





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				set:







			'''simple docstring'''

			UpperCAmelCase   		=				set()
			# edges = list of graph's edges
			UpperCAmelCase   		=				get_edges(UpperCamelCase__ )

			# While there are still elements in edges list, take an arbitrary edge
			# (from_node, to_node) and add his extremity to chosen_vertices and then
			# remove all arcs adjacent to the from_node and to_node
			while edges:
						UpperCAmelCase		,			UpperCAmelCase   		=				edges.pop()
						chosen_vertices.add(UpperCamelCase__ )
						chosen_vertices.add(UpperCamelCase__ )
						for edge in edges.copy():
									if from_node in edge or to_node in edge:
												edges.discard(UpperCamelCase__ )
			return chosen_vertices





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				set:







			'''simple docstring'''

			UpperCAmelCase   		=				set()
			for from_node, to_nodes in graph.items():
						for to_node in to_nodes:
									edges.add((from_node, to_node) )
			return edges


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")










import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 	A_       :
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				UNetaDConditionModel(
				    sample_size=3_2							,					layers_per_block=1							,					block_out_channels=[3_2, 6_4]							,					down_block_types=[
				        '''ResnetDownsampleBlock2D''',
				        '''SimpleCrossAttnDownBlock2D''',
				    ]							,					mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,					up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,					in_channels=3							,					out_channels=6							,					cross_attention_dim=3_2							,					encoder_hid_dim=3_2							,					attention_head_dim=8							,					addition_embed_type='''text'''							,					addition_embed_type_num_heads=2							,					cross_attention_norm='''group_norm'''							,					resnet_time_scale_shift='''scale_shift'''							,					act_fn='''gelu'''							,					)
				unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					thresholding=_A							,					dynamic_thresholding_ratio=0.95							,					sample_max_value=1.0							,					prediction_type='''epsilon'''							,					variance_type='''learned_range'''							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				IFWatermarker()

				return {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "watermarker": watermarker,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				UNetaDConditionModel(
				    sample_size=3_2							,					layers_per_block=[1, 2]							,					block_out_channels=[3_2, 6_4]							,					down_block_types=[
				        '''ResnetDownsampleBlock2D''',
				        '''SimpleCrossAttnDownBlock2D''',
				    ]							,					mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,					up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,					in_channels=6							,					out_channels=6							,					cross_attention_dim=3_2							,					encoder_hid_dim=3_2							,					attention_head_dim=8							,					addition_embed_type='''text'''							,					addition_embed_type_num_heads=2							,					cross_attention_norm='''group_norm'''							,					resnet_time_scale_shift='''scale_shift'''							,					act_fn='''gelu'''							,					class_embed_type='''timestep'''							,					mid_block_scale_factor=1.4_14							,					time_embedding_act_fn='''gelu'''							,					time_embedding_dim=3_2							,					)
				unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					thresholding=_A							,					dynamic_thresholding_ratio=0.95							,					sample_max_value=1.0							,					prediction_type='''epsilon'''							,					variance_type='''learned_range'''							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				IFWatermarker()

				return {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "image_noising_scheduler": image_noising_scheduler,
				    "watermarker": watermarker,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_dummy_components()
				UpperCAmelCase   		=				self.pipeline_class(**_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)

				UpperCAmelCase   		=				inputs['''prompt''']
				UpperCAmelCase   		=				inputs['''generator''']
				UpperCAmelCase   		=				inputs['''num_inference_steps''']
				UpperCAmelCase   		=				inputs['''output_type''']

				if "image" in inputs:
							UpperCAmelCase   		=				inputs['''image''']
				else:
							UpperCAmelCase   		=				None

				if "mask_image" in inputs:
							UpperCAmelCase   		=				inputs['''mask_image''']
				else:
							UpperCAmelCase   		=				None

				if "original_image" in inputs:
							UpperCAmelCase   		=				inputs['''original_image''']
				else:
							UpperCAmelCase   		=				None

				UpperCAmelCase		,			UpperCAmelCase   		=				pipe.encode_prompt(_A		)

				# inputs with prompt converted to embeddings
				UpperCAmelCase   		=				{
				    '''prompt_embeds''': prompt_embeds,
				    '''negative_prompt_embeds''': negative_prompt_embeds,
				    '''generator''': generator,
				    '''num_inference_steps''': num_inference_steps,
				    '''output_type''': output_type,
				}

				if image is not None:
							UpperCAmelCase   		=				image

				if mask_image is not None:
							UpperCAmelCase   		=				mask_image

				if original_image is not None:
							UpperCAmelCase   		=				original_image

				# set all optional components to None
				for optional_component in pipe._optional_components:
							setattr(_A							,					_A							,					_A		)

				UpperCAmelCase   		=				pipe(**_A		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(_A		)
							UpperCAmelCase   		=				self.pipeline_class.from_pretrained(_A		)
							pipe_loaded.to(_A		)
							pipe_loaded.set_progress_bar_config(disable=_A		)

				pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				for optional_component in pipe._optional_components:
							self.assertTrue(
							    getattr(_A							,					_A		) is None							,					F"""`{optional_component}` did not stay set to None after loading."""							,					)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)

				UpperCAmelCase   		=				inputs['''generator''']
				UpperCAmelCase   		=				inputs['''num_inference_steps''']
				UpperCAmelCase   		=				inputs['''output_type''']

				# inputs with prompt converted to embeddings
				UpperCAmelCase   		=				{
				    '''prompt_embeds''': prompt_embeds,
				    '''negative_prompt_embeds''': negative_prompt_embeds,
				    '''generator''': generator,
				    '''num_inference_steps''': num_inference_steps,
				    '''output_type''': output_type,
				}

				if image is not None:
							UpperCAmelCase   		=				image

				if mask_image is not None:
							UpperCAmelCase   		=				mask_image

				if original_image is not None:
							UpperCAmelCase   		=				original_image

				UpperCAmelCase   		=				pipe_loaded(**_A		)[0]

				UpperCAmelCase   		=				np.abs(to_np(_A		) - to_np(_A		)		).max()
				self.assertLess(_A							,					1E-4		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_dummy_components()
				UpperCAmelCase   		=				self.pipeline_class(**_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)
				UpperCAmelCase   		=				pipe(**_A		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(_A		)
							UpperCAmelCase   		=				self.pipeline_class.from_pretrained(_A		)
							pipe_loaded.to(_A		)
							pipe_loaded.set_progress_bar_config(disable=_A		)

				pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)
				UpperCAmelCase   		=				pipe_loaded(**_A		)[0]

				UpperCAmelCase   		=				np.abs(to_np(_A		) - to_np(_A		)		).max()
				self.assertLess(_A							,					1E-4		)



