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#pragma once

#include <array>
#include <memory>
#include <vector>

#include "DataTypes_Half.hpp"

#include <tensilelitehost/export.h>

namespace TensileLite
{
    /**
     * \ingroup Tensile
     * \defgroup MLPClassification MLP Classification
     *
     * @brief Classification model using multilayer perceptron
     *
     * Neural net used to estimate efficiency values for solutions in the
     * library. Used for MLPClassificationLibrary.
     *
     * See MLPNet.cpp
     */

    /**
     * \ingroup MLPClassification
     */
    namespace MLPClassification
    {

        // using dtype = TensileLite::Half;
        using dtype = float;
        // using dtype = _Float16;   // very slow, -mavx512fp16
        // using dtype = __bf16;     // compiler errors, tried with -march=native

        struct StandardScaler
        {
            void operator()(std::vector<dtype>& F) const;
            bool valid(bool verbose = false) const;

            std::vector<dtype> mean, scale;
        };

        // forward declaration
        struct WeightMatrix;

        struct DenseLayer
        {
            DenseLayer() = default;
            DenseLayer(const std::vector<float>& weights, const std::vector<float>& bias);

            std::vector<dtype> operator()(const std::vector<dtype>& F) const;

            bool valid(bool verbose = false) const;

            std::size_t size_in;
            std::size_t size_out;

            std::vector<dtype>            B;
            std::shared_ptr<WeightMatrix> W;
        };

        struct ResBlock
        {
            ResBlock() = default;

            std::vector<dtype> operator()(const std::vector<dtype>& F) const;

            bool valid(bool verbose = false) const;

            DenseLayer linear1, linear2, res;
        };

        struct MLPNet
        {
            MLPNet() = default;

            std::vector<dtype> predict(std::vector<float> const& probkey) const;

            bool valid(bool verbose = false) const;

            std::string description() const
            {
                return "MLPNet";
            }

            std::vector<ResBlock> res_blocks;
            DenseLayer            dense;
            StandardScaler        scaler;
        };

    } // namespace MLPClassification
} // namespace TensileLite