/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 * Copyright (c) 2019 Konduit K.K.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://www.apache.org/licenses/LICENSE-2.0.
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
//


#include<ops/declarable/helpers/transforms.h>
#include <array/ResultSet.h>
#include <helpers/ShapeUtils.h>
#include <numeric>
#include <array/NDArrayFactory.h>
#include <helpers/TAD.h>
#include <exceptions/cuda_exception.h>
#include <helpers/PointersManager.h>
#include <helpers/ConstantTadHelper.h>

namespace sd 	  {
namespace ops 	  {
namespace helpers {

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void invertPermutationCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {

    const T* x = reinterpret_cast<const T*>(vx);
          T* z = reinterpret_cast<T*>(vz);

    __shared__ Nd4jLong len, totalThreads;

    if (threadIdx.x == 0) {

        len  = shape::length(xShapeInfo);
        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

    const auto tid = blockIdx.x * blockDim.x + threadIdx.x;

    for (Nd4jLong i = tid; i < len; i += totalThreads) {

        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
        const Nd4jLong index = x[xOffset];
        const auto zOffset = shape::getIndexOffset(index, zShapeInfo);
        z[zOffset] = i;
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
__host__ static void invertPermutationCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
                                                   const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {

    invertPermutationCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo);
}

////////////////////////////////////////////////////////////////////////
void invertPermutation(sd::LaunchContext* context, const NDArray& input, NDArray& output) {

    const int threadsPerBlock = MAX_NUM_THREADS;
    const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;

    PointersManager manager(context, "invertPermutation");

    NDArray::prepareSpecialUse({&output}, {&input});
    BUILD_SINGLE_SELECTOR(input.dataType(), invertPermutationCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo()), LIBND4J_TYPES);
    NDArray::registerSpecialUse({&output}, {&input});

    manager.synchronize();
}

//////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint diagLen) {

    const auto x = reinterpret_cast<const T*>(vx);
          auto z = reinterpret_cast<T*>(vz);

    __shared__ T* sharedMem;
    __shared__ int xRank, zRank, *coordsMem;        // xRank = zRank + 2
    __shared__ Nd4jLong xLen, zLen;

    if (threadIdx.x == 0) {
        extern __shared__ unsigned char shmem[];
        sharedMem = reinterpret_cast<T*>(shmem);
        coordsMem = reinterpret_cast<int*>(shmem + blockDim.x * sizeof(T));

        xRank = shape::rank(xShapeInfo);
        zRank = shape::rank(zShapeInfo);
        xLen = shape::length(xShapeInfo);
        zLen = shape::length(zShapeInfo);   // corresponds to number of matrices

    }
    __syncthreads();

    auto coords = coordsMem + threadIdx.x * xRank;

    for (uint m = blockIdx.x; m < zLen; m += gridDim.x) {   // one block per each element of z, that is per each matrix

        shape::index2coords(m, zShapeInfo, coords);
        const auto zOffset = shape::getOffset(zShapeInfo, coords);

        sharedMem[threadIdx.x] = 0;

          for (uint i = threadIdx.x; i < diagLen; i += blockDim.x) {

            coords[zRank] = coords[zRank + 1] = i;
            const auto xOffset = shape::getOffset(xShapeInfo, coords);
            sharedMem[threadIdx.x] += x[xOffset];
          }

          __syncthreads();

        // aggregate sum
        for (Nd4jLong activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
            if (threadIdx.x < activeThreads)
                sharedMem[threadIdx.x] += sharedMem[threadIdx.x + activeThreads];
            __syncthreads();
        }

        if (threadIdx.x == 0)
            z[zOffset] = *sharedMem;
        __syncthreads();
    }

}

///////////////////////////////////////////////////////////////////
template<typename T>
static void traceCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
                              const void *vx, const Nd4jLong *xShapeInfo,
                                    void *vz, const Nd4jLong *zShapeInfo,
                                    const uint diagLen) {

    traceCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diagLen);
}


///////////////////////////////////////////////////////////////////
void trace(sd::LaunchContext* context, const NDArray& input, NDArray& output) {

    PointersManager manager(context, "trace");

    const uint diagLen = input.sizeAt(-1) < input.sizeAt(-2) ? input.sizeAt(-1) : input.sizeAt(-2);
    const int threadsPerBlock = MAX_NUM_THREADS / 4;
    const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = threadsPerBlock * (sizeof(int) * input.rankOf() + input.sizeOfT()) + 128;

    NDArray::prepareSpecialUse({&output}, {&input});
    BUILD_SINGLE_SELECTOR(input.dataType(), traceCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), diagLen), LIBND4J_TYPES);
    NDArray::registerSpecialUse({&output}, {&input});

    manager.synchronize();
}

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void triuBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {

    // x and z have same shapes
    const auto x = reinterpret_cast<const T*>(vx);  // gradO
          auto z = reinterpret_cast<T*>(vz);        // gradI

    __shared__ int rank, areSameOffsets, *sharedMem;                // xRank = zRank
    __shared__ Nd4jLong len, totalThreads;  // xLen = zLen

    if (threadIdx.x == 0) {

        extern __shared__ unsigned char shmem[];
        sharedMem = reinterpret_cast<int*>(shmem);
        areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
        rank = shape::rank(xShapeInfo);
        len  = shape::length(zShapeInfo);
        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

    auto coords = sharedMem + threadIdx.x * rank;

    const auto tid = blockIdx.x * blockDim.x + threadIdx.x;

    for (Nd4jLong i = tid; i < len; i += totalThreads) {

        shape::index2coords(i, zShapeInfo, coords);

        const auto zOffset = shape::getOffset(zShapeInfo, coords);

        if((coords[rank - 2] + diag > coords[rank - 1]))    // row + diag > col
            z[zOffset] = 0;
        else
            z[zOffset] = x[areSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords)];
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
static void triuBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,  const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {

    triuBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diag);
}

///////////////////////////////////////////////////////////////////
void triuBP(sd::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {

    const int threadsPerBlock = MAX_NUM_THREADS / 4;
    const int blocksPerGrid = (gradO.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = threadsPerBlock * sizeof(int) * gradO.rankOf() + 128;

    PointersManager manager(context, "triuBP");

    NDArray::prepareSpecialUse({&gradI}, {&gradO});
    BUILD_SINGLE_SELECTOR(gradI.dataType(), triuBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), diagonal), LIBND4J_TYPES);
    NDArray::registerSpecialUse({&gradI}, {&gradO});

    manager.synchronize();
}

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void tileBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {

    // x and z have same shapes
    const auto x = reinterpret_cast<const T*>(vx);  // gradO
          auto z = reinterpret_cast<T*>(vz);        // gradI

    __shared__ int xRank, zRank, *sharedMem;                // xRank >= zRank
    __shared__ Nd4jLong numOfXOffsets, zLen, totalThreads;  // xLen >= zLen

    if (threadIdx.x == 0) {

        extern __shared__ unsigned char shmem[];
        sharedMem = reinterpret_cast<int*>(shmem);

        xRank = shape::rank(zShapeInfo);
        zLen  = shape::length(zShapeInfo);
        numOfXOffsets = shape::length(xShapeInfo) / zLen;

        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

    const auto tid = blockIdx.x * blockDim.x + threadIdx.x;

    auto memBuff  = sharedMem + threadIdx.x * 2 * xRank;
    auto xOffsets = globMem + tid * numOfXOffsets;

    for (Nd4jLong i = tid; i < zLen; i += totalThreads) {

        const auto zOffset = shape::getIndexOffset(i, zShapeInfo);

        shape::outerArrayOffsets(xOffsets, i, xShapeInfo, zShapeInfo, memBuff);

        z[zOffset] = x[xOffsets[0]];                    // first offset
        for (Nd4jLong j = 1; j < numOfXOffsets; ++j)    // rest offsets
            z[zOffset] += x[xOffsets[j]];
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
static void tileBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,  const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {

    tileBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, globMem);
}


//////////////////////////////////////////////////////////////////////////
void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {

    NDArray memBuff('c', gradO.getShapeAsVector(), sd::DataType::INT64, context);        // empty auxiliary array for storing device memory which will be used in kernel calculations

    const int threadsPerBlock = MAX_NUM_THREADS / 4;
    const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = threadsPerBlock * sizeof(int) * 2 * gradO.rankOf() + 128;

    PointersManager manager(context, "tileBP");

    NDArray::prepareSpecialUse({&gradI}, {&gradO, &memBuff});
    BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), reinterpret_cast<Nd4jLong*>(memBuff.specialBuffer())), FLOAT_TYPES);
    NDArray::registerSpecialUse({&gradI}, {&gradO, &memBuff});

    manager.synchronize();
}

    template <typename T>
    static __global__ void swapShuffleKernel(T* input, Nd4jLong const* shape, Nd4jLong firstDim, sd::graph::RandomGenerator* rng) {
        auto tid = blockIdx.x * blockDim.x;
        auto step = blockDim.x * gridDim.x;

        for (int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
            int r = rng->relativeInt(i) % i;
            if (i != r) {
                const auto iOffset = shape::getIndexOffset(i, shape);
                const auto rOffset = shape::getIndexOffset(r, shape);
                T e0 = input[iOffset];
                T e1 = input[rOffset];
                //math::nd4j_swap<T>(input(i), input(r));
                input[iOffset] = e1;
                input[rOffset] = e0;
            }
        }
    }
    template <typename T>
    static __global__ void fillShuffleKernel(T* input, Nd4jLong const* inputShape, T* output, Nd4jLong const* outputShape, Nd4jLong firstDim, int* indices, sd::graph::RandomGenerator* rng) {

//        PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
        auto tid = blockIdx.x * blockDim.x;
        auto step = blockDim.x * gridDim.x;

        for(int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
            int r = rng->relativeInt(i) % i;
            output[shape::getIndexOffset(i, outputShape)] = input[shape::getIndexOffset(indices[r], inputShape)];
            if(i != r) {
                output[shape::getIndexOffset(r, outputShape)] = input[shape::getIndexOffset(indices[i], inputShape)];
//                output.p(r, input.e<T>(indices[i]));
//                math::nd4j_swap<int>(indices[i], indices[r]);
                atomicExch(&indices[i], indices[r]);
            }
        }

    }
    //////////////////////////////////////////////////////////////////////////
    template <typename T>
    void randomShuffle_(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) {

        // check edge cases first
        int temp;
        const int firstDim = input.sizeAt(0);
        auto stream = context->getCudaStream();
        NDArray::prepareSpecialUse({&output}, {&input});
        if(input.lengthOf() == 1 || firstDim == 1) {
            if(!isInplace)
                output.assign(input);
        }
        else if (input.isVector() || shape::isLikeVector(input.shapeInfo(), temp)) {

            // apply Fisher-Yates shuffle
            sd::graph::RandomGenerator* dRandom = nullptr;
            cudaMalloc(&dRandom, sizeof(sd::graph::RandomGenerator));
            cudaMemcpy(dRandom, &rng, sizeof(sd::graph::RandomGenerator), cudaMemcpyHostToDevice);
            T* inputBuf = reinterpret_cast<T*>(input.specialBuffer());
            if(isInplace) {
                swapShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), firstDim, dRandom);
            }
            else {
                std::vector<int> indices(firstDim);
                std::iota(indices.begin(), indices.end(), 0);
                cudaMemcpy(output.specialBuffer(), input.specialBuffer(), sizeof(T), cudaMemcpyDeviceToDevice);
                //output.p<T>(Nd4jLong(0), input.e<T>(0));
                PointersManager pointersManager(context, "helper::randomShuffle_");
                int* indicesDev = reinterpret_cast<int*>(pointersManager.replicatePointer(indices.data(), indices.size() * sizeof(int)));
                T* outputBuf = reinterpret_cast<T*>(output.specialBuffer());
                fillShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), outputBuf, output.specialShapeInfo(), firstDim, indicesDev, dRandom);
                pointersManager.synchronize();
            }
//            rng.rewindH(firstDim - 1);
            cudaFree(dRandom);
        }
        else {

            // evaluate sub-arrays list of input array through all dimensions excluding first one
            std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input.rankOf(), {0});
            auto subArrsListIn = input.allTensorsAlongDimension(dimensions);

            // apply Fisher-Yates shuffle
            if(isInplace) {
                for(int i = firstDim - 1; i > 0; --i) {
                    int r = rng.relativeInt(i) % i;

                    if(i != r)
                        subArrsListIn.at(i)->swapUnsafe(*subArrsListIn.at(r));
                }
            }
            else {
                // evaluate sub-arrays list of output array through all dimensions excluding first one
                auto subArrsListOut = output.allTensorsAlongDimension(dimensions);
                std::vector<int> indices(firstDim);
                std::iota(indices.begin(), indices.end(), 0);
                bool isZeroShuffled = false;

                for(int i = firstDim - 1; i > 0; --i) {
                    int r = rng.relativeInt(i) % i;
                    subArrsListOut.at(i)->assign(subArrsListIn.at(indices[r]));
                    if(r == 0)
                        isZeroShuffled = true;

                    if(i != r) {
                        subArrsListOut.at(r)->assign(subArrsListIn.at(indices[i]));
                        math::nd4j_swap<int>(indices[i], indices[r]);
                    }
                }
                if(!isZeroShuffled)
                    subArrsListOut.at(0)->assign(subArrsListIn.at(0));
            }
            rng.rewindH(firstDim-1);
        }
        NDArray::registerSpecialUse({&output}, {&input});

    }

    void randomShuffle(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) {
        BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (context, input, output, rng, isInplace), LIBND4J_TYPES);
    }

    BUILD_SINGLE_TEMPLATE(template void randomShuffle_, (sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace), LIBND4J_TYPES);


    //////////////////////////////////////////////////////////////////////////
    void eye(sd::LaunchContext * context, NDArray& output) {

        output.setIdentity();
    }

}
}
}