


'''simple docstring'''







import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






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

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


A   :  Optional[int]       		=				'''\
@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}
}
'''

A   :  Optional[Any]       		=				'''\
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.
'''

A   :  Dict       		=				'''
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 				__lowerCamelCase				( datasets.Metric ):
    """simple docstring"""


    def      A					(						self    :							Any):
           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   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]=None   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=None   ,							SCREAMING_SNAKE_CASE    :							str=None   ,							SCREAMING_SNAKE_CASE    :							List[Any]=False   ,							SCREAMING_SNAKE_CASE    :							Tuple=False   ,							SCREAMING_SNAKE_CASE    :							int=None   ,							SCREAMING_SNAKE_CASE    :							List[Any]=64   ,							SCREAMING_SNAKE_CASE    :							int=4   ,							SCREAMING_SNAKE_CASE    :							str=False   ,							SCREAMING_SNAKE_CASE    :							str=False   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=None   ,							SCREAMING_SNAKE_CASE    :							Tuple=False   ,							):
           _A  :			List[str]											=      bert_score.utils.get_hash
           _A  :			Tuple											=      bert_score.BERTScorer

           if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
                  _A  :			Optional[int]											=      functools.partial(SCREAMING_SNAKE_CASE   ,							use_fast_tokenizer=SCREAMING_SNAKE_CASE)
                  _A  :			List[Any]											=      functools.partial(SCREAMING_SNAKE_CASE   ,							use_fast_tokenizer=SCREAMING_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"
                  _A  :			Dict											=      bert_score.utils.langamodel[lang.lower()]

           if num_layers is None:
                  _A  :			Optional[int]											=      bert_score.utils.modelalayers[model_type]

           _A  :			List[str]											=      get_hash(
               model=SCREAMING_SNAKE_CASE   ,							num_layers=SCREAMING_SNAKE_CASE   ,							idf=SCREAMING_SNAKE_CASE   ,							rescale_with_baseline=SCREAMING_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:
                         _A  :			List[str]											=      scorer(
                             model_type=SCREAMING_SNAKE_CASE   ,							num_layers=SCREAMING_SNAKE_CASE   ,							batch_size=SCREAMING_SNAKE_CASE   ,							nthreads=SCREAMING_SNAKE_CASE   ,							all_layers=SCREAMING_SNAKE_CASE   ,							idf=SCREAMING_SNAKE_CASE   ,							device=SCREAMING_SNAKE_CASE   ,							lang=SCREAMING_SNAKE_CASE   ,							rescale_with_baseline=SCREAMING_SNAKE_CASE   ,							baseline_path=SCREAMING_SNAKE_CASE   ,							)

           (_A)  :			int											=      self.cached_bertscorer.score(
               cands=SCREAMING_SNAKE_CASE   ,							refs=SCREAMING_SNAKE_CASE   ,							verbose=SCREAMING_SNAKE_CASE   ,							batch_size=SCREAMING_SNAKE_CASE   ,							)
           _A  :			List[str]											=      {
               'precision': P.tolist(),
               'recall': R.tolist(),
               'f1': F.tolist(),
               'hashcode': hashcode,
           }
           return output_dict


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=None   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=None   ,							**SCREAMING_SNAKE_CASE    :							Any):
           # References can be strings or lists of strings
           # Let's change strings to lists of strings with one element
           if references is not None:
                  _A  :			List[Any]											=      [[ref] if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE) else ref for ref in references]
           super().add_batch(predictions=SCREAMING_SNAKE_CASE   ,							references=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							int=None   ,							SCREAMING_SNAKE_CASE    :							int=None   ,							**SCREAMING_SNAKE_CASE    :							Dict):
           # References can be strings or lists of strings
           # Let's change strings to lists of strings with one element
           if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  _A  :			List[str]											=      [reference]
           super().add(prediction=SCREAMING_SNAKE_CASE   ,							reference=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)










'''simple docstring'''







import itertools






def    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000000     ):
       _A  :			str											=      [1]

       for i in itertools.count(len(lowerCamelCase     )     ):
              _A  :			Optional[Any]											=      0
              for j in itertools.count(1     ):
                     _A  :			int											=      -1 if j % 2 == 0 else +1
                     _A  :			Optional[Any]											=      (j * j * 3 - j) // 2
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number
                     index += j
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number

              if item == 0:
                     return i
              partitions.append(lowerCamelCase     )

       return 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






