/**
 * 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_amin.h"
#include "reduce_min.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 "op_api/aclnn_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/tensor_view_utils.h"
#include "opdev/op_errno.h"
#include <bitset>
#include "opdev/platform.h"

using namespace op;
using std::bitset;

#ifdef __cplusplus
extern "C" {
#endif

constexpr size_t MAX_MASK_LEN = 64;
constexpr size_t MAX_DIM_LEN = 8;

// 算子支持的所有dtype
static const std::initializer_list<op::DataType> ASCEND910_DTYPE_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_FLOAT, op::DataType::DT_FLOAT16, op::DataType::DT_UINT8, op::DataType::DT_INT32,   // AiCore
    op::DataType::DT_INT64, op::DataType::DT_INT16,   op::DataType::DT_INT8,  op::DataType::DT_DOUBLE,  // AiCpu
    op::DataType::DT_BOOL};  // AiCore(cast to float)

static const std::initializer_list<op::DataType> ASCEND910B_DTYPE_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_FLOAT, op::DataType::DT_FLOAT16, op::DataType::DT_UINT8, op::DataType::DT_INT32,   // AiCore
    op::DataType::DT_INT64, op::DataType::DT_INT16,   op::DataType::DT_INT8,  op::DataType::DT_DOUBLE,  // AiCpu
    op::DataType::DT_BOOL, op::DataType::DT_BF16};  // AiCore(cast to float)

static inline uint64_t GetPosDim(int64_t dim, int64_t dimNum) {
  if (dimNum <= 0) {
    dimNum = 1;
  }
  return dim >= 0 ? dim : dim + dimNum;
}

static inline const aclIntArray* GetAllDims(const aclTensor* self, aclOpExecutor* executor) {
  auto inputShape = self->GetViewShape();
  size_t inputDimNum = inputShape.GetDimNum();
  FVector<int64_t> dims;
  for (size_t idx = 0; idx < inputDimNum; idx++) {
    dims.emplace_back(idx);
  }
  return executor->AllocIntArray(dims.data(), dims.size());
}

static void AminInferShape(const op::Shape& selfShape, const aclIntArray* dim, bool keepDim, op::Shape& reduceShape) {
  bitset<MAX_MASK_LEN> dimMask = bitset<MAX_MASK_LEN>();
  if (dim->Size() == 0) {
    dimMask.flip();
  }
  for (size_t i = 0; i < dim->Size(); i++) {
    int64_t index = GetPosDim((*dim)[i], selfShape.GetDimNum());
    // 前序已检查， 此处如果dim不会重复
    dimMask.set(index);
  }

  for (size_t i = 0; i < selfShape.GetDimNum(); i++) {
    if (!dimMask[i]) {
      reduceShape.AppendDim(selfShape.GetDim(i));
    } else if (keepDim) {
      reduceShape.AppendDim(1);
    }
  }
}

static inline bool CheckNotNull(const aclTensor* self, const aclIntArray* dim, const aclTensor* out) {
  // dim也需要判空，需要获取dim中元素和个数
  OP_CHECK_NULL(self, return false);
  OP_CHECK_NULL(dim, return false);
  OP_CHECK_NULL(out, return false);
  return true;
}

static const std::initializer_list<DataType>& GetDtypeSupportList() {
  if (GetCurrentPlatformInfo().GetSocVersion() == SocVersion::ASCEND910B ||
      GetCurrentPlatformInfo().GetSocVersion() == SocVersion::ASCEND910_93 ||
      IsRegBase()) {
    return ASCEND910B_DTYPE_DTYPE_SUPPORT_LIST;
  } else {
    return ASCEND910_DTYPE_DTYPE_SUPPORT_LIST;
  }
}

static bool CheckDtypeValid(const aclTensor* self, const aclTensor* out) {
  const auto& supportList = GetDtypeSupportList();
  // 检查self与out的数据类型是否一致
  OP_CHECK_DTYPE_NOT_MATCH(out, self->GetDataType(), return false);
  // 检查self的数据类型是否支持, out与self一致，不需要额外检查
  OP_CHECK_DTYPE_NOT_SUPPORT(self, supportList, return false);

  return true;
}

static bool CheckDimValid(const aclTensor* self, const aclIntArray* dim) {
  auto selfViewShape = self->GetViewShape();
  auto selfDimNum = static_cast<int64_t>(selfViewShape.GetDimNum());
  bool isScalar = false;
  // self为标量时，dim range [-1, 0]
  if (selfDimNum <= 0) {
    selfDimNum = 1;
    isScalar = true;
  }
  // dim为负时需要转正校验
  bitset<MAX_MASK_LEN> dimMask = bitset<MAX_MASK_LEN>();

  for (size_t i = 0; i < dim->Size(); i++) {
    if ((*dim)[i] >= selfDimNum || (*dim)[i] < (-selfDimNum)) {
      OP_LOGE(ACLNN_ERR_PARAM_INVALID, "Provided dim %ld must be in the range of [%ld, %ld].",
              (*dim)[i], -selfDimNum, selfDimNum - 1);
      return false;
    }
    uint64_t index = GetPosDim((*dim)[i], selfDimNum);
    // 非标量reduce的dim的size不能为0
    if (!isScalar && selfViewShape.GetDim(index) == 0) {
      OP_LOGE(ACLNN_ERR_PARAM_INVALID, "Expected reducution dim %lu to have non-zero size.", index);
      return false;
    }
    // dim重复
    if (dimMask[index]) {
      OP_LOGE(ACLNN_ERR_PARAM_INVALID, "Dim %lu appears multiple times in the list of dims.", index);
      return false;
    }

    dimMask.set(index);
  }

  return true;
}

static bool CheckShape(const aclTensor* self, const aclIntArray* dim, const bool keepDim, const aclTensor* out) {
  OP_CHECK_MAX_DIM(self, MAX_DIM_LEN, return false);
  OP_CHECK_MAX_DIM(out, MAX_DIM_LEN, return false);
  op::Shape reduceShape;
  AminInferShape(self->GetViewShape(), dim, keepDim, reduceShape);

  // out的shape必须满足Infer shape
  OP_CHECK_SHAPE_NOT_EQUAL_WITH_EXPECTED_SIZE(out, reduceShape, return false);
  return true;
}

static aclnnStatus CheckParams(const aclTensor* self, const aclIntArray* dim, const bool keepDim,
                               const aclTensor* out) {
  // 1. 检查参数是否为空指针
  CHECK_RET(CheckNotNull(self, dim, 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
  CHECK_RET(CheckDtypeValid(self, out), ACLNN_ERR_PARAM_INVALID);

  // 3. 检查reduce的轴是否超出self维度范围或者重复
  CHECK_RET(CheckDimValid(self, dim), ACLNN_ERR_PARAM_INVALID);

  // 4. 检查out的shape是否满足reduce推导
  CHECK_RET(CheckShape(self, dim, keepDim, out), ACLNN_ERR_PARAM_INVALID);
  return ACLNN_SUCCESS;
}

aclnnStatus aclnnAminGetWorkspaceSize(const aclTensor* self, const aclIntArray* dim, bool keepDim, aclTensor* out,
                                      uint64_t* workspaceSize, aclOpExecutor** executor) {
  L2_DFX_PHASE_1(aclnnAmin, DFX_IN(self, dim, keepDim), DFX_OUT(out));
  // 创建OpExecutor
  auto uniqueExecutor = CREATE_EXECUTOR();
  CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);

  // 参数检查
  auto ret = CheckParams(self, dim, keepDim, out);
  CHECK_RET(ret == ACLNN_SUCCESS, ret);

  // 算子的空tensor处理
  if (self->IsEmpty()) {
    *workspaceSize = 0;
    uniqueExecutor.ReleaseTo(executor);
    return ACLNN_SUCCESS;
  }

  // 当输入tensor是0维时，直接将输入tensor作为输出返回
  if (self->GetViewShape().GetDimNum() == 0) {
    auto viewCopyResult = l0op::ViewCopy(self, out, uniqueExecutor.get());
    CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);
    *workspaceSize = uniqueExecutor->GetWorkspaceSize();
    uniqueExecutor.ReleaseTo(executor);
    return ACLNN_SUCCESS;
  }

  // 空dim处理
  if (dim->Size() == 0) {
    dim = GetAllDims(self, uniqueExecutor.get());
    CHECK_RET(dim != nullptr, ACLNN_ERR_INNER_NULLPTR);
  }

  // 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的数据类型转换成目标数据类型, bool 转为float, 其余保持原类型
  op::DataType selfCastType =
      (self->GetDataType() == op::DataType::DT_BOOL) ? op::DataType::DT_FLOAT : self->GetDataType();

  auto selfCasted = l0op::Cast(selfContiguous, selfCastType, uniqueExecutor.get());
  CHECK_RET(selfCasted != nullptr, ACLNN_ERR_INNER_NULLPTR);

  // 调用min算子kernel
  auto minResult = l0op::ReduceMin(selfCasted, dim, keepDim, uniqueExecutor.get());
  CHECK_RET(minResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

  // 将min算子的输出转换成目标数据类型，
  auto castMinOut = l0op::Cast(minResult, out->GetDataType(), uniqueExecutor.get());
  CHECK_RET(castMinOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

  // 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(castMinOut, out, uniqueExecutor.get());
  CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

  // 获取计算过程中需要使用的workspace大小
  *workspaceSize = uniqueExecutor->GetWorkspaceSize();
  // 需要把 uniqueExecutor持有executor转移给executor
  uniqueExecutor.ReleaseTo(executor);

  return ACLNN_SUCCESS;
}

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

#ifdef __cplusplus
}
#endif