
"""simple docstring"""







import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def       __UpperCAmelCase		(		)  ->							str:
   def filter_log(a_      :		Dict					):
      return False if "This IS expected if you are initializing" in record.msg else True

   _UpperCAmelCase 						=       datasets.utils.logging.get_logger("transformers.modeling_utils"					)
   logger.addFilter(a_					)
   try:
      yield
   finally:
      logger.removeFilter(a_					)


SCREAMING_SNAKE_CASE_  					= '\\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'

SCREAMING_SNAKE_CASE_  					= '\\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'

SCREAMING_SNAKE_CASE_  					= '\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      ):







       def  _snake_case      (       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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_=None				,lowerCamelCase_=None				,lowerCamelCase_=None				,lowerCamelCase_=False				,lowerCamelCase_=False				,lowerCamelCase_=None				,lowerCamelCase_=64				,lowerCamelCase_=4				,lowerCamelCase_=False				,lowerCamelCase_=False				,lowerCamelCase_=None				,lowerCamelCase_=False				,):



          '''simple docstring'''

          _UpperCAmelCase 						=       bert_score.utils.get_hash
          _UpperCAmelCase 						=       bert_score.BERTScorer

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

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

          _UpperCAmelCase 						=       get_hash(
              model=lowerCamelCase_				,num_layers=lowerCamelCase_				,idf=lowerCamelCase_				,rescale_with_baseline=lowerCamelCase_				,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 						=       scorer(
                    model_type=lowerCamelCase_				,num_layers=lowerCamelCase_				,batch_size=lowerCamelCase_				,nthreads=lowerCamelCase_				,all_layers=lowerCamelCase_				,idf=lowerCamelCase_				,device=lowerCamelCase_				,lang=lowerCamelCase_				,rescale_with_baseline=lowerCamelCase_				,baseline_path=lowerCamelCase_				,)

          ((_UpperCAmelCase)						,				(_UpperCAmelCase)						,				(_UpperCAmelCase)) 						=       self.cached_bertscorer.score(
              cands=lowerCamelCase_				,refs=lowerCamelCase_				,verbose=lowerCamelCase_				,batch_size=lowerCamelCase_				,)
          _UpperCAmelCase 						=       {
              "precision": P.tolist(),
              "recall": R.tolist(),
              "f1": F.tolist(),
              "hashcode": hashcode,
          }
          return output_dict







       def  _snake_case      (       self				,lowerCamelCase_=None				,lowerCamelCase_=None				,**lowerCamelCase_		):



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_=None				,lowerCamelCase_=None				,**lowerCamelCase_		):



          '''simple docstring'''

          if isinstance(lowerCamelCase_				,lowerCamelCase_		):
             _UpperCAmelCase 						=       [reference]
          super().add(prediction=lowerCamelCase_				,reference=lowerCamelCase_				,**lowerCamelCase_		)



"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[int]					)  ->							int:

   # validate:
   if not isinstance(a_     ,	a_					) or not all(
       isinstance(a_     ,	a_					) and item >= 0 for item in citations					):
      raise ValueError("The citations should be a list of non negative integers."					)

   citations.sort()
   _UpperCAmelCase 						=       len(a_					)

   for i in range(a_					):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
  import doctest

  doctest.testmod()

