





"""simple docstring"""





from __future__ import annotations

import pandas as pd
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[int]:





  """simple docstring"""


  lowerCAmelCase__     :int											=				[0] * no_of_processes
  lowerCAmelCase__     :int											=				[0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Union[str, Any]											=				burst_time[i]

  lowerCAmelCase__     :Union[str, Any]											=				0
  lowerCAmelCase__     :List[Any]											=				0
  lowerCAmelCase__     :List[Any]											=				9_9999_9999
  lowerCAmelCase__     :Union[str, Any]											=				0
  lowerCAmelCase__     :Dict											=				False

  # Process until all processes are completed
  while complete != no_of_processes:
    for j in range(_SCREAMING_SNAKE_CASE     ):
      if arrival_time[j] <= increment_time and remaining_time[j] > 0:
        if remaining_time[j] < minm:
          lowerCAmelCase__     :Dict											=				remaining_time[j]
          lowerCAmelCase__     :List[str]											=				j
          lowerCAmelCase__     :int											=				True

    if not check:
      increment_time += 1
      continue
    remaining_time[short] -= 1

    lowerCAmelCase__     :Optional[int]											=				remaining_time[short]
    if minm == 0:
      lowerCAmelCase__     :str											=				9_9999_9999

    if remaining_time[short] == 0:
      complete += 1
      lowerCAmelCase__     :str											=				False

      # Find finish time of current process
      lowerCAmelCase__     :Optional[Any]											=				increment_time + 1

      # Calculate waiting time
      lowerCAmelCase__     :Union[str, Any]											=				finish_time - arrival_time[short]
      lowerCAmelCase__     :Union[str, Any]											=				finar - burst_time[short]

      if waiting_time[short] < 0:
        lowerCAmelCase__     :List[Any]											=				0

        # Increment time
    increment_time += 1
  return waiting_time
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[int]:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				[0] * no_of_processes
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Any											=				burst_time[i] + waiting_time[i]
  return turn_around_time
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				0
  lowerCAmelCase__     :Optional[Any]											=				0
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Tuple											=				total_waiting_time + waiting_time[i]
    lowerCAmelCase__     :Tuple											=				total_turn_around_time + turn_around_time[i]
  print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}"     )
  print('Average turn around time ='	,  total_turn_around_time / no_of_processes     )


if __name__ == "__main__":
  print("""Enter how many process you want to analyze""")
  __A											=						int(input())
  __A											=						[0] * no_of_processes
  __A											=						[0] * no_of_processes
  __A											=						list(range(1, no_of_processes + 1))

  for i in range(no_of_processes):
    print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
    __A							,				__A											=						map(int, input().split())

  __A											=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  __A											=						burst_time
  __A											=						no_of_processes
  __A											=						waiting_time
  __A											=						calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  __A											=						pd.DataFrame(
      list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
      columns=[
          """Process""",
          """BurstTime""",
          """ArrivalTime""",
          """WaitingTime""",
          """TurnAroundTime""",
      ],
  )

  # Printing the dataFrame
  pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
  print(fcfs)





from sklearn.metrics import matthews_corrcoef

import datasets


a__   :					Any 						=      "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

a__   :					Any 						=      "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

a__   :					str 						=      "\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"



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

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[Any])						->					int:




         """simple docstring"""

         return datasets.MetricInfo(
             description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
                 {
                     'predictions': datasets.Value('int32'),
                     'references': datasets.Value('int32'),
                 })      ,					reference_urls=[
                 'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
             ]      ,					)



   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]=None)						->					Any:




         """simple docstring"""

         return {
             "matthews_correlation": float(matthews_corrcoef(lowerCAmelCase      ,					lowerCAmelCase      ,					sample_weight=lowerCAmelCase)),
         }
