code: import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  @contextmanager
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							def filter_log(_A					):
  														return False if "This IS expected if you are initializing" in record.msg else True
  
  							_lowercase				=	datasets.utils.logging.get_logger("""transformers.modeling_utils"""					)
  							logger.addFilter(_A					)
  							try:
  														yield
  							finally:
  														logger.removeFilter(_A					)
  
  
  A_:      List[str]        =			'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'
  
  A_:      List[str]        =			'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'
  
  A_:      int        =			'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'
  
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,				_KWARGS_DESCRIPTION						)
  class _lowercase					(       datasets.Metric						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return datasets.MetricInfo(
  														    description=_DESCRIPTION     ,   citation=_CITATION     ,   homepage="""https://github.com/Tiiiger/bert_score"""     ,   inputs_description=_KWARGS_DESCRIPTION     ,   features=datasets.Features(
  														        {
  														            """predictions""": datasets.Value("""string"""     ,   id="""sequence"""  ),
  														            """references""": datasets.Sequence(datasets.Value("""string"""     ,   id="""sequence"""  )     ,   id="""references"""  ),
  														        }  )     ,   codebase_urls=["""https://github.com/Tiiiger/bert_score"""]     ,   reference_urls=[
  														        """https://github.com/Tiiiger/bert_score""",
  														        """https://arxiv.org/abs/1904.09675""",
  														    ]     ,   )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   UpperCAmelCase=False     ,   UpperCAmelCase=False     ,   UpperCAmelCase=None     ,   UpperCAmelCase=64     ,   UpperCAmelCase=4     ,   UpperCAmelCase=False     ,   UpperCAmelCase=False     ,   UpperCAmelCase=None     ,   UpperCAmelCase=False     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	bert_score.utils.get_hash
  														_lowercase				=	bert_score.BERTScorer
  
  														if version.parse(bert_score.__version__  ) >= version.parse("""0.3.10"""  ):
  																					_lowercase				=	functools.partial(UpperCAmelCase     ,   use_fast_tokenizer=UpperCAmelCase  )
  																					_lowercase				=	functools.partial(UpperCAmelCase     ,   use_fast_tokenizer=UpperCAmelCase  )
  														elif use_fast_tokenizer:
  																					raise ImportWarning(
  																					    """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
  																					    """You can install it with `pip install \"bert-score>=0.3.10\"`."""  )
  
  														if model_type is None:
  																					assert lang is not None, "either lang or model_type should be specified"
  																					_lowercase				=	bert_score.utils.langamodel[lang.lower()]
  
  														if num_layers is None:
  																					_lowercase				=	bert_score.utils.modelalayers[model_type]
  
  														_lowercase				=	get_hash(
  														    model=UpperCAmelCase     ,   num_layers=UpperCAmelCase     ,   idf=UpperCAmelCase     ,   rescale_with_baseline=UpperCAmelCase     ,   use_custom_baseline=baseline_path is not None     ,   )
  
  														with filter_logging_context():
  																					if not hasattr(self     ,   """cached_bertscorer"""  ) or self.cached_bertscorer.hash != hashcode:
  																												_lowercase				=	scorer(
  																												    model_type=UpperCAmelCase     ,   num_layers=UpperCAmelCase     ,   batch_size=UpperCAmelCase     ,   nthreads=UpperCAmelCase     ,   all_layers=UpperCAmelCase     ,   idf=UpperCAmelCase     ,   device=UpperCAmelCase     ,   lang=UpperCAmelCase     ,   rescale_with_baseline=UpperCAmelCase     ,   baseline_path=UpperCAmelCase     ,   )
  
  														((_lowercase)				,       (_lowercase)				,       (_lowercase))				=	self.cached_bertscorer.score(
  														    cands=UpperCAmelCase     ,   refs=UpperCAmelCase     ,   verbose=UpperCAmelCase     ,   batch_size=UpperCAmelCase     ,   )
  														_lowercase				=	{
  														    """precision""": P.tolist(),
  														    """recall""": R.tolist(),
  														    """f1""": F.tolist(),
  														    """hashcode""": hashcode,
  														}
  														return output_dict
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if references is not None:
  																					_lowercase				=	[[ref] if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ) else ref for ref in references]
  														super().add_batch(predictions=UpperCAmelCase     ,   references=UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					_lowercase				=	[reference]
  														super().add(prediction=UpperCAmelCase     ,   reference=UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  
style_context: import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  A_:      Tuple        =			logging.get_logger(__name__)
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														warnings.warn(
  														    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
  														    """ Please use MobileNetV2ImageProcessor instead."""     ,   UpperCAmelCase     ,   )
  														super().__init__(*UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  