'''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 ViTImageProcessor






class 						A   (   unittest.TestCase   ):





							def __init__(      self				:							str     ,		__a				:							str     ,		__a				:							int=1_3     ,		__a				:							Optional[Any]=3     ,		__a				:							List[str]=2_2_4     ,		__a				:							List[Any]=3_0     ,		__a				:							Optional[int]=4_0_0     ,		__a				:							List[Any]=True     ,		__a				:							Dict=None     ,		__a				:							Union[str, Any]=True     ,		__a				:							Dict=[0.5, 0.5, 0.5]     ,		__a				:							int=[0.5, 0.5, 0.5]     ,		)							->	Tuple:
								__UpperCAmelCase							=			size if size is not None else {'''height''': 1_8, '''width''': 1_8}
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			image_size
								__UpperCAmelCase							=			min_resolution
								__UpperCAmelCase							=			max_resolution
								__UpperCAmelCase							=			do_resize
								__UpperCAmelCase							=			size
								__UpperCAmelCase							=			do_normalize
								__UpperCAmelCase							=			image_mean
								__UpperCAmelCase							=			image_std






							def       snake_case__						(      self				:							int	)							->	str:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}






@require_torch
@require_vision
class 						A   (   lowercase_       ,	unittest.TestCase   ):
							a_        =      ViTImageProcessor if is_vision_available() else None





							def       snake_case__						(      self				:							Tuple	)							->	Union[str, Any]:
								__UpperCAmelCase							=			EfficientFormerImageProcessorTester(self	)





							@property
							def       snake_case__						(      self				:							List[str]	)							->	Tuple:
								return self.image_proc_tester.prepare_image_processor_dict()





							def       snake_case__						(      self				:							Optional[Any]	)							->	int:
								__UpperCAmelCase							=			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__     ,		'''size'''	)	)





							def       snake_case__						(      self				:							Dict	)							->	Union[str, Any]:
								pass





							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								__UpperCAmelCase							=			prepare_image_inputs(self.image_proc_tester     ,		equal_resolution=UpperCamelCase__	)
								for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__     ,		Image.Image	)

								# Test not batched input
								__UpperCAmelCase							=			image_processor(image_inputs[0]     ,		return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape     ,		(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )     ,		)

								# Test batched
								__UpperCAmelCase							=			image_processor(UpperCamelCase__     ,		return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape     ,		(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )     ,		)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								__UpperCAmelCase							=			prepare_image_inputs(self.image_proc_tester     ,		equal_resolution=UpperCamelCase__     ,		numpify=UpperCamelCase__	)
								for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__     ,		np.ndarray	)

								# Test not batched input
								__UpperCAmelCase							=			image_processor(image_inputs[0]     ,		return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape     ,		(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )     ,		)

								# Test batched
								__UpperCAmelCase							=			image_processor(UpperCamelCase__     ,		return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape     ,		(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )     ,		)






							def       snake_case__						(      self				:							str	)							->	List[Any]:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								__UpperCAmelCase							=			prepare_image_inputs(self.image_proc_tester     ,		equal_resolution=UpperCamelCase__     ,		torchify=UpperCamelCase__	)
								for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__     ,		torch.Tensor	)

								# Test not batched input
								__UpperCAmelCase							=			image_processor(image_inputs[0]     ,		return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape     ,		(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )     ,		)

								# Test batched
								__UpperCAmelCase							=			image_processor(UpperCamelCase__     ,		return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape     ,		(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )     ,		)

'''simple docstring'''


from __future__ import annotations

from statistics import mean





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	__UpperCAmelCase							=			[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i]
	__UpperCAmelCase							=			[]

	__UpperCAmelCase							=			0
	__UpperCAmelCase							=			0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			-1

		for i in range(UpperCamelCase__       ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(UpperCamelCase__       )

		if len(UpperCamelCase__       ) > 0:
			__UpperCAmelCase							=			ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					__UpperCAmelCase							=			i
			total_time += burst_time[target_process]
			completed += 1
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   list[int]       ):


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


if __name__ == "__main__":
					print("[TEST CASE 01]")

					__lowerCAmelCase	:       List[Any]						       =	4
					__lowerCAmelCase	:       List[Any]						       =	[2, 5, 3, 7]
					__lowerCAmelCase	:       Tuple						       =	[0, 0, 0, 0]
					__lowerCAmelCase	:       Optional[int]						       =	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					__lowerCAmelCase	:       Dict						       =	calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
										    F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
										)
					print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
					print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")
