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

import datasets


lowercase__  :      Optional[int]        =					"\\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__  :      int        =					"\\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__  :      List[Any]        =					"\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       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  simple_accuracy(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  float(fa_score(y_true=_UpperCamelCase							,    y_pred=_UpperCamelCase					)					)
      return {
          "accuracy": acc,
          "f1": fa,
      }



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  np.array(_UpperCamelCase					)
      UpperCAmelCase_            =  np.array(_UpperCamelCase					)
      UpperCAmelCase_            =  en_sentvecs.shape[0]

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

      UpperCAmelCase_            =  cdist(_UpperCamelCase							,    _UpperCamelCase							,    '''cosine'''					)
      UpperCAmelCase_            =  np.array(range(_UpperCamelCase					)					)
      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 lowerCamelCase							(	datasets.Metric					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[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  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Any  )				->Optional[int]:
        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'''
from __future__ import annotations



def  __lowerCamelCase       (      _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  position
      UpperCAmelCase_            =  [
          (y + 1, x + 2),
          (y - 1, x + 2),
          (y + 1, x - 2),
          (y - 1, x - 2),
          (y + 2, x + 1),
          (y + 2, x - 1),
          (y - 2, x + 1),
          (y - 2, x - 1),
      ]
      UpperCAmelCase_            =  []

      for position in positions:
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position
            if 0 <= y_test < n and 0 <= x_test < n:
                  permissible_positions.append(_UpperCamelCase					)

      return permissible_positions



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]					):


      '''simple docstring'''


      return not any(elem == 0 for row in board for elem in row					)



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if is_complete(_UpperCamelCase					):
            return True

      for position in get_valid_pos(_UpperCamelCase							,    len(_UpperCamelCase					)					):
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position

            if board[y][x] == 0:
                  UpperCAmelCase_            =  curr + 1
                  if open_knight_tour_helper(_UpperCamelCase							,    _UpperCamelCase							,    curr + 1					):
                        return True
                  UpperCAmelCase_            =  0

      return False



def  __lowerCamelCase       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  [[0 for i in range(_UpperCamelCase					)] for j in range(_UpperCamelCase					)]

      for i in range(_UpperCamelCase					):
            for j in range(_UpperCamelCase					):
                  UpperCAmelCase_            =  1
                  if open_knight_tour_helper(_UpperCamelCase							,    (i, j)							,    1					):
                        return board
                  UpperCAmelCase_            =  0

      UpperCAmelCase_            =  F"""Open Kight Tour cannot be performed on a board of size {n}"""
      raise ValueError(_UpperCamelCase					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





