


"""simple docstring"""







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

import datasets


__lowercase    =				"""\
    @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},
}
"""

__lowercase    =				"""\
    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.
"""

__lowercase    =				"""
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 					lowercase      (    A_  ,       A_	)->				Union[str, Any]:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		int											=      simple_accuracy(A_  ,       A_	)
  a						:		Optional[int]											=      float(fa_score(y_true=A_  ,       y_pred=A_	)	)
  return {
      "accuracy": acc,
      "f1": fa,
  }


def 					lowercase      (    A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		Union[str, Any]											=      np.array(A_	)
  a						:		List[str]											=      np.array(A_	)
  a						:		Tuple											=      en_sentvecs.shape[0]

  # mean centering
  a						:		Tuple											=      en_sentvecs - np.mean(A_  ,       axis=0	)
  a						:		Dict											=      in_sentvecs - np.mean(A_  ,       axis=0	)

  a						:		Optional[Any]											=      cdist(A_  ,       A_  ,       "cosine"	)
  a						:		Optional[Any]											=      np.array(range(A_	)	)
  a						:		Optional[Any]											=      sim.argsort(axis=1	)[:, :10]
  a						:		Any											=      np.any(preds == actual[:, None]  ,       axis=1	)
  return float(matches.mean()	)







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







    """simple docstring"""




    def      __snake_case							( self    :   Union[str, 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      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   Dict):
      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"""







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


__lowercase    =				logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			*__UpperCAmelCase    :   int    ,			**__UpperCAmelCase    :   List[Any]):
      warnings.warn(
          "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use DPTImageProcessor instead."    ,			__UpperCAmelCase    ,			)
      super().__init__(*__UpperCAmelCase    ,			**__UpperCAmelCase)






