/* ************************************************************************
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#pragma once
#ifndef TESTING_SPMM_BATCHED_COO_HPP
#define TESTING_SPMM_BATCHED_COO_HPP

#include "display.hpp"
#include "flops.hpp"
#include "gbyte.hpp"
#include "hipsparse.hpp"
#include "hipsparse_arguments.hpp"
#include "hipsparse_test_unique_ptr.hpp"
#include "unit.hpp"
#include "utility.hpp"

#include <hipsparse.h>
#include <string>
#include <typeinfo>

using namespace hipsparse;
using namespace hipsparse_test;

void testing_spmm_batched_coo_bad_arg(void)
{
#if(!defined(CUDART_VERSION))
    int32_t              m         = 100;
    int32_t              n         = 100;
    int32_t              k         = 100;
    int64_t              nnz       = 100;
    float                alpha     = 0.6;
    float                beta      = 0.2;
    size_t               safe_size = 100;
    hipsparseOperation_t transA    = HIPSPARSE_OPERATION_NON_TRANSPOSE;
    hipsparseOperation_t transB    = HIPSPARSE_OPERATION_NON_TRANSPOSE;
    hipsparseOrder_t     order     = HIPSPARSE_ORDER_COL;
    hipsparseIndexBase_t idxBase   = HIPSPARSE_INDEX_BASE_ZERO;
    hipsparseIndexType_t idxTypeI  = HIPSPARSE_INDEX_32I;
    hipDataType          dataType  = HIP_R_32F;

#if(CUDART_VERSION >= 11003)
    hipsparseSpMMAlg_t alg = HIPSPARSE_SPMM_COO_ALG1;
#else
    hipsparseSpMMAlg_t alg = HIPSPARSE_COOMM_ALG1;
#endif

    std::unique_ptr<handle_struct> unique_ptr_handle(new handle_struct);
    hipsparseHandle_t              handle = unique_ptr_handle->handle;

    auto drow_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(int32_t) * safe_size), device_free};
    auto dcol_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(int32_t) * safe_size), device_free};
    auto dval_managed = hipsparse_unique_ptr{device_malloc(sizeof(float) * safe_size), device_free};
    auto dB_managed   = hipsparse_unique_ptr{device_malloc(sizeof(float) * safe_size), device_free};
    auto dC_managed   = hipsparse_unique_ptr{device_malloc(sizeof(float) * safe_size), device_free};
    auto dbuf_managed = hipsparse_unique_ptr{device_malloc(sizeof(char) * safe_size), device_free};

    int32_t* drow = (int32_t*)drow_managed.get();
    int32_t* dcol = (int32_t*)dcol_managed.get();
    float*   dval = (float*)dval_managed.get();
    float*   dB   = (float*)dB_managed.get();
    float*   dC   = (float*)dC_managed.get();
    void*    dbuf = (void*)dbuf_managed.get();

    // SpMM structures
    hipsparseSpMatDescr_t A;
    hipsparseDnMatDescr_t B, C;

    // Create SpMM structures
    verify_hipsparse_status_success(
        hipsparseCreateCoo(&A, m, k, nnz, drow, dcol, dval, idxTypeI, idxBase, dataType),
        "success");
    verify_hipsparse_status_success(hipsparseCreateDnMat(&B, k, n, k, dB, dataType, order),
                                    "success");
    verify_hipsparse_status_success(hipsparseCreateDnMat(&C, m, n, m, dC, dataType, order),
                                    "success");

    int     batch_count_A;
    int     batch_count_B;
    int     batch_count_C;
    int64_t batch_stride_A;
    int64_t batch_stride_B;
    int64_t batch_stride_C;

    // C_i = A * B_i
    batch_count_A  = 1;
    batch_count_B  = 10;
    batch_count_C  = 5;
    batch_stride_A = 0;
    batch_stride_B = k * n;
    batch_stride_C = m * n;
    verify_hipsparse_status_success(hipsparseCooSetStridedBatch(A, batch_count_A, batch_stride_A),
                                    "success");
    verify_hipsparse_status_success(hipsparseDnMatSetStridedBatch(B, batch_count_B, batch_stride_B),
                                    "success");
    verify_hipsparse_status_success(hipsparseDnMatSetStridedBatch(C, batch_count_C, batch_stride_C),
                                    "success");

    verify_hipsparse_status_invalid_value(
        hipsparseSpMM(handle, transA, transB, &alpha, A, B, &beta, C, dataType, alg, dbuf),
        "Error: Combination of strided batch parameters is invald");

    // C_i = A_i * B
    batch_count_A  = 10;
    batch_count_B  = 1;
    batch_count_C  = 5;
    batch_stride_A = nnz;
    batch_stride_B = 0;
    batch_stride_C = m * n;
    verify_hipsparse_status_success(hipsparseCooSetStridedBatch(A, batch_count_A, batch_stride_A),
                                    "success");
    verify_hipsparse_status_success(hipsparseDnMatSetStridedBatch(B, batch_count_B, batch_stride_B),
                                    "success");
    verify_hipsparse_status_success(hipsparseDnMatSetStridedBatch(C, batch_count_C, batch_stride_C),
                                    "success");

    verify_hipsparse_status_invalid_value(
        hipsparseSpMM(handle, transA, transB, &alpha, A, B, &beta, C, dataType, alg, dbuf),
        "Error: Combination of strided batch parameters is invald");

    // C_i = A_i * B_i
    batch_count_A  = 10;
    batch_count_B  = 10;
    batch_count_C  = 5;
    batch_stride_A = nnz;
    batch_stride_B = k * n;
    batch_stride_C = m * n;
    verify_hipsparse_status_success(hipsparseCooSetStridedBatch(A, batch_count_A, batch_stride_A),
                                    "success");
    verify_hipsparse_status_success(hipsparseDnMatSetStridedBatch(B, batch_count_B, batch_stride_B),
                                    "success");
    verify_hipsparse_status_success(hipsparseDnMatSetStridedBatch(C, batch_count_C, batch_stride_C),
                                    "success");

    verify_hipsparse_status_invalid_value(
        hipsparseSpMM(handle, transA, transB, &alpha, A, B, &beta, C, dataType, alg, dbuf),
        "Error: Combination of strided batch parameters is invald");

    // Destruct
    verify_hipsparse_status_success(hipsparseDestroySpMat(A), "success");
    verify_hipsparse_status_success(hipsparseDestroyDnMat(B), "success");
    verify_hipsparse_status_success(hipsparseDestroyDnMat(C), "success");
#endif
}

template <typename I, typename T>
hipsparseStatus_t testing_spmm_batched_coo(Arguments argus)
{
#if(!defined(CUDART_VERSION))
    I                    m        = argus.M;
    I                    n        = argus.N;
    I                    k        = argus.K;
    T                    h_alpha  = make_DataType<T>(argus.alpha);
    T                    h_beta   = make_DataType<T>(argus.beta);
    hipsparseOperation_t transA   = argus.transA;
    hipsparseOperation_t transB   = argus.transB;
    hipsparseOrder_t     orderB   = argus.orderB;
    hipsparseOrder_t     orderC   = argus.orderC;
    hipsparseIndexBase_t idx_base = argus.baseA;

    I batch_count_A = 1;
    I batch_count_B = 10;
    I batch_count_C = 10;

#if(CUDART_VERSION >= 11003)
    hipsparseSpMMAlg_t alg = HIPSPARSE_SPMM_COO_ALG1;
#else
    hipsparseSpMMAlg_t alg = HIPSPARSE_COOMM_ALG1;
#endif

    std::string filename = argus.filename;

#if(defined(CUDART_VERSION))
    if(orderB != orderC || orderB != HIPSPARSE_ORDER_COL)
    {
        return HIPSPARSE_STATUS_SUCCESS;
    }
#endif

    // Index and data type
    hipsparseIndexType_t typeI = getIndexType<I>();
    hipDataType          typeT = getDataType<T>();

    // hipSPARSE handle
    std::unique_ptr<handle_struct> unique_ptr_handle(new handle_struct);
    hipsparseHandle_t              handle = unique_ptr_handle->handle;

    // Host structures
    std::vector<I> hrow_ptr;
    std::vector<I> hcol_ind;
    std::vector<T> hval;

    // Initial Data on CPU
    srand(12345ULL);

    I nnz_A;
    if(!generate_csr_matrix(filename,
                            (transA == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? m : k,
                            (transA == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? k : m,
                            nnz_A,
                            hrow_ptr,
                            hcol_ind,
                            hval,
                            idx_base))
    {
        fprintf(stderr, "Cannot open [read] %s\ncol", filename.c_str());
        return HIPSPARSE_STATUS_INTERNAL_ERROR;
    }

    std::vector<I> hrow_ind(nnz_A);

    // Convert to COO
    I mk = (transA == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? m : k;
    for(I i = 0; i < mk; ++i)
    {
        for(I j = hrow_ptr[i]; j < hrow_ptr[i + 1]; ++j)
        {
            hrow_ind[j - idx_base] = i + idx_base;
        }
    }

    // Some matrix properties
    I A_m = (transA == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? m : k;
    I A_n = (transA == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? k : m;
    I B_m = (transB == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? k : n;
    I B_n = (transB == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? n : k;
    I C_m = m;
    I C_n = n;

    int ld_multiplier_B = 1;
    int ld_multiplier_C = 1;

    int64_t ldb
        = (orderB == HIPSPARSE_ORDER_COL)
              ? ((transB == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? (int64_t(ld_multiplier_B) * k)
                                                               : (int64_t(ld_multiplier_B) * n))
              : ((transB == HIPSPARSE_OPERATION_NON_TRANSPOSE) ? (int64_t(ld_multiplier_B) * n)
                                                               : (int64_t(ld_multiplier_B) * k));
    int64_t ldc = (orderC == HIPSPARSE_ORDER_COL) ? (int64_t(ld_multiplier_C) * m)
                                                  : (int64_t(ld_multiplier_C) * n);

    ldb = std::max(int64_t(1), ldb);
    ldc = std::max(int64_t(1), ldc);

    int64_t nrowB = (orderB == HIPSPARSE_ORDER_COL) ? ldb : B_m;
    int64_t ncolB = (orderB == HIPSPARSE_ORDER_COL) ? B_n : ldb;
    int64_t nrowC = (orderC == HIPSPARSE_ORDER_COL) ? ldc : C_m;
    int64_t ncolC = (orderC == HIPSPARSE_ORDER_COL) ? C_n : ldc;

    int64_t nnz_B = nrowB * ncolB;
    int64_t nnz_C = nrowC * ncolC;

    int64_t batch_stride_A = (batch_count_A > 1) ? nnz_A : 0;
    int64_t batch_stride_B = (batch_count_B > 1) ? nnz_B : 0;
    int64_t batch_stride_C = (batch_count_C > 1) ? nnz_C : 0;

    // Allocate host memory for all batches of A matrix
    std::vector<I> hcoo_row_ind(batch_count_A * nnz_A);
    std::vector<I> hcoo_col_ind(batch_count_A * nnz_A);
    std::vector<T> hcoo_val(batch_count_A * nnz_A);

    for(I i = 0; i < batch_count_A; i++)
    {
        for(int64_t j = 0; j < nnz_A; j++)
        {
            hcoo_row_ind[nnz_A * i + j] = hrow_ind[j];
            hcoo_col_ind[nnz_A * i + j] = hcol_ind[j];
            hcoo_val[nnz_A * i + j]     = hval[j];
        }
    }

    std::vector<T> hB(batch_count_B * nnz_B);
    std::vector<T> hC_1(batch_count_C * nnz_C);
    std::vector<T> hC_2(batch_count_C * nnz_C);
    std::vector<T> hC_gold(batch_count_C * nnz_C);

    hipsparseInit<T>(hB, batch_count_B * nnz_B, 1);
    hipsparseInit<T>(hC_1, batch_count_C * nnz_C, 1);

    // copy vector is easy in STL; hC_gold = hC: save a copy in hy_gold which will be output of CPU
    hC_2    = hC_1;
    hC_gold = hC_1;

    // allocate memory on device
    auto drow_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(I) * batch_count_A * nnz_A), device_free};
    auto dcol_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(I) * batch_count_A * nnz_A), device_free};
    auto dval_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_A * nnz_A), device_free};
    auto dB_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_B * nnz_B), device_free};
    auto dC_1_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_C * nnz_C), device_free};
    auto dC_2_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_C * nnz_C), device_free};
    auto d_alpha_managed = hipsparse_unique_ptr{device_malloc(sizeof(T)), device_free};
    auto d_beta_managed  = hipsparse_unique_ptr{device_malloc(sizeof(T)), device_free};

    I* dcoo_row_ind = (I*)drow_managed.get();
    I* dcoo_col_ind = (I*)dcol_managed.get();
    T* dcoo_val     = (T*)dval_managed.get();
    T* dB           = (T*)dB_managed.get();
    T* dC_1         = (T*)dC_1_managed.get();
    T* dC_2         = (T*)dC_2_managed.get();
    T* d_alpha      = (T*)d_alpha_managed.get();
    T* d_beta       = (T*)d_beta_managed.get();

    // copy data from CPU to device
    CHECK_HIP_ERROR(hipMemcpy(dcoo_row_ind,
                              hcoo_row_ind.data(),
                              sizeof(I) * batch_count_A * nnz_A,
                              hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(dcoo_col_ind,
                              hcoo_col_ind.data(),
                              sizeof(I) * batch_count_A * nnz_A,
                              hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(
        dcoo_val, hcoo_val.data(), sizeof(T) * batch_count_A * nnz_A, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(
        hipMemcpy(dB, hB.data(), sizeof(T) * batch_count_B * nnz_B, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(
        hipMemcpy(dC_1, hC_1.data(), sizeof(T) * batch_count_C * nnz_C, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(
        hipMemcpy(dC_2, hC_2.data(), sizeof(T) * batch_count_C * nnz_C, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(d_alpha, &h_alpha, sizeof(T), hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(d_beta, &h_beta, sizeof(T), hipMemcpyHostToDevice));

    // Create matrices
    hipsparseSpMatDescr_t A;
    CHECK_HIPSPARSE_ERROR(hipsparseCreateCoo(
        &A, A_m, A_n, nnz_A, dcoo_row_ind, dcoo_col_ind, dcoo_val, typeI, idx_base, typeT));

    // Create dense matrices
    hipsparseDnMatDescr_t B, C1, C2;
    CHECK_HIPSPARSE_ERROR(hipsparseCreateDnMat(&B, B_m, B_n, ldb, dB, typeT, orderB));
    CHECK_HIPSPARSE_ERROR(hipsparseCreateDnMat(&C1, C_m, C_n, ldc, dC_1, typeT, orderC));
    CHECK_HIPSPARSE_ERROR(hipsparseCreateDnMat(&C2, C_m, C_n, ldc, dC_2, typeT, orderC));

    CHECK_HIPSPARSE_ERROR(hipsparseCooSetStridedBatch(A, batch_count_A, batch_stride_A));
    CHECK_HIPSPARSE_ERROR(hipsparseDnMatSetStridedBatch(B, batch_count_B, batch_stride_B));
    CHECK_HIPSPARSE_ERROR(hipsparseDnMatSetStridedBatch(C1, batch_count_C, batch_stride_C));
    CHECK_HIPSPARSE_ERROR(hipsparseDnMatSetStridedBatch(C2, batch_count_C, batch_stride_C));

    // Query SpMM buffer
    size_t bufferSize;
    CHECK_HIPSPARSE_ERROR(hipsparseSpMM_bufferSize(
        handle, transA, transB, &h_alpha, A, B, &h_beta, C1, typeT, alg, &bufferSize));

#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11021)
    //When using cusparse backend, cant pass nullptr for buffer to preprocess
    if(bufferSize == 0)
    {
        bufferSize = 4;
    }
#endif

    void* buffer;
    CHECK_HIP_ERROR(hipMalloc(&buffer, bufferSize));

    // HIPSPARSE pointer mode host
#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11021)
    CHECK_HIPSPARSE_ERROR(hipsparseSetPointerMode(handle, HIPSPARSE_POINTER_MODE_HOST));
    CHECK_HIPSPARSE_ERROR(hipsparseSpMM_preprocess(
        handle, transA, transB, &h_alpha, A, B, &h_beta, C1, typeT, alg, buffer));
#endif

    // HIPSPARSE pointer mode device
#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11021)
    CHECK_HIPSPARSE_ERROR(hipsparseSetPointerMode(handle, HIPSPARSE_POINTER_MODE_DEVICE));
    CHECK_HIPSPARSE_ERROR(hipsparseSpMM_preprocess(
        handle, transA, transB, d_alpha, A, B, d_beta, C2, typeT, alg, buffer));
#endif

    if(argus.unit_check)
    {
        CHECK_HIPSPARSE_ERROR(hipsparseSetPointerMode(handle, HIPSPARSE_POINTER_MODE_HOST));
        CHECK_HIPSPARSE_ERROR(
            hipsparseSpMM(handle, transA, transB, &h_alpha, A, B, &h_beta, C1, typeT, alg, buffer));

        CHECK_HIPSPARSE_ERROR(hipsparseSetPointerMode(handle, HIPSPARSE_POINTER_MODE_DEVICE));
        CHECK_HIPSPARSE_ERROR(
            hipsparseSpMM(handle, transA, transB, d_alpha, A, B, d_beta, C2, typeT, alg, buffer));

        // copy output from device to CPU
        CHECK_HIP_ERROR(
            hipMemcpy(hC_1.data(), dC_1, sizeof(T) * batch_count_C * nnz_C, hipMemcpyDeviceToHost));
        CHECK_HIP_ERROR(
            hipMemcpy(hC_2.data(), dC_2, sizeof(T) * batch_count_C * nnz_C, hipMemcpyDeviceToHost));

        // CPU
        host_coomm_batched(A_m,
                           n,
                           A_n,
                           nnz_A,
                           batch_count_A,
                           (I)batch_stride_A,
                           transA,
                           transB,
                           h_alpha,
                           hrow_ind.data(),
                           hcol_ind.data(),
                           hval.data(),
                           hB.data(),
                           (I)ldb,
                           batch_count_B,
                           (I)batch_stride_B,
                           orderB,
                           h_beta,
                           hC_gold.data(),
                           (I)ldc,
                           batch_count_C,
                           (I)batch_stride_C,
                           orderC,
                           idx_base);

        unit_check_near(1, batch_count_C * nnz_C, 1, hC_gold.data(), hC_1.data());
        unit_check_near(1, batch_count_C * nnz_C, 1, hC_gold.data(), hC_2.data());
    }

    if(argus.timing)
    {
        int number_cold_calls = 2;
        int number_hot_calls  = argus.iters;

        CHECK_HIPSPARSE_ERROR(hipsparseSetPointerMode(handle, HIPSPARSE_POINTER_MODE_HOST));

        // Warm up
        for(int iter = 0; iter < number_cold_calls; ++iter)
        {
            CHECK_HIPSPARSE_ERROR(hipsparseSpMM(
                handle, transA, transB, &h_alpha, A, B, &h_beta, C1, typeT, alg, buffer));
        }

        double gpu_time_used = get_time_us();

        // Performance run
        for(int iter = 0; iter < number_hot_calls; ++iter)
        {
            CHECK_HIPSPARSE_ERROR(hipsparseSpMM(
                handle, transA, transB, &h_alpha, A, B, &h_beta, C1, typeT, alg, buffer));
        }

        gpu_time_used = (get_time_us() - gpu_time_used) / number_hot_calls;

        double gflop_count
            = batch_count_C
              * spmm_gflop_count(n, nnz_A, (I)C_m * (I)C_n, h_beta != make_DataType<T>(0));
        double gpu_gflops = get_gpu_gflops(gpu_time_used, gflop_count);

        double gbyte_count = coomm_batched_gbyte_count<T>(A_m,
                                                          nnz_A,
                                                          (I)B_m * (I)B_n,
                                                          (I)C_m * (I)C_n,
                                                          batch_count_A,
                                                          batch_count_B,
                                                          batch_count_C,
                                                          h_beta != make_DataType<T>(0));
        double gpu_gbyte   = get_gpu_gbyte(gpu_time_used, gbyte_count);

        display_timing_info(display_key_t::M,
                            m,
                            display_key_t::N,
                            n,
                            display_key_t::K,
                            k,
                            display_key_t::nnzA,
                            nnz_A,
                            display_key_t::batch_countA,
                            batch_count_A,
                            display_key_t::batch_countB,
                            batch_count_B,
                            display_key_t::batch_countC,
                            batch_count_C,
                            display_key_t::alpha,
                            h_alpha,
                            display_key_t::beta,
                            h_beta,
                            display_key_t::algorithm,
                            hipsparse_spmmalg2string(alg),
                            display_key_t::gflops,
                            gpu_gflops,
                            display_key_t::bandwidth,
                            gpu_gbyte,
                            display_key_t::time_ms,
                            get_gpu_time_msec(gpu_time_used));
    }

    CHECK_HIP_ERROR(hipFree(buffer));
    CHECK_HIPSPARSE_ERROR(hipsparseDestroySpMat(A));
    CHECK_HIPSPARSE_ERROR(hipsparseDestroyDnMat(B));
    CHECK_HIPSPARSE_ERROR(hipsparseDestroyDnMat(C1));
    CHECK_HIPSPARSE_ERROR(hipsparseDestroyDnMat(C2));

#endif

    return HIPSPARSE_STATUS_SUCCESS;
}

#endif // TESTING_SPMM_BATCHED_COO_HPP
