code: 
  
  
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  @require_tokenizers
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   unittest.TestCase	):
  				lowerCamelCase__									=    BartTokenizer
  				lowerCamelCase__									=    BartTokenizerFast
  				lowerCamelCase__									=    True
  				lowerCamelCase__									=    filter_roberta_detectors
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											super().setUp()
  											SCREAMING_SNAKE_CASE			: str	     =[
  											    '''l''',
  											    '''o''',
  											    '''w''',
  											    '''e''',
  											    '''r''',
  											    '''s''',
  											    '''t''',
  											    '''i''',
  											    '''d''',
  											    '''n''',
  											    '''\u0120''',
  											    '''\u0120l''',
  											    '''\u0120n''',
  											    '''\u0120lo''',
  											    '''\u0120low''',
  											    '''er''',
  											    '''\u0120lowest''',
  											    '''\u0120newer''',
  											    '''\u0120wider''',
  											    '''<unk>''',
  											]
  											SCREAMING_SNAKE_CASE			: List[str]	     =dict(zip(snake_case_		,		range(len(snake_case_							)							)							)							)
  											SCREAMING_SNAKE_CASE			: Any	     =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
  											SCREAMING_SNAKE_CASE			: Dict	     ={'''unk_token''': '''<unk>'''}
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['''vocab_file''']							)
  											SCREAMING_SNAKE_CASE			: int	     =os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['''merges_file''']							)
  											with open(self.vocab_file		,		'''w'''		,		encoding='''utf-8'''							) as fp:
  																		fp.write(json.dumps(snake_case_							) + '''\n'''							)
  											with open(self.merges_file		,		'''w'''		,		encoding='''utf-8'''							) as fp:
  																		fp.write('''\n'''.join(snake_case_							)							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      int:
  											kwargs.update(self.special_tokens_map							)
  											return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**snake_case_							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Tuple:
  											kwargs.update(self.special_tokens_map							)
  											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
  											return "lower newer", "lower newer"
  
  
  
  
  				@cached_property
  				def     __a		( self							)    ->      List[str]:
  											return BartTokenizer.from_pretrained('''facebook/bart-large'''							)
  
  
  
  
  				@cached_property
  				def     __a		( self							)    ->      Any:
  											return BartTokenizerFast.from_pretrained('''facebook/bart-large'''							)
  
  
  
  
  				@require_torch
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: int	     =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
  											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																		SCREAMING_SNAKE_CASE			: int	     =tokenizer(snake_case_		,		max_length=len(snake_case_							)		,		padding=snake_case_		,		return_tensors='''pt'''							)
  																		self.assertIsInstance(snake_case_		,		snake_case_							)
  
  																		self.assertEqual((2, 9)		,		batch.input_ids.shape							)
  																		self.assertEqual((2, 9)		,		batch.attention_mask.shape							)
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =batch.input_ids.tolist()[0]
  																		self.assertListEqual(snake_case_		,		snake_case_							)
  																		# Test that special tokens are reset
  
  
  
  
  				@require_torch
  				def     __a		( self							)    ->      int:
  											SCREAMING_SNAKE_CASE			: Any	     =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																		SCREAMING_SNAKE_CASE			: Any	     =tokenizer(snake_case_		,		padding=snake_case_		,		return_tensors='''pt'''							)
  																		# check if input_ids are returned and no labels
  																		self.assertIn('''input_ids'''		,		snake_case_							)
  																		self.assertIn('''attention_mask'''		,		snake_case_							)
  																		self.assertNotIn('''labels'''		,		snake_case_							)
  																		self.assertNotIn('''decoder_attention_mask'''		,		snake_case_							)
  
  
  
  
  				@require_torch
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =[
  											    '''Summary of the text.''',
  											    '''Another summary.''',
  											]
  											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																		SCREAMING_SNAKE_CASE			: int	     =tokenizer(text_target=snake_case_		,		max_length=32		,		padding='''max_length'''		,		return_tensors='''pt'''							)
  																		self.assertEqual(32		,		targets['''input_ids'''].shape[1]							)
  
  
  
  
  				@require_torch
  				def     __a		( self							)    ->      Optional[int]:
  											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =tokenizer(
  																		    ['''I am a small frog''' * 1_024, '''I am a small frog''']		,		padding=snake_case_		,		truncation=snake_case_		,		return_tensors='''pt'''							)
  																		self.assertIsInstance(snake_case_		,		snake_case_							)
  																		self.assertEqual(batch.input_ids.shape		,		(2, 1_024)							)
  
  
  
  
  				@require_torch
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =['''A long paragraph for summarization.''']
  											SCREAMING_SNAKE_CASE			: Any	     =[
  											    '''Summary of the text.''',
  											]
  											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =tokenizer(snake_case_		,		return_tensors='''pt'''							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tokenizer(text_target=snake_case_		,		return_tensors='''pt'''							)
  																		SCREAMING_SNAKE_CASE			: List[str]	     =inputs['''input_ids''']
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =targets['''input_ids''']
  																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
  																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
  																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
  																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)
  
  
  
  
  				def     __a		( self							)    ->      Union[str, Any]:
  											pass
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																		with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
  																									SCREAMING_SNAKE_CASE			: str	     =self.rust_tokenizer_class.from_pretrained(snake_case_		,		**snake_case_							)
  																									SCREAMING_SNAKE_CASE			: int	     =self.tokenizer_class.from_pretrained(snake_case_		,		**snake_case_							)
  																									SCREAMING_SNAKE_CASE			: int	     ='''A, <mask> AllenNLP sentence.'''
  																									SCREAMING_SNAKE_CASE			: Tuple	     =tokenizer_r.encode_plus(snake_case_		,		add_special_tokens=snake_case_		,		return_token_type_ids=snake_case_							)
  																									SCREAMING_SNAKE_CASE			: Optional[Any]	     =tokenizer_p.encode_plus(snake_case_		,		add_special_tokens=snake_case_		,		return_token_type_ids=snake_case_							)
  
  																									# token_type_ids should put 0 everywhere
  																									self.assertEqual(sum(tokens_r['''token_type_ids''']							)		,		sum(tokens_p['''token_type_ids''']							)							)
  
  																									# attention_mask should put 1 everywhere, so sum over length should be 1
  																									self.assertEqual(
  																									    sum(tokens_r['''attention_mask''']							) / len(tokens_r['''attention_mask''']							)		,		sum(tokens_p['''attention_mask''']							) / len(tokens_p['''attention_mask''']							)		,		)
  
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
  																									SCREAMING_SNAKE_CASE			: int	     =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']							)
  
  																									# Rust correctly handles the space before the mask while python doesnt
  																									self.assertSequenceEqual(tokens_p['''input_ids''']		,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)
  																									self.assertSequenceEqual(tokens_r['''input_ids''']		,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)
  
  																									self.assertSequenceEqual(
  																									    snake_case_		,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
  																									self.assertSequenceEqual(
  																									    snake_case_		,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
  
  
  
  
  
  
  
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()
  
  
  
  
  
  
  