code: 
  
  import importlib
  import importlib.metadata
  import os
  import warnings
  from distutils.util import strtobool
  from functools import lru_cache
  
  import torch
  from packaging import version
  from packaging.version import parse
  
  from .environment import parse_flag_from_env
  from .versions import compare_versions, is_torch_version
  
  
  try:
         import torch_xla.core.xla_model as xm  # noqa: F401
  
         lowerCamelCase     =      True
  except ImportError:
         lowerCamelCase     =      False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  lowerCamelCase     =      torch.distributed.is_available()
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Tuple							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=importlib.util.find_spec(UpperCamelCase_							) is not None
    if package_exists:
      try:
        a__						=importlib.metadata.metadata(UpperCamelCase_							)
        return True
      except importlib.metadata.PackageNotFoundError:
        return False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _torch_distributed_available
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    try:
      pass
    except ImportError:
      print(
          '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
          ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
          ''' Bindings for PyTorch*.'''							)
    return (
        importlib.util.find_spec('''torch_ccl'''							) is not None
        or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''							) is not None
    )
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return importlib.metadata.version('''oneccl_bind_pt'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''transformer_engine'''							)
  
  
  
  
  @lru_cache
  def     UpperCAmelCase__       (    _A   :				Optional[Any]=True							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if torch.cuda.is_available():
      return False
    if _tpu_available and check_device:
      try:
        # Will raise a RuntimeError if no XLA configuration is found
        a__						=xm.xla_device()
        return True
      except RuntimeError:
        return False
    return _tpu_available
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''deepspeed'''							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if is_tpu_available():
      return not ignore_tpu
    if torch.cuda.is_available():
      return torch.cuda.is_bfaa_supported()
    if is_npu_available():
      return False
    return True
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=_is_package_available('''bitsandbytes'''							)
    if package_exists:
      a__						=version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
      return compare_versions(UpperCamelCase_			,   '''>='''			,   '''0.39.0'''							)
    return False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=_is_package_available('''bitsandbytes'''							)
    if package_exists:
      a__						=version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
      return compare_versions(UpperCamelCase_			,   '''>='''			,   '''0.37.2'''							)
    return False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''bitsandbytes'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''			,   '''False'''							)							) == 1:
      a__						=importlib.util.find_spec('''megatron'''							) is not None
      if package_exists:
        try:
          a__						=parse(importlib.metadata.version('''megatron-lm'''							)							)
          return compare_versions(UpperCamelCase_			,   '''>='''			,   '''2.2.0'''							)
        except Exception as e:
          warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""							)
          return False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''safetensors'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''transformers'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''datasets'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''aim'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''tensorboard'''							) or _is_package_available('''tensorboardX'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''wandb'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''comet_ml'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''boto3'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if _is_package_available('''rich'''							):
      if "ACCELERATE_DISABLE_RICH" in os.environ:
        warnings.warn(
            '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''							)
        return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''			,   UpperCamelCase_							)
      return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''			,   UpperCamelCase_							)
    return False
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''sagemaker'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''tqdm'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return _is_package_available('''mlflow'''							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return is_torch_version('''>='''			,   '''1.12'''							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def get_major_and_minor_from_version(_A   :				List[str]							):
      return str(version.parse(UpperCamelCase_							).major							) + "." + str(version.parse(UpperCamelCase_							).minor							)
  
    a__						=importlib.metadata.version('''torch'''							)
    if importlib.util.find_spec('''intel_extension_for_pytorch'''							) is None:
      return False
    a__						='''N/A'''
    try:
      a__						=importlib.metadata.version('''intel_extension_for_pytorch'''							)
    except importlib.metadata.PackageNotFoundError:
      return False
    a__						=get_major_and_minor_from_version(_torch_version							)
    a__						=get_major_and_minor_from_version(_ipex_version							)
    if torch_major_and_minor != ipex_major_and_minor:
      warnings.warn(
          F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
          F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""							)
      return False
    return True
  
  
  
  
  @lru_cache
  def     UpperCAmelCase__       (    _A   :				str=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if importlib.util.find_spec('''torch'''							) is None or importlib.util.find_spec('''torch_npu'''							) is None:
      return False
  
    import torch
    import torch_npu  # noqa: F401
  
    if check_device:
      try:
        # Will raise a RuntimeError if no NPU is found
        a__						=torch.npu.device_count()
        return torch.npu.is_available()
      except RuntimeError:
        return False
    return hasattr(UpperCamelCase_			,   '''npu'''							) and torch.npu.is_available()
  
  
  
  
  @lru_cache
  def     UpperCAmelCase__       (    _A   :				Any=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if not parse_flag_from_env('''ACCELERATE_USE_XPU'''			,   default=UpperCamelCase_							):
      return False
    if is_ipex_available():
      import torch
  
      if is_torch_version('''<='''			,   '''1.12'''							):
        return False
    else:
      return False
  
    import intel_extension_for_pytorch  # noqa: F401
  
    if check_device:
      try:
        # Will raise a RuntimeError if no XPU  is found
        a__						=torch.xpu.device_count()
        return torch.xpu.is_available()
      except RuntimeError:
        return False
    return hasattr(UpperCamelCase_			,   '''xpu'''							) and torch.xpu.is_available()
  
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 ) )}
  