



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

import datasets


__a      =				"\\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"

__a      =				"\\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"

__a      =				"\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_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						return float((preds == labels).mean()	)
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			simple_accuracy(_lowercase,					_lowercase	)
						snake_case_ :List[str]						=			float(fa_score(y_true=_lowercase,					y_pred=_lowercase	)	)
						return {
						    "accuracy": acc,
						    "f1": fa,
						}
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			np.array(_lowercase	)
						snake_case_ :Tuple						=			np.array(_lowercase	)
						snake_case_ :Dict						=			en_sentvecs.shape[0]

						# mean centering
						snake_case_ :int						=			en_sentvecs - np.mean(_lowercase,					axis=0	)
						snake_case_ :Dict						=			in_sentvecs - np.mean(_lowercase,					axis=0	)

						snake_case_ :int						=			cdist(_lowercase,					_lowercase,					"""cosine"""	)
						snake_case_ :Tuple						=			np.array(range(_lowercase	)	)
						snake_case_ :Optional[Any]						=			sim.argsort(axis=1	)[:, :10]
						snake_case_ :List[str]						=			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    lowerCAmelCase_				(       self:							List[str]						)   ->	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    lowerCAmelCase_				(       self:							int						,     snake_case:							Tuple						,     snake_case:							Optional[int]						)   ->	Tuple:
											if self.config_name == "cvit-mkb-clsr":
																	return {"precision@10": precision_at_aa(snake_case						,     snake_case						)}
											elif self.config_name in ["wiki-ner"]:
																	return acc_and_fa(snake_case						,     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(snake_case						,     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"""
import re
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			re.compile(
						    r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$"""	)

						return bool(re.search(_lowercase,					_lowercase	)	)


if __name__ == "__main__":
						__a      =				"0094702343221"

						print(is_sri_lankan_phone_number(phone))
