"""
Threshold Network for 4-input OR Gate
"""

import torch
from safetensors.torch import load_file


class ThresholdOR4:
    """
    4-input OR gate implemented as a threshold neuron.

    Circuit: output = (w1*x1 + w2*x2 + w3*x3 + w4*x4 + bias >= 0)
    With weights=[1,1,1,1], bias=-1: any single input reaches threshold.
    """

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

    def __call__(self, x1, x2, x3, x4):
        inputs = torch.tensor([float(x1), float(x2), float(x3), float(x4)])
        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 [..., 4]
        weights: Dict with 'weight' and 'bias' tensors

    Returns:
        OR(x[0], x[1], x[2], x[3])
    """
    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 = ThresholdOR4(weights)

    print("4-input OR Gate Truth Table:")
    print("-" * 35)
    correct = 0
    for x1 in [0, 1]:
        for x2 in [0, 1]:
            for x3 in [0, 1]:
                for x4 in [0, 1]:
                    out = int(model(x1, x2, x3, x4).item())
                    expected = x1 | x2 | x3 | x4
                    status = "OK" if out == expected else "FAIL"
                    if out == expected:
                        correct += 1
                    print(f"OR4({x1}, {x2}, {x3}, {x4}) = {out}  [{status}]")
    print(f"\nTotal: {correct}/16 correct")