// Copyright © Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier:  MIT
#include <gtest/gtest.h>
#include <hipdnn_frontend/attributes/ConvolutionDgradAttributes.hpp>

TEST(TestConvolutionDgradAttributes, CreateConvolutionDgradAttributes)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    // Set tensors
    convAttributes.set_dy(std::make_shared<hipdnn_frontend::graph::TensorAttributes>());
    convAttributes.set_w(std::make_shared<hipdnn_frontend::graph::TensorAttributes>());
    convAttributes.set_dx(std::make_shared<hipdnn_frontend::graph::TensorAttributes>());

    // Set convolution parameters
    convAttributes.set_pre_padding({1, 1});
    convAttributes.set_post_padding({1, 1});
    convAttributes.set_stride({1, 1});
    convAttributes.set_dilation({1, 1});
    convAttributes.set_convolution_mode(hipdnn_frontend::ConvolutionMode::CROSS_CORRELATION);

    // Configure dy tensor (gradient of output)
    auto dyTensor = convAttributes.get_dy();
    dyTensor->set_uid(1)
        .set_name("DyTensor")
        .set_data_type(hipdnn_frontend::DataType::FLOAT)
        .set_dim({1, 64, 32, 32}) // NCHW format
        .set_stride({65536, 1024, 32, 1});

    // Configure weights tensor
    auto wTensor = convAttributes.get_w();
    wTensor->set_uid(2)
        .set_name("WeightsTensor")
        .set_data_type(hipdnn_frontend::DataType::FLOAT)
        .set_dim({64, 3, 3, 3}) // KCHW format
        .set_stride({27, 9, 3, 1});

    // Configure dx tensor (gradient of input)
    auto dxTensor = convAttributes.get_dx();
    dxTensor->set_uid(3)
        .set_name("DxTensor")
        .set_data_type(hipdnn_frontend::DataType::FLOAT)
        .set_dim({1, 3, 32, 32}) // NCHW format
        .set_stride({3072, 1024, 32, 1});

    // Verify tensor attributes
    EXPECT_EQ(dyTensor->get_uid(), 1);
    EXPECT_EQ(dyTensor->get_name(), "DyTensor");
    EXPECT_EQ(dyTensor->get_data_type(), hipdnn_frontend::DataType::FLOAT);
    EXPECT_EQ(dyTensor->get_dim(), (std::vector<int64_t>{1, 64, 32, 32}));
    EXPECT_EQ(dyTensor->get_stride(), (std::vector<int64_t>{65536, 1024, 32, 1}));

    EXPECT_EQ(wTensor->get_uid(), 2);
    EXPECT_EQ(wTensor->get_name(), "WeightsTensor");
    EXPECT_EQ(wTensor->get_data_type(), hipdnn_frontend::DataType::FLOAT);
    EXPECT_EQ(wTensor->get_dim(), (std::vector<int64_t>{64, 3, 3, 3}));
    EXPECT_EQ(wTensor->get_stride(), (std::vector<int64_t>{27, 9, 3, 1}));

    EXPECT_EQ(dxTensor->get_uid(), 3);
    EXPECT_EQ(dxTensor->get_name(), "DxTensor");
    EXPECT_EQ(dxTensor->get_data_type(), hipdnn_frontend::DataType::FLOAT);
    EXPECT_EQ(dxTensor->get_dim(), (std::vector<int64_t>{1, 3, 32, 32}));
    EXPECT_EQ(dxTensor->get_stride(), (std::vector<int64_t>{3072, 1024, 32, 1}));

    // Verify convolution parameters
    EXPECT_EQ(convAttributes.get_pre_padding(), (std::vector<int64_t>{1, 1}));
    EXPECT_EQ(convAttributes.get_post_padding(), (std::vector<int64_t>{1, 1}));
    EXPECT_EQ(convAttributes.get_stride(), (std::vector<int64_t>{1, 1}));
    EXPECT_EQ(convAttributes.get_dilation(), (std::vector<int64_t>{1, 1}));
    EXPECT_EQ(convAttributes.get_convolution_mode(),
              hipdnn_frontend::ConvolutionMode::CROSS_CORRELATION);
}

TEST(TestConvolutionDgradAttributes, DefaultValues)
{
    const hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    // Check default convolution mode
    EXPECT_EQ(convAttributes.get_convolution_mode(),
              hipdnn_frontend::ConvolutionMode::CROSS_CORRELATION);

    // Check that parameters are empty by default
    EXPECT_TRUE(convAttributes.get_pre_padding().empty());
    EXPECT_TRUE(convAttributes.get_post_padding().empty());
    EXPECT_TRUE(convAttributes.get_stride().empty());
    EXPECT_TRUE(convAttributes.get_dilation().empty());

    // Check that tensors are null by default
    EXPECT_EQ(convAttributes.get_dy(), nullptr);
    EXPECT_EQ(convAttributes.get_w(), nullptr);
    EXPECT_EQ(convAttributes.get_dx(), nullptr);
}

TEST(TestConvolutionDgradAttributes, SetPrePaddingWithMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    std::vector<int64_t> prePaddingMove = {3, 3};
    convAttributes.set_pre_padding(std::move(prePaddingMove));

    EXPECT_EQ(convAttributes.get_pre_padding(), (std::vector<int64_t>{3, 3}));
}

TEST(TestConvolutionDgradAttributes, SetPostPaddingWithMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    std::vector<int64_t> postPaddingMove = {4, 4};
    convAttributes.set_post_padding(std::move(postPaddingMove));

    EXPECT_EQ(convAttributes.get_post_padding(), (std::vector<int64_t>{4, 4}));
}

TEST(TestConvolutionDgradAttributes, SetStrideWithMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    std::vector<int64_t> strideMove = {2, 2};
    convAttributes.set_stride(std::move(strideMove));

    EXPECT_EQ(convAttributes.get_stride(), (std::vector<int64_t>{2, 2}));
}

TEST(TestConvolutionDgradAttributes, SetDilationWithMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    std::vector<int64_t> dilationMove = {2, 2};
    convAttributes.set_dilation(std::move(dilationMove));

    EXPECT_EQ(convAttributes.get_dilation(), (std::vector<int64_t>{2, 2}));
}

TEST(TestConvolutionDgradAttributes, SetPaddingBothPreAndPost)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    const std::vector<int64_t> padding = {5, 5};
    convAttributes.set_padding(padding);

    // set_padding should set both pre and post padding to the same value
    EXPECT_EQ(convAttributes.get_pre_padding(), (std::vector<int64_t>{5, 5}));
    EXPECT_EQ(convAttributes.get_post_padding(), (std::vector<int64_t>{5, 5}));
}

TEST(TestConvolutionDgradAttributes, SetDyMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    auto dyTensor = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    dyTensor->set_uid(10)
        .set_name("MovedDyTensor")
        .set_data_type(hipdnn_frontend::DataType::FLOAT)
        .set_dim({1, 64, 224, 224})
        .set_stride({3211264, 50176, 224, 1});

    // Store the raw pointer before moving
    auto rawPtr = dyTensor.get();

    convAttributes.set_dy(std::move(dyTensor));

    // After move, dyTensor should be nullptr
    EXPECT_EQ(dyTensor, nullptr);

    // The moved tensor should be accessible through get_dy()
    auto retrievedTensor = convAttributes.get_dy();
    EXPECT_EQ(retrievedTensor.get(), rawPtr);
}

TEST(TestConvolutionDgradAttributes, SetWMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    auto wTensor = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    wTensor->set_uid(20)
        .set_name("MovedWeightTensor")
        .set_data_type(hipdnn_frontend::DataType::HALF)
        .set_dim({128, 64, 5, 5})
        .set_stride({1600, 25, 5, 1});

    // Store the raw pointer before moving
    auto rawPtr = wTensor.get();

    convAttributes.set_w(std::move(wTensor));

    // After move, wTensor should be nullptr
    EXPECT_EQ(wTensor, nullptr);

    // The moved tensor should be accessible through get_w()
    auto retrievedTensor = convAttributes.get_w();
    EXPECT_EQ(retrievedTensor.get(), rawPtr);
}

TEST(TestConvolutionDgradAttributes, SetDxWithMove)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    auto dxTensor = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    dxTensor->set_uid(30)
        .set_name("MovedDxTensor")
        .set_data_type(hipdnn_frontend::DataType::BFLOAT16)
        .set_dim({1, 128, 112, 112})
        .set_stride({1605632, 12544, 112, 1});

    // Store the raw pointer before moving
    auto rawPtr = dxTensor.get();

    convAttributes.set_dx(std::move(dxTensor));

    // After move, dxTensor should be nullptr
    EXPECT_EQ(dxTensor, nullptr);

    // The moved tensor should be accessible through get_dx()
    auto retrievedTensor = convAttributes.get_dx();
    EXPECT_EQ(retrievedTensor.get(), rawPtr);
}

TEST(TestConvolutionDgradAttributes, SetTensorsConstRef)
{
    hipdnn_frontend::graph::ConvDgradAttributes convAttributes;

    // Create tensors
    auto dyTensor = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    dyTensor->set_uid(40).set_name("DyConstRef");

    auto wTensor = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    wTensor->set_uid(50).set_name("WeightConstRef");

    auto dxTensor = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    dxTensor->set_uid(60).set_name("DxConstRef");

    // Set using const reference (copy)
    convAttributes.set_dy(dyTensor);
    convAttributes.set_w(wTensor);
    convAttributes.set_dx(dxTensor);

    // Original tensors should still be valid
    EXPECT_NE(dyTensor, nullptr);
    EXPECT_NE(wTensor, nullptr);
    EXPECT_NE(dxTensor, nullptr);
}

TEST(TestConvolutionDgradAttributes, LogicalAndStrictEquality)
{
    hipdnn_frontend::graph::ConvDgradAttributes attrA;
    hipdnn_frontend::graph::ConvDgradAttributes attrB;
    hipdnn_frontend::graph::ConvDgradAttributes attrC;
    hipdnn_frontend::graph::ConvDgradAttributes attrD;

    // Common structurally defined sharing tensors
    auto dyShared = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    dyShared->set_uid(10)
        .set_name("dy")
        .set_dim({1, 64, 32, 32})
        .set_data_type(hipdnn_frontend::DataType::FLOAT);

    auto wShared = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    wShared->set_uid(20)
        .set_name("w")
        .set_dim({64, 3, 3, 3})
        .set_data_type(hipdnn_frontend::DataType::FLOAT);

    auto dxShared = std::make_shared<hipdnn_frontend::graph::TensorAttributes>();
    dxShared->set_uid(30)
        .set_name("dx")
        .set_dim({1, 3, 32, 32})
        .set_data_type(hipdnn_frontend::DataType::FLOAT);

    // Identical Baseline Setup
    attrA.set_dy(dyShared)
        .set_w(wShared)
        .set_dx(dxShared)
        .set_padding({1, 1})
        .set_stride({1, 1})
        .set_dilation({1, 1});

    attrB.set_dy(dyShared)
        .set_w(wShared)
        .set_dx(dxShared)
        .set_padding({1, 1})
        .set_stride({1, 1})
        .set_dilation({1, 1});

    EXPECT_TRUE(attrA.logicallyEquals(attrB));
    EXPECT_TRUE(attrA == attrB);

    //  Metadata Discrepancy (Names/UID variance, identical layout shapes)
    auto dyMetaDiff = std::make_shared<hipdnn_frontend::graph::TensorAttributes>(*dyShared);
    dyMetaDiff->set_uid(999).set_name("altered_dy_name");

    attrC.set_dy(dyMetaDiff)
        .set_w(wShared)
        .set_dx(dxShared)
        .set_padding({1, 1})
        .set_stride({1, 1})
        .set_dilation({1, 1});

    EXPECT_TRUE(attrA.logicallyEquals(attrC));
    EXPECT_FALSE(attrA == attrC);

    // Functional/Mathematical Attribute Divergence
    // Variant 1: Padding mismatched
    attrD.set_dy(dyShared)
        .set_w(wShared)
        .set_dx(dxShared)
        .set_padding({0, 0})
        .set_stride({1, 1})
        .set_dilation({1, 1});
    EXPECT_FALSE(attrA.logicallyEquals(attrD));
    EXPECT_FALSE(attrA == attrD);

    // Variant 2: Stride mismatched
    attrD.set_padding({1, 1}).set_stride({2, 2});
    EXPECT_FALSE(attrA.logicallyEquals(attrD));
    EXPECT_FALSE(attrA == attrD);

    // Variant 3: Dilation mismatched
    attrD.set_stride({1, 1}).set_dilation({2, 2});
    EXPECT_FALSE(attrA.logicallyEquals(attrD));
    EXPECT_FALSE(attrA == attrD);

    // Variant 4: Math Mode mismatched
    attrD.set_dilation({1, 1}).set_convolution_mode(hipdnn_frontend::ConvolutionMode::CONVOLUTION);
    EXPECT_FALSE(attrA.logicallyEquals(attrD));
    EXPECT_FALSE(attrA == attrD);
}