



def 	__a					(   lowerCAmelCase_       : Dict    )				->			Dict:






						'''simple docstring'''



						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 	__a					(   lowerCAmelCase_       : dict[int, list[int]]    )				->			list[tuple[int, int]]:






						'''simple docstring'''



						UpperCAmelCase_=       0
						UpperCAmelCase_=       len(lowerCAmelCase_    )  # No of vertices in graph
						UpperCAmelCase_=       [0] * n
						UpperCAmelCase_=       [False] * n

						def dfs(lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    ):
												UpperCAmelCase_=       True
												UpperCAmelCase_=       id_
												id_ += 1
												for to in graph[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,id_    )
																								UpperCAmelCase_=       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
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							KandinskyInpaintPipeline
	a__				: Tuple		       =							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a__				: str		       =							[
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a__				: List[Any]		       =							[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a__				: Union[str, Any]		       =							False




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Union[str, Any]:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[Any]:
							return self.time_input_dim




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							return self.time_input_dim * 4




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[str]:
							return 100




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
							return tokenizer




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       MCLIPConfig(
							    numDims=self.cross_attention_dim						,  transformerDimensions=self.text_embedder_hidden_size						,  hidden_size=self.text_embedder_hidden_size						,  intermediate_size=37						,  num_attention_heads=4						,  num_hidden_layers=5						,  vocab_size=1_005						,  )

							UpperCAmelCase_=       MultilingualCLIP(__UpperCAmelCase					)
							UpperCAmelCase_=       text_encoder.eval()

							return text_encoder




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							torch.manual_seed(0					)

							UpperCAmelCase_=       {
							    """in_channels""": 9,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							UpperCAmelCase_=       UNetaDConditionModel(**__UpperCAmelCase					)
							return model




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[int]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       VQModel(**self.dummy_movq_kwargs					)
							return model




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.dummy_text_encoder
							UpperCAmelCase_=       self.dummy_tokenizer
							UpperCAmelCase_=       self.dummy_unet
							UpperCAmelCase_=       self.dummy_movq

							UpperCAmelCase_=       DDIMScheduler(
							    num_train_timesteps=1_000						,  beta_schedule="""linear"""						,  beta_start=0.00_085						,  beta_end=0.012						,  clip_sample=__UpperCAmelCase						,  set_alpha_to_one=__UpperCAmelCase						,  steps_offset=1						,  prediction_type="""epsilon"""						,  thresholding=__UpperCAmelCase						,  )

							UpperCAmelCase_=       {
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Dict=0					)      ->			Dict:
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(seed + 1					)					).to(__UpperCAmelCase					)
							# create init_image
							UpperCAmelCase_=       floats_tensor((1, 3, 64, 64)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					)[0]
							UpperCAmelCase_=       Image.fromarray(np.uinta(__UpperCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
							# create mask
							UpperCAmelCase_=       np.ones((64, 64)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							if str(__UpperCAmelCase					).startswith("""mps"""					):
													UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       {
							    """prompt""": """horse""",
							    """image""": init_image,
							    """mask_image""": mask,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 4.0,
							    """output_type""": """np""",
							}
							return inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       """cpu"""

							UpperCAmelCase_=       self.get_dummy_components()

							UpperCAmelCase_=       self.pipeline_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)

							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       pipe(**self.get_dummy_inputs(__UpperCAmelCase					)					)
							UpperCAmelCase_=       output.images

							UpperCAmelCase_=       pipe(
							    **self.get_dummy_inputs(__UpperCAmelCase					)						,  return_dict=__UpperCAmelCase						,  )[0]

							UpperCAmelCase_=       image[0, -3:, -3:, -1]
							UpperCAmelCase_=       image_from_tuple[0, -3:, -3:, -1]

							print(F"""image.shape {image.shape}"""					)

							assert image.shape == (1, 64, 64, 3)

							UpperCAmelCase_=       np.array(
							    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

							assert (
							    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






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






	"""simple docstring"""


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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

							UpperCAmelCase_=       load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
							UpperCAmelCase_=       np.ones((768, 768)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							UpperCAmelCase_=       """a hat"""

							UpperCAmelCase_=       KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""						,  torch_dtype=torch.floataa					)
							pipe_prior.to(__UpperCAmelCase					)

							UpperCAmelCase_=       KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""						,  torch_dtype=torch.floataa					)
							UpperCAmelCase_=       pipeline.to(__UpperCAmelCase					)
							pipeline.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       torch.Generator(device="""cpu"""					).manual_seed(0					)
							UpperCAmelCase_,       UpperCAmelCase_=       pipe_prior(
							    __UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=5						,  negative_prompt=""""""						,  ).to_tuple()

							UpperCAmelCase_=       pipeline(
							    __UpperCAmelCase						,  image=__UpperCAmelCase						,  mask_image=__UpperCAmelCase						,  image_embeds=__UpperCAmelCase						,  negative_image_embeds=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=100						,  height=768						,  width=768						,  output_type="""np"""						,  )

							UpperCAmelCase_=       output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(__UpperCAmelCase						,  __UpperCAmelCase					)




