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     UpperCAmelCase_		(      unittest.TestCase				):
  
  
  
        """simple docstring"""
        def 					UpperCAmelCase		(       self				)       ->   Any:
            UpperCamelCase :int										=     '''<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(''' '''				)
            UpperCamelCase :str										=     dict(zip(SCREAMING_SNAKE_CASE_					,       range(len(SCREAMING_SNAKE_CASE_				)				)				)				)
  
            UpperCamelCase :List[Any]										=     {
                '''pad_token''': '''<pad>''',
                '''unk_token''': '''<unk>''',
                '''bos_token''': '''<s>''',
                '''eos_token''': '''</s>''',
            }
            UpperCamelCase :List[Any]										=     {
                '''feature_size''': 1,
                '''padding_value''': 0.0,
                '''sampling_rate''': 1_6000,
                '''return_attention_mask''': False,
                '''do_normalize''': True,
            }
  
            UpperCamelCase :List[str]										=     tempfile.mkdtemp()
            UpperCamelCase :Optional[int]										=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['''vocab_file''']				)
            UpperCamelCase :str										=     os.path.join(self.tmpdirname					,       SCREAMING_SNAKE_CASE_				)
            with open(self.vocab_file					,       '''w'''					,       encoding='''utf-8'''				) as fp:
                fp.write(json.dumps(SCREAMING_SNAKE_CASE_				) + '''\n'''				)
  
            with open(self.feature_extraction_file					,       '''w'''					,       encoding='''utf-8'''				) as fp:
                fp.write(json.dumps(SCREAMING_SNAKE_CASE_				) + '''\n'''				)
        def 					UpperCAmelCase		(       self					,       **SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            UpperCamelCase :Dict										=     self.add_kwargs_tokens_map.copy()
            kwargs.update(SCREAMING_SNAKE_CASE_				)
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       **SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   int:
            shutil.rmtree(self.tmpdirname				)
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            UpperCamelCase :List[str]										=     self.get_tokenizer()
            UpperCamelCase :Tuple										=     self.get_feature_extractor()
  
            UpperCamelCase :Tuple										=     WavaVecaProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,       feature_extractor=SCREAMING_SNAKE_CASE_				)
  
            processor.save_pretrained(self.tmpdirname				)
            UpperCamelCase :int										=     WavaVecaProcessor.from_pretrained(self.tmpdirname				)
  
            self.assertEqual(processor.tokenizer.get_vocab()					,       tokenizer.get_vocab()				)
            self.assertIsInstance(processor.tokenizer					,       SCREAMING_SNAKE_CASE_				)
  
            self.assertEqual(processor.feature_extractor.to_json_string()					,       feature_extractor.to_json_string()				)
            self.assertIsInstance(processor.feature_extractor					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   str:
            UpperCamelCase :str										=     WavaVecaProcessor(tokenizer=self.get_tokenizer()					,       feature_extractor=self.get_feature_extractor()				)
            processor.save_pretrained(self.tmpdirname				)
  
            UpperCamelCase :Union[str, Any]										=     self.get_tokenizer(bos_token='''(BOS)'''					,       eos_token='''(EOS)'''				)
            UpperCamelCase :List[Any]										=     self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_					,       padding_value=1.0				)
  
            UpperCamelCase :str										=     WavaVecaProcessor.from_pretrained(
                self.tmpdirname					,       bos_token='''(BOS)'''					,       eos_token='''(EOS)'''					,       do_normalize=SCREAMING_SNAKE_CASE_					,       padding_value=1.0				)
  
            self.assertEqual(processor.tokenizer.get_vocab()					,       tokenizer_add_kwargs.get_vocab()				)
            self.assertIsInstance(processor.tokenizer					,       SCREAMING_SNAKE_CASE_				)
  
            self.assertEqual(processor.feature_extractor.to_json_string()					,       feature_extractor_add_kwargs.to_json_string()				)
            self.assertIsInstance(processor.feature_extractor					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
            UpperCamelCase :Optional[int]										=     self.get_feature_extractor()
            UpperCamelCase :Optional[Any]										=     self.get_tokenizer()
  
            UpperCamelCase :int										=     WavaVecaProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,       feature_extractor=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Any										=     floats_list((3, 1000)				)
  
            UpperCamelCase :Any										=     feature_extractor(SCREAMING_SNAKE_CASE_					,       return_tensors='''np'''				)
            UpperCamelCase :Tuple										=     processor(SCREAMING_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 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            UpperCamelCase :str										=     self.get_feature_extractor()
            UpperCamelCase :Tuple										=     self.get_tokenizer()
  
            UpperCamelCase :List[Any]										=     WavaVecaProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,       feature_extractor=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :str										=     '''This is a test string'''
  
            UpperCamelCase :Union[str, Any]										=     processor(text=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Union[str, Any]										=     tokenizer(SCREAMING_SNAKE_CASE_				)
  
            for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]				)
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase :Union[str, Any]										=     self.get_feature_extractor()
            UpperCamelCase :Dict										=     self.get_tokenizer()
  
            UpperCamelCase :Tuple										=     WavaVecaProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,       feature_extractor=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :int										=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
            UpperCamelCase :str										=     processor.batch_decode(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Union[str, Any]										=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				)
  
            self.assertListEqual(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
  
  
  
        def 					UpperCAmelCase		(       self				)       ->   List[Any]:
            UpperCamelCase :List[Any]										=     self.get_feature_extractor()
            UpperCamelCase :List[str]										=     self.get_tokenizer()
  
            UpperCamelCase :Optional[Any]										=     WavaVecaProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,       feature_extractor=SCREAMING_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
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            UpperCamelCase :Tuple										=     value
            UpperCamelCase :Node | None										=     None
            UpperCamelCase :Node | None										=     None
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      int
        def __init__(       self				)       ->   None:
            UpperCamelCase :List[Any]										=     0
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if node is None:
                return
  
            if path_sum == self.target:
                self.paths += 1
  
            if node.left:
                self.depth_first_search(node.left					,       path_sum + node.left.value				)
            if node.right:
                self.depth_first_search(node.right					,       path_sum + node.right.value				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None				)       ->   int:
            if node is None:
                return 0
            if target is not None:
                UpperCamelCase :Tuple										=     target
  
            self.depth_first_search(SCREAMING_SNAKE_CASE_					,       node.value				)
            self.path_sum(node.left				)
            self.path_sum(node.right				)
  
            return self.paths
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  