

'''simple docstring'''

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

import datasets


lowercase__         =      "\\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__         =      "\\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__         =      "\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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
	return float((preds == labels).mean()     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
	UpperCAmelCase	:     Dict						 =			simple_accuracy(A__  ,	A__     )
	UpperCAmelCase	:     List[str]						 =			float(fa_score(y_true=A__  ,	y_pred=A__     )     )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
	UpperCAmelCase	:     int						 =			np.array(A__     )
	UpperCAmelCase	:     List[Any]						 =			np.array(A__     )
	UpperCAmelCase	:     Optional[Any]						 =			en_sentvecs.shape[0]

	# mean centering
	UpperCAmelCase	:     int						 =			en_sentvecs - np.mean(A__  ,	axis=0     )
	UpperCAmelCase	:     Any						 =			in_sentvecs - np.mean(A__  ,	axis=0     )

	UpperCAmelCase	:     int						 =			cdist(A__  ,	A__  ,	'cosine'     )
	UpperCAmelCase	:     List[str]						 =			np.array(range(A__     )     )
	UpperCAmelCase	:     Union[str, Any]						 =			sim.argsort(axis=1     )[:, :10]
	UpperCAmelCase	:     Tuple						 =			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        UpperCAmelCase_	(		self			:					int	)						->     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			:					Dict					,      lowercase_			:					List[Any]					,      lowercase_			:					Union[str, Any]	)						->     Any:
						if self.config_name == "cvit-mkb-clsr":
							return {"precision@10": precision_at_aa(lowercase_					,      lowercase_	)}
						elif self.config_name in ["wiki-ner"]:
							return acc_and_fa(lowercase_					,      lowercase_	)
						elif self.config_name in [
						    "wnli",
						    "copa",
						    "sna",
						    "csqa",
						    "wstp",
						    "inltkh",
						    "bbca",
						    "iitp-mr",
						    "iitp-pr",
						    "actsa-sc",
						    "md",
						]:
							return {"accuracy": simple_accuracy(lowercase_					,      lowercase_	)}
						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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			(
     first_str_length if first_str_length > second_str_length else second_str_length
 )
 UpperCAmelCase	:     list						 =			[]
 for char_count in range(UpperCAmelCase_     ):
  if char_count < first_str_length:
   output_list.append(first_str[char_count]     )
  if char_count < second_str_length:
   output_list.append(second_str[char_count]     )
 return "".join(UpperCAmelCase_     )


if __name__ == "__main__":
 print(alternative_string_arrange("AB", "XYZ"), end=" ")




