/**
 * 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 "aclnn_randperm.h"
#include "stateless_randperm.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/shape_utils.h"
#include "opdev/data_type_utils.h"
#include "opdev/format_utils.h"
#include "opdev/op_executor.h"
#include "opdev/op_log.h"
#include "opdev/tensor_view_utils.h"
#include "opdev/op_dfx.h"
#include "opdev/platform.h"

using namespace op;

#ifdef __cplusplus
extern "C" {
#endif

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

static const std::initializer_list<op::DataType> DTYPE_SUPPORT_LIST_2201 = {
    op::DataType::DT_FLOAT,     op::DataType::DT_FLOAT16,  op::DataType::DT_INT32,      op::DataType::DT_DOUBLE,
    op::DataType::DT_INT64,      op::DataType::DT_INT8,     op::DataType::DT_UINT8,      op::DataType::DT_INT16,
    op::DataType::DT_BF16};

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

static bool CheckShapeValid(const aclTensor *out, int64_t n)
{
  op::Shape outShape;
  outShape.SetDimNum(1);
  outShape.SetDim(0, n);
  OP_CHECK_SHAPE_NOT_EQUAL_WITH_EXPECTED_SIZE(out, outShape, return false);
  return true;
}

static bool CheckDtypeValid(const aclTensor *out) {
  auto npuArch = op::GetCurrentPlatformInfo().GetCurNpuArch();
  bool isBf16Support = ((npuArch == NpuArch::DAV_2201) || (npuArch == NpuArch::DAV_3510));
  const std::initializer_list<op::DataType> DTYPE_SUPPORT_LIST =
      isBf16Support ? DTYPE_SUPPORT_LIST_2201 : DTYPE_SUPPORT_LIST_DEFAULT;
  OP_CHECK_DTYPE_NOT_SUPPORT(out, DTYPE_SUPPORT_LIST, return false);
  return true;
}

static aclnnStatus CheckParams(int64_t n, aclTensor* out) {
  CHECK_RET(CheckNotNull(out), ACLNN_ERR_INNER_NULLPTR);

  CHECK_RET(CheckDtypeValid(out), ACLNN_ERR_PARAM_INVALID);

  CHECK_RET(CheckShapeValid(out, n), ACLNN_ERR_PARAM_INVALID);
 
  CHECK_RET(n >= 0, ACLNN_ERR_PARAM_INVALID);

  return ACLNN_SUCCESS;
}

aclnnStatus aclnnRandpermGetWorkspaceSize(int64_t n, int64_t seed, int64_t offset,
                                          aclTensor* out, uint64_t *workspaceSize,
                                          aclOpExecutor **executor) {
  OP_CHECK_COMM_INPUT(workspaceSize, executor);
  
  L2_DFX_PHASE_1(aclnnRandperm, DFX_IN(n, seed, offset), DFX_OUT(out));
  // 固定写法，创建OpExecutor
  auto uniqueExecutor = CREATE_EXECUTOR();
  CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);

  // 固定写法，参数检查
  auto ret = CheckParams(n, out);
  CHECK_RET(ret == ACLNN_SUCCESS, ret);
  // n = 0 时返回空tensor
  if (n == 0) {
    *workspaceSize = 0;
    uniqueExecutor.ReleaseTo(executor);
    return ACLNN_SUCCESS;
  }

// 调用算子kernel
  auto executorP = uniqueExecutor.get();
  FVector<int64_t> nVector;
  nVector.push_back(n);
  auto nTensor = executorP->ConvertToTensor(nVector.data(), nVector.size(), op::DataType::DT_INT64);
  FVector<int64_t> seedVector;
  seedVector.push_back(seed);
  auto seedTensor = executorP->ConvertToTensor(seedVector.data(), seedVector.size(), op::DataType::DT_INT64);
  FVector<int64_t> offsetVector;
  offsetVector.push_back(offset);
  auto offsetTensor = executorP->ConvertToTensor(offsetVector.data(), offsetVector.size(), op::DataType::DT_INT64);
  const int64_t layout = 1;
  op::Shape shape({n});
  op::DataType outDataType = out->GetDataType();
  auto randpermOut = l0op::StatelessRandperm(shape, nTensor, seedTensor, offsetTensor, layout,
                                             outDataType, uniqueExecutor.get());
  CHECK_RET(randpermOut != 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 viewCopyResult = l0op::ViewCopy(randpermOut, out, uniqueExecutor.get());
  CHECK_RET(viewCopyResult != 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 aclnnRandperm(void *workspace, uint64_t workspaceSize,
                          aclOpExecutor *executor, aclrtStream stream) {
  L2_DFX_PHASE_2(aclnnRandperm);
  // 固定写法，调用框架能力，完成计算
  return CommonOpExecutorRun(workspace, workspaceSize, executor, stream);
}


#ifdef __cplusplus
}
#endif