






"""simple docstring"""





import os

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

import datasets


A__					: List[Any]=	datasets.logging.get_logger(__name__)


A__					: str=	"""\
@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__					: int=	"""\
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__					: Dict=	"""
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__					: Optional[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  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       # 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\').'      )
           UpperCamelCase__         =    'bleurt-base-128'

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

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







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Tuple:
       UpperCamelCase__         =    self.scorer.score(references=snake_case_ ,				candidates=snake_case_      )
       return {"scores": scores}












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__					: List[str]=	logging.get_logger(__name__)
class    __lowerCamelCase    (  _a  ):
   a      :				Optional[int] 						="""timm_backbone"""

   def __init__(					self ,				snake_case_=None ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=None ,				**snake_case_ ,				)		->       Dict:
       super().__init__(**snake_case_      )
       UpperCamelCase__         =    backbone
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    features_only
       UpperCamelCase__         =    use_pretrained_backbone
       UpperCamelCase__         =    True
       UpperCamelCase__         =    out_indices if out_indices is not None else (-1,)




