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
  
  
  __SCREAMING_SNAKE_CASE					=						get_tests_dir("""fixtures/test_sentencepiece.model""")
  
  
  
  
  
  @require_torch
  @require_torchaudio
  @require_sentencepiece
  class __snake_case		(					unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            _a          =				tempfile.mkdtemp()
  
            _a          =				['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
            _a          =				dict(zip(UpperCamelCase__				,  range(len(UpperCamelCase__       )       )       )       )
            _a          =				Path(self.tmpdirname       )
            save_json(UpperCamelCase__				,  save_dir / VOCAB_FILES_NAMES["vocab_file"]       )
            if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                   copyfile(UpperCamelCase__				,  save_dir / VOCAB_FILES_NAMES["spm_file"]       )
  
            _a          =				SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
            tokenizer.save_pretrained(self.tmpdirname       )
  
            _a          =				{
                'feature_size': 24,
                'num_mel_bins': 24,
                'padding_value': 0.0,
                'sampling_rate': 16_000,
                'return_attention_mask': False,
                'do_normalize': True,
            }
            save_json(UpperCamelCase__				,  save_dir / FEATURE_EXTRACTOR_NAME       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  **UpperCamelCase__							:Optional[int]       ):
            return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  **UpperCamelCase__							:Optional[Any]       ):
            return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
            shutil.rmtree(self.tmpdirname       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            _a          =				self.get_tokenizer()
            _a          =				self.get_feature_extractor()
  
            _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )
  
            processor.save_pretrained(self.tmpdirname       )
            _a          =				SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a          =				SpeechaTextProcessor(
                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=UpperCamelCase__				,  padding_value=1.0       )
  
            _a          =				SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
            _a          =				self.get_feature_extractor()
            _a          =				self.get_tokenizer()
  
            _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )
  
            _a          =				floats_list((3, 1_000)       )
  
            _a          =				feature_extractor(UpperCamelCase__				,  return_tensors="np"       )
            _a          =				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 SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				self.get_feature_extractor()
            _a          =				self.get_tokenizer()
  
            _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )
  
            _a          =				'This is a test string'
  
            _a          =				processor(text=UpperCamelCase__       )
  
            _a          =				tokenizer(UpperCamelCase__       )
  
            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key]				,  encoded_processor[key]       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				self.get_feature_extractor()
            _a          =				self.get_tokenizer()
  
            _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )
  
            _a          =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
            _a          =				processor.batch_decode(UpperCamelCase__       )
            _a          =				tokenizer.batch_decode(UpperCamelCase__       )
  
            self.assertListEqual(UpperCamelCase__				,  UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
            _a          =				self.get_feature_extractor()
            _a          =				self.get_tokenizer()
  
            _a          =				SpeechaTextProcessor(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"""
  
  
  
  
  from __future__ import annotations
  
  __SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
            _a          =				-1
            _a          =				-1
            _a          =				[-1] * length
            _a          =				None
            _a          =				None
  
  
  
  
     def __repr__(		self							:Optional[Any]       ):
            return f'Node(min_value={self.minn} max_value={self.maxx})'
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				Node(len(a					)					)
         _a   , _a          =				min(a					), max(a					)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         _a          =				(root.minn + root.maxx) // 2
  
         _a          =				[]
         _a          =				[]
  
         for index, num in enumerate(a					):
                if num <= pivot:
                       left_arr.append(a					)
                else:
                       right_arr.append(a					)
                _a          =				len(a					)
         _a          =				build_tree(a					)
         _a          =				build_tree(a					)
         return root
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index < 0 or node is None:
                return 0
         # Leaf node cases
         if node.minn == node.maxx:
                return index + 1 if node.minn == num else 0
         _a          =				(node.minn + node.maxx) // 2
         if num <= pivot:
                # go the left subtree and map index to the left subtree
                return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right,		a,		index - node.map_left[index]					)
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if start > end:
                return 0
         _a          =				rank_till_index(a,		a,		a					)
         _a          =				rank_till_index(a,		a,		start - 1					)
         return rank_till_end - rank_before_start
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index > (end - start) or start > end or node is None:
                return -1
         # Leaf node case
         if node.minn == node.maxx:
                return node.minn
         # Number of elements in the left subtree in interval [start, end]
         _a          =				node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
         else:
                return quantile(
                    node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)
  
  
  
  def 						__a      (  a,		a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if (
             start > end
             or node is None
             or start_num > end_num
             or node.minn > end_num
             or node.maxx < start_num
         ):
                return 0
         if start_num <= node.minn and node.maxx <= end_num:
                return end - start + 1
         _a          =				range_counting(
             node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
         _a          =				range_counting(
             node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  