


import os

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

import datasets


a			:			Tuple												=					datasets.logging.get_logger(__name__)


a			:			Dict												=					'''\
@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}
}
'''

a			:			Any												=					'''\
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.
'''

a			:			Tuple												=					'''
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]
'''

a			:			List[Any]												=					{
    '''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 A   (					self    ) ->      str:







								'''simple docstring'''

								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 A   (					self			,							snake_case_    ) ->      Optional[Any]:







								'''simple docstring'''

								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\').'''    )
											__lowercase			       = '''bleurt-base-128'''

								if self.config_name.lower() in CHECKPOINT_URLS:
											__lowercase			       = self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
											__lowercase			       = 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
								__lowercase			       = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
								__lowercase			       = score.BleurtScorer(os.path.join(snake_case_			,							snake_case_    )    )






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = self.scorer.score(references=snake_case_			,							candidates=snake_case_    )
								return {"scores": scores}






from maths.prime_factors import prime_factors


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase							,      _UpperCamelCase	):
						__lowercase			       = F'Input value of [number={number}] must be an integer'
						raise TypeError(_UpperCamelCase	)
			if number < 1:
						raise ValueError('''Input must be a positive integer'''	)
			return -1 if len(prime_factors(_UpperCamelCase	)	) % 2 else 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()


