




from collections import deque
class 	_UpperCamelCase					:
      def __init__(	self      :List[Any]       ,					lowerCamelCase      :str       ,					lowerCamelCase      :int       ,					lowerCamelCase      :int					)   ->	None:
       UpperCAmelCase__							       =  process_name  # process name
       UpperCAmelCase__							       =  arrival_time  # arrival time of the process
       # completion time of finished process or last interrupted time
       UpperCAmelCase__							       =  arrival_time
       UpperCAmelCase__							       =  burst_time  # remaining burst time
       UpperCAmelCase__							       =  0  # total time of the process wait in ready queue
       UpperCAmelCase__							       =  0  # time from arrival time to completion time



class 	_UpperCamelCase					:
      def __init__(	self      :List[str]       ,					lowerCamelCase      :int       ,					lowerCamelCase      :list[int]       ,					lowerCamelCase      :deque[Process]       ,					lowerCamelCase      :int       ,					)   ->	None:
       UpperCAmelCase__							       =  number_of_queues
       # time slice of queues that round robin algorithm applied
       UpperCAmelCase__							       =  time_slices
       # unfinished process is in this ready_queue
       UpperCAmelCase__							       =  queue
       # current time
       UpperCAmelCase__							       =  current_time
       # finished process is in this sequence queue
       UpperCAmelCase__							       =  deque()
      def 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	list[str]:
       UpperCAmelCase__							       =  []
       for i in range(len(self.finish_queue					)					):
        sequence.append(self.finish_queue[i].process_name					)
       return sequence
      def 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :list[Process]					)   ->	list[int]:
       UpperCAmelCase__							       =  []
       for i in range(len(_a					)					):
        waiting_times.append(queue[i].waiting_time					)
       return waiting_times
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :list[Process]					)   ->	list[int]:
       UpperCAmelCase__							       =  []
       for i in range(len(_a					)					):
        turnaround_times.append(queue[i].turnaround_time					)
       return turnaround_times
      def 		UpperCAmelCase_  (	self      :str       ,					lowerCamelCase      :list[Process]					)   ->	list[int]:
       UpperCAmelCase__							       =  []
       for i in range(len(_a					)					):
        completion_times.append(queue[i].stop_time					)
       return completion_times
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :deque[Process]					)   ->	list[int]:
       return [q.burst_time for q in queue]
      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :Process					)   ->	int:
       process.waiting_time += self.current_time - process.stop_time
       return process.waiting_time
      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :deque[Process]					)   ->	deque[Process]:
       UpperCAmelCase__							       =  deque()  # sequence deque of finished process
       while len(_a					) != 0:
        UpperCAmelCase__							       =  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(_a					)
        # update current time
        self.current_time += cp.burst_time
        # finish the process and set the process's burst-time 0
        UpperCAmelCase__							       =  0
        # set the process's turnaround time because it is finished
        UpperCAmelCase__							       =  self.current_time - cp.arrival_time
        # set the completion time
        UpperCAmelCase__							       =  self.current_time
        # add the process to queue that has finished queue
        finished.append(_a					)

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

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







      def 		UpperCAmelCase_  (	self      :Any					)   ->	deque[Process]:






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

  _lowerCAmelCase  :      Tuple						=      Process("P1", 0, 5_3)
  _lowerCAmelCase  :      str						=      Process("P2", 0, 1_7)
  _lowerCAmelCase  :      Any						=      Process("P3", 0, 6_8)
  _lowerCAmelCase  :      Any						=      Process("P4", 0, 2_4)
  _lowerCAmelCase  :      Optional[int]						=      3
  _lowerCAmelCase  :      Dict						=      [1_7, 2_5]
  _lowerCAmelCase  :      int						=      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])})

  _lowerCAmelCase  :      Optional[int]						=      Process("P1", 0, 5_3)
  _lowerCAmelCase  :      List[str]						=      Process("P2", 0, 1_7)
  _lowerCAmelCase  :      Union[str, Any]						=      Process("P3", 0, 6_8)
  _lowerCAmelCase  :      List[str]						=      Process("P4", 0, 2_4)
  _lowerCAmelCase  :      List[str]						=      3
  _lowerCAmelCase  :      Optional[int]						=      [1_7, 2_5]
  _lowerCAmelCase  :      str						=      deque([Pa, Pa, Pa, Pa])
  _lowerCAmelCase  :      Tuple						=      MLFQ(number_of_queues, time_slices, queue, 0)
  _lowerCAmelCase  :      int						=      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()}'''
  )








'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image







class 							A__    (       A__  ):
  A__		 =				(
      'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
      'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
      'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
