


import os

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

import datasets


lowercase_	   =     datasets.logging.get_logger(__name__)


lowercase_	   =     '\\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'

lowercase_	   =     '\\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'

lowercase_	   =     '\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'

lowercase_	   =     {
    '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   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	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 					_snake_case     (    self:	Tuple						,				a:	List[Any]    ):
       # 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		:							Optional[int]		 =						'bleurt-base-128'

       if self.config_name.lower() in CHECKPOINT_URLS:
        __lowerCamelCase		:							List[str]		 =						self.config_name.lower()

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




      def 					_snake_case     (    self:	List[Any]						,				a:	Union[str, Any]						,				a:	str    ):
       __lowerCamelCase		:							Tuple		 =						self.scorer.score(references=a						,				candidates=a    )
       return {"scores": scores}





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						str(SCREAMING_SNAKE_CASE__						)
 return len(SCREAMING_SNAKE_CASE__						) == 9 and set(SCREAMING_SNAKE_CASE__						) == set('123456789'						)
def 				UpperCamelCase__					(  ):
 for base_num in range(9_999      ,	4_999      ,	-1						):
  __lowerCamelCase		:							Tuple		 =						100_002 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 for base_num in range(333      ,	99      ,	-1						):
  __lowerCamelCase		:							Union[str, Any]		 =						1_002_003 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 return None


if __name__ == "__main__":
       print(F"""{solution() = }""")

