Tensor.hpp

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#ifndef _H_Tensor_
#define _H_Tensor_
 
#include "Type.hpp"
#include "cytnx_error.hpp"
#include "Storage.hpp"
#include "Device.hpp"
#include "intrusive_ptr_base.hpp"
#include <iostream>
#include <fstream>
#include "utils/vec_range.hpp"
#include "utils/dynamic_arg_resolver.hpp"
//#include "linalg.hpp"
#include "Accessor.hpp"
#include <vector>
#include <initializer_list>
#include <string>
#include "Scalar.hpp"
 
namespace cytnx{
 
 
    // real implementation
    class Tensor_impl: public intrusive_ptr_base<Tensor_impl>{
        private:
 
            //Interface:
            Storage_init_interface __SII;
            
 
            //Memory:
            Storage _storage;
 
            //tensor shape
            std::vector<cytnx_uint64> _shape;
            
            // pseudo-perm info
            std::vector<cytnx_uint64> _mapper;
            std::vector<cytnx_uint64> _invmapper;
            bool _contiguous;
 
        public:
 
            friend class Tensor;
            boost::intrusive_ptr<Tensor_impl> _clone_meta_only() const{
                boost::intrusive_ptr<Tensor_impl> out(new Tensor_impl());
                out->_mapper = this->_mapper;
                out->_invmapper = this->_invmapper;
                out->_shape = this->_shape;
                out->_contiguous = this->_contiguous;
                return out;
            }
            Tensor_impl(): _contiguous(true){};
 
            void Init(const std::vector<cytnx_uint64> &shape, const unsigned int &dtype=Type.Double, int device=-1);
            void Init(const Storage &in);                
            /*
            template<class T>
            void From_vec(const T &ndvec){
                cytnx_error_msg(std::string(typeid(T).name()).find("vector") == std::string::npos,"[ERROR][Tensor][From_vec] the input argument should be a nd vector.%s","\n");
                //dispatch the rank!:
                                
                
 
            }
            */
            //clone&assignment constr., use intrusive_ptr's
            Tensor_impl(const Tensor_impl &rhs);
            Tensor_impl& operator=(const Tensor_impl &rhs); // add const
            
            unsigned int dtype() const{
                return this->_storage.dtype();
            }
            int device() const {
                return this->_storage.device();
            }
 
            std::string dtype_str() const {
                return Type.getname(this->_storage.dtype());
            }
            std::string device_str() const{
                return Device.getname(this->_storage.device());
            }
 
            const std::vector<cytnx_uint64>& shape() const{
                return _shape;
            }
 
            const bool& is_contiguous() const{
                return this->_contiguous;
            }
 
            const std::vector<cytnx_uint64>& mapper() const{
                return this->_mapper;
            }
            const std::vector<cytnx_uint64>& invmapper() const{
                return this->_invmapper;
            }
            Storage& storage(){
                return _storage;
            }
 
            const Storage& storage() const{
                return _storage;
            }
 
            boost::intrusive_ptr<Tensor_impl> clone() const{
                boost::intrusive_ptr<Tensor_impl> out = this->_clone_meta_only();
                out->_storage = this->_storage.clone();
                return out;
            }
            
 
            void to_(const int &device){
                this->_storage.to_(device);
            }
            boost::intrusive_ptr<Tensor_impl> to(const int &device){
                if(this->device()==device){
                    //boost::intrusive_ptr<Tensor_impl> out(this);
                    return this;
                }else{
                    
                    boost::intrusive_ptr<Tensor_impl> out = this->_clone_meta_only();
                    out->_storage = this->_storage.to(device);
                    return out;
                }
            }
 
            void permute_(const std::vector<cytnx_uint64> &rnks);
 
            boost::intrusive_ptr<Tensor_impl> permute(const std::vector<cytnx_uint64> &rnks);
 
            template<class T> 
            T& at(const std::vector<cytnx_uint64> &locator) const {
                cytnx_error_msg(locator.size() != this->_shape.size(), "%s", "The input index does not match Tensor's rank.");
 
                cytnx_uint64 RealRank,mtplyr;
                std::vector<cytnx_uint64> c_shape(this->_shape.size());
                std::vector<cytnx_uint64> c_loc(this->_shape.size());
 
                RealRank=0;
                mtplyr = 1;
 
                for(cytnx_int64 i=this->_shape.size()-1; i>=0; i--){
                    if(locator[i]>=this->_shape[i]){
                        cytnx_error_msg(true, "%s", "Attempting to access out-of-bound index in Tensor.");
                    }
                    c_shape[i] = this->_shape[this->_invmapper[i]];
                    c_loc[i] = locator[this->_invmapper[i]];
                    RealRank += mtplyr*c_loc[i];
                    mtplyr *= c_shape[i];
                }
                return this->_storage.at<T>(RealRank);
            }
            
            boost::intrusive_ptr<Tensor_impl> get(const std::vector<cytnx::Accessor> &accessors);
            boost::intrusive_ptr<Tensor_impl> get_deprecated(const std::vector<cytnx::Accessor> &accessors);
            void set(const std::vector<cytnx::Accessor> &accessors, const boost::intrusive_ptr<Tensor_impl> &rhs);
 
            template<class T>
            void set(const std::vector<cytnx::Accessor> &accessors, const T& rc);
            
            void set(const std::vector<cytnx::Accessor> &accessors, const Scalar::Sproxy& rc);
 
            template<class Tx>
            void fill(const Tx& val){
                this->storage().fill(val);
            } 
                        
            boost::intrusive_ptr<Tensor_impl> contiguous(){
                // return new instance if act on non-contiguous tensor
                // return self if act on contiguous tensor
                if(this->_contiguous){
                    boost::intrusive_ptr<Tensor_impl> out(this);
                    //out->_storage = this->_storage;
                    return out;
                }else{
                    boost::intrusive_ptr<Tensor_impl> out(new Tensor_impl());
                    std::vector<cytnx_uint64> oldshape(this->_shape.size());
                    for(cytnx_uint64 i=0;i<this->_shape.size();i++){
                        oldshape[i] = this->_shape[this->_invmapper[i]];
                    }
        
                    out->_storage._impl = this->_storage._impl->Move_memory(oldshape,this->_mapper, this->_invmapper);
                    //std::cout << out->_storage << std::endl;
                    out->_invmapper = vec_range(this->_invmapper.size());
                    out->_mapper = out->_invmapper;
                    out->_shape = this->_shape;
                    out->_contiguous = true;
                    return out;
                }
            }
            
            void contiguous_(){
                // return new instance if act on non-contiguous tensor
                // return self if act on contiguous tensor
                if(!this->_contiguous){
                    std::vector<cytnx_uint64> oldshape(this->_shape.size());
                    for(cytnx_uint64 i=0;i<this->_shape.size();i++){
                        oldshape[i] = this->_shape[this->_invmapper[i]];
                    }
                    
                    this->_storage._impl = this->_storage._impl->Move_memory(oldshape,this->_mapper, this->_invmapper);
                    this->_mapper = vec_range(this->_invmapper.size());
                    this->_invmapper = this->_mapper;
                    this->_contiguous = true;
                }
            }
 
 
            void reshape_(const std::vector<cytnx_int64> &new_shape){
                if(!this->_contiguous){
                    this->contiguous_();
                }
                std::vector<cytnx_uint64> result_shape(new_shape.size());
                cytnx_uint64 new_N = 1;
                bool has_undetermine = false;
                unsigned int Udet_id = 0;
                for(int i=0;i<new_shape.size();i++){
                    if(new_shape[i]<0){
                        if(new_shape[i]!=-1) cytnx_error_msg(new_shape[i]!=-1,"%s","[ERROR] reshape can only have dimension > 0 and one undetermine rank specify as -1");
                        if(has_undetermine) cytnx_error_msg(new_shape[i]!=-1,"%s","[ERROR] reshape can only have dimension > 0 and one undetermine rank specify as -1");
                        Udet_id = i;
                        has_undetermine = true;
                    }else{
                        new_N *= new_shape[i];
                        result_shape[i] = new_shape[i];
                    }
                }
 
                            
                if(has_undetermine){
                    cytnx_error_msg(new_N > this->_storage.size(),"%s","[ERROR] new shape exceed the total number of elements.");
                    cytnx_error_msg(this->_storage.size()%new_N,"%s","[ERROR] unmatch size when reshape with undetermine dimension");
                    result_shape[Udet_id] = this->_storage.size()/new_N;
                }else{
                    cytnx_error_msg(new_N != this->_storage.size(),"%s","[ERROR] new shape does not match the number of elements.");
                }
            
                this->_shape = result_shape;
                this->_mapper = vec_range(result_shape.size());
                this->_invmapper = this->_mapper; 
            }
 
 
            boost::intrusive_ptr<Tensor_impl> reshape(const std::vector<cytnx_int64> &new_shape){
                boost::intrusive_ptr<Tensor_impl> out(new Tensor_impl());
                if(this->is_contiguous()){
                    out = this->_clone_meta_only();
                    out->_storage = this->_storage;
                }else{
                    out = this->contiguous();
                }
                //out = this->clone();
                 
                out->reshape_(new_shape);
                return out;
            }
 
 
            
            boost::intrusive_ptr<Tensor_impl> astype(const int& new_type){
                //boost::intrusive_ptr<Tensor_impl> out(new Tensor_impl());
                //out->_storage = this->_storage.astype(new_type);
                if(this->dtype() == new_type){
                    return this;
                }else{
                   boost::intrusive_ptr<Tensor_impl> out = this->_clone_meta_only();
                   out->_storage = this->_storage.astype(new_type);
                   return out;
                }
            }
 
    };
 
    class Tensor;
 
    // [Note] these are fwd from linalg.hpp
    template<class T> Tensor operator+(const Tensor &lhs, const T &rc);
    template<class T> Tensor operator-(const Tensor &lhs, const T &rhs);
    template<class T> Tensor operator*(const Tensor &lhs, const T &rhs);
    template<class T> Tensor operator/(const Tensor &lhs, const T &rhs);
 
    class Tensor{
        private:
        public:
 
            // this is a proxy class to allow get/set element using [] as python!
            struct Tproxy
            {
                boost::intrusive_ptr<Tensor_impl> _insimpl;
                std::vector<cytnx::Accessor> _accs;
                Tproxy(boost::intrusive_ptr<Tensor_impl> _ptr,const std::vector<cytnx::Accessor> &accs) : _insimpl(_ptr), _accs(accs){}
 
                // when used to set elems:
                const Tensor& operator=(const Tensor &rhs){
                    this->_insimpl->set(_accs,rhs._impl);
                    return rhs;
                }
 
                template<class T>
                const T& operator=(const T &rc){
                    this->_insimpl->set(_accs,rc);
                    return rc;
                }
                const Tproxy& operator=(const Tproxy &rc){
                    Tensor tmp = Tensor(rc);
                    this->_insimpl->set(_accs,tmp._impl);
                    return rc;
                }            
 
 
                template<class T>
                Tensor operator+=(const T &rc){
                    Tensor self;
                    self._impl = _insimpl->get(_accs);
                    self += rc;
                    _insimpl->set(_accs,self._impl);
                    self._impl = this->_insimpl;
                    return self;
                }
                Tensor operator+=(const Tproxy &rc);
 
                template<class T>
                Tensor operator-=(const T &rc){
                    Tensor self;
                    self._impl = _insimpl->get(_accs);
                    self -= rc;
                    _insimpl->set(_accs,self._impl);
                    self._impl = this->_insimpl;
                    return self;
                }
                Tensor operator-=(const Tproxy &rc);
 
                template<class T>
                Tensor operator/=(const T &rc){
                    Tensor self;
                    self._impl = _insimpl->get(_accs);
                    self /= rc;
                    _insimpl->set(_accs,self._impl);
                    self._impl = this->_insimpl;
                    return self;
                }
                Tensor operator/=(const Tproxy &rc);
 
                template<class T>
                Tensor operator*=(const T &rc){
                    Tensor self;
                    self._impl = _insimpl->get(_accs);
                    self *= rc;
                    _insimpl->set(_accs,self._impl);
                    self._impl = this->_insimpl;
                    return self;
                }
                Tensor operator*=(const Tproxy &rc);
 
 
                //alias to resolve conflict with op ovld for rc=Tensor
                /*
                template<class T>
                Tensor _operatorADD(const T &rc) const{
                    Tensor out;
                    out._impl = _insimpl->get(_accs);
                    return out.Add(rc);
                }   
                */
                Tensor operator+(const cytnx_complex128 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_complex64 &rc)  const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_double &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_float &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_uint64 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_int64 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_uint32 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_int32 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_uint16 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_int16 &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const cytnx_bool &rc) const;//{return this->_operatorADD(rc);};
                Tensor operator+(const Tproxy &rc) const;
 
                /*
                template<class T>
                Tensor _operatorSUB(const T &rc) const{
                    Tensor out;
                    out._impl = _insimpl->get(_accs);
                    return out.Sub(rc);
                }
                */
                Tensor operator-(const cytnx_complex128 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_complex64 &rc)  const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_double &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_float &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_uint64 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_int64 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_uint32 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_int32 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_uint16 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_int16 &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const cytnx_bool &rc) const;//{return this->_operatorSUB(rc);};
                Tensor operator-(const Tproxy &rc) const;
    
                Tensor operator-() const;
 
                /*
                template<class T>
                Tensor _operatorMUL(const T &rc) const{
                    Tensor out;
                    out._impl = _insimpl->get(_accs);
                    return out.Mul(rc);
                }
                */
                Tensor operator*(const cytnx_complex128 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_complex64 &rc)  const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_double &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_float &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_uint64 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_int64 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_uint32 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_int32 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_uint16 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_int16 &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const cytnx_bool &rc) const;//{return this->_operatorMUL(rc);};
                Tensor operator*(const Tproxy &rc) const;
                
                /*
                template<class T>
                Tensor _operatorDIV(const T &rc) const{
                    Tensor out;
                    out._impl = _insimpl->get(_accs);
                    return out.Div(rc);
                }
                */
                Tensor operator/(const cytnx_complex128 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_complex64 &rc)  const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_double &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_float &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_uint64 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_int64 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_uint32 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_int32 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_uint16 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_int16 &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const cytnx_bool &rc) const;//{return this->_operatorDIV(rc);};
                Tensor operator/(const Tproxy &rc) const;
 
                template<class T>
                T item() const{
                    Tensor out;
                    out._impl = _insimpl->get(_accs);
                    return out.item<T>(); 
                }
               
                Scalar::Sproxy item() const{
                    Tensor out;
                    out._impl = _insimpl;
                    return out.item();
                }
 
                // when used to get elems:
                operator Tensor () const{
                    Tensor out;
                    out._impl = _insimpl->get(_accs);
                    return out;
                }
 
            };// proxy class of Tensor. 
 
 
            // these two are using the python way!
            //----------------------------------------
            template<class ... Ts>
            Tproxy operator()(const std::string &e1, const Ts&...elems){
                //std::cout << e1 << std::endl;
                std::vector<cytnx::Accessor> tmp = Indices_resolver(e1,elems...);
                return (*this)[tmp];
            }
            template<class ... Ts>
            Tproxy operator()(const cytnx_int64 &e1, const Ts&...elems){
                //std::cout << e1<< std::endl;
                std::vector<cytnx::Accessor> tmp = Indices_resolver(e1,elems...);
                return (*this)[tmp];
            }
            template<class ... Ts>   
            Tproxy operator()(const cytnx::Accessor &e1, const Ts&...elems){
                //std::cout << e1 << std::endl;
                std::vector<cytnx::Accessor> tmp = Indices_resolver(e1,elems...);
                return (*this)[tmp];
            }   
            
            template<class ... Ts>
            const Tproxy operator()(const cytnx_int64 &e1, const Ts&...elems) const{
                std::vector<cytnx::Accessor> tmp = Indices_resolver(e1,elems...);
                return (*this)[tmp];
            }
            template<class ... Ts>   
            const Tproxy operator()(const cytnx::Accessor &e1, const Ts&...elems) const{
                std::vector<cytnx::Accessor> tmp = Indices_resolver(e1,elems...);
                return (*this)[tmp];
            }   
            //-----------------------------------------
 
             
            Tproxy operator[](const std::initializer_list<cytnx::Accessor> &accs){
                std::vector<cytnx::Accessor> tmp = accs;
                return (*this)[tmp];
            }
            Tproxy operator[](const std::vector<cytnx::Accessor> &accs){
                return Tproxy(this->_impl,accs);
            }
 
 
            const Tproxy operator[](const std::vector<cytnx::Accessor> &accs) const{
                return Tproxy(this->_impl,accs);
            }
            const Tproxy operator[](const std::initializer_list<cytnx::Accessor> &accs) const{
                std::vector<cytnx::Accessor> tmp = accs;
                return (*this)[tmp];
            }
 
            Tproxy operator[](const std::initializer_list<cytnx_int64> &accs){
                std::vector<cytnx_int64> tmp = accs;
                return (*this)[tmp];
            }
            Tproxy operator[](const std::vector<cytnx_int64>  &accs){
                std::vector<cytnx::Accessor> acc_in;
                for(int i=0;i<accs.size();i++){
                    acc_in.push_back(cytnx::Accessor(accs[i]));
                }
                return Tproxy(this->_impl,acc_in);
            }
            const Tproxy operator[](const std::initializer_list<cytnx_int64> &accs) const{
                std::vector<cytnx_int64> tmp = accs;
                return (*this)[tmp];
            }
            const Tproxy operator[](const std::vector<cytnx_int64>  &accs) const{
                std::vector<cytnx::Accessor> acc_in;
                for(int i=0;i<accs.size();i++){
                    acc_in.push_back(cytnx::Accessor(accs[i]));
                }
                return Tproxy(this->_impl,acc_in);
            }
            //-------------------------------------------
            
 
            void _Save(std::fstream &f) const;
            void _Load(std::fstream &f);
 
 
            void Save(const std::string &fname) const;
            void Save(const char* fname) const;
            void Tofile(const std::string &fname) const;
            void Tofile(const char* fname) const;
            void Tofile(std::fstream &f) const;
            static Tensor Load(const std::string &fname);
            static Tensor Load(const char* fname);
            static Tensor Fromfile(const std::string &fname, const unsigned int &dtype, const cytnx_int64 &count=-1);
            static Tensor Fromfile(const char* fname, const unsigned int &dtype, const cytnx_int64 &count=-1);
 
            //static Tensor Frombinary(const std::string &fname);
            
 
 
            boost::intrusive_ptr<Tensor_impl> _impl;
            Tensor(): _impl(new Tensor_impl()){};
            Tensor(const Tensor &rhs){
                _impl = rhs._impl;
            }
 
            /*
            template<class Tp>
            Tensor(const std::initializer_list<Tp> &rhs){
                Storage stmp = std::vector<Tp>(rhs);
                boost::intrusive_ptr<Tensor_impl> tmp(new Tensor_impl());
                tmp->Init(stmp);
                this->_impl = tmp;
            }
            */
 
            Tensor& operator=(const Tensor &rhs){
                _impl = rhs._impl;
                return *this;
            }
 
            void operator=(const Tproxy &rhsp){ // this is used to handle proxy assignment
                this->_impl = rhsp._insimpl->get(rhsp._accs);
                
            }
 
            //default device==Device.cpu (-1)
            void Init(const std::vector<cytnx_uint64> &shape, const unsigned int &dtype=Type.Double, const int &device=-1){
                boost::intrusive_ptr<Tensor_impl> tmp(new Tensor_impl());
                this->_impl = tmp;
                this->_impl->Init(shape,dtype,device);
            }
            Tensor(const std::vector<cytnx_uint64> &shape, const unsigned int &dtype=Type.Double, const int &device=-1):_impl(new Tensor_impl()){
                this->Init(shape,dtype,device);
            }
 
            static Tensor from_storage(const Storage &in){
                Tensor out;
                boost::intrusive_ptr<Tensor_impl> tmp(new Tensor_impl());
                out._impl = tmp;
                out._impl->Init(in);
                return out;
            }
            
 
            unsigned int dtype() const {return this->_impl->dtype();}
 
            int device() const { return this->_impl->device();}
            
            std::string dtype_str() const { return this->_impl->dtype_str();}
 
            std::string device_str() const{ return this->_impl->device_str();}
 
            const std::vector<cytnx_uint64>& shape() const{
                return this->_impl->shape();
            }
 
            cytnx_uint64 rank() const{
                return this->_impl->shape().size();
            }
 
            Tensor clone() const{
                Tensor out;
                out._impl = this->_impl->clone();
                return out;
            }
 
            Tensor to(const int &device) const{
                Tensor out;
                out._impl = this->_impl->to(device);
                return out;
            }
 
            void to_(const int &device){
                this->_impl->to_(device);
            }
            
            const bool& is_contiguous() const{
                return this->_impl->is_contiguous();
            }
 
            Tensor permute_(const std::vector<cytnx_uint64> &rnks){
                this->_impl->permute_(rnks);
                return *this;
            }
            template<class ... Ts>
            Tensor permute_(const cytnx_uint64 &e1, const Ts&... elems){
                std::vector<cytnx_uint64> argv = dynamic_arg_uint64_resolver(e1,elems...);
                this->_impl->permute_(argv);
                return *this;
            }
            
            Tensor permute(const std::vector<cytnx_uint64> &rnks) const{
                Tensor out;
                out._impl = this->_impl->permute(rnks);
                return out;
            }
            template<class ... Ts>
            Tensor permute(const cytnx_uint64 &e1, const Ts&... elems) const{  
                std::vector<cytnx_uint64> argv = dynamic_arg_uint64_resolver(e1,elems...);
                return this->permute(argv);
            }
 
 
            Tensor contiguous()const {
                Tensor out;
                out._impl = this->_impl->contiguous();
                return out;
            }
            
            Tensor contiguous_(){
                this->_impl->contiguous_();
                return *this;
            }
 
            void reshape_(const std::vector<cytnx_int64> &new_shape){
                this->_impl->reshape_(new_shape);
            }
            void reshape_(const std::vector<cytnx_uint64> &new_shape){
                std::vector<cytnx_int64> shape(new_shape.begin(),new_shape.end());
                this->_impl->reshape_(shape);
            }           
            void reshape_(const std::initializer_list<cytnx_int64> &new_shape){
                std::vector<cytnx_int64> shape = new_shape;
                this->_impl->reshape_(shape);
            }
            template<class ...Ts>
            void reshape_(const cytnx_int64 &e1, const Ts...elems){
                std::vector<cytnx_int64> shape = dynamic_arg_int64_resolver(e1,elems...);
                //std::cout << shape << std::endl;
                this->_impl->reshape_(shape);
            }
 
            Tensor reshape(const std::vector<cytnx_int64> &new_shape) const {
                Tensor out;
                out._impl = this->_impl->reshape(new_shape);
                return out;
            }
            template<class ... Ts>
            Tensor reshape(const cytnx_int64 &e1, const Ts&...elems) const{
                std::vector<cytnx_int64> argv = dynamic_arg_int64_resolver(e1,elems...);
                return this->reshape(argv);
            }
 
            Tensor astype(const int &new_type) const{
                Tensor out;
                out._impl = this->_impl->astype(new_type);
                return out;
            }
 
 
            
            //Tensor diagonal(){
            //    for(unsigned int i=0;i<this->shape().size();i++){
            //        if(this->shape()[i] != this->shape()[0],"[ERROR] Tensor.diagonal() can only be called when the subject has equal dimension in each rank.%s","\n");
            //    }
            //    
            //}
 
            template<class T>
            T& at(const std::vector<cytnx_uint64> &locator){
                return this->_impl->at<T>(locator);
            }
            template<class T>
            const T& at(const std::vector<cytnx_uint64> &locator) const{
                return this->_impl->at<T>(locator);
            }
            template<class T, class...Ts>
            const T& at(const cytnx_uint64 &e1, const Ts&...elems) const{   
                std::vector<cytnx_uint64> argv = dynamic_arg_uint64_resolver(e1,elems...);
                return this->at<T>(argv);
            }
            template<class T, class...Ts>
            T& at(const cytnx_uint64 &e1, const Ts&...elems){   
                std::vector<cytnx_uint64> argv = dynamic_arg_uint64_resolver(e1,elems...);
                return this->at<T>(argv);
            }
 
            template<class T>
            T& item(){
                cytnx_error_msg(this->_impl->storage().size()!=1,"[ERROR][Tensor.item<T>]%s","item can only be called from a Tensor with only one element\n");
                return this->_impl->storage().at<T>(0);
            }
 
            template<class T>
            const T& item() const{
                cytnx_error_msg(this->_impl->storage().size()!=1,"[ERROR][Tensor.item<T>]%s","item can only be called from a Tensor with only one element\n");
                return this->_impl->storage().at<T>(0);
            }
 
            const Scalar::Sproxy item() const{
                Scalar::Sproxy out(this->storage()._impl,0);
                return out;
            }
 
            Scalar::Sproxy item(){
                Scalar::Sproxy out(this->storage()._impl,0);
                return out;
            }
 
 
            Tensor get(const std::vector<cytnx::Accessor> &accessors)const {
                Tensor out;
                out._impl = this->_impl->get(accessors);
                return out;
            }
            
            /*
            Tensor get_v2(const std::vector<cytnx::Accessor> &accessors) const{
                Tensor out;     
                out._impl = this->_impl->get_v2(accessors);
                return out;
            }
            */
 
            void set(const std::vector<cytnx::Accessor> &accessors, const Tensor &rhs){
                this->_impl->set(accessors,rhs._impl);
            }
            
 
            
            template<class T>
            void set(const std::vector<cytnx::Accessor> &accessors, const T &rc){
                this->_impl->set(accessors,rc);
            }
            template<class T>
            void set(const std::initializer_list<cytnx::Accessor> &accessors, const T &rc){
                std::vector<cytnx::Accessor> args = accessors;
                this->set(args,rc);
            }
 
            Storage& storage() const{
                return this->_impl->storage();
            } 
 
            template<class T>
            void fill(const T& val){
                this->_impl->fill(val);
            }
 
 
            bool equiv(const Tensor &rhs){
                if(this->shape() != rhs.shape()) return false;
                return true; 
            }
 
            Tensor real();
            Tensor imag();
 
 
               
            // Arithmic:
           template<class T>           
           Tensor& operator+=(const T &rc);
           template<class T>           
           Tensor& operator-=(const T &rc);
           template<class T>           
           Tensor& operator*=(const T &rc);
           template<class T>           
           Tensor& operator/=(const T &rc);
 
 
           //Tensor &operator+=(const Tproxy &rc);
           //Tensor &operator-=(const Tproxy &rc);
           //Tensor &operator*=(const Tproxy &rc);
           //Tensor &operator/=(const Tproxy &rc);
           /*
           Tensor operator+(const Tproxy &rc){
                return *this + Tensor(rc);
           }
           Tensor operator-(const Tproxy &rc){
                return *this - Tensor(rc);
           }
           Tensor operator*(const Tproxy &rc){
                return *this * Tensor(rc);
           }
           Tensor operator/(const Tproxy &rc){
                return *this / Tensor(rc);
           }
           */
           
           template<class T>
           Tensor Add(const T &rhs){
                return *this + rhs;
           }
           template<class T>
           Tensor& Add_(const T &rhs){
                return *this += rhs;
           }
            
           template<class T>
           Tensor Sub(const T &rhs){
                return *this - rhs;
           }
           template<class T>
           Tensor& Sub_(const T &rhs){
                return *this -= rhs;
           }
            
           template<class T>
           Tensor Mul(const T &rhs){
                return *this * rhs;
           }
           template<class T>
           Tensor& Mul_(const T &rhs){
                return *this *= rhs;
           }
           
           template<class T>
           Tensor Div(const T &rhs){
                return *this / rhs;
           }
           template<class T>
           Tensor& Div_(const T &rhs){
                return *this /= rhs; 
           }
           
           template<class T>
           Tensor Cpr(const T &rhs){
                return *this == rhs;
           }
 
           //template<class T>
           //Tensor& Cpr_(const T &rhs){
           //     
           //     return *this == rhs; 
           //}
 
           template<class T>
           Tensor Mod(const T &rhs){
                return *this % rhs;
           }
 
            Tensor operator-(){
                return this->Mul(-1.);
            }
 
            Tensor flatten() const{
                Tensor out = this->clone();
                out.contiguous_();
                out.reshape_({-1});
                return out;
            }
 
            void flatten_(){
                this->contiguous_();
                this->reshape_({-1});
                
            }
 
 
            void append(const Tensor &rhs){
                //Tensor in;
                if(!this->is_contiguous())
                    this->contiguous_();
 
                // check Tensor in shape:
                cytnx_error_msg(rhs.shape().size()==0 || this->shape().size()==0,"[ERROR] try to append a null Tensor.%s","\n");
                cytnx_error_msg(rhs.shape().size()!=(this->shape().size()-1),"[ERROR] try to append a Tensor with rank not match.%s","\n");
                cytnx_uint64 Nelem = 1; 
                for(unsigned int i=0;i<rhs.shape().size();i++){
                    cytnx_error_msg(rhs.shape()[i]!=this->shape()[i+1],"[ERROR] dimension mismatch @ rhs.rank: [%d] this: [%d] rhs: [%d]\n",i,this->shape()[i+1],rhs.shape()[i]);
                    Nelem*=rhs.shape()[i];
                }
 
                //check type:
                Tensor in;
                if(rhs.dtype() != this->dtype()){
                    in = rhs.astype(this->dtype());        
                    if(!in.is_contiguous())
                        in.contiguous_();
                }else{
                    if(!in.is_contiguous())
                        in = rhs.contiguous();
                    else
                        in = rhs;
                }     
                this->_impl->_shape[0]+=1;
                cytnx_uint64 oldsize = this->_impl->_storage.size();
                this->_impl->_storage.resize(oldsize+Nelem);
                memcpy(((char*)this->_impl->_storage.data()) + oldsize*Type.typeSize(this->dtype())/sizeof(char),
                       in._impl->_storage.data(),
                       Type.typeSize(in.dtype())*Nelem);
 
            }
            void append(const Storage &srhs){
                if(!this->is_contiguous())
                    this->contiguous_();
 
                // check Tensor in shape:
                cytnx_error_msg(srhs.size()==0 || this->shape().size()==0,"[ERROR] try to append a null Tensor.%s","\n");
                cytnx_error_msg((this->shape().size()-1)!=1,"[ERROR] append a storage to Tensor can only accept rank-2 Tensor.%s","\n");
                cytnx_error_msg(this->shape().back()!=srhs.size(),"[ERROR] Tensor dmension mismatch!%s","\n");
 
               
                //check type:
                Storage in;
                if(srhs.dtype() != this->dtype()){
                    in = srhs.astype(this->dtype());        
                }else{
                    in = srhs;
                }     
                this->_impl->_shape[0]+=1;
                cytnx_uint64 oldsize = this->_impl->_storage.size();
                this->_impl->_storage.resize(oldsize+in.size());
                memcpy(((char*)this->_impl->_storage.data()) + oldsize*Type.typeSize(this->dtype())/sizeof(char),
                       in._impl->Mem,
                       Type.typeSize(in.dtype())*in.size());
 
            }
            /*
            void append(const Tensor &rhs){
                // convert to the same type.
                Tensor in;
                if(rhs.dtype() != this->dtype()){
                    in = rhs.astype(this->dtype());        
                }else{
                    in = rhs;
                }       
 
                // 1) check rank
                if(this->shape().size()==1){
                    // check if rhs is a scalar tensor (only one element)
                    cytnx_error_msg(!(rhs.shape().size()==1 && rhs.shape()[0]==1),"[ERROR] trying to append a scalar into multidimentional Tensor is not allow.\n Only rank-1 Tensor can accept scalar append.%s","\n");
                    this->_impl->_shape[0]+=1;
                    this->_impl->_storage.append(0);
 
                }else{
                    cytnx_error_msg(rhs.shape().size() != this->shape().size()-1,"[ERROR] try to append a Tensor with rank not match.%s","\n");
                    
                }
                cytnx_error_msg(!this->is_contiguous(),"[ERROR] append require the Tensor to be contiguous. suggestion: call contiguous() or contiguous_() first.","\n");
            }
            */
            template<class T>
            void append(const T &rhs){
                cytnx_error_msg(this->shape().size()!=1,"[ERROR] trying to append a scalar into multidimentional Tensor is not allow.\n Only rank-1 Tensor can accept scalar append.%s","\n");
                cytnx_error_msg(!this->is_contiguous(),"[ERROR] append require the Tensor to be contiguous. suggestion: call contiguous() or contiguous_() first.","\n");
                this->_impl->_shape[0]+=1;
                this->_impl->_storage.append(rhs);         
            }
 
            bool same_data(const Tensor &rhs) const;
 
            // linalg:
            std::vector<Tensor> Svd(const bool &is_U=true, const bool &is_vT=true) const;
            std::vector<Tensor> Eigh(const bool &is_V=true,const bool &row_v=false) const;
            Tensor& InvM_();
            Tensor InvM() const; 
            Tensor& Inv_(const double &clip);
            Tensor Inv(const double &clip) const; 
 
            Tensor& Conj_();
            Tensor Conj() const;
            Tensor& Exp_();
            Tensor Exp() const;
            Tensor Norm() const;
            Tensor Pow(const cytnx_double &p) const;
            Tensor& Pow_(const cytnx_double &p);
            Tensor Trace(const cytnx_uint64 &a, const cytnx_uint64 &b) const;
            Tensor Abs() const;
            Tensor& Abs_();
            Tensor Max() const;
            Tensor Min() const;
 
    };// class Tensor
 
    Tensor operator+(const Tensor &lhs, const Tensor::Tproxy &rhs);
    Tensor operator-(const Tensor &lhs, const Tensor::Tproxy &rhs);
    Tensor operator*(const Tensor &lhs, const Tensor::Tproxy &rhs);
    Tensor operator/(const Tensor &lhs, const Tensor::Tproxy &rhs);
    
    Tensor operator+(const Tensor &lhs, const Scalar::Sproxy &rhs);
    Tensor operator-(const Tensor &lhs, const Scalar::Sproxy &rhs);
    Tensor operator*(const Tensor &lhs, const Scalar::Sproxy &rhs);
    Tensor operator/(const Tensor &lhs, const Scalar::Sproxy &rhs);
 
    std::ostream& operator<<(std::ostream& os, const Tensor &in);
    std::ostream& operator<<(std::ostream& os, const Tensor::Tproxy &in);
    //{ os << Tensor(in);};
}
 
#endif