/**
 * Copyright 2020 Huawei Technologies Co., Ltd
 *
 * 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 <fstream>
#include <iostream>
#include <memory>
#include <vector>
#include <string>

#include "utils/log_adapter.h"
#include "common/utils.h"
#include "common/common.h"
#include "gtest/gtest.h"
#include "securec.h"
#include "minddata/dataset/include/datasets.h"
#include "minddata/dataset/include/status.h"
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/include/iterator.h"
#include "minddata/dataset/core/constants.h"
#include "minddata/dataset/core/tensor_shape.h"
#include "minddata/dataset/core/tensor.h"
#include "minddata/dataset/include/samplers.h"
#include "minddata/dataset/engine/datasetops/source/voc_op.h"


using namespace mindspore::dataset::api;
using mindspore::MsLogLevel::ERROR;
using mindspore::ExceptionType::NoExceptionType;
using mindspore::LogStream;
using mindspore::dataset::Tensor;
using mindspore::dataset::TensorShape;
using mindspore::dataset::TensorImpl;
using mindspore::dataset::DataType;
using mindspore::dataset::Status;
using mindspore::dataset::BorderType;
using mindspore::dataset::dsize_t;


class MindDataTestPipeline : public UT::DatasetOpTesting {
 protected:
};


TEST_F(MindDataTestPipeline, TestBatchAndRepeat) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestBatchAndRepeat.";

  // Create a Mnist Dataset
  std::string folder_path = datasets_root_path_ + "/testMnistData/";
  std::shared_ptr<Dataset> ds = Mnist(folder_path, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 2;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestMnistFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMnistFail1.";

  // Create a Mnist Dataset
  std::shared_ptr<Dataset> ds = Mnist("", RandomSampler(false, 10));
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestTensorOpsAndMap) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTensorOpsAndMap.";

  // Create a Mnist Dataset
  std::string folder_path = datasets_root_path_ + "/testMnistData/";
  std::shared_ptr<Dataset> ds = Mnist(folder_path, RandomSampler(false, 20));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> resize_op = vision::Resize({30, 30});
  EXPECT_NE(resize_op, nullptr);

  std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
  EXPECT_NE(center_crop_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({resize_op, center_crop_op});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 40);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestUniformAugWithOps) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugWithOps.";

  // Create a Mnist Dataset
  std::string folder_path = datasets_root_path_ + "/testMnistData/";
  std::shared_ptr<Dataset> ds = Mnist(folder_path, RandomSampler(false, 20));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 1;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> resize_op = vision::Resize({30, 30});
  EXPECT_NE(resize_op, nullptr);

  std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
  EXPECT_NE(random_crop_op, nullptr);

  std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
  EXPECT_NE(center_crop_op, nullptr);

  std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, 2);
  EXPECT_NE(uniform_aug_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({resize_op, uniform_aug_op});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomFlip) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomFlip.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
  EXPECT_NE(random_vertical_flip_op, nullptr);

  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(0.5);
  EXPECT_NE(random_horizontal_flip_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_vertical_flip_op, random_horizontal_flip_op});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestImageFolderBatchAndRepeat) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestImageFolderBatchAndRepeat.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 2;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestImageFolderFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestImageFolderFail1.";

  // Create an ImageFolder Dataset
  std::shared_ptr<Dataset> ds = ImageFolder("", true, nullptr);
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestImageFolderWithSamplers) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestImageFolderWithSamplers.";

  std::shared_ptr<SamplerObj> sampl = DistributedSampler(2, 1);
  EXPECT_NE(sampl, nullptr);

  sampl = PKSampler(3);
  EXPECT_NE(sampl, nullptr);

  sampl = RandomSampler(false, 12);
  EXPECT_NE(sampl, nullptr);

  sampl = SequentialSampler(0, 12);
  EXPECT_NE(sampl, nullptr);

  std::vector<double> weights = {0.9, 0.8, 0.68, 0.7, 0.71, 0.6, 0.5, 0.4, 0.3, 0.5, 0.2, 0.1};
  sampl = WeightedRandomSampler(weights, 12);
  EXPECT_NE(sampl, nullptr);

  std::vector<int64_t> indices = {1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23};
  sampl = SubsetRandomSampler(indices);
  EXPECT_NE(sampl, nullptr);

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, sampl);
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 2;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 12);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestPad) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestPad.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> pad_op1 = vision::Pad({1, 2, 3, 4}, {0}, BorderType::kSymmetric);
  EXPECT_NE(pad_op1, nullptr);

  std::shared_ptr<TensorOperation> pad_op2 = vision::Pad({1}, {1, 1, 1}, BorderType::kEdge);
  EXPECT_NE(pad_op2, nullptr);

  std::shared_ptr<TensorOperation> pad_op3 = vision::Pad({1, 4});
  EXPECT_NE(pad_op3, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({pad_op1, pad_op2, pad_op3});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
  i++;
  auto image = row["image"];
  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
  iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCutOut) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutOut.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> cut_out1 = vision::CutOut(30, 5);
  EXPECT_NE(cut_out1, nullptr);

  std::shared_ptr<TensorOperation> cut_out2 = vision::CutOut(30);
  EXPECT_NE(cut_out2, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({cut_out1, cut_out2});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
  i++;
  auto image = row["image"];
  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
  iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestNormalize) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestNormalize.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> normalize = vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0});
  EXPECT_NE(normalize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({normalize});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestDecode) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestDecode.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> decode = vision::Decode(true);
  EXPECT_NE(decode, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({decode});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }
  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestShuffleDataset) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestShuffleDataset.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Shuffle operation on ds
  int32_t shuffle_size = 10;
  ds = ds->Shuffle(shuffle_size);
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 2;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestSkipDataset) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSkipDataset.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Skip operation on ds
  int32_t count = 3;
  ds = ds->Skip(count);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }
  MS_LOG(INFO) << "Number of rows: " << i;

  // Expect 10-3=7 rows
  EXPECT_EQ(i, 7);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestSkipDatasetError1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSkipDatasetError1.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Skip operation on ds with invalid count input
  int32_t count = -1;
  ds = ds->Skip(count);
  // Expect nullptr for invalid input skip_count
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestTakeDatasetDefault) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTakeDatasetDefault.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 7));
  EXPECT_NE(ds, nullptr);

  // Create a Take operation on ds, dafault count = -1
  ds = ds->Take();
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }
  MS_LOG(INFO) << "Number of rows: " << i;

  // Expect 7 rows
  EXPECT_EQ(i, 7);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestTakeDatasetNormal) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTakeDatasetNormal.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 8));
  EXPECT_NE(ds, nullptr);

  // Create a Take operation on ds
  ds = ds->Take(5);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }
  MS_LOG(INFO) << "Number of rows: " << i;

  // Expect 5 rows
  EXPECT_EQ(i, 5);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestTakeDatasetError1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTakeDatasetError1.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Take operation on ds with invalid count input
  int32_t count = -5;
  ds = ds->Take(count);
  // Expect nullptr for invalid input take_count
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestCifar10Dataset) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar10Dataset.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  EXPECT_NE(row.find("image"), row.end());
  EXPECT_NE(row.find("label"), row.end());

  uint64_t i = 0;
  while (row.size() != 0) {
  i++;
  auto image = row["image"];
  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
  iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCifar10DatasetFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar10DatasetFail1.";

  // Create a Cifar10 Dataset
  std::shared_ptr<Dataset> ds = Cifar10("", RandomSampler(false, 10));
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestCifar100Dataset) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar100Dataset.";

  // Create a Cifar100 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar100Data/";
  std::shared_ptr<Dataset> ds = Cifar100(folder_path, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  EXPECT_NE(row.find("image"), row.end());
  EXPECT_NE(row.find("coarse_label"), row.end());
  EXPECT_NE(row.find("fine_label"), row.end());

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCifar100DatasetFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar100DatasetFail1.";

  // Create a Cifar100 Dataset
  std::shared_ptr<Dataset> ds = Cifar100("", RandomSampler(false, 10));
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomColorAdjust) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomColorAdjust.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_color_adjust1 = vision::RandomColorAdjust({1.0}, {0.0}, {0.5}, {0.5});
  EXPECT_NE(random_color_adjust1, nullptr);

  std::shared_ptr<TensorOperation> random_color_adjust2 = vision::RandomColorAdjust({1.0, 1.0}, {0.0, 0.0}, {0.5, 0.5},
                                                                                    {0.5, 0.5});
  EXPECT_NE(random_color_adjust2, nullptr);

  std::shared_ptr<TensorOperation> random_color_adjust3 = vision::RandomColorAdjust({0.5, 1.0}, {0.0, 0.5}, {0.25, 0.5},
                                                                             {0.25, 0.5});
  EXPECT_NE(random_color_adjust3, nullptr);

  std::shared_ptr<TensorOperation> random_color_adjust4 = vision::RandomColorAdjust();
  EXPECT_NE(random_color_adjust4, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_color_adjust1, random_color_adjust2, random_color_adjust3, random_color_adjust4});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
  i++;
  auto image = row["image"];
  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
  iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomRotation) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomRotation.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_rotation_op = vision::RandomRotation({-180, 180});
  EXPECT_NE(random_rotation_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_rotation_op});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestProjectMap) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestProjectMap.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
  EXPECT_NE(random_vertical_flip_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_vertical_flip_op}, {}, {}, {"image", "label"});
  EXPECT_NE(ds, nullptr);

  // Create a Project operation on ds
  std::vector<std::string> column_project = {"image"};
  ds = ds->Project(column_project);
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestZipSuccess) {
  // Testing the member zip() function
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestZipSuccess.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Project operation on ds
  std::vector<std::string> column_project = {"image"};
  ds = ds->Project(column_project);
  EXPECT_NE(ds, nullptr);

  // Create an ImageFolder Dataset
  std::shared_ptr<Dataset> ds1 = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds1, nullptr);

  // Create a Rename operation on ds (so that the 3 datasets we are going to zip have distinct column names)
  ds1 = ds1->Rename({"image", "label"}, {"col1", "col2"});
  EXPECT_NE(ds1, nullptr);

  folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds2 = Cifar10(folder_path, RandomSampler(false, 10));
  EXPECT_NE(ds2, nullptr);

  // Create a Project operation on ds
  column_project = {"label"};
  ds2 = ds2->Project(column_project);
  EXPECT_NE(ds2, nullptr);

  // Create a Zip operation on the datasets
  ds = ds->Zip({ds1, ds2});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  // Check zipped column names
  EXPECT_EQ(row.size(), 4);
  EXPECT_NE(row.find("image"), row.end());
  EXPECT_NE(row.find("label"), row.end());
  EXPECT_NE(row.find("col1"), row.end());
  EXPECT_NE(row.find("col2"), row.end());

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestZipSuccess2) {
  // Testing the static zip() function
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestZipSuccess2.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 9));
  EXPECT_NE(ds, nullptr);
  std::shared_ptr<Dataset> ds2 = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds2, nullptr);

  // Create a Rename operation on ds (so that the 2 datasets we are going to zip have distinct column names)
  ds = ds->Rename({"image", "label"}, {"col1", "col2"});
  EXPECT_NE(ds, nullptr);

  // Create a Zip operation on the datasets
  ds = Zip({ds, ds2});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  // Check zipped column names
  EXPECT_EQ(row.size(), 4);
  EXPECT_NE(row.find("image"), row.end());
  EXPECT_NE(row.find("label"), row.end());
  EXPECT_NE(row.find("col1"), row.end());
  EXPECT_NE(row.find("col2"), row.end());

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 9);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestZipFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestZipFail.";
  // We expect this test to fail because we are the both datasets we are zipping have "image" and "label" columns
  // and zip doesn't accept datasets with same column names

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create an ImageFolder Dataset
  std::shared_ptr<Dataset> ds1 = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds1, nullptr);

  // Create a Zip operation on the datasets
  ds = Zip({ds, ds1});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_EQ(iter, nullptr);
}

TEST_F(MindDataTestPipeline, TestZipFail2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestZipFail2.";
  // This case is expected to fail because the input dataset is empty.

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Zip operation on the datasets
  // Input dataset to zip is empty
  ds = Zip({});
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestRenameSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRenameSuccess.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create a Rename operation on ds
  ds = ds->Rename({"image", "label"}, {"col1", "col2"});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  EXPECT_NE(row.find("col1"), row.end());
  EXPECT_NE(row.find("col2"), row.end());
  EXPECT_EQ(row.find("image"), row.end());
  EXPECT_EQ(row.find("label"), row.end());

  while (row.size() != 0) {
    i++;
    auto image = row["col1"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRenameFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRenameFail.";
  // We expect this test to fail because input and output in Rename are not the same size

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create a Rename operation on ds
  ds = ds->Rename({"image", "label"}, {"col2"});
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestVOCSegmentation) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCSegmentation.";

  // Create a VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Segmentation", "train", {}, false, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 2;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  // Check if VOCOp read correct images/targets
  using Tensor = mindspore::dataset::Tensor;
  std::string expect_file[] = {"32", "33", "39", "32", "33", "39"};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto target = row["target"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    MS_LOG(INFO) << "Tensor target shape: " << target->shape();

    std::shared_ptr<Tensor> expect_image;
    Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
    EXPECT_EQ(*image, *expect_image);

    std::shared_ptr<Tensor> expect_target;
    Tensor::CreateFromFile(folder_path + "/SegmentationClass/" + expect_file[i] + ".png", &expect_target);
    EXPECT_EQ(*target, *expect_target);

    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestVOCSegmentationError1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCSegmentationError1.";

  // Create a VOC Dataset
  std::map<std::string, int32_t> class_index;
  class_index["car"] = 0;
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Segmentation", "train", class_index, false, RandomSampler(false, 6));

  // Expect nullptr for segmentation task with class_index
  EXPECT_EQ(ds, nullptr);
}

TEST_F(MindDataTestPipeline, TestVOCInvalidTaskOrMode) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCInvalidTaskOrMode.";

  // Create a VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds_1 = VOC(folder_path, "Classification", "train", {}, false, SequentialSampler(0, 3));
  // Expect nullptr for invalid task
  EXPECT_EQ(ds_1, nullptr);

  std::shared_ptr<Dataset> ds_2 = VOC(folder_path, "Segmentation", "validation", {}, false, RandomSampler(false, 4));
  // Expect nullptr for invalid mode
  EXPECT_EQ(ds_2, nullptr);
}

TEST_F(MindDataTestPipeline, TestVOCDetection) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCDetection.";

  // Create a VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, false, SequentialSampler(0, 4));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  // Check if VOCOp read correct images/labels
  std::string expect_file[] = {"15", "32", "33", "39"};
  uint32_t expect_num[] = {5, 5, 4, 3};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto label = row["label"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    MS_LOG(INFO) << "Tensor label shape: " << label->shape();

    std::shared_ptr<Tensor> expect_image;
    Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
    EXPECT_EQ(*image, *expect_image);

    std::shared_ptr<Tensor> expect_label;
    Tensor::CreateFromMemory(TensorShape({1, 1}), DataType(DataType::DE_UINT32), nullptr, &expect_label);
    expect_label->SetItemAt({0, 0}, expect_num[i]);
    EXPECT_EQ(*label, *expect_label);

    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 4);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestVOCClassIndex) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCClassIndex.";

  // Create a VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::map<std::string, int32_t> class_index;
  class_index["car"] = 0;
  class_index["cat"] = 1;
  class_index["train"] = 9;

  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", class_index, false, SequentialSampler(0, 6));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  // Check if VOCOp read correct labels
  // When we provide class_index, label of ["car","cat","train"] become [0,1,9]
  std::shared_ptr<Tensor> expect_label;
  Tensor::CreateFromMemory(TensorShape({1, 1}), DataType(DataType::DE_UINT32), nullptr, &expect_label);

  uint32_t expect[] = {9, 9, 9, 1, 1, 0};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto label = row["label"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    MS_LOG(INFO) << "Tensor label shape: " << label->shape();
    expect_label->SetItemAt({0, 0}, expect[i]);
    EXPECT_EQ(*label, *expect_label);

    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCocoDetection) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoDetection.";
  // Create a Coco Dataset
  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";

  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Detection", false, SequentialSampler(0, 6));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  std::string expect_file[] = {"000000391895", "000000318219", "000000554625", "000000574769", "000000060623",
                               "000000309022"};
  std::vector<std::vector<float>> expect_bbox_vector = {{10.0, 10.0, 10.0, 10.0, 70.0, 70.0, 70.0, 70.0},
                                                        {20.0, 20.0, 20.0, 20.0, 80.0, 80.0, 80.0, 80.0},
                                                        {30.0, 30.0, 30.0, 30.0}, {40.0, 40.0, 40.0, 40.0},
                                                        {50.0, 50.0, 50.0, 50.0}, {60.0, 60.0, 60.0, 60.0}};
  std::vector<std::vector<uint32_t>> expect_catagoryid_list = {{1, 7}, {2, 8}, {3}, {4}, {5}, {6}};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto bbox = row["bbox"];
    auto category_id = row["category_id"];
    std::shared_ptr<Tensor> expect_image;
    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
    EXPECT_EQ(*image, *expect_image);
    std::shared_ptr<Tensor> expect_bbox;
    dsize_t bbox_num = static_cast<dsize_t>(expect_bbox_vector[i].size() / 4);
    Tensor::CreateFromVector(expect_bbox_vector[i], TensorShape({bbox_num, 4}), &expect_bbox);
    EXPECT_EQ(*bbox, *expect_bbox);
    std::shared_ptr<Tensor> expect_categoryid;
    Tensor::CreateFromVector(expect_catagoryid_list[i], TensorShape({bbox_num, 1}), &expect_categoryid);
    EXPECT_EQ(*category_id, *expect_categoryid);
    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCocoStuff) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoStuff.";
  // Create a Coco Dataset
  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";

  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Stuff", false, SequentialSampler(0, 6));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  std::string expect_file[] = {"000000391895", "000000318219", "000000554625", "000000574769", "000000060623",
                               "000000309022"};
  std::vector<std::vector<float>> expect_segmentation_vector =
    {{10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0,
      70.0, 72.0, 73.0, 74.0, 75.0, -1.0, -1.0, -1.0, -1.0, -1.0},
     {20.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0,
      10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, -1.0},
     {40.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 40.0, 41.0, 42.0},
     {50.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0},
     {60.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0},
     {60.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0}};
  std::vector<std::vector<dsize_t>> expect_size = {{2, 10}, {2, 11}, {1, 12}, {1, 13}, {1, 14}, {2, 7}};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto segmentation = row["segmentation"];
    auto iscrowd = row["iscrowd"];
    std::shared_ptr<Tensor> expect_image;
    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
    EXPECT_EQ(*image, *expect_image);
    std::shared_ptr<Tensor> expect_segmentation;
    Tensor::CreateFromVector(expect_segmentation_vector[i], TensorShape(expect_size[i]), &expect_segmentation);
    EXPECT_EQ(*segmentation, *expect_segmentation);
    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCocoKeypoint) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoKeypoint.";
  // Create a Coco Dataset
  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/key_point.json";

  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Keypoint", false, SequentialSampler(0, 2));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  std::string expect_file[] = {"000000391895", "000000318219"};
  std::vector<std::vector<float>> expect_keypoint_vector =
    {{368.0, 61.0, 1.0, 369.0, 52.0, 2.0, 0.0, 0.0, 0.0, 382.0, 48.0, 2.0, 0.0, 0.0, 0.0, 368.0, 84.0, 2.0, 435.0,
       81.0, 2.0, 362.0, 125.0, 2.0, 446.0, 125.0, 2.0, 360.0, 153.0, 2.0, 0.0, 0.0, 0.0, 397.0, 167.0, 1.0, 439.0,
       166.0, 1.0, 369.0, 193.0, 2.0, 461.0, 234.0, 2.0, 361.0, 246.0, 2.0, 474.0, 287.0, 2.0},
     {244.0, 139.0, 2.0, 0.0, 0.0, 0.0, 226.0, 118.0, 2.0, 0.0, 0.0, 0.0, 154.0, 159.0, 2.0, 143.0, 261.0, 2.0, 135.0,
       312.0, 2.0, 271.0, 423.0, 2.0, 184.0, 530.0, 2.0, 261.0, 280.0, 2.0, 347.0, 592.0, 2.0, 0.0, 0.0, 0.0, 123.0,
       596.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}};
  std::vector<std::vector<dsize_t>> expect_size = {{1, 51}, {1, 51}};
  std::vector<std::vector<uint32_t>> expect_num_keypoints_list = {{14}, {10}};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto keypoints = row["keypoints"];
    auto num_keypoints = row["num_keypoints"];
    std::shared_ptr<Tensor> expect_image;
    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
    EXPECT_EQ(*image, *expect_image);
    std::shared_ptr<Tensor> expect_keypoints;
    dsize_t keypoints_size = expect_size[i][0];
    Tensor::CreateFromVector(expect_keypoint_vector[i], TensorShape(expect_size[i]), &expect_keypoints);
    EXPECT_EQ(*keypoints, *expect_keypoints);
    std::shared_ptr<Tensor> expect_num_keypoints;
    Tensor::CreateFromVector(expect_num_keypoints_list[i], TensorShape({keypoints_size, 1}), &expect_num_keypoints);
    EXPECT_EQ(*num_keypoints, *expect_num_keypoints);
    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCocoPanoptic) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoPanoptic.";
  // Create a Coco Dataset
  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/panoptic.json";

  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Panoptic", false, SequentialSampler(0, 2));
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  std::string expect_file[] = {"000000391895", "000000574769"};
  std::vector<std::vector<float>> expect_bbox_vector = {{472, 173, 36, 48, 340, 22, 154, 301, 486, 183, 30, 35},
                                                        {103, 133, 229, 422, 243, 175, 93, 164}};
  std::vector<std::vector<uint32_t>> expect_categoryid_vector = {{1, 1, 2}, {1, 3}};
  std::vector<std::vector<uint32_t>> expect_iscrowd_vector = {{0, 0, 0}, {0, 0}};
  std::vector<std::vector<uint32_t>> expect_area_vector = {{705, 14062, 626}, {43102, 6079}};
  std::vector<std::vector<dsize_t>> expect_size = {{3, 4}, {2, 4}};
  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto bbox = row["bbox"];
    auto category_id = row["category_id"];
    auto iscrowd = row["iscrowd"];
    auto area = row["area"];
    std::shared_ptr<Tensor> expect_image;
    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
    EXPECT_EQ(*image, *expect_image);
    std::shared_ptr<Tensor> expect_bbox;
    dsize_t bbox_size = expect_size[i][0];
    Tensor::CreateFromVector(expect_bbox_vector[i], TensorShape(expect_size[i]), &expect_bbox);
    EXPECT_EQ(*bbox, *expect_bbox);
    std::shared_ptr<Tensor> expect_categoryid;
    Tensor::CreateFromVector(expect_categoryid_vector[i], TensorShape({bbox_size, 1}), &expect_categoryid);
    EXPECT_EQ(*category_id, *expect_categoryid);
    std::shared_ptr<Tensor> expect_iscrowd;
    Tensor::CreateFromVector(expect_iscrowd_vector[i], TensorShape({bbox_size, 1}), &expect_iscrowd);
    EXPECT_EQ(*iscrowd, *expect_iscrowd);
    std::shared_ptr<Tensor> expect_area;
    Tensor::CreateFromVector(expect_area_vector[i], TensorShape({bbox_size, 1}), &expect_area);
    EXPECT_EQ(*area, *expect_area);
    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCocoDefault) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoDetection.";
  // Create a Coco Dataset
  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";

  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    auto image = row["image"];
    auto bbox = row["bbox"];
    auto category_id = row["category_id"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    MS_LOG(INFO) << "Tensor bbox shape: " << bbox->shape();
    MS_LOG(INFO) << "Tensor category_id shape: " << category_id->shape();
    iter->GetNextRow(&row);
    i++;
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCocoException) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoDetection.";
  // Create a Coco Dataset
  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";
  std::string invalid_folder_path = "./NotExist";
  std::string invalid_annotation_file = "./NotExistFile";

  std::shared_ptr<Dataset> ds = Coco(invalid_folder_path, annotation_file);
  EXPECT_EQ(ds, nullptr);

  std::shared_ptr<Dataset> ds1 = Coco(folder_path, invalid_annotation_file);
  EXPECT_EQ(ds1, nullptr);

  std::shared_ptr<Dataset> ds2 = Coco(folder_path, annotation_file, "valid_mode");
  EXPECT_EQ(ds2, nullptr);
}

TEST_F(MindDataTestPipeline, TestConcatSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestConcatSuccess.";

  // Create an ImageFolder Dataset
  // Column names: {"image", "label"}
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Cifar10 Dataset
  // Column names: {"image", "label"}
  folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds2 = Cifar10(folder_path, RandomSampler(false, 9));
  EXPECT_NE(ds2, nullptr);

  // Create a Project operation on ds
  ds = ds->Project({"image"});
  EXPECT_NE(ds, nullptr);
  ds2 = ds2->Project({"image"});
  EXPECT_NE(ds, nullptr);

  // Create a Concat operation on the ds
  ds = ds->Concat({ds2});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);
  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 19);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestConcatSuccess2) {
  // Test "+" operator to concat two datasets
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestConcatSuccess2.";

  // Create an ImageFolder Dataset
  // Column names: {"image", "label"}
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Cifar10 Dataset
  // Column names: {"image", "label"}
  folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds2 = Cifar10(folder_path, RandomSampler(false, 9));
  EXPECT_NE(ds2, nullptr);

  // Create a Project operation on ds
  ds = ds->Project({"image"});
  EXPECT_NE(ds, nullptr);
  ds2 = ds2->Project({"image"});
  EXPECT_NE(ds, nullptr);

  // Create a Concat operation on the ds
  ds = ds + ds2;
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);
  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 19);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestConcatFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestConcatFail1.";
  // This case is expected to fail because the input column names of concatenated datasets are not the same

  // Create an ImageFolder Dataset
  // Column names: {"image", "label"}
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);
  std::shared_ptr<Dataset> ds2 = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Rename operation on ds
  ds2 = ds2->Rename({"image", "label"}, {"col1", "col2"});
  EXPECT_NE(ds, nullptr);

  // Create a Project operation on the ds
  // Name of datasets to concat doesn't not match
  ds = ds->Concat({ds2});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 1;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_EQ(iter, nullptr);
}

TEST_F(MindDataTestPipeline, TestConcatFail2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestConcatFail2.";
  // This case is expected to fail because the input dataset is empty.

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Project operation on the ds
  // Input dataset to concat is empty
  ds = ds->Concat({});
  EXPECT_EQ(ds, nullptr);
}