code: 
  
  
  
  
  
  
  """simple docstring"""
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  @contextmanager
  def        A	(							)						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         def filter_log(__snake_case:   Union[str, Any]     ):
                return False if "This IS expected if you are initializing" in record.msg else True
  
         __magic_name__										=						datasets.utils.logging.get_logger('transformers.modeling_utils'     )
         logger.addFilter(lowercase__     )
         try:
                yield
         finally:
                logger.removeFilter(lowercase__     )
  
  
  snake_case   :   Optional[int]    		=		'\\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'
  
  snake_case   :   Optional[Any]    		=		'\\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'
  
  snake_case   :   str    		=		'\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 					UpperCamelCase__   (							datasets.Metric):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''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 							a__ (		self   :  Dict							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  str=None							,						UpperCamelCase_   :  List[Any]=None							,						UpperCamelCase_   :  Any=None							,						UpperCamelCase_   :  Tuple=False							,						UpperCamelCase_   :  Dict=False							,						UpperCamelCase_   :  Union[str, Any]=None							,						UpperCamelCase_   :  Any=6_4							,						UpperCamelCase_   :  int=4							,						UpperCamelCase_   :  Optional[Any]=False							,						UpperCamelCase_   :  Optional[Any]=False							,						UpperCamelCase_   :  Union[str, Any]=None							,						UpperCamelCase_   :  Tuple=False							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						bert_score.utils.get_hash
             __magic_name__										=						bert_score.BERTScorer
  
             if version.parse(bert_score.__version__   ) >= version.parse('0.3.10'   ):
                    __magic_name__										=						functools.partial(A_							,						use_fast_tokenizer=A_   )
                    __magic_name__										=						functools.partial(A_							,						use_fast_tokenizer=A_   )
             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"
                    __magic_name__										=						bert_score.utils.langamodel[lang.lower()]
  
             if num_layers is None:
                    __magic_name__										=						bert_score.utils.modelalayers[model_type]
  
             __magic_name__										=						get_hash(
                 model=A_							,						num_layers=A_							,						idf=A_							,						rescale_with_baseline=A_							,						use_custom_baseline=baseline_path is not None							,						)
  
             with filter_logging_context():
                    if not hasattr(self							,						'cached_bertscorer'   ) or self.cached_bertscorer.hash != hashcode:
                           __magic_name__										=						scorer(
                               model_type=A_							,						num_layers=A_							,						batch_size=A_							,						nthreads=A_							,						all_layers=A_							,						idf=A_							,						device=A_							,						lang=A_							,						rescale_with_baseline=A_							,						baseline_path=A_							,						)
  
             ((__magic_name__)							,     (__magic_name__)							,     (__magic_name__))										=						self.cached_bertscorer.score(
                 cands=A_							,						refs=A_							,						verbose=A_							,						batch_size=A_							,						)
             __magic_name__										=						{
                 'precision': P.tolist(),
                 'recall': R.tolist(),
                 'f1': F.tolist(),
                 'hashcode': hashcode,
             }
             return output_dict
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Tuple=None							,						UpperCamelCase_   :  Dict=None							,						**UpperCamelCase_   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             if references is not None:
                    __magic_name__										=						[[ref] if isinstance(A_							,						A_   ) else ref for ref in references]
             super().add_batch(predictions=A_							,						references=A_							,						**A_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  List[Any]=None							,						UpperCamelCase_   :  Any=None							,						**UpperCamelCase_   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             if isinstance(A_							,						A_   ):
                    __magic_name__										=						[reference]
             super().add(prediction=A_							,						reference=A_							,						**A_   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SEWDForCTC""",
       """SEWDForSequenceClassification""",
       """SEWDModel""",
       """SEWDPreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_sew_d import (
        SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWDForCTC,
        SEWDForSequenceClassification,
        SEWDModel,
        SEWDPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)