



"""simple docstring"""
from collections import deque
class 							_UpperCAmelCase							:





     '''simple docstring'''



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



class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A       ,	A       ,	A       ,	)							->					Optional[int]:
         # total number of mlfq's queues
         _UpperCAmelCase							:  Union[str, Any]											=    number_of_queues
         # time slice of queues that round robin algorithm applied
         _UpperCAmelCase							:  str											=    time_slices
         # unfinished process is in this ready_queue
         _UpperCAmelCase							:  Dict											=    queue
         # current time
         _UpperCAmelCase							:  Optional[int]											=    current_time
         # finished process is in this sequence queue
         _UpperCAmelCase							:  Dict											=    deque()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  int											=    []
         for i in range(len(self.finish_queue		)		):
             sequence.append(self.finish_queue[i].process_name		)
         return sequence





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[Any]:
         _UpperCAmelCase							:  Dict											=    []
         for i in range(len(_lowerCAmelCase		)		):
             waiting_times.append(queue[i].waiting_time		)
         return waiting_times





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         _UpperCAmelCase							:  Tuple											=    []
         for i in range(len(_lowerCAmelCase		)		):
             turnaround_times.append(queue[i].turnaround_time		)
         return turnaround_times





     def  __lowerCAmelCase	(					self       ,	A		)							->					Union[str, Any]:
         _UpperCAmelCase							:  str											=    []
         for i in range(len(_lowerCAmelCase		)		):
             completion_times.append(queue[i].stop_time		)
         return completion_times





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[int]:
         return [q.burst_time for q in queue]





     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:
         process.waiting_time += self.current_time - process.stop_time
         return process.waiting_time





     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:
         _UpperCAmelCase							:  Optional[int]											=    deque()  # sequence deque of finished process
         while len(_lowerCAmelCase		) != 0:
             _UpperCAmelCase							:  Optional[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(_lowerCAmelCase		)
             # update current time
             self.current_time += cp.burst_time
             # finish the process and set the process's burst-time 0
             _UpperCAmelCase							:  Dict											=    0
             # set the process's turnaround time because it is finished
             _UpperCAmelCase							:  Tuple											=    self.current_time - cp.arrival_time
             # set the completion time
             _UpperCAmelCase							:  str											=    self.current_time
             # add the process to queue that has finished queue
             finished.append(_lowerCAmelCase		)

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





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

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





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

         #  all queues except last one have round_robin algorithm
         for i in range(self.number_of_queues - 1		):
             _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    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, 53)
  _lowerCAmelCase    :Optional[int]     =					Process('P2', 0, 17)
  _lowerCAmelCase    :Tuple     =					Process('P3', 0, 68)
  _lowerCAmelCase    :Optional[Any]     =					Process('P4', 0, 24)
  _lowerCAmelCase    :Dict     =					3
  _lowerCAmelCase    :int     =					[17, 25]
  _lowerCAmelCase    :str     =					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    :Tuple     =					Process('P1', 0, 53)
  _lowerCAmelCase    :Optional[int]     =					Process('P2', 0, 17)
  _lowerCAmelCase    :Union[str, Any]     =					Process('P3', 0, 68)
  _lowerCAmelCase    :Any     =					Process('P4', 0, 24)
  _lowerCAmelCase    :Tuple     =					3
  _lowerCAmelCase    :str     =					[17, 25]
  _lowerCAmelCase    :Dict     =					deque([Pa, Pa, Pa, Pa])
  _lowerCAmelCase    :List[Any]     =					MLFQ(number_of_queues, time_slices, queue, 0)
  _lowerCAmelCase    :str     =					mlfq.multi_level_feedback_queue()

  # print total waiting times of processes(P1, P2, P3, P4)
  print(
      f"waiting time:\\n        \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:\\n        \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:\\n        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
  )
  # print sequence of finished processes
  print(
      f"sequence of finished processes:\\n        {mlfq.calculate_sequence_of_finish_queue()}"
  )








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2