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#pragma once
#ifndef TESTING_SPMM_BATCHED_CSR_HPP
#define TESTING_SPMM_BATCHED_CSR_HPP

#include "hipsparse.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_csr_bad_arg(void)
{
#ifdef __HIP_PLATFORM_NVIDIA__
    // do not test for bad args
    return;
#endif

#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11000)
    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_COLUMN;
    hipsparseIndexBase_t idxBase   = HIPSPARSE_INDEX_BASE_ZERO;
    hipsparseIndexType_t idxTypeI  = HIPSPARSE_INDEX_64I;
    hipsparseIndexType_t idxTypeJ  = HIPSPARSE_INDEX_32I;
    hipDataType          dataType  = HIP_R_32F;

#if(CUDART_VERSION >= 11003)
    hipsparseSpMMAlg_t alg = HIPSPARSE_SPMM_CSR_ALG1;
#else
    hipsparseSpMMAlg_t alg = HIPSPARSE_MM_ALG_DEFAULT;
#endif

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

    auto dptr_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(int64_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};

    int64_t* dptr = (int64_t*)dptr_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();

    if(!dval || !dptr || !dcol || !dB || !dC || !dbuf)
    {
        PRINT_IF_HIP_ERROR(hipErrorOutOfMemory);
        return;
    }

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

    // Create SpMM structures
    verify_hipsparse_status_success(
        hipsparseCreateCsr(&A, m, k, nnz, dptr, dcol, dval, idxTypeI, idxTypeJ, 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 offsets_batch_stride_A;
    int64_t columns_values_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;
    offsets_batch_stride_A        = 0;
    columns_values_batch_stride_A = 0;
    batch_stride_B                = k * n;
    batch_stride_C                = m * n;
    verify_hipsparse_status_success(
        hipsparseCsrSetStridedBatch(
            A, batch_count_A, offsets_batch_stride_A, columns_values_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;
    offsets_batch_stride_A        = (m + 1);
    columns_values_batch_stride_A = nnz;
    batch_stride_B                = 0;
    batch_stride_C                = m * n;
    verify_hipsparse_status_success(
        hipsparseCsrSetStridedBatch(
            A, batch_count_A, offsets_batch_stride_A, columns_values_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;
    offsets_batch_stride_A        = (m + 1);
    columns_values_batch_stride_A = nnz;
    batch_stride_B                = k * n;
    batch_stride_C                = m * n;
    verify_hipsparse_status_success(
        hipsparseCsrSetStridedBatch(
            A, batch_count_A, offsets_batch_stride_A, columns_values_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 J, typename T>
hipsparseStatus_t testing_spmm_batched_csr()
{
#ifdef __HIP_PLATFORM_NVIDIA__
    // do not test for bad args
    return HIPSPARSE_STATUS_SUCCESS;
#endif

#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11000)
    T                    h_alpha  = make_DataType<T>(1.0);
    T                    h_beta   = make_DataType<T>(1.0);
    hipsparseOperation_t transA   = HIPSPARSE_OPERATION_NON_TRANSPOSE;
    hipsparseOperation_t transB   = HIPSPARSE_OPERATION_NON_TRANSPOSE;
    hipsparseOrder_t     order    = HIPSPARSE_ORDER_COLUMN;
    hipsparseIndexBase_t idx_base = HIPSPARSE_INDEX_BASE_ZERO;

    J batch_count_A = 1;
    J batch_count_B = 3;
    J batch_count_C = 3;

#if(CUDART_VERSION >= 11003)
    hipsparseSpMMAlg_t alg = HIPSPARSE_SPMM_CSR_ALG1;
#else
    hipsparseSpMMAlg_t alg = HIPSPARSE_MM_ALG_DEFAULT;
#endif

    // Matrices are stored at the same path in matrices directory
    std::string filename = hipsparse_exepath() + "../matrices/nos3.bin";

    // Index and data type
    hipsparseIndexType_t typeI
        = (typeid(I) == typeid(int32_t)) ? HIPSPARSE_INDEX_32I : HIPSPARSE_INDEX_64I;
    hipsparseIndexType_t typeJ
        = (typeid(J) == typeid(int32_t)) ? HIPSPARSE_INDEX_32I : HIPSPARSE_INDEX_64I;
    hipDataType typeT = (typeid(T) == typeid(float))
                            ? HIP_R_32F
                            : ((typeid(T) == typeid(double))
                                   ? HIP_R_64F
                                   : ((typeid(T) == typeid(hipComplex) ? HIP_C_32F : HIP_C_64F)));

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

    // Host structures
    std::vector<I> hcsr_row_ptr;
    std::vector<J> hcsr_col_ind;
    std::vector<T> hcsr_val;

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

    J m;
    J k;
    I nnz;

    if(read_bin_matrix(filename.c_str(), m, k, nnz, hcsr_row_ptr, hcsr_col_ind, hcsr_val, idx_base)
       != 0)
    {
        fprintf(stderr, "Cannot open [read] %s\n", filename.c_str());
        return HIPSPARSE_STATUS_INTERNAL_ERROR;
    }

    J n   = 5;
    J ldb = k;
    J ldc = m;

    J offsets_batch_stride_A        = 0;
    I columns_values_batch_stride_A = 0;
    I batch_stride_B                = k * n;
    I batch_stride_C                = m * n;

    std::vector<T> hB(batch_count_B * k * n);
    std::vector<T> hC_1(batch_count_C * m * n);
    std::vector<T> hC_2(batch_count_C * m * n);
    std::vector<T> hC_gold(batch_count_C * m * n);

    hipsparseInit<T>(hB, batch_count_B * k * n, 1);
    hipsparseInit<T>(hC_1, batch_count_C * m * n, 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 dptr_managed = hipsparse_unique_ptr{device_malloc(sizeof(I) * (m + 1)), device_free};
    auto dcol_managed = hipsparse_unique_ptr{device_malloc(sizeof(J) * nnz), device_free};
    auto dval_managed = hipsparse_unique_ptr{device_malloc(sizeof(T) * nnz), device_free};
    auto dB_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_B * k * n), device_free};
    auto dC_1_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_C * m * n), device_free};
    auto dC_2_managed
        = hipsparse_unique_ptr{device_malloc(sizeof(T) * batch_count_C * m * n), 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* dptr    = (I*)dptr_managed.get();
    J* dcol    = (J*)dcol_managed.get();
    T* dval    = (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();

    if(!dval || !dptr || !dcol || !dB || !dC_1 || !dC_2 || !d_alpha || !d_beta)
    {
        verify_hipsparse_status_success(HIPSPARSE_STATUS_ALLOC_FAILED,
                                        "!dval || !dptr || !dcol || !dB || "
                                        "!dC_1 || !dC_2 || !d_alpha || !d_beta");
        return HIPSPARSE_STATUS_ALLOC_FAILED;
    }

    // copy data from CPU to device
    CHECK_HIP_ERROR(
        hipMemcpy(dptr, hcsr_row_ptr.data(), sizeof(I) * (m + 1), hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(dcol, hcsr_col_ind.data(), sizeof(J) * nnz, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(dval, hcsr_val.data(), sizeof(T) * nnz, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(
        hipMemcpy(dB, hB.data(), sizeof(T) * batch_count_B * k * n, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(
        hipMemcpy(dC_1, hC_1.data(), sizeof(T) * batch_count_C * m * n, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(
        hipMemcpy(dC_2, hC_2.data(), sizeof(T) * batch_count_C * m * n, 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(
        hipsparseCreateCsr(&A, m, k, nnz, dptr, dcol, dval, typeI, typeJ, idx_base, typeT));

    // Create dense matrices
    hipsparseDnMatDescr_t B, C1, C2;
    CHECK_HIPSPARSE_ERROR(hipsparseCreateDnMat(&B, k, n, ldb, dB, typeT, order));
    CHECK_HIPSPARSE_ERROR(hipsparseCreateDnMat(&C1, m, n, ldc, dC_1, typeT, order));
    CHECK_HIPSPARSE_ERROR(hipsparseCreateDnMat(&C2, m, n, ldc, dC_2, typeT, order));

    CHECK_HIPSPARSE_ERROR(hipsparseCsrSetStridedBatch(
        A, batch_count_A, offsets_batch_stride_A, columns_values_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));

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

    // ROCSPARSE pointer mode device
    CHECK_HIPSPARSE_ERROR(hipsparseSetPointerMode(handle, HIPSPARSE_POINTER_MODE_DEVICE));
#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 11021)
    CHECK_HIPSPARSE_ERROR(hipsparseSpMM_preprocess(
        handle, transA, transB, d_alpha, A, B, d_beta, C2, typeT, alg, buffer));
#endif
    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 * m * n, hipMemcpyDeviceToHost));
    CHECK_HIP_ERROR(
        hipMemcpy(hC_2.data(), dC_2, sizeof(T) * batch_count_C * m * n, hipMemcpyDeviceToHost));

    // CPU
    double cpu_time_used = get_time_us();

    host_csrmm_batched(m,
                       n,
                       k,
                       batch_count_A,
                       offsets_batch_stride_A,
                       columns_values_batch_stride_A,
                       transA,
                       transB,
                       h_alpha,
                       hcsr_row_ptr.data(),
                       hcsr_col_ind.data(),
                       hcsr_val.data(),
                       hB.data(),
                       ldb,
                       batch_count_B,
                       batch_stride_B,
                       h_beta,
                       hC_gold.data(),
                       ldc,
                       batch_count_C,
                       batch_stride_C,
                       order,
                       idx_base,
                       false);

    cpu_time_used = get_time_us() - cpu_time_used;

    unit_check_near(1, batch_count_C * m * n, 1, hC_gold.data(), hC_1.data());
    unit_check_near(1, batch_count_C * m * n, 1, hC_gold.data(), hC_2.data());

    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_CSR_HPP
