/**
 * 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_bitwise_not.h"
#include "math/logical_not/op_api/logical_not.h"
#include "math/invert/op_api/invert.h"
#include "aclnn_kernels/cast.h"
#include "aclnn_kernels/contiguous.h"
#include "op_api/aclnn_check.h"
#include "op_api/op_api_def.h"
#include "opdev/common_types.h"
#include "opdev/data_type_utils.h"
#include "opdev/format_utils.h"
#include "opdev/op_dfx.h"
#include "opdev/op_executor.h"
#include "opdev/op_log.h"
#include "opdev/shape_utils.h"
#include "opdev/tensor_view_utils.h"
#include "aclnn_kernels/common/op_error_check.h"

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

/* BitwiseNot 算子的完整计算流程如下：
 * seld.dtype == bool
 *       self
 *        |
 * Contiguous(workspace_0)
 *        |
 * LogicalNot(workspace_1)
 *        |
 *    ViewCopy
 *        |
 *     result
 * self.dtype == INT
 *       self
 *        |
 * Contiguous(workspace_0)
 *        |
 * Invert(workspace_1)
 *        |
 *    ViewCopy
 *        |
 *      result
 */

// 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 = {op::DataType::DT_INT16, op::DataType::DT_INT32,
                                                                       op::DataType::DT_INT64, op::DataType::DT_INT8,
                                                                       op::DataType::DT_UINT8, op::DataType::DT_BOOL};
static const std::initializer_list<op::DataType> DTYPE_SUPPORT_LIST_950 = {
  op::DataType::DT_INT8, op::DataType::DT_INT16, op::DataType::DT_INT32, op::DataType::DT_INT64, 
  op::DataType::DT_UINT8, op::DataType::DT_UINT16, op::DataType::DT_UINT32, op::DataType::DT_UINT64, 
  op::DataType::DT_BOOL};

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

  return true;
}

static bool CheckDtypeValid(const aclTensor* self, const aclTensor* y) {
  // 检查self的数据类型是否在bitwise_not算子的支持列表内
  if (IsRegBase()) {
    OP_CHECK_DTYPE_NOT_SUPPORT(self, DTYPE_SUPPORT_LIST_950, return false);
  } else {
    OP_CHECK_DTYPE_NOT_SUPPORT(self, DTYPE_SUPPORT_LIST, return false);
  }
  
  OP_CHECK_DTYPE_NOT_MATCH(self, y->GetDataType(), return false);

  return true;
}

static bool CheckFormat(const aclTensor* self, const aclTensor* y) {
  // 如果输入格式是私有格式，记录日志，直接报错
  if (op::IsPrivateFormat(self->GetStorageFormat()) || op::IsPrivateFormat(y->GetStorageFormat())) {
    OP_LOGE(ACLNN_ERR_PARAM_INVALID, "Format only support ND,NCHW,NHWC,HWCN,NDHWC,NCDHW.");
    return false;
  }
  return true;
}

static bool CheckShape(const aclTensor* self, const aclTensor* y) {
  OP_CHECK_MAX_DIM(self, MAX_SUPPORT_DIMS_NUMS, return false);
  OP_CHECK_MAX_DIM(y, MAX_SUPPORT_DIMS_NUMS, return false);

  OP_CHECK_SHAPE_NOT_EQUAL(self, y, return false);

  return true;
}

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

  // 3. 检查数据格式是否支持
  CHECK_RET(CheckFormat(self, y), ACLNN_ERR_PARAM_INVALID);

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

  return ACLNN_SUCCESS;
}

aclnnStatus aclnnBitwiseNotGetWorkspaceSize(const aclTensor* self, aclTensor* out, uint64_t* workspaceSize,
                                            aclOpExecutor** executor) {
  L2_DFX_PHASE_1(aclnnBitwiseNot, DFX_IN(self), 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(self, out);
  CHECK_RET(ret == ACLNN_SUCCESS, ret);

  // BitwiseNot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()) {
    // 根据实际支持情况补充
    *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);

  // 进行BitwiseNot计算
  const aclTensor* notOpOut = nullptr;
  if (self->GetDataType() == op::DataType::DT_BOOL) {
    notOpOut = l0op::LogicalNot(selfContiguous, uniqueExecutor.get());
  } else {
    notOpOut = l0op::Invert(selfContiguous, uniqueExecutor.get());
  }
  CHECK_RET(notOpOut != 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(notOpOut, 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);  // 需要把uniqueExecutor持有executor转移给executor
  return ACLNN_SUCCESS;
}

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

#ifdef __cplusplus
}
#endif