import torch
from safetensors.torch import save_file

# Input order: a3, a2, a1, a0 (MSB to LSB)
# Output sign bit (MSB = a3)
# In 2's complement, a3=1 means negative
weights = {
    'neuron.weight': torch.tensor([[1.0, 0.0, 0.0, 0.0]], dtype=torch.float32),
    'neuron.bias': torch.tensor([-1.0], dtype=torch.float32)
}
save_file(weights, 'model.safetensors')

def signbit4(a3, a2, a1, a0):
    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())

print("Verifying signbit4...")
errors = 0
for i in range(16):
    a3, a2, a1, a0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
    result = signbit4(a3, a2, a1, a0)
    expected = a3  # sign bit is MSB
    if result != expected:
        errors += 1
        print(f"ERROR: {a3}{a2}{a1}{a0} -> {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}")