


'''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_			:  int										=   '\\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_			:  List[Any]										=   '\\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_			:  str										=   '\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_			:  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					):




 '''simple docstring'''


 def 			snake_case__ (   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 			snake_case__ (   self   ,   __lowercase    ):

  """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\').'    )
   __A	:		str	  =      'bleurt-base-128'

  if self.config_name.lower() in CHECKPOINT_URLS:
   __A	:		Tuple	  =      self.config_name.lower()

  elif self.config_name.upper() in CHECKPOINT_URLS:
   __A	:		str	  =      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
  __A	:		Any	  =      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
  __A	:		Union[str, Any]	  =      score.BleurtScorer(os.path.join(__lowercase   ,   __lowercase    )    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      self.scorer.score(references=__lowercase   ,   candidates=__lowercase    )
  return {"scores": scores}










'''simple docstring'''
from math import ceil


def       _lowercase							(					UpperCamelCase__							:       int = 1001      ):
 __A	:		int	  =      1

 for i in range(1,					int(ceil(n / 2.0      )      )      ):
  __A	:		Any	  =      2 * i + 1
  __A	:		List[str]	  =      2 * i
  __A	:		str	  =      total + 4 * odd**2 - 6 * even

 return total


if __name__ == "__main__":
 import sys

 if len(sys.argv) == 1:
  print(solution())
 else:
  try:
   UpperCAmelCase_			:  int										=   int(sys.argv[1])
   print(solution(n))
  except ValueError:
   print('Invalid entry - please enter a number')






