




import os

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

import datasets


__SCREAMING_SNAKE_CASE			    =datasets.logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE			    ="""\
@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}
}
"""

__SCREAMING_SNAKE_CASE			    ="""\
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.
"""

__SCREAMING_SNAKE_CASE			    ="""
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]
"""

__SCREAMING_SNAKE_CASE			    ={
    """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 			__magic_name__    (	datasets.Metric):
				'''simple docstring'''







				def      _A				(				self: Optional[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      _A				(				self: int		,       _lowerCamelCase: Optional[Any]		):
											# 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\').'''		)
																		SCREAMING_SNAKE_CASE_ 						=     'bleurt-base-128'

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

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





				def      _A				(				self: Any		,       _lowerCamelCase: Union[str, Any]		,       _lowerCamelCase: Tuple		):
											SCREAMING_SNAKE_CASE_ 						=     self.scorer.score(references=_UpperCAmelCase		,       candidates=_UpperCAmelCase		)
											return {"scores": scores}







'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


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




