




import os

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

import datasets


UpperCamelCase								=	datasets.logging.get_logger(__name__)


UpperCamelCase								=	'\\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								=	'\\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								=	'\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								=	{
    '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   a (			self	:	Any  ) ->			Dict:
         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	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ) ->			Tuple:
         # 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')."  )
              lowerCAmelCase__            =					"""bleurt-base-128"""

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

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


    def   a (			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Any  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ) ->			Optional[Any]:
         lowerCAmelCase__            =					self.scorer.score(references=__snake_case  ,      candidates=__snake_case  )
         return {"scores": scores}








"""simple docstring"""





def   a    (     __UpperCAmelCase      :    list[int]  )    ->							float:
						if not nums:  # Makes sure that the list is not empty
												raise ValueError("""List is empty"""  )

						__magic_name__:	Dict							=       sum(__UpperCAmelCase  ) / len(__UpperCAmelCase  )  # Calculate the average
						return sum(abs(x - average  ) for x in nums  ) / len(__UpperCAmelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



