


"""simple docstring"""




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

import datasets


lowercase_			     =  "\\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"

lowercase_			     =  "\\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"

lowercase_			     =  "\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        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     str			) ->      List[Any]:
		return float((preds == labels).mean()			)

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Dict			) ->      int:
		__a           =			simple_accuracy(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			float(fa_score(y_true=lowerCAmelCase__    ,					y_pred=lowerCAmelCase__			)			)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     str			) ->      List[Any]:
		__a           =			float(pearsonr(lowerCAmelCase__    ,					lowerCAmelCase__			)[0]			)
		__a           =			float(spearmanr(lowerCAmelCase__    ,					lowerCAmelCase__			)[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 			__UpperCAmelCase       (  self      ):
				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 			__UpperCAmelCase       (  self							, _a							, _a      ):
				if self.config_name == "cola":
						return {"matthews_correlation": matthews_corrcoef(_a							, _a      )}
				elif self.config_name == "stsb":
						return pearson_and_spearman(_a							, _a      )
				elif self.config_name in ["mrpc", "qqp"]:
						return acc_and_fa(_a							, _a      )
				elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
						return {"accuracy": simple_accuracy(_a							, _a      )}
				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"""




lowercase_			     =  [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_			     =  [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_			     =  {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int			) ->      str:
		assert len(str(lowerCAmelCase__			)			) > 2, "year should be in YYYY format"
		assert 1 <= month <= 12, "month should be between 1 to 12"
		assert 1 <= day <= 31, "day should be between 1 to 31"

		# Doomsday algorithm:
		__a           =			year // 100
		__a           =			(5 * (century % 4) + 2) % 7
		__a           =			year % 100
		__a           =			centurian % 12
		__a           =			(
		    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
		) % 7
		__a           =			(
		    DOOMSDAY_NOT_LEAP[month - 1]
		    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
		    else DOOMSDAY_LEAP[month - 1]
		)
		__a           =			(dooms_day + day - day_anchor) % 7
		return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
				import doctest

				doctest.testmod()






