# Copyright 2023 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
from mindspore import Tensor
from mindspore import ops


class Net(nn.Cell):
    def __init__(self, dim=0, start=None, end=None, step=1):
        super(Net, self).__init__()
        self.dim = dim
        self.start = start
        self.end = end
        self.step = step

    def construct(self, input_x, src):
        return ops.slice_scatter(input_x, src, self.dim, self.start, self.end, self.step)


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_ops_slice_scatter(mode):
    """
    Feature: ops.slice_scatter
    Description: Verify the result of slice_scatter
    Expectation: success
    """
    ms.set_context(mode=mode)
    x = Tensor([[[0, 1, 2],
                 [3, 4, 5],
                 [6, 7, 8]],
                [[9, 10, 11],
                 [12, 13, 14],
                 [15, 16, 17]]], ms.float32)
    y = Tensor([[[28, 29, 30]],
                [[31, 32, 33]]], ms.float32)
    net = Net(1, 0, 1, 1)
    output = net(x, y)
    expect_output = [[[28., 29., 30.],
                      [3., 4., 5.],
                      [6., 7., 8.]],
                     [[31., 32., 33.],
                      [12., 13., 14.],
                      [15., 16., 17.]]]
    assert np.allclose(output.asnumpy(), expect_output)
