





"""simple docstring"""





from __future__ import annotations

import pandas as pd


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[int]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						[0] * no_of_processes
  __UpperCAmelCase    :						Any       =						[0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(UpperCAmelCase_  ):
    __UpperCAmelCase    :						Tuple       =						burst_time[i]

  __UpperCAmelCase    :						Tuple       =						0
  __UpperCAmelCase    :						List[str]       =						0
  __UpperCAmelCase    :						Any       =						9_99_99_99_99
  __UpperCAmelCase    :						List[str]       =						0
  __UpperCAmelCase    :						List[Any]       =						False

  # Process until all processes are completed
  while complete != no_of_processes:
    for j in range(UpperCAmelCase_  ):
      if arrival_time[j] <= increment_time and remaining_time[j] > 0:
        if remaining_time[j] < minm:
          __UpperCAmelCase    :						List[Any]       =						remaining_time[j]
          __UpperCAmelCase    :						List[Any]       =						j
          __UpperCAmelCase    :						Union[str, Any]       =						True

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

    __UpperCAmelCase    :						Tuple       =						remaining_time[short]
    if minm == 0:
      __UpperCAmelCase    :						Optional[Any]       =						9_99_99_99_99

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

      # Find finish time of current process
      __UpperCAmelCase    :						int       =						increment_time + 1

      # Calculate waiting time
      __UpperCAmelCase    :						Dict       =						finish_time - arrival_time[short]
      __UpperCAmelCase    :						str       =						finar - burst_time[short]

      if waiting_time[short] < 0:
        __UpperCAmelCase    :						str       =						0

        # Increment time
    increment_time += 1
  return waiting_time


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[int]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						[0] * no_of_processes
  for i in range(UpperCAmelCase_  ):
    __UpperCAmelCase    :						str       =						burst_time[i] + waiting_time[i]
  return turn_around_time


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->None:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						0
  __UpperCAmelCase    :						int       =						0
  for i in range(UpperCAmelCase_  ):
    __UpperCAmelCase    :						List[Any]       =						total_waiting_time + waiting_time[i]
    __UpperCAmelCase    :						List[Any]       =						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')
  lowercase__				:Any				   =       int(input())
  lowercase__				:Optional[Any]				   =       [0] * no_of_processes
  lowercase__				:Union[str, Any]				   =       [0] * no_of_processes
  lowercase__				:Tuple				   =       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))
    lowercase__				:Optional[int]				   =       map(int, input().split())

  lowercase__				:Optional[int]				   =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  lowercase__				:Any				   =       burst_time
  lowercase__				:Any				   =       no_of_processes
  lowercase__				:int				   =       waiting_time
  lowercase__				:str				   =       calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  lowercase__				:List[str]				   =       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)






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						DDIMScheduler(
      timesteps=config.model.params.timesteps  ,			beta_schedule='''scaled_linear'''  ,			beta_start=config.model.params.linear_start  ,			beta_end=config.model.params.linear_end  ,			clip_sample=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  lowercase__				:Dict				   =       parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)