




"""simple docstring"""






from __future__ import annotations

import pandas as pd







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Union[str, Any]       =       [0] * no_of_processes
				_lowerCamelCase	:     Union[str, Any]       =       [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(a_						):
								_lowerCamelCase	:     int       =       burst_time[i]

				_lowerCamelCase	:     Dict       =       0
				_lowerCamelCase	:     Tuple       =       0
				_lowerCamelCase	:     Union[str, Any]       =       999999999
				_lowerCamelCase	:     str       =       0
				_lowerCamelCase	:     Tuple       =       False

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

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

								_lowerCamelCase	:     Tuple       =       remaining_time[short]
								if minm == 0:
												_lowerCamelCase	:     Dict       =       999999999

								if remaining_time[short] == 0:
												complete += 1
												_lowerCamelCase	:     List[Any]       =       False

												# Find finish time of current process
												_lowerCamelCase	:     str       =       increment_time + 1

												# Calculate waiting time
												_lowerCamelCase	:     List[Any]       =       finish_time - arrival_time[short]
												_lowerCamelCase	:     Optional[int]       =       finar - burst_time[short]

												if waiting_time[short] < 0:
																_lowerCamelCase	:     Tuple       =       0

        # Increment time
								increment_time += 1
				return waiting_time







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Tuple       =       [0] * no_of_processes
				for i in range(a_						):
								_lowerCamelCase	:     Optional[int]       =       burst_time[i] + waiting_time[i]
				return turn_around_time







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Union[str, Any]       =       0
				_lowerCamelCase	:     Dict       =       0
				for i in range(a_						):
								_lowerCamelCase	:     Optional[int]       =       total_waiting_time + waiting_time[i]
								_lowerCamelCase	:     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__			=		int(input())
			lowercase__			=		[0] * no_of_processes
			lowercase__			=		[0] * no_of_processes
			lowercase__			=		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__		,				lowercase__			=		map(int, input().split())

			lowercase__			=		calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

			calculate_average_times(waiting_time, turn_around_time, no_of_processes)

			lowercase__			=		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 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      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				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      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

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

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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






