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 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Optional[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(''' '''							)
  											SCREAMING_SNAKE_CASE			: Dict	     =dict(zip(snake_case_		,		range(len(snake_case_							)							)							)							)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     ={
  											    '''pad_token''': '''<pad>''',
  											    '''unk_token''': '''<unk>''',
  											    '''bos_token''': '''<s>''',
  											    '''eos_token''': '''</s>''',
  											}
  											SCREAMING_SNAKE_CASE			: Tuple	     ={
  											    '''feature_size''': 1,
  											    '''padding_value''': 0.0,
  											    '''sampling_rate''': 16_000,
  											    '''return_attention_mask''': False,
  											    '''do_normalize''': True,
  											}
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =tempfile.mkdtemp()
  											SCREAMING_SNAKE_CASE			: Tuple	     =os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['''vocab_file''']							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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		,		**snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.add_kwargs_tokens_map.copy()
  											kwargs.update(snake_case_							)
  											return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,		**snake_case_							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Tuple:
  											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,		**snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											shutil.rmtree(self.tmpdirname							)
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Dict	     =self.get_tokenizer()
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.get_feature_extractor()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											processor.save_pretrained(self.tmpdirname							)
  											SCREAMING_SNAKE_CASE			: 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							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Tuple	     =WavaVecaProcessor(tokenizer=self.get_tokenizer()		,		feature_extractor=self.get_feature_extractor()							)
  											processor.save_pretrained(self.tmpdirname							)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.get_tokenizer(bos_token='''(BOS)'''		,		eos_token='''(EOS)'''							)
  											SCREAMING_SNAKE_CASE			: Dict	     =self.get_feature_extractor(do_normalize=snake_case_		,		padding_value=1.0							)
  
  											SCREAMING_SNAKE_CASE			: int	     =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							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: int	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: int	     =floats_list((3, 1_000)							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =feature_extractor(snake_case_		,		return_tensors='''np'''							)
  											SCREAMING_SNAKE_CASE			: Any	     =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]:
  											SCREAMING_SNAKE_CASE			: str	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: Dict	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: int	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     ='''This is a test string'''
  
  											SCREAMING_SNAKE_CASE			: str	     =processor(text=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =tokenizer(snake_case_							)
  
  											for key in encoded_tok.keys():
  																		self.assertListEqual(encoded_tok[key]		,		encoded_processor[key]							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =processor.batch_decode(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tokenizer.batch_decode(snake_case_							)
  
  											self.assertListEqual(snake_case_		,		snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Dict	     =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: 
  
  
  
  
  def 						lowerCAmelCase_       (	__a = "mytext.txt"	,	__a = False )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							import socket
  
  							SCREAMING_SNAKE_CASE			: int	     =12312  # Reserve a port for your service.
  							SCREAMING_SNAKE_CASE			: Dict	     =socket.socket()  # Create a socket object
  							SCREAMING_SNAKE_CASE			: Any	     =socket.gethostname()  # Get local machine name
  							sock.bind((host, port) )  # Bind to the port
  							sock.listen(5 )  # Now wait for client connection.
  
  							print('''Server listening....''' )
  
  							while True:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =sock.accept()  # Establish connection with client.
  														print(f'Got connection from {addr}' )
  														SCREAMING_SNAKE_CASE			: List[str]	     =conn.recv(1024 )
  														print(f'Server received: {data = }' )
  
  														with open(__a	,	'''rb''' ) as in_file:
  																					SCREAMING_SNAKE_CASE			: Dict	     =in_file.read(1024 )
  																					while data:
  																												conn.send(__a )
  																												print(f'Sent {data!r}' )
  																												SCREAMING_SNAKE_CASE			: str	     =in_file.read(1024 )
  
  														print('''Done sending''' )
  														conn.close()
  														if testing:  # Allow the test to complete
  																					break
  
  							sock.shutdown(1 )
  							sock.close()
  
  
  if __name__ == "__main__":
  							send_file()
  
  
  
  
  
  
  