






'''simple docstring'''

import os

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

import datasets


UpperCAmelCase__						:int          =			datasets.logging.get_logger(__name__)


UpperCAmelCase__						:Any          =			"""\
@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}
}
"""

UpperCAmelCase__						:List[str]          =			"""\
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.
"""

UpperCAmelCase__						:int          =			"""
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]
"""

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 	SCREAMING_SNAKE_CASE	(      datasets.Metric  ):




       def 					a_					(					self      : Optional[int]					):


            """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      : Tuple	,    A__      : Tuple					):


            """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')."""					)
                 __lowerCamelCase						:       List[Any]				   =				"""bleurt-base-128"""

            if self.config_name.lower() in CHECKPOINT_URLS:
                 __lowerCamelCase						:       Dict				   =				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						:       List[Any]				   =				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]					)
            __lowerCamelCase						:       Union[str, Any]				   =				score.BleurtScorer(os.path.join(A__	,    A__					)					)




       def 					a_					(					self      : List[Any]	,    A__      : Union[str, Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.scorer.score(references=A__	,    candidates=A__					)
            return {"scores": scores}











'''simple docstring'''

from ..utils import DummyObject, requires_backends







class 	SCREAMING_SNAKE_CASE	(      metaclass=lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			['torch', 'torchsde']




       def __init__(					self      : int	,    *A__      : Optional[Any]	,    **A__      : Dict					):


            """simple docstring"""







            requires_backends(self	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Optional[Any]	,    *A__      : str	,    **A__      : Optional[Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Union[str, Any]	,    *A__      : Any	,    **A__      : Union[str, Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)



