# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect


def hardswish(x: np.ndarray) -> np.ndarray:
    alfa = float(1 / 6)
    beta = 0.5
    return x * np.maximum(0, np.minimum(1, alfa * x + beta))


class HardSwish(Base):
    @staticmethod
    def export() -> None:
        node = onnx.helper.make_node(
            "HardSwish",
            inputs=["x"],
            outputs=["y"],
        )
        x = np.random.randn(3, 4, 5).astype(np.float32)
        y = hardswish(x)

        expect(node, inputs=[x], outputs=[y], name="test_hardswish")