




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase	:		Tuple							       =   get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_lowerCamelCase	:		Tuple							       =   256047
_lowerCamelCase	:		int							       =   256145


@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      NllbTokenizer
    UpperCAmelCase__							  =      NllbTokenizerFast
    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      {}


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[int]:





           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           A__       =    NllbTokenizer(UpperCAmelCase__					, keep_accents=UpperCAmelCase__)
           tokenizer.save_pretrained(self.tmpdirname)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->int:





           '''simple docstring'''

           A__       =    NllbTokenizer(UpperCAmelCase__					, keep_accents=UpperCAmelCase__)

           A__       =    tokenizer.tokenize('''This is a test''')
           self.assertListEqual(UpperCAmelCase__					, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCAmelCase__)					, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					, )

           A__       =    tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               UpperCAmelCase__					, [
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''9''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''é''',
                   '''.''',
               ]					, )
           A__       =    tokenizer.convert_tokens_to_ids(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__					, [
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]					, )

           A__       =    tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__					, [
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''<unk>''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''<unk>''',
                   '''.''',
               ]					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Dict:





           '''simple docstring'''

           A__       =    (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         A__       =    self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    self.tokenizer_class.from_pretrained(UpperCAmelCase__					, **UpperCAmelCase__)

                         A__       =    tempfile.mkdtemp()

                         A__       =    tokenizer_r.save_pretrained(UpperCAmelCase__)
                         A__       =    tokenizer_p.save_pretrained(UpperCAmelCase__)

                         # Checks it save with the same files + the tokenizer.json file for the fast one
                         self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files))
                         A__       =    tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f)
                         self.assertSequenceEqual(UpperCAmelCase__					, UpperCAmelCase__)

                         # Checks everything loads correctly in the same way
                         A__       =    tokenizer_r.from_pretrained(UpperCAmelCase__)
                         A__       =    tokenizer_p.from_pretrained(UpperCAmelCase__)

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                                self.assertTrue(hasattr(UpperCAmelCase__					, UpperCAmelCase__))

                         shutil.rmtree(UpperCAmelCase__)

                         # Save tokenizer rust, legacy_format=True
                         A__       =    tempfile.mkdtemp()

                         A__       =    tokenizer_r.save_pretrained(UpperCAmelCase__					, legacy_format=UpperCAmelCase__)
                         A__       =    tokenizer_p.save_pretrained(UpperCAmelCase__)

                         # Checks it save with the same files
                         self.assertSequenceEqual(UpperCAmelCase__					, UpperCAmelCase__)

                         # Checks everything loads correctly in the same way
                         A__       =    tokenizer_r.from_pretrained(UpperCAmelCase__)
                         A__       =    tokenizer_p.from_pretrained(UpperCAmelCase__)

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                                self.assertTrue(hasattr(UpperCAmelCase__					, UpperCAmelCase__))

                         shutil.rmtree(UpperCAmelCase__)

                         # Save tokenizer rust, legacy_format=False
                         A__       =    tempfile.mkdtemp()

                         A__       =    tokenizer_r.save_pretrained(UpperCAmelCase__					, legacy_format=UpperCAmelCase__)
                         A__       =    tokenizer_p.save_pretrained(UpperCAmelCase__)

                         # Checks it saved the tokenizer.json file
                         self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files))

                         # Checks everything loads correctly in the same way
                         A__       =    tokenizer_r.from_pretrained(UpperCAmelCase__)
                         A__       =    tokenizer_p.from_pretrained(UpperCAmelCase__)

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                                self.assertTrue(hasattr(UpperCAmelCase__					, UpperCAmelCase__))

                         shutil.rmtree(UpperCAmelCase__)


    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Any:





           '''simple docstring'''

           if not self.test_seqaseq:
                  return

           A__       =    self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f"""{tokenizer.__class__.__name__}"""):
                         # Longer text that will definitely require truncation.
                         A__       =    [
                             ''' UN Chief Says There Is No Military Solution in Syria''',
                             ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
                             ''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
                             ''' will only worsen the violence and misery for millions of people.''',
                         ]
                         A__       =    [
                             '''Şeful ONU declară că nu există o soluţie militară în Siria''',
                             '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
                             ''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
                             ''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
                         ]
                         try:
                                A__       =    tokenizer.prepare_seqaseq_batch(
                                    src_texts=UpperCAmelCase__					, tgt_texts=UpperCAmelCase__					, max_length=3					, max_target_length=10					, return_tensors='''pt'''					, src_lang='''eng_Latn'''					, tgt_lang='''ron_Latn'''					, )
                         except NotImplementedError:
                                return
                         self.assertEqual(batch.input_ids.shape[1]					, 3)
                         self.assertEqual(batch.labels.shape[1]					, 10)
                         # max_target_length will default to max_length if not specified
                         A__       =    tokenizer.prepare_seqaseq_batch(
                             UpperCAmelCase__					, tgt_texts=UpperCAmelCase__					, max_length=3					, return_tensors='''pt''')
                         self.assertEqual(batch.input_ids.shape[1]					, 3)
                         self.assertEqual(batch.labels.shape[1]					, 3)

                         A__       =    tokenizer.prepare_seqaseq_batch(
                             src_texts=UpperCAmelCase__					, max_length=3					, max_target_length=10					, return_tensors='''pt''')
                         self.assertEqual(batch_encoder_only.input_ids.shape[1]					, 3)
                         self.assertEqual(batch_encoder_only.attention_mask.shape[1]					, 3)
                         self.assertNotIn('''decoder_input_ids'''					, UpperCAmelCase__)


    @unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.''')
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[Any]:





           '''simple docstring'''

           pass





    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[Any]:





           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         A__       =    [AddedToken('''<special>'''					, lstrip=UpperCAmelCase__)]

                         A__       =    self.rust_tokenizer_class.from_pretrained(
                             UpperCAmelCase__					, additional_special_tokens=UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    tokenizer_r.encode('''Hey this is a <special> token''')

                         A__       =    tokenizer_r.encode('''<special>'''					, add_special_tokens=UpperCAmelCase__)[0]

                         self.assertTrue(special_token_id in r_output)

                         if self.test_slow_tokenizer:
                                A__       =    self.rust_tokenizer_class.from_pretrained(
                                    UpperCAmelCase__					, additional_special_tokens=UpperCAmelCase__					, **UpperCAmelCase__					, )
                                A__       =    self.tokenizer_class.from_pretrained(
                                    UpperCAmelCase__					, additional_special_tokens=UpperCAmelCase__					, **UpperCAmelCase__)

                                A__       =    tokenizer_p.encode('''Hey this is a <special> token''')

                                A__       =    tokenizer_cr.encode('''Hey this is a <special> token''')

                                self.assertEqual(UpperCAmelCase__					, UpperCAmelCase__)
                                self.assertEqual(UpperCAmelCase__					, UpperCAmelCase__)
                                self.assertTrue(special_token_id in p_output)
                                self.assertTrue(special_token_id in cr_output)


@require_torch
@require_sentencepiece
@require_tokenizers
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''facebook/nllb-200-distilled-600M'''
    UpperCAmelCase__							  =      [
        ''' UN Chief Says There Is No Military Solution in Syria''',
        ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
    ]
    UpperCAmelCase__							  =      [
        '''Şeful ONU declară că nu există o soluţie militară în Siria''',
        '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
        ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
        ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
    ]
    UpperCAmelCase__							  =      [
        25_6047,
        1_6297,
        13_4408,
        8165,
        24_8066,
        1_4734,
        950,
        1135,
        10_5721,
        3573,
        83,
        2_7352,
        108,
        4_9486,
        2,
    ]


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Tuple)						->int:





           '''simple docstring'''

           A__       =    NllbTokenizer.from_pretrained(
               cls.checkpoint_name					, src_lang='''eng_Latn'''					, tgt_lang='''ron_Latn''')
           A__       =    1
           return cls


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Dict:





           '''simple docstring'''

           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab''']					, 256_001)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn''']					, 256_002)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn''']					, 256_057)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Tuple:





           '''simple docstring'''

           A__       =    self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
           self.assertListEqual(self.expected_src_tokens					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           self.assertIn(UpperCAmelCase__					, self.tokenizer.all_special_ids)
           # fmt: off
           A__       =    [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
           # fmt: on

           A__       =    self.tokenizer.decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
           A__       =    self.tokenizer.decode(generated_ids[1:]					, skip_special_tokens=UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__					, UpperCAmelCase__)
           self.assertNotIn(self.tokenizer.eos_token					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__       =    ['''this is gunna be a long sentence ''' * 20]
           assert isinstance(src_text[0]					, UpperCAmelCase__)
           A__       =    10
           A__       =    self.tokenizer(UpperCAmelCase__					, max_length=UpperCAmelCase__					, truncation=UpperCAmelCase__).input_ids[0]
           self.assertEqual(ids[-1]					, 2)
           self.assertEqual(ids[0]					, UpperCAmelCase__)
           self.assertEqual(len(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[str]:





           '''simple docstring'''

           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''])					, [256_203, 3])


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->str:





           '''simple docstring'''

           A__       =    tempfile.mkdtemp()
           A__       =    self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(UpperCAmelCase__)
           A__       =    NllbTokenizer.from_pretrained(UpperCAmelCase__)
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids					, UpperCAmelCase__)


    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[Any]:





           '''simple docstring'''

           A__       =    self.tokenizer(
               self.src_text					, text_target=self.tgt_text					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, max_length=len(self.expected_src_tokens)					, return_tensors='''pt'''					, )
           A__       =    shift_tokens_right(
               batch['''labels''']					, self.tokenizer.pad_token_id					, self.tokenizer.lang_code_to_id['''ron_Latn'''])

           self.assertIsInstance(UpperCAmelCase__					, UpperCAmelCase__)

           self.assertEqual((2, 15)					, batch.input_ids.shape)
           self.assertEqual((2, 15)					, batch.attention_mask.shape)
           A__       =    batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens					, UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__					, batch.decoder_input_ids[0, 0])  # EOS
           # Test that special tokens are reset
           self.assertEqual(self.tokenizer.prefix_tokens					, [EN_CODE])
           self.assertEqual(self.tokenizer.suffix_tokens					, [self.tokenizer.eos_token_id])


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Tuple:





           '''simple docstring'''

           A__       =    self.tokenizer(self.src_text					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, max_length=3					, return_tensors='''pt''')
           A__       =    self.tokenizer(
               text_target=self.tgt_text					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, max_length=10					, return_tensors='''pt''')
           A__       =    targets['''input_ids''']
           A__       =    shift_tokens_right(
               UpperCAmelCase__					, self.tokenizer.pad_token_id					, decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]					, )

           self.assertEqual(batch.input_ids.shape[1]					, 3)
           self.assertEqual(batch.decoder_input_ids.shape[1]					, 10)


    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__       =    self.tokenizer._build_translation_inputs(
               '''A test'''					, return_tensors='''pt'''					, src_lang='''eng_Latn'''					, tgt_lang='''fra_Latn''')

           self.assertEqual(
               nested_simplify(UpperCAmelCase__)					, {
                   # A, test, EOS, en_XX
                   '''input_ids''': [[256_047, 70, 7_356, 2]],
                   '''attention_mask''': [[1, 1, 1, 1]],
                   # ar_AR
                   '''forced_bos_token_id''': 256_057,
               }					, )





    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           A__       =    True
           A__       =    self.tokenizer(
               '''UN Chief says there is no military solution in Syria'''					, src_lang='''eng_Latn'''					, tgt_lang='''fra_Latn''')
           self.assertEqual(
               inputs.input_ids					, [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047])

           A__       =    False
           A__       =    self.tokenizer(
               '''UN Chief says there is no military solution in Syria'''					, src_lang='''eng_Latn'''					, tgt_lang='''fra_Latn''')
           self.assertEqual(
               inputs.input_ids					, [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2])








import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    args.log_outputs
       A__       =    '''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

       # load metric
       A__       =    load_metric('''wer'''						)
       A__       =    load_metric('''cer'''						)

       # compute metrics
       A__       =    wer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)
       A__       =    cer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)

       # print & log results
       A__       =    f"""WER: {wer_result}\nCER: {cer_result}"""
       print(lowercase_						)

       with open(f"""{dataset_id}_eval_results.txt""" ,				'''w'''						) as f:
              f.write(lowercase_						)

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              A__       =    f"""log_{dataset_id}_predictions.txt"""
              A__       =    f"""log_{dataset_id}_targets.txt"""

              with open(lowercase_ ,				'''w'''						) as p, open(lowercase_ ,				'''w'''						) as t:
                     # mapping function to write output
                     def write_to_file(lowercase_ ,				lowercase_						):
                            p.write(f"""{i}""" + '''\n'''						)
                            p.write(batch['''prediction'''] + '''\n'''						)
                            t.write(f"""{i}""" + '''\n'''						)
                            t.write(batch['''target'''] + '''\n'''						)

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

       # In addition, we can normalize the target text, e.g. removing new lines characters etc...
       # note that order is important here!
       A__       =    ['''\n\n''', '''\n''', '''   ''', '''  ''']

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

       # for testing: only process the first two examples as a test
       # dataset = dataset.select(range(10))

       # load processor
       A__       =    AutoFeatureExtractor.from_pretrained(args.model_id						)
       A__       =    feature_extractor.sampling_rate

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

       # load eval pipeline
       if args.device is None:
              A__       =    0 if torch.cuda.is_available() else -1
       A__       =    pipeline('''automatic-speech-recognition''' ,				model=args.model_id ,				device=args.device						)

       # map function to decode audio
       def map_to_pred(lowercase_						):
              A__       =    asr(
                  batch['''audio''']['''array'''] ,				chunk_length_s=args.chunk_length_s ,				stride_length_s=args.stride_length_s						)

              A__       =    prediction['''text''']
              A__       =    normalize_text(batch['''sentence''']						)
              return batch

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

       # compute and log_results
       # do not change function below
       log_results(lowercase_ ,				lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase	:		List[Any]							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


