/**
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This program is free software, you can redistribute it and/or modify it under the terms and conditions of
 * CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */

#include <iostream>
#include <vector>
#include "asdsip.h"
#include "acl/acl.h"
#include "acl_meta.h"

using namespace AsdSip;

int64_t GetShapeSize(const std::vector<int64_t> &shape)
{
    int64_t shapeSize = 1;
    for (auto i : shape) {
        shapeSize *= i;
    }
    return shapeSize;
}

int Init(int32_t deviceId, aclrtStream *stream)
{
    // 固定写法，acl初始化
    aclInit(nullptr);
    aclrtSetDevice(deviceId);
    aclrtCreateStream(stream);
    return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
    aclDataType dataType, aclTensor **tensor)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // 调用aclrtMalloc申请device侧内存
    aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
    // 调用aclrtMemcpy将host侧数据复制到device侧内存上
    aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);

    // 计算连续tensor的strides
    std::vector<int64_t> strides(shape.size(), 1);
    for (int64_t i = shape.size() - 2; i >= 0; i--) {
        strides[i] = shape[i + 1] * strides[i + 1];
    }

    // 调用aclCreateTensor接口创建aclTensor
    *tensor = aclCreateTensor(shape.data(),
        shape.size(),
        dataType,
        strides.data(),
        0,
        aclFormat::ACL_FORMAT_ND,
        shape.data(),
        shape.size(),
        *deviceAddr);
    return 0;
}

int main(int argc, char **argv)
{
    // 设置算子使用的device id
    int deviceId = 0;
    //（固定写法）创建执行流
    aclrtStream stream;
    Init(deviceId, &stream);

    // 创造tensor的Host侧数据
    int64_t n = 5;
    int64_t incx = 1;
    int64_t incy = 1;

    int64_t xSize = 5;
    std::vector<float> tensorInXData;
    tensorInXData.reserve(xSize);
    for (int64_t i = 0; i < xSize; i++) {
        tensorInXData[i] = 1.0 + i;
    }

    int64_t ySize = 5;
    std::vector<float> tensorInYData;
    tensorInYData.reserve(xSize);
    for (int64_t i = 0; i < ySize; i++) {
        tensorInYData[i] = 10.0 + i;
    }

    int64_t resultSize = 1;
    std::vector<float> resultData;
    resultData.reserve(resultSize);

    std::cout << "------- input x -------" << std::endl;
    for (int64_t i = 0; i < xSize; i++) {
        std::cout << tensorInXData[i] << " ";
    }
    std::cout << std::endl;

    std::cout << "------- input y -------" << std::endl;
    for (int64_t i = 0; i < ySize; i++) {
        std::cout << tensorInYData[i] << " ";
    }
    std::cout << std::endl;

    // 创造输入/输出tensor
    std::vector<int64_t> xShape = {xSize};
    std::vector<int64_t> yShape = {ySize};
    std::vector<int64_t> resultShape = {resultSize};
    aclTensor *inputX = nullptr;
    aclTensor *inputY = nullptr;
    aclTensor *result = nullptr;
    void *inputXDeviceAddr = nullptr;
    void *inputYDeviceAddr = nullptr;
    void *resultDeviceAddr = nullptr;
    CreateAclTensor(tensorInXData, xShape, &inputXDeviceAddr, aclDataType::ACL_FLOAT, &inputX);
    CreateAclTensor(tensorInYData, yShape, &inputYDeviceAddr, aclDataType::ACL_FLOAT, &inputY);
    CreateAclTensor(resultData, resultShape, &resultDeviceAddr, aclDataType::ACL_FLOAT, &result);

    // 创建算子执行句柄
    asdBlasHandle handle;
    asdBlasCreate(handle);

    // 创造算子执行所需workspace
    size_t lwork = 0;
    void *buffer = nullptr;
    asdBlasMakeDotPlan(handle);
    asdBlasGetWorkspaceSize(handle, lwork);
    if (lwork > 0) {
        aclrtMalloc(&buffer, static_cast<int64_t>(lwork), ACL_MEM_MALLOC_HUGE_FIRST);
    }
    asdBlasSetWorkspace(handle, buffer);

    // 配置算子执行信息
    asdBlasSetStream(handle, stream);

    // 调用接口执行算子（固定调用逻辑）
    asdBlasSdot(handle, n, inputX, incx, inputY, incy, result);
    asdBlasSynchronize(handle);

    // 调用算子后销毁算子句柄
    asdBlasDestroy(handle);

    // 将输出tensor的Device侧数据复制到Host侧内存上
    aclrtMemcpy(resultData.data(),
        resultSize * sizeof(float),
        resultDeviceAddr,
        resultSize * sizeof(float),
        ACL_MEMCPY_DEVICE_TO_HOST);

    std::cout << "------- result -------" << std::endl;
    for (int64_t i = 0; i < 1; i++) {
        std::cout << resultData[i] << " ";
    }
    std::cout << std::endl;

    // 资源释放
    aclDestroyTensor(inputX);
    aclDestroyTensor(inputY);
    aclDestroyTensor(result);
    aclrtFree(inputXDeviceAddr);
    aclrtFree(inputYDeviceAddr);
    aclrtFree(resultDeviceAddr);
    if (lwork > 0) {
        aclrtFree(buffer);
    }

    // 调用算子后重置算子使用的deviceId
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}