





from collections import deque





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       process_name  # process name
       A_          =       arrival_time  # arrival time of the process
       # completion time of finished process or last interrupted time
       A_          =       arrival_time
       A_          =       burst_time  # remaining burst time
       A_          =       0  # total time of the process wait in ready queue
       A_          =       0  # time from arrival time to completion time





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,):



       """simple docstring"""





       A_          =       number_of_queues
       # time slice of queues that round robin algorithm applied
       A_          =       time_slices
       # unfinished process is in this ready_queue
       A_          =       queue
       # current time
       A_          =       current_time
       # finished process is in this sequence queue
       A_          =       deque()


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       []
       for i in range(len(self.finish_queue ) ):
            sequence.append(self.finish_queue[i].process_name )
       return sequence


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       []
       for i in range(len(__snake_case ) ):
            waiting_times.append(queue[i].waiting_time )
       return waiting_times


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       []
       for i in range(len(__snake_case ) ):
            turnaround_times.append(queue[i].turnaround_time )
       return turnaround_times


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       []
       for i in range(len(__snake_case ) ):
            completion_times.append(queue[i].stop_time )
       return completion_times


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       return [q.burst_time for q in queue]


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       process.waiting_time += self.current_time - process.stop_time
       return process.waiting_time


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





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

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


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





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

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




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for i in range(self.number_of_queues - 1 ):
            A_   ,    A_          =       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__      :     str         =		Process('P1', 0, 53)
      a__      :     Tuple         =		Process('P2', 0, 17)
      a__      :     List[str]         =		Process('P3', 0, 68)
      a__      :     List[str]         =		Process('P4', 0, 24)
      a__      :     int         =		3
      a__      :     Optional[Any]         =		[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__      :     Union[str, Any]         =		Process('P1', 0, 53)
      a__      :     int         =		Process('P2', 0, 17)
      a__      :     str         =		Process('P3', 0, 68)
      a__      :     List[str]         =		Process('P4', 0, 24)
      a__      :     Union[str, Any]         =		3
      a__      :     int         =		[17, 25]
      a__      :     Optional[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()}'''
      )










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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


a__      :     Dict         =		logging.get_logger(__name__)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Optional[int]							=   ["""pixel_values"""]


  def __init__(							self ,__snake_case = True ,__snake_case = None ,__snake_case = None ,__snake_case = PILImageResampling.BILINEAR ,__snake_case = True ,__snake_case = 1 / 2_5_5 ,__snake_case = True ,__snake_case = None ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       super().__init__(**__snake_case )
       A_          =       size if size is not None else {'''shortest_edge''': 3_8_4}
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       do_resize
       A_          =       size
       # Default value set here for backwards compatibility where the value in config is None
       A_          =       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       A_          =       resample
       A_          =       do_rescale
       A_          =       rescale_factor
       A_          =       do_normalize
       A_          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A_          =       image_std if image_std is not None else IMAGENET_STANDARD_STD


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = PILImageResampling.BICUBIC ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )
       if "shortest_edge" not in size:
            raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
       A_          =       size['''shortest_edge''']

       if shortest_edge < 3_8_4:
            # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
            A_          =       int(shortest_edge / crop_pct )
            A_          =       get_resize_output_image_size(__snake_case ,size=__snake_case ,default_to_square=__snake_case )
            A_          =       resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
            # then crop to (shortest_edge, shortest_edge)
            return center_crop(image=__snake_case ,size=(shortest_edge, shortest_edge) ,data_format=__snake_case ,**__snake_case )
       else:
            # warping (no cropping) when evaluated at 384 or larger
            return resize(
                __snake_case ,size=(shortest_edge, shortest_edge) ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





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




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = ChannelDimension.FIRST ,**__snake_case ,):



       """simple docstring"""





       A_          =       do_resize if do_resize is not None else self.do_resize
       A_          =       crop_pct if crop_pct is not None else self.crop_pct
       A_          =       resample if resample is not None else self.resample
       A_          =       do_rescale if do_rescale is not None else self.do_rescale
       A_          =       rescale_factor if rescale_factor is not None else self.rescale_factor
       A_          =       do_normalize if do_normalize is not None else self.do_normalize
       A_          =       image_mean if image_mean is not None else self.image_mean
       A_          =       image_std if image_std is not None else self.image_std

       A_          =       size if size is not None else self.size
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       make_list_of_images(__snake_case )

       if not valid_images(__snake_case ):
            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 or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.''' )

       if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
            raise ValueError('''crop_pct must be specified if size < 384.''' )

       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.
       A_          =       [to_numpy_array(__snake_case ) for image in images]

       if do_resize:
            A_          =       [self.resize(image=__snake_case ,size=__snake_case ,crop_pct=__snake_case ,resample=__snake_case ) for image in images]

       if do_rescale:
            A_          =       [self.rescale(image=__snake_case ,scale=__snake_case ) for image in images]

       if do_normalize:
            A_          =       [self.normalize(image=__snake_case ,mean=__snake_case ,std=__snake_case ) for image in images]

       A_          =       [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]

       A_          =       {'''pixel_values''': images}
       return BatchFeature(data=__snake_case ,tensor_type=__snake_case )



