/****************************************************************************
 *
 *    Copyright (c) 2020-2023 Vivante Corporation
 *
 *    Permission is hereby granted, free of charge, to any person obtaining a
 *    copy of this software and associated documentation files (the "Software"),
 *    to deal in the Software without restriction, including without limitation
 *    the rights to use, copy, modify, merge, publish, distribute, sublicense,
 *    and/or sell copies of the Software, and to permit persons to whom the
 *    Software is furnished to do so, subject to the following conditions:
 *
 *    The above copyright notice and this permission notice shall be included in
 *    all copies or substantial portions of the Software.
 *
 *    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 *    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 *    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 *    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 *    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 *    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 *    DEALINGS IN THE SOFTWARE.
 *
 *****************************************************************************/
#ifndef TIM_LAYOUT_INFER_POOL2D_LAYOUT_INFERENCE_H_
#define TIM_LAYOUT_INFER_POOL2D_LAYOUT_INFERENCE_H_

#include "ops/op_layout_inference.h"
#include "permute_vector.h"
#include "builtin_op_impl.h"
#include "tim/vx/ops/pool2d.h"

namespace tim {
namespace transform {
class Pool2dLayoutInfer : public OpLayoutInfer {
 public:
  Pool2dLayoutInfer(
      const std::shared_ptr<vx::Operation> op,
      std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
      : OpLayoutInfer(op, context) {}
  void OnInputs(
      std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
    vx::DataLayout layout = op_->impl()->layout_;
    auto required_pv = MakeShared(4);
    if (layout == vx::DataLayout::CWHN) {
      required_pv = std::make_shared<PermuteVector<4>>(kCWHN2WHCN);
    }
    auto input_tensors = op_->impl()->InputsTensor();

    auto pv = context_->GetPermuteVector(input_tensors[0]);
    auto final_pv = pv->Reverse()->Add(required_pv);
    if (!final_pv->IsAligned()) {
      auto perm_out =
          InsertPermute(context_->GetMappedTensor(input_tensors[0]), final_pv);
      context_->UpdateTensorMap(input_tensors[0], perm_out);
      context_->SetPermuteVector(input_tensors[0], required_pv);
    }

    std::shared_ptr<tim::vx::Operation> pool2d;
    std::array<uint32_t, 4> pad;
    auto pool_type = TranslatePoolType(op_->impl()->node()->nn_param.pool.type);
    auto round_type =
        TranslateRoundType(op_->impl()->node()->nn_param.pool.round_type);
    auto pad_type =
        TranslatePadType(op_->impl()->node()->nn_param.pool.pad_type);

    std::array<uint32_t, 2> ksize = {
        op_->impl()->node()->nn_param.pool.ksize[0],
        op_->impl()->node()->nn_param.pool.ksize[1]};
    std::array<uint32_t, 2> stride = {
        op_->impl()->node()->nn_param.pool.stride[0],
        op_->impl()->node()->nn_param.pool.stride[1]};

    if (pad_type == tim::vx::PadType::AUTO) {
      pad = {op_->impl()->node()->nn_param.pool.pad[0],
             op_->impl()->node()->nn_param.pool.pad[1],
             op_->impl()->node()->nn_param.pool.pad[2],
             op_->impl()->node()->nn_param.pool.pad[3]};
      pool2d = context_->infer_graph_->CreateOperation<vx::ops::Pool2d>(
          pool_type, pad, ksize, stride, round_type, vx::DataLayout::WHCN);
    } else {
        pool2d = context_->infer_graph_->CreateOperation<vx::ops::Pool2d>(
          pool_type, pad_type, ksize, stride, round_type, vx::DataLayout::WHCN);
    }
    auto otensor_infer = CreateOutputsTensor(required_pv);
    (*pool2d).BindInput(context_->GetMappedTensor(input_tensors[0]));
    (*pool2d).BindOutput(otensor_infer[0]);
    context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], required_pv);
    // Add out tensor of src_graph into next_tensor
    next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
  }
};

}  // namespace transform
}  // namespace tim

#endif