


"""simple docstring"""







from __future__ import annotations

from statistics import mean
def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							[0] * no_of_processes
  lowerCAmelCase					:					Union[str, Any]								=							[0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					List[Any]								=							burst_time[i]
  lowerCAmelCase					:					list[int]								=							[]

  lowerCAmelCase					:					Optional[int]								=							0
  lowerCAmelCase					:					Dict								=							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:
    lowerCAmelCase					:					Optional[Any]								=							[]
    lowerCAmelCase					:					List[Any]								=							-1

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

    if len(SCREAMING_SNAKE_CASE    ) > 0:
      lowerCAmelCase					:					Optional[Any]								=							ready_process[0]
      for i in ready_process:
        if remaining_time[i] < remaining_time[target_process]:
          lowerCAmelCase					:					List[str]								=							i
      total_time += burst_time[target_process]
      completed += 1
      lowerCAmelCase					:					Union[str, Any]								=							0
      lowerCAmelCase					:					str								=							(
          total_time - arrival_time[target_process] - burst_time[target_process]
      )
    else:
      total_time += 1

  return waiting_time
def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					int								=							[0] * no_of_processes
  for i in range(SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Union[str, Any]								=							burst_time[i] + waiting_time[i]
  return turn_around_time


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

     lowerCAmelCase__				    =       4
     lowerCAmelCase__				    =       [2, 5, 3, 7]
     lowerCAmelCase__				    =       [0, 0, 0, 0]
     lowerCAmelCase__				    =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
     lowerCAmelCase__				    =       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"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     lowerCAmelCase__				    =       argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
     def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


       '''simple docstring'''







       if string == "True":
         return True
       elif string == "False":
         return False
       else:
         raise ValueError(f"""could not parse string as bool {string}"""    )

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     lowerCAmelCase__				    =       parser.parse_args()

     lowerCAmelCase__				    =       download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





