"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import GLPNImageProcessor
class     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=7		,__UpperCAmelCase=3		,__UpperCAmelCase=18		,__UpperCAmelCase=30		,__UpperCAmelCase=4_00		,__UpperCAmelCase=True		,__UpperCAmelCase=32		,__UpperCAmelCase=True		,)					->				List[str]:
													A__	     =							parent
													A__	     =							batch_size
													A__	     =							num_channels
													A__	     =							image_size
													A__	     =							min_resolution
													A__	     =							max_resolution
													A__	     =							do_resize
													A__	     =							size_divisor
													A__	     =							do_rescale





						def 				snake_case__	(       self     )					->				Optional[Any]:
													return {
													    "do_resize": self.do_resize,
													    "size_divisor": self.size_divisor,
													    "do_rescale": self.do_rescale,
													}






@require_torch
@require_vision
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Optional[Any]													=		GLPNImageProcessor if is_vision_available() else None





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							GLPNImageProcessingTester(self     )





						@property
						def 				snake_case__	(       self     )					->				Any:
													return self.image_processor_tester.prepare_image_processor_dict()





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'do_resize'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'size_divisor'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'resample'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'do_rescale'     )     )





						def 				snake_case__	(       self     )					->				Dict:
													pass





						def 				snake_case__	(       self     )					->				Tuple:
													# Initialize image_processing
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													# create random PIL images
													A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=__UpperCAmelCase     )
													for image in image_inputs:
																				self.assertIsInstance(__UpperCAmelCase		,Image.Image     )

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													A__	     =							image_processing(image_inputs[0]		,return_tensors='pt'     ).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )





						def 				snake_case__	(       self     )					->				Dict:
													# Initialize image_processing
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													# create random numpy tensors
													A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=__UpperCAmelCase		,numpify=__UpperCAmelCase     )
													for image in image_inputs:
																				self.assertIsInstance(__UpperCAmelCase		,np.ndarray     )

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													A__	     =							image_processing(image_inputs[0]		,return_tensors='pt'     ).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )





						def 				snake_case__	(       self     )					->				Any:
													# Initialize image_processing
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													# create random PyTorch tensors
													A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=__UpperCAmelCase		,torchify=__UpperCAmelCase     )
													for image in image_inputs:
																				self.assertIsInstance(__UpperCAmelCase		,torch.Tensor     )

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													A__	     =							image_processing(image_inputs[0]		,return_tensors='pt'     ).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )






"""simple docstring"""




from __future__ import annotations

import pandas as pd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							0
							A__	     =							0
							A__	     =							999_999_999
							A__	     =							0
							A__	     =							False

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

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

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

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

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

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

																					if waiting_time[short] < 0:
																												A__	     =							0

        # Increment time
														increment_time += 1
							return waiting_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0
							A__	     =							0
							for i in range(UpperCamelCase__				):
														A__	     =							total_waiting_time + waiting_time[i]
														A__	     =							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)





