'''simple docstring'''





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     _lowerCAmelCase  :Optional[int]  				=    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     __lowerCAmelCase						(       a_		)      ->	Optional[int]:





          '''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  :Tuple  				=    parser.parse_args()

     _lowerCAmelCase  :Tuple  				=    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)




'''simple docstring'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





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

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

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

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

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

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


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

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



