/* 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. */

#include "../test_helper.h"
#include "../test_include.h"
#include "operators/elementwise_sub_op.h"

namespace paddle_mobile {
namespace framework {

template <typename Dtype>
class TestElementwiseSubOp {
 public:
  explicit TestElementwiseSubOp(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_sub" &&
            op->Input("X")[0] == "sigmoid_1.tmp_0") {
          DLOG << " elementwise_sub attr size: " << op->GetAttrMap().size();
          DLOG << " inputs size: " << op->GetInputs().size();
          DLOG << " outputs size: " << op->GetOutputs().size();

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

  std::shared_ptr<Tensor> predict_bn(const Tensor &t1, const Tensor &t2) {
    // feed
    auto scope = program_.scope.get();
    Variable *x1_feed_value = scope->Var("tmp_0");
    auto tensor_x1 = x1_feed_value->GetMutable<LoDTensor>();
    tensor_x1->ShareDataWith(t1);

    Variable *x2_feed_value = scope->Var("sigmoid_1.tmp_0");
    auto tensor_x2 = x2_feed_value->GetMutable<LoDTensor>();
    tensor_x2->ShareDataWith(t2);

    Variable *output = scope->Var("tmp_1");
    auto *output_tensor = output->GetMutable<LoDTensor>();
    output_tensor->mutable_data<float>({1, 1, 6, 6});
    //  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_bn(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_bn(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 TestElementwiseSubOp<CPU>;
}  // namespace framework
}  // namespace paddle_mobile

int main() {
  DLOG << "----------**********----------";
  DLOG << "begin to run ElementwiseSub Test";
  paddle_mobile::framework::Loader<paddle_mobile::CPU> loader;
  auto program = loader.Load(std::string(g_ocr) + "/model",
                             std::string(g_ocr) + "/params");

  /// input x1 (1,1,6,6)
  paddle_mobile::framework::Tensor inputx1;
  SetupTensor<float>(&inputx1, {1, 1, 6, 6}, static_cast<float>(0),
                     static_cast<float>(1));
  auto *inputx1_ptr = inputx1.data<float>();

  /// input x2 (1,1,6,6)
  paddle_mobile::framework::Tensor inputx2;
  SetupTensor<float>(&inputx2, {1, 1, 6, 6}, static_cast<float>(0),
                     static_cast<float>(1));
  auto *inputx2_ptr = inputx2.data<float>();

  paddle_mobile::framework::TestElementwiseSubOp<paddle_mobile::CPU>
      testElementwiseSubOp(program);

  auto output_op = testElementwiseSubOp.predict_bn(inputx1, inputx2);
  auto *output_op_ptr = output_op->data<float>();

  auto inputx1_dim = inputx1.numel() / inputx1.dims()[0];
  DLOG << " input1 : ";
  for (int i = 0; i < inputx1.dims()[0]; ++i) {
    for (int j = 0; j < inputx1_dim; ++j) {
      DLOGF("%f ", inputx1_ptr[i * inputx1_dim + j]);
    }
    DLOGF("\n");
  }

  auto inputx2_dim = inputx2.numel() / inputx2.dims()[0];
  DLOG << " input2 : ";
  for (int i = 0; i < inputx2.dims()[0]; ++i) {
    for (int j = 0; j < inputx2_dim; ++j) {
      DLOGF("%f ", inputx2_ptr[i * inputx2_dim + j]);
    }
    DLOGF("\n");
  }

  auto output_dim = output_op->numel() / output_op->dims()[0];
  DLOG << " output : ";
  for (int i = 0; i < output_op->dims()[0]; ++i) {
    for (int j = 0; j < output_dim; ++j) {
      DLOGF("%f ", output_op_ptr[i * output_dim + j]);
    }
    DLOGF("\n");
  }

  return 0;
}
