




class       a_       :





	def __init__(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						{}





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				if vertex not in self.adjacency:
							SCREAMING_SNAKE_CASE_								=						{}
							self.num_vertices += 1





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				self.add_vertex(SCREAMING_SNAKE_CASE      )
				self.add_vertex(SCREAMING_SNAKE_CASE      )

				if head == tail:
							return

				SCREAMING_SNAKE_CASE_								=						weight
				SCREAMING_SNAKE_CASE_								=						weight





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_edges()
				for edge in edges:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						edge
							edges.remove((tail, head, weight)      )
				for i in range(len(SCREAMING_SNAKE_CASE      )      ):
							SCREAMING_SNAKE_CASE_								=						list(edges[i]      )

				edges.sort(key=lambda SCREAMING_SNAKE_CASE      : e[2]      )
				for i in range(len(SCREAMING_SNAKE_CASE      ) - 1      ):
							if edges[i][2] >= edges[i + 1][2]:
										SCREAMING_SNAKE_CASE_								=						edges[i][2] + 1
				for edge in edges:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						edge
							SCREAMING_SNAKE_CASE_								=						weight
							SCREAMING_SNAKE_CASE_								=						weight





	def __str__(							self      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for tail in self.adjacency:
							for head in self.adjacency[tail]:
										SCREAMING_SNAKE_CASE_								=						self.adjacency[head][tail]
										string += f'{head} -> {tail} == {weight}\n'
				return string.rstrip('\n'      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				for tail in self.adjacency:
							for head in self.adjacency[tail]:
										output.append((tail, head, self.adjacency[head][tail])      )
				return output





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				return self.adjacency.keys()





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						Graph()
				if vertices is None:
							SCREAMING_SNAKE_CASE_								=						[]
				if edges is None:
							SCREAMING_SNAKE_CASE_								=						[]
				for vertex in vertices:
							g.add_vertex(SCREAMING_SNAKE_CASE      )
				for edge in edges:
							g.add_edge(*SCREAMING_SNAKE_CASE      )
				return g



	class       a_       :





		def __init__(							self      )  ->					str:




					"""simple docstring"""



					SCREAMING_SNAKE_CASE_								=						{}
					SCREAMING_SNAKE_CASE_								=						{}





		def __len__(							self      )  ->					Union[str, Any]:




					"""simple docstring"""



					return len(self.parent      )





		def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




					"""simple docstring"""



					if item in self.parent:
								return self.find(SCREAMING_SNAKE_CASE      )

					SCREAMING_SNAKE_CASE_								=						item
					SCREAMING_SNAKE_CASE_								=						0
					return item





		def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int:




					"""simple docstring"""



					if item not in self.parent:
								return self.make_set(SCREAMING_SNAKE_CASE      )
					if item != self.parent[item]:
								SCREAMING_SNAKE_CASE_								=						self.find(self.parent[item]      )
					return self.parent[item]





		def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




					"""simple docstring"""



					SCREAMING_SNAKE_CASE_								=						self.find(SCREAMING_SNAKE_CASE      )
					SCREAMING_SNAKE_CASE_								=						self.find(SCREAMING_SNAKE_CASE      )

					if roota == roota:
								return roota

					if self.rank[roota] > self.rank[roota]:
								SCREAMING_SNAKE_CASE_								=						roota
								return roota

					if self.rank[roota] < self.rank[roota]:
								SCREAMING_SNAKE_CASE_								=						roota
								return roota

					if self.rank[roota] == self.rank[roota]:
								self.rank[roota] += 1
								SCREAMING_SNAKE_CASE_								=						roota
								return roota
					return None





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						graph.num_vertices

				SCREAMING_SNAKE_CASE_								=						Graph.UnionFind()
				SCREAMING_SNAKE_CASE_								=						[]
				while num_components > 1:
							SCREAMING_SNAKE_CASE_								=						{}
							for vertex in graph.get_vertices():
										SCREAMING_SNAKE_CASE_								=						-1

							SCREAMING_SNAKE_CASE_								=						graph.get_edges()
							for edge in edges:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						edge
										edges.remove((tail, head, weight)      )
							for edge in edges:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						edge
										SCREAMING_SNAKE_CASE_								=						union_find.find(SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						union_find.find(SCREAMING_SNAKE_CASE      )
										if seta != seta:
													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																SCREAMING_SNAKE_CASE_								=						[head, tail, weight]

													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																SCREAMING_SNAKE_CASE_								=						[head, tail, weight]
							for vertex in cheap_edge:
										if cheap_edge[vertex] != -1:
													SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						cheap_edge[vertex]
													if union_find.find(SCREAMING_SNAKE_CASE      ) != union_find.find(SCREAMING_SNAKE_CASE      ):
																union_find.union(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
																mst_edges.append(cheap_edge[vertex]      )
																SCREAMING_SNAKE_CASE_								=						num_components - 1
				SCREAMING_SNAKE_CASE_								=						Graph.build(edges=SCREAMING_SNAKE_CASE      )
				return mst






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference



class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= ShapEPipeline
	A    							= ['''prompt''']
	A    							= ['''prompt''']
	A    							= [
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	A    							= False





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				return 32





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return 32





	@property
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				return self.time_input_dim * 4





	@property
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				return 8





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
				return tokenizer





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						CLIPTextConfig(
				    bos_token_id=0      ,     eos_token_id=2      ,     hidden_size=self.text_embedder_hidden_size      ,     projection_dim=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     layer_norm_eps=1e-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=1000      ,     )
				return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE      )





	@property
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				torch.manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						{
				    'num_attention_heads': 2,
				    'attention_head_dim': 16,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 32,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				SCREAMING_SNAKE_CASE_								=						PriorTransformer(**SCREAMING_SNAKE_CASE      )
				return model





	@property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				torch.manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						{
				    'param_shapes': (
				        (self.renderer_dim, 93),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 12,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				SCREAMING_SNAKE_CASE_								=						ShapERenderer(**SCREAMING_SNAKE_CASE      )
				return model





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.dummy_prior
				SCREAMING_SNAKE_CASE_								=						self.dummy_text_encoder
				SCREAMING_SNAKE_CASE_								=						self.dummy_tokenizer
				SCREAMING_SNAKE_CASE_								=						self.dummy_renderer

				SCREAMING_SNAKE_CASE_								=						HeunDiscreteScheduler(
				    beta_schedule='exp'      ,     num_train_timesteps=1024      ,     prediction_type='sample'      ,     use_karras_sigmas=SCREAMING_SNAKE_CASE      ,     clip_sample=SCREAMING_SNAKE_CASE      ,     clip_sample_range=1.0      ,     )
				SCREAMING_SNAKE_CASE_								=						{
				    'prior': prior,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=0      )  ->					str:




				"""simple docstring"""



				if str(SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    'prompt': 'horse',
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 32,
				    'output_type': 'np',
				}
				return inputs





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'cpu'

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()

				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )

				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )      )
				SCREAMING_SNAKE_CASE_								=						output.images[0]
				SCREAMING_SNAKE_CASE_								=						image[0, -3:, -3:, -1]

				assert image.shape == (20, 32, 32, 3)

				SCREAMING_SNAKE_CASE_								=						np.array(
				    [
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				    ]      )

				assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				self._test_inference_batch_consistent(batch_sizes=[1, 2]      )





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch_device == 'cpu'
				SCREAMING_SNAKE_CASE_								=						True

				self._test_inference_batch_single_identical(
				    batch_size=2      ,     test_max_difference=SCREAMING_SNAKE_CASE      ,     relax_max_difference=SCREAMING_SNAKE_CASE      ,     )





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						1
				SCREAMING_SNAKE_CASE_								=						2

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )

				for key in inputs.keys():
							if key in self.batch_params:
										SCREAMING_SNAKE_CASE_								=						batch_size * [inputs[key]]

				SCREAMING_SNAKE_CASE_								=						pipe(**SCREAMING_SNAKE_CASE      ,     num_images_per_prompt=SCREAMING_SNAKE_CASE      )[0]

				assert images.shape[0] == batch_size * num_images_per_prompt




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





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_np_out.npy'      )
				SCREAMING_SNAKE_CASE_								=						ShapEPipeline.from_pretrained('openai/shap-e'      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						pipe(
				    'a shark'      ,     generator=SCREAMING_SNAKE_CASE      ,     guidance_scale=1_5.0      ,     num_inference_steps=64      ,     frame_size=64      ,     output_type='np'      ,     ).images[0]

				assert images.shape == (20, 64, 64, 3)

				assert_mean_pixel_difference(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
