# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import numpy as np
import pytest

import mindspore as ms
import mindspore.nn as nn


class Net(nn.Cell):
    def construct(self, x, nan, posinf, neginf):
        output = x.nan_to_num(nan, posinf, neginf)
        return output


@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_nan_to_num_normal(mode):
    """
    Feature: nan_to_num
    Description: Verify the result of nan_to_num
    Expectation: success
    """
    ms.set_context(mode=mode)
    net = Net()
    x = ms.Tensor(np.array([float('nan'), float('inf'), -float('inf'), 3.14]), ms.float32)
    out = net(x, 1.0, 2.0, 3.0)
    expect_out = np.array([1., 2., 3., 3.14])
    assert np.allclose(out.asnumpy(), expect_out)
