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      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> List[str]:
  							_lowercase					:  Tuple    			=      "<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(" "						)
  							_lowercase					:  Optional[int]    			=      dict(zip(snake_case			,  range(len(snake_case						)						)						)						)
  
  							_lowercase					:  Optional[Any]    			=      {
  							    "pad_token": "<pad>",
  							    "unk_token": "<unk>",
  							    "bos_token": "<s>",
  							    "eos_token": "</s>",
  							}
  							_lowercase					:  str    			=      {
  							    "feature_size": 1,
  							    "padding_value": 0.0,
  							    "sampling_rate": 1_6000,
  							    "return_attention_mask": False,
  							    "do_normalize": True,
  							}
  
  							_lowercase					:  Tuple    			=      tempfile.mkdtemp()
  							_lowercase					:  Dict    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]						)
  							_lowercase					:  List[str]    			=      os.path.join(self.tmpdirname			,  snake_case						)
  							with open(self.vocab_file			,  "w"			,  encoding="utf-8"						) as fp:
  													fp.write(json.dumps(snake_case						) + "\n"						)
  
  							with open(self.feature_extraction_file			,  "w"			,  encoding="utf-8"						) as fp:
  													fp.write(json.dumps(snake_case						) + "\n"						)
  
  
  
  
  
  
  	def 			_a(self							:   int			,  **snake_case							:   List[str]						)   -> Optional[Any]:
  							_lowercase					:  Any    			=      self.add_kwargs_tokens_map.copy()
  							kwargs.update(snake_case						)
  							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,  **snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any			,  **snake_case							:   Dict						)   -> Any:
  							return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,  **snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> str:
  							shutil.rmtree(self.tmpdirname						)
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Optional[int]:
  							_lowercase					:  Dict    			=      self.get_tokenizer()
  							_lowercase					:  Optional[Any]    			=      self.get_feature_extractor()
  
  							_lowercase					:  List[Any]    			=      WavaVecaProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)
  
  							processor.save_pretrained(self.tmpdirname						)
  							_lowercase					:  Optional[Any]    			=      WavaVecaProcessor.from_pretrained(self.tmpdirname						)
  
  							self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer.get_vocab()						)
  							self.assertIsInstance(processor.tokenizer			,  snake_case						)
  
  							self.assertEqual(processor.feature_extractor.to_json_string()			,  feature_extractor.to_json_string()						)
  							self.assertIsInstance(processor.feature_extractor			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> Union[str, Any]:
  							_lowercase					:  Any    			=      WavaVecaProcessor(tokenizer=self.get_tokenizer()			,  feature_extractor=self.get_feature_extractor()						)
  							processor.save_pretrained(self.tmpdirname						)
  
  							_lowercase					:  List[Any]    			=      self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"						)
  							_lowercase					:  Optional[Any]    			=      self.get_feature_extractor(do_normalize=snake_case			,  padding_value=1.0						)
  
  							_lowercase					:  str    			=      WavaVecaProcessor.from_pretrained(
  							    self.tmpdirname			,  bos_token="(BOS)"			,  eos_token="(EOS)"			,  do_normalize=snake_case			,  padding_value=1.0						)
  
  							self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer_add_kwargs.get_vocab()						)
  							self.assertIsInstance(processor.tokenizer			,  snake_case						)
  
  							self.assertEqual(processor.feature_extractor.to_json_string()			,  feature_extractor_add_kwargs.to_json_string()						)
  							self.assertIsInstance(processor.feature_extractor			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> str:
  							_lowercase					:  Dict    			=      self.get_feature_extractor()
  							_lowercase					:  List[Any]    			=      self.get_tokenizer()
  
  							_lowercase					:  Optional[int]    			=      WavaVecaProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)
  
  							_lowercase					:  Union[str, Any]    			=      floats_list((3, 1000)						)
  
  							_lowercase					:  Any    			=      feature_extractor(snake_case			,  return_tensors="np"						)
  							_lowercase					:  Tuple    			=      processor(snake_case			,  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 			_a(self							:   Optional[Any]						)   -> str:
  							_lowercase					:  Optional[Any]    			=      self.get_feature_extractor()
  							_lowercase					:  Any    			=      self.get_tokenizer()
  
  							_lowercase					:  Any    			=      WavaVecaProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)
  
  							_lowercase					:  Tuple    			=      "This is a test string"
  
  							_lowercase					:  List[str]    			=      processor(text=snake_case						)
  
  							_lowercase					:  Any    			=      tokenizer(snake_case						)
  
  							for key in encoded_tok.keys():
  													self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> int:
  							_lowercase					:  Any    			=      self.get_feature_extractor()
  							_lowercase					:  Union[str, Any]    			=      self.get_tokenizer()
  
  							_lowercase					:  Any    			=      WavaVecaProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)
  
  							_lowercase					:  Optional[Any]    			=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  							_lowercase					:  int    			=      processor.batch_decode(snake_case						)
  							_lowercase					:  List[str]    			=      tokenizer.batch_decode(snake_case						)
  
  							self.assertListEqual(snake_case			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> Tuple:
  							_lowercase					:  List[str]    			=      self.get_feature_extractor()
  							_lowercase					:  List[Any]    			=      self.get_tokenizer()
  
  							_lowercase					:  List[Any]    			=      WavaVecaProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)
  
  							self.assertListEqual(
  							    processor.model_input_names			,  feature_extractor.model_input_names			,  msg="`processor` and `feature_extractor` model input names do not match"			,  )
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
  						while start_ind <= end_ind:
  												if array[start_ind] == search_item:
  																		return start_ind
  												elif array[end_ind] == search_item:
  																		return end_ind
  												else:
  																		start_ind += 1
  																		end_ind -= 1
      # returns -1 if search_item is not found in array
  						return -1
  
  
  if __name__ == "__main__":
  							print(double_linear_search(list(range(100)), 40))
  
  
  
  
  