import torch
from safetensors.torch import save_file

# exactly2: (sum >= 2) AND (sum <= 2)
weights = {
    'layer1.weight': torch.tensor([
        [1.0, 1.0, 1.0, 1.0],   # N1: sum >= 2
        [-1.0, -1.0, -1.0, -1.0] # N2: sum <= 2
    ], dtype=torch.float32),
    'layer1.bias': torch.tensor([-2.0, 2.0], dtype=torch.float32),
    'layer2.weight': torch.tensor([[1.0, 1.0]], dtype=torch.float32),
    'layer2.bias': torch.tensor([-2.0], dtype=torch.float32)
}
save_file(weights, 'model.safetensors')

def exactly2of4(a, b, c, d):
    inp = torch.tensor([float(a), float(b), float(c), float(d)])
    l1 = (inp @ weights['layer1.weight'].T + weights['layer1.bias'] >= 0).float()
    out = (l1 @ weights['layer2.weight'].T + weights['layer2.bias'] >= 0).float()
    return int(out.item())

print("Verifying exactly2outof4...")
errors = 0
for i in range(16):
    a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
    result = exactly2of4(a, b, c, d)
    expected = 1 if (a + b + c + d) == 2 else 0
    if result != expected:
        errors += 1
        print(f"ERROR: {a}{b}{c}{d} -> {result}, expected {expected}")
if errors == 0:
    print("All 16 test cases passed!")
print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}")