# 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


class HardSigmoid(Base):
    @staticmethod
    def export() -> None:
        node = onnx.helper.make_node(
            "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6
        )

        x = np.array([-1, 0, 1]).astype(np.float32)
        y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
        expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example")

        x = np.random.randn(3, 4, 5).astype(np.float32)
        y = np.clip(x * 0.5 + 0.6, 0, 1)
        expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid")

    @staticmethod
    def export_hardsigmoid_default() -> None:
        default_alpha = 0.2
        default_beta = 0.5
        node = onnx.helper.make_node(
            "HardSigmoid",
            inputs=["x"],
            outputs=["y"],
        )
        x = np.random.randn(3, 4, 5).astype(np.float32)
        y = np.clip(x * default_alpha + default_beta, 0, 1)
        expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default")