

'''simple docstring'''
from collections import deque







class 							A__    :





  def __init__(		self      :	List[Any]							,					_a      :	str							,					_a      :	int							,					_a      :	int )	->    None:





       '''simple docstring'''






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







class 							A__    :





  def __init__(		self      :	List[str]							,					_a      :	int							,					_a      :	list[int]							,					_a      :	deque[Process]							,					_a      :	int							,					)	->    None:





       '''simple docstring'''






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





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





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					_a      :	list[Process] )	->    list[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	List[str]							,					_a      :	list[Process] )	->    list[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	str							,					_a      :	list[Process] )	->    list[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	Tuple							,					_a      :	deque[Process] )	->    list[int]:





       '''simple docstring'''






       return [q.burst_time for q in queue]





  def 						A  (		self      :	Optional[int]							,					_a      :	Process )	->    int:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[int]							,					_a      :	deque[Process] )	->    deque[Process]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =deque()  # sequence deque of finished process
       while len(_a ) != 0:
            _SCREAMING_SNAKE_CASE							    =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
            _SCREAMING_SNAKE_CASE							    =0
            # set the process's turnaround time because it is finished
            _SCREAMING_SNAKE_CASE							    =self.current_time - cp.arrival_time
            # set the completion time
            _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	Any							,					_a      :	deque[Process]							,					_a      :	int )	->    tuple[deque[Process], deque[Process]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =deque()  # sequence deque of terminated process
       # just for 1 cycle and unfinished processes will go back to queue
       for _ in range(len(_a ) ):
            _SCREAMING_SNAKE_CASE							    =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
                 _SCREAMING_SNAKE_CASE							    =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
                 _SCREAMING_SNAKE_CASE							    =0
                 # set the finish time
                 _SCREAMING_SNAKE_CASE							    =self.current_time
                 # update the process' turnaround time because it is finished
                 _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	Any )	->    deque[Process]:





       '''simple docstring'''






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