






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

import datasets


SCREAMING_SNAKE_CASE 							=						'\\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'

SCREAMING_SNAKE_CASE 							=						'\\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'

SCREAMING_SNAKE_CASE 							=						'\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      _lowerCamelCase	(						__A    :							List[str]		,		__A    :							Dict )      ->					int:
    return float((preds == labels).mean() )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Optional[int] )      ->					List[Any]:
    _UpperCAmelCase				:      int			  =				simple_accuracy(lowerCamelCase_		,		lowerCamelCase_ )
    _UpperCAmelCase				:      Any			  =				float(fa_score(y_true=lowerCamelCase_		,		y_pred=lowerCamelCase_ ) )
    return {
        "accuracy": acc,
        "f1": fa,
    }

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Optional[Any] )      ->					Tuple:
    _UpperCAmelCase				:      Union[str, Any]			  =				float(pearsonr(lowerCamelCase_		,		lowerCamelCase_ )[0] )
    _UpperCAmelCase				:      int			  =				float(spearmanr(lowerCamelCase_		,		lowerCamelCase_ )[0] )
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
    }


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






 '''simple docstring'''




 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     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  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     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"]''')












import unittest

from knapsack import knapsack as k
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				0
     _UpperCAmelCase				:      List[Any]			  =				[0]
     _UpperCAmelCase				:      Optional[Any]			  =				[0]
     _UpperCAmelCase				:      Optional[int]			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  0)

     _UpperCAmelCase				:      Optional[int]			  =				[60]
     _UpperCAmelCase				:      List[str]			  =				[10]
     _UpperCAmelCase				:      str			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  0)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				3
     _UpperCAmelCase				:      int			  =				[1, 2, 3]
     _UpperCAmelCase				:      List[str]			  =				[3, 2, 1]
     _UpperCAmelCase				:      Union[str, Any]			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  5)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				50
     _UpperCAmelCase				:      Tuple			  =				[60, 100, 120]
     _UpperCAmelCase				:      Optional[int]			  =				[10, 20, 30]
     _UpperCAmelCase				:      Optional[Any]			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  220)


if __name__ == "__main__":
  unittest.main()




