text: # Copyright 2019 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.
  # ==============================================================================
  """
  Test Cifar10 and Cifar100 dataset operators
  """
  import os
  import pytest
  import numpy as np
  import matplotlib.pyplot as plt
  import mindspore.dataset as ds
  from mindspore import log as logger
  
  DATA_DIR_10 = "../data/dataset/testCifar10Data"
  DATA_DIR_100 = "../data/dataset/testCifar100Data"
  NO_BIN_DIR = "../data/dataset/testMnistData"
  
  
  def load_cifar(path, kind="cifar10"):
      """
      load Cifar10/100 data
      """
      raw = np.empty(0, dtype=np.uint8)
      for file_name in os.listdir(path):
          if file_name.endswith(".bin"):
              with open(os.path.join(path, file_name), mode='rb') as file:
                  raw = np.append(raw, np.fromfile(file, dtype=np.uint8), axis=0)
      if kind == "cifar10":
          raw = raw.reshape(-1, 3073)
          labels = raw[:, 0]
          images = raw[:, 1:]
      elif kind == "cifar100":
          raw = raw.reshape(-1, 3074)
          labels = raw[:, :2]
          images = raw[:, 2:]
      else:
          raise ValueError("Invalid parameter value")
      images = images.reshape(-1, 3, 32, 32)
      images = images.transpose(0, 2, 3, 1)
      return images, labels
  
  
  def visualize_dataset(images, labels):
      """
      Helper function to visualize the dataset samples
      """
      num_samples = len(images)
      for i in range(num_samples):
          plt.subplot(1, num_samples, i + 1)
          plt.imshow(images[i])
          plt.title(labels[i])
      plt.show()
  
  
  ### Testcases for Cifar10Dataset Op ###
  
  
  def test_cifar10_content_check():
      """
      Validate Cifar10Dataset image readings
      """
      logger.info("Test Cifar10Dataset Op with content check")
      data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100, shuffle=False)
      images, labels = load_cifar(DATA_DIR_10)
      num_iter = 0
      # in this example, each dictionary has keys "image" and "label"
      for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
          np.testing.assert_array_equal(d["image"], images[i])
          np.testing.assert_array_equal(d["label"], labels[i])
          num_iter += 1
      assert num_iter == 100
  
  
  def test_cifar10_basic():
      """
      Validate CIFAR10
      """
      logger.info("Test Cifar10Dataset Op")
  
      # case 0: test loading the whole dataset
      data0 = ds.Cifar10Dataset(DATA_DIR_10)
      num_iter0 = 0
      for _ in data0.create_dict_iterator(num_epochs=1):
          num_iter0 += 1
      assert num_iter0 == 10000
  
      # case 1: test num_samples
      data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
      num_iter1 = 0
      for _ in data1.create_dict_iterator(num_epochs=1):
          num_iter1 += 1
      assert num_iter1 == 100
  
      # case 2: test num_parallel_workers
      data2 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=50, num_parallel_workers=1)
      num_iter2 = 0
      for _ in data2.create_dict_iterator(num_epochs=1):
          num_iter2 += 1
      assert num_iter2 == 50
  
      # case 3: test repeat
      data3 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
      data3 = data3.repeat(3)
      num_iter3 = 0
      for _ in data3.create_dict_iterator(num_epochs=1):
          num_iter3 += 1
      assert num_iter3 == 300
  
      # case 4: test batch with drop_remainder=False
      data4 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
      assert data4.get_dataset_size() == 100
      assert data4.get_batch_size() == 1
      data4 = data4.batch(batch_size=7)  # drop_remainder is default to be False
      assert data4.get_dataset_size() == 15
      assert data4.get_batch_size() == 7
      num_iter4 = 0
      for _ in data4.create_dict_iterator(num_epochs=1):
          num_iter4 += 1
      assert num_iter4 == 15
  
      # case 5: test batch with drop_remainder=True
      data5 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
      assert data5.get_dataset_size() == 100
      assert data5.get_batch_size() == 1
      data5 = data5.batch(batch_size=7, drop_remainder=True)  # the rest of incomplete batch will be dropped
      assert data5.get_dataset_size() == 14
      assert data5.get_batch_size() == 7
      num_iter5 = 0
      for _ in data5.create_dict_iterator(num_epochs=1):
          num_iter5 += 1
      assert num_iter5 == 14
  
  
  def test_cifar10_pk_sampler():
      """
      Test Cifar10Dataset with PKSampler
      """
      logger.info("Test Cifar10Dataset Op with PKSampler")
      golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
                5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
      sampler = ds.PKSampler(3)
      data = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
      num_iter = 0
      label_list = []
      for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
          label_list.append(item["label"])
          num_iter += 1
      np.testing.assert_array_equal(golden, label_list)
      assert num_iter == 30
  
  
  def test_cifar10_sequential_sampler():
      """
      Test Cifar10Dataset with SequentialSampler
      """
      logger.info("Test Cifar10Dataset Op with SequentialSampler")
      num_samples = 30
      sampler = ds.SequentialSampler(num_samples=num_samples)
      data1 = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
      data2 = ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_samples=num_samples)
      num_iter = 0
      for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                              data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
          np.testing.assert_equal(item1["label"], item2["label"])
          num_iter += 1
      assert num_iter == num_samples
  
  
  def test_cifar10_exception():
      """
      Test error cases for Cifar10Dataset
      """
      logger.info("Test error cases for Cifar10Dataset")
      error_msg_1 = "sampler and shuffle cannot be specified at the same time"
      with pytest.raises(RuntimeError, match=error_msg_1):
          ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, sampler=ds.PKSampler(3))
  
      error_msg_2 = "sampler and sharding cannot be specified at the same time"
      with pytest.raises(RuntimeError, match=error_msg_2):
          ds.Cifar10Dataset(DATA_DIR_10, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
  
      error_msg_3 = "num_shards is specified and currently requires shard_id as well"
      with pytest.raises(RuntimeError, match=error_msg_3):
          ds.Cifar10Dataset(DATA_DIR_10, num_shards=10)
  
      error_msg_4 = "shard_id is specified but num_shards is not"
      with pytest.raises(RuntimeError, match=error_msg_4):
          ds.Cifar10Dataset(DATA_DIR_10, shard_id=0)
  
      error_msg_5 = "Input shard_id is not within the required interval"
      with pytest.raises(ValueError, match=error_msg_5):
          ds.Cifar10Dataset(DATA_DIR_10, num_shards=2, shard_id=-1)
      with pytest.raises(ValueError, match=error_msg_5):
          ds.Cifar10Dataset(DATA_DIR_10, num_shards=2, shard_id=5)
  
      error_msg_6 = "num_parallel_workers exceeds"
      with pytest.raises(ValueError, match=error_msg_6):
          ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_parallel_workers=0)
      with pytest.raises(ValueError, match=error_msg_6):
          ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_parallel_workers=88)
  
      error_msg_7 = "no .bin files found"
      with pytest.raises(RuntimeError, match=error_msg_7):
          ds1 = ds.Cifar10Dataset(NO_BIN_DIR)
          for _ in ds1.__iter__():
              pass
  
  
  def test_cifar10_visualize(plot=False):
      """
      Visualize Cifar10Dataset results
      """
      logger.info("Test Cifar10Dataset visualization")
  
      data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=10, shuffle=False)
      num_iter = 0
      image_list, label_list = [], []
      for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
          image = item["image"]
          label = item["label"]
          image_list.append(image)
          label_list.append("label {}".format(label))
          assert isinstance(image, np.ndarray)
          assert image.shape == (32, 32, 3)
          assert image.dtype == np.uint8
          assert label.dtype == np.uint32
          num_iter += 1
      assert num_iter == 10
      if plot:
          visualize_dataset(image_list, label_list)
  
  
  ### Testcases for Cifar100Dataset Op ###
  
  def test_cifar100_content_check():
      """
      Validate Cifar100Dataset image readings
      """
      logger.info("Test Cifar100Dataset with content check")
      data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, shuffle=False)
      images, labels = load_cifar(DATA_DIR_100, kind="cifar100")
      num_iter = 0
      # in this example, each dictionary has keys "image", "coarse_label" and "fine_image"
      for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
          np.testing.assert_array_equal(d["image"], images[i])
          np.testing.assert_array_equal(d["coarse_label"], labels[i][0])
          np.testing.assert_array_equal(d["fine_label"], labels[i][1])
          num_iter += 1
      assert num_iter == 100
  
  
  def test_cifar100_basic():
      """
      Test Cifar100Dataset
      """
      logger.info("Test Cifar100Dataset")
  
      # case 1: test num_samples
      data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
      num_iter1 = 0
      for _ in data1.create_dict_iterator(num_epochs=1):
          num_iter1 += 1
      assert num_iter1 == 100
  
      # case 2: test repeat
      data1 = data1.repeat(2)
      num_iter2 = 0
      for _ in data1.create_dict_iterator(num_epochs=1):
          num_iter2 += 1
      assert num_iter2 == 200
  
      # case 3: test num_parallel_workers
      data2 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, num_parallel_workers=1)
      num_iter3 = 0
      for _ in data2.create_dict_iterator(num_epochs=1):
          num_iter3 += 1
      assert num_iter3 == 100
  
      # case 4: test batch with drop_remainder=False
      data3 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
      assert data3.get_dataset_size() == 100
      assert data3.get_batch_size() == 1
      data3 = data3.batch(batch_size=3)
      assert data3.get_dataset_size() == 34
      assert data3.get_batch_size() == 3
      num_iter4 = 0
      for _ in data3.create_dict_iterator(num_epochs=1):
          num_iter4 += 1
      assert num_iter4 == 34
  
      # case 4: test batch with drop_remainder=True
      data4 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
      data4 = data4.batch(batch_size=3, drop_remainder=True)
      assert data4.get_dataset_size() == 33
      assert data4.get_batch_size() == 3
      num_iter5 = 0
      for _ in data4.create_dict_iterator(num_epochs=1):
          num_iter5 += 1
      assert num_iter5 == 33
  
  
  def test_cifar100_pk_sampler():
      """
      Test Cifar100Dataset with PKSampler
      """
      logger.info("Test Cifar100Dataset with PKSampler")
      golden = [i for i in range(20)]
      sampler = ds.PKSampler(1)
      data = ds.Cifar100Dataset(DATA_DIR_100, sampler=sampler)
      num_iter = 0
      label_list = []
      for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
          label_list.append(item["coarse_label"])
          num_iter += 1
      np.testing.assert_array_equal(golden, label_list)
      assert num_iter == 20
  
  
  def test_cifar100_exception():
      """
      Test error cases for Cifar100Dataset
      """
      logger.info("Test error cases for Cifar100Dataset")
      error_msg_1 = "sampler and shuffle cannot be specified at the same time"
      with pytest.raises(RuntimeError, match=error_msg_1):
          ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, sampler=ds.PKSampler(3))
  
      error_msg_2 = "sampler and sharding cannot be specified at the same time"
      with pytest.raises(RuntimeError, match=error_msg_2):
          ds.Cifar100Dataset(DATA_DIR_100, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
  
      error_msg_3 = "num_shards is specified and currently requires shard_id as well"
      with pytest.raises(RuntimeError, match=error_msg_3):
          ds.Cifar100Dataset(DATA_DIR_100, num_shards=10)
  
      error_msg_4 = "shard_id is specified but num_shards is not"
      with pytest.raises(RuntimeError, match=error_msg_4):
          ds.Cifar100Dataset(DATA_DIR_100, shard_id=0)
  
      error_msg_5 = "Input shard_id is not within the required interval"
      with pytest.raises(ValueError, match=error_msg_5):
          ds.Cifar100Dataset(DATA_DIR_100, num_shards=2, shard_id=-1)
      with pytest.raises(ValueError, match=error_msg_5):
          ds.Cifar10Dataset(DATA_DIR_100, num_shards=2, shard_id=5)
  
      error_msg_6 = "num_parallel_workers exceeds"
      with pytest.raises(ValueError, match=error_msg_6):
          ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=0)
      with pytest.raises(ValueError, match=error_msg_6):
          ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=88)
  
      error_msg_7 = "no .bin files found"
      with pytest.raises(RuntimeError, match=error_msg_7):
          ds1 = ds.Cifar100Dataset(NO_BIN_DIR)
          for _ in ds1.__iter__():
              pass
  
  
  def test_cifar100_visualize(plot=False):
      """
      Visualize Cifar100Dataset results
      """
      logger.info("Test Cifar100Dataset visualization")
  
      data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=10, shuffle=False)
      num_iter = 0
      image_list, label_list = [], []
      for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
          image = item["image"]
          coarse_label = item["coarse_label"]
          fine_label = item["fine_label"]
          image_list.append(image)
          label_list.append("coarse_label {}\nfine_label {}".format(coarse_label, fine_label))
          assert isinstance(image, np.ndarray)
          assert image.shape == (32, 32, 3)
          assert image.dtype == np.uint8
          assert coarse_label.dtype == np.uint32
          assert fine_label.dtype == np.uint32
          num_iter += 1
      assert num_iter == 10
      if plot:
          visualize_dataset(image_list, label_list)
  
  
  def test_cifar_usage():
      """
      test usage of cifar
      """
      logger.info("Test Cifar100Dataset usage flag")
  
      # flag, if True, test cifar10 else test cifar100
      def test_config(usage, flag=True, cifar_path=None):
          if cifar_path is None:
              cifar_path = DATA_DIR_10 if flag else DATA_DIR_100
          try:
              data = ds.Cifar10Dataset(cifar_path, usage=usage) if flag else ds.Cifar100Dataset(cifar_path, usage=usage)
              num_rows = 0
              for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
                  num_rows += 1
          except (ValueError, TypeError, RuntimeError) as e:
              return str(e)
          return num_rows
  
      # test the usage of CIFAR100
      assert test_config("train") == 10000
      assert test_config("all") == 10000
      assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config("invalid")
      assert "Argument usage with value ['list'] is not of type (<class 'str'>,)" in test_config(["list"])
      assert "no valid data matching the dataset API Cifar10Dataset" in test_config("test")
  
      # test the usage of CIFAR10
      assert test_config("test", False) == 10000
      assert test_config("all", False) == 10000
      assert "no valid data matching the dataset API Cifar100Dataset" in test_config("train", False)
      assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config("invalid", False)
  
      # change this directory to the folder that contains all cifar10 files
      all_cifar10 = None
      if all_cifar10 is not None:
          assert test_config("train", True, all_cifar10) == 50000
          assert test_config("test", True, all_cifar10) == 10000
          assert test_config("all", True, all_cifar10) == 60000
          assert ds.Cifar10Dataset(all_cifar10, usage="train").get_dataset_size() == 50000
          assert ds.Cifar10Dataset(all_cifar10, usage="test").get_dataset_size() == 10000
          assert ds.Cifar10Dataset(all_cifar10, usage="all").get_dataset_size() == 60000
  
      # change this directory to the folder that contains all cifar100 files
      all_cifar100 = None
      if all_cifar100 is not None:
          assert test_config("train", False, all_cifar100) == 50000
          assert test_config("test", False, all_cifar100) == 10000
          assert test_config("all", False, all_cifar100) == 60000
          assert ds.Cifar100Dataset(all_cifar100, usage="train").get_dataset_size() == 50000
          assert ds.Cifar100Dataset(all_cifar100, usage="test").get_dataset_size() == 10000
          assert ds.Cifar100Dataset(all_cifar100, usage="all").get_dataset_size() == 60000
  
  
  if __name__ == '__main__':
      test_cifar10_content_check()
      test_cifar10_basic()
      test_cifar10_pk_sampler()
      test_cifar10_sequential_sampler()
      test_cifar10_exception()
      test_cifar10_visualize(plot=False)
  
      test_cifar100_content_check()
      test_cifar100_basic()
      test_cifar100_pk_sampler()
      test_cifar100_exception()
      test_cifar100_visualize(plot=False)
  
      test_cifar_usage()
  