"""
Threshold Network for 4-input XOR Gate (Magnitude 21)

Cascade of three magnitude-7 optimal XORs.
"""

import torch
from safetensors.torch import load_file


def xor2(x1, x2, w, prefix):
    inp = torch.tensor([float(x1), float(x2)])
    n1 = int((inp * w[f'{prefix}.layer1.n1.weight']).sum() + w[f'{prefix}.layer1.n1.bias'] >= 0)
    n2 = int((inp * w[f'{prefix}.layer1.n2.weight']).sum() + w[f'{prefix}.layer1.n2.bias'] >= 0)
    h = torch.tensor([float(n1), float(n2)])
    return int((h * w[f'{prefix}.layer2.weight']).sum() + w[f'{prefix}.layer2.bias'] >= 0)


class ThresholdXOR4:
    def __init__(self, weights_dict):
        self.w = weights_dict

    def __call__(self, a, b, c, d):
        xor_ab = xor2(a, b, self.w, 'xor1')
        xor_abc = xor2(xor_ab, c, self.w, 'xor2')
        xor_abcd = xor2(xor_abc, d, self.w, 'xor3')
        return float(xor_abcd)

    @classmethod
    def from_safetensors(cls, path="model.safetensors"):
        return cls(load_file(path))


if __name__ == "__main__":
    weights = load_file("model.safetensors")
    model = ThresholdXOR4(weights)

    print("4-input XOR Gate (mag21):")
    correct = 0
    for a in [0, 1]:
        for b in [0, 1]:
            for c in [0, 1]:
                for d in [0, 1]:
                    out = int(model(a, b, c, d))
                    expected = a ^ b ^ c ^ d
                    if out == expected:
                        correct += 1
                    status = "OK" if out == expected else "FAIL"
                    print(f"  XOR4({a},{b},{c},{d}) = {out} [{status}]")
    print(f"Total: {correct}/16")