code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import shutil
  import tempfile
  import unittest
  from pathlib import Path
  from shutil import copyfile
  
  from transformers import SpeechaTextTokenizer, is_speech_available
  from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
  from transformers.utils import FEATURE_EXTRACTOR_NAME
  
  from .test_feature_extraction_speech_to_text import floats_list
  
  
  if is_speech_available():
         from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor
  
  
  lowercase__			=	get_tests_dir('''fixtures/test_sentencepiece.model''')
  
  
  
  
  
  
  @require_torch
  @require_torchaudio
  @require_sentencepiece
  class 						lowercase     (   unittest.TestCase):
  
  
  
  
  
  
        def    A__(self					)       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= tempfile.mkdtemp()
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
           SCREAMING_SNAKE_CASE__			:				Dict       						= dict(zip(a__ ,  range(len(a__					)					)					)					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= Path(self.tmpdirname					)
           save_json(a__ ,  save_dir / VOCAB_FILES_NAMES['''vocab_file''']					)
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
              copyfile(a__ ,  save_dir / VOCAB_FILES_NAMES['''spm_file''']					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
           tokenizer.save_pretrained(self.tmpdirname					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= {
               '''feature_size''': 2_4,
               '''num_mel_bins''': 2_4,
               '''padding_value''': 0.0,
               '''sampling_rate''': 1_6_0_0_0,
               '''return_attention_mask''': False,
               '''do_normalize''': True,
           }
           save_json(a__ ,  save_dir / FEATURE_EXTRACTOR_NAME					)
  
  
  
  
  
  
        def    A__(self ,  **a__					)       ->   Tuple:
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname ,  **a__					)
  
  
  
  
  
  
        def    A__(self ,  **a__					)       ->   Any:
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname ,  **a__					)
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           shutil.rmtree(self.tmpdirname					)
  
  
  
  
  
  
        def    A__(self					)       ->   Union[str, Any]:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer()
           SCREAMING_SNAKE_CASE__			:				Tuple       						= self.get_feature_extractor()
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= SpeechaTextProcessor(tokenizer=a__ ,  feature_extractor=a__					)
  
           processor.save_pretrained(self.tmpdirname					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= SpeechaTextProcessor.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    A__(self					)       ->   Union[str, Any]:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= SpeechaTextProcessor(
               tokenizer=self.get_tokenizer() ,  feature_extractor=self.get_feature_extractor()					)
           processor.save_pretrained(self.tmpdirname					)
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.get_tokenizer(bos_token='''(BOS)''' ,  eos_token='''(EOS)'''					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.get_feature_extractor(do_normalize=a__ ,  padding_value=1.0					)
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= SpeechaTextProcessor.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    A__(self					)       ->   Union[str, Any]:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.get_feature_extractor()
           SCREAMING_SNAKE_CASE__			:				str       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= SpeechaTextProcessor(tokenizer=a__ ,  feature_extractor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= floats_list((3, 1_0_0_0)					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= feature_extractor(a__ ,  return_tensors='''np'''					)
           SCREAMING_SNAKE_CASE__			:				Union[str, 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    A__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				int       						= self.get_feature_extractor()
           SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				str       						= SpeechaTextProcessor(tokenizer=a__ ,  feature_extractor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= '''This is a test string'''
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= processor(text=a__					)
  
           SCREAMING_SNAKE_CASE__			:				Any       						= tokenizer(a__					)
  
           for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key] ,  encoded_processor[key]					)
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				int       						= self.get_feature_extractor()
           SCREAMING_SNAKE_CASE__			:				Any       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= SpeechaTextProcessor(tokenizer=a__ ,  feature_extractor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
           SCREAMING_SNAKE_CASE__			:				int       						= processor.batch_decode(a__					)
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= tokenizer.batch_decode(a__					)
  
           self.assertListEqual(a__ ,  a__					)
  
  
  
  
  
  
        def    A__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= self.get_feature_extractor()
           SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= SpeechaTextProcessor(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: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  lowercase__			=	{
      '''+''': lambda x, y: x + y,
      '''-''': lambda x, y: x - y,
      '''*''': lambda x, y: x * y,
      '''/''': lambda x, y: x / y,
  }
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return c.isdigit()
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= []
  
     # iterate over the string in reverse order
     for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(snake_case   ):
           stack.append(int(snake_case   )   )
  
        else:
           # pop values from stack can calculate the result
           # push the result onto the stack again
           SCREAMING_SNAKE_CASE__			:				Dict       						= stack.pop()
           SCREAMING_SNAKE_CASE__			:				Any       						= stack.pop()
           stack.append(calc[c](snake_case	,     snake_case   )   )
  
     return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
         lowercase__			=	'''+ 9 * 2 6'''
         print(evaluate(test_expression))
  
         lowercase__			=	'''/ * 10 2 + 4 1 '''
         print(evaluate(test_expression))
  
  