code: 
  
  import os
  import unittest
  
  from transformers import RealmTokenizerFast
  from transformers.models.bert.tokenization_bert import (
      VOCAB_FILES_NAMES,
      BasicTokenizer,
      WordpieceTokenizer,
      _is_control,
      _is_punctuation,
      _is_whitespace,
  )
  from transformers.models.realm.tokenization_realm import RealmTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
  
  
  
  
  
  
  
  @require_tokenizers
  class 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Dict												=     RealmTokenizer
     lowerCamelCase__				:			Optional[Any]												=     RealmTokenizerFast
     lowerCamelCase__				:			Optional[int]												=     True
     lowerCamelCase__				:			List[str]												=     True
     lowerCamelCase__				:			Optional[int]												=     filter_non_english
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       super().setUp()
  
       a__						=[
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       a__						=os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file'''] )
       with open(self.vocab_file,					'''w''',					encoding='''utf-8''' ) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						='''UNwant\u00E9d,running'''
       a__						='''unwanted, running'''
       return input_text, output_text
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.tokenizer_class(self.vocab_file )
  
       a__						=tokenizer.tokenize('''UNwant\u00E9d,running''' )
       self.assertListEqual(lowercase_,					['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ),					[9, 6, 7, 12, 10, 11] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.test_rust_tokenizer:
         return
  
       a__						=self.get_tokenizer()
       a__						=self.get_rust_tokenizer()
  
       a__						='''UNwant\u00E9d,running'''
  
       a__						=tokenizer.tokenize(lowercase_ )
       a__						=rust_tokenizer.tokenize(lowercase_ )
       self.assertListEqual(lowercase_,					lowercase_ )
  
       a__						=tokenizer.encode(lowercase_,					add_special_tokens=lowercase_ )
       a__						=rust_tokenizer.encode(lowercase_,					add_special_tokens=lowercase_ )
       self.assertListEqual(lowercase_,					lowercase_ )
  
       a__						=self.get_rust_tokenizer()
       a__						=tokenizer.encode(lowercase_ )
       a__						=rust_tokenizer.encode(lowercase_ )
       self.assertListEqual(lowercase_,					lowercase_ )
  
       # With lower casing
       a__						=self.get_tokenizer(do_lower_case=lowercase_ )
       a__						=self.get_rust_tokenizer(do_lower_case=lowercase_ )
  
       a__						='''UNwant\u00E9d,running'''
  
       a__						=tokenizer.tokenize(lowercase_ )
       a__						=rust_tokenizer.tokenize(lowercase_ )
       self.assertListEqual(lowercase_,					lowercase_ )
  
       a__						=tokenizer.encode(lowercase_,					add_special_tokens=lowercase_ )
       a__						=rust_tokenizer.encode(lowercase_,					add_special_tokens=lowercase_ )
       self.assertListEqual(lowercase_,					lowercase_ )
  
       a__						=self.get_rust_tokenizer()
       a__						=tokenizer.encode(lowercase_ )
       a__						=rust_tokenizer.encode(lowercase_ )
       self.assertListEqual(lowercase_,					lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer()
  
       self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ),					['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' ),					['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),					['''hello'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_,					strip_accents=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),					['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),					['''h\u00E9llo'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_,					strip_accents=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),					['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),					['''hello'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),					['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),					['''hello'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' ),					['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_,					strip_accents=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),					['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_,					strip_accents=lowercase_ )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),					['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=BasicTokenizer(do_lower_case=lowercase_,					never_split=['''[UNK]'''] )
  
       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''' ),					['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']
  
       a__						={}
       for i, token in enumerate(lowercase_ ):
         a__						=i
       a__						=WordpieceTokenizer(vocab=lowercase_,					unk_token='''[UNK]''' )
  
       self.assertListEqual(tokenizer.tokenize('''''' ),					[] )
  
       self.assertListEqual(tokenizer.tokenize('''unwanted running''' ),					['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )
  
       self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ),					['''[UNK]''', '''runn''', '''##ing'''] )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.assertTrue(_is_whitespace(''' ''' ) )
       self.assertTrue(_is_whitespace('''\t''' ) )
       self.assertTrue(_is_whitespace('''\r''' ) )
       self.assertTrue(_is_whitespace('''\n''' ) )
       self.assertTrue(_is_whitespace('''\u00A0''' ) )
  
       self.assertFalse(_is_whitespace('''A''' ) )
       self.assertFalse(_is_whitespace('''-''' ) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.assertTrue(_is_control('''\u0005''' ) )
  
       self.assertFalse(_is_control('''A''' ) )
       self.assertFalse(_is_control(''' ''' ) )
       self.assertFalse(_is_control('''\t''' ) )
       self.assertFalse(_is_control('''\r''' ) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.assertTrue(_is_punctuation('''-''' ) )
       self.assertTrue(_is_punctuation('''$''' ) )
       self.assertTrue(_is_punctuation('''`''' ) )
       self.assertTrue(_is_punctuation('''.''' ) )
  
       self.assertFalse(_is_punctuation('''A''' ) )
       self.assertFalse(_is_punctuation(''' ''' ) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.get_tokenizer()
  
       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(lowercase_ ) for t in ['''Test''', '''\xad''', '''test''']],					[['''[UNK]'''], [], ['''[UNK]''']] )
  
       if self.test_rust_tokenizer:
         a__						=self.get_rust_tokenizer()
         self.assertListEqual(
             [rust_tokenizer.tokenize(lowercase_ ) for t in ['''Test''', '''\xad''', '''test''']],					[['''[UNK]'''], [], ['''[UNK]''']] )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.tokenizer_class.from_pretrained('''bert-base-uncased''' )
  
       a__						=tokenizer.encode('''sequence builders''',					add_special_tokens=lowercase_ )
       a__						=tokenizer.encode('''multi-sequence build''',					add_special_tokens=lowercase_ )
  
       a__						=tokenizer.build_inputs_with_special_tokens(lowercase_ )
       a__						=tokenizer.build_inputs_with_special_tokens(lowercase_,					lowercase_ )
  
       assert encoded_sentence == [101] + text + [102]
       assert encoded_pair == [101] + text + [102] + text_a + [102]
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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(lowercase_,					**lowercase_ )
  
           a__						=F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
           a__						=tokenizer_r.encode_plus(
               lowercase_,					return_attention_mask=lowercase_,					return_token_type_ids=lowercase_,					return_offsets_mapping=lowercase_,					add_special_tokens=lowercase_,					)
  
           a__						=tokenizer_r.do_lower_case if hasattr(lowercase_,					'''do_lower_case''' ) else False
           a__						=(
               [
                   ((0, 0), tokenizer_r.cls_token),
                   ((0, 1), '''A'''),
                   ((1, 2), ''','''),
                   ((3, 5), '''na'''),
                   ((5, 6), '''##ï'''),
                   ((6, 8), '''##ve'''),
                   ((9, 15), tokenizer_r.mask_token),
                   ((16, 21), '''Allen'''),
                   ((21, 23), '''##NL'''),
                   ((23, 24), '''##P'''),
                   ((25, 33), '''sentence'''),
                   ((33, 34), '''.'''),
                   ((0, 0), tokenizer_r.sep_token),
               ]
               if not do_lower_case
               else [
                   ((0, 0), tokenizer_r.cls_token),
                   ((0, 1), '''a'''),
                   ((1, 2), ''','''),
                   ((3, 8), '''naive'''),
                   ((9, 15), tokenizer_r.mask_token),
                   ((16, 21), '''allen'''),
                   ((21, 23), '''##nl'''),
                   ((23, 24), '''##p'''),
                   ((25, 33), '''sentence'''),
                   ((33, 34), '''.'''),
                   ((0, 0), tokenizer_r.sep_token),
               ]
           )
  
           self.assertEqual(
               [e[1] for e in expected_results],					tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
           self.assertEqual([e[0] for e in expected_results],					tokens['''offset_mapping'''] )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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(lowercase_,					**lowercase_ )
           a__						=self.tokenizer_class.from_pretrained(lowercase_,					**lowercase_ )
           a__						=[['''Hello world!''', '''Nice to meet you!'''], ['''The cute cat.''', '''The adorable dog.''']]
  
           a__						=tokenizer_r.batch_encode_candidates(lowercase_,					max_length=10,					return_tensors='''np''' )
           a__						=tokenizer_p.batch_encode_candidates(lowercase_,					max_length=10,					return_tensors='''np''' )
  
           a__						=(2, 2, 10)
  
           self.assertEqual(encoded_sentence_r['''input_ids'''].shape,					lowercase_ )
           self.assertEqual(encoded_sentence_r['''attention_mask'''].shape,					lowercase_ )
           self.assertEqual(encoded_sentence_r['''token_type_ids'''].shape,					lowercase_ )
  
           self.assertEqual(encoded_sentence_p['''input_ids'''].shape,					lowercase_ )
           self.assertEqual(encoded_sentence_p['''attention_mask'''].shape,					lowercase_ )
           self.assertEqual(encoded_sentence_p['''token_type_ids'''].shape,					lowercase_ )
  
style_context: 
  
  import torch
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
  
  import datasets
  
  
  lowerCamelCase     =      '''\
  @article{eddine2021frugalscore,
    title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
    author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
    journal={arXiv preprint arXiv:2110.08559},
    year={2021}
  }
  '''
  
  lowerCamelCase     =      '''\
  FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
  '''
  
  
  lowerCamelCase     =      '''
  Calculates how good are predictions given some references, using certain scores.
  Args:
      predictions (list of str): list of predictions to score. Each predictions
          should be a string.
      references (list of str): list of reference for each prediction. Each
          reference should be a string.
      batch_size (int): the batch size for predictions.
      max_length (int): maximum sequence length.
      device (str): either gpu or cpu
  Returns:
      scores (list of int): list of scores.
  Examples:
      >>> frugalscore = datasets.load_metric("frugalscore")
      >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
      >>> print([round(s, 3) for s in results["scores"]])
      [0.631, 0.645]
  '''
  
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	, _KWARGS_DESCRIPTION							)
  class 		__magic_name__    (							datasets.Metric							):
  
     '''simple docstring'''
  
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return datasets.MetricInfo(
           description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string''' ),
                   '''references''': datasets.Value('''string''' ),
               } ),					homepage='''https://github.com/moussaKam/FrugalScore''',					)
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.config_name == "default":
         a__						='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
       else:
         a__						=self.config_name
       a__						=AutoModelForSequenceClassification.from_pretrained(lowercase_ )
       a__						=AutoTokenizer.from_pretrained(lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_=32,					lowercase_=128,					lowercase_=None,					)							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       assert len(lowercase_ ) == len(
           lowercase_ ), "predictions and references should have the same number of sentences."
       if device is not None:
         assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
       else:
         a__						='''gpu''' if torch.cuda.is_available() else '''cpu'''
       a__						=TrainingArguments(
           '''trainer''',					fpaa=(device == '''gpu'''),					per_device_eval_batch_size=lowercase_,					report_to='''all''',					no_cuda=(device == '''cpu'''),					log_level='''warning''',					)
       a__						={'''sentence1''': predictions, '''sentence2''': references}
       a__						=datasets.Dataset.from_dict(lowercase_ )
  
       def tokenize_function(lowercase_ ):
         return self.tokenizer(
             data['''sentence1'''],					data['''sentence2'''],					max_length=lowercase_,					truncation=lowercase_,					padding=lowercase_ )
  
       a__						=raw_datasets.map(lowercase_,					batched=lowercase_ )
       tokenized_datasets.remove_columns(['''sentence1''', '''sentence2'''] )
       a__						=Trainer(self.model,					lowercase_,					tokenizer=self.tokenizer )
       a__						=trainer.predict(lowercase_ )
       return {"scores": list(predictions.predictions.squeeze(-1 ) )}
  