/**
 * Copyright (c) 2026 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.
 */

/* Generated By CANNBot */

/**
 * @file test_aclnn_acosh.cpp
 * @brief aclnnAcosh（非原地）调用示例
 *
 * 演示标准 aclnn 两段式调用流程：
 *   1) aclInit / aclrtSetDevice / aclrtCreateStream
 *   2) Host 构造输入张量并拷贝到 Device
 *   3) aclnnAcoshGetWorkspaceSize  → workspaceSize / executor
 *   4) 申请 workspace 后调用 aclnnAcosh
 *   5) aclrtSynchronizeStream 等待执行结束
 *   6) Device → Host 拷回输出并打印
 *   7) 资源释放 + aclFinalize
 *
 * 前置条件：算子已编译并安装到 ${ASCEND_HOME_PATH}/opp/vendors/acosh_custom/
 */

#include <cmath>
#include <cstdio>
#include <iostream>
#include <vector>

#include "acl/acl.h"
#include "aclnn_acosh.h"

#define CHECK_RET(cond, return_expr)            \
    do {                                        \
        if (!(cond)) {                          \
            return_expr;                        \
        }                                       \
    } while (0)

#define LOG_PRINT(message, ...)                 \
    do {                                        \
        printf(message, ##__VA_ARGS__);         \
    } while (0)

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

static int Init(int32_t deviceId, aclrtStream *stream)
{
    auto ret = aclInit(nullptr);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
    ret = aclrtSetDevice(deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
    ret = aclrtCreateStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
    return 0;
}

template <typename T>
static int CreateAclTensor(const std::vector<T> &hostData,
                           const std::vector<int64_t> &shape,
                           void **deviceAddr,
                           aclDataType dataType,
                           aclTensor **tensor)
{
    auto bytes = GetShapeSize(shape) * sizeof(T);
    auto ret = aclrtMalloc(deviceAddr, bytes, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
    ret = aclrtMemcpy(*deviceAddr, bytes, hostData.data(), bytes, ACL_MEMCPY_HOST_TO_DEVICE);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy H2D failed. ERROR: %d\n", ret); return ret);

    // 行主序连续 strides
    std::vector<int64_t> strides(shape.size(), 1);
    for (int64_t i = static_cast<int64_t>(shape.size()) - 2; i >= 0; --i) {
        strides[i] = shape[i + 1] * strides[i + 1];
    }

    *tensor = aclCreateTensor(shape.data(), shape.size(), dataType,
                              strides.data(), 0, aclFormat::ACL_FORMAT_ND,
                              shape.data(), shape.size(), *deviceAddr);
    return 0;
}

int main()
{
    // 1. 初始化 ACL
    int32_t deviceId = 0;
    aclrtStream stream = nullptr;
    auto ret = Init(deviceId, &stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

    // 2. 构造输入：shape = [2, 4]，全部位于定义域 [1, +∞)
    std::vector<int64_t> selfShape = {2, 4};
    std::vector<int64_t> outShape  = {2, 4};
    std::vector<float>   selfHostData = {1.0f, 2.0f, 3.0f, 5.0f,
                                         10.0f, 1.5f, 2.5f, 4.0f};
    std::vector<float>   outHostData(static_cast<size_t>(GetShapeSize(outShape)), 0.0f);

    aclTensor *selfTensor = nullptr;
    void      *selfDevice = nullptr;
    ret = CreateAclTensor(selfHostData, selfShape, &selfDevice,
                          aclDataType::ACL_FLOAT, &selfTensor);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    aclTensor *outTensor = nullptr;
    void      *outDevice = nullptr;
    ret = CreateAclTensor(outHostData, outShape, &outDevice,
                          aclDataType::ACL_FLOAT, &outTensor);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 3. 第一段接口：获取 workspace 大小 + executor
    uint64_t       workspaceSize = 0;
    aclOpExecutor *executor      = nullptr;
    ret = aclnnAcoshGetWorkspaceSize(selfTensor, outTensor, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS,
              LOG_PRINT("aclnnAcoshGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);

    // 4. 申请 workspace（如有）
    void *workspaceAddr = nullptr;
    if (workspaceSize > 0) {
        ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
        CHECK_RET(ret == ACL_SUCCESS,
                  LOG_PRINT("workspace aclrtMalloc failed. ERROR: %d\n", ret); return ret);
    }

    // 5. 第二段接口：实际执行算子
    ret = aclnnAcosh(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS,
              LOG_PRINT("aclnnAcosh failed. ERROR: %d\n", ret); return ret);

    // 6. 同步等待
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS,
              LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

    // 7. 拷回输出并打印（同时用 std::acosh 粗略对照）
    auto outBytes = GetShapeSize(outShape) * sizeof(float);
    std::vector<float> npuResult(static_cast<size_t>(GetShapeSize(outShape)), 0.0f);
    ret = aclrtMemcpy(npuResult.data(), outBytes, outDevice, outBytes,
                      ACL_MEMCPY_DEVICE_TO_HOST);
    CHECK_RET(ret == ACL_SUCCESS,
              LOG_PRINT("aclrtMemcpy D2H failed. ERROR: %d\n", ret); return ret);

    LOG_PRINT("\n=== aclnnAcosh result (shape=[2,4], dtype=FLOAT) ===\n");
    int allClose = 1;
    for (size_t i = 0; i < npuResult.size(); ++i) {
        float expected = std::acosh(selfHostData[i]);
        float diff     = std::fabs(npuResult[i] - expected);
        // FP32 阈值参考：MERE 2^-13 ≈ 1.22e-4，这里取 1e-4 作为示例粗对照
        const char *flag = (diff < 1e-4f) ? "OK" : "DIFF";
        if (diff >= 1e-4f) {
            allClose = 0;
        }
        LOG_PRINT("  self[%zu]=%-9.4f  npu=%-12.7f  std::acosh=%-12.7f  diff=%-10.3e  [%s]\n",
                  i, selfHostData[i], npuResult[i], expected, diff, flag);
    }
    LOG_PRINT("=== aclnnAcosh %s ===\n", allClose ? "PASS (粗对照)" : "DIFF (粗对照)");

    // 8. 释放资源
    aclDestroyTensor(selfTensor);
    aclDestroyTensor(outTensor);
    aclrtFree(selfDevice);
    aclrtFree(outDevice);
    if (workspaceSize > 0 && workspaceAddr != nullptr) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();

    return allClose ? 0 : 1;
}