


import os

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

import datasets


lowerCAmelCase__      		= datasets.logging.get_logger(__name__)


lowerCAmelCase__      		= '\\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'

lowerCAmelCase__      		= '\\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'

lowerCAmelCase__      		= '\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'

lowerCAmelCase__      		= {
    '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  _lowerCamelCase (						self)			->				Tuple:
            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  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, 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').")
                   _A      :       Tuple				    = "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      :       str				    = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
            _A      :       Dict				    = score.BleurtScorer(os.path.join(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[Any]:
            _A      :       Dict				    = self.scorer.score(references=__lowerCamelCase ,						candidates=__lowerCamelCase)
            return {"scores": scores}




'''simple docstring'''


def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):

		if density <= 0:
				raise ValueError("Impossible fluid density"      )
		if bulk_modulus <= 0:
				raise ValueError("Impossible bulk modulus"      )

		return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()



