



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def       UpperCAmelCase__	(							)				->	Any:
  def filter_log(__snake_case							):
    return False if "This IS expected if you are initializing" in record.msg else True

  _A          =      datasets.utils.logging.get_logger('''transformers.modeling_utils'''							)
  logger.addFilter(__snake_case							)
  try:
    yield
  finally:
    logger.removeFilter(__snake_case							)


__lowerCamelCase								=   """\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
"""

__lowerCamelCase								=   """\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
"""

__lowerCamelCase								=   """
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. 'en').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bertscore = datasets.load_metric(\"bertscore\")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")
    >>> print([round(v, 2) for v in results[\"f1\"]])
    [1.0, 1.0]
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION			)
class 						_snake_case (     datasets.Metric			):





 """simple docstring"""



 def lowercase_						(  self   )		->							str:




   """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 lowercase_						(  self		,      a		,      a		,      a=None		,      a=None		,      a=None		,      a=False		,      a=False		,      a=None		,      a=6_4		,      a=4		,      a=False		,      a=False		,      a=None		,      a=False		,      )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      bert_score.utils.get_hash
   _A          =      bert_score.BERTScorer

   if version.parse(bert_score.__version__   ) >= version.parse('''0.3.10'''   ):
     _A          =      functools.partial(a		,      use_fast_tokenizer=a   )
     _A          =      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"
     _A          =      bert_score.utils.langamodel[lang.lower()]

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

   _A          =      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:
       _A          =      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		,      )

   ((_A)      ,				(_A)      ,				(_A))          =      self.cached_bertscorer.score(
       cands=a		,      refs=a		,      verbose=a		,      batch_size=a		,      )
   _A          =      {
       '''precision''': P.tolist(),
       '''recall''': R.tolist(),
       '''f1''': F.tolist(),
       '''hashcode''': hashcode,
   }
   return output_dict
 def lowercase_						(  self		,      a=None		,      a=None		,      **a   )		->							Dict:




   """simple docstring"""







   if references is not None:
     _A          =      [[ref] if isinstance(a		,      a   ) else ref for ref in references]
   super().add_batch(predictions=a		,      references=a		,      **a   )



 def lowercase_						(  self		,      a=None		,      a=None		,      **a   )		->							List[Any]:




   """simple docstring"""







   if isinstance(a		,      a   ):
     _A          =      [reference]
   super().add(prediction=a		,      reference=a		,      **a   )




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:

  # Base Case
  if counter == 0 or capacity == 0:
    return 0

  # If weight of the nth item is more than Knapsack of capacity,
  #   then this item cannot be included in the optimal solution,
  # else return the maximum of two cases:
  #   (1) nth item included
  #   (2) not included
  if weights[counter - 1] > capacity:
    return knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()