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: 
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
  
  _A							    =	{
      """vocab_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
      },
      """tokenizer_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
      },
  }
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token
  
  											super().__init__(
  											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =3
  											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
  											SCREAMING_SNAKE_CASE			: int	     =remove_space
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
  											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True
  
  											try:
  																		import jieba
  											except ModuleNotFoundError as error:
  																		raise error.__class__(
  																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
  																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
  											SCREAMING_SNAKE_CASE			: str	     =jieba
  											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
  											if token_ids_a is None:
  																		return token_ids_a + sep + cls
  											return token_ids_a + sep + token_ids_a + sep + cls
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]
  
  											if token_ids_a is None:
  																		return len(token_ids_a + sep							) * [0] + cls_segment_id
  											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
  											if not self.can_save_slow_tokenizer:
  																		raise ValueError(
  																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
  																		    '''tokenizer.'''							)
  
  											if not os.path.isdir(snake_case_							):
  																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  																		return
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
  											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
  
  											if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
  																		copyfile(self.vocab_file		,		snake_case_							)
  
  											return (out_vocab_file,)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =[
  											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
  											    for text in batch_text_or_text_pairs
  											]
  											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
  											return text
  
  
  
  
  
  
  