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 					_UpperCamelCase (      UpperCamelCase__			,							unittest.TestCase	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  BartTokenizer
  							_A  				=  BartTokenizerFast
  							_A  				=  True
  							_A  				=  filter_roberta_detectors
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		):
  													super().setUp()
  													_a     =			[
  													    '''l''',
  													    '''o''',
  													    '''w''',
  													    '''e''',
  													    '''r''',
  													    '''s''',
  													    '''t''',
  													    '''i''',
  													    '''d''',
  													    '''n''',
  													    '''\u0120''',
  													    '''\u0120l''',
  													    '''\u0120n''',
  													    '''\u0120lo''',
  													    '''\u0120low''',
  													    '''er''',
  													    '''\u0120lowest''',
  													    '''\u0120newer''',
  													    '''\u0120wider''',
  													    '''<unk>''',
  													]
  													_a     =			dict(zip(snake_case_		,				range(len(snake_case_		)		)		)		)
  													_a     =			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
  													_a     =			{'''unk_token''': '''<unk>'''}
  
  													_a     =			os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['vocab_file']		)
  													_a     =			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 		_UpperCAmelCase	(		self  :				Any		,				**SCREAMING_SNAKE_CASE_  :				str		):
  													kwargs.update(self.special_tokens_map		)
  													return self.tokenizer_class.from_pretrained(self.tmpdirname		,				**snake_case_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				**SCREAMING_SNAKE_CASE_  :				str		):
  													kwargs.update(self.special_tokens_map		)
  													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,				**snake_case_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Any		):
  													return "lower newer", "lower newer"
  
  
  
  
  
  
  
  							@cached_property
  							def 		_UpperCAmelCase	(		self  :				Any		):
  													return BartTokenizer.from_pretrained('facebook/bart-large'		)
  
  
  
  
  
  
  
  							@cached_property
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													return BartTokenizerFast.from_pretrained('facebook/bart-large'		)
  
  
  
  
  
  
  
  							@require_torch
  							def 		_UpperCAmelCase	(		self  :				str		):
  													_a     =			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  													_a     =			[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			_a     =			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		)
  																			_a     =			batch.input_ids.tolist()[0]
  																			self.assertListEqual(snake_case_		,				snake_case_		)
  																			# Test that special tokens are reset
  
  
  
  
  
  
  
  							@require_torch
  							def 		_UpperCAmelCase	(		self  :				Tuple		):
  													_a     =			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			_a     =			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 		_UpperCAmelCase	(		self  :				Optional[Any]		):
  													_a     =			[
  													    '''Summary of the text.''',
  													    '''Another summary.''',
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			_a     =			tokenizer(text_target=snake_case_		,				max_length=3_2		,				padding='max_length'		,				return_tensors='pt'		)
  																			self.assertEqual(3_2		,				targets['input_ids'].shape[1]		)
  
  
  
  
  
  
  
  							@require_torch
  							def 		_UpperCAmelCase	(		self  :				Dict		):
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			_a     =			tokenizer(
  																			    ['I am a small frog' * 1_0_2_4, '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_0_2_4)		)
  
  
  
  
  
  
  
  							@require_torch
  							def 		_UpperCAmelCase	(		self  :				str		):
  													_a     =			['''A long paragraph for summarization.''']
  													_a     =			[
  													    '''Summary of the text.''',
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			_a     =			tokenizer(snake_case_		,				return_tensors='pt'		)
  																			_a     =			tokenizer(text_target=snake_case_		,				return_tensors='pt'		)
  																			_a     =			inputs['''input_ids''']
  																			_a     =			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 		_UpperCAmelCase	(		self  :				Optional[Any]		):
  													pass
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				int		):
  													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
  																									_a     =			self.rust_tokenizer_class.from_pretrained(snake_case_		,				**snake_case_		)
  																									_a     =			self.tokenizer_class.from_pretrained(snake_case_		,				**snake_case_		)
  																									_a     =			'''A, <mask> AllenNLP sentence.'''
  																									_a     =			tokenizer_r.encode_plus(snake_case_		,				add_special_tokens=snake_case_		,				return_token_type_ids=snake_case_		)
  																									_a     =			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']		)		,				)
  
  																									_a     =			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
  																									_a     =			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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)
  																									self.assertSequenceEqual(tokens_r['input_ids']		,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 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: 
  
  
  lowercase_								=    [0.485, 0.456, 0.406]
  lowercase_								=    [0.229, 0.224, 0.225]
  lowercase_								=    [0.5, 0.5, 0.5]
  lowercase_								=    [0.5, 0.5, 0.5]
  lowercase_								=    [0.48145466, 0.4578275, 0.40821073]
  lowercase_								=    [0.26862954, 0.26130258, 0.27577711]
  
  
  