



import heapq as hq
import math
from collections.abc import Iterator


class   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							List[Any]					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     str(id_       )
								A__	  =     None
								A__	  =     None
								A__	  =     []
								A__	  =     {}  # {vertex:distance}



					def __lt__(  self       :							List[str]					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								return self.key < other.key



					def __repr__(  self       :							int       ):






								"""simple docstring"""


								return self.id



					def      _a				(  self       :							str					,       _snake_case       :							str       ):






								"""simple docstring"""


								self.neighbors.append(_snake_case       )



					def      _a				(  self       :							Tuple					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     weight







def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			# add the neighbors:
			graph[a - 1].add_neighbor(graph[b - 1]       )
			graph[b - 1].add_neighbor(graph[a - 1]       )
			# add the edges:
			graph[a - 1].add_edge(graph[b - 1]			,						__UpperCamelCase       )
			graph[b - 1].add_edge(graph[a - 1]			,						__UpperCamelCase       )


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   list:
			A__	  =     []
			for u in graph:
						A__	  =     math.inf
						A__	  =     None
			A__	  =     0
			A__	  =     graph[:]
			while q:
						A__	  =     min(__UpperCamelCase       )
						q.remove(__UpperCamelCase       )
						for v in u.neighbors:
									if (v in q) and (u.edges[v.id] < v.key):
												A__	  =     u
												A__	  =     u.edges[v.id]
			for i in range(1			,						len(__UpperCamelCase       )       ):
						a.append((int(graph[i].id       ) + 1, int(graph[i].pi.id       ) + 1)       )
			return a


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Iterator[tuple]:
			for u in graph:
						A__	  =     math.inf
						A__	  =     None
			A__	  =     0

			A__	  =     list(__UpperCamelCase       )
			hq.heapify(__UpperCamelCase       )

			while h:
						A__	  =     hq.heappop(__UpperCamelCase       )
						for v in u.neighbors:
									if (v in h) and (u.edges[v.id] < v.key):
												A__	  =     u
												A__	  =     u.edges[v.id]
												hq.heapify(__UpperCamelCase       )

			for i in range(1			,						len(__UpperCamelCase       )       ):
						yield (int(graph[i].id       ) + 1, int(graph[i].pi.id       ) + 1)


def A    (							)    ->   None:
			pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			KandinskyVaaInpaintPipeline
 SCREAMING_SNAKE_CASE__ =			['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
 SCREAMING_SNAKE_CASE__ =			[
     'image_embeds',
     'negative_image_embeds',
     'image',
     'mask_image',
 ]
 SCREAMING_SNAKE_CASE__ =			[
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 SCREAMING_SNAKE_CASE__ =			False




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.time_input_dim




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




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 100




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )

       a		:Tuple									=			{
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''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''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       a		:int									=			UNetaDConditionModel(**_lowerCamelCase       )
       return model




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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       ):
       torch.manual_seed(0       )
       a		:Any									=			VQModel(**self.dummy_movq_kwargs       )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_unet
       a		:Any									=			self.dummy_movq

       a		:Dict									=			DDIMScheduler(
           num_train_timesteps=1000   ,    beta_schedule='''linear'''   ,    beta_start=0.0_0085   ,    beta_end=0.012   ,    clip_sample=_lowerCamelCase   ,    set_alpha_to_one=_lowerCamelCase   ,    steps_offset=1   ,    prediction_type='''epsilon'''   ,    thresholding=_lowerCamelCase   ,    )

       a		:Tuple									=			{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=0       ):
       a		:Tuple									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:int									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(seed + 1       )       ).to(
           _lowerCamelCase       )
       # create init_image
       a		:int									=			floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:Union[str, Any]									=			image.cpu().permute(0   ,    2   ,    3   ,    1       )[0]
       a		:Any									=			Image.fromarray(np.uinta(_lowerCamelCase       )       ).convert('''RGB'''       ).resize((256, 256)       )
       # create mask
       a		:Dict									=			np.ones((64, 64)   ,    dtype=np.floataa       )
       a		:Tuple									=			0

       if str(_lowerCamelCase       ).startswith('''mps'''       ):
             a		:Dict									=			torch.manual_seed(_lowerCamelCase       )
       else:
             a		:Union[str, Any]									=			torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
       a		:Tuple									=			{
           '''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       ):
       a		:Optional[Any]									=			'''cpu'''

       a		:List[Any]									=			self.get_dummy_components()

       a		:int									=			self.pipeline_class(**_lowerCamelCase       )
       a		:Tuple									=			pipe.to(_lowerCamelCase       )

       pipe.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:str									=			pipe(**self.get_dummy_inputs(_lowerCamelCase       )       )
       a		:Optional[int]									=			output.images

       a		:List[str]									=			pipe(
           **self.get_dummy_inputs(_lowerCamelCase       )   ,    return_dict=_lowerCamelCase   ,    )[0]

       a		:Optional[int]									=			image[0, -3:, -3:, -1]
       a		:Tuple									=			image_from_tuple[0, -3:, -3:, -1]

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

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

       a		:str									=			np.array(
           [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]       )

       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       ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''       )

       a		:Tuple									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
       a		:List[Any]									=			np.ones((768, 768)   ,    dtype=np.floataa       )
       a		:Optional[Any]									=			0

       a		:List[Any]									=			'''a hat'''

       a		:int									=			KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''   ,    torch_dtype=torch.floataa       )
       pipe_prior.to(_lowerCamelCase       )

       a		:List[Any]									=			KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,    torch_dtype=torch.floataa       )
       a		:str									=			pipeline.to(_lowerCamelCase       )
       pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Union[str, Any]									=			torch.Generator(device='''cpu'''       ).manual_seed(0       )
       a				,							a		:Optional[Any]									=			pipe_prior(
           _lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=5   ,    negative_prompt=''''''   ,    ).to_tuple()

       a		:List[str]									=			pipeline(
           image=_lowerCamelCase   ,    mask_image=_lowerCamelCase   ,    image_embeds=_lowerCamelCase   ,    negative_image_embeds=_lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=100   ,    height=768   ,    width=768   ,    output_type='''np'''   ,    )

       a		:Any									=			output.images[0]

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

       assert_mean_pixel_difference(_lowerCamelCase   ,    _lowerCamelCase       )





