'''simple docstring'''







from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


UpperCamelCase_      	=	'''\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
'''

UpperCamelCase_      	=	'''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

UpperCamelCase_      	=	'''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  List[Any]		)			->	List[str]:


							'''simple docstring'''






							return float((preds == labels).mean()		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  Any		)			->	Any:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = simple_accuracy(UpperCAmelCase__				,						UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Dict						    = float(fa_score(y_true=UpperCAmelCase__				,						y_pred=UpperCAmelCase__		)		)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple				,						UpperCAmelCase__				:  List[str]		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = float(pearsonr(UpperCAmelCase__				,						UpperCAmelCase__		)[0]		)
							SCREAMING_SNAKE_CASE__     :int						    = float(spearmanr(UpperCAmelCase__				,						UpperCAmelCase__		)[0]		)
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}





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




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[int]:
													if self.config_name not in [
													    "sst2",
													    "mnli",
													    "mnli_mismatched",
													    "mnli_matched",
													    "cola",
													    "stsb",
													    "mrpc",
													    "qqp",
													    "qnli",
													    "rte",
													    "wnli",
													    "hans",
													]:
																				raise KeyError(
																				    'You should supply a configuration name selected in '
																				    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
																				    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'      )
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
													        {
													            'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'      ),
													            'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'      ),
													        }      )							,							codebase_urls=[]							,							reference_urls=[]							,							format='numpy'							,							)




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Union[str, Any]      )     ->				Any:
													if self.config_name == "cola":
																				return {"matthews_correlation": matthews_corrcoef(UpperCamelCase_							,							UpperCamelCase_      )}
													elif self.config_name == "stsb":
																				return pearson_and_spearman(UpperCamelCase_							,							UpperCamelCase_      )
													elif self.config_name in ["mrpc", "qqp"]:
																				return acc_and_fa(UpperCamelCase_							,							UpperCamelCase_      )
													elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																				return {"accuracy": simple_accuracy(UpperCamelCase_							,							UpperCamelCase_      )}
													else:
																				raise KeyError(
																				    'You should supply a configuration name selected in '
																				    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
																				    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'      )







'''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






