


"""simple docstring"""






from __future__ import annotations

import pandas as pd
def 					A_      (      _lowerCAmelCase   :    Any,   _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Any				):






     """simple docstring"""

     _a					= [0] * no_of_processes
     _a					= [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(lowercase_				):
          _a					= burst_time[i]

     _a					= 0
     _a					= 0
     _a					= 9_99_99_99_99
     _a					= 0
     _a					= False

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

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

          _a					= remaining_time[short]
          if minm == 0:
               _a					= 9_99_99_99_99

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

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

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

               if waiting_time[short] < 0:
                    _a					= 0

        # Increment time
          increment_time += 1
     return waiting_time
def 					A_      (      _lowerCAmelCase   :    int,   _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    int				):






     """simple docstring"""

     _a					= [0] * no_of_processes
     for i in range(lowercase_				):
          _a					= burst_time[i] + waiting_time[i]
     return turn_around_time
def 					A_      (      _lowerCAmelCase   :    Optional[Any],   _lowerCAmelCase   :    str,   _lowerCAmelCase   :    Tuple				):






     """simple docstring"""

     _a					= 0
     _a					= 0
     for i in range(lowercase_				):
          _a					= total_waiting_time + waiting_time[i]
          _a					= 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''')
   __snake_case				=				int(input())
   __snake_case				=				[0] * no_of_processes
   __snake_case				=				[0] * no_of_processes
   __snake_case				=				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))
      __snake_case				=				map(int, input().split())

   __snake_case				=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

   __snake_case				=				burst_time
   __snake_case				=				no_of_processes
   __snake_case				=				waiting_time
   __snake_case				=				calculate_turnaroundtime(bt, n, wt)

   calculate_average_times(waiting_time, turn_around_time, no_of_processes)

   __snake_case				=				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)





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       A__       =    []

       # directly update weight in diffusers model
       for key in state_dict:
              # it is suggested to print out the key, it usually will be something like below
              # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

              # as we have set the alpha beforehand, so just skip
              if ".alpha" in key or key in visited:
                     continue

              if "text" in key:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.text_encoder
              else:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_UNET + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.unet

              # find the target layer
              A__       =    layer_infos.pop(0						)
              while len(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 0:
                                   temp_name += "_" + layer_infos.pop(0						)
                            else:
                                   A__       =    layer_infos.pop(0						)

              A__       =    []
              if "lora_down" in key:
                     pair_keys.append(key.replace('''lora_down''' ,				'''lora_up'''						)						)
                     pair_keys.append(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     pair_keys.append(key.replace('''lora_up''' ,				'''lora_down'''						)						)

              # update weight
              if len(state_dict[pair_keys[0]].shape						) == 4:
                     A__       =    state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						).unsqueeze(2						).unsqueeze(3						)
              else:
                     A__       =    state_dict[pair_keys[0]].to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						)

              # update visited list
              for item in pair_keys:
                     visited.append(lowercase_						)

       return pipeline


if __name__ == "__main__":
  _lowerCamelCase	:		Tuple							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument(
      """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
  )
  parser.add_argument(
      """--lora_prefix_text_encoder""",
      default="""lora_te""",
      type=str,
      help="""The prefix of text encoder weight in safetensors""",
  )
  parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
  parser.add_argument(
      """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
  )
  parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

  _lowerCamelCase	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


