



def      __lowercase       (			a__       )    ->			Dict:
							return [
							    {
							        0: [1, 2],
							        1: [0, 2],
							        2: [0, 1, 3, 5],
							        3: [2, 4],
							        4: [3],
							        5: [2, 6, 8],
							        6: [5, 7],
							        7: [6, 8],
							        8: [5, 7],
							    },
							    {
							        0: [6],
							        1: [9],
							        2: [4, 5],
							        3: [4],
							        4: [2, 3],
							        5: [2],
							        6: [0, 7],
							        7: [6],
							        8: [],
							        9: [1],
							    },
							    {
							        0: [4],
							        1: [6],
							        2: [],
							        3: [5, 6, 7],
							        4: [0, 6],
							        5: [3, 8, 9],
							        6: [1, 3, 4, 7],
							        7: [3, 6, 8, 9],
							        8: [5, 7],
							        9: [5, 7],
							    },
							    {
							        0: [1, 3],
							        1: [0, 2, 4],
							        2: [1, 3, 4],
							        3: [0, 2, 4],
							        4: [1, 2, 3],
							    },
							][index]






def      __lowercase       (			a__       )    ->			List[str]:
							__SCREAMING_SNAKE_CASE									= 0
							__SCREAMING_SNAKE_CASE									= len(a_       )  # No of vertices in graph
							__SCREAMING_SNAKE_CASE									= [0] * n
							__SCREAMING_SNAKE_CASE									= [False] * n

							def dfs(a__							,       a__							,       a__							,       a__       ):
														__SCREAMING_SNAKE_CASE									= True
														__SCREAMING_SNAKE_CASE									= id_
														id_ += 1
														for to in graph[at]:
																					if to == parent:
																												pass
																					elif not visited[to]:
																												dfs(a_							,       a_							,       a_							,       id_       )
																												__SCREAMING_SNAKE_CASE									= min(low[at]							,       low[to]       )
																												if id_ <= low[to]:
																																			bridges.append((at, to) if at < to else (to, at)       )
																					else:
																												# This edge is a back edge and cannot be a bridge
																												__SCREAMING_SNAKE_CASE									= min(low[at]							,       low[to]       )

							__SCREAMING_SNAKE_CASE									= []
							for i in range(a_       ):
														if not visited[i]:
																					dfs(a_							,       -1							,       a_							,       id_       )
							return bridges


if __name__ == "__main__":
			import doctest

			doctest.testmod()











import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  CycleDiffusionPipeline
						UpperCamelCase__      :			int      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    '''negative_prompt''',
						    '''height''',
						    '''width''',
						    '''negative_prompt_embeds''',
						}
						UpperCamelCase__      :			Union[str, Any]      =  PipelineTesterMixin.required_optional_params - {'''latents'''}
						UpperCamelCase__      :			List[str]      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}	)
						UpperCamelCase__      :			Optional[int]      =  IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase__      :			str      =  IMAGE_TO_IMAGE_IMAGE_PARAMS






						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= UNetaDConditionModel(
													    block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='scaled_linear'				,							num_train_timesteps=1_000				,							clip_sample=_A				,							set_alpha_to_one=_A				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= AutoencoderKL(
													    block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,							latent_channels=4				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= 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=1_000				,							)
													__SCREAMING_SNAKE_CASE									= CLIPTextModel(_A		)
													__SCREAMING_SNAKE_CASE									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

													__SCREAMING_SNAKE_CASE									= {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vae': vae,
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'safety_checker': None,
													    'feature_extractor': None,
													}
													return components






						def 	_A     (   self				,							_A				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor((1, 3, 32, 32)				,							rng=random.Random(_A		)		).to(_A		)
													__SCREAMING_SNAKE_CASE									= image / 2 + 0.5
													if str(_A		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'prompt': 'An astronaut riding an elephant',
													    'source_prompt': 'An astronaut riding a horse',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 2,
													    'eta': 0.1,
													    'strength': 0.8,
													    'guidance_scale': 3,
													    'source_guidance_scale': 1,
													    'output_type': 'numpy',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'  # ensure determinism for the device-dependent torch.Generator

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]		)

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






						@unittest.skipIf(torch_device != 'cuda'				,							'This test requires a GPU'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(_A				,							'half'		):
																											__SCREAMING_SNAKE_CASE									= module.half()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]		)

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






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_local()






						@unittest.skip('non-deterministic pipeline'		)
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_inference_batch_single_identical()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_optional_components()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_attention_slicing_forward_pass()







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

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'		)
													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'		)
													__SCREAMING_SNAKE_CASE									= init_image.resize((512, 512)		)

													__SCREAMING_SNAKE_CASE									= 'CompVis/stable-diffusion-v1-4'
													__SCREAMING_SNAKE_CASE									= DDIMScheduler.from_pretrained(_A				,							subfolder='scheduler'		)
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline.from_pretrained(
													    _A				,							scheduler=_A				,							safety_checker=_A				,							torch_dtype=torch.floataa				,							revision='fp16'		)

													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)
													pipe.enable_attention_slicing()

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image		).max() < 5e-1






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'		)
													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'		)
													__SCREAMING_SNAKE_CASE									= init_image.resize((512, 512)		)

													__SCREAMING_SNAKE_CASE									= 'CompVis/stable-diffusion-v1-4'
													__SCREAMING_SNAKE_CASE									= DDIMScheduler.from_pretrained(_A				,							subfolder='scheduler'		)
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline.from_pretrained(_A				,							scheduler=_A				,							safety_checker=_A		)

													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)
													pipe.enable_attention_slicing()

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

													assert np.abs(image - expected_image		).max() < 2e-2






