"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class    a__    (   unittest.TestCase	):


			def 	snake_case__	(     self  ):
								'''simple docstring'''



								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								torch.manual_seed(0  )
								lowercase__				 =							UNetaDModel(
								    sample_size=(32, 64),				in_channels=1,				out_channels=1,				layers_per_block=2,				block_out_channels=(128, 128),				down_block_types=("AttnDownBlock2D", "DownBlock2D"),				up_block_types=("UpBlock2D", "AttnUpBlock2D"),				)
								return model


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								torch.manual_seed(0  )
								lowercase__				 =							UNetaDConditionModel(
								    sample_size=(64, 32),				in_channels=1,				out_channels=1,				layers_per_block=2,				block_out_channels=(128, 128),				down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),				up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),				cross_attention_dim=10,				)
								return model


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								torch.manual_seed(0  )
								lowercase__				 =							AutoencoderKL(
								    sample_size=(128, 64),				in_channels=1,				out_channels=1,				latent_channels=1,				layers_per_block=2,				block_out_channels=(128, 128),				down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"),				up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"),				)
								lowercase__				 =							UNetaDModel(
								    sample_size=(64, 32),				in_channels=1,				out_channels=1,				layers_per_block=2,				block_out_channels=(128, 128),				down_block_types=("AttnDownBlock2D", "DownBlock2D"),				up_block_types=("UpBlock2D", "AttnUpBlock2D"),				)
								return vqvae, unet


			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								lowercase__				 =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								lowercase__				 =							Mel(
								    x_res=self.dummy_unet.config.sample_size[1],				y_res=self.dummy_unet.config.sample_size[0],				)

								lowercase__				 =							DDPMScheduler()
								lowercase__				 =							AudioDiffusionPipeline(vqvae=__a,				unet=self.dummy_unet,				mel=__a,				scheduler=__a  )
								lowercase__				 =							pipe.to(__a  )
								pipe.set_progress_bar_config(disable=__a  )

								lowercase__				 =							torch.Generator(device=__a  ).manual_seed(42  )
								lowercase__				 =							pipe(generator=__a,				steps=4  )
								lowercase__				 =							output.audios[0]
								lowercase__				 =							output.images[0]

								lowercase__				 =							torch.Generator(device=__a  ).manual_seed(42  )
								lowercase__				 =							pipe(generator=__a,				steps=4,				return_dict=__a  )
								lowercase__				 =							output[0][0]

								assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
								assert (
								    image.height == self.dummy_unet.config.sample_size[0]
								    and image.width == self.dummy_unet.config.sample_size[1]
								)
								lowercase__				 =							np.frombuffer(image.tobytes(),				dtype="uint8"  )[:10]
								lowercase__				 =							np.frombuffer(image_from_tuple.tobytes(),				dtype="uint8"  )[:10]
								lowercase__				 =							np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() == 0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() == 0

								lowercase__				 =							Mel(
								    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1],				y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0],				)

								lowercase__				 =							DDIMScheduler()
								lowercase__				 =							self.dummy_vqvae_and_unet
								lowercase__				 =							AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0],				unet=dummy_vqvae_and_unet[1],				mel=__a,				scheduler=__a  )
								lowercase__				 =							pipe.to(__a  )
								pipe.set_progress_bar_config(disable=__a  )

								np.random.seed(0  )
								lowercase__				 =							np.random.uniform(-1,				1,				((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)  )
								lowercase__				 =							torch.Generator(device=__a  ).manual_seed(42  )
								lowercase__				 =							pipe(raw_audio=__a,				generator=__a,				start_step=5,				steps=10  )
								lowercase__				 =							output.images[0]

								assert (
								    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
								    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
								)
								lowercase__				 =							np.frombuffer(image.tobytes(),				dtype="uint8"  )[:10]
								lowercase__				 =							np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() == 0

								lowercase__				 =							self.dummy_unet_condition
								lowercase__				 =							AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0],				unet=__a,				mel=__a,				scheduler=__a  )
								lowercase__				 =							pipe.to(__a  )
								pipe.set_progress_bar_config(disable=__a  )

								np.random.seed(0  )
								lowercase__				 =							torch.rand((1, 1, 10)  )
								lowercase__				 =							pipe(generator=__a,				encoding=__a  )
								lowercase__				 =							output.images[0]
								lowercase__				 =							np.frombuffer(image.tobytes(),				dtype="uint8"  )[:10]
								lowercase__				 =							np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() == 0






@slow
@require_torch_gpu
class    a__    (   unittest.TestCase	):


			def 	snake_case__	(     self  ):
								'''simple docstring'''



								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


			def 	snake_case__	(     self  ):
								'''simple docstring'''



								lowercase__				 =							torch_device

								lowercase__				 =							DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256"  )
								lowercase__				 =							pipe.to(__a  )
								pipe.set_progress_bar_config(disable=__a  )

								lowercase__				 =							torch.Generator(device=__a  ).manual_seed(42  )
								lowercase__				 =							pipe(generator=__a  )
								lowercase__				 =							output.audios[0]
								lowercase__				 =							output.images[0]

								assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
								assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
								lowercase__				 =							np.frombuffer(image.tobytes(),				dtype="uint8"  )[:10]
								lowercase__				 =							np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() == 0





"""simple docstring"""


from collections import deque






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return [q.burst_time for q in queue]


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for i in range(self.number_of_queues - 1  ):
             lowercase__ ,						lowercase__				 =							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_:					Optional[int]     =					Process("P1", 0, 5_3)
 lowerCAmelCase_:					Union[str, Any]     =					Process("P2", 0, 1_7)
 lowerCAmelCase_:					str     =					Process("P3", 0, 6_8)
 lowerCAmelCase_:					int     =					Process("P4", 0, 2_4)
 lowerCAmelCase_:					Dict     =					3
 lowerCAmelCase_:					Any     =					[1_7, 2_5]
 lowerCAmelCase_:					Tuple     =					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_:					Any     =					Process("P1", 0, 5_3)
 lowerCAmelCase_:					Tuple     =					Process("P2", 0, 1_7)
 lowerCAmelCase_:					Optional[int]     =					Process("P3", 0, 6_8)
 lowerCAmelCase_:					List[Any]     =					Process("P4", 0, 2_4)
 lowerCAmelCase_:					Union[str, Any]     =					3
 lowerCAmelCase_:					Any     =					[1_7, 2_5]
 lowerCAmelCase_:					Optional[Any]     =					deque([Pa, Pa, Pa, Pa])
 lowerCAmelCase_:					Union[str, Any]     =					MLFQ(number_of_queues, time_slices, queue, 0)
 lowerCAmelCase_:					Tuple     =					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()}'
 )




