'''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_      	=	'''\
@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}
}
'''

UpperCamelCase_      	=	'''\
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.
'''

UpperCamelCase_      	=	'''
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]
'''

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 _SCREAMING_SNAKE_CASE(							datasets.Metric					):




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				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      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       Optional[int]      )     ->				Tuple:
													# 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__     :Any						    = 'bleurt-base-128'

													if self.config_name.lower() in CHECKPOINT_URLS:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.config_name.lower()

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




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Optional[int]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scorer.score(references=UpperCamelCase_							,							candidates=UpperCamelCase_      )
													return {"scores": scores}







'''simple docstring'''







import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


UpperCamelCase_      	=	'''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

UpperCamelCase_      	=	'''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

UpperCamelCase_      	=	'''
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,						_KWARGS_DESCRIPTION					)
class _SCREAMING_SNAKE_CASE(							datasets.Metric					):




						def      __lowerCamelCase       (    self :       Any      )     ->				Union[str, Any]:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							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/google-research/tree/master/rouge']							,							reference_urls=[
													        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
													        'https://github.com/google-research/google-research/tree/master/rouge',
													    ]							,							)




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Tuple=True							,							UpperCamelCase_ :       str=False      )     ->				Optional[Any]:
													if rouge_types is None:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rouge_scorer.RougeScorer(rouge_types=UpperCamelCase_							,							use_stemmer=UpperCamelCase_      )
													if use_aggregator:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = scoring.BootstrapAggregator()
													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []

													for ref, pred in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :Tuple						    = scorer.score(UpperCamelCase_							,							UpperCamelCase_      )
																				if use_aggregator:
																											aggregator.add_scores(UpperCamelCase_      )
																				else:
																											scores.append(UpperCamelCase_      )

													if use_aggregator:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = aggregator.aggregate()
													else:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = {}
																				for key in scores[0]:
																											SCREAMING_SNAKE_CASE__     :int						    = [score[key] for score in scores]

													return result






