


import os

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

import datasets


SCREAMING_SNAKE_CASE__					:     int       =     datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__					:     str       =     '\\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'

SCREAMING_SNAKE_CASE__					:     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'

SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     '\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'

SCREAMING_SNAKE_CASE__					:     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 		_SCREAMING_SNAKE_CASE     (						datasets.Metric     ):


     def        __snake_case(  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        __snake_case(  self							,					A_    ):
       # 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')."""    )
         _UpperCAmelCase		:							Optional[Any]							=     """bleurt-base-128"""

       if self.config_name.lower() in CHECKPOINT_URLS:
         _UpperCAmelCase		:							List[Any]							=     self.config_name.lower()

       elif self.config_name.upper() in CHECKPOINT_URLS:
         _UpperCAmelCase		:							Optional[Any]							=     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
       _UpperCAmelCase		:							List[str]							=     dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
       _UpperCAmelCase		:							int							=     score.BleurtScorer(os.path.join(A_							,					A_    )    )




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							str							=     self.scorer.score(references=A_							,					candidates=A_    )
       return {"scores": scores}









SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





