'''simple docstring'''





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

import datasets


_lowerCAmelCase  :Dict  				=    """\
    @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},
}
"""

_lowerCAmelCase  :Dict  				=    """\
    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.
"""

_lowerCAmelCase  :List[str]  				=    """
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     __lowerCAmelCase						(       a_       ,      a_		)      ->	List[str]:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       simple_accuracy(a_       ,      a_		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       float(fa_score(y_true=a_       ,      y_pred=a_		)		)
     return {
         "accuracy": acc,
         "f1": fa,
     }


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       np.array(a_		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       np.array(a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       en_sentvecs.shape[0]

     # mean centering
     SCREAMING_SNAKE_CASE					:			Any    	=       en_sentvecs - np.mean(a_       ,      axis=0		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       in_sentvecs - np.mean(a_       ,      axis=0		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       cdist(a_       ,      a_       ,      'cosine'		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.array(range(a_		)		)
     SCREAMING_SNAKE_CASE					:			int    	=       sim.argsort(axis=1		)[:, :10]
     SCREAMING_SNAKE_CASE					:			str    	=       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 )							-> 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    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         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'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -1

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

          if len(a_		) > 0:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


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

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, Any]  				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase  :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}""")



