"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				return not any(
				    neighbour == 1 and colored_vertices[i] == color
				    for i, neighbour in enumerate(lowerCamelCase       )       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):

				# Base Case
				if index == len(lowerCamelCase       ):
								return True

				# Recursive Step
				for i in range(lowerCamelCase       ):
								if valid_coloring(graph[index]					,			lowerCamelCase					,			lowerCamelCase       ):
												# Color current vertex
												UpperCAmelCase__             =							i
												# Validate coloring
												if util_color(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			index + 1       ):
																return True
												# Backtrack
												UpperCAmelCase__             =							-1
				return False







def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							[-1] * len(lowerCamelCase       )

				if util_color(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			0       ):
								return colored_vertices

				return []




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = ShapEImgaImgPipeline
					snake_case__				    = ["image"]
					snake_case__				    = ["image"]
					snake_case__				    = [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return 8







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size    ,image_size=64    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_channels=3    ,num_hidden_layers=5    ,patch_size=1    ,)

									UpperCAmelCase__             =							CLIPVisionModel(lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							CLIPImageProcessor(
									    crop_size=224    ,do_center_crop=lowerCamelCase__    ,do_normalize=lowerCamelCase__    ,do_resize=lowerCamelCase__    ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,resample=3    ,size=224    ,)

									return image_processor







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    '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',
									    'embedding_proj_norm_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									UpperCAmelCase__             =							PriorTransformer(**lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    '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,
									    ),
									}
									UpperCAmelCase__             =							ShapERenderer(**lowerCamelCase__			)
									return model







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							self.dummy_prior
									UpperCAmelCase__             =							self.dummy_image_encoder
									UpperCAmelCase__             =							self.dummy_image_processor
									UpperCAmelCase__             =							self.dummy_renderer

									UpperCAmelCase__             =							HeunDiscreteScheduler(
									    beta_schedule='exp'    ,num_train_timesteps=1_024    ,prediction_type='sample'    ,use_karras_sigmas=lowerCamelCase__    ,clip_sample=lowerCamelCase__    ,clip_sample_range=1.0    ,)
									UpperCAmelCase__             =							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'image_processor': image_processor,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': input_image,
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)

									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

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

									UpperCAmelCase__             =							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 		__lowerCAmelCase						(				self   :	Tuple			):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							torch_device == 'cpu'
									UpperCAmelCase__             =							True
									self._test_inference_batch_single_identical(
									    batch_size=2    ,test_max_difference=lowerCamelCase__    ,relax_max_difference=lowerCamelCase__    ,)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							2

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)

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

									UpperCAmelCase__             =							pipe(**lowerCamelCase__    ,num_images_per_prompt=lowerCamelCase__			)[0]

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






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_img2img_out.npy'			)
									UpperCAmelCase__             =							ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(0			)

									UpperCAmelCase__             =							pipe(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,guidance_scale=3.0    ,num_inference_steps=64    ,frame_size=64    ,output_type='np'    ,).images[0]

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

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)



