





'''simple docstring'''




from __future__ import annotations

from statistics import mean






def        _lowerCAmelCase			(  __snake_case  : list[int] ,							__snake_case  : list[int] ,							__snake_case  : int			)     ->     list[int]:
     __A						: List[str]      =       [0] * no_of_processes
     __A						: Union[str, Any]      =       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(__snake_case			):
          __A						: Optional[int]      =       burst_time[i]
     __A						: list[int]      =       []

     __A						: List[Any]      =       0
     __A						: Optional[int]      =       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						: str      =       []
          __A						: Any      =       -1

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

          if len(__snake_case			) > 0:
               __A						: int      =       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         __A						: List[Any]      =       i
               total_time += burst_time[target_process]
               completed += 1
               __A						: List[str]      =       0
               __A						: Any      =       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time






def        _lowerCAmelCase			(  __snake_case  : list[int] ,							__snake_case  : int ,							__snake_case  : list[int]			)     ->     list[int]:
     __A						: Tuple      =       [0] * no_of_processes
     for i in range(__snake_case			):
          __A						: str      =       burst_time[i] + waiting_time[i]
     return turn_around_time


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

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






'''simple docstring'''




lowercase__    :      Dict						=    {
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}