code: 
  
  """simple docstring"""
  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 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						"""<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__ :       Optional[Any]						=						dict(zip(_lowerCamelCase       ,					range(len(_lowerCamelCase  )  )  )  )
  
          UpperCAmelCase__ :       Optional[Any]						=						{
              """pad_token""": """<pad>""",
              """unk_token""": """<unk>""",
              """bos_token""": """<s>""",
              """eos_token""": """</s>""",
          }
          UpperCAmelCase__ :       Union[str, Any]						=						{
              """feature_size""": 1,
              """padding_value""": 0.0,
              """sampling_rate""": 16000,
              """return_attention_mask""": False,
              """do_normalize""": True,
          }
  
          UpperCAmelCase__ :       Optional[Any]						=						tempfile.mkdtemp()
          UpperCAmelCase__ :       Optional[int]						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
          UpperCAmelCase__ :       Optional[Any]						=						os.path.join(self.tmpdirname       ,					_lowerCamelCase  )
          with open(self.vocab_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
                fp.write(json.dumps(_lowerCamelCase  ) + """\n"""  )
  
          with open(self.feature_extraction_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
                fp.write(json.dumps(_lowerCamelCase  ) + """\n"""  )
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.add_kwargs_tokens_map.copy()
          kwargs.update(_lowerCamelCase  )
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          shutil.rmtree(self.tmpdirname  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.get_tokenizer()
          UpperCAmelCase__ :       str						=						self.get_feature_extractor()
  
          UpperCAmelCase__ :       List[Any]						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          processor.save_pretrained(self.tmpdirname  )
          UpperCAmelCase__ :       Tuple						=						WavaVecaProcessor.from_pretrained(self.tmpdirname  )
  
          self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer.get_vocab()  )
          self.assertIsInstance(processor.tokenizer       ,					_lowerCamelCase  )
  
          self.assertEqual(processor.feature_extractor.to_json_string()       ,					feature_extractor.to_json_string()  )
          self.assertIsInstance(processor.feature_extractor       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,					feature_extractor=self.get_feature_extractor()  )
          processor.save_pretrained(self.tmpdirname  )
  
          UpperCAmelCase__ :       Any						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
          UpperCAmelCase__ :       Any						=						self.get_feature_extractor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						WavaVecaProcessor.from_pretrained(
              self.tmpdirname       ,					bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""       ,					do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab()  )
          self.assertIsInstance(processor.tokenizer       ,					_lowerCamelCase  )
  
          self.assertEqual(processor.feature_extractor.to_json_string()       ,					feature_extractor_add_kwargs.to_json_string()  )
          self.assertIsInstance(processor.feature_extractor       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.get_feature_extractor()
          UpperCAmelCase__ :       Tuple						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       int						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       int						=						floats_list((3, 1000)  )
  
          UpperCAmelCase__ :       Optional[int]						=						feature_extractor(_lowerCamelCase       ,					return_tensors="""np"""  )
          UpperCAmelCase__ :       List[str]						=						processor(_lowerCamelCase       ,					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  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.get_feature_extractor()
          UpperCAmelCase__ :       Dict						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Dict						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						"""This is a test string"""
  
          UpperCAmelCase__ :       Optional[Any]						=						processor(text=_lowerCamelCase  )
  
          UpperCAmelCase__ :       int						=						tokenizer(_lowerCamelCase  )
  
          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key]  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.get_feature_extractor()
          UpperCAmelCase__ :       int						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       str						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
          UpperCAmelCase__ :       Dict						=						processor.batch_decode(_lowerCamelCase  )
          UpperCAmelCase__ :       List[Any]						=						tokenizer.batch_decode(_lowerCamelCase  )
  
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						self.get_feature_extractor()
          UpperCAmelCase__ :       str						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Any						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          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 typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  