






'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


_A     :      Tuple   					=logging.get_logger(__name__)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[List[ImageInput]]:
							if isinstance(UpperCamelCase    ,      (list, tuple)				) and isinstance(videos[0]    ,      (list, tuple)				) and is_valid_image(videos[0][0]				):
														return videos

							elif isinstance(UpperCamelCase    ,      (list, tuple)				) and is_valid_image(videos[0]				):
														return [videos]

							elif is_valid_image(UpperCamelCase				):
														return [[videos]]

							raise ValueError(f'''Could not make batched video from {videos}'''				)



class _lowercase     (    _lowercase		):
							a    				=	["""pixel_values"""]




							def __init__(							self:    Optional[Any]      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Union[int, float] = 1 / 255      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__:    List[str]      ,							):
														super().__init__(**UpperCamelCase__					)
														lowerCamelCase__ :     List[str]	  =	size if size is not None else {"""shortest_edge""": 224}
														lowerCamelCase__ :     List[Any]	  =	get_size_dict(UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
														lowerCamelCase__ :     Union[str, Any]	  =	get_size_dict(UpperCamelCase__      ,							param_name="""crop_size"""					)

														lowerCamelCase__ :     Dict	  =	do_resize
														lowerCamelCase__ :     Union[str, Any]	  =	size
														lowerCamelCase__ :     Optional[Any]	  =	do_center_crop
														lowerCamelCase__ :     Optional[Any]	  =	crop_size
														lowerCamelCase__ :     Tuple	  =	resample
														lowerCamelCase__ :     Dict	  =	do_rescale
														lowerCamelCase__ :     int	  =	rescale_factor
														lowerCamelCase__ :     Any	  =	do_normalize
														lowerCamelCase__ :     List[Any]	  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														lowerCamelCase__ :     Dict	  =	image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Dict[str, int]      ,							UpperCamelCase__:    PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Any      ,							):
														lowerCamelCase__ :     Any	  =	get_size_dict(UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														if "shortest_edge" in size:
																					lowerCamelCase__ :     Optional[int]	  =	get_resize_output_image_size(UpperCamelCase__      ,							size["""shortest_edge"""]      ,							default_to_square=UpperCamelCase__					)
														elif "height" in size and "width" in size:
																					lowerCamelCase__ :     str	  =	(size["""height"""], size["""width"""])
														else:
																					raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
														return resize(UpperCamelCase__      ,							size=UpperCamelCase__      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Dict[str, int]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    List[str]      ,							):
														lowerCamelCase__ :     int	  =	get_size_dict(UpperCamelCase__					)
														if "height" not in size or "width" not in size:
																					raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
														return center_crop(UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Union[int, float]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    List[Any]      ,							):
														return rescale(UpperCamelCase__      ,							scale=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Union[float, List[float]]      ,							UpperCamelCase__:    Union[float, List[float]]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Tuple      ,							):
														return normalize(UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    ImageInput      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    PILImageResampling = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    float = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[ChannelDimension] = ChannelDimension.FIRST      ,							):
														if do_resize and size is None or resample is None:
																					raise ValueError("""Size and resample must be specified if do_resize is True."""					)

														if do_center_crop and crop_size is None:
																					raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

														if do_rescale and rescale_factor is None:
																					raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

														# All transformations expect numpy arrays.
														lowerCamelCase__ :     Dict	  =	to_numpy_array(UpperCamelCase__					)

														if do_resize:
																					lowerCamelCase__ :     Optional[int]	  =	self.resize(image=UpperCamelCase__      ,							size=UpperCamelCase__      ,							resample=UpperCamelCase__					)

														if do_center_crop:
																					lowerCamelCase__ :     List[Any]	  =	self.center_crop(UpperCamelCase__      ,							size=UpperCamelCase__					)

														if do_rescale:
																					lowerCamelCase__ :     Dict	  =	self.rescale(image=UpperCamelCase__      ,							scale=UpperCamelCase__					)

														if do_normalize:
																					lowerCamelCase__ :     int	  =	self.normalize(image=UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	to_channel_dimension_format(UpperCamelCase__      ,							UpperCamelCase__					)
														return image




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    ImageInput      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    PILImageResampling = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    float = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__:    ChannelDimension = ChannelDimension.FIRST      ,							**UpperCamelCase__:    str      ,							):
														lowerCamelCase__ :     Optional[Any]	  =	do_resize if do_resize is not None else self.do_resize
														lowerCamelCase__ :     Tuple	  =	resample if resample is not None else self.resample
														lowerCamelCase__ :     Optional[int]	  =	do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCamelCase__ :     Dict	  =	do_rescale if do_rescale is not None else self.do_rescale
														lowerCamelCase__ :     List[Any]	  =	rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCamelCase__ :     Optional[Any]	  =	do_normalize if do_normalize is not None else self.do_normalize
														lowerCamelCase__ :     List[Any]	  =	image_mean if image_mean is not None else self.image_mean
														lowerCamelCase__ :     List[Any]	  =	image_std if image_std is not None else self.image_std

														lowerCamelCase__ :     List[Any]	  =	size if size is not None else self.size
														lowerCamelCase__ :     Dict	  =	get_size_dict(UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	crop_size if crop_size is not None else self.crop_size
														lowerCamelCase__ :     Optional[Any]	  =	get_size_dict(UpperCamelCase__      ,							param_name="""crop_size"""					)

														if not valid_images(UpperCamelCase__					):
																					raise ValueError(
																					    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																					    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

														lowerCamelCase__ :     Union[str, Any]	  =	make_batched(UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	[
														    [
														        self._preprocess_image(
														            image=UpperCamelCase__      ,							do_resize=UpperCamelCase__      ,							size=UpperCamelCase__      ,							resample=UpperCamelCase__      ,							do_center_crop=UpperCamelCase__      ,							crop_size=UpperCamelCase__      ,							do_rescale=UpperCamelCase__      ,							rescale_factor=UpperCamelCase__      ,							do_normalize=UpperCamelCase__      ,							image_mean=UpperCamelCase__      ,							image_std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							)
														        for img in video
														    ]
														    for video in videos
														]

														lowerCamelCase__ :     Optional[Any]	  =	{"""pixel_values""": videos}
														return BatchFeature(data=UpperCamelCase__      ,							tensor_type=UpperCamelCase__					)









'''simple docstring'''







from collections import deque

class _lowercase     :




							def __init__(							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     int	  =	process_name  # process name
														lowerCamelCase__ :     int	  =	arrival_time  # arrival time of the process
														# completion time of finished process or last interrupted time
														lowerCamelCase__ :     List[str]	  =	arrival_time
														lowerCamelCase__ :     Tuple	  =	burst_time  # remaining burst time
														lowerCamelCase__ :     str	  =	0  # total time of the process wait in ready queue
														lowerCamelCase__ :     Optional[Any]	  =	0  # time from arrival time to completion time



class _lowercase     :




							def __init__(							self:    Any      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    list[int]      ,							UpperCamelCase__:    deque[Process]      ,							UpperCamelCase__:    int      ,							):
														# total number of mlfq's queues
														lowerCamelCase__ :     Tuple	  =	number_of_queues
														# time slice of queues that round robin algorithm applied
														lowerCamelCase__ :     List[Any]	  =	time_slices
														# unfinished process is in this ready_queue
														lowerCamelCase__ :     int	  =	queue
														# current time
														lowerCamelCase__ :     Optional[int]	  =	current_time
														# finished process is in this sequence queue
														lowerCamelCase__ :     deque[Process]	  =	deque()




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	[]
														for i in range(len(self.finish_queue					)					):
																					sequence.append(self.finish_queue[i].process_name					)
														return sequence




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     int	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					waiting_times.append(queue[i].waiting_time					)
														return waiting_times




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     Optional[int]	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					turnaround_times.append(queue[i].turnaround_time					)
														return turnaround_times




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     List[Any]	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					completion_times.append(queue[i].stop_time					)
														return completion_times




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    deque[Process]					):
														return [q.burst_time for q in queue]




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Process					):
														process.waiting_time += self.current_time - process.stop_time
														return process.waiting_time




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    deque[Process]					):
														lowerCamelCase__ :     deque[Process]	  =	deque()  # sequence deque of finished process
														while len(UpperCamelCase__					) != 0:
																					lowerCamelCase__ :     List[Any]	  =	ready_queue.popleft()  # current process

																					# if process's arrival time is later than current time, update current time
																					if self.current_time < cp.arrival_time:
																												self.current_time += cp.arrival_time

																					# update waiting time of current process
																					self.update_waiting_time(UpperCamelCase__					)
																					# update current time
																					self.current_time += cp.burst_time
																					# finish the process and set the process's burst-time 0
																					lowerCamelCase__ :     Dict	  =	0
																					# set the process's turnaround time because it is finished
																					lowerCamelCase__ :     List[str]	  =	self.current_time - cp.arrival_time
																					# set the completion time
																					lowerCamelCase__ :     int	  =	self.current_time
																					# add the process to queue that has finished queue
																					finished.append(UpperCamelCase__					)

														self.finish_queue.extend(UpperCamelCase__					)  # add finished process to finish queue
														# FCFS will finish all remaining processes
														return finished




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    deque[Process]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     deque[Process]	  =	deque()  # sequence deque of terminated process
														# just for 1 cycle and unfinished processes will go back to queue
														for _ in range(len(UpperCamelCase__					)					):
																					lowerCamelCase__ :     Optional[int]	  =	ready_queue.popleft()  # current process

																					# if process's arrival time is later than current time, update current time
																					if self.current_time < cp.arrival_time:
																												self.current_time += cp.arrival_time

																					# update waiting time of unfinished processes
																					self.update_waiting_time(UpperCamelCase__					)
																					# if the burst time of process is bigger than time-slice
																					if cp.burst_time > time_slice:
																												# use CPU for only time-slice
																												self.current_time += time_slice
																												# update remaining burst time
																												cp.burst_time -= time_slice
																												# update end point time
																												lowerCamelCase__ :     Dict	  =	self.current_time
																												# locate the process behind the queue because it is not finished
																												ready_queue.append(UpperCamelCase__					)
																					else:
																												# use CPU for remaining burst time
																												self.current_time += cp.burst_time
																												# set burst time 0 because the process is finished
																												lowerCamelCase__ :     Any	  =	0
																												# set the finish time
																												lowerCamelCase__ :     List[Any]	  =	self.current_time
																												# update the process' turnaround time because it is finished
																												lowerCamelCase__ :     Any	  =	self.current_time - cp.arrival_time
																												# add the process to queue that has finished queue
																												finished.append(UpperCamelCase__					)

														self.finish_queue.extend(UpperCamelCase__					)  # add finished process to finish queue
														# return finished processes queue and remaining processes queue
														return finished, ready_queue




							def 		lowerCamelCase_    (							self:    Tuple					):

														#  all queues except last one have round_robin algorithm
														for i in range(self.number_of_queues - 1					):
																					lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	self.round_robin(
																					    self.ready_queue      ,							self.time_slices[i]					)
														#  the last queue has first_come_first_served algorithm
														self.first_come_first_served(self.ready_queue					)

														return self.finish_queue


if __name__ == "__main__":
			import doctest

			_A     :      Optional[Any]   					=Process('''P1''', 0, 53)
			_A     :      List[Any]   					=Process('''P2''', 0, 17)
			_A     :      Any   					=Process('''P3''', 0, 68)
			_A     :      Tuple   					=Process('''P4''', 0, 24)
			_A     :      int   					=3
			_A     :      Tuple   					=[17, 25]
			_A     :      List[Any]   					=deque([Pa, Pa, Pa, Pa])

			if len(time_slices) != number_of_queues - 1:
						raise SystemExit(0)

			doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

			_A     :      Dict   					=Process('''P1''', 0, 53)
			_A     :      Union[str, Any]   					=Process('''P2''', 0, 17)
			_A     :      int   					=Process('''P3''', 0, 68)
			_A     :      Dict   					=Process('''P4''', 0, 24)
			_A     :      List[str]   					=3
			_A     :      List[Any]   					=[17, 25]
			_A     :      Any   					=deque([Pa, Pa, Pa, Pa])
			_A     :      List[str]   					=MLFQ(number_of_queues, time_slices, queue, 0)
			_A     :      Union[str, Any]   					=mlfq.multi_level_feedback_queue()

			# print total waiting times of processes(P1, P2, P3, P4)
			print(
			    F'waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'
			)
			# print completion times of processes(P1, P2, P3, P4)
			print(
			    F'completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'
			)
			# print total turnaround times of processes(P1, P2, P3, P4)
			print(
			    F'turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'
			)
			# print sequence of finished processes
			print(
			    F'sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'
			)

