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      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      '<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(' '		)
  		snake_case__  					=      dict(zip(UpperCamelCase_			,					range(len(UpperCamelCase_		)		)		)		)
  
  		snake_case__  					=      {
  		    'pad_token': '<pad>',
  		    'unk_token': '<unk>',
  		    'bos_token': '<s>',
  		    'eos_token': '</s>',
  		}
  		snake_case__  					=      {
  		    'feature_size': 1,
  		    'padding_value': 0.0,
  		    'sampling_rate': 1_6000,
  		    'return_attention_mask': False,
  		    'do_normalize': True,
  		}
  
  		snake_case__  					=      tempfile.mkdtemp()
  		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)
  		snake_case__  					=      os.path.join(self.tmpdirname			,					UpperCamelCase_		)
  		with open(self.vocab_file			,					'w'			,					encoding='utf-8'		) as fp:
  			fp.write(json.dumps(UpperCamelCase_		) + '\n'		)
  
  		with open(self.feature_extraction_file			,					'w'			,					encoding='utf-8'		) as fp:
  			fp.write(json.dumps(UpperCamelCase_		) + '\n'		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
  		snake_case__  					=      self.add_kwargs_tokens_map.copy()
  		kwargs.update(UpperCamelCase_		)
  		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		str:
  		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      self.get_tokenizer()
  		snake_case__  					=      self.get_feature_extractor()
  
  		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		processor.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      WavaVecaProcessor.from_pretrained(self.tmpdirname		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor.to_json_string()		)
  		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      WavaVecaProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		processor.save_pretrained(self.tmpdirname		)
  
  		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
  		snake_case__  					=      self.get_feature_extractor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		snake_case__  					=      WavaVecaProcessor.from_pretrained(
  		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor_add_kwargs.to_json_string()		)
  		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      floats_list((3, 1000)		)
  
  		snake_case__  					=      feature_extractor(UpperCamelCase_			,					return_tensors='np'		)
  		snake_case__  					=      processor(UpperCamelCase_			,					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      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      'This is a test string'
  
  		snake_case__  					=      processor(text=UpperCamelCase_		)
  
  		snake_case__  					=      tokenizer(UpperCamelCase_		)
  
  		for key in encoded_tok.keys():
  			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
  		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)
  
  		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		self.assertListEqual(
  		    processor.model_input_names			,					feature_extractor.model_input_names			,					msg='`processor` and `feature_extractor` model input names do not match'			,					)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  