
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def 			A_			(					)     ->							Tuple:


     """simple docstring"""







     def filter_log(lowercase      ):
          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__      )


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 		UpperCAmelCase_	(datasets.Metric						):
 """simple docstring"""




 def   a      ( self       : int )->					Dict:



      """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   a      ( self       : Tuple     ,   a_       : Dict     ,   a_       : Union[str, Any]     ,   a_       : Optional[int]=None     ,   a_       : List[Any]=None     ,   a_       : Dict=None     ,   a_       : Tuple=False     ,   a_       : List[str]=False     ,   a_       : Any=None     ,   a_       : Optional[Any]=64     ,   a_       : Dict=4     ,   a_       : Union[str, Any]=False     ,   a_       : int=False     ,   a_       : str=None     ,   a_       : Optional[Any]=False     ,   )->					Tuple:



      """simple docstring"""

      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(a_     ,   use_fast_tokenizer=a_ )
           UpperCAmelCase_						:    List[str]    					=  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"
           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=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:
                UpperCAmelCase_						:    Union[str, Any]    					=  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_     ,   )

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




 def   a      ( self       : int     ,   a_       : Any=None     ,   a_       : Tuple=None     ,   **a_       : Optional[int] )->					Tuple:



      """simple docstring"""

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






 def   a      ( self       : List[str]     ,   a_       : List[str]=None     ,   a_       : Union[str, Any]=None     ,   **a_       : Optional[Any] )->					Any:



      """simple docstring"""

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



"""simple docstring"""







def 			A_			(					lowercase = 99_8001      )     ->							int:


     """simple docstring"""







     for number in range(n - 1						, 9999						, -1      ):
          UpperCAmelCase_						:    Union[str, Any]    					=  str(lowercase      )

          # checks whether 'str_number' is a palindrome.
          if str_number == str_number[::-1]:
               UpperCAmelCase_						:    List[str]    					=  999

               # 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() = }""")

