x3, x2, x1, x0, w)
        print(f'{x3}{x2}{x1}{x0} -> {y3}{y2}{y1}{y0}')