#include <iostream>

#include <hipdnn_data_sdk/utilities/Tensor.hpp>
#include <hipdnn_frontend.hpp>

#include "hipdnn_data_sdk/utilities/Workspace.hpp"
#include "utils.hpp"

int main()
{
    using InputType = float;

    const int64_t n = 16; // Batch size
    // Input
    const int64_t c = 16; // Number of channels
    const int64_t h = 16; // Height
    const int64_t w = 16; // Width

    auto buildBnBackwarWeightdGraph = [=](hipdnnHandle_t handle) {
        auto graph = std::make_shared<hipdnn_frontend::graph::Graph>();
        graph->set_name("bn_backward_weight_graph")
            .set_io_data_type(hipdnn_frontend::getDataTypeEnumFromType<InputType>())
            .set_intermediate_data_type(hipdnn_frontend::getDataTypeEnumFromType<InputType>())
            .set_compute_data_type(hipdnn_frontend::DataType::FLOAT);

        auto dy = std::make_shared<hipdnn_frontend::graph::TensorAttributes>(
            hipdnn_frontend::graph::Tensor_attributes()
                .set_name("dy")
                .set_dim({n, c, h, w})
                .set_stride({c * h * w, 1, c * w, c}));

        auto x = std::make_shared<hipdnn_frontend::graph::TensorAttributes>(
            hipdnn_frontend::graph::Tensor_attributes()
                .set_name("x")
                .set_dim({n, c, h, w})
                .set_stride({c * h * w, 1, c * w, c}));

        auto scale = std::make_shared<hipdnn_frontend::graph::TensorAttributes>(
            hipdnn_frontend::graph::Tensor_attributes()
                .set_name("scale")
                .set_dim({1, c, 1, 1})
                .set_stride({c, 1, c, c}));

        auto savedMean = std::make_shared<hipdnn_frontend::graph::TensorAttributes>(
            hipdnn_frontend::graph::Tensor_attributes()
                .set_name("save_mean")
                .set_dim({1, c, 1, 1})
                .set_stride({c, 1, c, c}));

        auto savedInvVariance = std::make_shared<hipdnn_frontend::graph::TensorAttributes>(
            hipdnn_frontend::graph::Tensor_attributes()
                .set_name("save_inv_variance")
                .set_dim({1, c, 1, 1})
                .set_stride({c, 1, c, c}));

        auto bnBwdWeightAttributes
            = hipdnn_frontend::graph::BatchnormBackwardWeightAttributes().set_name(
                "bn_backward_weight_node");

        auto [dscale, dbias, eqScaleDy, eqScaleX, eqBias]
            = graph->dbn_weight(dy, x, savedMean, savedInvVariance, scale, bnBwdWeightAttributes);
        dscale->set_output(true);
        dbias->set_output(true);
        eqScaleDy->set_output(true);
        eqScaleX->set_output(true);
        eqBias->set_output(true);

        // build graph
        HIPDNN_FE_CHECK(graph->build(handle));

        return std::make_tuple(graph,
                               dy,
                               x,
                               scale,
                               savedMean,
                               savedInvVariance,
                               dscale,
                               dbias,
                               eqScaleDy,
                               eqScaleX,
                               eqBias);
    };

    auto backend = hipdnn_frontend::detail::hipdnnBackend();
    if(!backend)
    {
        std::cout << "Creat backend failed. \n";
        return 1;
    }

    hipdnnHandle_t handle;
    HIPDNN_CHECK(backend->create(&handle));

    auto [graph,
          dy,
          x,
          scale,
          savedMean,
          savedInvVariance,
          dscale,
          dbias,
          eqScaleDy,
          eqScaleX,
          eqBias]
        = buildBnBackwarWeightdGraph(handle);

    hipdnn_data_sdk::utilities::Tensor<InputType> dyTensor(dy->get_dim(), dy->get_stride());
    hipdnn_data_sdk::utilities::Tensor<InputType> xTensor(x->get_dim(), x->get_stride());
    hipdnn_data_sdk::utilities::Tensor<InputType> scaleTensor(scale->get_dim());
    hipdnn_data_sdk::utilities::Tensor<InputType> savedMeanTensor(savedMean->get_dim());
    hipdnn_data_sdk::utilities::Tensor<InputType> savedInvVarTensor(savedInvVariance->get_dim());
    hipdnn_data_sdk::utilities::Tensor<InputType> dscaleTensor(dscale->get_dim());
    hipdnn_data_sdk::utilities::Tensor<InputType> dbiasTensor(dbias->get_dim());
    hipdnn_data_sdk::utilities::Tensor<InputType> eqScaleDyTensor(eqScaleDy->get_dim(),
                                                                  eqScaleDy->get_stride());
    hipdnn_data_sdk::utilities::Tensor<InputType> eqScaleXTensor(eqScaleX->get_dim(),
                                                                 eqScaleX->get_stride());
    hipdnn_data_sdk::utilities::Tensor<InputType> eqBiasTensor(eqBias->get_dim(),
                                                               eqBias->get_stride());

    std::unordered_map<int64_t, void*> variantPack;

    variantPack[dy->get_uid()] = dyTensor.memory().deviceData();
    variantPack[x->get_uid()] = xTensor.memory().deviceData();
    variantPack[scale->get_uid()] = scaleTensor.memory().deviceData();
    variantPack[savedMean->get_uid()] = savedMeanTensor.memory().deviceData();
    variantPack[savedInvVariance->get_uid()] = savedInvVarTensor.memory().deviceData();
    variantPack[dscale->get_uid()] = dscaleTensor.memory().deviceData();
    variantPack[dbias->get_uid()] = dbiasTensor.memory().deviceData();
    variantPack[eqScaleDy->get_uid()] = eqScaleDyTensor.memory().deviceData();
    variantPack[eqScaleX->get_uid()] = eqScaleXTensor.memory().deviceData();
    variantPack[eqBias->get_uid()] = eqBiasTensor.memory().deviceData();

    int64_t workspaceSize = 0;
    HIPDNN_FE_CHECK(graph->get_workspace_size(workspaceSize));
    const hipdnn_data_sdk::utilities::Workspace workspace(static_cast<size_t>(workspaceSize));

    HIPDNN_FE_CHECK(graph->execute(handle, variantPack, workspace.get()));

    std::cout << "nBatch normalization backward weight graph execution complete.  \n";

    HIPDNN_CHECK(backend->destroy(handle));
    return 0;
}