



"""simple docstring"""







from statistics import mean

import numpy as np

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





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

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

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

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

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

	return turn_around_time

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





	A__											=					[0] * no_of_process
	for i in range(0,	UpperCAmelCase_     ):
		A__											=					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}')






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_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=3				,			__UpperCamelCase=10				,			__UpperCamelCase=18				,			__UpperCamelCase=30				,			__UpperCamelCase=400				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=True				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=None				,			):
									_lowercase					       =					size if size is not None else {"""shortest_edge""": 18}
									_lowercase					       =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_frames
									_lowercase					       =					image_size
									_lowercase					       =					min_resolution
									_lowercase					       =					max_resolution
									_lowercase					       =					do_resize
									_lowercase					       =					size
									_lowercase					       =					do_normalize
									_lowercase					       =					image_mean
									_lowercase					       =					image_std
									_lowercase					       =					crop_size






			def    UpperCamelCase_				(      self  ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			str					   =							VivitImageProcessor if is_vision_available() else None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					VivitImageProcessingTester(self  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_mean"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_std"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_normalize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_resize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_center_crop"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""size"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 18}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 18, """width""": 18}  )

									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 42}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 84, """width""": 84}  )






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL videos
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			Image.Image  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			numpify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			np.ndarray  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			torchify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			torch.Tensor  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)