


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

import datasets


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

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

lowerCAmelCase__      		= '\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[Any]						):
       return float((preds == labels).mean()						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  int				,							UpperCamelCase__     :  Optional[int]						):
       _A      :       Optional[int]				    = simple_accuracy(UpperCamelCase__				,							UpperCamelCase__						)
       _A      :       Optional[Any]				    = float(fa_score(y_true=UpperCamelCase__				,							y_pred=UpperCamelCase__						)						)
       return {
           "accuracy": acc,
           "f1": fa,
       }
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       Optional[Any]				    = float(pearsonr(UpperCamelCase__				,							UpperCamelCase__						)[0]						)
       _A      :       Union[str, Any]				    = float(spearmanr(UpperCamelCase__				,							UpperCamelCase__						)[0]						)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }







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




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				Tuple:
            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 ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[int]:
            if self.config_name == "cola":
                   return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase ,						__lowerCamelCase)}
            elif self.config_name == "stsb":
                   return pearson_and_spearman(__lowerCamelCase ,						__lowerCamelCase)
            elif self.config_name in ["mrpc", "qqp"]:
                   return acc_and_fa(__lowerCamelCase ,						__lowerCamelCase)
            elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                   return {"accuracy": simple_accuracy(__lowerCamelCase ,						__lowerCamelCase)}
            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\"]")







class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase ):
        A__											=						set_counts
        A__											=						max(__lowerCamelCase )
        A__											=						len(__lowerCamelCase )
        A__											=						[1] * num_sets
        A__											=						list(range(__lowerCamelCase ) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.get_parent(__lowerCamelCase )
        A__											=						self.get_parent(__lowerCamelCase )

        if src_parent == dst_parent:
               return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
               self.set_counts[dst_parent] += self.set_counts[src_parent]
               A__											=						0
               A__											=						dst_parent
               if self.ranks[dst_parent] == self.ranks[src_parent]:
                      self.ranks[dst_parent] += 1
               A__											=						self.set_counts[dst_parent]
        else:
               self.set_counts[src_parent] += self.set_counts[dst_parent]
               A__											=						0
               A__											=						src_parent
               A__											=						self.set_counts[src_parent]

        A__											=						max(self.max_set,__lowerCamelCase )
        return True




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if self.parents[disj_set] == disj_set:
               return disj_set
        A__											=						self.get_parent(self.parents[disj_set] )
        return self.parents[disj_set]





