




'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 						a				(   _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		KandinskyVaaControlnetPipeline
				snake_case_      =		["image_embeds", "negative_image_embeds", "hint"]
				snake_case_      =		["image_embeds", "negative_image_embeds", "hint"]
				snake_case_      =		[
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				snake_case_      =		False


				@property
				def   A_ (		self    :					Union[str, Any]					):
									return 32


				@property
				def   A_ (		self    :					List[Any]					):
									return 32


				@property
				def   A_ (		self    :					List[Any]					):
									return self.time_input_dim


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


				@property
				def   A_ (		self    :					Optional[int]					):
									return 100


				@property
				def   A_ (		self    :					Any					):
									torch.manual_seed(0					)

									snake_case_									=				{
									    '''in_channels''': 8,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''image_hint''',
									    '''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,
									}

									snake_case_									=				UNetaDConditionModel(**lowercase_					)
									return model


				@property
				def   A_ (		self    :					Any					):
									return {
									    "block_out_channels": [32, 32, 64, 64],
									    "down_block_types": [
									        "DownEncoderBlock2D",
									        "DownEncoderBlock2D",
									        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "vq_embed_dim": 4,
									}


				@property
				def   A_ (		self    :					int					):
									torch.manual_seed(0					)
									snake_case_									=				VQModel(**self.dummy_movq_kwargs					)
									return model


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.dummy_unet
									snake_case_									=				self.dummy_movq

									snake_case_									=				DDIMScheduler(
									    num_train_timesteps=1000       ,				beta_schedule='''linear'''       ,				beta_start=0.0_0085       ,				beta_end=0.012       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				steps_offset=1       ,				prediction_type='''epsilon'''       ,				thresholding=lowercase_       ,				)

									snake_case_									=				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}
									return components


				def   A_ (		self    :					Dict       ,				lowercase_    :					Dict       ,				lowercase_    :					Any=0					):
									snake_case_									=				floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(seed + 1					)					).to(
									    lowercase_					)

									# create hint
									snake_case_									=				floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)

									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									snake_case_									=				{
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''hint''': hint,
									    '''generator''': generator,
									    '''height''': 64,
									    '''width''': 64,
									    '''guidance_scale''': 4.0,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''np''',
									}
									return inputs


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				'''cpu'''

									snake_case_									=				self.get_dummy_components()

									snake_case_									=				self.pipeline_class(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)

									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				pipe(**self.get_dummy_inputs(lowercase_					)					)
									snake_case_									=				output.images

									snake_case_									=				pipe(
									    **self.get_dummy_inputs(lowercase_					)       ,				return_dict=lowercase_       ,				)[0]

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

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

									snake_case_									=				np.array(
									    [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]					)

									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()}"







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


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


				def   A_ (		self    :					Tuple					):
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''					)

									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/hint_image_cat.png'''					)
									snake_case_									=				torch.from_numpy(np.array(lowercase_					)					).float() / 255.0
									snake_case_									=				hint.permute(2       ,				0       ,				1					).unsqueeze(0					)

									snake_case_									=				KandinskyVaaPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-prior'''       ,				torch_dtype=torch.floataa					)
									pipe_prior.to(lowercase_					)

									snake_case_									=				KandinskyVaaControlnetPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''       ,				torch_dtype=torch.floataa					)
									snake_case_									=				pipeline.to(lowercase_					)
									pipeline.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				'''A robot, 4k photo'''

									snake_case_									=				torch.Generator(device='''cuda'''					).manual_seed(0					)
									snake_case_			,snake_case_									=				pipe_prior(
									    lowercase_       ,				generator=lowercase_       ,				num_inference_steps=5       ,				negative_prompt=''''''       ,				).to_tuple()

									snake_case_									=				torch.Generator(device='''cuda'''					).manual_seed(0					)
									snake_case_									=				pipeline(
									    image_embeds=lowercase_       ,				negative_image_embeds=lowercase_       ,				hint=lowercase_       ,				generator=lowercase_       ,				num_inference_steps=100       ,				output_type='''np'''       ,				)

									snake_case_									=				output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(lowercase_       ,				lowercase_					)







'''simple docstring'''

from collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


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

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

