code: 
  
  
  '''simple docstring'''
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  A__	:      Tuple				=						logging.get_logger(__name__)
  
  
  
  
  
  class      UpperCAmelCase_						(a_						):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				def __init__(							self    ,  *SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)       ->  None:
  									warnings.warn(
  									    'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
  									    ' use ViltImageProcessor instead.'    ,  _snake_case    ,  )
  									super().__init__(*_snake_case    ,  **_snake_case			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  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"""
  
  
  
  
  
  
      lowerCamelCase						:      Any							  =    BartTokenizer
      lowerCamelCase						:      Optional[Any]							  =    BartTokenizerFast
      lowerCamelCase						:      Tuple							  =    True
      lowerCamelCase						:      Optional[Any]							  =    filter_roberta_detectors
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           super().setUp()
           __lowerCamelCase     :			List[Any]              =	[
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           __lowerCamelCase     :			Optional[Any]              =	dict(zip(SCREAMING_SNAKE_CASE_    ,  range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
           __lowerCamelCase     :			Union[str, Any]              =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           __lowerCamelCase     :			int              =	{'unk_token': '<unk>'}
  
           __lowerCamelCase     :			int              =	os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES['vocab_file']			)
           __lowerCamelCase     :			str              =	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(SCREAMING_SNAKE_CASE_			) + '\n'			)
           with open(self.merges_file    ,  'w'    ,  encoding='utf-8'			) as fp:
                fp.write('\n'.join(SCREAMING_SNAKE_CASE_			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           kwargs.update(self.special_tokens_map			)
           return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  int:
           kwargs.update(self.special_tokens_map			)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           return "lower newer", "lower newer"
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  str:
           return BartTokenizer.from_pretrained('facebook/bart-large'			)
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  Any:
           return BartTokenizerFast.from_pretrained('facebook/bart-large'			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			str              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
           __lowerCamelCase     :			Union[str, Any]              =	[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
  
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  max_length=len(SCREAMING_SNAKE_CASE_			)    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                self.assertEqual((2, 9)    ,  batch.input_ids.shape			)
                self.assertEqual((2, 9)    ,  batch.attention_mask.shape			)
                __lowerCamelCase     :			str              =	batch.input_ids.tolist()[0]
                self.assertListEqual(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                # Test that special tokens are reset
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Optional[int]              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Tuple              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                # check if input_ids are returned and no labels
                self.assertIn('input_ids'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertIn('attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertNotIn('labels'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertNotIn('decoder_attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Any              =	[
               'Summary of the text.',
               'Another summary.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  max_length=32    ,  padding='max_length'    ,  return_tensors='pt'			)
                self.assertEqual(32    ,  targets['input_ids'].shape[1]			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Tuple:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Optional[Any]              =	tokenizer(
                    ['I am a small frog' * 10_24, 'I am a small frog']    ,  padding=SCREAMING_SNAKE_CASE_    ,  truncation=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                self.assertEqual(batch.input_ids.shape    ,  (2, 10_24)			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			Dict              =	['A long paragraph for summarization.']
           __lowerCamelCase     :			str              =	[
               'Summary of the text.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                __lowerCamelCase     :			Optional[Any]              =	inputs['input_ids']
                __lowerCamelCase     :			Dict              =	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    lowercase_       (							self			)       ->  List[Any]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                     __lowerCamelCase     :			List[str]              =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Optional[int]              =	self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Any              =	'A, <mask> AllenNLP sentence.'
                     __lowerCamelCase     :			Tuple              =	tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Union[str, Any]              =	tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_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']			)    ,  )
  
                     __lowerCamelCase     :			int              =	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                     __lowerCamelCase     :			Optional[Any]              =	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_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
                     self.assertSequenceEqual(tokens_r['input_ids']    ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
  
                     self.assertSequenceEqual(
                         SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                     self.assertSequenceEqual(
                         SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
  
  
  
  
  
  
  