code: 
  
  import logging
  import os
  import sys
  import threading
  from logging import (
      CRITICAL,  # NOQA
      DEBUG,  # NOQA
      ERROR,  # NOQA
      FATAL,  # NOQA
      INFO,  # NOQA
      NOTSET,  # NOQA
      WARN,  # NOQA
      WARNING,  # NOQA
  )
  from typing import Optional
  
  from tqdm import auto as tqdm_lib
  
  
  lowerCamelCase     =      threading.Lock()
  lowerCamelCase     =      None
  
  lowerCamelCase     =      {
      '''debug''': logging.DEBUG,
      '''info''': logging.INFO,
      '''warning''': logging.WARNING,
      '''error''': logging.ERROR,
      '''critical''': logging.CRITICAL,
  }
  
  lowerCamelCase     =      logging.WARNING
  
  lowerCamelCase     =      True
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=os.getenv('''DIFFUSERS_VERBOSITY'''			,   _A							)
    if env_level_str:
      if env_level_str in log_levels:
        return log_levels[env_level_str]
      else:
        logging.getLogger().warning(
            F"""Unknown option DIFFUSERS_VERBOSITY={env_level_str}, """
            F"""has to be one of: { ", ".join(log_levels.keys()							) }"""							)
    return _default_log_level
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return __name__.split('''.'''							)[0]
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return logging.getLogger(_get_library_name()							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    global _default_handler
  
    with _lock:
      if _default_handler:
        # This library has already configured the library root logger.
        return
      a__						=logging.StreamHandler()  # Set sys.stderr as stream.
      a__						=sys.stderr.flush
  
      # Apply our default configuration to the library root logger.
      a__						=_get_library_root_logger()
      library_root_logger.addHandler(_default_handler							)
      library_root_logger.setLevel(_get_default_logging_level()							)
      a__						=False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    global _default_handler
  
    with _lock:
      if not _default_handler:
        return
  
      a__						=_get_library_root_logger()
      library_root_logger.removeHandler(_default_handler							)
      library_root_logger.setLevel(logging.NOTSET							)
      a__						=None
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return log_levels
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[str] = None							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if name is None:
      a__						=_get_library_name()
  
    _configure_library_root_logger()
    return logging.getLogger(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
    return _get_library_root_logger().getEffectiveLevel()
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
    _get_library_root_logger().setLevel(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return set_verbosity(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return set_verbosity(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return set_verbosity(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return set_verbosity(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
  
    assert _default_handler is not None
    _get_library_root_logger().removeHandler(_default_handler							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
  
    assert _default_handler is not None
    _get_library_root_logger().addHandler(_default_handler							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				logging.Handler							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
  
    assert handler is not None
    _get_library_root_logger().addHandler(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				logging.Handler							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
  
    assert handler is not None and handler not in _get_library_root_logger().handlers
    _get_library_root_logger().removeHandler(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
    a__						=False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    _configure_library_root_logger()
    a__						=True
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=_get_library_root_logger().handlers
  
    for handler in handlers:
      a__						=logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''							)
      handler.setFormatter(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=_get_library_root_logger().handlers
  
    for handler in handlers:
      handler.setFormatter(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    self   :				List[Any]			,   *_A   :				int			,   **_A   :				Any							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=os.getenv('''DIFFUSERS_NO_ADVISORY_WARNINGS'''			,   _A							)
    if no_advisory_warnings:
      return
    self.warning(*_A			,   **_A							)
  
  
  lowerCamelCase     =      warning_advice
  
  
  
  
  
  
  
  class 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					*lowercase_,					**lowercase_ )							->					Optional[Any]:  # pylint: disable=unused-argument
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=args[0] if args else None
  
  
  
     def __iter__(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return iter(self._iterator )
  
  
  
     def __getattr__(       self,					lowercase_ )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       def empty_fn(*lowercase_,					**lowercase_ ):  # pylint: disable=unused-argument
         return
  
       return empty_fn
  
  
  
     def __enter__(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self
  
  
  
     def __exit__(       self,					lowercase_,					lowercase_,					lowercase_ )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return
  
  
  
  
  
  
  
  class 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __call__(       self,					*lowercase_,					**lowercase_ )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if _tqdm_active:
         return tqdm_lib.tqdm(*lowercase_,					**lowercase_ )
       else:
         return EmptyTqdm(*lowercase_,					**lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					*lowercase_,					**lowercase_ )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=None
       if _tqdm_active:
         return tqdm_lib.tqdm.set_lock(*lowercase_,					**lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if _tqdm_active:
         return tqdm_lib.tqdm.get_lock()
  
  
  
  
  
  lowerCamelCase     =      _tqdm_cls()
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    global _tqdm_active
    return bool(_tqdm_active							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    global _tqdm_active
    a__						=True
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    global _tqdm_active
    a__						=False
  
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 ) )}
  