/**
 * 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.
 */

#include "aclnn_lt_scalar.h"
#include "less.h"
#include "aclnn_kernels/cast.h"
#include "aclnn_kernels/contiguous.h"
#include "aclnn/aclnn_base.h"
#include "aclnn_kernels/common/op_error_check.h"
#include "opdev/common_types.h"
#include "opdev/data_type_utils.h"
#include "opdev/shape_utils.h"
#include "opdev/format_utils.h"
#include "opdev/op_dfx.h"
#include "opdev/op_executor.h"
#include "opdev/op_log.h"
#include "opdev/platform.h"
#include "opdev/tensor_view_utils.h"
#include "op_api/aclnn_check.h"

using namespace op;
#ifdef __cplusplus
extern "C" {
#endif

/* Less 算子的完整计算流程如下:
 * self                               other
 *   |                                  |
 *   \                                  /
 * Contiguous(workspace_0)    Contiguous(workspace_2)
 *      \                             /
 *     Cast(workspace_1)     Cast(workspace_3)
 *               \            /
 *              Less(workspace_4)
 *                    |
 *              Cast(workspace_5)
 *                    |
 *                ViewCopy
 *                    |
 *                  result
 */

constexpr size_t MAX_DIM_LEN = 8;

// 根据API定义，需要列出所能支持的所有dtype
static const std::initializer_list<op::DataType> ASCEND910_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_FLOAT,  op::DataType::DT_INT32,  op::DataType::DT_INT64, op::DataType::DT_FLOAT16,
    op::DataType::DT_INT16,  op::DataType::DT_INT8,   op::DataType::DT_UINT8, op::DataType::DT_DOUBLE,
    op::DataType::DT_UINT32, op::DataType::DT_UINT64, op::DataType::DT_BOOL,  op::DataType::DT_UINT16};

static const std::initializer_list<op::DataType> ASCEND910B_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_FLOAT,  op::DataType::DT_INT32,  op::DataType::DT_INT64, op::DataType::DT_FLOAT16,
    op::DataType::DT_INT16,  op::DataType::DT_INT8,   op::DataType::DT_UINT8, op::DataType::DT_DOUBLE,
    op::DataType::DT_UINT32, op::DataType::DT_UINT64, op::DataType::DT_BOOL,  op::DataType::DT_UINT16,
    op::DataType::DT_BF16};

static const std::initializer_list<op::DataType> ASCEND910_OUT_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_INT64,      op::DataType::DT_UINT64,   op::DataType::DT_INT32,   op::DataType::DT_UINT32,
    op::DataType::DT_INT16,      op::DataType::DT_UINT16,   op::DataType::DT_INT8,    op::DataType::DT_UINT8,
    op::DataType::DT_DOUBLE,     op::DataType::DT_FLOAT,    op::DataType::DT_FLOAT16, op::DataType::DT_BOOL,
    op::DataType::DT_COMPLEX128, op::DataType::DT_COMPLEX64};

static const std::initializer_list<op::DataType> ASCEND910B_OUT_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_INT64,  op::DataType::DT_UINT64,     op::DataType::DT_INT32,    op::DataType::DT_UINT32,
    op::DataType::DT_INT16,  op::DataType::DT_UINT16,     op::DataType::DT_INT8,     op::DataType::DT_UINT8,
    op::DataType::DT_DOUBLE, op::DataType::DT_FLOAT,      op::DataType::DT_FLOAT16,  op::DataType::DT_BF16,
    op::DataType::DT_BOOL,   op::DataType::DT_COMPLEX128, op::DataType::DT_COMPLEX64};

static op::DataType InnerTypeToComplexType(const op::DataType input)
{
    switch (input) {
        case op::DataType::DT_BF16:
            // BFloat16 has range equivalent to Float,
            // so we map it to ComplexFloat.
            return op::DataType::DT_COMPLEX64;
        case op::DataType::DT_FLOAT16:
            return op::DataType::DT_COMPLEX32;
        case op::DataType::DT_FLOAT:
            return op::DataType::DT_COMPLEX64;
        case op::DataType::DT_DOUBLE:
            return op::DataType::DT_COMPLEX128;
        case op::DataType::DT_COMPLEX32:
            return op::DataType::DT_COMPLEX32;
        case op::DataType::DT_COMPLEX64:
            return op::DataType::DT_COMPLEX64;
        case op::DataType::DT_COMPLEX128:
            return op::DataType::DT_COMPLEX128;
        default:
            OP_LOGE(ACLNN_ERR_PARAM_INVALID, "Unknown Complex ScalarType for [%s]", ToString(input).GetString());
            return op::DataType::DT_UNDEFINED;
    }
}

static op::DataType CombineCategoriesWithComplex(const op::DataType higher, const op::DataType lower)
{
    if (IsComplexType(higher)) {
        return higher;
    } else if (IsComplexType(lower)) {
        // preserve value type of higher if it is floating type.
        if (IsFloatingType(higher)) {
            return InnerTypeToComplexType(higher);
        }
        // in case of integral input
        // lower complex takes precedence.
        return lower;
    } else if (IsFloatingType(higher)) {
        return higher;
    }
    if (higher == op::DataType::DT_BOOL || IsFloatingType(lower)) {
        return op::PromoteType(higher, lower);
    }
    if (higher != op::DataType::DT_UNDEFINED) {
        return higher;
    }
    return lower;
}

static op::DataType GetScalarDefaultDtype(const op::DataType input)
{
    if (IsComplexType(input)) {
        return op::DataType::DT_COMPLEX64;
    } else if (IsFloatingType(input)) {
        return op::DataType::DT_FLOAT;
    }
    return input;
}

static bool CheckNotNull(const aclTensor* self, const aclTensor* other, const aclTensor* out)
{
    OP_CHECK_NULL(self, return false);
    OP_CHECK_NULL(other, return false);
    OP_CHECK_NULL(out, return false);
    return true;
}

static inline const std::initializer_list<op::DataType>& GetDtypeSupportList()
{
    auto socVersion = GetCurrentPlatformInfo().GetSocVersion();
    if (socVersion >= SocVersion::ASCEND910B && socVersion <= SocVersion::ASCEND910E) {
        return ASCEND910B_DTYPE_SUPPORT_LIST;
    } else {
        return ASCEND910_DTYPE_SUPPORT_LIST;
    }
}

static inline const std::initializer_list<op::DataType>& GetOutDtypeSupportList()
{
    auto socVersion = GetCurrentPlatformInfo().GetSocVersion();
    if (socVersion >= SocVersion::ASCEND910B && socVersion <= SocVersion::ASCEND910E) {
        return ASCEND910B_OUT_DTYPE_SUPPORT_LIST;
    }

    return ASCEND910_OUT_DTYPE_SUPPORT_LIST;
}

static bool CheckDtypeValid(const aclTensor* self, const aclTensor* other, const aclTensor* out)
{
    auto supportList = GetDtypeSupportList();
    // 检查self的数据类型是否在Less算子的支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(self, supportList, return false);

    // 检查other的数据类型是否在Less算子的支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(other, supportList, return false);

    // 检查out的数据类型是否在Less算子的支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(out, supportList, return false);
    return true;
}

static bool CheckPromoteType(const aclTensor* self, const aclTensor* other, const aclTensor* out)
{
    // 检查self和other能否做数据类型推导
    op::DataType promoteType = op::PromoteType(self->GetDataType(), other->GetDataType());
    if (promoteType == DataType::DT_UNDEFINED) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "Self dtype %s and other dtype %s can not promote dtype.",
            op::ToString(self->GetDataType()).GetString(), op::ToString(other->GetDataType()).GetString());
        return false;
    }

    // 检查BOOL类型能否转换为输出的数据类型(算子返回的都是BOOL类型)
    OP_CHECK_RESULT_DTYPE_CAST_FAILED(DataType::DT_BOOL, out->GetDataType(), return false);
    return true;
}

static bool CheckShape(const aclTensor* self, const aclTensor* other, const aclTensor* out)
{
    OP_CHECK_MAX_DIM(self, MAX_DIM_LEN, return false);
    OP_CHECK_MAX_DIM(other, MAX_DIM_LEN, return false);

    op::Shape broadcastShape;
    OP_CHECK_BROADCAST_AND_INFER_SHAPE(self, other, broadcastShape, return false);

    if (broadcastShape != out->GetViewShape()) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "Shape of out should be %s, but current is %s.",
            op::ToString(broadcastShape).GetString(), op::ToString(out->GetViewShape()).GetString());
        return false;
    }
    return true;
}

static aclnnStatus CheckParams(const aclTensor* self, const aclTensor* other, const aclTensor* out)
{
    // 1. 检查参数是否为空指针
    CHECK_RET(CheckNotNull(self, other, out), ACLNN_ERR_PARAM_NULLPTR);

    // 2. 检查输入的数据类型是否在API支持的数据类型范围之内，需要根据api定义校验
    CHECK_RET(CheckDtypeValid(self, other, out), ACLNN_ERR_PARAM_INVALID);

    // 3. 检查self和other能否做数据类型推导以及推导的数据类型能否转换为输出数据类型
    CHECK_RET(CheckPromoteType(self, other, out), ACLNN_ERR_PARAM_INVALID);

    // 4. 检查双输入是否能broadcast
    CHECK_RET(CheckShape(self, other, out), ACLNN_ERR_PARAM_INVALID);

    return ACLNN_SUCCESS;
}

static bool CheckNotNullScalar(const aclTensor* self, const aclScalar* other, const aclTensor* out)
{
    OP_CHECK_NULL(self, return false);
    OP_CHECK_NULL(other, return false);
    OP_CHECK_NULL(out, return false);
    return true;
}

static bool CheckDtypeValidScalar(const aclTensor* self, const aclScalar* other, const aclTensor* out)
{
    auto supportList = GetDtypeSupportList();
    auto outSupportList = GetOutDtypeSupportList();
    // 检查self的数据类型是否在Less算子的支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(self, supportList, return false);

    // 检查other的数据类型是否在Less算子的支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(other, supportList, return false);

    // 检查out的数据类型是否在Less算子的支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(out, outSupportList, return false);
    return true;
}

static bool CheckPromoteTypeScalar(
    const aclTensor* self, const aclScalar* other, const aclTensor* out, DataType promoteType)
{
    // 检查self和other能否做数据类型推导
    if (promoteType == DataType::DT_UNDEFINED) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "Self dtype %s and other dtype %s can not promote dtype.",
            op::ToString(self->GetDataType()).GetString(), op::ToString(other->GetDataType()).GetString());
        return false;
    }

    // 检查promote后的数据类型是否在Less算子的支持列表内
    auto supportList = GetDtypeSupportList();
    if (!CheckType(promoteType, supportList)) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "aclnnLtScalar not implemented for input promote dtype %s.",
            ToString(promoteType).GetString());
        return false;
    }

    // 检查self和other能否转换为promote后的数据类型
    OP_CHECK_RESULT_DTYPE_CAST_FAILED(self->GetDataType(), promoteType, return false);
    OP_CHECK_RESULT_DTYPE_CAST_FAILED(other->GetDataType(), promoteType, return false);

    // 检查BOOL类型能否转换为输出的数据类型(算子返回的都是BOOL类型)
    OP_CHECK_RESULT_DTYPE_CAST_FAILED(DataType::DT_BOOL, out->GetDataType(), return false);
    return true;
}

static bool CheckShapeScalar(const aclTensor* self, const aclTensor* out)
{
    OP_CHECK_MAX_DIM(self, MAX_DIM_LEN, return false);
    OP_CHECK_MAX_DIM(out, MAX_DIM_LEN, return false);
    OP_CHECK_SHAPE_NOT_EQUAL(self, out, return false);
    return true;
}

static aclnnStatus CheckParamsScalar(
    const aclTensor* self, const aclScalar* other, const aclTensor* out, DataType promote)
{
    // 1. 检查参数是否为空指针
    CHECK_RET(CheckNotNullScalar(self, other, out), ACLNN_ERR_PARAM_NULLPTR);

    // 2. 检查输入的数据类型是否在API支持的数据类型范围之内，需要根据api定义校验
    CHECK_RET(CheckDtypeValidScalar(self, other, out), ACLNN_ERR_PARAM_INVALID);

    // 3. 检查self和other能否做数据类型推导以及推导的数据类型能否转换为输出数据类型
    CHECK_RET(CheckPromoteTypeScalar(self, other, out, promote), ACLNN_ERR_PARAM_INVALID);

    // 4. 检查双输入是否能broadcast
    CHECK_RET(CheckShapeScalar(self, out), ACLNN_ERR_PARAM_INVALID);

    return ACLNN_SUCCESS;
}

aclnnStatus aclnnLtScalarGetWorkspaceSizeV35(
    const aclTensor* self, const aclScalar* other, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)
{
    // 固定写法，创建OpExecutor
    auto uniqueExecutor = CREATE_EXECUTOR();
    CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);

    // 将other scalar转换为tensor
    CHECK_RET(other != nullptr, ACLNN_ERR_PARAM_NULLPTR);
    CHECK_RET(self != nullptr, ACLNN_ERR_PARAM_NULLPTR);

    // Less算子需要对self和other两个输入做隐式数据类型转换，根据具体算子语义按需调用
    auto scalarDefaultDtype = GetScalarDefaultDtype(other->GetDataType());
    auto promoteType = CombineCategoriesWithComplex(self->GetDataType(), scalarDefaultDtype);
    if (promoteType == DataType::DT_BOOL) {
        promoteType = DataType::DT_UINT8;
    }

    // 固定写法，参数检查
    auto ret = CheckParamsScalar(self, other, out, promoteType);
    CHECK_RET(ret == ACLNN_SUCCESS, ret);

    // Less算子的空tensor在kernel中支持，对标竞品根据算子实际情况补充
    if (self->IsEmpty()) {
        *workspaceSize = 0;
        uniqueExecutor.ReleaseTo(executor);
        return ACLNN_SUCCESS;
    }

    // 固定写法，将输入self转换成连续的tensor
    auto selfContiguous = l0op::Contiguous(self, uniqueExecutor.get());
    CHECK_RET(selfContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 将other转换为tensor，并且数据类型转换为推导后的数据类型
    auto otherTensor = uniqueExecutor.get()->ConvertToTensor(other, promoteType);
    CHECK_RET(otherTensor != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 将输入self的数据类型转换成隐式数据类型，根据具体算子语义按需调用
    auto selfCasted = l0op::Cast(selfContiguous, promoteType, uniqueExecutor.get());
    CHECK_RET(selfCasted != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 将输入otherTensor的数据类型转换为推导后的数据类型
    auto otherCasted = l0op::Cast(otherTensor, promoteType, uniqueExecutor.get());
    CHECK_RET(otherCasted != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 调用Less算子kernel
    auto ltOpOut = l0op::Less(selfCasted, otherCasted, uniqueExecutor.get());
    CHECK_RET(ltOpOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，将计算结果(BOOL)转换成输出out的数据类型
    auto castOut = l0op::Cast(ltOpOut, out->GetDataType(), uniqueExecutor.get());
    CHECK_RET(castOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，将计算结果拷贝到输出out上，out可能是非连续的tensor
    auto view_copy_result = l0op::ViewCopy(castOut, out, uniqueExecutor.get());
    CHECK_RET(view_copy_result != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，获取计算过程中需要使用的workspace大小
    *workspaceSize = uniqueExecutor->GetWorkspaceSize();
    uniqueExecutor.ReleaseTo(executor);
    return ACLNN_SUCCESS;
}

aclnnStatus aclnnLtScalarGetWorkspaceSize(
    const aclTensor* self, const aclScalar* other, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)
{
    L2_DFX_PHASE_1(aclnnLtScalar, DFX_IN(self, other), DFX_OUT(out));
    auto npuArch = op::GetCurrentPlatformInfo().GetCurNpuArch();
    if (IsRegBase(npuArch)) {
        return aclnnLtScalarGetWorkspaceSizeV35(self, other, out, workspaceSize, executor);
    }

    // 固定写法，创建OpExecutor
    auto uniqueExecutor = CREATE_EXECUTOR();
    CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);

    // 将other scalar转换为tensor
    CHECK_RET(other != nullptr, ACLNN_ERR_INNER_NULLPTR);
    CHECK_RET(self != nullptr, ACLNN_ERR_INNER_NULLPTR);

    auto otherPromoteType = other->GetDataType();
    if (IsFloatingType(self->GetDataType())) {
        otherPromoteType = self->GetDataType();
    } else if (other->GetDataType() == op::DataType::DT_DOUBLE) {
        otherPromoteType = op::DataType::DT_FLOAT;
    }
    auto otherTensor = uniqueExecutor.get()->ConvertToTensor(other, otherPromoteType);
    CHECK_RET(otherTensor != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，参数检查
    auto ret = CheckParams(self, otherTensor, out);
    CHECK_RET(ret == ACLNN_SUCCESS, ret);

    // Less算子的空tensor在kernel中支持，对标竞品根据算子实际情况补充
    if (self->IsEmpty()) {
        *workspaceSize = 0;
        uniqueExecutor.ReleaseTo(executor);
        return ACLNN_SUCCESS;
    }

    // Less算子需要对self和otherTensor两个输入做隐式数据类型转换，根据具体算子语义按需调用
    auto promoteType = op::PromoteType(self->GetDataType(), otherTensor->GetDataType());
    if (promoteType == DataType::DT_BOOL) {
        promoteType = DataType::DT_UINT8;
    }

    // 固定写法，将输入self转换成连续的tensor
    auto selfContiguous = l0op::Contiguous(self, uniqueExecutor.get());
    CHECK_RET(selfContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 将输入self的数据类型转换成隐式数据类型，根据具体算子语义按需调用
    auto selfCasted = l0op::Cast(selfContiguous, promoteType, uniqueExecutor.get());
    CHECK_RET(selfCasted != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 将输入otherTensor的数据类型转换为推导后的数据类型
    auto otherCasted = l0op::Cast(otherTensor, promoteType, uniqueExecutor.get());
    CHECK_RET(otherCasted != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 调用Less算子kernel
    auto ltOpOut = l0op::Less(selfCasted, otherCasted, uniqueExecutor.get());
    CHECK_RET(ltOpOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，将计算结果(BOOL)转换成输出out的数据类型
    auto castOut = l0op::Cast(ltOpOut, out->GetDataType(), uniqueExecutor.get());
    CHECK_RET(castOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，将计算结果拷贝到输出out上，out可能是非连续的tensor
    auto view_copy_result = l0op::ViewCopy(castOut, out, uniqueExecutor.get());
    CHECK_RET(view_copy_result != nullptr, ACLNN_ERR_INNER_NULLPTR);

    // 固定写法，获取计算过程中需要使用的workspace大小
    *workspaceSize = uniqueExecutor->GetWorkspaceSize();
    uniqueExecutor.ReleaseTo(executor);
    return ACLNN_SUCCESS;
}

aclnnStatus aclnnLtScalar(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream)
{
    L2_DFX_PHASE_2(aclnnLtScalar);
    // 固定写法，调用框架能力，完成计算
    return CommonOpExecutorRun(workspace, workspaceSize, executor, stream);
}

// inplace lt scalar
aclnnStatus aclnnInplaceLtScalarGetWorkspaceSize(
    const aclTensor* selfRef, const aclScalar* other, uint64_t* workspaceSize, aclOpExecutor** executor)
{
    auto out = const_cast<aclTensor*>(selfRef);
    return aclnnLtScalarGetWorkspaceSize(selfRef, other, out, workspaceSize, executor);
}

aclnnStatus aclnnInplaceLtScalar(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream)
{
    L2_DFX_PHASE_2(aclnnInplaceLtScalar);
    // 固定写法，调用框架能力，完成计算
    return CommonOpExecutorRun(workspace, workspaceSize, executor, stream);
}

#ifdef __cplusplus
}
#endif