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):
   __SCREAMING_SNAKE_CASE					      =				BartTokenizer
   __SCREAMING_SNAKE_CASE					      =				BartTokenizerFast
   __SCREAMING_SNAKE_CASE					      =				True
   __SCREAMING_SNAKE_CASE					      =				filter_roberta_detectors
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    super().setUp()
    __UpperCamelCase				      =  [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    __UpperCamelCase				      =  dict(zip(lowercase		,	range(len(lowercase			)			)			)			)
    __UpperCamelCase				      =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    __UpperCamelCase				      =  {"""unk_token""": """<unk>"""}
  
    __UpperCamelCase				      =  os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES["""vocab_file"""]			)
    __UpperCamelCase				      =  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(lowercase			) + """\n"""			)
    with open(self.merges_file		,	"""w"""		,	encoding="""utf-8"""			) as fp:
     fp.write("""\n""".join(lowercase			)			)
  
  
  
   def    __lowerCamelCase				(  self		,	**lowercase			)  ->							Union[str, Any]:
    kwargs.update(self.special_tokens_map			)
    return self.tokenizer_class.from_pretrained(self.tmpdirname		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	**lowercase			)  ->							List[Any]:
    kwargs.update(self.special_tokens_map			)
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[Any]:
    return "lower newer", "lower newer"
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    return BartTokenizer.from_pretrained("""facebook/bart-large"""			)
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							Any:
    return BartTokenizerFast.from_pretrained("""facebook/bart-large"""			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    __UpperCamelCase				      =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    __UpperCamelCase				      =  [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]:
     __UpperCamelCase				      =  tokenizer(lowercase		,	max_length=len(lowercase			)		,	padding=lowercase		,	return_tensors="""pt"""			)
     self.assertIsInstance(lowercase		,	lowercase			)
  
     self.assertEqual((2, 9)		,	batch.input_ids.shape			)
     self.assertEqual((2, 9)		,	batch.attention_mask.shape			)
     __UpperCamelCase				      =  batch.input_ids.tolist()[0]
     self.assertListEqual(lowercase		,	lowercase			)
     # Test that special tokens are reset
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(lowercase		,	padding=lowercase		,	return_tensors="""pt"""			)
     # check if input_ids are returned and no labels
     self.assertIn("""input_ids"""		,	lowercase			)
     self.assertIn("""attention_mask"""		,	lowercase			)
     self.assertNotIn("""labels"""		,	lowercase			)
     self.assertNotIn("""decoder_attention_mask"""		,	lowercase			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  [
        """Summary of the text.""",
        """Another summary.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(text_target=lowercase		,	max_length=3_2		,	padding="""max_length"""		,	return_tensors="""pt"""			)
     self.assertEqual(3_2		,	targets["""input_ids"""].shape[1]			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(
         ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]		,	padding=lowercase		,	truncation=lowercase		,	return_tensors="""pt"""			)
     self.assertIsInstance(lowercase		,	lowercase			)
     self.assertEqual(batch.input_ids.shape		,	(2, 1_0_2_4)			)
  
  
  
   @require_torch
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  ["""A long paragraph for summarization."""]
    __UpperCamelCase				      =  [
        """Summary of the text.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase				      =  tokenizer(lowercase		,	return_tensors="""pt"""			)
     __UpperCamelCase				      =  tokenizer(text_target=lowercase		,	return_tensors="""pt"""			)
     __UpperCamelCase				      =  inputs["""input_ids"""]
     __UpperCamelCase				      =  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    __lowerCamelCase				(  self			)  ->							str:
    pass
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
      __UpperCamelCase				      =  self.rust_tokenizer_class.from_pretrained(lowercase		,	**lowercase			)
      __UpperCamelCase				      =  self.tokenizer_class.from_pretrained(lowercase		,	**lowercase			)
      __UpperCamelCase				      =  """A, <mask> AllenNLP sentence."""
      __UpperCamelCase				      =  tokenizer_r.encode_plus(lowercase		,	add_special_tokens=lowercase		,	return_token_type_ids=lowercase			)
      __UpperCamelCase				      =  tokenizer_p.encode_plus(lowercase		,	add_special_tokens=lowercase		,	return_token_type_ids=lowercase			)
  
      # 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"""]			)		,	)
  
      __UpperCamelCase				      =  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]			)
      __UpperCamelCase				      =  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(
          lowercase		,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)
      self.assertSequenceEqual(
          lowercase		,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  