






from statistics import mean

import numpy as np

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							list:
  UpperCamelCase_:   Any        =							0
  # Number of processes finished
  UpperCamelCase_:   str        =							0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  UpperCamelCase_:   Union[str, Any]        =							[0] * no_of_process
  # List to include calculation results
  UpperCamelCase_:   Union[str, Any]        =							[0] * no_of_process

  # Sort by arrival time.
  UpperCamelCase_:   List[Any]        =							[burst_time[i] for i in np.argsort(UpperCAmelCase__    )]
  UpperCamelCase_:   List[Any]        =							[process_name[i] for i in np.argsort(UpperCAmelCase__    )]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    UpperCamelCase_:   str        =							0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      UpperCamelCase_:   List[Any]        =							arrival_time[i]

    UpperCamelCase_:   Optional[Any]        =							0
    # Index showing the location of the process being performed
    UpperCamelCase_:   Dict        =							0
    # Saves the current response ratio.
    UpperCamelCase_:   int        =							0
    for i in range(0      ,       UpperCAmelCase__    ):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        UpperCamelCase_:   Union[str, Any]        =							(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        UpperCamelCase_:   Dict        =							temp
        UpperCamelCase_:   Union[str, Any]        =							i

        # Calculate the turn around time
    UpperCamelCase_:   int        =							current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    UpperCamelCase_:   Union[str, Any]        =							1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							list:
  UpperCamelCase_:   Any        =							[0] * no_of_process
  for i in range(0      ,       UpperCAmelCase__    ):
    UpperCamelCase_:   Tuple        =							turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
   A_						:						List[str]						      =					5
   A_						:						Dict						      =					['A', 'B', 'C', 'D', 'E']
   A_						:						str						      =					[1, 2, 3, 4, 5]
   A_						:						Union[str, Any]						      =					[1, 2, 3, 4, 5]

   A_						:						Dict						      =					calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   A_						:						Tuple						      =					calculate_waiting_time(
       process_name, turn_around_time, burst_time, no_of_process
   )

   print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
   for i in range(0, no_of_process):
      print(
          F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
          F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
      )

   print(F'''average waiting time : {mean(waiting_time):.5f}''')
   print(F'''average turn around time : {mean(turn_around_time):.5f}''')



'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_dpmpp_2m'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe(
                [prompt]			,     generator=_UpperCamelCase			,     guidance_scale=7.5			,     num_inference_steps=15			,     output_type='np'			,     use_karras_sigmas=_UpperCamelCase			,     )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2





