






import math






def 				lowerCamelCase__				(_UpperCAmelCase):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5							,       int(math.sqrt(_UpperCAmelCase) + 1)							,       6):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True






def 				lowerCamelCase__				(_UpperCAmelCase = 0.1):
			SCREAMING_SNAKE_CASE						= 3
			SCREAMING_SNAKE_CASE						= 3

			while primes / (2 * j - 1) >= ratio:
						for i in range(j * j + j + 1							,       (j + 2) * (j + 2)							,       j + 1):
									primes += is_prime(_UpperCAmelCase)
						j += 2
			return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()












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

import datasets


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

a_		:						Tuple             =    '\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'

a_		:						Optional[Any]             =    '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "precision": Precision@10\nExamples:\n\n    >>> 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"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'precision@10\': 1.0}\n\n'






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return float((preds == labels).mean())






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= simple_accuracy(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= float(fa_score(y_true=_UpperCAmelCase							,       y_pred=_UpperCAmelCase))
			return {
			    "accuracy": acc,
			    "f1": fa,
			}






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= en_sentvecs.shape[0]

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

			SCREAMING_SNAKE_CASE						= cdist(_UpperCAmelCase							,       _UpperCAmelCase							,       'cosine')
			SCREAMING_SNAKE_CASE						= np.array(range(_UpperCAmelCase))
			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 		_snake_case  (  datasets.Metric					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						if self.config_name == "cvit-mkb-clsr":
									return {"precision@10": precision_at_aa(a		,					a)}
						elif self.config_name in ["wiki-ner"]:
									return acc_and_fa(a		,					a)
						elif self.config_name in [
						    "wnli",
						    "copa",
						    "sna",
						    "csqa",
						    "wstp",
						    "inltkh",
						    "bbca",
						    "iitp-mr",
						    "iitp-pr",
						    "actsa-sc",
						    "md",
						]:
									return {"accuracy": simple_accuracy(a		,					a)}
						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"]')




