import torch
from safetensors.torch import save_file

weights = {}

# Input order: a3, a2, a1, a0
# Output: y3=a0, y2=a1, y1=a2, y0=a3
# Each output copies from reversed position

# y3 = a0 (copy from position 3)
weights['y3.weight'] = torch.tensor([[0.0, 0.0, 0.0, 1.0]], dtype=torch.float32)
weights['y3.bias'] = torch.tensor([-1.0], dtype=torch.float32)

# y2 = a1 (copy from position 2)
weights['y2.weight'] = torch.tensor([[0.0, 0.0, 1.0, 0.0]], dtype=torch.float32)
weights['y2.bias'] = torch.tensor([-1.0], dtype=torch.float32)

# y1 = a2 (copy from position 1)
weights['y1.weight'] = torch.tensor([[0.0, 1.0, 0.0, 0.0]], dtype=torch.float32)
weights['y1.bias'] = torch.tensor([-1.0], dtype=torch.float32)

# y0 = a3 (copy from position 0)
weights['y0.weight'] = torch.tensor([[1.0, 0.0, 0.0, 0.0]], dtype=torch.float32)
weights['y0.bias'] = torch.tensor([-1.0], dtype=torch.float32)

save_file(weights, 'model.safetensors')

# Verify
def reverse4(a3, a2, a1, a0):
    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
    y3 = int((inp @ weights['y3.weight'].T + weights['y3.bias'] >= 0).item())
    y2 = int((inp @ weights['y2.weight'].T + weights['y2.bias'] >= 0).item())
    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
    return [y3, y2, y1, y0]

print("Verifying reverse4...")
errors = 0
for i in range(16):
    a3, a2, a1, a0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
    result = reverse4(a3, a2, a1, a0)
    expected = [a0, a1, a2, a3]
    if result != expected:
        errors += 1
        print(f"ERROR: {a3}{a2}{a1}{a0} -> {result}, expected {expected}")

if errors == 0:
    print("All 16 test cases passed!")
else:
    print(f"FAILED: {errors} errors")

mag = sum(t.abs().sum().item() for t in weights.values())
print(f"Magnitude: {mag:.0f}")