# Copyright 2019-2021 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.
# ==============================================================================
import pytest
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
from mindspore import log as logger

DATA_DIR = "../data/dataset/testPK/data"


def test_imagefolder_basic():
    logger.info("Test Case basic")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 44


def test_imagefolder_numsamples():
    logger.info("Test Case numSamples")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, num_samples=10, num_parallel_workers=2)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 10

    random_sampler = ds.RandomSampler(num_samples=3, replacement=True)
    data1 = ds.ImageFolderDataset(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):
        num_iter += 1

    assert num_iter == 3

    random_sampler = ds.RandomSampler(num_samples=3, replacement=False)
    data1 = ds.ImageFolderDataset(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):
        num_iter += 1

    assert num_iter == 3


def test_imagefolder_numshards():
    logger.info("Test Case numShards")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, num_shards=4, shard_id=3)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 11


def test_imagefolder_shardid():
    logger.info("Test Case withShardID")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, num_shards=4, shard_id=1)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 11


def test_imagefolder_noshuffle():
    logger.info("Test Case noShuffle")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, shuffle=False)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 44


def test_imagefolder_extrashuffle():
    logger.info("Test Case extraShuffle")
    # define parameters
    repeat_count = 2

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, shuffle=True)
    data1 = data1.shuffle(buffer_size=5)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 88


def test_imagefolder_classindex():
    logger.info("Test Case classIndex")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    class_index = {"class3": 333, "class1": 111}
    data1 = ds.ImageFolderDataset(DATA_DIR, class_indexing=class_index, shuffle=False)
    data1 = data1.repeat(repeat_count)

    golden = [111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111,
              333, 333, 333, 333, 333, 333, 333, 333, 333, 333, 333]

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        assert item["label"] == golden[num_iter]
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 22


def test_imagefolder_negative_classindex():
    logger.info("Test Case negative classIndex")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    class_index = {"class3": -333, "class1": 111}
    data1 = ds.ImageFolderDataset(DATA_DIR, class_indexing=class_index, shuffle=False)
    data1 = data1.repeat(repeat_count)

    golden = [111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111,
              -333, -333, -333, -333, -333, -333, -333, -333, -333, -333, -333]

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        assert item["label"] == golden[num_iter]
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 22


def test_imagefolder_extensions():
    logger.info("Test Case extensions")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    ext = [".jpg", ".JPEG"]
    data1 = ds.ImageFolderDataset(DATA_DIR, extensions=ext)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 44


def test_imagefolder_decode():
    logger.info("Test Case decode")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    ext = [".jpg", ".JPEG"]
    data1 = ds.ImageFolderDataset(DATA_DIR, extensions=ext, decode=True)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 44


def test_sequential_sampler():
    logger.info("Test Case SequentialSampler")

    golden = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
              1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
              2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
              3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]

    # define parameters
    repeat_count = 1

    # apply dataset operations
    sampler = ds.SequentialSampler()
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
    data1 = data1.repeat(repeat_count)

    result = []
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        result.append(item["label"])
        num_iter += 1

    assert num_iter == 44
    logger.info("Result: {}".format(result))
    assert result == golden


def test_random_sampler():
    logger.info("Test Case RandomSampler")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    sampler = ds.RandomSampler()
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 44


def test_distributed_sampler():
    logger.info("Test Case DistributedSampler")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    sampler = ds.DistributedSampler(10, 1)
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 5


def test_pk_sampler():
    logger.info("Test Case PKSampler")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    sampler = ds.PKSampler(3)
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 12


def test_subset_random_sampler():
    logger.info("Test Case SubsetRandomSampler")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
    sampler = ds.SubsetRandomSampler(indices)
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 12


def test_weighted_random_sampler():
    logger.info("Test Case WeightedRandomSampler")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 1.1]
    sampler = ds.WeightedRandomSampler(weights, 11)
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 11


def test_weighted_random_sampler_exception():
    """
    Test error cases for WeightedRandomSampler
    """
    logger.info("Test error cases for WeightedRandomSampler")
    error_msg_1 = "type of weights element must be number"
    with pytest.raises(TypeError, match=error_msg_1):
        weights = ""
        ds.WeightedRandomSampler(weights)

    error_msg_2 = "type of weights element must be number"
    with pytest.raises(TypeError, match=error_msg_2):
        weights = (0.9, 0.8, 1.1)
        ds.WeightedRandomSampler(weights)

    error_msg_3 = "WeightedRandomSampler: weights vector must not be empty"
    with pytest.raises(RuntimeError, match=error_msg_3):
        weights = []
        sampler = ds.WeightedRandomSampler(weights)
        sampler.parse()

    error_msg_4 = "WeightedRandomSampler: weights vector must not contain negative number, got: "
    with pytest.raises(RuntimeError, match=error_msg_4):
        weights = [1.0, 0.1, 0.02, 0.3, -0.4]
        sampler = ds.WeightedRandomSampler(weights)
        sampler.parse()

    error_msg_5 = "WeightedRandomSampler: elements of weights vector must not be all zero"
    with pytest.raises(RuntimeError, match=error_msg_5):
        weights = [0, 0, 0, 0, 0]
        sampler = ds.WeightedRandomSampler(weights)
        sampler.parse()


def test_chained_sampler_01():
    logger.info("Test Case Chained Sampler - Random and Sequential, with repeat")

    # Create chained sampler, random and sequential
    sampler = ds.RandomSampler()
    child_sampler = ds.SequentialSampler()
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    data1 = data1.repeat(count=3)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 132

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 132


def test_chained_sampler_02():
    logger.info("Test Case Chained Sampler - Random and Sequential, with batch then repeat")

    # Create chained sampler, random and sequential
    sampler = ds.RandomSampler()
    child_sampler = ds.SequentialSampler()
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    data1 = data1.batch(batch_size=5, drop_remainder=True)
    data1 = data1.repeat(count=2)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 16

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 16


def test_chained_sampler_03():
    logger.info("Test Case Chained Sampler - Random and Sequential, with repeat then batch")

    # Create chained sampler, random and sequential
    sampler = ds.RandomSampler()
    child_sampler = ds.SequentialSampler()
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    data1 = data1.repeat(count=2)
    data1 = data1.batch(batch_size=5, drop_remainder=False)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 18

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 18


def test_chained_sampler_04():
    logger.info("Test Case Chained Sampler - Distributed and Random, with batch then repeat")

    # Create chained sampler, distributed and random
    sampler = ds.DistributedSampler(num_shards=4, shard_id=3)
    child_sampler = ds.RandomSampler()
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    data1 = data1.batch(batch_size=5, drop_remainder=True)
    data1 = data1.repeat(count=3)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 6

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    # Note: Each of the 4 shards has 44/4=11 samples
    # Note: Number of iterations is (11/5 = 2) * 3 = 6
    assert num_iter == 6


def skip_test_chained_sampler_05():
    logger.info("Test Case Chained Sampler - PKSampler and WeightedRandom")

    # Create chained sampler, PKSampler and WeightedRandom
    sampler = ds.PKSampler(num_val=3)  # Number of elements per class is 3 (and there are 4 classes)
    weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
    child_sampler = ds.WeightedRandomSampler(weights, num_samples=12)
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 12

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    # Note: PKSampler produces 4x3=12 samples
    # Note: Child WeightedRandomSampler produces 12 samples
    assert num_iter == 12


def test_chained_sampler_06():
    logger.info("Test Case Chained Sampler - WeightedRandom and PKSampler")

    # Create chained sampler, WeightedRandom and PKSampler
    weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
    sampler = ds.WeightedRandomSampler(weights=weights, num_samples=12)
    child_sampler = ds.PKSampler(num_val=3)  # Number of elements per class is 3 (and there are 4 classes)
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 12

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    # Note: WeightedRandomSampler produces 12 samples
    # Note: Child PKSampler produces 12 samples
    assert num_iter == 12


def test_chained_sampler_07():
    logger.info("Test Case Chained Sampler - SubsetRandom and Distributed, 2 shards")

    # Create chained sampler, subset random and distributed
    indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
    sampler = ds.SubsetRandomSampler(indices, num_samples=12)
    child_sampler = ds.DistributedSampler(num_shards=2, shard_id=1)
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 12

    # Verify number of iterations

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    # Note: SubsetRandomSampler produces 12 samples
    # Note: Each of 2 shards has 6 samples
    # FIXME: Uncomment the following assert when code issue is resolved; at runtime, number of samples is 12 not 6
    # assert num_iter == 6


def skip_test_chained_sampler_08():
    logger.info("Test Case Chained Sampler - SubsetRandom and Distributed, 4 shards")

    # Create chained sampler, subset random and distributed
    indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
    sampler = ds.SubsetRandomSampler(indices, num_samples=12)
    child_sampler = ds.DistributedSampler(num_shards=4, shard_id=1)
    sampler.add_child(child_sampler)
    # Create ImageFolderDataset with sampler
    data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)

    # Verify dataset size
    data1_size = data1.get_dataset_size()
    logger.info("dataset size is: {}".format(data1_size))
    assert data1_size == 3

    # Verify number of iterations
    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    # Note: SubsetRandomSampler returns 12 samples
    # Note: Each of 4 shards has 3 samples
    assert num_iter == 3


def test_imagefolder_rename():
    logger.info("Test Case rename")
    # define parameters
    repeat_count = 1

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, num_samples=10)
    data1 = data1.repeat(repeat_count)

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 10

    data1 = data1.rename(input_columns=["image"], output_columns="image2")

    num_iter = 0
    for item in data1.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image2"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 10


def test_imagefolder_zip():
    logger.info("Test Case zip")
    # define parameters
    repeat_count = 2

    # apply dataset operations
    data1 = ds.ImageFolderDataset(DATA_DIR, num_samples=10)
    data2 = ds.ImageFolderDataset(DATA_DIR, num_samples=10)

    data1 = data1.repeat(repeat_count)
    # rename dataset2 for no conflict
    data2 = data2.rename(input_columns=["image", "label"], output_columns=["image1", "label1"])
    data3 = ds.zip((data1, data2))

    num_iter = 0
    for item in data3.create_dict_iterator(num_epochs=1):  # each data is a dictionary
        # in this example, each dictionary has keys "image" and "label"
        logger.info("image is {}".format(item["image"]))
        logger.info("label is {}".format(item["label"]))
        num_iter += 1

    logger.info("Number of data in data1: {}".format(num_iter))
    assert num_iter == 10


def test_imagefolder_exception():
    logger.info("Test imagefolder exception")

    def exception_func(item):
        raise Exception("Error occur!")

    def exception_func2(image, label):
        raise Exception("Error occur!")

    try:
        data = ds.ImageFolderDataset(DATA_DIR)
        data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
        for _ in data.__iter__():
            pass
        assert False
    except RuntimeError as e:
        assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)

    try:
        data = ds.ImageFolderDataset(DATA_DIR)
        data = data.map(operations=exception_func2, input_columns=["image", "label"],
                        output_columns=["image", "label", "label1"],
                        column_order=["image", "label", "label1"], num_parallel_workers=1)
        for _ in data.__iter__():
            pass
        assert False
    except RuntimeError as e:
        assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)

    try:
        data = ds.ImageFolderDataset(DATA_DIR)
        data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
        data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
        for _ in data.__iter__():
            pass
        assert False
    except RuntimeError as e:
        assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)

    data_dir_invalid = "../data/dataset/testPK"
    try:
        data = ds.ImageFolderDataset(data_dir_invalid)
        for _ in data.__iter__():
            pass
        assert False
    except RuntimeError as e:
        assert "should be file, but got directory" in str(e)


if __name__ == '__main__':
    test_imagefolder_basic()
    logger.info('test_imagefolder_basic Ended.\n')

    test_imagefolder_numsamples()
    logger.info('test_imagefolder_numsamples Ended.\n')

    test_sequential_sampler()
    logger.info('test_sequential_sampler Ended.\n')

    test_random_sampler()
    logger.info('test_random_sampler Ended.\n')

    test_distributed_sampler()
    logger.info('test_distributed_sampler Ended.\n')

    test_pk_sampler()
    logger.info('test_pk_sampler Ended.\n')

    test_subset_random_sampler()
    logger.info('test_subset_random_sampler Ended.\n')

    test_weighted_random_sampler()
    logger.info('test_weighted_random_sampler Ended.\n')

    test_weighted_random_sampler_exception()
    logger.info('test_weighted_random_sampler_exception Ended.\n')

    test_chained_sampler_01()
    logger.info('test_chained_sampler_01 Ended.\n')

    test_chained_sampler_02()
    logger.info('test_chained_sampler_02 Ended.\n')

    test_chained_sampler_03()
    logger.info('test_chained_sampler_03 Ended.\n')

    test_chained_sampler_04()
    logger.info('test_chained_sampler_04 Ended.\n')

    # test_chained_sampler_05()
    # logger.info('test_chained_sampler_05 Ended.\n')

    test_chained_sampler_06()
    logger.info('test_chained_sampler_06 Ended.\n')

    test_chained_sampler_07()
    logger.info('test_chained_sampler_07 Ended.\n')

    # test_chained_sampler_08()
    # logger.info('test_chained_sampler_07 Ended.\n')

    test_imagefolder_numshards()
    logger.info('test_imagefolder_numshards Ended.\n')

    test_imagefolder_shardid()
    logger.info('test_imagefolder_shardid Ended.\n')

    test_imagefolder_noshuffle()
    logger.info('test_imagefolder_noshuffle Ended.\n')

    test_imagefolder_extrashuffle()
    logger.info('test_imagefolder_extrashuffle Ended.\n')

    test_imagefolder_classindex()
    logger.info('test_imagefolder_classindex Ended.\n')

    test_imagefolder_negative_classindex()
    logger.info('test_imagefolder_negative_classindex Ended.\n')

    test_imagefolder_extensions()
    logger.info('test_imagefolder_extensions Ended.\n')

    test_imagefolder_decode()
    logger.info('test_imagefolder_decode Ended.\n')

    test_imagefolder_rename()
    logger.info('test_imagefolder_rename Ended.\n')

    test_imagefolder_zip()
    logger.info('test_imagefolder_zip Ended.\n')

    test_imagefolder_exception()
    logger.info('test_imagefolder_exception Ended.\n')