



'''simple docstring'''
from __future__ import annotations

from statistics import mean






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):






						"""simple docstring"""




						_UpperCamelCase :		Optional[Any]			    =       [0] * no_of_processes
						_UpperCamelCase :		Union[str, Any]			    =       [0] * no_of_processes

						# Initialize remaining_time to waiting_time.

						for i in range(lowerCamelCase__							):
												_UpperCamelCase :		int			    =       burst_time[i]
						_UpperCamelCase :		Dict			    =       []

						_UpperCamelCase :		Tuple			    =       0
						_UpperCamelCase :		str			    =       0

						# When processes are not completed,
						# A process whose arrival time has passed \
						# and has remaining execution time is put into the ready_process.
						# The shortest process in the ready_process, target_process is executed.

						while completed != no_of_processes:
												_UpperCamelCase :		Dict			    =       []
												_UpperCamelCase :		Union[str, Any]			    =       -1

												for i in range(lowerCamelCase__							):
																		if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
																								ready_process.append(lowerCamelCase__							)

												if len(lowerCamelCase__							) > 0:
																		_UpperCamelCase :		Any			    =       ready_process[0]
																		for i in ready_process:
																								if remaining_time[i] < remaining_time[target_process]:
																														_UpperCamelCase :		List[str]			    =       i
																		total_time += burst_time[target_process]
																		completed += 1
																		_UpperCamelCase :		List[Any]			    =       0
																		_UpperCamelCase :		Optional[int]			    =       (
																		    total_time - arrival_time[target_process] - burst_time[target_process]
																		)
												else:
																		total_time += 1

						return waiting_time






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):






						"""simple docstring"""




						_UpperCamelCase :		int			    =       [0] * no_of_processes
						for i in range(lowerCamelCase__							):
												_UpperCamelCase :		Any			    =       burst_time[i] + waiting_time[i]
						return turn_around_time


if __name__ == "__main__":
					print('[TEST CASE 01]')

					snake_case_			:     Union[str, Any]  	=  4
					snake_case_			:     str  	=  [2, 5, 3, 7]
					snake_case_			:     str  	=  [0, 0, 0, 0]
					snake_case_			:     Optional[Any]  	=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					snake_case_			:     str  	=  calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
										    F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
										)
					print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
					print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							lowercase__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      DanceDiffusionPipeline
	lowercase__      =      UNCONDITIONAL_AUDIO_GENERATION_PARAMS
	lowercase__      =      PipelineTesterMixin.required_optional_params - {
	    """callback""",
	    """latents""",
	    """callback_steps""",
	    """output_type""",
	    """num_images_per_prompt""",
	}
	lowercase__      =      UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
	lowercase__      =      False
	lowercase__      =      False





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		str			    =       UNetaDModel(
							    block_out_channels=(32, 32, 64)			,extra_in_channels=16			,sample_size=512			,sample_rate=16000			,in_channels=2			,out_channels=2			,flip_sin_to_cos=lowerCamelCase__			,use_timestep_embedding=lowerCamelCase__			,time_embedding_type='fourier'			,mid_block_type='UNetMidBlock1D'			,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')			,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')			,)
							_UpperCamelCase :		int			    =       IPNDMScheduler()

							_UpperCamelCase :		List[str]			    =       {
							    'unet': unet,
							    'scheduler': scheduler,
							}
							return components





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    int=0			):







							'''simple docstring'''
							if str(lowerCamelCase__			).startswith('mps'			):
													_UpperCamelCase :		Union[str, Any]			    =       torch.manual_seed(lowerCamelCase__			)
							else:
													_UpperCamelCase :		str			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		str			    =       {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 4,
							}
							return inputs





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase :		List[str]			    =       self.get_dummy_components()
							_UpperCamelCase :		int			    =       DanceDiffusionPipeline(**lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       self.get_dummy_inputs(lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       pipe(**lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       output.audios

							_UpperCamelCase :		List[str]			    =       audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							_UpperCamelCase :		Dict			    =       np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]			)
							assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1E-2





	@skip_mps
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return super().test_save_load_local()





	@skip_mps
	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)





	@skip_mps
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return super().test_save_load_optional_components()





	@skip_mps
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return super().test_attention_slicing_forward_pass()





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							super().test_inference_batch_single_identical(expected_max_diff=3E-3			)






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





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       torch_device

							_UpperCamelCase :		Dict			    =       DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'			)
							_UpperCamelCase :		str			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		int			    =       torch.manual_seed(0			)
							_UpperCamelCase :		Optional[Any]			    =       pipe(generator=lowerCamelCase__			,num_inference_steps=100			,audio_length_in_s=4.0_9_6			)
							_UpperCamelCase :		Optional[int]			    =       output.audios

							_UpperCamelCase :		int			    =       audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							_UpperCamelCase :		Tuple			    =       np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]			)

							assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1E-2





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       torch_device

							_UpperCamelCase :		int			    =       DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'			,torch_dtype=torch.floataa			)
							_UpperCamelCase :		Any			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       torch.manual_seed(0			)
							_UpperCamelCase :		Union[str, Any]			    =       pipe(generator=lowerCamelCase__			,num_inference_steps=100			,audio_length_in_s=4.0_9_6			)
							_UpperCamelCase :		Any			    =       output.audios

							_UpperCamelCase :		str			    =       audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							_UpperCamelCase :		Any			    =       np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]			)

							assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1E-2


