
"""simple docstring"""




import os

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

import datasets


UpperCAmelCase__   :					Optional[Any]												=				datasets.logging.get_logger(__name__)


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

UpperCAmelCase__   :					str												=				'\\nBLEURT 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)\nand 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\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

UpperCAmelCase__   :					List[Any]												=				'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

UpperCAmelCase__   :					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 			__magic_name__					(self					)      ->	Dict:



											"""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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	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')."""					)
																		SCREAMING_SNAKE_CASE__		: Any     =     """bleurt-base-128"""

											if self.config_name.lower() in CHECKPOINT_URLS:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.config_name.lower()

											elif self.config_name.upper() in CHECKPOINT_URLS:
																		SCREAMING_SNAKE_CASE__		: int     =     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__		: Any     =     dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




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






"""simple docstring"""




UpperCAmelCase__   :					Dict												=				[
    (1_0_0_0, 'M'),
    (9_0_0, 'CM'),
    (5_0_0, 'D'),
    (4_0_0, 'CD'),
    (1_0_0, 'C'),
    (9_0, 'XC'),
    (5_0, 'L'),
    (4_0, 'XL'),
    (1_0, 'X'),
    (9, 'IX'),
    (5, 'V'),
    (4, 'IV'),
    (1, 'I'),
]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							while place < len(_snake_case		):
														if (place + 1 < len(_snake_case		)) and (vals[roman[place]] < vals[roman[place + 1]]):
																					total += vals[roman[place + 1]] - vals[roman[place]]
																					place += 2
														else:
																					total += vals[roman[place]]
																					place += 1
							return total

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
							for arabic, roman in ROMAN:
														((SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__))		: List[Any]     =     divmod(_snake_case     ,_snake_case		)
														result.append(roman * factor		)
														if number == 0:
																					break
							return "".join(_snake_case		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




