


'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCAmelCase       							=				False




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )

														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase_  )
																					lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained(lowerCamelCase_   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      generator.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''cyberpunk 2077'''
														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt=lowerCamelCase_   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''   ,       ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]  )

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

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger '''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.text_to_image(
														    prompt=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]  )

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

														lowerCAmelCase__			  =      pipe.image_variation(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]  )

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

"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# 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(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    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
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# 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]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{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)}""")






