



"""simple docstring"""






__snake_case							:   List[Any]      =    [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

__snake_case							:   Union[str, Any]      =    [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

__snake_case							:   str      =    [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

__snake_case							:   Optional[int]      =    [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

__snake_case							:   List[Any]      =    [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

__snake_case							:   Any      =    [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

__snake_case							:   Optional[Any]      =    [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

__snake_case							:   List[str]      =    [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]




"""simple docstring"""






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

import datasets


__snake_case							:   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'

__snake_case							:   str      =    '\\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'

__snake_case							:   str      =    '\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 			_lowercase				(  __snake_case					,__snake_case	)						->   Union[str, Any]:
			return float((preds == labels).mean()	)


def 			_lowercase				(  __snake_case					,__snake_case	)						->   str:
			__lowerCAmelCase      :  str												=       simple_accuracy(__snake_case					,__snake_case	)
			__lowerCAmelCase      :  Any												=       float(fa_score(y_true=__snake_case					,y_pred=__snake_case	)	)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}


def 			_lowercase				(  __snake_case					,__snake_case	)						->   int:
			__lowerCAmelCase      :  Union[str, Any]												=       np.array(__snake_case	)
			__lowerCAmelCase      :  Tuple												=       np.array(__snake_case	)
			__lowerCAmelCase      :  List[Any]												=       en_sentvecs.shape[0]

			# mean centering
			__lowerCAmelCase      :  Union[str, Any]												=       en_sentvecs - np.mean(__snake_case					,axis=0	)
			__lowerCAmelCase      :  int												=       in_sentvecs - np.mean(__snake_case					,axis=0	)

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





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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       str:

				"""simple docstring"""

				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:		List[str]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       int:

				"""simple docstring"""

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