#pragma once

#include <unordered_map>
#include "simplesenonehmm.h"
#include "latticearchive.h"
#include "latticesource.h"
#include "ssematrix.h"
#include "Matrix.h"
#include "CUDAPageLockedMemAllocator.h"

#include <memory>
#include <vector>

#pragma warning(disable : 4127) // conditional expression is constant

namespace msra { namespace lattices {

struct SeqGammarCalParam
{
    double amf;
    double lmf;
    double wp;
    double bMMIfactor;
    bool sMBRmode;
    SeqGammarCalParam()
    {
        amf = 14.0;
        lmf = 14.0;
        wp = 0.0;
        bMMIfactor = 0.0;
        sMBRmode = false;
    }
};

template <class ElemType>
class GammaCalculation
{
    bool cpumode;

public:
    GammaCalculation()
        : cpumode(false)
    {
        initialmark = false;
        lmf = 7.0f; // Note that 9 was best for Fisher  --these should best be configurable
        wp = 0.0f;
        amf = 7.0f;
        boostmmifactor = 0.0f;
        seqsMBRmode = false;
    }
    ~GammaCalculation()
    {
    }

    // ========================================
    // Sec. 1 init functions
    // ========================================
    void init(msra::asr::simplesenonehmm hset, int DeviceId)
    {
        m_deviceid = DeviceId;
        if (!initialmark)
        {
            m_hset = hset;
            m_maxframenum = 0;

            // prep for parallel implementation (CUDA)
            parallellattice.setdevice(DeviceId);

            if (parallellattice.enabled())                             // send hmm set to GPU if GPU computation enabled
                parallellattice.entercomputation(m_hset, mbrclassdef); // cache senone2classmap if mpemode
            initialmark = true;
        }
    }

    // ========================================
    // Sec. 2 set functions
    // ========================================
    void SetGammarCalculationParams(const SeqGammarCalParam& gammarParam)
    {
        lmf = (float) gammarParam.lmf;
        amf = (float) gammarParam.amf;
        wp = (float) gammarParam.wp;
        seqsMBRmode = gammarParam.sMBRmode;
        boostmmifactor = (float) gammarParam.bMMIfactor;
    }

    // ========================================
    // Sec. 3 calculation functions
    // ========================================
    void calgammaformb(Microsoft::MSR::CNTK::Matrix<ElemType>& functionValues,
                       std::vector<std::shared_ptr<const msra::dbn::latticepair>>& lattices,
                       const Microsoft::MSR::CNTK::Matrix<ElemType>& loglikelihood,
                       Microsoft::MSR::CNTK::Matrix<ElemType>& labels,
                       Microsoft::MSR::CNTK::Matrix<ElemType>& gammafromlattice,
                       std::vector<size_t>& uids, std::vector<size_t>& boundaries,
                       size_t samplesInRecurrentStep, /* numParallelUtterance ? */
                       std::shared_ptr<Microsoft::MSR::CNTK::MBLayout> pMBLayout,
                       std::vector<size_t>& extrauttmap,
                       bool doreferencealign)
    {
        // check total frame number to be added ?
        // int deviceid = loglikelihood.GetDeviceId();
        size_t boundaryframenum;
        std::vector<size_t> validframes; // [s] cursor pointing to next utterance begin within a single parallel sequence [s]
        validframes.assign(samplesInRecurrentStep, 0);
        ElemType objectValue = 0.0;
        // convert from Microsoft::MSR::CNTK::Matrix to  msra::math::ssematrixbase
        size_t numrows = loglikelihood.GetNumRows();
        size_t numcols = loglikelihood.GetNumCols();
        Microsoft::MSR::CNTK::Matrix<ElemType> tempmatrix(m_deviceid);

        // copy loglikelihood to pred
        if (numcols > pred.cols())
        {
            pred.resize(numrows, numcols);
            dengammas.resize(numrows, numcols);
        }

        if (doreferencealign)
            labels.SetValue((ElemType)(0.0f));

        size_t T = numcols / samplesInRecurrentStep; // number of time steps in minibatch
        if (samplesInRecurrentStep > 1)
        {
            assert(extrauttmap.size() == lattices.size());
            assert(T == pMBLayout->GetNumTimeSteps());
        }

        size_t mapi = 0; // parallel-sequence index for utterance [i]
        // cal gamma for each utterance
        size_t ts = 0;
        for (size_t i = 0; i < lattices.size(); i++)
        {
            const size_t numframes = lattices[i]->getnumframes();

            msra::dbn::matrixstripe predstripe(pred, ts, numframes);           // logLLs for this utterance
            msra::dbn::matrixstripe dengammasstripe(dengammas, ts, numframes); // denominator gammas

            if (samplesInRecurrentStep == 1) // no sequence parallelism
            {
                tempmatrix = loglikelihood.ColumnSlice(ts, numframes);
                // if (m_deviceid == CPUDEVICE)
                {
                    CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe);
                }

                if (m_deviceid != CPUDEVICE)
                    parallellattice.setloglls(tempmatrix);
            }
            else // multiple parallel sequences
            {
                // get number of frames for the utterance
                mapi = extrauttmap[i]; // parallel-sequence index; in case of >1 utterance within this parallel sequence, this is in order of concatenation

                // scan MBLayout for end of utterance
                size_t mapframenum = SIZE_MAX; // duration of utterance [i] as determined from MBLayout
                for (size_t t = validframes[mapi]; t < T; t++)
                {
                    // TODO: Adapt this to new MBLayout, m_sequences would be easier to work off.
                    if (pMBLayout->IsEnd(mapi, t))
                    {
                        mapframenum = t - validframes[mapi] + 1;
                        break;
                    }
                }

                // must match the explicit information we get from the reader
                if (numframes != mapframenum)
                    LogicError("gammacalculation: IsEnd() not working, numframes (%d) vs. mapframenum (%d)", (int) numframes, (int) mapframenum);
                assert(numframes == mapframenum);

                if (numframes > tempmatrix.GetNumCols())
                    tempmatrix.Resize(numrows, numframes);

                Microsoft::MSR::CNTK::Matrix<ElemType> loglikelihoodForCurrentParallelUtterance = loglikelihood.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1);
                tempmatrix.CopyColumnsStrided(loglikelihoodForCurrentParallelUtterance, numframes, samplesInRecurrentStep, 1);

                // if (doreferencealign || m_deviceid == CPUDEVICE)
                {
                    CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe);
                }

                if (m_deviceid != CPUDEVICE)
                {
                    parallellattice.setloglls(tempmatrix);
                }
            }

            array_ref<size_t> uidsstripe(&uids[ts], numframes);

            if (doreferencealign)
            {
                boundaryframenum = numframes;
            }
            else
                boundaryframenum = 0;
            array_ref<size_t> boundariesstripe(&boundaries[ts], boundaryframenum);

            double numavlogp = 0;
            foreach_column (t, dengammasstripe) // we do not allocate memory for numgamma now, should be the same as numgammasstripe
            {
                const size_t s = uidsstripe[t];
                numavlogp += predstripe(s, t) / amf;
            }
            numavlogp /= numframes;

            // auto_timer dengammatimer;
            double denavlogp = lattices[i]->second.forwardbackward(parallellattice,
                                                                   (const msra::math::ssematrixbase&) predstripe, (const msra::asr::simplesenonehmm&) m_hset,
                                                                   (msra::math::ssematrixbase&) dengammasstripe, (msra::math::ssematrixbase&) gammasbuffer /*empty, not used*/,
                                                                   lmf, wp, amf, boostmmifactor, seqsMBRmode, uidsstripe, boundariesstripe);
            objectValue += (ElemType)((numavlogp - denavlogp) * numframes);

            if (samplesInRecurrentStep == 1)
            {
                tempmatrix = gammafromlattice.ColumnSlice(ts, numframes);
            }

            // copy gamma to tempmatrix
            if (m_deviceid == CPUDEVICE)
            {
                CopyFromSSEMatrixToCNTKMatrix(dengammas, numrows, numframes, tempmatrix, gammafromlattice.GetDeviceId());
            }
            else
                parallellattice.getgamma(tempmatrix);

            // set gamma for multi channel
            if (samplesInRecurrentStep > 1)
            {
                Microsoft::MSR::CNTK::Matrix<ElemType> gammaFromLatticeForCurrentParallelUtterance = gammafromlattice.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1);
                gammaFromLatticeForCurrentParallelUtterance.CopyColumnsStrided(tempmatrix, numframes, 1, samplesInRecurrentStep);
            }

            if (doreferencealign)
            {
                for (size_t nframe = 0; nframe < numframes; nframe++)
                {
                    size_t uid = uidsstripe[nframe];
                    if (samplesInRecurrentStep > 1)
                        labels(uid, (nframe + validframes[mapi]) * samplesInRecurrentStep + mapi) = 1.0;
                    else
                        labels(uid, ts + nframe) = 1.0;
                }
            }
            if (samplesInRecurrentStep > 1)
                validframes[mapi] += numframes; // advance the cursor within the parallel sequence
            fprintf(stderr, "dengamma value %f\n", denavlogp);
            ts += numframes;
        }
        functionValues.SetValue(objectValue);
    }

private:
    // Helper methods for copying between ssematrix objects and CNTK matrices
    void CopyFromCNTKMatrixToSSEMatrix(const Microsoft::MSR::CNTK::Matrix<ElemType>& src, size_t numCols, msra::math::ssematrixbase& dest)
    {
        if (!std::is_same<ElemType, float>::value)
        {
            LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!");
        }

        size_t numRows = src.GetNumRows();
        const Microsoft::MSR::CNTK::Matrix<ElemType> srcSlice = src.ColumnSlice(0, numCols);
        if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < srcSlice.GetNumElements()))
        {
            m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(srcSlice.GetDeviceId(), srcSlice.GetNumElements());
            m_intermediateCUDACopyBufferSize = srcSlice.GetNumElements();
        }

        ElemType* pBuf = m_intermediateCUDACopyBuffer.get();
        srcSlice.CopyToArray(pBuf, m_intermediateCUDACopyBufferSize);
        if (pBuf != m_intermediateCUDACopyBuffer.get())
        {
            LogicError("Unexpected re-allocation of destination CPU buffer in Matrix::CopyToArray!");
        }

        if ((dest.getcolstride() == dest.rows()) && (numRows == dest.rows()))
        {
            memcpy(&dest(0, 0), (float*) pBuf, sizeof(ElemType) * numRows * numCols);
        }
        else
        {
            // We need to copy columnwise
            for (size_t i = 0; i < numCols; ++i)
            {
                memcpy(&dest(0, i), (float*) (pBuf + (i * numRows)), sizeof(ElemType) * numRows);
            }
        }
    }

    void CopyFromSSEMatrixToCNTKMatrix(const msra::math::ssematrixbase& src, size_t numRows, size_t numCols, Microsoft::MSR::CNTK::Matrix<ElemType>& dest, int deviceId)
    {
        if (!std::is_same<ElemType, float>::value)
        {
            LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!");
        }

        size_t numElements = numRows * numCols;
        if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < numElements))
        {
            m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(deviceId, numElements);
            m_intermediateCUDACopyBufferSize = numElements;
        }

        if ((src.getcolstride() == src.rows()) && (numRows == src.rows()))
        {
            memcpy((float*) m_intermediateCUDACopyBuffer.get(), &src(0, 0), sizeof(float) * numRows * numCols);
        }
        else
        {
            // We need to copy columnwise
            for (size_t i = 0; i < numCols; ++i)
            {
                memcpy((float*) (m_intermediateCUDACopyBuffer.get() + (i * numRows)), &src(0, i), sizeof(float) * numRows);
            }
        }

        dest.SetValue(numRows, numCols, deviceId, m_intermediateCUDACopyBuffer.get(), 0);
    }

    // TODO: This function is duplicate of the one in HTLMLFReader.
    // This should be moved to a common utils library and removed from here as well as HTLMLFReader
    std::unique_ptr<Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator>& GetCUDAAllocator(int deviceID)
    {
        if (m_cudaAllocator != nullptr)
        {
            if (m_cudaAllocator->GetDeviceId() != deviceID)
            {
                m_cudaAllocator.reset(nullptr);
            }
        }

        if (m_cudaAllocator == nullptr)
        {
            m_cudaAllocator.reset(new Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator(deviceID));
        }

        return m_cudaAllocator;
    }

    // TODO: This function is duplicate of the one in HTLMLFReader.
    // This should be moved to a common utils library and removed from here as well as HTLMLFReader
    std::shared_ptr<ElemType> AllocateIntermediateBuffer(int deviceID, size_t numElements)
    {
        if (deviceID >= 0)
        {
            // Use pinned memory for GPU devices for better copy performance
            size_t totalSize = sizeof(ElemType) * numElements;
            return std::shared_ptr<ElemType>((ElemType*) GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p)
                                             {
                                                 this->GetCUDAAllocator(deviceID)->Free((char*) p);
                                             });
        }
        else
        {
            return std::shared_ptr<ElemType>(new ElemType[numElements], [](ElemType* p)
                                             {
                                                 delete[] p;
                                             });
        }
    }

protected:
    msra::asr::simplesenonehmm m_hset;
    msra::lattices::lattice::parallelstate parallellattice;
    msra::lattices::mbrclassdefinition mbrclassdef = msra::lattices::senone; // defines the unit for minimum bayesian risk
    bool initialmark;
    msra::dbn::matrix dengammas;
    msra::dbn::matrix pred;
    int m_deviceid; // -1: cpu
    size_t m_maxframenum;
    float lmf; // Note that 9 was best for Fisher  --these should best be configurable
    float wp;
    float amf;
    msra::dbn::matrix gammasbuffer;
    std::vector<size_t> boundary;
    float boostmmifactor;
    bool seqsMBRmode;

private:
    std::unique_ptr<Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator> m_cudaAllocator;
    std::shared_ptr<ElemType> m_intermediateCUDACopyBuffer;
    size_t m_intermediateCUDACopyBufferSize;
};

}}