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

import datasets


__a  			=					'''\
    @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},
}
'''

__a  			=					'''\
    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.
'''

__a  			=					'''
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						(							snake_case__:					Union[str, Any]						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				return float((preds == labels).mean()					)




def 				a						(							snake_case__:					List[str]						,   snake_case__:					Dict					):


				'''simple docstring'''
				lowercase_       =					simple_accuracy(UpperCAmelCase_						,   UpperCAmelCase_					)
				lowercase_       =					float(fa_score(y_true=UpperCAmelCase_						,   y_pred=UpperCAmelCase_					)					)
				return {
				    "accuracy": acc,
				    "f1": fa,
				}




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					np.array(UpperCAmelCase_					)
				lowercase_       =					np.array(UpperCAmelCase_					)
				lowercase_       =					en_sentvecs.shape[0]

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

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


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



	"""simple docstring"""




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








from maths.prime_factors import prime_factors
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Dict 							=       F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





