code: 
  
  
  
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
  from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
  from transformers.utils import FEATURE_EXTRACTOR_NAME
  
  from .test_feature_extraction_wavaveca import floats_list
  
  
  
  class 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''					)
     _A           =    dict(zip(a	,    range(len(a					)					)					)					)
  
     _A           =    {
         '''pad_token''': '''<pad>''',
         '''unk_token''': '''<unk>''',
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
     }
     _A           =    {
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_6_0_0_0,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }
  
     _A           =    tempfile.mkdtemp()
     _A           =    os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''vocab_file''']					)
     _A           =    os.path.join(self.tmpdirname	,    a					)
     with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''					) as fp:
       fp.write(json.dumps(a					) + '''\n'''					)
  
     with open(self.feature_extraction_file	,    '''w'''	,    encoding='''utf-8'''					) as fp:
       fp.write(json.dumps(a					) + '''\n'''					)
   def lowercase_		(						self	,    **a					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.add_kwargs_tokens_map.copy()
     kwargs.update(a					)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,    **a					)
   def lowercase_		(						self	,    **a					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,    **a					)
   def lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     shutil.rmtree(self.tmpdirname					)
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_tokenizer()
     _A           =    self.get_feature_extractor()
  
     _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)
  
     processor.save_pretrained(self.tmpdirname					)
     _A           =    WavaVecaProcessor.from_pretrained(self.tmpdirname					)
  
     self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()					)
     self.assertIsInstance(processor.tokenizer	,    a					)
  
     self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()					)
     self.assertIsInstance(processor.feature_extractor	,    a					)
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    WavaVecaProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()					)
     processor.save_pretrained(self.tmpdirname					)
  
     _A           =    self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''					)
     _A           =    self.get_feature_extractor(do_normalize=a	,    padding_value=1.0					)
  
     _A           =    WavaVecaProcessor.from_pretrained(
         self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=a	,    padding_value=1.0					)
  
     self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()					)
     self.assertIsInstance(processor.tokenizer	,    a					)
  
     self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()					)
     self.assertIsInstance(processor.feature_extractor	,    a					)
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)
  
     _A           =    floats_list((3, 1_0_0_0)					)
  
     _A           =    feature_extractor(a	,    return_tensors='''np'''					)
     _A           =    processor(a	,    return_tensors='''np'''					)
  
     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1e-2					)
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)
  
     _A           =    '''This is a test string'''
  
     _A           =    processor(text=a					)
  
     _A           =    tokenizer(a					)
  
     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]					)
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)
  
     _A           =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
     _A           =    processor.batch_decode(a					)
     _A           =    tokenizer.batch_decode(a					)
  
     self.assertListEqual(a	,    a					)
  
  
  
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    WavaVecaProcessor(tokenizer=a	,    feature_extractor=a					)
  
     self.assertListEqual(
         processor.model_input_names	,    feature_extractor.model_input_names	,    msg='''`processor` and `feature_extractor` model input names do not match'''	,    )
style_context: 
  
  
  
  import platform
  from argparse import ArgumentParser
  
  import huggingface_hub
  import pandas
  import pyarrow
  
  from datasets import __version__ as version
  from datasets.commands import BaseDatasetsCLICommand
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Optional[Any]:
    return EnvironmentCommand()
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    parser.add_parser('''env'''	,    help='''Print relevant system environment info.'''					)
     download_parser.set_defaults(func=a					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    {
         '''`datasets` version''': version,
         '''Platform''': platform.platform(),
         '''Python version''': platform.python_version(),
         '''Huggingface_hub version''': huggingface_hub.__version__,
         '''PyArrow version''': pyarrow.__version__,
         '''Pandas version''': pandas.__version__,
     }
  
     print('''\nCopy-and-paste the text below in your GitHub issue.\n'''					)
     print(self.format_dict(a					)					)
  
     return info
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"