





import os

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

import datasets


a__:      Tuple									=		datasets.logging.get_logger(__name__)


a__:      Dict									=		'\\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'

a__:      Tuple									=		'\\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'

a__:      int									=		'\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'

a__:      Optional[int]									=		{
    '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     SCREAMING_SNAKE_CASE__ (						datasets.Metric      ):



 def 				UpperCamelCase			(							self ):
        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,__lowerCamelCase ):
        # 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\').''' )
               A__											=						"bleurt-base-128"

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

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




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.scorer.score(references=__lowerCamelCase,candidates=__lowerCamelCase )
        return {"scores": scores}









def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  bool = False						):

       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Optional[Any]				    = f"Expected string as input, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Union[str, Any]				    = f"Expected boolean as use_pascal parameter, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)

       _A      :       int				    = input_str.split("_"						)

       _A      :       str				    = 0 if use_pascal else 1

       _A      :       str				    = words[start_index:]

       _A      :       Optional[Any]				    = [word[0].upper() + word[1:] for word in words_to_capitalize]

       _A      :       Any				    = "" if use_pascal else words[0]

       return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
