


from __future__ import annotations

from statistics import mean




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  [0] * no_of_processes
   __a					=  [0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCAmelCase__   ):
      __a					=  burst_time[i]
   __a					=  []

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

   return waiting_time




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  [0] * no_of_processes
   for i in range(lowerCAmelCase__   ):
      __a					=  burst_time[i] + waiting_time[i]
   return turn_around_time


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

  SCREAMING_SNAKE_CASE							    =							4
  SCREAMING_SNAKE_CASE							    =							[2, 5, 3, 7]
  SCREAMING_SNAKE_CASE							    =							[0, 0, 0, 0]
  SCREAMING_SNAKE_CASE							    =							calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  SCREAMING_SNAKE_CASE							    =							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}''')







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



