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 						A__      (					lowerCAmelCase__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	BartTokenizer
      __magic_name__    						=	BartTokenizerFast
      __magic_name__    						=	True
      __magic_name__    						=	filter_roberta_detectors
      def 	a_    ( self					):
           super().setUp()
           snake_case					=       [
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''\u0120''',
               '''\u0120l''',
               '''\u0120n''',
               '''\u0120lo''',
               '''\u0120low''',
               '''er''',
               '''\u0120lowest''',
               '''\u0120newer''',
               '''\u0120wider''',
               '''<unk>''',
           ]
           snake_case					=       dict(zip(lowerCAmelCase_    ,      range(len(lowerCAmelCase_					)					)					)					)
           snake_case					=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
           snake_case					=       {'''unk_token''': '''<unk>'''}
  
           snake_case					=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']					)
           snake_case					=       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(lowerCAmelCase_					) + '''\n'''					)
           with open(self.merges_file    ,      '''w'''    ,      encoding='''utf-8'''					) as fp:
                fp.write('''\n'''.join(lowerCAmelCase_					)					)
      def 	a_    ( self    ,      **__snake_case					):
           kwargs.update(self.special_tokens_map					)
           return self.tokenizer_class.from_pretrained(self.tmpdirname    ,      **lowerCAmelCase_					)
      def 	a_    ( self    ,      **__snake_case					):
           kwargs.update(self.special_tokens_map					)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,      **lowerCAmelCase_					)
      def 	a_    ( self    ,      __snake_case					):
           return "lower newer", "lower newer"
      @cached_property
      def 	a_    ( self					):
           return BartTokenizer.from_pretrained('''facebook/bart-large'''					)
      @cached_property
      def 	a_    ( self					):
           return BartTokenizerFast.from_pretrained('''facebook/bart-large'''					)
      @require_torch
      def 	a_    ( self					):
           snake_case					=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           snake_case					=       [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]:
                snake_case					=       tokenizer(lowerCAmelCase_    ,      max_length=len(lowerCAmelCase_					)    ,      padding=lowerCAmelCase_    ,      return_tensors='''pt'''					)
                self.assertIsInstance(lowerCAmelCase_    ,      lowerCAmelCase_					)
  
                self.assertEqual((2, 9)    ,      batch.input_ids.shape					)
                self.assertEqual((2, 9)    ,      batch.attention_mask.shape					)
                snake_case					=       batch.input_ids.tolist()[0]
                self.assertListEqual(lowerCAmelCase_    ,      lowerCAmelCase_					)
                # Test that special tokens are reset
      @require_torch
      def 	a_    ( self					):
           snake_case					=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case					=       tokenizer(lowerCAmelCase_    ,      padding=lowerCAmelCase_    ,      return_tensors='''pt'''					)
                # check if input_ids are returned and no labels
                self.assertIn('''input_ids'''    ,      lowerCAmelCase_					)
                self.assertIn('''attention_mask'''    ,      lowerCAmelCase_					)
                self.assertNotIn('''labels'''    ,      lowerCAmelCase_					)
                self.assertNotIn('''decoder_attention_mask'''    ,      lowerCAmelCase_					)
      @require_torch
      def 	a_    ( self					):
           snake_case					=       [
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case					=       tokenizer(text_target=lowerCAmelCase_    ,      max_length=3_2    ,      padding='''max_length'''    ,      return_tensors='''pt'''					)
                self.assertEqual(3_2    ,      targets['''input_ids'''].shape[1]					)
      @require_torch
      def 	a_    ( self					):
  
  
  
  
  
  
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case					=       tokenizer(
                    ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']    ,      padding=lowerCAmelCase_    ,      truncation=lowerCAmelCase_    ,      return_tensors='''pt'''					)
                self.assertIsInstance(lowerCAmelCase_    ,      lowerCAmelCase_					)
                self.assertEqual(batch.input_ids.shape    ,      (2, 1_0_2_4)					)
      @require_torch
      def 	a_    ( self					):
           snake_case					=       ['''A long paragraph for summarization.''']
           snake_case					=       [
               '''Summary of the text.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case					=       tokenizer(lowerCAmelCase_    ,      return_tensors='''pt'''					)
                snake_case					=       tokenizer(text_target=lowerCAmelCase_    ,      return_tensors='''pt'''					)
                snake_case					=       inputs['''input_ids''']
                snake_case					=       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					):
           pass
  
  
  
  
      def 	a_    ( self					):
  
  
  
  
  
  
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                     snake_case					=       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_    ,      **lowerCAmelCase_					)
                     snake_case					=       self.tokenizer_class.from_pretrained(lowerCAmelCase_    ,      **lowerCAmelCase_					)
                     snake_case					=       '''A, <mask> AllenNLP sentence.'''
                     snake_case					=       tokenizer_r.encode_plus(lowerCAmelCase_    ,      add_special_tokens=lowerCAmelCase_    ,      return_token_type_ids=lowerCAmelCase_					)
                     snake_case					=       tokenizer_p.encode_plus(lowerCAmelCase_    ,      add_special_tokens=lowerCAmelCase_    ,      return_token_type_ids=lowerCAmelCase_					)
  
                     # 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''']					)    ,      )
  
                     snake_case					=       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
                     snake_case					=       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(
                         lowerCAmelCase_    ,      ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
                     self.assertSequenceEqual(
                         lowerCAmelCase_    ,      ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  