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    SCREAMING_SNAKE_CASE			(							datasets.Metric					):





							def 						UpperCamelCase_					(   self		:		Any	):

											'''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 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											# 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')."""	)
															__a       =		"""bleurt-base-128"""

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

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







							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str      ,				__lowercase		:		Dict	):

											'''simple docstring'''
											__a       =		self.scorer.score(references=__lowercase      ,				candidates=__lowercase	)
											return {"scores": scores}





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				if isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ) and isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
								__a       =		len(set_a.intersection(_SCREAMING_SNAKE_CASE    )    )

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
								else:
												__a       =		len(set_a.union(_SCREAMING_SNAKE_CASE    )    )

								return intersection / union

				if isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ) and isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ):
								__a       =		[element for element in set_a if element in set_b]

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
												return len(_SCREAMING_SNAKE_CASE    ) / union
								else:
												__a       =		set_a + [element for element in set_b if element not in set_a]
												return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )

								return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )
				return None


if __name__ == "__main__":
						lowerCamelCase__			= {"""a""", """b""", """c""", """d""", """e"""}
						lowerCamelCase__			= {"""c""", """d""", """e""", """f""", """h""", """i"""}
						print(jaccard_similarity(set_a, set_b))
