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       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Union[str, Any]			     =  "<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(" "	)
  				__magic_name__	:	Union[str, Any]			     =  dict(zip(_a		,     range(len(_a	)	)	)	)
  
  				__magic_name__	:	Optional[Any]			     =  {
  				    "pad_token": "<pad>",
  				    "unk_token": "<unk>",
  				    "bos_token": "<s>",
  				    "eos_token": "</s>",
  				}
  				__magic_name__	:	Union[str, Any]			     =  {
  				    "feature_size": 1,
  				    "padding_value": 0.0,
  				    "sampling_rate": 16_000,
  				    "return_attention_mask": False,
  				    "do_normalize": True,
  				}
  
  				__magic_name__	:	int			     =  tempfile.mkdtemp()
  				__magic_name__	:	str			     =  os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["vocab_file"]	)
  				__magic_name__	:	Dict			     =  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     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
  				__magic_name__	:	int			     =  self.add_kwargs_tokens_map.copy()
  				kwargs.update(_a	)
  				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
  				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,     **_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				shutil.rmtree(self.tmpdirname	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Union[str, Any]			     =  self.get_tokenizer()
  				__magic_name__	:	List[str]			     =  self.get_feature_extractor()
  
  				__magic_name__	:	Optional[int]			     =  WavaVecaProcessor(tokenizer=_a		,     feature_extractor=_a	)
  
  				processor.save_pretrained(self.tmpdirname	)
  				__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Union[str, Any]			     =  WavaVecaProcessor(tokenizer=self.get_tokenizer()		,     feature_extractor=self.get_feature_extractor()	)
  				processor.save_pretrained(self.tmpdirname	)
  
  				__magic_name__	:	List[Any]			     =  self.get_tokenizer(bos_token="(BOS)"		,     eos_token="(EOS)"	)
  				__magic_name__	:	List[Any]			     =  self.get_feature_extractor(do_normalize=_a		,     padding_value=1.0	)
  
  				__magic_name__	:	Any			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  self.get_feature_extractor()
  				__magic_name__	:	Any			     =  self.get_tokenizer()
  
  				__magic_name__	:	Optional[int]			     =  WavaVecaProcessor(tokenizer=_a		,     feature_extractor=_a	)
  
  				__magic_name__	:	Optional[int]			     =  floats_list((3, 1_000)	)
  
  				__magic_name__	:	List[Any]			     =  feature_extractor(_a		,     return_tensors="np"	)
  				__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	int			     =  self.get_feature_extractor()
  				__magic_name__	:	Optional[Any]			     =  self.get_tokenizer()
  
  				__magic_name__	:	Tuple			     =  WavaVecaProcessor(tokenizer=_a		,     feature_extractor=_a	)
  
  				__magic_name__	:	Optional[int]			     =  "This is a test string"
  
  				__magic_name__	:	List[str]			     =  processor(text=_a	)
  
  				__magic_name__	:	int			     =  tokenizer(_a	)
  
  				for key in encoded_tok.keys():
  					self.assertListEqual(encoded_tok[key]		,     encoded_processor[key]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  self.get_feature_extractor()
  				__magic_name__	:	Optional[int]			     =  self.get_tokenizer()
  
  				__magic_name__	:	int			     =  WavaVecaProcessor(tokenizer=_a		,     feature_extractor=_a	)
  
  				__magic_name__	:	Tuple			     =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  				__magic_name__	:	int			     =  processor.batch_decode(_a	)
  				__magic_name__	:	int			     =  tokenizer.batch_decode(_a	)
  
  				self.assertListEqual(_a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	List[Any]			     =  self.get_feature_extractor()
  				__magic_name__	:	Dict			     =  self.get_tokenizer()
  
  				__magic_name__	:	Optional[int]			     =  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: 
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if not isinstance(_snake_case   ,     _snake_case     ) or number < 0:
  		raise ValueError("Input must be a non-negative integer"     )
  
  	__magic_name__	:	Dict			     =  number & ~(number - 1)
  	__magic_name__	:	Optional[Any]			     =  0
  	while intermediate:
  		intermediate >>= 1
  		index += 1
  	return index - 1
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod(verbose=True)
  