/* Copyright (c) 2018 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 "../test_include.h"
#include "operators/elementwise_add_op.h"

namespace paddle_mobile {
namespace framework {

template <typename Dtype> class TestElementwiseAddOp {
public:
  explicit TestElementwiseAddOp(const Program<Dtype> p) : program_(p) {
    if (use_optimize_) {
      to_predict_program_ = program_.optimizeProgram;
    } else {
      to_predict_program_ = program_.originProgram;
    }

    const std::vector<std::shared_ptr<BlockDesc>> blocks =
        to_predict_program_->Blocks();
    //  DLOG << " **block size " << blocks.size();
    for (int i = 0; i < blocks.size(); ++i) {
      std::shared_ptr<BlockDesc> block_desc = blocks[i];
      std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
      //    DLOG << " ops " << ops.size();
      for (int j = 0; j < ops.size(); ++j) {
        std::shared_ptr<OpDesc> op = ops[j];
        if (op->Type() == "elementwise_add" &&
            op->Input("X")[0] == "batch_norm_2.tmp_2") {
          DLOG << " elementwise_add attr size: " << op->GetAttrMap().size();
          DLOG << " inputs size: " << op->GetInputs().size();
          DLOG << " outputs size: " << op->GetOutputs().size();
          DLOG << " Input X is : " << op->Input("X")[0];
          DLOG << " Input Y is : " << op->Input("Y")[0];
          DLOG << " Output Out is : " << op->Output("Out")[0];
          Attribute axis_attr = op->GetAttrMap().at("axis");
          int axis = axis_attr.Get<int>();
          DLOG << " Attr axis is : " << axis;

          std::shared_ptr<operators::ElementwiseAddOp<Dtype, float>> add =
              std::make_shared<operators::ElementwiseAddOp<Dtype, float>>(
                  op->Type(), op->GetInputs(), op->GetOutputs(),
                  op->GetAttrMap(), program_.scope);
          ops_of_block_[*block_desc.get()].push_back(add);
        }
      }
    }
  }

  std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
    // feed
    auto scope = program_.scope;
    Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
    auto tensor_x = x_feed_value->GetMutable<Tensor>();
    tensor_x->ShareDataWith(t1);

    Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
    auto tensor_y = y_feed_value->GetMutable<Tensor>();
    tensor_y->ShareDataWith(t2);

    Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
    auto *output_tensor = con_output->GetMutable<Tensor>();
    output_tensor->mutable_data<float>({1, 3, 224, 224});
    //  DLOG << typeid(output_tensor).name();
    //  DLOG << "output_tensor dims: " << output_tensor->dims();

    std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
    out_tensor.reset(output_tensor);

    predict_add(t1, t2, 0);
    return out_tensor;
  }

private:
  const framework::Program<Dtype> program_;
  std::shared_ptr<ProgramDesc> to_predict_program_;
  std::map<framework::BlockDesc,
           std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
      ops_of_block_;
  bool use_optimize_ = false;

  void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
    std::shared_ptr<BlockDesc> to_predict_block =
        to_predict_program_->Block(block_id);
    for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
      auto op = ops_of_block_[*to_predict_block.get()][j];
      DLOG << "op -> run()";
      op->Run();
    }
  }
};

template class TestElementwiseAddOp<CPU>;
} // namespace framework
} // namespace paddle_mobile
int main() {
  DLOG << "----------**********----------";
  DLOG << "begin to run ElementAddOp Test";
  paddle_mobile::Loader<paddle_mobile::CPU> loader;
  auto program =
      loader.Load(std::string("../../test/models/"
                              "image_classification_resnet.inference.model"));

  /// input x (1,3,224,224)
  paddle_mobile::framework::Tensor inputx;
  SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
                     static_cast<float>(1));
  auto *inputx_ptr = inputx.data<float>();
  /// input y (224,)
  paddle_mobile::framework::Tensor inputy;
  SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
                     static_cast<float>(1));
  auto *inputy_ptr = inputy.data<float>();

  paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
      testElementwiseAddOp(program);

  auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
  auto *output_add_ptr = output_add->data<float>();
  //            for (int j = 0; j < output_add->numel(); ++j) {
  //                DLOG << "value of output: " << output_add_ptr[j];
  //            }

  /// output (1,3,224,224)
  DLOG << "output memory size : " << output_add->memory_size();
  DLOG << "output numel : " << output_add->numel();

  DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
       << output_add_ptr[226];
  return 0;
}