code: 
  
  import concurrent.futures
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers import AutoTokenizer, PreTrainedTokenizerFast
  from transformers.testing_utils import require_tokenizers
  
  from ..test_tokenization_common import TokenizerTesterMixin
  @require_tokenizers
  class 					A_   (      __lowerCamelCase ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       PreTrainedTokenizerFast
      a__     							=       False
      a__     							=       True
      a__     							=       "tokenizer_file"
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          __UpperCAmelCase									=			True
  
          __UpperCAmelCase									=			["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
          __UpperCAmelCase									=			"""SaulLu/dummy-tokenizer-bytelevel-bpe"""
  
          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          __UpperCAmelCase									=			[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
  
          __UpperCAmelCase									=			PreTrainedTokenizerFast.from_pretrained(model_paths[0]  )
          tokenizer.save_pretrained(self.tmpdirname  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      List[str]:
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                  try:
                      __UpperCAmelCase									=			tempfile.mkdtemp()
                      __UpperCAmelCase									=			self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase     ,      **_lowerCAmelCase  )
  
                      tokenizer.save_pretrained(self.tmpdirname  )
                      super().test_training_new_tokenizer()
                  finally:
                      # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                      # is restored
                      shutil.rmtree(self.tmpdirname  )
                      __UpperCAmelCase									=			tmpdirname_orig
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase									=			self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                  try:
                      __UpperCAmelCase									=			tempfile.mkdtemp()
                      __UpperCAmelCase									=			self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase     ,      **_lowerCAmelCase  )
  
                      tokenizer.save_pretrained(self.tmpdirname  )
                      super().test_training_new_tokenizer_with_special_tokens_change()
                  finally:
                      # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                      # is restored
                      shutil.rmtree(self.tmpdirname  )
                      __UpperCAmelCase									=			tmpdirname_orig
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          __UpperCAmelCase									=			self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name  )
  
          __UpperCAmelCase									=			("""a""" for _ in range(1_000  ))
          __UpperCAmelCase									=			tokenizer.train_new_from_iterator(text_iterator=_lowerCAmelCase     ,      length=1_000     ,      vocab_size=50  )
  
          __UpperCAmelCase									=			new_tokenizer.encode('''a🤗'''  )
          self.assertEqual(_lowerCAmelCase     ,      [64, 172, 253, 97, 245]  )
  
  
  
  
  
  
  @require_tokenizers
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase									=			AutoTokenizer.from_pretrained('''bert-base-cased'''  )
          __UpperCAmelCase									=			json.loads(tokenizer._tokenizer.to_str()  )
          __UpperCAmelCase									=			len(_lowerCAmelCase  )
  
          with tempfile.TemporaryDirectory() as tmp_dir:
              # Hack to save this in the tokenizer_config.json
              __UpperCAmelCase									=			["""tokenizer.4.0.0.json"""]
              tokenizer.save_pretrained(_lowerCAmelCase  )
              json.dump(_lowerCAmelCase     ,      open(os.path.join(_lowerCAmelCase     ,      '''tokenizer.4.0.0.json'''  )     ,      '''w'''  )  )
  
              # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
              __UpperCAmelCase									=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
              self.assertEqual(len(_lowerCAmelCase  )     ,      len(_lowerCAmelCase  ) + 1  )
              __UpperCAmelCase									=			json.loads(new_tokenizer._tokenizer.to_str()  )
              self.assertIn('''huggingface'''     ,      json_tokenizer['''model''']['''vocab''']  )
  
              # Will need to be adjusted if we reach v42 and this test is still here.
              # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
              shutil.move(os.path.join(_lowerCAmelCase     ,      '''tokenizer.4.0.0.json'''  )     ,      os.path.join(_lowerCAmelCase     ,      '''tokenizer.42.0.0.json'''  )  )
              __UpperCAmelCase									=			["""tokenizer.42.0.0.json"""]
              tokenizer.save_pretrained(_lowerCAmelCase  )
              __UpperCAmelCase									=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
              self.assertEqual(len(_lowerCAmelCase  )     ,      len(_lowerCAmelCase  )  )
              __UpperCAmelCase									=			json.loads(new_tokenizer._tokenizer.to_str()  )
              self.assertNotIn('''huggingface'''     ,      json_tokenizer['''model''']['''vocab''']  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
          __UpperCAmelCase									=			"""hf-internal-testing/test-two-tokenizers"""
  
          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          __UpperCAmelCase									=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
          self.assertEqual(len(_lowerCAmelCase  )     ,      28_997  )
          __UpperCAmelCase									=			json.loads(tokenizer._tokenizer.to_str()  )
          self.assertIn('''huggingface'''     ,      json_tokenizer['''model''']['''vocab''']  )
  
          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers
  
          __UpperCAmelCase									=			"""3.0.0"""
          __UpperCAmelCase									=			old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCAmelCase  )
          self.assertEqual(len(_lowerCAmelCase  )     ,      28_996  )
          __UpperCAmelCase									=			json.loads(old_tokenizer._tokenizer.to_str()  )
          self.assertNotIn('''huggingface'''     ,      json_tokenizer['''model''']['''vocab''']  )
  
  
  
  
  
  
  @require_tokenizers
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      List[str]:
          # See https://github.com/huggingface/transformers/pull/12550
          # and https://github.com/huggingface/tokenizers/issues/537
          __UpperCAmelCase									=			PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''  )
          __UpperCAmelCase									=			"""The Matrix is a 1999 science fiction action film."""
  
          with concurrent.futures.ThreadPoolExecutor() as executor:
              __UpperCAmelCase									=			[executor.submit(self.fetch     ,      _lowerCAmelCase     ,      _lowerCAmelCase  ) for i in range(10  )]
              __UpperCAmelCase									=			[future.result() for future in futures]
              self.assertEqual(_lowerCAmelCase     ,      [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10  )]  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Union[str, Any]:
          return tokenizer.encode(_lowerCAmelCase     ,      truncation='''longest_first'''     ,      padding='''longest'''  )
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  