






"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      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=5_1_2,
     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       (      lowerCAmelCase		):







        '''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_	:    int            =      parser.parse_args()

 lowerCAmelCase_	:    Optional[int]            =      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

import pandas as pd






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       UpperCAmelCase	    =			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i]

       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			999999999
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			False

       # Process until all processes are completed
       while complete != no_of_processes:
              for j in range(lowerCAmelCase		):
                     if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                            if remaining_time[j] < minm:
                                   UpperCAmelCase	    =			remaining_time[j]
                                   UpperCAmelCase	    =			j
                                   UpperCAmelCase	    =			True

              if not check:
                     increment_time += 1
                     continue
              remaining_time[short] -= 1

              UpperCAmelCase	    =			remaining_time[short]
              if minm == 0:
                     UpperCAmelCase	    =			999999999

              if remaining_time[short] == 0:
                     complete += 1
                     UpperCAmelCase	    =			False

                     # Find finish time of current process
                     UpperCAmelCase	    =			increment_time + 1

                     # Calculate waiting time
                     UpperCAmelCase	    =			finish_time - arrival_time[short]
                     UpperCAmelCase	    =			finar - burst_time[short]

                     if waiting_time[short] < 0:
                            UpperCAmelCase	    =			0

        # Increment time
              increment_time += 1
       return waiting_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i] + waiting_time[i]
       return turn_around_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			total_waiting_time + waiting_time[i]
              UpperCAmelCase	    =			total_turn_around_time + turn_around_time[i]
       print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''		)
       print("""Average turn around time ="""	,      total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
 print('''Enter how many process you want to analyze''')
 lowerCAmelCase_	:    Optional[Any]            =      int(input())
 lowerCAmelCase_	:    List[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    Optional[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    int            =      list(range(1, no_of_processes + 1))

 for i in range(no_of_processes):
  print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
  lowerCAmelCase_     ,					lowerCAmelCase_	:    Optional[Any]            =      map(int, input().split())

 lowerCAmelCase_	:    str            =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

 lowerCAmelCase_	:    str            =      burst_time
 lowerCAmelCase_	:    List[Any]            =      no_of_processes
 lowerCAmelCase_	:    int            =      waiting_time
 lowerCAmelCase_	:    int            =      calculate_turnaroundtime(bt, n, wt)

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase_	:    List[Any]            =      pd.DataFrame(
     list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
     columns=[
         '''Process''',
         '''BurstTime''',
         '''ArrivalTime''',
         '''WaitingTime''',
         '''TurnAroundTime''',
     ],
 )

 # Printing the dataFrame
 pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
 print(fcfs)
