"""
Threshold Network for MOD-4 Circuit

A formally verified threshold network computing Hamming weight mod 4.
Uses the algebraic weight pattern [1, 1, 1, -3, 1, 1, 1, -3].
"""

import torch
from safetensors.torch import load_file


class ThresholdMod4:
    """
    MOD-4 circuit using threshold logic.

    Weight pattern: (1, 1, 1, 1-m) repeating for m=4
    Computes cumulative sum that cycles mod 4.
    """

    def __init__(self, weights_dict):
        self.weight = weights_dict['weight']
        self.bias = weights_dict['bias']

    def __call__(self, bits):
        inputs = torch.tensor([float(b) for b in bits])
        weighted_sum = (inputs * self.weight).sum() + self.bias
        return weighted_sum

    def get_residue(self, bits):
        """Returns Hamming weight mod 4."""
        return sum(bits) % 4

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


def forward(x, weights):
    x = torch.as_tensor(x, dtype=torch.float32)
    weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
    return weighted_sum


if __name__ == "__main__":
    weights = load_file("model.safetensors")
    model = ThresholdMod4(weights)

    print("MOD-4 Circuit Tests:")
    print("-" * 40)
    for hw in range(9):
        bits = [1]*hw + [0]*(8-hw)
        out = model(bits).item()
        expected_residue = hw % 4
        print(f"HW={hw}: weighted_sum={out:.0f}, HW mod 4 = {expected_residue}")