//*****************************************************************************
// Copyright 2017-2020 Intel Corporation
//
// 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.
//*****************************************************************************

#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/runtime/tensor.hpp"
#include "runtime/backend.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/known_element_types.hpp"
#include "util/ndarray.hpp"
#include "util/test_case.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"

using namespace std;
using namespace ngraph;

static string s_manifest = "${MANIFEST}";

NGRAPH_TEST(${BACKEND_NAME}, dyn_group_convolution_backprop_data)
{
    Shape shape_filter{6, 1, 3, 3};
    auto filters = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    Shape shape_delta{2, 6, 3, 3};
    auto deltas = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    Shape shape_data_batch{2, 3, 5, 5};
    auto data_batch = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    auto strides = Strides{1, 1};
    auto dilations = Strides{1, 1};
    auto padding_begin = CoordinateDiff{0, 0};
    auto padding_end = CoordinateDiff{0, 0};
    size_t groups = 3;

    auto conv_bprop_data = make_shared<op::GroupConvolutionBackpropData>(
        data_batch, filters, deltas, strides, dilations, padding_begin, padding_end, groups);

    auto f = make_shared<Function>(conv_bprop_data, ParameterVector{data_batch, filters, deltas});

    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);

    auto handle = backend->compile(f);

    auto result = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());

    vector<float> filter, delta, data, expected_result;

    for (int i = 0; i < 6 * 1 * 3 * 3; i++)
        filter.emplace_back(i);

    for (int i = 0; i < 2 * 6 * 3 * 3; i++)
        delta.emplace_back(i);

    for (int i = 0; i < 2 * 3 * 5 * 5; i++)
        data.emplace_back(i);

    for (int i = 0; i < 2 * 3 * 5 * 5; i++)
        expected_result.emplace_back(i);

    auto a = backend->create_tensor(element::f32, shape_data_batch);
    copy_data(a, data);
    auto b = backend->create_tensor(element::f32, shape_filter);
    copy_data(b, filter);
    auto c = backend->create_tensor(element::f32, shape_delta);
    copy_data(c, delta);
    handle->call_with_validate({result}, {a, b, c});
    EXPECT_FALSE(test::all_close_f(vector<float>{expected_result}, read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, dyn_group_convolution_backprop_filters)
{
    Shape shape_filter{6, 1, 3, 3};
    auto filters = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    Shape shape_delta{2, 6, 3, 3};
    auto deltas = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    Shape shape_data_batch{2, 3, 5, 5};
    auto data_batch = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    auto strides = Strides{1, 1};
    auto dilations = Strides{1, 1};
    auto padding_begin = CoordinateDiff{0, 0};
    auto padding_end = CoordinateDiff{0, 0};
    size_t groups = 3;

    auto conv_bprop_filters = make_shared<op::GroupConvolutionBackpropFilters>(
        data_batch, filters, deltas, strides, dilations, padding_begin, padding_end, groups);

    auto f =
        make_shared<Function>(conv_bprop_filters, ParameterVector{data_batch, filters, deltas});

    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);

    auto handle = backend->compile(f);

    auto result = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());

    vector<float> filter, delta, data, expected_result;

    for (int i = 0; i < 6 * 1 * 3 * 3; i++)
        filter.emplace_back(i);

    for (int i = 0; i < 2 * 6 * 3 * 3; i++)
        delta.emplace_back(i);

    for (int i = 0; i < 2 * 3 * 5 * 5; i++)
        data.emplace_back(i);

    for (int i = 0; i < 6 * 1 * 3 * 3; i++)
        expected_result.emplace_back(i);

    auto a = backend->create_tensor(element::f32, shape_data_batch);
    copy_data(a, data);
    auto b = backend->create_tensor(element::f32, shape_filter);
    copy_data(b, filter);
    auto c = backend->create_tensor(element::f32, shape_delta);
    copy_data(c, delta);
    handle->call_with_validate({result}, {a, b, c});
    EXPECT_FALSE(test::all_close_f(vector<float>{expected_result}, read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, v1_group_conv_backprop_data)
{
    const CoordinateDiff output_padding{1, 1};
    const CoordinateDiff pads_begin{1, 1};
    const CoordinateDiff pads_end{1, 1};
    Strides strides{2, 2};
    Strides dilations{1, 1};
    const op::PadType auto_pad{op::PadType::EXPLICIT};

    auto data = make_shared<op::Parameter>(element::f32, Shape{1, 1, 3, 3});
    auto filters = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 3, 3});

    auto gcbd = make_shared<op::v1::GroupConvolutionBackpropData>(
        data, filters, strides, pads_begin, pads_end, dilations, auto_pad, output_padding);

    auto function = make_shared<Function>(NodeVector{gcbd}, ParameterVector{data, filters});
    auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");

    // X
    test_case.add_input<float>(vector<float>{0.16857791f,
                                             -0.15161794f,
                                             0.08540368f,
                                             0.1820628f,
                                             -0.21746576f,
                                             0.08245695f,
                                             0.1431433f,
                                             -0.43156421f,
                                             0.30591947f});
    // W
    test_case.add_input<float>({-0.06230065f,
                                0.37932432f,
                                -0.25388849f,
                                0.33878803f,
                                0.43709868f,
                                -0.22477469f,
                                0.04118127f,
                                -0.44696793f,
                                0.06373066f});
    test_case.add_expected_output(
        Shape{1, 1, 6, 6},
        vector<float>{
            0.07368518f,  -0.08925839f, -0.06627201f, 0.06301362f,  0.03732984f,  -0.01919658f,
            -0.00628807f, -0.02817563f, -0.01472169f, 0.04392925f,  -0.00689478f, -0.01549204f,
            0.07957941f,  -0.11459791f, -0.09505399f, 0.07681622f,  0.03604182f,  -0.01853423f,
            -0.0270785f,  -0.00680824f, -0.06650258f, 0.08004665f,  0.07918708f,  -0.0724144f,
            0.06256775f,  -0.17838378f, -0.18863615f, 0.20064656f,  0.133717f,    -0.06876295f,
            -0.06398046f, -0.00864975f, 0.19289537f,  -0.01490572f, -0.13673618f, 0.01949645f});
    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
}

NGRAPH_TEST(${BACKEND_NAME}, v1_group_conv_backprop_data_output_shape)
{
    Strides strides{1, 1};
    Strides dilations{1, 1};

    auto data = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 10});
    auto filters = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 1, 5});
    auto output_shape = op::Constant::create(element::i64, Shape{2}, {1, 14});

    auto gcbd = make_shared<op::v1::GroupConvolutionBackpropData>(
        data, filters, output_shape, strides, dilations, op::PadType::SAME_UPPER);

    auto function = make_shared<Function>(NodeVector{gcbd}, ParameterVector{data, filters});
    auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");

    // X
    test_case.add_input<float>(
        vector<float>{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f});
    // W
    test_case.add_input<float>({1.0f, 2.0f, 3.0f, 2.0f, 1.0f});
    test_case.add_expected_output(Shape{1, 1, 1, 14},
                                  vector<float>{0.0f,
                                                1.0f,
                                                4.0f,
                                                10.0f,
                                                18.0f,
                                                27.0f,
                                                36.0f,
                                                45.0f,
                                                54.0f,
                                                63.0f,
                                                62.0f,
                                                50.0f,
                                                26.0f,
                                                9.0f});
    test_case.run();
}
