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

import datasets


lowercase__       ='\\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'

lowercase__       ='\\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'

lowercase__       ='\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    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Union[str, Any]  ):
 return float((preds == labels).mean()  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				str  ):
 __a :  Optional[int]						=  simple_accuracy(lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a :  Optional[int]						=  float(fa_score(y_true=lowerCAmelCase__  ,   y_pred=lowerCAmelCase__  )  )
 return {
     "accuracy": acc,
     "f1": fa,
 }




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Any  ):
 __a :  Optional[Any]						=  np.array(lowerCAmelCase__  )
 __a :  Optional[Any]						=  np.array(lowerCAmelCase__  )
 __a :  Any						=  en_sentvecs.shape[0]

 # mean centering
 __a :  Optional[int]						=  en_sentvecs - np.mean(lowerCAmelCase__  ,   axis=0  )
 __a :  Any						=  in_sentvecs - np.mean(lowerCAmelCase__  ,   axis=0  )

 __a :  Dict						=  cdist(lowerCAmelCase__  ,   lowerCAmelCase__  ,   '''cosine'''  )
 __a :  int						=  np.array(range(lowerCAmelCase__  )  )
 __a :  List[Any]						=  sim.argsort(axis=1  )[:, :1_0]
 __a :  int						=  np.any(preds == actual[:, None]  ,   axis=1  )
 return float(matches.mean()  )



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION    )
class 		UpperCamelCase__  (       datasets.Metric    ):
   def       lowerCAmelCase			(self :					Tuple   ):
    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       lowerCAmelCase			(self :					str					,				snake_case_ :					Optional[Any]					,				snake_case_ :					List[str]   ):
    if self.config_name == "cvit-mkb-clsr":
     return {"precision@10": precision_at_aa(snake_case_					,				snake_case_   )}
    elif self.config_name in ["wiki-ner"]:
     return acc_and_fa(snake_case_					,				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(snake_case_					,				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"]'''   )


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 if n_term == "":
  return []
 __a :  list						=  []
 for temp in range(int(lowerCAmelCase__  )  ):
  series.append(f"1/{temp + 1}" if series else '''1'''  )
 return series


if __name__ == "__main__":
      lowercase__       =input('Enter the last number (nth term) of the Harmonic Series')
      print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
      print(harmonic_series(nth_term))
