



'''simple docstring'''

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

import datasets


__lowerCAmelCase									=   '''\
    @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},
}
'''

__lowerCAmelCase									=   '''\
    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.
'''

__lowerCAmelCase									=   '''
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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Optional[int]:
		return float((preds == labels).mean()  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		_a     :      Dict        =							simple_accuracy(lowerCAmelCase_						,			lowerCAmelCase_  )
		_a     :      Dict        =							float(fa_score(y_true=lowerCAmelCase_						,			y_pred=lowerCAmelCase_  )  )
		return {
		    "accuracy": acc,
		    "f1": fa,
		}



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      List[Any]        =							np.array(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							np.array(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							en_sentvecs.shape[0]

		# mean centering
		_a     :      Dict        =							en_sentvecs - np.mean(lowerCAmelCase_						,			axis=0  )
		_a     :      Optional[Any]        =							in_sentvecs - np.mean(lowerCAmelCase_						,			axis=0  )

		_a     :      str        =							cdist(lowerCAmelCase_						,			lowerCAmelCase_						,			'cosine'  )
		_a     :      str        =							np.array(range(lowerCAmelCase_  )  )
		_a     :      Any        =							sim.argsort(axis=1  )[:, :10]
		_a     :      Optional[int]        =							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 							__lowercase			(							self					:       Optional[int]		):
									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					:       Union[str, Any]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       str		):
									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'''

import math



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      bool:
		_a     :      Optional[int]        =							math.loga(math.sqrt(4 * positive_integer + 1  ) / 2 + 1 / 2  )

		return exponent == int(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_ = 1 / 12345  )			->      int:
		_a     :      int        =							0
		_a     :      Optional[Any]        =							0

		_a     :      int        =							3
		while True:
				_a     :      Tuple        =							(integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							int(lowerCAmelCase_  )
						total_partitions += 1
						if check_partition_perfect(lowerCAmelCase_  ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(lowerCAmelCase_  )
				integer += 1


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




