/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#pragma once

#include <string>

#include "paddle/phi/backends/gpu/gpu_dnn.h"

namespace phi {

using PoolingMode = phi::backends::gpu::PoolingMode;
using ScopedPoolingDescriptor = phi::backends::gpu::ScopedPoolingDescriptor;
using ScopedTensorDescriptor = phi::backends::gpu::ScopedTensorDescriptor;

template <typename T>
using ScalingParamType =
    typename phi::backends::gpu::CudnnDataType<T>::ScalingParamType;

template <typename T>
class CudnnIndexType;

template <>
class CudnnIndexType<int> {
 public:
#ifdef PADDLE_WITH_CUDA
  static const dnnDataType_t type = CUDNN_DATA_INT32;
#else
  static const dnnDataType_t type = miopenInt32;
#endif
};

template <>
class CudnnIndexType<int8_t> {
 public:
#ifdef PADDLE_WITH_CUDA
  static const dnnDataType_t type = CUDNN_DATA_INT8;
#else
  static const dnnDataType_t type = miopenInt8;
#endif
};

inline DataLayout GetLayoutFromStr(std::string data_format) {
  if (data_format == "NHWC") {
    return DataLayout::NHWC;
  } else if (data_format == "NCHW") {
    return DataLayout::NCHW;
  } else if (data_format == "NCDHW") {
    return DataLayout::NCDHW;
  } else {
    return DataLayout::NCDHW;
  }
}

}  // namespace phi