"""
Threshold Network for Biconditional Gate

A formally verified two-layer threshold network computing logical equivalence (iff).
Biconditional is not linearly separable, requiring at least 2 layers.
Architecture: Layer1 computes NOR and AND, Layer2 computes OR of results.
"""

import torch
from safetensors.torch import load_file


class ThresholdBiImplies:
    """
    Biconditional gate implemented as a 2-layer threshold network.

    Layer 1: NOR (neuron 1) and AND (neuron 2) in parallel
    Layer 2: OR of layer 1 outputs

    BiImplies(x,y) = OR(NOR(x,y), AND(x,y)) = (x iff y)
    """

    def __init__(self, weights_dict):
        self.l1_n1_weight = weights_dict['layer1.neuron1.weight']
        self.l1_n1_bias = weights_dict['layer1.neuron1.bias']
        self.l1_n2_weight = weights_dict['layer1.neuron2.weight']
        self.l1_n2_bias = weights_dict['layer1.neuron2.bias']
        self.l2_weight = weights_dict['layer2.weight']
        self.l2_bias = weights_dict['layer2.bias']

    def __call__(self, x1, x2):
        inputs = torch.tensor([float(x1), float(x2)])

        nor_out = ((inputs * self.l1_n1_weight).sum() + self.l1_n1_bias >= 0).float()
        and_out = ((inputs * self.l1_n2_weight).sum() + self.l1_n2_bias >= 0).float()

        layer1_out = torch.tensor([nor_out, and_out])
        output = ((layer1_out * self.l2_weight).sum() + self.l2_bias >= 0).float()

        return output

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


def forward(x1, x2, weights):
    """
    Forward pass with Heaviside activation.

    Args:
        x1, x2: Input values (0 or 1)
        weights: Dict with layer weights and biases

    Returns:
        BiImplies(x1, x2) = (x1 iff x2)
    """
    inputs = torch.tensor([float(x1), float(x2)])

    nor_out = ((inputs * weights['layer1.neuron1.weight']).sum() + weights['layer1.neuron1.bias'] >= 0).float()
    and_out = ((inputs * weights['layer1.neuron2.weight']).sum() + weights['layer1.neuron2.bias'] >= 0).float()

    layer1_out = torch.tensor([nor_out, and_out])
    output = ((layer1_out * weights['layer2.weight']).sum() + weights['layer2.bias'] >= 0).float()

    return output


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

    print("Biconditional Gate Truth Table:")
    print("-" * 30)
    for x1 in [0, 1]:
        for x2 in [0, 1]:
            out = int(model(x1, x2).item())
            expected = 1 if x1 == x2 else 0
            status = "OK" if out == expected else "FAIL"
            print(f"BiImplies({x1}, {x2}) = {out}  [{status}]")