/* ************************************************************************
 * Copyright (C) 2016-2022 Advanced Micro Devices, Inc. All rights reserved.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 *
 * ************************************************************************ */

#include <fstream>
#include <iostream>
#include <stdlib.h>
#include <vector>

#include "testing_common.hpp"

/* ============================================================================================ */

using hipblasTpmvStridedBatchedModel
    = ArgumentModel<e_uplo, e_transA, e_diag, e_M, e_incx, e_stride_scale, e_batch_count>;

inline void testname_tpmv_strided_batched(const Arguments& arg, std::string& name)
{
    hipblasTpmvStridedBatchedModel{}.test_name(arg, name);
}

template <typename T>
inline hipblasStatus_t testing_tpmv_strided_batched(const Arguments& arg)
{
    bool FORTRAN = arg.fortran;
    auto hipblasTpmvStridedBatchedFn
        = FORTRAN ? hipblasTpmvStridedBatched<T, true> : hipblasTpmvStridedBatched<T, false>;

    hipblasFillMode_t  uplo         = char2hipblas_fill(arg.uplo);
    hipblasOperation_t transA       = char2hipblas_operation(arg.transA);
    hipblasDiagType_t  diag         = char2hipblas_diagonal(arg.diag);
    int                M            = arg.M;
    int                incx         = arg.incx;
    double             stride_scale = arg.stride_scale;
    int                batch_count  = arg.batch_count;

    int           abs_incx = incx >= 0 ? incx : -incx;
    size_t        dim_A    = size_t(M) * (M + 1) / 2;
    hipblasStride stride_A = dim_A * stride_scale;
    hipblasStride stride_x = size_t(M) * abs_incx * stride_scale;

    size_t A_size = stride_A * batch_count;
    size_t X_size = stride_x * batch_count;

    hipblasLocalHandle handle(arg);

    // argument sanity check, quick return if input parameters are invalid before allocating invalid
    // memory
    bool invalid_size = M < 0 || !incx || batch_count < 0;
    if(invalid_size || !M || !batch_count)
    {
        hipblasStatus_t actual = hipblasTpmvStridedBatchedFn(
            handle, uplo, transA, diag, M, nullptr, stride_A, nullptr, incx, stride_x, batch_count);
        EXPECT_HIPBLAS_STATUS(
            actual, (invalid_size ? HIPBLAS_STATUS_INVALID_VALUE : HIPBLAS_STATUS_SUCCESS));
        return actual;
    }

    // Naming: dK is in GPU (device) memory. hK is in CPU (host) memory
    host_vector<T> hA(A_size);
    host_vector<T> hx(X_size);
    host_vector<T> hres(X_size);

    device_vector<T> dA(A_size);
    device_vector<T> dx(X_size);

    double gpu_time_used, hipblas_error;

    // Initial Data on CPU
    hipblas_init_matrix(
        hA, arg, dim_A, 1, 1, stride_A, batch_count, hipblas_client_never_set_nan, true);
    hipblas_init_vector(
        hx, arg, M, abs_incx, stride_x, batch_count, hipblas_client_never_set_nan, false, true);
    hres = hx;

    // copy data from CPU to device
    CHECK_HIP_ERROR(hipMemcpy(dA, hA.data(), sizeof(T) * A_size, hipMemcpyHostToDevice));
    CHECK_HIP_ERROR(hipMemcpy(dx, hx.data(), sizeof(T) * X_size, hipMemcpyHostToDevice));

    if(arg.unit_check || arg.norm_check)
    {
        /* =====================================================================
            HIPBLAS
        =================================================================== */
        CHECK_HIPBLAS_ERROR(hipblasTpmvStridedBatchedFn(
            handle, uplo, transA, diag, M, dA, stride_A, dx, incx, stride_x, batch_count));

        // copy output from device to CPU
        CHECK_HIP_ERROR(hipMemcpy(hres.data(), dx, sizeof(T) * X_size, hipMemcpyDeviceToHost));

        /* =====================================================================
           CPU BLAS
        =================================================================== */
        for(int b = 0; b < batch_count; b++)
        {
            cblas_tpmv<T>(
                uplo, transA, diag, M, hA.data() + b * stride_A, hx.data() + b * stride_x, incx);
        }

        // enable unit check, notice unit check is not invasive, but norm check is,
        // unit check and norm check can not be interchanged their order
        if(arg.unit_check)
        {
            unit_check_general<T>(1, M, batch_count, abs_incx, stride_x, hx, hres);
        }
        if(arg.norm_check)
        {
            hipblas_error = norm_check_general<T>(
                'F', 1, M, abs_incx, stride_x, hx.data(), hres.data(), batch_count);
        }
    }

    if(arg.timing)
    {
        hipStream_t stream;
        CHECK_HIPBLAS_ERROR(hipblasGetStream(handle, &stream));

        int runs = arg.cold_iters + arg.iters;
        for(int iter = 0; iter < runs; iter++)
        {
            if(iter == arg.cold_iters)
                gpu_time_used = get_time_us_sync(stream);

            CHECK_HIPBLAS_ERROR(hipblasTpmvStridedBatchedFn(
                handle, uplo, transA, diag, M, dA, stride_A, dx, incx, stride_x, batch_count));
        }
        gpu_time_used = get_time_us_sync(stream) - gpu_time_used; // in microseconds

        hipblasTpmvStridedBatchedModel{}.log_args<T>(std::cout,
                                                     arg,
                                                     gpu_time_used,
                                                     tpmv_gflop_count<T>(M),
                                                     tpmv_gbyte_count<T>(M),
                                                     hipblas_error);
    }

    return HIPBLAS_STATUS_SUCCESS;
}
