



'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







 """simple docstring"""
 lowerCAmelCase   =     [0] * no_of_processes
 lowerCAmelCase   =     [0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(_SCREAMING_SNAKE_CASE							):
  lowerCAmelCase   =     burst_time[i]

 lowerCAmelCase   =     0
 lowerCAmelCase   =     0
 lowerCAmelCase   =     999_999_999
 lowerCAmelCase   =     0
 lowerCAmelCase   =     False

 # Process until all processes are completed
 while complete != no_of_processes:
  for j in range(_SCREAMING_SNAKE_CASE							):
   if arrival_time[j] <= increment_time and remaining_time[j] > 0:
    if remaining_time[j] < minm:
     lowerCAmelCase   =     remaining_time[j]
     lowerCAmelCase   =     j
     lowerCAmelCase   =     True

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

  lowerCAmelCase   =     remaining_time[short]
  if minm == 0:
   lowerCAmelCase   =     999_999_999

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

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

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

   if waiting_time[short] < 0:
    lowerCAmelCase   =     0

        # Increment time
  increment_time += 1
 return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







 """simple docstring"""
 lowerCAmelCase   =     [0] * no_of_processes
 for i in range(_SCREAMING_SNAKE_CASE							):
  lowerCAmelCase   =     burst_time[i] + waiting_time[i]
 return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







 """simple docstring"""
 lowerCAmelCase   =     0
 lowerCAmelCase   =     0
 for i in range(_SCREAMING_SNAKE_CASE							):
  lowerCAmelCase   =     total_waiting_time + waiting_time[i]
  lowerCAmelCase   =     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')
    UpperCAmelCase     		=   int(input())
    UpperCAmelCase     		=   [0] * no_of_processes
    UpperCAmelCase     		=   [0] * no_of_processes
    UpperCAmelCase     		=   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))
        UpperCAmelCase		,       UpperCAmelCase     		=   map(int, input().split())

    UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    UpperCAmelCase     		=   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

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)