
"""simple docstring"""




import os

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

import datasets


UpperCAmelCase__   :					Optional[Any]												=				datasets.logging.get_logger(__name__)


UpperCAmelCase__   :					int												=				'\\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__   :					str												=				'\\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__   :					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'

UpperCAmelCase__   :					List[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 			lowerCAmelCase_		(datasets.Metric						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	Dict:



											"""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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""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')."""					)
																		SCREAMING_SNAKE_CASE__		: Any     =     """bleurt-base-128"""

											if self.config_name.lower() in CHECKPOINT_URLS:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.config_name.lower()

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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     self.scorer.score(references=SCREAMING_SNAKE_CASE__     , candidates=SCREAMING_SNAKE_CASE__					)
											return {"scores": scores}



"""simple docstring"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




