


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)			->  Tuple:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4')
           _lowercase			:				Optional[Any] 						=						sd_pipe.to(lowerCamelCase)
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase)

           sd_pipe.set_scheduler('sample_euler')

           _lowercase			:				str 						=						'A painting of a squirrel eating a burger'
           _lowercase			:				int 						=						torch.manual_seed(0)
           _lowercase			:				int 						=						sd_pipe([prompt], generator=lowerCamelCase, guidance_scale=9.0, num_inference_steps=20, output_type='np')

           _lowercase			:				Union[str, Any] 						=						output.images

           _lowercase			:				Optional[Any] 						=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase			:				Dict 						=						np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				int 						=						StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
           _lowercase			:				Optional[int] 						=						sd_pipe.to(lowerCamelCase)
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase)

           sd_pipe.set_scheduler('sample_euler')

           _lowercase			:				Any 						=						'A painting of a squirrel eating a burger'
           _lowercase			:				Any 						=						torch.manual_seed(0)
           _lowercase			:				Union[str, Any] 						=						sd_pipe([prompt], generator=lowerCamelCase, guidance_scale=9.0, num_inference_steps=20, output_type='np')

           _lowercase			:				List[str] 						=						output.images

           _lowercase			:				str 						=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase			:				Tuple 						=						np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2])

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




       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Any 						=						StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
           _lowercase			:				List[str] 						=						sd_pipe.to(lowerCamelCase)
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase)

           sd_pipe.set_scheduler('sample_dpmpp_2m')

           _lowercase			:				Optional[Any] 						=						'A painting of a squirrel eating a burger'
           _lowercase			:				Optional[Any] 						=						torch.manual_seed(0)
           _lowercase			:				Union[str, Any] 						=						sd_pipe(
               [prompt], generator=lowerCamelCase, guidance_scale=7.5, num_inference_steps=15, output_type='np', use_karras_sigmas=lowerCamelCase, )

           _lowercase			:				List[Any] 						=						output.images

           _lowercase			:				Tuple 						=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase			:				Dict 						=						np.array(
               [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8])

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





from __future__ import annotations

from statistics import mean
def 	a (     A__      :  list[int] ,  A__      :  list[int] ,  A__      :  int	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      _lowercase      			=[0] * no_of_processes

      # Initialize remaining_time to waiting_time.

      for i in range(A__	):
            _lowercase      			=burst_time[i]
      _lowercase      			=[]

      _lowercase      			=0
      _lowercase      			=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:
            _lowercase      			=[]
            _lowercase      			=-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:
                  _lowercase      			=ready_process[0]
                  for i in ready_process:
                        if remaining_time[i] < remaining_time[target_process]:
                              _lowercase      			=i
                  total_time += burst_time[target_process]
                  completed += 1
                  _lowercase      			=0
                  _lowercase      			=(
                      total_time - arrival_time[target_process] - burst_time[target_process]
                  )
            else:
                  total_time += 1

      return waiting_time
def 	a (     A__      :  list[int] ,  A__      :  int ,  A__      :  list[int]	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      for i in range(A__	):
            _lowercase      			=burst_time[i] + waiting_time[i]
      return turn_around_time


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

  lowercase_   							=  4
  lowercase_   							=  [2, 5, 3, 7]
  lowercase_   							=  [0, 0, 0, 0]
  lowercase_   							=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  lowercase_   							=  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}")


