# Copyright 2024 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 pytest
import numpy as np

import mindspore.context as context
from mindspore import Tensor
from mindspore import nn
import tests.st.utils.test_utils as test_utils


@test_utils.run_with_cell
def channel_shuffle(x):
    return nn.ChannelShuffle(2)(x)


@pytest.mark.level0
@pytest.mark.env_onecard
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.parametrize("context_mode", [context.GRAPH_MODE, context.PYNATIVE_MODE])
def test_net_channelshuffle_float32(context_mode):
    """
    Feature: channelshuffle
    Description: test channelshuffle
    Expectation: expect correct result.
    """
    context.set_context(mode=context_mode, device_target="Ascend")
    x = Tensor(np.arange(16).astype(np.int32).reshape(1, 4, 2, 2))
    output = channel_shuffle(x)
    expected = np.array([[[[0, 1],
                           [2, 3]],
                          [[8, 9],
                           [10, 11]],
                          [[4, 5],
                           [6, 7]],
                          [[12, 13],
                           [14, 15]]]], np.int32)
    assert np.all(output.asnumpy() == expected)