



"""simple docstring"""







from __future__ import annotations

from statistics import mean

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       list[int]   ):
						"""simple docstring"""






						snake_case      : Any							=   2
						snake_case      : Any							=   list(lowerCamelCase_   )
						snake_case      : Optional[int]							=   [0] * len(lowerCamelCase_   )
						snake_case      : Union[str, Any]							=   0
						while True:
												snake_case      : List[Any]							=   True
												for i, burst_time in enumerate(lowerCamelCase_   ):
																		if rem_burst_times[i] > 0:
																								snake_case      : Union[str, Any]							=   False
																								if rem_burst_times[i] > quantum:
																														t += quantum
																														rem_burst_times[i] -= quantum
																								else:
																														t += rem_burst_times[i]
																														snake_case      : Union[str, Any]							=   t - burst_time
																														snake_case      : Tuple							=   0
												if done is True:
																		return waiting_times

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       list[int]    ,		lowerCamelCase_:       list[int]   ):
						"""simple docstring"""






						return [burst + waiting for burst, waiting in zip(lowerCamelCase_    ,		lowerCamelCase_   )]


if __name__ == "__main__":
			A    =	[3, 5, 7]
			A    =	calculate_waiting_times(burst_times)
			A    =	calculate_turn_around_times(burst_times, waiting_times)
			print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
			for i, burst_time in enumerate(burst_times):
						print(
						    F'''  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '''
						    F'''{turn_around_times[i]}'''
						)
			print(F'''\nAverage waiting time = {mean(waiting_times):.5f}''')
			print(F'''Average turn around time = {mean(turn_around_times):.5f}''')








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


A    =	{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileViTV2ForImageClassification',
			    'MobileViTV2ForSemanticSegmentation',
			    'MobileViTV2Model',
			    'MobileViTV2PreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_mobilevitva import (
			    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileViTVaConfig,
			    MobileViTVaOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilevitva import (
						    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileViTVaForImageClassification,
						    MobileViTVaForSemanticSegmentation,
						    MobileViTVaModel,
						    MobileViTVaPreTrainedModel,
						)

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



