# Copyright 2020 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.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.common.api import ms_function
from mindspore.ops.operations import _grad_ops as G
from mindspore.ops import operations as P

context.set_context(mode=context.GRAPH_MODE, device_target='CPU')


class SliceGrad(nn.Cell):
    def __init__(self):
        super(SliceGrad, self).__init__()

        self.slicegrad = G.SliceGrad()

    @ms_function
    def construct(self, dy, x):
        return self.slicegrad(dy, x, (0, 1, 0), (2, 1, 3))


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_slice_grad():
    x = Tensor(np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]), mstype.float32)
    dy = Tensor(np.array([[[3., 1., 2.]], [[4., 1., 4.]]]), mstype.float32)
    slicegrad = SliceGrad()
    output = slicegrad(dy, x)
    expect = [[[0., 0., 0.],
               [3., 1., 2.]],
              [[0., 0., 0.],
               [4., 1., 4.]],
              [[0., 0., 0.],
               [0., 0., 0.]]]
    print("output:\n", output)
    assert (output.asnumpy() == expect).all()


class SliceGrad2(nn.Cell):
    def __init__(self):
        super(SliceGrad2, self).__init__()
        self.slicegrad = G.SliceGrad()

    def construct(self, dy, x):
        return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2))


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_slice_grad2():
    dy = Tensor(np.array([[[2., 3.], [4., 5.]], [[8., 9.], [10., 11.]]]), mstype.float32)
    x = Tensor(np.arange(2 * 3 * 2).reshape(2, 3, 2), mstype.float32)
    grad = SliceGrad2()
    output = grad(dy, x)
    print("output:\n", output)
    expect = [[[0., 0.], [2., 3.], [4., 5.]],
              [[0., 0.], [8., 9.], [10., 11.]]]
    assert (output.asnumpy() == expect).all()

class StridedSliceGrad(nn.Cell):
    def __init__(self, x, begin, end, stride):
        super(StridedSliceGrad, self).__init__()
        self.shape_op = P.Shape()
        self.shapex = self.shape_op(x)
        self.begin = begin
        self.end = end
        self.stride = stride
        self.stride_slice = G.StridedSliceGrad()

    def construct(self, dy):
        return self.stride_slice(dy, self.shapex, self.begin, self.end, self.stride)

@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_strided_slice_grad_bool_type():
    x = Tensor([[[False, False, True], [False, True, False]], [[False, True, False], [True, False, False]],
                [[False, True, True], [True, False, True]]], mstype.bool_)
    dy = Tensor([False, True, False], mstype.bool_)
    begin = (1, 0, 0)
    end = (2, 1, 3)
    stride = (1, 1, 1)
    slice_op = StridedSliceGrad(x, begin, end, stride)
    output = slice_op(dy)
    expected_output = np.array([[[False, False, False], [False, False, False]],
                                [[False, True, False], [False, False, False]],
                                [[False, False, False], [False, False, False]]])
    assert (output.asnumpy() == expected_output).all()

@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_strided_slice_grad_float32_type():
    x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]], mstype.float32)
    dy = Tensor([3, 3, 3], mstype.float32)
    begin = (1, 0, 0)
    end = (2, 1, 3)
    stride = (1, 1, 1)
    slice_op = StridedSliceGrad(x, begin, end, stride)
    output = slice_op(dy)
    expected_output = np.array([[[0, 0, 0], [0, 0, 0]], [[3, 3, 3], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]])
    assert (output.asnumpy() == expected_output).all()

if __name__ == '__main__':
    test_slice_grad()
    test_slice_grad2()
    test_strided_slice_grad_bool_type()
    test_strided_slice_grad_float32_type()