






"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def      snake_case    ():







  '''simple docstring'''

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

  a	:     str							=  datasets.utils.logging.get_logger('transformers.modeling_utils' )
  logger.addFilter(A_ )
  try:
    yield
  finally:
    logger.removeFilter(A_ )


_UpperCamelCase							:       List[Any]       					=		'\\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							:       List[str]       					=		'\\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							:       int       					=		'\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 		snake_case    (    datasets.Metric			):





    def 							lowerCamelCase__ (				self      :   int     ):


      '''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 							lowerCamelCase__ (				self      :   int							,						A      :   str							,						A      :   Union[str, Any]							,						A      :   List[Any]=None							,						A      :   str=None							,						A      :   Optional[int]=None							,						A      :   Optional[int]=False							,						A      :   Optional[Any]=False							,						A      :   int=None							,						A      :   str=6_4							,						A      :   Union[str, Any]=4							,						A      :   Union[str, Any]=False							,						A      :   Dict=False							,						A      :   Optional[int]=None							,						A      :   List[Any]=False							,						):


      '''simple docstring'''




      a	:     Union[str, Any]							=  bert_score.utils.get_hash
      a	:     Union[str, Any]							=  bert_score.BERTScorer

      if version.parse(bert_score.__version__     ) >= version.parse('0.3.10'     ):
        a	:     Dict							=  functools.partial(A							,						use_fast_tokenizer=A     )
        a	:     Union[str, 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"
        a	:     Dict							=  bert_score.utils.langamodel[lang.lower()]

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

      a	:     Optional[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:
          a	:     Optional[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							,						)

      ((a),	(a),	(a))	:     Dict							=  self.cached_bertscorer.score(
          cands=A							,						refs=A							,						verbose=A							,						batch_size=A							,						)
      a	:     List[Any]							=  {
          'precision': P.tolist(),
          'recall': R.tolist(),
          'f1': F.tolist(),
          'hashcode': hashcode,
      }
      return output_dict





    def 							lowerCamelCase__ (				self      :   str							,						A      :   List[Any]=None							,						A      :   List[str]=None							,						**A      :   Tuple     ):


      '''simple docstring'''




      if references is not None:
        a	:     str							=  [[ref] if isinstance(A							,						A     ) else ref for ref in references]
      super().add_batch(predictions=A							,						references=A							,						**A     )





    def 							lowerCamelCase__ (				self      :   Union[str, Any]							,						A      :   List[str]=None							,						A      :   Any=None							,						**A      :   Union[str, Any]     ):


      '''simple docstring'''




      if isinstance(A							,						A     ):
        a	:     int							=  [reference]
      super().add(prediction=A							,						reference=A							,						**A     )












"""simple docstring"""


from doctest import testmod
from math import sqrt




def      snake_case    (A_ :int ):







  '''simple docstring'''

  a	:     list[int]							=  []
  if num < 1:
    return facs
  facs.append(1 )
  if num == 1:
    return facs
  facs.append(A_ )
  for i in range(2      ,     int(sqrt(A_ ) ) + 1 ):
    if num % i == 0:  # If i is a factor of num
      facs.append(A_ )
      a	:     Optional[Any]							=  num // i  # num//i is the other factor of num
      if d != i:  # If d and i are distinct
        facs.append(A_ )  # we have found another factor
  facs.sort()
  return facs


if __name__ == "__main__":
   testmod(name='factors_of_a_number', verbose=True)




