    // Copyright (C) 2015  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_DNn_TENSOR_H_
    #define DLIB_DNn_TENSOR_H_

    #include "tensor_abstract.h"
    #include <cstring>
    #include "../matrix.h"
    #include "cudnn_dlibapi.h"
    #include "gpu_data.h"
    #include "../byte_orderer.h"
    #include <memory>
    #include "../any.h"

    namespace dlib
    {
    // ----------------------------------------------------------------------------------------

        class tensor
        {
        public:

            tensor (
            ) :
                m_n(0), m_k(0), m_nr(0), m_nc(0), m_size(0)
            {
            }

            virtual ~tensor() {}

            long num_samples() const { return m_n; }
            long k() const { return m_k; }
            long nr() const { return m_nr; }
            long nc() const { return m_nc; }
            size_t size() const { return m_size; }

            typedef float* iterator;
            typedef const float* const_iterator;
            iterator       begin()       { return host(); }
            const_iterator begin() const { return host(); }
            iterator       end()         { return host()+size(); }
            const_iterator end() const   { return host()+size(); }

            void async_copy_to_device() const
            {
                data().async_copy_to_device();
            }

            virtual const float* host() const = 0;
            virtual float*       host() = 0;
            virtual float*       host_write_only() = 0;
            virtual const float* device() const = 0;
            virtual float*       device() = 0;
            virtual float*       device_write_only() = 0;

            virtual const any&   annotation() const = 0;
            virtual any&         annotation() = 0;

            int device_id() const { return data().device_id(); }

            tensor& operator= (float val)
            {
    #ifdef DLIB_USE_CUDA
                // If you are using CUDA then presumably you will be mostly using tensors on
                // the GPU.  So unless you seem to be actively working with the host side's
                // data then we do this initialization on the device side since this avoids a
                // host to device transfer that would likely immediately follow.
                if (data().device_ready())
                {
                    cuda::set_tensor(*this, val);
                    return *this;
                }
    #endif
                auto d = host_write_only();
                for (size_t i = 0; i < size(); ++i)
                    d[i] = val;

                return *this;
            }

            tensor& operator*= (float val)
            {
    #ifdef DLIB_USE_CUDA
                cuda::scale_tensor(*this, val);
                return *this;
    #else
                for (auto& d : *this)
                    d *= val;

                return *this;
    #endif
            }

            tensor& operator/= (float val)
            {
                *this *= 1.0/val;
                return *this;
            }

            template <typename EXP>
            tensor& operator= (const matrix_exp<EXP>& item)
            {
                DLIB_CASSERT(num_samples() == item.nr() &&
                             nr()*nc()*k() == item.nc());
                static_assert((is_same_type<float, typename EXP::type>::value == true),
                    "To assign a matrix to a tensor the matrix must contain float values");

                set_ptrm(host_write_only(), m_n, m_nr*m_nc*m_k) = item;
                return *this;
            }

            template <typename EXP>
            tensor& operator+= (const matrix_exp<EXP>& item)
            {
                DLIB_CASSERT(num_samples() == item.nr() &&
                             nr()*nc()*k() == item.nc());
                static_assert((is_same_type<float, typename EXP::type>::value == true),
                    "To assign a matrix to a tensor the matrix must contain float values");
                set_ptrm(host(), m_n, m_nr*m_nc*m_k) += item;
                return *this;
            }

            template <typename EXP>
            tensor& operator-= (const matrix_exp<EXP>& item)
            {
                DLIB_CASSERT(num_samples() == item.nr() &&
                             nr()*nc()*k() == item.nc());
                static_assert((is_same_type<float, typename EXP::type>::value == true),
                    "To assign a matrix to a tensor the matrix must contain float values");
                set_ptrm(host(), m_n, m_nr*m_nc*m_k) -= item;
                return *this;
            }

            template <typename EXP>
            void set_sample (
                unsigned long idx,
                const matrix_exp<EXP>& item
            )
            {
                DLIB_CASSERT(idx < (unsigned long)num_samples());
                DLIB_CASSERT(item.size() == nr()*nc()*k());
                static_assert((is_same_type<float, typename EXP::type>::value == true),
                    "To assign a matrix to a tensor the matrix must contain float values");
                set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) = item;
            }

            template <typename EXP>
            void add_to_sample (
                unsigned long idx,
                const matrix_exp<EXP>& item
            )
            {
                DLIB_CASSERT(idx < num_samples());
                DLIB_CASSERT(item.size() == nr()*nc()*k());
                static_assert((is_same_type<float, typename EXP::type>::value == true),
                    "To assign a matrix to a tensor the matrix must contain float values");
                set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) += item;
            }

    #ifdef DLIB_USE_CUDA
            virtual const cuda::tensor_descriptor& get_cudnn_tensor_descriptor (
            ) const = 0;
    #endif

            friend void memcpy (
                tensor& dest,
                const tensor& src
            )
            {
                DLIB_CASSERT(dest.size() == src.size());
                memcpy(dest.data(), dest.get_alias_offset(),
                       src.data(),  src.get_alias_offset(),
                       src.size());
            }

        protected:

            friend class alias_tensor;

            virtual gpu_data& data() = 0;
            virtual const gpu_data& data() const = 0;
            virtual size_t get_alias_offset() const { return 0; } // needed by alias_tensor.

            long m_n;
            long m_k;
            long m_nr;
            long m_nc;
            long m_size; // always equal to m_n*m_k*m_nr*m_nc
        };

    // ----------------------------------------------------------------------------------------

        inline const matrix_op<op_pointer_to_mat<float> > mat (
            const tensor& t,
            long nr,
            long nc
        )
        {
            DLIB_ASSERT(nr >= 0 && nc >= 0 ,
                        "\tconst matrix_exp mat(tensor, nr, nc)"
                        << "\n\t nr and nc must be >= 0"
                        << "\n\t nr: " << nr
                        << "\n\t nc: " << nc
            );
            DLIB_ASSERT(nr*nc == (long)t.size() ,
                        "\tconst matrix_exp mat(tensor, nr, nc)"
                        << "\n\t The sizes don't match up."
                        << "\n\t nr*nc:    " << nr*nc
                        << "\n\t t.size(): " << t.size()
            );
            typedef op_pointer_to_mat<float> op;
            return matrix_op<op>(op(t.host(),nr,nc));
        }

        inline const matrix_op<op_pointer_to_mat<float> > mat (
            const tensor& t
        )
        {
            if (t.size() != 0)
                return mat(t, t.num_samples(), t.size()/t.num_samples());
            else
                return mat((float*)0,0,0);
        }

        inline const matrix_op<op_pointer_to_mat<float> > image_plane (
            const tensor& t,
            long sample = 0,
            long k = 0
        )
        {
            DLIB_ASSERT(0 <= sample && sample < t.num_samples() &&
                        0 <= k && k < t.k() &&
                        t.size() != 0,
                        "\tconst matrix_exp image_plane(tensor,sample,k)"
                        << "\n\t Invalid arguments were given to this function."
                        << "\n\t sample: " << sample
                        << "\n\t k:      " << k
                        << "\n\t t.num_samples(): " << t.num_samples()
                        << "\n\t t.k():           " << t.k()
                        << "\n\t t.size():        " << t.size()
            );

            typedef op_pointer_to_mat<float> op;
            return matrix_op<op>(op(t.host() + ((sample*t.k() + k)*t.nr())*t.nc(),
                                    t.nr(),
                                    t.nc()));
        }

    // ----------------------------------------------------------------------------------------

        inline bool have_same_dimensions (
            const tensor& a,
            const tensor& b
        )
        {
            return a.num_samples() == b.num_samples() &&
                   a.k()  == b.k() &&
                   a.nr() == b.nr() &&
                   a.nc() == b.nc();
        }

    // ----------------------------------------------------------------------------------------

        class resizable_tensor : public tensor
        {
        public:
            resizable_tensor(
            )
            {}

            template <typename EXP>
            resizable_tensor(
                const matrix_exp<EXP>& item
            )
            {
                set_size(item.nr(), item.nc());
                *this = item;
            }

            explicit resizable_tensor(
                long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
            )
            {
                DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0);

                set_size(n_,k_,nr_,nc_);
            }

            resizable_tensor(const resizable_tensor& item) : _annotation(item.annotation())
            {
                copy_size(item);
                memcpy(data_instance, item.data_instance);
            }
            resizable_tensor(const tensor& item) : _annotation(item.annotation())
            {
                copy_size(item);
                memcpy(*this, item);
            }

            resizable_tensor(resizable_tensor&& item) { swap(item); }
            resizable_tensor& operator=(resizable_tensor&& item) { swap(item); return *this; }

            virtual const float* host() const { return data_instance.host(); }
            virtual float*       host()       { return data_instance.host(); }
            virtual float*       host_write_only() { return data_instance.host_write_only(); }
            virtual const float* device() const { return data_instance.device(); }
            virtual float*       device()       { return data_instance.device(); }
            virtual float*       device_write_only() { return data_instance.device_write_only(); }

            virtual const any&   annotation() const { return _annotation; }
            virtual any&         annotation() { return _annotation; }

            void clear(
            )
            {
                set_size(0,0,0,0);
                _annotation.clear();
            }

            void copy_size (
                const tensor& item
            )
            {
                set_size(item.num_samples(), item.k(), item.nr(), item.nc());
            }

            resizable_tensor& operator= (float val)
            {
                tensor::operator=(val);
                return *this;
            }

            template <typename EXP>
            resizable_tensor& operator= (
                const matrix_exp<EXP>& item
            )
            {
                if (!(num_samples() == item.nr() && k()*nr()*nc() == item.nc()))
                    set_size(item.nr(), item.nc());
                tensor::operator=(item);
                return *this;
            }

            void set_size(
                long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
            )
            {
                DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0);

                m_n = n_;
                m_k = k_;
                m_nr = nr_;
                m_nc = nc_;
                m_size = n_*k_*nr_*nc_;
                data_instance.set_size(m_size);
    #ifdef DLIB_USE_CUDA
                cudnn_descriptor.set_size(m_n,m_k,m_nr,m_nc);
    #endif
            }

            resizable_tensor& operator= (const resizable_tensor& item)
            {
                resizable_tensor temp(item);
                temp.swap(*this);
                return *this;
            }

            resizable_tensor& operator= (const tensor& item)
            {
                resizable_tensor temp(item);
                temp.swap(*this);
                return *this;
            }

            void swap(resizable_tensor& item)
            {
                std::swap(m_n,    item.m_n);
                std::swap(m_k,    item.m_k);
                std::swap(m_nr,   item.m_nr);
                std::swap(m_nc,   item.m_nc);
                std::swap(m_size, item.m_size);
                std::swap(data_instance, item.data_instance);
                std::swap(_annotation, item._annotation);
    #ifdef DLIB_USE_CUDA
                std::swap(cudnn_descriptor, item.cudnn_descriptor);
    #endif
            }

    #ifdef DLIB_USE_CUDA
            virtual const cuda::tensor_descriptor& get_cudnn_tensor_descriptor (
            ) const { return cudnn_descriptor; }
    #endif

        private:

    #ifdef DLIB_USE_CUDA
            cuda::tensor_descriptor cudnn_descriptor;
    #endif

            gpu_data data_instance;
            any _annotation;
            virtual gpu_data& data() { return data_instance; }
            virtual const gpu_data& data() const { return data_instance; }
        };

        inline void serialize(const tensor& item, std::ostream& out)
        {
            int version = 2;
            serialize(version, out);
            serialize(item.num_samples(), out);
            serialize(item.k(), out);
            serialize(item.nr(), out);
            serialize(item.nc(), out);
            byte_orderer bo;
            auto sbuf = out.rdbuf();
            for (auto d : item)
            {
                // Write out our data as 4byte little endian IEEE floats rather than using
                // dlib's default float serialization.  We do this because it will result in
                // more compact outputs.  It's slightly less portable but it seems doubtful
                // that any CUDA enabled platform isn't going to use IEEE floats.  But if one
                // does we can just update the serialization code here to handle it if such a
                // platform is encountered.
                bo.host_to_little(d);
                static_assert(sizeof(d)==4, "This serialization code assumes we are writing 4 byte floats");
                sbuf->sputn((char*)&d, sizeof(d));
            }
        }

        inline void deserialize(resizable_tensor& item, std::istream& in)
        {
            int version;
            deserialize(version, in);
            if (version != 2)
                throw serialization_error("Unexpected version found while deserializing dlib::resizable_tensor.");

            long num_samples=0, k=0, nr=0, nc=0;
            deserialize(num_samples, in);
            deserialize(k, in);
            deserialize(nr, in);
            deserialize(nc, in);
            item.set_size(num_samples, k, nr, nc);
            byte_orderer bo;
            auto sbuf = in.rdbuf();
            for (auto& d : item)
            {
                static_assert(sizeof(d)==4, "This serialization code assumes we are writing 4 byte floats");
                if (sbuf->sgetn((char*)&d,sizeof(d)) != sizeof(d))
                {
                    in.setstate(std::ios::badbit);
                    throw serialization_error("Error reading data while deserializing dlib::resizable_tensor.");
                }
                bo.little_to_host(d);
            }
        }

    // ----------------------------------------------------------------------------------------

        inline double dot(
            const tensor& a,
            const tensor& b
        )
        {
            DLIB_CASSERT(a.size() == b.size());
            const float* da = a.host();
            const float* db = b.host();
            double sum = 0;
            for (size_t i = 0; i < a.size(); ++i)
                sum += da[i]*db[i];
            return sum;
        }

    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------

        class alias_tensor_instance : public tensor
        {
            alias_tensor_instance(
            ) : data_instance(0), _annotation(0), data_offset(0) {}

        public:
            friend class alias_tensor;
            friend class alias_tensor_const_instance;

            alias_tensor_instance& operator= (float val)
            {
                tensor::operator=(val);
                return *this;
            }

            template <typename EXP>
            alias_tensor_instance& operator= (const matrix_exp<EXP>& item)
            {
                tensor::operator=(item);
                return *this;
            }

            virtual const float* host() const { return data_instance->host()+data_offset; }
            virtual float*       host()       { return data_instance->host()+data_offset; }
            virtual float*       host_write_only()    { return data_instance->host()+data_offset; }
            virtual const float* device() const { return data_instance->device()+data_offset; }
            virtual float*       device()       { return data_instance->device()+data_offset; }
            virtual float*       device_write_only()  { return data_instance->device()+data_offset; }

            virtual const any&   annotation() const { return *_annotation; }
            virtual any&         annotation() { return *_annotation; }

    #ifdef DLIB_USE_CUDA
            virtual const cuda::tensor_descriptor& get_cudnn_tensor_descriptor (
            ) const { return *cudnn_descriptor; }
    #endif
        private:

            virtual size_t get_alias_offset() const { return data_offset; }

    #ifdef DLIB_USE_CUDA
            std::shared_ptr<cuda::tensor_descriptor> cudnn_descriptor;
    #endif
            gpu_data* data_instance;
            any* _annotation;
            size_t data_offset;
            virtual gpu_data& data() { return *data_instance; }
            virtual const gpu_data& data() const { return *data_instance; }
        };

    // ----------------------------------------------------------------------------------------

        class alias_tensor_const_instance
        {
        public:
            const tensor& get() const { return inst; }
            operator const tensor& () { return inst; }

        private:
            alias_tensor_instance inst;

            friend class alias_tensor;
            alias_tensor_const_instance() {}
        };

    // ----------------------------------------------------------------------------------------

        class alias_tensor
        {
        public:

            alias_tensor (
            ) {}

            alias_tensor (
                long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
            )
            {
                DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0);

                inst.m_n = n_;
                inst.m_k = k_;
                inst.m_nr = nr_;
                inst.m_nc = nc_;
                inst.m_size = n_*k_*nr_*nc_;
            }

            long num_samples(
            ) const { return inst.m_n; }

            long k(
            ) const { return inst.m_k; }

            long nr(
            ) const { return inst.m_nr; }

            long nc(
            ) const { return inst.m_nc; }

            size_t size(
            ) const { return inst.m_size; }

            alias_tensor_instance operator() (
                tensor& t,
                size_t offset
            )
            {
                DLIB_CASSERT(offset+size() <= t.size());

    #ifdef DLIB_USE_CUDA
                if (!inst.cudnn_descriptor)
                {
                    inst.cudnn_descriptor = std::make_shared<cuda::tensor_descriptor>();
                    inst.cudnn_descriptor->set_size(inst.m_n, inst.m_k, inst.m_nr, inst.m_nc);
                }
    #endif
                inst.data_instance = &t.data();
                inst._annotation   = &t.annotation();
                // Note that t might already be an aliasing tensor so we need to take that into
                // account.
                inst.data_offset = t.get_alias_offset()+offset;
                return inst;
            }

            alias_tensor_const_instance operator() (
                const tensor& t,
                size_t offset
            )
            {
                alias_tensor_const_instance temp;
                temp.inst = (*this)(const_cast<tensor&>(t),offset);
                return temp;
            }

        private:
            alias_tensor_instance inst;
        };

        inline void serialize(const alias_tensor& item, std::ostream& out)
        {
            int version = 1;
            serialize(version, out);
            serialize(item.num_samples(), out);
            serialize(item.k(), out);
            serialize(item.nr(), out);
            serialize(item.nc(), out);
        }

        inline void deserialize(alias_tensor& item, std::istream& in)
        {
            int version = 0;
            deserialize(version, in);
            if (version != 1)
                throw serialization_error("Unexpected version found while deserializing dlib::alias_tensor.");
            long num_samples, k, nr, nc;
            deserialize(num_samples, in);
            deserialize(k, in);
            deserialize(nr, in);
            deserialize(nc, in);
            item = alias_tensor(num_samples, k, nr, nc);
        }

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_DNn_TENSOR_H_