
'''simple docstring'''







import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


_snake_case				:   Tuple     =				"""\
    @inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}
"""

_snake_case				:   Optional[int]     =				"""\
    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""

_snake_case				:   int     =				"""
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"precision\": Precision@10
Examples:

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'precision@10': 1.0}

"""



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						return float((preds == labels).mean()  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= simple_accuracy(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= float(fa_score(y_true=_SCREAMING_SNAKE_CASE	,			y_pred=_SCREAMING_SNAKE_CASE  )  )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= np.array(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= np.array(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= en_sentvecs.shape[0]

						# mean centering
						_SCREAMING_SNAKE_CASE				= en_sentvecs - np.mean(_SCREAMING_SNAKE_CASE	,			axis=0  )
						_SCREAMING_SNAKE_CASE				= in_sentvecs - np.mean(_SCREAMING_SNAKE_CASE	,			axis=0  )

						_SCREAMING_SNAKE_CASE				= cdist(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			"cosine"  )
						_SCREAMING_SNAKE_CASE				= np.array(range(_SCREAMING_SNAKE_CASE  )  )
						_SCREAMING_SNAKE_CASE				= sim.argsort(axis=1  )[:, :10]
						_SCREAMING_SNAKE_CASE				= np.any(preds == actual[:, None]	,			axis=1  )
						return float(matches.mean()  )




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




		def     lowercase       (				self      ):
								if self.config_name not in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "cvit-mkb-clsr",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								    "wiki-ner",
								]:
														raise KeyError(
														    "You should supply a configuration name selected in "
														    "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
														    "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
														    "\"wiki-ner\"]"      )
								return datasets.MetricInfo(
								    description=_DESCRIPTION					,						citation=_CITATION					,						inputs_description=_KWARGS_DESCRIPTION					,						features=datasets.Features(
								        {
								            "predictions": datasets.Value("int64"      )
								            if self.config_name != "cvit-mkb-clsr"
								            else datasets.Sequence(datasets.Value("float32"      )      ),
								            "references": datasets.Value("int64"      )
								            if self.config_name != "cvit-mkb-clsr"
								            else datasets.Sequence(datasets.Value("float32"      )      ),
								        }      )					,						codebase_urls=[]					,						reference_urls=[]					,						format="numpy" if self.config_name != "cvit-mkb-clsr" else None					,						)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								if self.config_name == "cvit-mkb-clsr":
														return {"precision@10": precision_at_aa(UpperCamelCase					,						UpperCamelCase      )}
								elif self.config_name in ["wiki-ner"]:
														return acc_and_fa(UpperCamelCase					,						UpperCamelCase      )
								elif self.config_name in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								]:
														return {"accuracy": simple_accuracy(UpperCamelCase					,						UpperCamelCase      )}
								else:
														raise KeyError(
														    "You should supply a configuration name selected in "
														    "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
														    "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
														    "\"wiki-ner\"]"      )

'''simple docstring'''







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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ) -> bool:
												_SCREAMING_SNAKE_CASE				= 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
						_SCREAMING_SNAKE_CASE				= mean(
						    int(is_in_circle(uniform(-1.0	,			1.0  )	,			uniform(-1.0	,			1.0  )  )  )
						    for _ in range(_SCREAMING_SNAKE_CASE  )  )
						# The ratio of the area for circle to square is pi/4.
						_SCREAMING_SNAKE_CASE				= 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 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Callable[[float], float]	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0	,			):
						return mean(
						    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  ) for _ in range(_SCREAMING_SNAKE_CASE  )  ) * (max_value - min_value)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0  ):

						def identity_function(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return x

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (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 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

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

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			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()