



'''simple docstring'''

from collections import deque

class __snake_case:


						'''simple docstring'''



						def __init__(   self      ,			A_      ,			A_      ,			A_ )      ->      None:
							lowerCAmelCase       =      process_name  # process name
							lowerCAmelCase       =      arrival_time  # arrival time of the process
							# completion time of finished process or last interrupted time
							lowerCAmelCase       =      arrival_time
							lowerCAmelCase       =      burst_time  # remaining burst time
							lowerCAmelCase       =      0  # total time of the process wait in ready queue
							lowerCAmelCase       =      0  # time from arrival time to completion time







class __snake_case:


						'''simple docstring'''



						def __init__(   self      ,			A_      ,			A_      ,			A_      ,			A_      ,			)      ->      None:
							lowerCAmelCase       =      number_of_queues
							# time slice of queues that round robin algorithm applied
							lowerCAmelCase       =      time_slices
							# unfinished process is in this ready_queue
							lowerCAmelCase       =      queue
							# current time
							lowerCAmelCase       =      current_time
							# finished process is in this sequence queue
							lowerCAmelCase       =      deque()


						def 							__snake_case       (   self )      ->      list[str]:
							lowerCAmelCase       =      []
							for i in range(len(self.finish_queue ) ):
								sequence.append(self.finish_queue[i].process_name )
							return sequence


						def 							__snake_case       (   self      ,			A_ )      ->      list[int]:
							lowerCAmelCase       =      []
							for i in range(len(lowercase_ ) ):
								waiting_times.append(queue[i].waiting_time )
							return waiting_times


						def 							__snake_case       (   self      ,			A_ )      ->      list[int]:
							lowerCAmelCase       =      []
							for i in range(len(lowercase_ ) ):
								turnaround_times.append(queue[i].turnaround_time )
							return turnaround_times


						def 							__snake_case       (   self      ,			A_ )      ->      list[int]:
							lowerCAmelCase       =      []
							for i in range(len(lowercase_ ) ):
								completion_times.append(queue[i].stop_time )
							return completion_times


						def 							__snake_case       (   self      ,			A_ )      ->      list[int]:
							return [q.burst_time for q in queue]


						def 							__snake_case       (   self      ,			A_ )      ->      int:
							process.waiting_time += self.current_time - process.stop_time
							return process.waiting_time


						def 							__snake_case       (   self      ,			A_ )      ->      deque[Process]:
							lowerCAmelCase       =      deque()  # sequence deque of finished process
							while len(lowercase_ ) != 0:
								lowerCAmelCase       =      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(lowercase_ )
								# update current time
								self.current_time += cp.burst_time
								# finish the process and set the process's burst-time 0
								lowerCAmelCase       =      0
								# set the process's turnaround time because it is finished
								lowerCAmelCase       =      self.current_time - cp.arrival_time
								# set the completion time
								lowerCAmelCase       =      self.current_time
								# add the process to queue that has finished queue
								finished.append(lowercase_ )

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


						def 							__snake_case       (   self      ,			A_      ,			A_ )      ->      tuple[deque[Process], deque[Process]]:
							lowerCAmelCase       =      deque()  # sequence deque of terminated process
							# just for 1 cycle and unfinished processes will go back to queue
							for _ in range(len(lowercase_ ) ):
								lowerCAmelCase       =      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(lowercase_ )
								# 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       =      self.current_time
									# locate the process behind the queue because it is not finished
									ready_queue.append(lowercase_ )
								else:
									# use CPU for remaining burst time
									self.current_time += cp.burst_time
									# set burst time 0 because the process is finished
									lowerCAmelCase       =      0
									# set the finish time
									lowerCAmelCase       =      self.current_time
									# update the process' turnaround time because it is finished
									lowerCAmelCase       =      self.current_time - cp.arrival_time
									# add the process to queue that has finished queue
									finished.append(lowercase_ )

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




						def 							__snake_case       (   self )      ->      deque[Process]:




							for i in range(self.number_of_queues - 1 ):
								lowerCAmelCase       =      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

				UpperCAmelCase      						=       Process('P1', 0, 53)
				UpperCAmelCase      						=       Process('P2', 0, 17)
				UpperCAmelCase      						=       Process('P3', 0, 68)
				UpperCAmelCase      						=       Process('P4', 0, 24)
				UpperCAmelCase      						=       3
				UpperCAmelCase      						=       [17, 25]
				UpperCAmelCase      						=       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])})

				UpperCAmelCase      						=       Process('P1', 0, 53)
				UpperCAmelCase      						=       Process('P2', 0, 17)
				UpperCAmelCase      						=       Process('P3', 0, 68)
				UpperCAmelCase      						=       Process('P4', 0, 24)
				UpperCAmelCase      						=       3
				UpperCAmelCase      						=       [17, 25]
				UpperCAmelCase      						=       deque([Pa, Pa, Pa, Pa])
				UpperCAmelCase      						=       MLFQ(number_of_queues, time_slices, queue, 0)
				UpperCAmelCase      						=       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()}'''
				)



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['pixel_values']






      def __init__(   self		:	Any     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	bool = True     ,  lowercase_		:	Union[int, float] = 1 / 255     ,  lowercase_		:	bool = True     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = True     ,  **lowercase_		:	str     ,  )   ->		None:
             """simple docstring"""




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

             _lowerCamelCase			:						Dict            =do_resize
             _lowerCamelCase			:						int            =size
             _lowerCamelCase			:						Optional[Any]            =resample
             _lowerCamelCase			:						Optional[int]            =do_center_crop
             _lowerCamelCase			:						Tuple            =crop_size
             _lowerCamelCase			:						Optional[int]            =do_rescale
             _lowerCamelCase			:						Optional[Any]            =rescale_factor
             _lowerCamelCase			:						Union[str, Any]            =do_normalize
             _lowerCamelCase			:						Optional[int]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _lowerCamelCase			:						Any            =image_std if image_std is not None else OPENAI_CLIP_STD
             _lowerCamelCase			:						Any            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             if "shortest_edge" not in size:
                    raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
             _lowerCamelCase			:						Union[str, Any]            =get_resize_output_image_size(lowercase_     ,  size=size['shortest_edge']     ,  default_to_square=lowercase_					)
             return resize(lowercase_     ,  size=lowercase_     ,  resample=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =get_size_dict(lowercase_					)
             if "height" not in size or "width" not in size:
                    raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
             return center_crop(lowercase_     ,  size=(size['height'], size['width'])     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




             return rescale(lowercase_     ,  scale=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             return normalize(lowercase_     ,  mean=lowercase_     ,  std=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ImageInput     ,  lowercase_		:	bool = None     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	int = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	float = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[str, TensorType]] = None     ,  lowercase_		:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **lowercase_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase			:						List[str]            =size if size is not None else self.size
             _lowerCamelCase			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =make_list_of_images(lowercase_					)

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

             if do_resize and size is None:
                    raise ValueError('Size 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.'					)

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    _lowerCamelCase			:						Tuple            =[convert_to_rgb(lowercase_					) for image in images]

             # All transformations expect numpy arrays.
             _lowerCamelCase			:						Optional[Any]            =[to_numpy_array(lowercase_					) for image in images]

             if do_resize:
                    _lowerCamelCase			:						Optional[Any]            =[self.resize(image=lowercase_     ,  size=lowercase_     ,  resample=lowercase_					) for image in images]

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

             if do_rescale:
                    _lowerCamelCase			:						Optional[Any]            =[self.rescale(image=lowercase_     ,  scale=lowercase_					) for image in images]

             if do_normalize:
                    _lowerCamelCase			:						List[Any]            =[self.normalize(image=lowercase_     ,  mean=lowercase_     ,  std=lowercase_					) for image in images]

             _lowerCamelCase			:						List[str]            =[to_channel_dimension_format(lowercase_     ,  lowercase_					) for image in images]

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


