





import os

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

import datasets


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


__lowercase	: Optional[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}
}
"""

__lowercase	: 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.
"""

__lowercase	: Optional[Any]       ="""
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]
"""

__lowercase	: str       ={
    """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   A							(						datasets.Metric    ):



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Union[str, 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 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    List[Any]							)    ->     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')."							)
            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(_lowerCAmelCase		,	_lowerCAmelCase							)							)






      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Tuple							)    ->     Any:
         '''simple docstring'''

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












import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



