





from collections import deque

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      str				,			a__       :      int				,			a__       :      int      ):





         """simple docstring"""



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






class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      List[str]				,			a__       :      int				,			a__       :      list[int]				,			a__       :      deque[Process]				,			a__       :      int				,			):





         """simple docstring"""



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


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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


      def    a (self       :      Tuple				,			a__       :      list[Process]      ):





         """simple docstring"""



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


      def    a (self       :      Any				,			a__       :      list[Process]      ):





         """simple docstring"""



         __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]      ):





         """simple docstring"""



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


      def    a (self       :      Optional[Any]				,			a__       :      deque[Process]      ):





         """simple docstring"""



         return [q.burst_time for q in queue]


      def    a (self       :      int				,			a__       :      Process      ):





         """simple docstring"""



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


      def    a (self       :      Tuple				,			a__       :      deque[Process]      ):





         """simple docstring"""



         __snake_case							       =   deque()  # sequence deque of finished process
         while len(a__      ) != 0:
            __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
            __snake_case							       =   0
            # set the process's turnaround time because it is finished
            __snake_case							       =   self.current_time - cp.arrival_time
            # set the completion time
            __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       :      Optional[int]				,			a__       :      deque[Process]				,			a__       :      int      ):





         """simple docstring"""



         __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__      )      ):
            __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
               __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
               __snake_case							       =   0
               # set the finish time
               __snake_case							       =   self.current_time
               # update the process' turnaround time because it is finished
               __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       :      List[Any]      ):





         """simple docstring"""



         for i in range(self.number_of_queues - 1      ):
            __snake_case       ,	__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

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

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








import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Tuple							=    'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'


      def    a (self       :      int				,			a__       :      List[Any]=0      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor((1, 3, 128, 128)				,			rng=random.Random(a__      )      )
         __snake_case							       =   np.random.RandomState(a__      )
         __snake_case							       =   {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.7_5,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]      )
         assert np.abs(image_slice - expected_slice      ).max() < 1E-1


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   PNDMScheduler.from_config(pipe.scheduler.config				,			skip_prk_steps=a__      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         # warmup pass to apply optimizations
         __snake_case							       =   pipe(**self.get_dummy_inputs()      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1






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


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   ort.SessionOptions()
         __snake_case							       =   False
         return options


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''      )
         __snake_case							       =   init_image.resize((768, 512)      )
         # using the PNDM scheduler by default
         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''				,			revision='''onnx'''				,			safety_checker=a__				,			feature_extractor=a__				,			provider=self.gpu_provider				,			sess_options=self.gpu_options				,			)
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A fantasy landscape, trending on artstation'''

         __snake_case							       =   np.random.RandomState(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			strength=0.7_5				,			guidance_scale=7.5				,			num_inference_steps=10				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         __snake_case							       =   images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case							       =   np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]      )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2E-2


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''      )
         __snake_case							       =   init_image.resize((768, 512)      )
         __snake_case							       =   LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''				,			subfolder='''scheduler'''				,			revision='''onnx'''      )
         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''				,			revision='''onnx'''				,			scheduler=a__				,			safety_checker=a__				,			feature_extractor=a__				,			provider=self.gpu_provider				,			sess_options=self.gpu_options				,			)
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A fantasy landscape, trending on artstation'''

         __snake_case							       =   np.random.RandomState(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			strength=0.7_5				,			guidance_scale=7.5				,			num_inference_steps=20				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         __snake_case							       =   images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case							       =   np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]      )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2E-2

