code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  @contextmanager
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     def filter_log(snake_case   ):
        return False if "This IS expected if you are initializing" in record.msg else True
  
     SCREAMING_SNAKE_CASE__			:				int       						= datasets.utils.logging.get_logger('''transformers.modeling_utils'''   )
     logger.addFilter(lowercase__   )
     try:
        yield
     finally:
        logger.removeFilter(lowercase__   )
  
  
  lowercase__				=	'\\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'
  
  lowercase__				=	'\\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'
  
  lowercase__				=	'\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 						lowercase     (   datasets.Metric):
  
  
  
  
  
  
        def    A__(self					)       ->   int:
           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 ,  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 ,  )       ->   str:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= bert_score.utils.get_hash
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= bert_score.BERTScorer
  
           if version.parse(bert_score.__version__					) >= version.parse('''0.3.10'''					):
              SCREAMING_SNAKE_CASE__			:				str       						= functools.partial(A_ ,  use_fast_tokenizer=A_					)
              SCREAMING_SNAKE_CASE__			:				Any       						= 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"
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= bert_score.utils.langamodel[lang.lower()]
  
           if num_layers is None:
              SCREAMING_SNAKE_CASE__			:				str       						= bert_score.utils.modelalayers[model_type]
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= 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:
                 SCREAMING_SNAKE_CASE__			:				int       						= 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_ ,  )
  
           ((SCREAMING_SNAKE_CASE__)						,				(SCREAMING_SNAKE_CASE__)						,				(SCREAMING_SNAKE_CASE__))			:				int       						= self.cached_bertscorer.score(
               cands=A_ ,  refs=A_ ,  verbose=A_ ,  batch_size=A_ ,  )
           SCREAMING_SNAKE_CASE__			:				int       						= {
               '''precision''': P.tolist(),
               '''recall''': R.tolist(),
               '''f1''': F.tolist(),
               '''hashcode''': hashcode,
           }
           return output_dict
  
  
  
  
  
  
        def    A__(self ,  a__=None ,  a__=None ,  **a__					)       ->   Optional[int]:
           # References can be strings or lists of strings
           # Let's change strings to lists of strings with one element
           if references is not None:
              SCREAMING_SNAKE_CASE__			:				str       						= [[ref] if isinstance(A_ ,  A_					) else ref for ref in references]
           super().add_batch(predictions=A_ ,  references=A_ ,  **A_					)
  
  
  
  
  
  
        def    A__(self ,  a__=None ,  a__=None ,  **a__					)       ->   Any:
           # References can be strings or lists of strings
           # Let's change strings to lists of strings with one element
           if isinstance(A_ ,  A_					):
              SCREAMING_SNAKE_CASE__			:				str       						= [reference]
           super().add(prediction=A_ ,  reference=A_ ,  **A_					)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  def 				UpperCamelCase	(  snake_case = 99_80_01   ):
  
  
  
  
     '''simple docstring'''
  
     for number in range(n - 1	,     99_99	,     -1   ):
        SCREAMING_SNAKE_CASE__			:				int       						= str(snake_case   )
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           SCREAMING_SNAKE_CASE__			:				Any       						= 9_99
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor   )   ) == 3.0):
                 return number
              divisor -= 1
     raise ValueError('''That number is larger than our acceptable range.'''   )
  
  
  if __name__ == "__main__":
         print(F"{solution() = }")
  
  