// Copyright (c) 2019 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.

#include "lite/kernels/npu/bridges/registry.h"
#include "lite/kernels/rknpu/bridges/graph.h"
// #include "lite/kernels/npu/bridges/utility.h"

namespace paddle {
namespace lite {
namespace subgraph {
namespace rknpu {

int ActConverter(void* ctx, OpLite* op, KernelBase* kernel) {
  CHECK(ctx != nullptr);
  CHECK(op != nullptr);
  auto graph = static_cast<Graph*>(ctx);
  auto scope = op->scope();
  auto op_info = op->op_info();
  auto op_type = op_info->Type();
  auto x_var_name = op_info->Input("X").front();
  auto x = scope->FindVar(x_var_name)->GetMutable<lite::Tensor>();
  auto x_dims = x->dims();
  auto output_var_name = op_info->Output("Out").front();
  auto output = scope->FindVar(output_var_name)->GetMutable<lite::Tensor>();
  auto output_dims = output->dims();
  const int64_t* x_shape_data = const_cast<const int64_t*>(&x_dims.data()[0]);
  const int64_t* output_shape_data =
      const_cast<const int64_t*>(&output_dims.data()[0]);
  std::vector<int32_t> i_x_shape_data(x_dims.size());
  std::vector<int32_t> i_output_shape_data(output_dims.size());

  VLOG(3) << "[RKNPU] Converting " + op_type + "...";

  auto x_type = kernel->GetInputDeclType("X");
  CHECK(x_type->precision() == PRECISION(kFloat));
  CHECK(x_type->layout() == DATALAYOUT(kNCHW));

  auto out_type = kernel->GetOutputDeclType("Out");
  CHECK(out_type->precision() == PRECISION(kFloat));
  CHECK(out_type->layout() == DATALAYOUT(kNCHW));

  for (size_t i = 0; i < x_dims.size(); i++) {
    i_x_shape_data[i] = static_cast<int>(x_shape_data[i]);
  }
  for (size_t i = 0; i < output_dims.size(); i++) {
    i_output_shape_data[i] = static_cast<int>(output_shape_data[i]);
  }
  CHECK_EQ(op_type, "relu");
  // X node
  std::shared_ptr<Node> x_node = nullptr;
  if (graph->Has(x_var_name)) {
    x_node = graph->Get(x_var_name);
  } else {
    x_node = graph->Add(x_var_name, *x, x_type->precision(), x_type->layout());
  }

  auto output_node = graph->Add(
      output_var_name, *output, out_type->precision(), out_type->layout());
  auto rGraph = graph->GetHandle();
  std::vector<std::shared_ptr<rk::nn::Tensor>> inputs;
  std::vector<std::shared_ptr<rk::nn::Tensor>> outputs;

  inputs.push_back(x_node->data());
  outputs.push_back(output_node->data());
  auto relu =
      rGraph->AddOperator(rk::nn::OperatorType::RELU, inputs, outputs, nullptr);

  return SUCCESS;
}

}  // namespace rknpu
}  // namespace subgraph
}  // namespace lite
}  // namespace paddle

REGISTER_SUBGRAPH_BRIDGE(relu,
                         kRKNPU,
                         paddle::lite::subgraph::rknpu::ActConverter);
