

'''simple docstring'''



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

import datasets


UpperCAmelCase :       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'

UpperCAmelCase :       Any			   =     '\\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'

UpperCAmelCase :       List[str]			   =     '\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    a__       (  a__      ,    a__						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array(a__						)
     __SCREAMING_SNAKE_CASE     =      np.array(a__						)
     __SCREAMING_SNAKE_CASE     =      en_sentvecs.shape[0]

     # mean centering
     __SCREAMING_SNAKE_CASE     =      en_sentvecs - np.mean(a__      ,    axis=0						)
     __SCREAMING_SNAKE_CASE     =      in_sentvecs - np.mean(a__      ,    axis=0						)

     __SCREAMING_SNAKE_CASE     =      cdist(a__      ,    a__      ,    """cosine"""						)
     __SCREAMING_SNAKE_CASE     =      np.array(range(a__						)						)
     __SCREAMING_SNAKE_CASE     =      sim.argsort(axis=1						)[:, :10]
     __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       str			)  -> Optional[int]:


      """simple docstring"""




      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       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       str			)  -> List[str]:


      """simple docstring"""




      if self.config_name == "cvit-mkb-clsr":
           return {"precision@10": precision_at_aa(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)}
      elif self.config_name in ["wiki-ner"]:
           return acc_and_fa(__SCREAMING_SNAKE_CASE					,							__SCREAMING_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(__SCREAMING_SNAKE_CASE					,							__SCREAMING_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\"]"""			)









'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      name
      __SCREAMING_SNAKE_CASE     =      value
      __SCREAMING_SNAKE_CASE     =      weight
 def __repr__(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      return self.value
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return self.name
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return self.weight


 def       UpperCAmelCase__		(		self					:       int			)  -> Tuple:


      """simple docstring"""




      return self.value / self.weight







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     for i in range(len(a__						)						):
          menu.append(Things(name[i]      ,    value[i]      ,    weight[i]						)						)
     return menu







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      sorted(a__      ,    key=a__      ,    reverse=a__						)
     __SCREAMING_SNAKE_CASE     =      []
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      0.0, 0.0
     for i in range(len(a__						)						):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]						)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)







def    a__       (  ):




     """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()






