

'''simple docstring'''

from statistics import mean

import numpy as np

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	list,       lowercase__    :	list,       lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=0
     # Number of processes finished
     __lowercase      		=0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     __lowercase      		=[0] * no_of_process
     # List to include calculation results
     __lowercase      		=[0] * no_of_process

     # Sort by arrival time.
     __lowercase      		=[burst_time[i] for i in np.argsort(lowercase__			)]
     __lowercase      		=[process_name[i] for i in np.argsort(lowercase__			)]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          __lowercase      		=0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               __lowercase      		=arrival_time[i]

          __lowercase      		=0
          # Index showing the location of the process being performed
          __lowercase      		=0
          # Saves the current response ratio.
          __lowercase      		=0
          for i in range(0,       lowercase__			):
               if finished_process[i] == 0 and arrival_time[i] <= current_time:
                    __lowercase      		=(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                        i
                    ]
               if response_ratio < temp:
                    __lowercase      		=temp
                    __lowercase      		=i

        # Calculate the turn around time
          __lowercase      		=current_time + burst_time[loc] - arrival_time[loc]
          current_time += burst_time[loc]
          # Indicates that the process has been performed.
          __lowercase      		=1
          # Increase finished_process_count by 1
          finished_process_count += 1

     return turn_around_time

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	list,       lowercase__    :	list,       lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=[0] * no_of_process
     for i in range(0,       lowercase__			):
          __lowercase      		=turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
       UpperCAmelCase					 =	5
       UpperCAmelCase					 =	['''A''', '''B''', '''C''', '''D''', '''E''']
       UpperCAmelCase					 =	[1, 2, 3, 4, 5]
       UpperCAmelCase					 =	[1, 2, 3, 4, 5]

       UpperCAmelCase					 =	calculate_turn_around_time(
           process_name, arrival_time, burst_time, no_of_process
       )
       UpperCAmelCase					 =	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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 	lowerCAmelCase  (   unittest.TestCase ):







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            torch.manual_seed(0   )
            __lowercase      		=UNetaDModel(
                block_out_channels=(32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')    ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')    ,   )
            return model







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.dummy_uncond_unet
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'   ).images

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'    ,   return_dict=__lowercase   )[0]

            __lowercase      		=image[0, -3:, -3:, -1]
            __lowercase      		=image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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






@slow
@require_torch
class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		='google/ddpm-cifar10-32'

            __lowercase      		=UNetaDModel.from_pretrained(__lowercase   )
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   output_type='numpy'   ).images

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

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

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


