


"""simple docstring"""







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

import datasets


a__    :							Optional[int]            =					'''\
    @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},
}
'''

a__    :							Union[str, Any]            =					'''\
    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.
'''

a__    :							Tuple            =					'''
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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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

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

					__SCREAMING_SNAKE_CASE						       =    cdist(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"cosine"       )
					__SCREAMING_SNAKE_CASE						       =    np.array(range(lowerCAmelCase_       )       )
					__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 			UpperCamelCase_     (			datasets.Metric):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Optional[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   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    Any       )	->     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"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if not nums:
										return 0
					__SCREAMING_SNAKE_CASE						       =    nums[0]
					__SCREAMING_SNAKE_CASE						       =    0
					for num in nums[1:]:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (
										    max_excluding + num,
										    max(lowerCAmelCase_  ,						lowerCAmelCase_       ),
										)
					return max(lowerCAmelCase_  ,						lowerCAmelCase_       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
