






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def     lowerCamelCase__				(   ):
  def filter_log(__lowerCamelCase      :					Dict		):
    return False if "This IS expected if you are initializing" in record.msg else True

  __UpperCAmelCase							:     Optional[Any]									=  datasets.utils.logging.get_logger("""transformers.modeling_utils"""		)
  logger.addFilter(__lowerCamelCase		)
  try:
    yield
  finally:
    logger.removeFilter(__lowerCamelCase		)


a		:	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"

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		:	List[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    a   (			datasets.Metric       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      str )							->				Dict:
        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 :      int		,				__lowercase :      int		,				__lowercase :      Union[str, Any]		,				__lowercase :      Optional[int]=None		,				__lowercase :      Dict=None		,				__lowercase :      int=None		,				__lowercase :      Dict=False		,				__lowercase :      str=False		,				__lowercase :      List[str]=None		,				__lowercase :      List[str]=64		,				__lowercase :      List[str]=4		,				__lowercase :      Tuple=False		,				__lowercase :      Union[str, Any]=False		,				__lowercase :      Optional[int]=None		,				__lowercase :      Optional[int]=False		,				)							->				Optional[Any]:
        __UpperCAmelCase							:     int									=  bert_score.utils.get_hash
        __UpperCAmelCase							:     Any									=  bert_score.BERTScorer

        if version.parse(bert_score.__version__ ) >= version.parse("""0.3.10""" ):
          __UpperCAmelCase							:     Union[str, Any]									=  functools.partial(__lowercase		,				use_fast_tokenizer=__lowercase )
          __UpperCAmelCase							:     int									=  functools.partial(__lowercase		,				use_fast_tokenizer=__lowercase )
        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"
          __UpperCAmelCase							:     Tuple									=  bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
          __UpperCAmelCase							:     Tuple									=  bert_score.utils.modelalayers[model_type]

        __UpperCAmelCase							:     Dict									=  get_hash(
            model=__lowercase		,				num_layers=__lowercase		,				idf=__lowercase		,				rescale_with_baseline=__lowercase		,				use_custom_baseline=baseline_path is not None		,				)

        with filter_logging_context():
          if not hasattr(self		,				"""cached_bertscorer""" ) or self.cached_bertscorer.hash != hashcode:
            __UpperCAmelCase							:     Optional[Any]									=  scorer(
                model_type=__lowercase		,				num_layers=__lowercase		,				batch_size=__lowercase		,				nthreads=__lowercase		,				all_layers=__lowercase		,				idf=__lowercase		,				device=__lowercase		,				lang=__lowercase		,				rescale_with_baseline=__lowercase		,				baseline_path=__lowercase		,				)

        ((__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase))							:     int									=  self.cached_bertscorer.score(
            cands=__lowercase		,				refs=__lowercase		,				verbose=__lowercase		,				batch_size=__lowercase		,				)
        __UpperCAmelCase							:     Optional[int]									=  {
            """precision""": P.tolist(),
            """recall""": R.tolist(),
            """f1""": F.tolist(),
            """hashcode""": hashcode,
        }
        return output_dict
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      Optional[int]=None		,				__lowercase :      List[Any]=None		,				**__lowercase :      Any )							->				Tuple:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if references is not None:
          __UpperCAmelCase							:     str									=  [[ref] if isinstance(__lowercase		,				__lowercase ) else ref for ref in references]
        super().add_batch(predictions=__lowercase		,				references=__lowercase		,				**__lowercase )




      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      Optional[int]=None		,				__lowercase :      Union[str, Any]=None		,				**__lowercase :      Optional[Any] )							->				Tuple:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if isinstance(__lowercase		,				__lowercase ):
          __UpperCAmelCase							:     Any									=  [reference]
        super().add(prediction=__lowercase		,				reference=__lowercase		,				**__lowercase )










def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  if len(__lowerCamelCase		) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]		),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]		),
    ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


