



'''simple docstring'''

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class      __magic_name__						(   unittest.TestCase       ):







							@property
							def 							__lowercase      (							self :	List[str]		):
									torch.manual_seed(0		)
									_a					:				Union[str, Any]				=							UNetaDModel(
									    block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=3     ,out_channels=3     ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')     ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')     ,)
									return model







							@property
							def 							__lowercase      (							self :	List[str]		):
									torch.manual_seed(0		)
									_a					:				Tuple				=							VQModel(
									    block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,latent_channels=3     ,)
									return model







							@property
							def 							__lowercase      (							self :	List[str]		):
									torch.manual_seed(0		)
									_a					:				Optional[Any]				=							CLIPTextConfig(
									    bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,)
									return CLIPTextModel(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Optional[Any]				=							self.dummy_uncond_unet
									_a					:				int				=							DDIMScheduler()
									_a					:				Optional[int]				=							self.dummy_vq_model

									_a					:				List[Any]				=							LDMPipeline(unet=_UpperCAmelCase     ,vqvae=_UpperCAmelCase     ,scheduler=_UpperCAmelCase		)
									ldm.to(_UpperCAmelCase		)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a					:				Dict				=							torch.manual_seed(0		)
									_a					:				Optional[int]				=							ldm(generator=_UpperCAmelCase     ,num_inference_steps=2     ,output_type='numpy'		).images

									_a					:				Union[str, Any]				=							torch.manual_seed(0		)
									_a					:				Union[str, Any]				=							ldm(generator=_UpperCAmelCase     ,num_inference_steps=2     ,output_type='numpy'     ,return_dict=_UpperCAmelCase		)[0]

									_a					:				Optional[int]				=							image[0, -3:, -3:, -1]
									_a					:				int				=							image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a					:				Optional[Any]				=							np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]		)
									_a					:				Tuple				=							1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice		).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < tolerance

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







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Any				=							LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'		)
									ldm.to(_UpperCAmelCase		)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a					:				Any				=							torch.manual_seed(0		)
									_a					:				Any				=							ldm(generator=_UpperCAmelCase     ,num_inference_steps=5     ,output_type='numpy'		).images

									_a					:				str				=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_a					:				Any				=							np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]		)
									_a					:				int				=							1E-2 if torch_device != 'mps' else 3E-2

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









'''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)}""")




