"""
Threshold Network for Implication Gate

A formally verified single-neuron threshold network computing material conditional.
Weights are integer-constrained and activation uses the Heaviside step function.
"""

import torch
from safetensors.torch import load_file


class ThresholdImplies:
    """
    Implication gate implemented as a threshold neuron.

    Circuit: output = (w1*x + w2*y + bias >= 0)
    With weights=[-1,1], bias=0: fails only when x=1 and y=0.
    """

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

    def __call__(self, x, y):
        inputs = torch.tensor([float(x), float(y)])
        weighted_sum = (inputs * self.weight).sum() + self.bias
        return (weighted_sum >= 0).float()

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


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

    Args:
        x: Input tensor of shape [..., 2]
        weights: Dict with 'weight' and 'bias' tensors

    Returns:
        Implies(x[0], x[1])
    """
    x = torch.as_tensor(x, dtype=torch.float32)
    weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
    return (weighted_sum >= 0).float()


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

    print("Implication Gate Truth Table:")
    print("-" * 30)
    for x in [0, 1]:
        for y in [0, 1]:
            out = int(model(x, y).item())
            expected = 1 if (x == 0 or y == 1) else 0
            status = "OK" if out == expected else "FAIL"
            print(f"Implies({x}, {y}) = {out}  [{status}]")