code: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
  
  import datasets
  
  
  __lowerCAmelCase							:			str				='''\
  @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							:			str				='''\
  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							:			Any				='''
  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      UpperCAmelCase			( datasets.Metric       ):
   def   UpperCAmelCase_							(       self   :Any							)->							Optional[int]:
       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   :Any		, lowercase_   :Dict							)->							Any:
       if self.config_name == "default":
           A__       =    "moussaKam/frugalscore_tiny_bert-base_bert-score"
       else:
           A__       =    self.config_name
       A__       =    AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE							)
       A__       =    AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE							)
  
  
  
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Union[str, Any]		, lowercase_   :Any=32		, lowercase_   :str=1_28		, lowercase_   :Dict=None		, )->							int:
       assert len(__SCREAMING_SNAKE_CASE							) == len(
           __SCREAMING_SNAKE_CASE							), "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=__SCREAMING_SNAKE_CASE		, report_to="all"		, no_cuda=(device == "cpu")		, log_level="warning"		, )
       A__       =    {"sentence1": predictions, "sentence2": references}
       A__       =    datasets.Dataset.from_dict(__SCREAMING_SNAKE_CASE							)
  
       def tokenize_function(lowercase_   :str							):
           return self.tokenizer(
               data["sentence1"]		, data["sentence2"]		, max_length=__SCREAMING_SNAKE_CASE		, truncation=__SCREAMING_SNAKE_CASE		, padding=__SCREAMING_SNAKE_CASE							)
  
       A__       =    raw_datasets.map(__SCREAMING_SNAKE_CASE		, batched=__SCREAMING_SNAKE_CASE							)
       tokenized_datasets.remove_columns(["sentence1", "sentence2"]							)
       A__       =    Trainer(self.model		, __SCREAMING_SNAKE_CASE		, tokenizer=self.tokenizer							)
       A__       =    trainer.predict(__SCREAMING_SNAKE_CASE							)
       return {"scores": list(predictions.predictions.squeeze(-1							)							)}
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class      UpperCAmelCase			( unittest.TestCase       ):
   __lowercase     	=       EsmTokenizer
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       super().setUp()
       A__       =    tempfile.mkdtemp()
       # fmt: off
       A__       =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
       # fmt: on
       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   :Any		, **lowercase_   :int							)->							List[PreTrainedTokenizerBase]:
       return [self.get_tokenizer(**lowercase_							)]
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :Dict							)->							PreTrainedTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Dict:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    tokenizer.tokenize("LAGVS"							)
       self.assertListEqual(lowercase_		, ["L", "A", "G", "V", "S"]							)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, [4, 5, 6, 7, 8]							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    "LAGVS"
       self.assertListEqual(tokenizer.encode(lowercase_							)		, [0, 4, 5, 6, 7, 8, 2]							)
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
   def   UpperCAmelCase_							(       self   :str							)->							Any:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]							)
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
       A__       =    self.get_tokenizers(fast=lowercase_							)
       for tokenizer in tokenizers:
           with self.subTest(F"{tokenizer.__class__.__name__}"							):
               A__       =    "<unk>"
               A__       =    "<mask>"
  
               A__       =    tokenizer.tokenize(lowercase_							)
               A__       =    tokenizer.tokenize(lowercase_							)
  
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(token_a[0]		, lowercase_							)
               self.assertEqual(token_a[0]		, lowercase_							)
  
  
  
  