






"""simple docstring"""



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  def filter_log(__snake_case     :    int       ):
    return False if "This IS expected if you are initializing" in record.msg else True

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


_UpperCamelCase						:    List[str]		=						'\\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'

_UpperCamelCase						:    Tuple		=						'\\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'

_UpperCamelCase						:    Dict		=						'\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      UpperCamelCase_ (     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      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=6_4				,						_lowerCamelCase=4				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						):
      lowercase				       =			bert_score.utils.get_hash
      lowercase				       =			bert_score.BERTScorer

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

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

      lowercase				       =			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:
          lowercase				       =			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				,						)

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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if references is not None:
        lowercase				       =			[[ref] if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else ref for ref in references]
      super().add_batch(predictions=_lowerCamelCase				,						references=_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if isinstance(_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			[reference]
      super().add(prediction=_lowerCamelCase				,						reference=_lowerCamelCase				,						**_lowerCamelCase			)









def      a    (   _lowercase :  Dict			,    _lowercase :  List[str]							)  ->List[Any]:
		if len(str(_lowercase							)							) == 1 or len(str(_lowercase							)							) == 1:
				return a * b

		UpperCamelCase__								=						max(len(str(_lowercase							)							)			,    len(str(_lowercase							)							)							)
		UpperCamelCase__								=						ma // 2

		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)
		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)

		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)
		UpperCamelCase__								=						karatsuba((aa + aa)			,    (ba + ba)							)
		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)

		return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)

def      a    (   )  ->List[Any]:
		print(karatsuba(1_5_4_6_3			,    2_3_4_8_9							)							)


if __name__ == "__main__":
				main()





