



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

import datasets


_lowercase		:				Optional[Any]      		="\\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		:				int      		="\\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		:				int      		="\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      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Dict)							->	Tuple:







			"""simple docstring"""
			return float((preds == labels).mean())

def      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       Optional[Any])							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Any							=  simple_accuracy(_lowercase						,       _lowercase)
			a__       :						int							=  float(fa_score(y_true=_lowercase						,       y_pred=_lowercase))
			return {
			    "accuracy": acc,
			    "f1": fa,
			}

def      lowerCAmelCase_					(  _lowercase    :       List[Any]						,       _lowercase    :       str)							->	int:







			"""simple docstring"""
			a__       :						Dict							=  np.array(_lowercase)
			a__       :						int							=  np.array(_lowercase)
			a__       :						Optional[int]							=  en_sentvecs.shape[0]

			# mean centering
			a__       :						List[str]							=  en_sentvecs - np.mean(_lowercase						,       axis=0)
			a__       :						Tuple							=  in_sentvecs - np.mean(_lowercase						,       axis=0)

			a__       :						Any							=  cdist(_lowercase						,       _lowercase						,       """cosine""")
			a__       :						Optional[int]							=  np.array(range(_lowercase))
			a__       :						Union[str, 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        snake_case__    (datasets.Metric		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									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\"]"""  )









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       int)							->	float:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  u
			for i in range(1						,       _lowercase):
						a__       :						Optional[int]							=  temp * (u - i)
			return temp

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						Tuple							=  int(input("""enter the numbers of values: """))
			a__       :						list[list[float]]							=  []
			for _ in range(_lowercase):
						y.append([])
			for i in range(_lowercase):
						for j in range(_lowercase):
									y[i].append(_lowercase)
									a__       :						Optional[Any]							=  0

			print("""enter the values of parameters in a list: """)
			a__       :						List[Any]							=  list(map(_lowercase						,       input().split()))

			print("""enter the values of corresponding parameters: """)
			for i in range(_lowercase):
						a__       :						Optional[Any]							=  float(input())

			a__       :						Tuple							=  int(input("""enter the value to interpolate: """))
			a__       :						int							=  (value - x[0]) / (x[1] - x[0])

			# for calculating forward difference table

			for i in range(1						,       _lowercase):
						for j in range(n - i):
									a__       :						int							=  y[j + 1][i - 1] - y[j][i - 1]

			a__       :						Optional[int]							=  y[0][0]
			for i in range(1						,       _lowercase):
						summ += (ucal(_lowercase						,       _lowercase) * y[0][i]) / math.factorial(_lowercase)

			print(F'''the value at {value} is {summ}''')


if __name__ == "__main__":
		main()




