// Copyright (c) 2021 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 "paddle/infrt/api/infrt_api.h"

#include <gtest/gtest.h>

#include <iostream>
#include <vector>

#include "llvm/Support/raw_ostream.h"
#include "paddle/infrt/common/buffer.h"
#include "paddle/infrt/common/dtype.h"

using infrt::InfRtConfig;
using infrt::InfRtPredictor;
using infrt::CreateInfRtPredictor;

namespace infrt {

TEST(InfRtPredictor, predictor) {
  std::vector<std::string> shared_libs;
  shared_libs.push_back("../../paddle/libexternal_kernels.so");

  InfRtConfig config;

  // set external shared libraries that contain kernels.
  config.set_shared_libs(shared_libs);
  // set model dir
  config.set_model_dir("../../paddle/paddle_1.8_fc_model");
  // set mlir path
  config.set_mlir_path("../../../infrt/dialect/mlir_tests/tensor_map.mlir");

  std::shared_ptr<InfRtPredictor> predictor = CreateInfRtPredictor(config);

  auto* input = predictor->GetInput(0);
  std::vector<int64_t> shape = {3, 3};
  input->Init(shape, infrt::GetDType<float>());
  llvm::outs() << input->shape() << "\n";

  // init input tensor
  auto* input_data = reinterpret_cast<float*>(input->buffer()->data()->memory);
  for (int i = 0; i < input->shape().GetNumElements(); i++) input_data[i] = 1.0;

  predictor->Run();

  // get and print output tensor
  auto* output = predictor->GetOutput(0);
  auto* output_data =
      reinterpret_cast<float*>(output->buffer()->data()->memory);

  std::vector<float> ans = {0.428458,
                            0.244493,
                            0.572342,
                            0.572008,
                            0.509771,
                            0.495599,
                            0.651287,
                            0.326426,
                            0.404649};

  ASSERT_EQ(output->shape().GetNumElements(), ans.size());
  for (int i = 0; i < output->shape().GetNumElements(); ++i) {
    ASSERT_NEAR(output_data[i], ans[i], 0.000001);
  }
}

}  // namespace infrt