



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

import datasets


__lowerCamelCase     :							Any  							=				"\\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"

__lowerCamelCase     :							Union[str, Any]  							=				"\\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"

__lowerCamelCase     :							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    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Dict:
						return float((preds == labels).mean()    )

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple:
						UpperCAmelCase         =     simple_accuracy(lowerCamelCase_    ,   lowerCamelCase_    )
						UpperCAmelCase         =     float(fa_score(y_true=lowerCamelCase_    ,   y_pred=lowerCamelCase_    )    )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     np.array(lowerCamelCase_    )
						UpperCAmelCase         =     np.array(lowerCamelCase_    )
						UpperCAmelCase         =     en_sentvecs.shape[0]

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

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





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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
								'''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      :     int				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Any      )				->					List[str]:
								'''simple docstring'''







								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\"]"      )








def    lowerCamelCase_(lowerCamelCase_ = 600_851_475_143    ) ->       int:

						try:
												UpperCAmelCase         =     int(lowerCamelCase_    )
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."    )
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."    )
						UpperCAmelCase         =     1
						UpperCAmelCase         =     2
						while i * i <= n:
												while n % i == 0:
																		UpperCAmelCase         =     i
																		n //= i
												i += 1
						if n > 1:
												UpperCAmelCase         =     n
						return int(lowerCamelCase_    )


if __name__ == "__main__":
		print(F'''{solution() = }''')



