

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

import datasets


snake_case_        =			'''\
    @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},
}
'''

snake_case_        =			'''\
    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.
'''

snake_case_        =			'''
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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     return float((preds == labels).mean()      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	simple_accuracy(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[Any]				=	float(fa_score(y_true=SCREAMING_SNAKE_CASE_     ,					y_pred=SCREAMING_SNAKE_CASE_      )      )
     return {
         "accuracy": acc,
         "f1": fa,
     }

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Tuple      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	np.array(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Dict				=	np.array(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       str				=	en_sentvecs.shape[0]

     # mean centering
     lowercase__   :       str				=	en_sentvecs - np.mean(SCREAMING_SNAKE_CASE_     ,					axis=0      )
     lowercase__   :       Optional[int]				=	in_sentvecs - np.mean(SCREAMING_SNAKE_CASE_     ,					axis=0      )

     lowercase__   :       Union[str, Any]				=	cdist(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					'cosine'      )
     lowercase__   :       List[Any]				=	np.array(range(SCREAMING_SNAKE_CASE_      )      )
     lowercase__   :       Dict				=	sim.argsort(axis=1      )[:, :10]
     lowercase__   :       Any				=	np.any(preds == actual[:, None]     ,					axis=1      )
     return float(matches.mean()      )


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





    def 				snake_case_    (				self):
         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 				snake_case_    (				self						,       a						,       a):
         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"]')




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 50_000_000      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	set()
     lowercase__   :       Any				=	int((limit - 24) ** (1 / 2)      )

     lowercase__   :       Any				=	set(range(3     ,					prime_square_limit + 1     ,					2      )      )
     primes.add(2      )
     for p in range(3     ,					prime_square_limit + 1     ,					2      ):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p     ,					prime_square_limit + 1     ,					SCREAMING_SNAKE_CASE_      )      )      )

     for primea in primes:
          lowercase__   :       Any				=	primea * primea
          for primea in primes:
               lowercase__   :       Optional[int]				=	primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    lowercase__   :       Dict				=	primea * primea * primea * primea
                    lowercase__   :       List[Any]				=	square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(SCREAMING_SNAKE_CASE_      )

     return len(SCREAMING_SNAKE_CASE_      )


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

