//  Copyright (c) 2021 Intel Corporation
//
//  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 <map>
#include <string>
#include "gtest/gtest.h"
#include "../../include/conf.hpp"
#include "../../include/common.hpp"
#include "../../include/operators/reduce_mean.hpp"

using executor::Tensor;
using executor::OperatorConfig;
using executor::TensorConfig;
using executor::AttrConfig;
using executor::MemoryAllocator;

struct OpArgs {
  std::vector<Tensor*> input;
  std::vector<Tensor*> output;
  OperatorConfig conf;
};

struct TestParams {
  std::pair<OpArgs, OpArgs> args;
  bool expect_to_fail;
};

void GetTrueData(const std::vector<Tensor*>& input, const std::vector<Tensor*>& output,
                  const OperatorConfig& conf) {
  auto attrs_map = conf.attributes();
  vector<int64_t> axes;
  executor::StringSplit<int64_t>(&axes, attrs_map["axis"], ",");
  if (axes.empty()) {  // default axis
    axes = {0};
  }

  auto src_tensor_shape = input[0]->shape();
  const auto src_tensor_data = static_cast<const float*>(input[0]->data());
  vector<int64_t> src_stride = executor::GetStrides(src_tensor_shape);
  int h = src_tensor_shape[0];
  int w = src_tensor_shape[1];
  vector<int64_t> dst_shape;
  bool h_flag = false;
  bool w_flag = false;
  for (const auto& axis : axes) {
    switch (axis) {
      case 0: {
        h_flag = true;
        dst_shape = {1, w};
        break;
      }
      case 1: {
        w_flag = true;
        dst_shape = {h, 1};
        break;
      }
      case -1: {
        w_flag = true;
        dst_shape = {h, 1};
        break;
      }
      case -2: {
        h_flag = true;
        dst_shape = {1, w};
        break;
      }
    }
  }

  // dst shape
  output[0]->set_shape(dst_shape);
  float* dst_data = static_cast<float*>(output[0]->mutable_data());
  if (h_flag && !w_flag) {
    vector<float> h_acc(w, 0);
    for (int i = 0; i < w; i++) {
      for (int j = 0; j < h; j++) {
        h_acc[i] += src_tensor_data[j * w + i];
      }
      dst_data[i] = h_acc[i] / h;
    }
  } else if (w_flag && !h_flag) {
    vector<float> w_acc(h, 0);
    for (int i = 0; i < h; i++) {
      for (int j = 0; j < w; j++) {
        w_acc[i] += src_tensor_data[i * w + j];
      }
      dst_data[i] = w_acc[i] / w;
    }
  }
}

bool CheckResult(const TestParams& t) {
  const auto& p = t.args.first;
  const auto& q = t.args.second;
  try {
    executor::ReduceMeanOperator reduce_mean(p.conf);
    reduce_mean.Reshape(p.input, p.output);
    reduce_mean.Forward(p.input, p.output);
  } catch (const dnnl::error& e) {
    if (e.status != dnnl_status_t::dnnl_success && t.expect_to_fail) {
      return true;
    } else {
      return false;
    }
  }
  if (!t.expect_to_fail) {
    GetTrueData(q.input, q.output, q.conf);
    // Should compare buffer with different addresses
    EXPECT_NE(p.output[0]->data(), q.output[0]->data());
    return executor::CompareData<float>(p.output[0]->data(), p.output[0]->size(),
                                        q.output[0]->data(), q.output[0]->size());
  }
  return false;
}

class ReduceMeanTest : public testing::TestWithParam<TestParams> {
 protected:
  ReduceMeanTest() {}
  ~ReduceMeanTest() {}
  void SetUp() override {}
  void TearDown() override {}
};

TEST_P(ReduceMeanTest, TestPostfix) {
  TestParams t = testing::TestWithParam<TestParams>::GetParam();
  EXPECT_TRUE(CheckResult(t));
}

std::pair<OpArgs, OpArgs> GenerateFp32Case(const std::vector<std::vector<int64_t>>& input_shape,
                                           std::string axis, std::string keep_dims = "") {
  // Step 1: Construct Tensor config ptr
  const auto& src_shape = input_shape[0];
  TensorConfig* src_config = new TensorConfig("src", src_shape);
  std::vector<TensorConfig*> input_config = {src_config};
  std::vector<int64_t> dst_shape = {};
  TensorConfig* dst_config = new TensorConfig("dst", dst_shape);
  std::vector<TensorConfig*> output_config = {dst_config};

  // Step 1.1: Construct Operator config obj
  std::map<std::string, std::string> attr_map;
  attr_map = {
    {"axis", axis},
    {"keep_dims", keep_dims}
    };

  AttrConfig* op_attr = new AttrConfig(attr_map);
  OperatorConfig op_config = OperatorConfig("reduce_mean", "fp32",
                             input_config, output_config, op_attr);

  // Step 2: Construct Tensor ptr
  auto make_tensor_obj = [&](const TensorConfig* a_tensor_config) {
    // step1: set shape
    Tensor* a_tensor = new Tensor(*a_tensor_config);
    // step2: set tensor life
    a_tensor->add_tensor_life(1);
    // step3: library buffer can only be obtained afterwards
    auto tensor_data = a_tensor->mutable_data();
    executor::InitVector(static_cast<float*>(tensor_data), a_tensor->size());

    Tensor* a_tensor_copy = new Tensor(*a_tensor_config);
    a_tensor_copy->add_tensor_life(1);
    auto tensor_data_copy = a_tensor_copy->mutable_data();
    memcpy(reinterpret_cast<void*>(tensor_data_copy), tensor_data,
           a_tensor_copy->size() * sizeof(float));
    return std::pair<Tensor*, Tensor*>{a_tensor, a_tensor_copy};
  };

  auto src_tensors = make_tensor_obj(src_config);
  Tensor* dst_tensor = new Tensor(*dst_config);
  dst_tensor->add_tensor_life(1);
  Tensor* dst_tensor_copy = new Tensor(*dst_config);
  dst_tensor_copy->add_tensor_life(1);

  OpArgs op_args = {{src_tensors.first},
                    {dst_tensor}, op_config};
  OpArgs op_args_copy = {{src_tensors.second},
                         {dst_tensor_copy}, op_config};

  return {op_args, op_args_copy};
}

static auto CasesFp32 = []() {
  std::string memory_strategy = getenv("DIRECT_BUFFER") == NULL \
                                ? "cycle_buffer" : "direct_buffer";
  MemoryAllocator::SetStrategy(memory_strategy);
  std::vector<TestParams> cases;

  // Config
  std::vector<int64_t> src_shape;

  // case: simple for 0 axis
  src_shape = {3, 2};
  cases.push_back({GenerateFp32Case({src_shape}, "0", "true"), false});
  // case: simple for 1 axis
  src_shape = {3, 2};
  cases.push_back({GenerateFp32Case({src_shape}, "1", "true"), false});
  // case: simple for -1 axis
  src_shape = {3, 2};
  cases.push_back({GenerateFp32Case({src_shape}, "-1", "true"), false});
  // case: simple for -2 axis with keep dims false
  src_shape = {3, 2};
  cases.push_back({GenerateFp32Case({src_shape}, "-2", "false"), false});
  // case: simple for large shape case
  src_shape = {100, 50};
  cases.push_back({GenerateFp32Case({src_shape}, "1", "false"), false});

  return ::testing::ValuesIn(cases);
};

INSTANTIATE_TEST_SUITE_P(Prefix, ReduceMeanTest, CasesFp32());
