



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


a_      :      Optional[Any]    =     '\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

a_      :      Optional[int]    =     '\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

a_      :      Optional[int]    =     '\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 return float((preds == labels).mean()       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= simple_accuracy(__UpperCAmelCase							,  __UpperCAmelCase       )
 a__												= float(fa_score(y_true=__UpperCAmelCase							,  y_pred=__UpperCAmelCase       )       )
 return {
     "accuracy": acc,
     "f1": fa,
 }






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= float(pearsonr(__UpperCAmelCase							,  __UpperCAmelCase       )[0]       )
 a__												= float(spearmanr(__UpperCAmelCase							,  __UpperCAmelCase       )[0]       )
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class    __UpperCamelCase			(       datasets.Metric				):

 """simple docstring"""



 def 						_UpperCAmelCase       (	self      ) ->			int:
  if self.config_name not in [
      "sst2",
      "mnli",
      "mnli_mismatched",
      "mnli_matched",
      "cola",
      "stsb",
      "mrpc",
      "qqp",
      "qnli",
      "rte",
      "wnli",
      "hans",
  ]:
   raise KeyError(
       '''You should supply a configuration name selected in '''
       '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
       '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''      )
  return datasets.MetricInfo(
      description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(
          {
              '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''      ),
              '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''      ),
          }      )		,				codebase_urls=[]		,				reference_urls=[]		,				format='''numpy'''		,				)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  if self.config_name == "cola":
   return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )}
  elif self.config_name == "stsb":
   return pearson_and_spearman(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  elif self.config_name in ["mrpc", "qqp"]:
   return acc_and_fa(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
   return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )}
  else:
   raise KeyError(
       '''You should supply a configuration name selected in '''
       '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
       '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''      )










from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





