

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def    _snake_case		()       ->		List[Any]:
  def filter_log(snake_case_				:    Optional[Any]     ):
    return False if "This IS expected if you are initializing" in record.msg else True

  __UpperCAmelCase  :     List[str]		=    datasets.utils.logging.get_logger('transformers.modeling_utils'     )
  logger.addFilter(lowercase__     )
  try:
    yield
  finally:
    logger.removeFilter(lowercase__     )


_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     lowerCamelCase_   ( datasets.Metric):




 def 						a					( self       ):
   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     ,     _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     ,     ):
   __UpperCAmelCase  :     Any		=    bert_score.utils.get_hash
   __UpperCAmelCase  :     List[str]		=    bert_score.BERTScorer

   if version.parse(bert_score.__version__       ) >= version.parse('0.3.10'       ):
     __UpperCAmelCase  :     List[str]		=    functools.partial(_UpperCAmelCase     ,     use_fast_tokenizer=_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    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"
     __UpperCAmelCase  :     Optional[Any]		=    bert_score.utils.langamodel[lang.lower()]

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

   __UpperCAmelCase  :     List[Any]		=    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:
       __UpperCAmelCase  :     Union[str, Any]		=    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     ,     )

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




 def 						a					( self     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     **_UpperCAmelCase       ):







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




 def 						a					( self     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     **_UpperCAmelCase       ):







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








def    _snake_case		(snake_case_				:    list						,					snake_case_				:    list     )       ->		list:
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    (
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  __UpperCAmelCase  :     list		=    []
  for char_count in range(snake_case_     ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count]     )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count]     )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





