





"""simple docstring"""




import os

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

import datasets


UpperCamelCase_									=datasets.logging.get_logger(__name__)


UpperCamelCase_									="\\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_									="\\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_									="\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_									={
    "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    ):






				def     snake_case     (			self		:   Tuple							)     ->				str:




										'''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		:   List[Any],			lowerCAmelCase__		:   List[str]							)     ->				List[str]:




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

										if self.config_name.lower() in CHECKPOINT_URLS:
																_UpperCamelCase      : Dict         =   self.config_name.lower()

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






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Any							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self.scorer.score(references=UpperCAmelCase_,			candidates=UpperCAmelCase_							)
										return {"scores": scores}













"""simple docstring"""




UpperCamelCase_									=[
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



