text: # Copyright 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.
  # ==============================================================================
  """
  Test LJSpeech dataset operators
  """
  import numpy as np
  import pytest
  
  import mindspore.dataset as ds
  import mindspore.dataset.audio.transforms as audio
  from mindspore import log as logger
  
  DATA_DIR = "../data/dataset/testLJSpeechData/"
  
  
  def test_lj_speech_basic():
      """
      Feature: LJSpeechDataset
      Description: basic test of LJSpeechDataset
      Expectation: the data is processed successfully
      """
      logger.info("Test LJSpeechDataset Op")
  
      # case 1: test loading whole dataset
      data1 = ds.LJSpeechDataset(DATA_DIR)
      num_iter1 = 0
      for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
          num_iter1 += 1
      assert num_iter1 == 3
  
      # case 2: test num_samples
      data2 = ds.LJSpeechDataset(DATA_DIR, num_samples=3)
      num_iter2 = 0
      for _ in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
          num_iter2 += 1
      assert num_iter2 == 3
  
      # case 3: test repeat
      data3 = ds.LJSpeechDataset(DATA_DIR, num_samples=3)
      data3 = data3.repeat(5)
      num_iter3 = 0
      for _ in data3.create_dict_iterator(num_epochs=1, output_numpy=True):
          num_iter3 += 1
      assert num_iter3 == 15
  
  
  def test_lj_speech_sequential_sampler():
      """
      Feature: LJSpeechDataset
      Description: test LJSpeechDataset with SequentialSampler
      Expectation: the data is processed successfully
      """
      logger.info("Test LJSpeechDataset Op with SequentialSampler")
      num_samples = 3
      sampler = ds.SequentialSampler(num_samples=num_samples)
      data1 = ds.LJSpeechDataset(DATA_DIR, sampler=sampler)
      data2 = ds.LJSpeechDataset(DATA_DIR, shuffle=False, num_samples=num_samples)
      sample_rate_list1, sample_rate_list2 = [], []
      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)):
          sample_rate_list1.append(item1["sample_rate"])
          sample_rate_list2.append(item2["sample_rate"])
          num_iter += 1
      np.testing.assert_array_equal(sample_rate_list1, sample_rate_list2)
      assert num_iter == num_samples
  
  
  def test_lj_speech_exception():
      """
      Feature: LJSpeechDataset
      Description: test error cases for LJSpeechDataset
      Expectation: throw correct error and message
      """
      logger.info("Test error cases for LJSpeechDataset")
      error_msg_1 = "sampler and shuffle cannot be specified at the same time"
      with pytest.raises(RuntimeError, match=error_msg_1):
          ds.LJSpeechDataset(DATA_DIR, 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.LJSpeechDataset(DATA_DIR, 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.LJSpeechDataset(DATA_DIR, num_shards=10)
  
      error_msg_4 = "shard_id is specified but num_shards is not"
      with pytest.raises(RuntimeError, match=error_msg_4):
          ds.LJSpeechDataset(DATA_DIR, shard_id=0)
  
      error_msg_5 = "Input shard_id is not within the required interval"
      with pytest.raises(ValueError, match=error_msg_5):
          ds.LJSpeechDataset(DATA_DIR, num_shards=5, shard_id=-1)
      with pytest.raises(ValueError, match=error_msg_5):
          ds.LJSpeechDataset(DATA_DIR, num_shards=5, shard_id=5)
      with pytest.raises(ValueError, match=error_msg_5):
          ds.LJSpeechDataset(DATA_DIR, num_shards=2, shard_id=5)
  
      error_msg_6 = "num_parallel_workers exceeds"
      with pytest.raises(ValueError, match=error_msg_6):
          ds.LJSpeechDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
      with pytest.raises(ValueError, match=error_msg_6):
          ds.LJSpeechDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
      with pytest.raises(ValueError, match=error_msg_6):
          ds.LJSpeechDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
  
      error_msg_7 = "Argument shard_id"
      with pytest.raises(TypeError, match=error_msg_7):
          ds.LJSpeechDataset(DATA_DIR, num_shards=2, shard_id="0")
  
      def exception_func(item):
          raise Exception("Error occur!")
  
      error_msg_8 = "The corresponding data files"
      with pytest.raises(RuntimeError, match=error_msg_8):
          data = ds.LJSpeechDataset(DATA_DIR)
          data = data.map(operations=exception_func, input_columns=["waveform"], num_parallel_workers=1)
          for _ in data.__iter__():
              pass
      with pytest.raises(RuntimeError, match=error_msg_8):
          data = ds.LJSpeechDataset(DATA_DIR)
          data = data.map(operations=exception_func, input_columns=["sample_rate"], num_parallel_workers=1)
          for _ in data.__iter__():
              pass
  
  
  def test_lj_speech_pipeline():
      """
      Feature: LJSpeechDataset
      Description: Read a sample
      Expectation: The amount of each function are equal
      """
      # Original waveform
      dataset = ds.LJSpeechDataset(DATA_DIR)
      band_biquad_op = audio.BandBiquad(8000, 200.0)
      # Filtered waveform by bandbiquad
      dataset = dataset.map(input_columns=["waveform"], operations=band_biquad_op, num_parallel_workers=2)
      i = 0
      for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
          i += 1
      assert i == 3
  
  
  if __name__ == '__main__':
      test_lj_speech_basic()
      test_lj_speech_sequential_sampler()
      test_lj_speech_exception()
      test_lj_speech_pipeline()
  