





"""simple docstring"""
import os

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

import datasets


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


__UpperCamelCase     :						Union[str, 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     :						Optional[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.
'''

__UpperCamelCase     :						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]
'''

__UpperCamelCase     :						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    UpperCamelCase__  (     self     ):







  """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				,       _snake_case     ):







  """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    				= '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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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












"""simple docstring"""
from __future__ import annotations

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list[float]			):
 lowerCAmelCase    				= 0.00
 lowerCAmelCase    				= 0
 for resistor in resistors:
  if resistor <= 0:
   lowerCAmelCase    				= F'Resistor at index {index} has a negative or zero value!'
   raise ValueError(_UpperCAmelCase			)
  first_sum += 1 / float(_UpperCAmelCase			)
  index += 1
 return 1 / first_sum

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list[float]			):
 lowerCAmelCase    				= 0.00
 lowerCAmelCase    				= 0
 for resistor in resistors:
  sum_r += resistor
  if resistor < 0:
   lowerCAmelCase    				= F'Resistor at index {index} has a negative value!'
   raise ValueError(_UpperCAmelCase			)
  index += 1
 return sum_r


if __name__ == "__main__":
       import doctest

       doctest.testmod()





