import torch
from safetensors.torch import save_file

weights = {
    'neuron.weight': torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float32),
    'neuron.bias': torch.tensor([-2.0], dtype=torch.float32)
}
save_file(weights, 'model.safetensors')

def test(a, b, c, d, e):
    inp = torch.tensor([float(a), float(b), float(c), float(d), float(e)])
    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())

print("Verifying 2outof5...")
errors = 0
for i in range(32):
    a, b, c, d, e = (i >> 4) & 1, (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
    result = test(a, b, c, d, e)
    expected = 1 if (a + b + c + d + e) >= 2 else 0
    if result != expected:
        errors += 1
if errors == 0:
    print("All 32 test cases passed!")
print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}")