






import os

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

import datasets


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


a_		:						Tuple             =    '\\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_		:						Union[str, 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'

a_		:						List[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'

a_		:						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 		_snake_case  (  datasets.Metric					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						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    SCREAMING_SNAKE_CASE__    (       self		,					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\').')
									SCREAMING_SNAKE_CASE						= 'bleurt-base-128'

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

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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.scorer.score(references=a		,					candidates=a)
						return {"scores": scores}












import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script

@require_cpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						debug_launcher(test_script.main)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						debug_launcher(test_ops.main)




