

'''simple docstring'''






from __future__ import annotations

import pandas as pd


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					[0] * no_of_processes
 __lowercase				    =					[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(A__ ):
  __lowercase				    =					burst_time[i]

 __lowercase				    =					0
 __lowercase				    =					0
 __lowercase				    =					999999999
 __lowercase				    =					0
 __lowercase				    =					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:
     __lowercase				    =					remaining_time[j]
     __lowercase				    =					j
     __lowercase				    =					True

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

  __lowercase				    =					remaining_time[short]
  if minm == 0:
   __lowercase				    =					999999999

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

   # Find finish time of current process
   __lowercase				    =					increment_time + 1

   # Calculate waiting time
   __lowercase				    =					finish_time - arrival_time[short]
   __lowercase				    =					finar - burst_time[short]

   if waiting_time[short] < 0:
    __lowercase				    =					0

        # Increment time
  increment_time += 1
 return waiting_time


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



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


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					0
 __lowercase				    =					0
 for i in range(A__ ):
  __lowercase				    =					total_waiting_time + waiting_time[i]
  __lowercase				    =					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''')
 lowerCAmelCase__   		=      int(input())
 lowerCAmelCase__   		=      [0] * no_of_processes
 lowerCAmelCase__   		=      [0] * no_of_processes
 lowerCAmelCase__   		=      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))
  lowerCAmelCase__		,     lowerCAmelCase__   		=      map(int, input().split())

 lowerCAmelCase__   		=      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase__   		=      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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCAmelCase     :  str											=    False
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Any       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				List[Any]       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				Any       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Any       =							VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							generator.manual_seed(0 )
            _lowerCAmelCase     :				Tuple       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							VersatileDiffusionTextToImagePipeline.from_pretrained(
                'shi-labs/versatile-diffusion'   ,						torch_dtype=torch.floataa )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Dict       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				Any       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=50   ,						output_type='numpy' ).images

            _lowerCAmelCase     :				Optional[int]       =							image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase     :				Dict       =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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

