




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCAmelCase_		:					int          =  "\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

UpperCAmelCase_		:					Any          =  "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

UpperCAmelCase_		:					Tuple          =  "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return float((preds == labels).mean() )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase="binary" ):
				__magic_name__						:						Optional[int]										=simple_accuracy(lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						Optional[int]										=float(fa_score(y_true=lowerCamelCase						,  y_pred=lowerCamelCase						,  average=lowerCamelCase ) )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Tuple										={}
				for id_pred, label in zip(lowerCamelCase						,  lowerCamelCase ):
								__magic_name__						:						List[Any]										=F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
								__magic_name__						:						Any										=id_pred["""prediction"""]
								if question_id in question_map:
												question_map[question_id].append((pred, label) )
								else:
												__magic_name__						:						Optional[int]										=[(pred, label)]
				__magic_name__					,			__magic_name__						:						Dict										=[], []
				for question, preds_labels in question_map.items():
								__magic_name__					,			__magic_name__						:						Tuple										=zip(*lowerCamelCase )
								__magic_name__						:						str										=fa_score(y_true=lowerCamelCase						,  y_pred=lowerCamelCase						,  average="""macro""" )
								fas.append(lowerCamelCase )
								__magic_name__						:						Optional[int]										=int(sum(pred == label for pred, label in preds_labels ) == len(lowerCamelCase ) )
								ems.append(lowerCamelCase )
				__magic_name__						:						Any										=float(sum(lowerCamelCase ) / len(lowerCamelCase ) )
				__magic_name__						:						str										=sum(lowerCamelCase ) / len(lowerCamelCase )
				__magic_name__						:						Any										=float(fa_score(y_true=lowerCamelCase						,  y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
				return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}




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

						def      A__    (		self :str			):
										'''simple docstring'''

										if self.config_name not in [
										    "boolq",
										    "cb",
										    "copa",
										    "multirc",
										    "record",
										    "rte",
										    "wic",
										    "wsc",
										    "wsc.fixed",
										    "axb",
										    "axg",
										]:
														raise KeyError(
														    """You should supply a configuration name selected in """
														    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""			)
										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(self._get_feature_types()			)     ,			codebase_urls=[]     ,			reference_urls=[]     ,			format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None     ,			)

						def      A__    (		self :int			):
										'''simple docstring'''

										if self.config_name == "record":
														return {
														    "predictions": {
														        "idx": {
														            "passage": datasets.Value("""int64"""			),
														            "query": datasets.Value("""int64"""			),
														        },
														        "prediction_text": datasets.Value("""string"""			),
														    },
														    "references": {
														        "idx": {
														            "passage": datasets.Value("""int64"""			),
														            "query": datasets.Value("""int64"""			),
														        },
														        "answers": datasets.Sequence(datasets.Value("""string"""			)			),
														    },
														}
										elif self.config_name == "multirc":
														return {
														    "predictions": {
														        "idx": {
														            "answer": datasets.Value("""int64"""			),
														            "paragraph": datasets.Value("""int64"""			),
														            "question": datasets.Value("""int64"""			),
														        },
														        "prediction": datasets.Value("""int64"""			),
														    },
														    "references": datasets.Value("""int64"""			),
														}
										else:
														return {
														    "predictions": datasets.Value("""int64"""			),
														    "references": datasets.Value("""int64"""			),
														}


						def      A__    (		self :Union[str, Any]     ,			__snake_case :List[str]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										if self.config_name == "axb":
														return {"matthews_correlation": matthews_corrcoef(__snake_case     ,			__snake_case			)}
										elif self.config_name == "cb":
														return acc_and_fa(__snake_case     ,			__snake_case     ,			fa_avg="""macro"""			)
										elif self.config_name == "record":
														__magic_name__						:						List[str]										=[
														    {
														        """qas""": [
														            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
														            for ref in references
														        ]
														    }
														]
														__magic_name__						:						Optional[int]										={pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
														return evaluate_record(__snake_case     ,			__snake_case			)[0]
										elif self.config_name == "multirc":
														return evaluate_multirc(__snake_case     ,			__snake_case			)
										elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
														return {"accuracy": simple_accuracy(__snake_case     ,			__snake_case			)}
										else:
														raise KeyError(
														    """You should supply a configuration name selected in """
														    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""			)










from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 		lowerCAmelCase_ (						lowerCamelCase ):

				# A local function to see if a dot lands in the circle.
				def is_in_circle(lowerCamelCase						,  lowerCamelCase ) -> bool:
								__magic_name__						:						Dict										=sqrt((x**2) + (y**2) )
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				__magic_name__						:						Union[str, Any]										=mean(
				    int(is_in_circle(uniform(-1.0						,  1.0 )						,  uniform(-1.0						,  1.0 ) ) )
				    for _ in range(lowerCamelCase ) )
				# The ratio of the area for circle to square is pi/4.
				__magic_name__						:						List[Any]										=proportion * 4
				print(F"The estimated value of pi is {pi_estimate}" )
				print(F"The numpy value of pi is {pi}" )
				print(F"The total error is {abs(pi - pi_estimate )}" )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0						,  ):
				return mean(
				    function_to_integrate(uniform(lowerCamelCase						,  lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0 ):

				def identity_function(lowerCamelCase ) -> float:
								return x

				__magic_name__						:						Optional[int]										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=(max_value * max_value - min_value * min_value) / 2

				print("""******************""" )
				print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {expected_value}" )
				print(F"Total error is {abs(estimated_value - expected_value )}" )
				print("""******************""" )




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def function_to_integrate(lowerCamelCase ) -> float:
								return sqrt(4.0 - x * x )

				__magic_name__						:						Dict										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  0.0						,  2.0 )

				print("""******************""" )
				print("""Estimating pi using area_under_curve_estimator""" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {pi}" )
				print(F"Total error is {abs(estimated_value - pi )}" )
				print("""******************""" )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




