"""
Threshold Network for NOT Gate

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

import torch
from safetensors.torch import load_file


class ThresholdNOT:
    """
    NOT gate implemented as a threshold neuron.

    Circuit: output = (weight * input + bias >= 0)
    With weight=-1, bias=0: NOT(0)=1, NOT(1)=0
    """

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

    def __call__(self, x):
        x = torch.as_tensor(x, dtype=torch.float32)
        return (x * self.weight + self.bias >= 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 (0 or 1)
        weights: Dict with 'weight' and 'bias' tensors

    Returns:
        NOT(x)
    """
    x = torch.as_tensor(x, dtype=torch.float32)
    return (x * weights['weight'] + weights['bias'] >= 0).float()


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

    print("NOT Gate Truth Table:")
    print("-" * 20)
    for inp in [0, 1]:
        out = int(model(inp).item())
        expected = 1 - inp
        status = "OK" if out == expected else "FAIL"
        print(f"NOT({inp}) = {out}  [{status}]")