

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

import datasets


lowerCamelCase :      List[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"

lowerCamelCase :      Optional[Any]					 =     "\\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"

lowerCamelCase :      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					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Tuple				)      ->			List[Any]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Any				)      ->			List[str]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Any							, _UpperCamelCase						:     int				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =en_sentvecs.shape[0]

     # mean centering
     _SCREAMING_SNAKE_CASE							    =en_sentvecs - np.mean(_UpperCamelCase							, axis=0				)
     _SCREAMING_SNAKE_CASE							    =in_sentvecs - np.mean(_UpperCamelCase							, axis=0				)

     _SCREAMING_SNAKE_CASE							    =cdist(_UpperCamelCase							, _UpperCamelCase							, 'cosine'				)
     _SCREAMING_SNAKE_CASE							    =np.array(range(_UpperCamelCase				)				)
     _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 							A__    (       datasets.Metric  ):





  def 						A  (		self      :	Any )	->    List[str]:





       '''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 						A  (		self      :	List[str]							,					_a      :	Tuple							,					_a      :	Optional[int] )	->    int:





       '''simple docstring'''






       if self.config_name == "cvit-mkb-clsr":
            return {"precision@10": precision_at_aa(_a							,					_a )}
       elif self.config_name in ["wiki-ner"]:
            return acc_and_fa(_a							,					_a )
       elif self.config_name in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
       ]:
            return {"accuracy": simple_accuracy(_a							,					_a )}
       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

from statistics import mean




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				__lowerCamelCase    					=	[0] * no_of_processes

				# Initialize remaining_time to waiting_time.

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i]
				__lowerCamelCase    					=	[]

				__lowerCamelCase    					=	0
				__lowerCamelCase    					=	0

				# When processes are not completed,
				# A process whose arrival time has passed \
				# and has remaining execution time is put into the ready_process.
				# The shortest process in the ready_process, target_process is executed.

				while completed != no_of_processes:
								__lowerCamelCase    					=	[]
								__lowerCamelCase    					=	-1

								for i in range(__lowerCAmelCase							):
												if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
																ready_process.append(__lowerCAmelCase							)

								if len(__lowerCAmelCase							) > 0:
												__lowerCamelCase    					=	ready_process[0]
												for i in ready_process:
																if remaining_time[i] < remaining_time[target_process]:
																				__lowerCamelCase    					=	i
												total_time += burst_time[target_process]
												completed += 1
												__lowerCamelCase    					=	0
												__lowerCamelCase    					=	(
												    total_time - arrival_time[target_process] - burst_time[target_process]
												)
								else:
												total_time += 1

				return waiting_time




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						list[int]							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i] + waiting_time[i]
				return turn_around_time


if __name__ == "__main__":
					print("[TEST CASE 01]")

					SCREAMING_SNAKE_CASE__   :			Tuple												=    4
					SCREAMING_SNAKE_CASE__   :			Optional[int]												=    [2, 5, 3, 7]
					SCREAMING_SNAKE_CASE__   :			List[str]												=    [0, 0, 0, 0]
					SCREAMING_SNAKE_CASE__   :			str												=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F'{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'
										    F'{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'
										)
					print(F'\nAverage waiting time = {mean(waiting_time):.5f}')
					print(F'Average turnaround time = {mean(turn_around_time):.5f}')



