

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



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 ) )}
