




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def   lowerCAmelCase_ (				)->  Tuple:






      '''simple docstring'''





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

      UpperCAmelCase    : Optional[int]      =datasets.utils.logging.get_logger('''transformers.modeling_utils'''						)
      logger.addFilter(__lowerCAmelCase						)
      try:
            yield
      finally:
            logger.removeFilter(__lowerCAmelCase						)


__snake_case			      =    '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

__snake_case			      =    '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

__snake_case			      =    '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,   _KWARGS_DESCRIPTION   )
class 			__snake_case   (     datasets.Metric   ):
 def 	UpperCAmelCase__       (  self						)	->							str:
       '''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 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__=None		,   snake_case__=None		,   snake_case__=None		,   snake_case__=False		,   snake_case__=False		,   snake_case__=None		,   snake_case__=64		,   snake_case__=4		,   snake_case__=False		,   snake_case__=False		,   snake_case__=None		,   snake_case__=False		,   )	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =bert_score.utils.get_hash
       UpperCAmelCase    : Any      =bert_score.BERTScorer

       if version.parse(bert_score.__version__						) >= version.parse('''0.3.10'''						):
             UpperCAmelCase    : List[Any]      =functools.partial(snake_case__		,   use_fast_tokenizer=snake_case__						)
             UpperCAmelCase    : Dict      =functools.partial(snake_case__		,   use_fast_tokenizer=snake_case__						)
       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    : List[Any]      =bert_score.utils.langamodel[lang.lower()]

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

       UpperCAmelCase    : Tuple      =get_hash(
           model=snake_case__		,   num_layers=snake_case__		,   idf=snake_case__		,   rescale_with_baseline=snake_case__		,   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    : Tuple      =scorer(
                       model_type=snake_case__		,   num_layers=snake_case__		,   batch_size=snake_case__		,   nthreads=snake_case__		,   all_layers=snake_case__		,   idf=snake_case__		,   device=snake_case__		,   lang=snake_case__		,   rescale_with_baseline=snake_case__		,   baseline_path=snake_case__		,   )

       (UpperCAmelCase)    : Tuple      =self.cached_bertscorer.score(
           cands=snake_case__		,   refs=snake_case__		,   verbose=snake_case__		,   batch_size=snake_case__		,   )
       UpperCAmelCase    : str      ={
           '''precision''': P.tolist(),
           '''recall''': R.tolist(),
           '''f1''': F.tolist(),
           '''hashcode''': hashcode,
       }
       return output_dict
 def 	UpperCAmelCase__       (  self		,   snake_case__=None		,   snake_case__=None		,   **snake_case__						)	->							Union[str, Any]:
       '''simple docstring'''





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






 def 	UpperCAmelCase__       (  self		,   snake_case__=None		,   snake_case__=None		,   **snake_case__						)	->							Optional[Any]:
       '''simple docstring'''





       if isinstance(snake_case__		,   snake_case__						):
             UpperCAmelCase    : Any      =[reference]
       super().add(prediction=snake_case__		,   reference=snake_case__		,   **snake_case__						)

from collections import defaultdict





def   lowerCAmelCase_ (				__lowerCAmelCase = 5						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      =defaultdict(__lowerCAmelCase						)
      UpperCAmelCase    : Tuple      =0

      while True:
            UpperCAmelCase    : Any      =get_digits(__lowerCAmelCase						)
            freqs[digits].append(__lowerCAmelCase						)

            if len(freqs[digits]						) == max_base:
                  UpperCAmelCase    : Optional[int]      =freqs[digits][0] ** 3
                  return base

            num += 1





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  str:






      '''simple docstring'''





      return "".join(sorted(str(num**3						)						)						)


if __name__ == "__main__":
     print(f'{solution() = }')
