#ifndef _NNUE_TRAINER_H_
#define _NNUE_TRAINER_H_

#include "nnue/nnue_common.h"
#include "nnue/features/index_list.h"

#include <sstream>

#if defined(USE_BLAS)
static_assert(std::is_same<LearnFloatType, float>::value, "");
#include <cblas.h>
#endif

// Common header of class template for learning NNUE evaluation function
namespace Eval::NNUE {

    // Ponanza constant used in the relation between evaluation value and winning percentage
    constexpr double kPonanzaConstant = 600.0;

    // Class that represents one index of learning feature
    class TrainingFeature {
        using StorageType = std::uint32_t;
        static_assert(std::is_unsigned<StorageType>::value, "");

    public:
        static constexpr std::uint32_t kIndexBits = 24;

        static_assert(kIndexBits < std::numeric_limits<StorageType>::digits, "");

        static constexpr std::uint32_t kCountBits =
            std::numeric_limits<StorageType>::digits - kIndexBits;

        explicit TrainingFeature(IndexType index) :
            index_and_count_((index << kCountBits) | 1) {

            assert(index < (1 << kIndexBits));
        }

        TrainingFeature& operator+=(const TrainingFeature& other) {
            assert(other.get_index() == get_index());
            assert(other.get_count() + get_count() < (1 << kCountBits));
            index_and_count_ += other.get_count();
            return *this;
        }

        IndexType get_index() const {
            return static_cast<IndexType>(index_and_count_ >> kCountBits);
        }

        void shift_index(IndexType offset) {
            assert(get_index() + offset < (1 << kIndexBits));
            index_and_count_ += offset << kCountBits;
        }

        IndexType get_count() const {
            return static_cast<IndexType>(index_and_count_ & ((1 << kCountBits) - 1));
        }

        bool operator<(const TrainingFeature& other) const {
            return index_and_count_ < other.index_and_count_;
        }

    private:
        StorageType index_and_count_;
    };

    // Structure that represents one sample of training data
    struct Example {
        std::vector<TrainingFeature> training_features[2];
        Learner::PackedSfenValue psv;
        Value discrete_nn_eval;
        int sign;
        double weight;
    };

    // Message used for setting hyperparameters
    struct Message {
        Message(const std::string& message_name, const std::string& message_value = "") :
            name(message_name), value(message_value), num_peekers(0), num_receivers(0)
        {
        }

        const std::string name;
        const std::string value;
        std::uint32_t num_peekers;
        std::uint32_t num_receivers;
    };

    // determine whether to accept the message
    bool receive_message(const std::string& name, Message* message) {
        const auto subscript = "[" + std::to_string(message->num_peekers) + "]";

        if (message->name.substr(0, name.size() + 1) == name + "[") {
            ++message->num_peekers;
        }

        if (message->name == name || message->name == name + subscript) {
            ++message->num_receivers;
            return true;
        }

        return false;
    }

    // round a floating point number to an integer
    template <typename IntType>
    IntType round(double value) {
        return static_cast<IntType>(std::floor(value + 0.5));
    }

    // make_shared with alignment
    template <typename T, typename... ArgumentTypes>
    std::shared_ptr<T> make_aligned_shared_ptr(ArgumentTypes&&... arguments) {
        const auto ptr = new(std_aligned_alloc(alignof(T), sizeof(T)))
            T(std::forward<ArgumentTypes>(arguments)...);

        return std::shared_ptr<T>(ptr, AlignedDeleter<T>());
    }

}  // namespace Eval::NNUE

#endif