# 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.api import ms_function
from mindspore.ops import operations as P

x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis0 = 3

x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis1 = 3

x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
axis2 = 2

x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
axis3 = 2

x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis4 = 1

x5 = np.random.rand(2, 3).astype(np.float32)
axis5 = 1

x6 = np.random.rand(1, 1, 1, 1).astype(np.float32)
axis6 = 0

context.set_context(device_target='GPU')


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

        self.x0 = Tensor(x0)
        self.axis0 = axis0

        self.x1 = Tensor(x1)
        self.axis1 = axis1

        self.x2 = Tensor(x2)
        self.axis2 = axis2

        self.x3 = Tensor(x3)
        self.axis3 = axis3

        self.x4 = Tensor(x4)
        self.axis4 = axis4

        self.x5 = Tensor(x5)
        self.axis5 = axis5

        self.x6 = Tensor(x6)
        self.axis6 = axis6

    @ms_function
    def construct(self):
        return (P.CumSum()(self.x0, self.axis0),
                P.CumSum()(self.x1, self.axis1),
                P.CumSum()(self.x2, self.axis2),
                P.CumSum()(self.x3, self.axis3),
                P.CumSum()(self.x4, self.axis4),
                P.CumSum()(self.x5, self.axis5),
                P.CumSum()(self.x6, self.axis6))


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_CumSum():
    cumsum = CumSum()
    output = cumsum()

    expect0 = np.cumsum(x0, axis=axis0)
    diff0 = abs(output[0].asnumpy() - expect0)
    error0 = np.ones(shape=expect0.shape) * 1.0e-5
    assert np.all(diff0 < error0)
    assert output[0].shape == expect0.shape

    expect1 = np.cumsum(x1, axis=axis1)
    diff1 = abs(output[1].asnumpy() - expect1)
    error1 = np.ones(shape=expect1.shape) * 1.0e-5
    assert np.all(diff1 < error1)
    assert output[1].shape == expect1.shape

    expect2 = np.cumsum(x2, axis=axis2)
    diff2 = abs(output[2].asnumpy() - expect2)
    error2 = np.ones(shape=expect2.shape) * 1.0e-5
    assert np.all(diff2 < error2)
    assert output[2].shape == expect2.shape

    expect3 = np.cumsum(x3, axis=axis3)
    diff3 = abs(output[3].asnumpy() - expect3)
    error3 = np.ones(shape=expect3.shape) * 1.0e-5
    assert np.all(diff3 < error3)
    assert output[3].shape == expect3.shape

    expect4 = np.cumsum(x4, axis=axis4)
    diff4 = abs(output[4].asnumpy() - expect4)
    error4 = np.ones(shape=expect4.shape) * 1.0e-5
    assert np.all(diff4 < error4)
    assert output[4].shape == expect4.shape

    expect5 = np.cumsum(x5, axis=axis5)
    diff5 = abs(output[5].asnumpy() - expect5)
    error5 = np.ones(shape=expect5.shape) * 1.0e-5
    assert np.all(diff5 < error5)
    assert output[5].shape == expect5.shape

    expect6 = np.cumsum(x6, axis=axis6)
    diff6 = abs(output[6].asnumpy() - expect6)
    error6 = np.ones(shape=expect6.shape) * 1.0e-5
    assert np.all(diff6 < error6)
    assert output[6].shape == expect6.shape
