



'''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      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Any           = DanceDiffusionPipeline
							lowerCAmelCase	:   int           = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
							lowerCAmelCase	:   Dict           = PipelineTesterMixin.required_optional_params - {
							    'callback',
							    'latents',
							    'callback_steps',
							    'output_type',
							    'num_images_per_prompt',
							}
							lowerCAmelCase	:   int           = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
							lowerCAmelCase	:   List[str]           = False
							lowerCAmelCase	:   List[Any]           = False







							def 							__lowercase      (							self :	Optional[int]		):
									torch.manual_seed(0		)
									_a					:				Dict				=							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=_UpperCAmelCase     ,use_timestep_embedding=_UpperCAmelCase     ,time_embedding_type='fourier'     ,mid_block_type='UNetMidBlock1D'     ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')     ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')     ,)
									_a					:				int				=							IPNDMScheduler()

									_a					:				Tuple				=							{
									    'unet': unet,
									    'scheduler': scheduler,
									}
									return components







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	List[str]=0		):
									if str(_UpperCAmelCase		).startswith('mps'		):
											_a					:				Tuple				=							torch.manual_seed(_UpperCAmelCase		)
									else:
											_a					:				int				=							torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
									_a					:				Optional[Any]				=							{
									    'batch_size': 1,
									    'generator': generator,
									    'num_inference_steps': 4,
									}
									return inputs







							def 							__lowercase      (							self :	str		):
									_a					:				int				=							'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a					:				Dict				=							self.get_dummy_components()
									_a					:				Union[str, Any]				=							DanceDiffusionPipeline(**_UpperCAmelCase		)
									_a					:				List[Any]				=							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a					:				int				=							self.get_dummy_inputs(_UpperCAmelCase		)
									_a					:				str				=							pipe(**_UpperCAmelCase		)
									_a					:				str				=							output.audios

									_a					:				Optional[Any]				=							audio[0, -3:, -3:]

									assert audio.shape == (1, 2, components["unet"].sample_size)
									_a					:				Any				=							np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]		)
									assert np.abs(audio_slice.flatten() - expected_slice		).max() < 1E-2







							@skip_mps
							def 							__lowercase      (							self :	str		):
									return super().test_save_load_local()







							@skip_mps
							def 							__lowercase      (							self :	Tuple		):
									return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)







							@skip_mps
							def 							__lowercase      (							self :	Tuple		):
									return super().test_save_load_optional_components()







							@skip_mps
							def 							__lowercase      (							self :	List[Any]		):
									return super().test_attention_slicing_forward_pass()







							def 							__lowercase      (							self :	List[Any]		):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3		)

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







							def 							__lowercase      (							self :	List[str]		):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 							__lowercase      (							self :	str		):
									_a					:				Dict				=							torch_device

									_a					:				Tuple				=							DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'		)
									_a					:				Optional[int]				=							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a					:				str				=							torch.manual_seed(0		)
									_a					:				int				=							pipe(generator=_UpperCAmelCase     ,num_inference_steps=100     ,audio_length_in_s=4.0_96		)
									_a					:				Tuple				=							output.audios

									_a					:				Union[str, Any]				=							audio[0, -3:, -3:]

									assert audio.shape == (1, 2, pipe.unet.sample_size)
									_a					:				Any				=							np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]		)

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







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				int				=							torch_device

									_a					:				List[Any]				=							DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'     ,torch_dtype=torch.floataa		)
									_a					:				Dict				=							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a					:				Dict				=							torch.manual_seed(0		)
									_a					:				int				=							pipe(generator=_UpperCAmelCase     ,num_inference_steps=100     ,audio_length_in_s=4.0_96		)
									_a					:				List[Any]				=							output.audios

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

									assert audio.shape == (1, 2, pipe.unet.sample_size)
									_a					:				str				=							np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]		)

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









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




