How to use the nlpaug.flow.Sequential function in nlpaug

def test_n_output_without_augmentation(self):
        texts = [
            'AAAAAAAAAAA AAAAAAAAAAAAAA'
        ]
        flows = [
            naf.Sequential([
                nac.OcrAug(),
                nac.OcrAug()
            ]),
            naf.Sometimes([
                nac.RandomCharAug(),
                nac.RandomCharAug()
            ], pipeline_p=0.00001)
        ]

        for flow in flows:
            for text in texts:
                for _ in range(5):
                    augmented_texts = flow.augment(text, n=3)
                    all_not_equal = False
                    for augmented_text in augmented_texts:
                        if augmented_text != text:

def test_n_output_audio(self):
        audio, sampling_rate = AudioLoader.load_audio(self.sample_wav_file)

        flows = [
            naf.Sequential([
                naa.CropAug(sampling_rate=sampling_rate),
                naa.LoudnessAug()
            ]),
            naf.Sometimes([
                naa.CropAug(sampling_rate=sampling_rate),
                naa.LoudnessAug()
            ], pipeline_p=0.9),
            naf.Sequential([
                naf.Sequential([
                    naa.CropAug(sampling_rate=sampling_rate),
                    naa.LoudnessAug()
                ]),
                naf.Sometimes([
                    naa.CropAug(sampling_rate=sampling_rate),
                    naa.LoudnessAug()
                ], pipeline_p=0.9)
            ])
        ]

        for flow in flows:
            augmented_audios = flow.augment(audio, n=3)
            self.assertGreater(len(augmented_audios), 1)
            for augmented_audio in augmented_audios:
                self.assertFalse(np.array_equal(audio, augmented_audio))

def test_multiple_actions(self):
        texts = [
            'The quick brown fox jumps over the lazy dog',
            'Zology raku123456 fasdasd asd4123414 1234584'
        ]

        flows = [
            naf.Sequential([
                naf.Sometimes([nac.RandomCharAug(action="insert"),
                               nac.RandomCharAug(action="delete")],
                              pipeline_p=0.9),
                naf.Sequential([
                    nac.RandomCharAug(action="substitute", aug_char_min=1, aug_char_p=0.6, aug_word_p=0.6)
                ], name='Sub_Seq')
            ]),
            naf.Sometimes([
                naf.Sometimes([nac.RandomCharAug(action="insert"),
                               nac.RandomCharAug(action="delete")]),
                naf.Sequential([nac.OcrAug(), nac.KeyboardAug(aug_char_min=1),
                                nac.RandomCharAug(action="substitute", aug_char_min=1, aug_char_p=0.6, aug_word_p=0.6)])
            ], pipeline_p=0.9)
        ]

        # Since prob may be low and causing do not perform data augmentation. Retry 5 times
        for flow in flows:
            for text in texts:
                at_least_one_not_equal = False
                for _ in range(5):

def test_dry_run(self):
        flow = naf.Sequential([naf.Sequential()])
        results = flow.augment([])
        self.assertEqual(0, len(results))

def test_n_output_spectrogram(self):
        mel_spectrogram = AudioLoader.load_mel_spectrogram(self.sample_wav_file, n_mels=128)
    #
        flows = [
            naf.Sequential([
                nas.FrequencyMaskingAug(mask_factor=80),
                nas.TimeMaskingAug(mask_factor=80)
            ]),
            naf.Sometimes([
                nas.FrequencyMaskingAug(mask_factor=80),
                nas.TimeMaskingAug(mask_factor=80)
            ], pipeline_p=0.9),
            naf.Sequential([
                naf.Sequential([
                    nas.FrequencyMaskingAug(mask_factor=80),
                    nas.TimeMaskingAug(mask_factor=80)
                ]),
                naf.Sometimes([
                    nas.FrequencyMaskingAug(mask_factor=80),
                    nas.TimeMaskingAug(mask_factor=80)
                ], pipeline_p=0.9)

naf.Sequential([
                naf.Sequential([
                    nac.OcrAug(),
                    naw.WordEmbsAug(
                        model_type='word2vec',
                        model_path=os.environ["MODEL_DIR"] + 'GoogleNews-vectors-negative300.bin')
                ]),
                naf.Sequential([
                    nac.RandomCharAug(),
                ]),
                naw.ContextualWordEmbsAug(
                    model_path='xlnet-base-cased', action="substitute",
                    skip_unknown_word=True, temperature=0.7, device='cpu')
            ]),
            naf.Sometimes([
                naf.Sequential([
                    nac.OcrAug(),
                    nac.RandomCharAug(),
                ]),
                naf.Sometimes([
                    naw.WordEmbsAug(model_type='word2vec',
                                    model_path=os.environ["MODEL_DIR"] + 'GoogleNews-vectors-negative300.bin')
                ], pipeline_p=0.999),
                naw.ContextualWordEmbsAug(
                    model_path='xlnet-base-cased', action="substitute",
                    skip_unknown_word=True, temperature=0.7, device='cpu')
            ], pipeline_p=0.9999)
        ]

        for num_thread in [1, 3]:
            for flow in flows:
                augmented_data = flow.augment(text, n=n, num_thread=num_thread)

texts = [
            'The quick brown fox jumps over the lazy dog',
            'Zology raku123456 fasdasd asd4123414 1234584',
            'AAAAAAAAAAA AAAAAAAAAAAAAA'
        ]
        flows = [
            naf.Sequential([
                nac.RandomCharAug(action="insert"),
                naw.RandomWordAug()
            ]),
            naf.Sometimes([
                nac.RandomCharAug(action="insert"),
                nac.RandomCharAug(action="delete")
            ], pipeline_p=0.9),
            naf.Sequential([
                naf.Sequential([
                    nac.RandomCharAug(action="insert"),
                    naw.RandomWordAug()
                ]),
                naf.Sometimes([
                    nac.RandomCharAug(action="insert"),
                    nac.RandomCharAug(action="delete")
                ], pipeline_p=0.9)
            ])
        ]

        for flow in flows:
            for text in texts:
                augmented_texts = flow.augment(text, n=3)
                self.assertGreater(len(augmented_texts), 1)
                for augmented_text in augmented_texts:
                    self.assertNotEqual(augmented_text, text)