



'''simple docstring'''

import os

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

import datasets


_A				:    Tuple     						=datasets.logging.get_logger(__name__)


_A				:    Optional[Any]     						='''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

_A				:    Dict     						='''\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

_A				:    Union[str, Any]     						='''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

_A				:    Dict     						={
    '''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 						__UpperCAmelCase     (  self				:	Any					)  ->       Optional[int]:



						'''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 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Optional[Any]					)  ->       Union[str, 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\').'					)
							_lowercase					:       Optional[int]       =		'bleurt-base-128'

						if self.config_name.lower() in CHECKPOINT_URLS:
							_lowercase					:       int       =		self.config_name.lower()

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




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[int]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.scorer.score(references=UpperCamelCase_     ,  candidates=UpperCamelCase_					)
						return {"scores": scores}





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
