



def 							A	(     _UpperCAmelCase				:   Union[str, Any]    )				-> List[Any]:







			'''simple docstring'''





			try:
						_UpperCAmelCase		       =   float(_UpperCAmelCase    )
			except ValueError:
						raise ValueError('Please enter a valid number'    )
			_UpperCAmelCase		       =   decimal - int(_UpperCAmelCase    )
			if fractional_part == 0:
						return int(_UpperCAmelCase    ), 1
			else:
						_UpperCAmelCase		       =   len(str(_UpperCAmelCase    ).split('.'    )[1]    )
						_UpperCAmelCase		       =   int(decimal * (10**number_of_frac_digits)    )
						_UpperCAmelCase		       =   10**number_of_frac_digits
						_UpperCAmelCase		       =   denominator, numerator
						while True:
									_UpperCAmelCase		       =   dividend % divisor
									if remainder == 0:
												break
									_UpperCAmelCase		       =   divisor, remainder
						_UpperCAmelCase		       =   numerator / divisor, denominator / divisor
						return int(_UpperCAmelCase    ), int(_UpperCAmelCase    )


if __name__ == "__main__":
							print(f"""{decimal_to_fraction(2) = }""")
							print(f"""{decimal_to_fraction(89.0) = }""")
							print(f"""{decimal_to_fraction("67") = }""")
							print(f"""{decimal_to_fraction("45.0") = }""")
							print(f"""{decimal_to_fraction(1.5) = }""")
							print(f"""{decimal_to_fraction("6.25") = }""")
							print(f"""{decimal_to_fraction("78td") = }""")





'''simple docstring'''





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCAmelCase      						=	datasets.logging.get_logger(__name__)


_lowerCAmelCase      						=	'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

_lowerCAmelCase      						=	'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

_lowerCAmelCase      						=	'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

_lowerCAmelCase      						=	{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						, _KWARGS_DESCRIPTION     )
class        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,citation=_CITATION  ,homepage="""https://github.com/google-research/bleurt"""  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""  ,id="""sequence"""      ),
                  """references""": datasets.Value("""string"""  ,id="""sequence"""      ),
              }      )  ,codebase_urls=["""https://github.com/google-research/bleurt"""]  ,reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""]  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      # check that config name specifies a valid BLEURT model
      if self.config_name == "default":
          logger.warning(
              """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
              """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""      )
          lowerCAmelCase__ :  str							      =					"""bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  List[Any]							      =					self.config_name.upper()

      else:
          raise KeyError(
              F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""      )

      # download the model checkpoint specified by self.config_name and set up the scorer
      lowerCAmelCase__ :  int							      =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
      lowerCAmelCase__ :  Dict							      =					score.BleurtScorer(os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.scorer.score(references=__UpperCAmelCase  ,candidates=__UpperCAmelCase      )
      return {"scores": scores}


