






'''simple docstring'''



from statistics import mean

import numpy as np


def 				lowercase_					(     __A			:							list       ,   __A			:							list       ,   __A			:							list       ,   __A			:							int      )							->   list:



 """simple docstring"""




 lowercase       : Dict						=0
 # Number of processes finished
 lowercase       : str						=0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 lowercase       : Optional[Any]						=[0] * no_of_process
 # List to include calculation results
 lowercase       : Any						=[0] * no_of_process

 # Sort by arrival time.
 lowercase       : int						=[burst_time[i] for i in np.argsort(__A      )]
 lowercase       : Optional[int]						=[process_name[i] for i in np.argsort(__A      )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  lowercase       : List[str]						=0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   lowercase       : Tuple						=arrival_time[i]

  lowercase       : Dict						=0
  # Index showing the location of the process being performed
  lowercase       : Any						=0
  # Saves the current response ratio.
  lowercase       : Union[str, Any]						=0
  for i in range(0       ,   __A      ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    lowercase       : int						=(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    lowercase       : Any						=temp
    lowercase       : Optional[int]						=i

        # Calculate the turn around time
  lowercase       : Union[str, Any]						=current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  lowercase       : str						=1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time


def 				lowercase_					(     __A			:							list       ,   __A			:							list       ,   __A			:							list       ,   __A			:							int      )							->   list:



 """simple docstring"""




 lowercase       : Dict						=[0] * no_of_process
 for i in range(0       ,   __A      ):
  lowercase       : str						=turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						5
      SCREAMING_SNAKE_CASE							=						['A', 'B', 'C', 'D', 'E']
      SCREAMING_SNAKE_CASE							=						[1, 2, 3, 4, 5]
      SCREAMING_SNAKE_CASE							=						[1, 2, 3, 4, 5]

      SCREAMING_SNAKE_CASE							=						calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      SCREAMING_SNAKE_CASE							=						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}""")










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__: str							   =						{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Union[str, Any]							   =						[
           'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MegaForCausalLM',
           'MegaForMaskedLM',
           'MegaForMultipleChoice',
           'MegaForQuestionAnswering',
           'MegaForSequenceClassification',
           'MegaForTokenClassification',
           'MegaModel',
           'MegaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mega import (
                  MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MegaForCausalLM,
                  MegaForMaskedLM,
                  MegaForMultipleChoice,
                  MegaForQuestionAnswering,
                  MegaForSequenceClassification,
                  MegaForTokenClassification,
                  MegaModel,
                  MegaPreTrainedModel,
              )

else:
       import sys

       a__: Union[str, Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





