code: import torch
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
  
  import datasets
  
  
  lowerCamelCase__           =				'\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'
  
  lowerCamelCase__           =				'\\nFrugalScore 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.\n'
  
  
  lowerCamelCase__           =				'\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,      _KWARGS_DESCRIPTION      )
  class 					UpperCamelCase	(     datasets.Metric      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					str						):
  
  
  							"""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 :					str      ,_lowerCAmelCase :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if self.config_name == "default":
  									__snake_case		       =     "moussaKam/frugalscore_tiny_bert-base_bert-score"
  							else:
  									__snake_case		       =     self.config_name
  							__snake_case		       =     AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase						)
  							__snake_case		       =     AutoTokenizer.from_pretrained(_lowerCAmelCase						)
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Tuple=32      ,_lowerCAmelCase :					Any=128      ,_lowerCAmelCase :					Any=None      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert len(_lowerCAmelCase						) == len(
  							    _lowerCAmelCase						), "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:
  									__snake_case		       =     "gpu" if torch.cuda.is_available() else "cpu"
  							__snake_case		       =     TrainingArguments(
  							    "trainer"      ,fpaa=(device == "gpu")      ,per_device_eval_batch_size=_lowerCAmelCase      ,report_to="all"      ,no_cuda=(device == "cpu")      ,log_level="warning"      ,)
  							__snake_case		       =     {"sentence1": predictions, "sentence2": references}
  							__snake_case		       =     datasets.Dataset.from_dict(_lowerCAmelCase						)
  
  							def tokenize_function(_lowerCAmelCase :					Optional[Any]						):
  									return self.tokenizer(
  									    data["sentence1"]      ,data["sentence2"]      ,max_length=_lowerCAmelCase      ,truncation=_lowerCAmelCase      ,padding=_lowerCAmelCase						)
  
  							__snake_case		       =     raw_datasets.map(_lowerCAmelCase      ,batched=_lowerCAmelCase						)
  							tokenized_datasets.remove_columns(["sentence1", "sentence2"]						)
  							__snake_case		       =     Trainer(self.model      ,_lowerCAmelCase      ,tokenizer=self.tokenizer						)
  							__snake_case		       =     trainer.predict(_lowerCAmelCase						)
  							return {"scores": list(predictions.predictions.squeeze(-1						)						)}
  
  
  
  
  
  
style_context: import os
  from shutil import copyfile
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  		from .tokenization_reformer import ReformerTokenizer
  else:
  		lowerCamelCase__           =				None
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)
  
  
  lowerCamelCase__           =				'▁'
  
  lowerCamelCase__           =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase__           =				{
      'vocab_file': {
          'google/reformer-crime-and-punishment': (
              'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
          )
      },
      'tokenizer_file': {
          'google/reformer-crime-and-punishment': (
              'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
          )
      },
  }
  
  lowerCamelCase__           =				{
      'google/reformer-crime-and-punishment': 52_42_88,
  }
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   VOCAB_FILES_NAMES
  					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
  					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]
  					__UpperCamelCase						=   ReformerTokenizer
  
  
  
  
  					def __init__( self :					Tuple      ,_lowerCAmelCase :					Dict=None      ,_lowerCAmelCase :					Union[str, Any]=None      ,_lowerCAmelCase :					int="</s>"      ,_lowerCAmelCase :					Dict="<unk>"      ,_lowerCAmelCase :					Dict=[]      ,**_lowerCAmelCase :					Optional[Any]      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__(
  							    _lowerCAmelCase      ,tokenizer_file=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,additional_special_tokens=_lowerCAmelCase      ,**_lowerCAmelCase      ,)
  
  							__snake_case		       =     vocab_file
  							__snake_case		       =     False if not self.vocab_file else True
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not self.can_save_slow_tokenizer:
  									raise ValueError(
  									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  									    "tokenizer."						)
  
  							if not os.path.isdir(_lowerCAmelCase						):
  									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
  									return
  							__snake_case		       =     os.path.join(
  							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
  
  							if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowerCAmelCase						):
  									copyfile(self.vocab_file      ,_lowerCAmelCase						)
  
  							return (out_vocab_file,)
  
  
  
  
  
  