/* ************************************************************************
 * Copyright (c) 2020-2021 Advanced Micro Devices, Inc.
 * ************************************************************************ */

#pragma once

#include "clientcommon.hpp"
#include "lapack_host_reference.hpp"
#include "norm.hpp"
#include "rocsolver.hpp"
#include "rocsolver_arguments.hpp"
#include "rocsolver_test.hpp"

template <bool BATCHED, bool STRIDED, typename U>
void gels_checkBadArgs(const rocblas_handle handle,
                       const rocblas_operation trans,
                       const rocblas_int m,
                       const rocblas_int n,
                       const rocblas_int nrhs,
                       U dA,
                       const rocblas_int lda,
                       const rocblas_stride stA,
                       U dB,
                       const rocblas_int ldb,
                       const rocblas_stride stB,
                       rocblas_int* info,
                       const rocblas_int bc)
{
    // handle
    EXPECT_ROCBLAS_STATUS(
        rocsolver_gels(STRIDED, nullptr, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, info, bc),
        rocblas_status_invalid_handle);

    // values
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, rocblas_operation(-1), m, n, nrhs, dA,
                                         lda, stA, dB, ldb, stB, info, bc),
                          rocblas_status_invalid_value)
        << "Must report error when operation is invalid";

    // sizes (only check batch_count if applicable)
    if(STRIDED)
        EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA, lda, stA, dB,
                                             ldb, stB, info, -1),
                              rocblas_status_invalid_size)
            << "Must report error when batch size is negative";

    // pointers
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, (U) nullptr, lda, stA,
                                         dB, ldb, stB, info, bc),
                          rocblas_status_invalid_pointer)
        << "Should normally report error when A is null";
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA, lda, stA,
                                         (U) nullptr, ldb, stB, info, bc),
                          rocblas_status_invalid_pointer)
        << "Should normally report error when B is null";
    EXPECT_ROCBLAS_STATUS(
        rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, nullptr, bc),
        rocblas_status_invalid_pointer)
        << "Should normally report error when info is null";

    // quick return with invalid pointers
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, 0, n, nrhs, (U) nullptr, lda, stA,
                                         dB, ldb, stB, info, bc),
                          rocblas_status_success)
        << "Matrix A may be null when m is 0 (empty matrix)";
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, 0, nrhs, (U) nullptr, lda, stA,
                                         dB, ldb, stB, info, bc),
                          rocblas_status_success)
        << "Matrix A may be null when n is 0 (empty matrix)";
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, 0, dA, lda, stA, (U) nullptr,
                                         ldb, stB, info, bc),
                          rocblas_status_success)
        << "Matrix B may be null when nhrs is 0 (empty matrix)";
    EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, 0, 0, nrhs, (U) nullptr, lda, stA,
                                         (U) nullptr, ldb, stB, info, bc),
                          rocblas_status_success)
        << "Matrices A and B may be null when m and n are 0 (empty matrix)";
    if(BATCHED)
        EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA, lda, stA, dB,
                                             ldb, stB, nullptr, 0),
                              rocblas_status_success)
            << "Info may be null when batch size is 0";

    // quick return with zero batch_count if applicable
    if(STRIDED)
        EXPECT_ROCBLAS_STATUS(
            rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, info, 0),
            rocblas_status_success);
}

template <bool BATCHED, bool STRIDED, typename T>
void testing_gels_bad_arg()
{
    // safe arguments
    rocblas_local_handle handle;
    rocblas_int m = 1;
    rocblas_int n = 1;
    rocblas_int nrhs = 1;
    rocblas_int lda = 1;
    rocblas_int ldb = 1;
    rocblas_stride stA = 1;
    rocblas_stride stB = 1;
    rocblas_int bc = 1;
    rocblas_operation trans = rocblas_operation_none;
    if(BATCHED)
    {
        // memory allocations
        device_batch_vector<T> dA(1, 1, 1);
        device_batch_vector<T> dB(1, 1, 1);
        device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, 1);
        CHECK_HIP_ERROR(dA.memcheck());
        CHECK_HIP_ERROR(dB.memcheck());
        CHECK_HIP_ERROR(dInfo.memcheck());

        // check bad arguments
        gels_checkBadArgs<BATCHED, STRIDED>(handle, trans, m, n, nrhs, dA.data(), lda, stA,
                                            dB.data(), ldb, stB, dInfo.data(), bc);
    }
    else
    {
        // memory allocations
        device_strided_batch_vector<T> dA(1, 1, 1, 1);
        device_strided_batch_vector<T> dB(1, 1, 1, 1);
        device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, 1);
        CHECK_HIP_ERROR(dA.memcheck());
        CHECK_HIP_ERROR(dB.memcheck());
        CHECK_HIP_ERROR(dInfo.memcheck());

        // check bad arguments
        gels_checkBadArgs<BATCHED, STRIDED>(handle, trans, m, n, nrhs, dA.data(), lda, stA,
                                            dB.data(), ldb, stB, dInfo.data(), bc);
    }
}

template <bool CPU, bool GPU, typename T, typename Td, typename Ud, typename Th, typename Uh>
void gels_initData(const rocblas_handle handle,
                   const rocblas_operation trans,
                   const rocblas_int m,
                   const rocblas_int n,
                   const rocblas_int nrhs,
                   Td& dA,
                   const rocblas_int lda,
                   const rocblas_stride stA,
                   Td& dB,
                   const rocblas_int ldb,
                   const rocblas_stride stB,
                   Ud& dInfo,
                   const rocblas_int bc,
                   Th& hA,
                   Th& hB,
                   Uh& hInfo,
                   const bool singular)
{
    if(CPU)
    {
        rocblas_init<T>(hA, true);
        rocblas_init<T>(hB, true);

        const rocblas_int max_index = std::max(0, std::min(m, n) - 1);
        std::uniform_int_distribution<int> sample_index(0, max_index);
        std::bernoulli_distribution coinflip(0.5);

        // scale A to avoid singularities
        for(rocblas_int b = 0; b < bc; ++b)
        {
            for(rocblas_int i = 0; i < m; i++)
            {
                for(rocblas_int j = 0; j < n; j++)
                {
                    if(i == j)
                        hA[b][i + j * lda] += 400;
                    else
                        hA[b][i + j * lda] -= 4;
                }
            }

            // add some singularities
            // always the same elements for debugging purposes
            if(singular && (b == bc / 4 || b == bc / 2 || b == bc - 1))
            {
                do
                {
                    if(n <= m)
                    {
                        // zero random col
                        rocblas_int j = sample_index(rocblas_rng);
                        for(rocblas_int i = 0; i < m; i++)
                            hA[b][i + j * lda] = 0;
                    }
                    else
                    {
                        // zero random row
                        rocblas_int i = sample_index(rocblas_rng);
                        for(rocblas_int j = 0; j < n; j++)
                            hA[b][i + j * lda] = 0;
                    }
                } while(coinflip(rocblas_rng));
            }
        }
    }

    if(GPU)
    {
        // now copy pivoting indices and matrices to the GPU
        CHECK_HIP_ERROR(dA.transfer_from(hA));
        CHECK_HIP_ERROR(dB.transfer_from(hB));
    }
}

template <bool STRIDED, typename T, typename Td, typename Ud, typename Th, typename Uh>
void gels_getError(const rocblas_handle handle,
                   const rocblas_operation trans,
                   const rocblas_int m,
                   const rocblas_int n,
                   const rocblas_int nrhs,
                   Td& dA,
                   const rocblas_int lda,
                   const rocblas_stride stA,
                   Td& dB,
                   const rocblas_int ldb,
                   const rocblas_stride stB,
                   Ud& dInfo,
                   const rocblas_int bc,
                   Th& hA,
                   Th& hB,
                   Th& hBRes,
                   Uh& hInfo,
                   Uh& hInfoRes,
                   double* max_err,
                   const bool singular)
{
    rocblas_int sizeW = max(1, min(m, n) + max(min(m, n), nrhs));
    std::vector<T> hW(sizeW);

    // input data initialization
    gels_initData<true, true, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc,
                                 hA, hB, hInfo, singular);

    // execute computations
    // GPU lapack
    CHECK_ROCBLAS_ERROR(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA.data(), lda, stA,
                                       dB.data(), ldb, stB, dInfo.data(), bc));
    CHECK_HIP_ERROR(hBRes.transfer_from(dB));
    CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));

    // CPU lapack
    for(rocblas_int b = 0; b < bc; ++b)
    {
        cblas_gels<T>(trans, m, n, nrhs, hA[b], lda, hB[b], ldb, hW.data(), sizeW, hInfo[b]);
    }

    // error is ||hB - hBRes|| / ||hB||
    // (THIS DOES NOT ACCOUNT FOR NUMERICAL REPRODUCIBILITY ISSUES.
    // IT MIGHT BE REVISITED IN THE FUTURE)
    // using vector-induced infinity norm
    double err;
    *max_err = 0;
    for(rocblas_int b = 0; b < bc; ++b)
    {
        err = norm_error('I', max(m, n), nrhs, ldb, hB[b], hBRes[b]);
        *max_err = err > *max_err ? err : *max_err;
    }

    // also check info for singularities
    err = 0;
    for(rocblas_int b = 0; b < bc; ++b)
        if(hInfo[b][0] != hInfoRes[b][0])
            err++;
    *max_err += err;
}

template <bool STRIDED, typename T, typename Td, typename Ud, typename Th, typename Uh>
void gels_getPerfData(const rocblas_handle handle,
                      const rocblas_operation trans,
                      const rocblas_int m,
                      const rocblas_int n,
                      const rocblas_int nrhs,
                      Td& dA,
                      const rocblas_int lda,
                      const rocblas_stride stA,
                      Td& dB,
                      const rocblas_int ldb,
                      const rocblas_stride stB,
                      Ud& dInfo,
                      const rocblas_int bc,
                      Th& hA,
                      Th& hB,
                      Uh& hInfo,
                      double* gpu_time_used,
                      double* cpu_time_used,
                      const rocblas_int hot_calls,
                      const int profile,
                      const bool perf,
                      const bool singular)
{
    rocblas_int sizeW = max(1, min(m, n) + max(min(m, n), nrhs));
    std::vector<T> hW(sizeW);

    if(!perf)
    {
        gels_initData<true, false, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo,
                                      bc, hA, hB, hInfo, singular);
        // cpu-lapack performance (only if not in perf mode)
        *cpu_time_used = get_time_us_no_sync();
        for(rocblas_int b = 0; b < bc; ++b)
        {
            cblas_gels<T>(trans, m, n, nrhs, hA[b], lda, hB[b], ldb, hW.data(), sizeW, hInfo[b]);
        }
        *cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
    }
    gels_initData<true, false, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc,
                                  hA, hB, hInfo, singular);
    // cold calls
    for(int iter = 0; iter < 2; iter++)
    {
        gels_initData<false, true, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo,
                                      bc, hA, hB, hInfo, singular);
        CHECK_ROCBLAS_ERROR(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA.data(), lda, stA,
                                           dB.data(), ldb, stB, dInfo.data(), bc));
    }

    // gpu-lapack performance
    hipStream_t stream;
    CHECK_ROCBLAS_ERROR(rocblas_get_stream(handle, &stream));
    double start;

    if(profile > 0)
    {
        rocsolver_log_set_layer_mode(rocblas_layer_mode_log_profile);
        rocsolver_log_set_max_levels(profile);
    }

    for(rocblas_int iter = 0; iter < hot_calls; iter++)
    {
        gels_initData<false, true, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo,
                                      bc, hA, hB, hInfo, singular);

        start = get_time_us_sync(stream);
        rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA.data(), lda, stA, dB.data(), ldb, stB,
                       dInfo.data(), bc);
        *gpu_time_used += get_time_us_sync(stream) - start;
    }
    *gpu_time_used /= hot_calls;
}

template <bool BATCHED, bool STRIDED, typename T, bool COMPLEX = is_complex<T>>
void testing_gels(Arguments& argus)
{
    // get arguments
    rocblas_local_handle handle;
    char transC = argus.get<char>("trans");
    rocblas_int m = argus.get<rocblas_int>("m");
    rocblas_int n = argus.get<rocblas_int>("n", m);
    rocblas_int nrhs = argus.get<rocblas_int>("nrhs", n);
    rocblas_int lda = argus.get<rocblas_int>("lda", m);
    rocblas_int ldb = argus.get<rocblas_int>("ldb", max(m, n));
    rocblas_stride stA = argus.get<rocblas_stride>("strideA", lda * n);
    rocblas_stride stB = argus.get<rocblas_stride>("strideB", ldb * nrhs);

    rocblas_operation trans = char2rocblas_operation(transC);
    rocblas_int bc = argus.batch_count;
    rocblas_int hot_calls = argus.iters;

    rocblas_stride stBRes = (argus.unit_check || argus.norm_check) ? stB : 0;

    // check non-supported values
    bool invalid_value = ((COMPLEX && trans == rocblas_operation_transpose)
                          || (!COMPLEX && trans == rocblas_operation_conjugate_transpose));
    if(invalid_value)
    {
        if(BATCHED)
            EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs,
                                                 (T* const*)nullptr, lda, stA, (T* const*)nullptr,
                                                 ldb, stB, (rocblas_int*)nullptr, bc),
                                  rocblas_status_invalid_value);
        else
            EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, (T*)nullptr,
                                                 lda, stA, (T*)nullptr, ldb, stB,
                                                 (rocblas_int*)nullptr, bc),
                                  rocblas_status_invalid_value);

        if(argus.timing)
            rocsolver_bench_inform(inform_invalid_args);

        return;
    }

    // determine sizes
    size_t size_A = size_t(lda) * n;
    size_t size_B = size_t(ldb) * nrhs;
    double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;

    size_t size_BRes = (argus.unit_check || argus.norm_check) ? size_B : 0;

    // check invalid sizes
    bool invalid_size = (m < 0 || n < 0 || nrhs < 0 || lda < m || ldb < m || ldb < n || bc < 0);
    if(invalid_size)
    {
        if(BATCHED)
            EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs,
                                                 (T* const*)nullptr, lda, stA, (T* const*)nullptr,
                                                 ldb, stB, (rocblas_int*)nullptr, bc),
                                  rocblas_status_invalid_size);
        else
            EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, (T*)nullptr,
                                                 lda, stA, (T*)nullptr, ldb, stB,
                                                 (rocblas_int*)nullptr, bc),
                                  rocblas_status_invalid_size);

        if(argus.timing)
            rocsolver_bench_inform(inform_invalid_size);

        return;
    }

    // memory size query is necessary
    if(argus.mem_query || !USE_ROCBLAS_REALLOC_ON_DEMAND)
    {
        CHECK_ROCBLAS_ERROR(rocblas_start_device_memory_size_query(handle));
        if(BATCHED)
            CHECK_ALLOC_QUERY(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, (T* const*)nullptr,
                                             lda, stA, (T* const*)nullptr, ldb, stB,
                                             (rocblas_int*)nullptr, bc));
        else
            CHECK_ALLOC_QUERY(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, (T*)nullptr, lda,
                                             stA, (T*)nullptr, ldb, stB, (rocblas_int*)nullptr, bc));

        size_t size;
        CHECK_ROCBLAS_ERROR(rocblas_stop_device_memory_size_query(handle, &size));
        if(argus.mem_query)
        {
            rocsolver_bench_inform(inform_mem_query, size);
            return;
        }

        CHECK_ROCBLAS_ERROR(rocblas_set_device_memory_size(handle, size));
    }

    if(BATCHED)
    {
        // memory allocations
        host_batch_vector<T> hA(size_A, 1, bc);
        host_batch_vector<T> hB(size_B, 1, bc);
        host_batch_vector<T> hBRes(size_BRes, 1, bc);
        host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, bc);
        host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, bc);
        device_batch_vector<T> dA(size_A, 1, bc);
        device_batch_vector<T> dB(size_B, 1, bc);
        device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
        if(size_A)
            CHECK_HIP_ERROR(dA.memcheck());
        if(size_B)
            CHECK_HIP_ERROR(dB.memcheck());
        if(bc)
            CHECK_HIP_ERROR(dInfo.memcheck());

        // check quick return
        if(m == 0 || n == 0 || nrhs == 0 || bc == 0)
        {
            EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA.data(), lda,
                                                 stA, dB.data(), ldb, stB, dInfo.data(), bc),
                                  rocblas_status_success);
            if(argus.timing)
                rocsolver_bench_inform(inform_quick_return);

            return;
        }

        // check computations
        if(argus.unit_check || argus.norm_check)
            gels_getError<STRIDED, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo,
                                      bc, hA, hB, hBRes, hInfo, hInfoRes, &max_error, argus.singular);

        // collect performance data
        if(argus.timing)
            gels_getPerfData<STRIDED, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB,
                                         dInfo, bc, hA, hB, hInfo, &gpu_time_used, &cpu_time_used,
                                         hot_calls, argus.profile, argus.perf, argus.singular);
    }
    else
    {
        // memory allocations
        host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
        host_strided_batch_vector<T> hB(size_B, 1, stB, bc);
        host_strided_batch_vector<T> hBRes(size_BRes, 1, stBRes, bc);
        host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, bc);
        host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, bc);
        device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
        device_strided_batch_vector<T> dB(size_B, 1, stB, bc);
        device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
        if(size_A)
            CHECK_HIP_ERROR(dA.memcheck());
        if(size_B)
            CHECK_HIP_ERROR(dB.memcheck());
        if(bc)
            CHECK_HIP_ERROR(dInfo.memcheck());

        // check quick return
        if(m == 0 || n == 0 || nrhs == 0 || bc == 0)
        {
            EXPECT_ROCBLAS_STATUS(rocsolver_gels(STRIDED, handle, trans, m, n, nrhs, dA.data(), lda,
                                                 stA, dB.data(), ldb, stB, dInfo.data(), bc),
                                  rocblas_status_success);
            if(argus.timing)
                rocsolver_bench_inform(inform_quick_return);

            return;
        }

        // check computations
        if(argus.unit_check || argus.norm_check)
            gels_getError<STRIDED, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo,
                                      bc, hA, hB, hBRes, hInfo, hInfoRes, &max_error, argus.singular);

        // collect performance data
        if(argus.timing)
            gels_getPerfData<STRIDED, T>(handle, trans, m, n, nrhs, dA, lda, stA, dB, ldb, stB,
                                         dInfo, bc, hA, hB, hInfo, &gpu_time_used, &cpu_time_used,
                                         hot_calls, argus.profile, argus.perf, argus.singular);
    }
    // validate results for rocsolver-test
    // using max(m,n) * machine_precision as tolerance
    if(argus.unit_check)
        ROCSOLVER_TEST_CHECK(T, max_error, max(m, n));

    // output results for rocsolver-bench
    if(argus.timing)
    {
        if(!argus.perf)
        {
            rocsolver_bench_header("Arguments:");
            if(BATCHED)
            {
                rocsolver_bench_output("trans", "m", "n", "nrhs", "lda", "ldb", "batch_c");
                rocsolver_bench_output(transC, m, n, nrhs, lda, ldb, bc);
            }
            else if(STRIDED)
            {
                rocsolver_bench_output("trans", "m", "n", "nrhs", "lda", "ldb", "strideA",
                                       "strideB", "batch_c");
                rocsolver_bench_output(transC, m, n, nrhs, lda, ldb, stA, stB, bc);
            }
            else
            {
                rocsolver_bench_output("trans", "m", "n", "nrhs", "lda", "ldb");
                rocsolver_bench_output(transC, m, n, nrhs, lda, ldb);
            }
            rocsolver_bench_header("Results:");
            if(argus.norm_check)
            {
                rocsolver_bench_output("cpu_time", "gpu_time", "error");
                rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
            }
            else
            {
                rocsolver_bench_output("cpu_time", "gpu_time");
                rocsolver_bench_output(cpu_time_used, gpu_time_used);
            }
            rocsolver_bench_endl();
        }
        else
        {
            if(argus.norm_check)
                rocsolver_bench_output(gpu_time_used, max_error);
            else
                rocsolver_bench_output(gpu_time_used);
        }
    }

    // ensure all arguments were consumed
    argus.validate_consumed();
}