


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

import datasets


UpperCAmelCase				=						"""\
    @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},
}
"""

UpperCAmelCase				=						"""\
    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.
"""

UpperCAmelCase				=						"""
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      (   __snake_case							:      List[Any]						, __snake_case							:      str   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      Optional[int]						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		simple_accuracy(__snake_case						, __snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		float(fa_score(y_true=__snake_case						, y_pred=__snake_case   )   )
   return {
       "accuracy": acc,
       "f1": fa,
   }

def 				_snake_case      (   __snake_case							:      Optional[Any]						, __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Dict   =		np.array(__snake_case   )
   _lowerCamelCase	:					Optional[Any]   =		np.array(__snake_case   )
   _lowerCamelCase	:					List[Any]   =		en_sentvecs.shape[0]

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

   _lowerCamelCase	:					Union[str, Any]   =		cdist(__snake_case						, __snake_case						, """cosine"""   )
   _lowerCamelCase	:					str   =		np.array(range(__snake_case   )   )
   _lowerCamelCase	:					Optional[Any]   =		sim.argsort(axis=1   )[:, :10]
   _lowerCamelCase	:					Dict   =		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				):





   def 		UpperCamelCase_  (							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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      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"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





