






"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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    __UpperCAmelCase(       a_  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= KandinskyVaaControlnetImgaImgPipeline
						__lowerCamelCase    	= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						__lowerCamelCase    	= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						__lowerCamelCase    	= [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''strength''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						__lowerCamelCase    	= False




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return 32




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return 32




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.time_input_dim




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.time_input_dim * 4




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return 100




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												torch.manual_seed(0      )

												lowercase__    :				Union[str, Any]=	{
												    "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,
												}

												lowercase__    :				Dict=	UNetaDConditionModel(**lowercase_      )
												return model




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												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 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												torch.manual_seed(0      )
												lowercase__    :				int=	VQModel(**self.dummy_movq_kwargs      )
												return model




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	self.dummy_unet
												lowercase__    :				Optional[int]=	self.dummy_movq

												lowercase__    :				str=	{
												    "num_train_timesteps": 1000,
												    "beta_schedule": "linear",
												    "beta_start": 0.0_00_85,
												    "beta_end": 0.0_12,
												    "clip_sample": False,
												    "set_alpha_to_one": False,
												    "steps_offset": 0,
												    "prediction_type": "epsilon",
												    "thresholding": False,
												}

												lowercase__    :				Tuple=	DDIMScheduler(**lowercase_      )

												lowercase__    :				Tuple=	{
												    "unet": unet,
												    "scheduler": scheduler,
												    "movq": movq,
												}

												return components




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__=0      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(lowercase_      )      ).to(lowercase_      )
												lowercase__    :				Any=	floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1      )      ).to(
												    lowercase_      )
												# create init_image
												lowercase__    :				str=	floats_tensor((1, 3, 64, 64)	,  rng=random.Random(lowercase_      )      ).to(lowercase_      )
												lowercase__    :				int=	image.cpu().permute(0	,  2	,  3	,  1      )[0]
												lowercase__    :				List[Any]=	Image.fromarray(np.uinta(lowercase_      )      ).convert("RGB"      ).resize((256, 256)      )
												# create hint
												lowercase__    :				Tuple=	floats_tensor((1, 3, 64, 64)	,  rng=random.Random(lowercase_      )      ).to(lowercase_      )

												if str(lowercase_      ).startswith("mps"      ):
																		lowercase__    :				Any=	torch.manual_seed(lowercase_      )
												else:
																		lowercase__    :				Union[str, Any]=	torch.Generator(device=lowercase_      ).manual_seed(lowercase_      )
												lowercase__    :				Dict=	{
												    "image": init_image,
												    "image_embeds": image_embeds,
												    "negative_image_embeds": negative_image_embeds,
												    "hint": hint,
												    "generator": generator,
												    "height": 64,
												    "width": 64,
												    "num_inference_steps": 10,
												    "guidance_scale": 7.0,
												    "strength": 0.2,
												    "output_type": "np",
												}
												return inputs




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	"cpu"

												lowercase__    :				Optional[int]=	self.get_dummy_components()

												lowercase__    :				Optional[Any]=	self.pipeline_class(**lowercase_      )
												lowercase__    :				Dict=	pipe.to(lowercase_      )

												pipe.set_progress_bar_config(disable=lowercase_      )

												lowercase__    :				Optional[Any]=	pipe(**self.get_dummy_inputs(lowercase_      )      )
												lowercase__    :				Any=	output.images

												lowercase__    :				Optional[Any]=	pipe(
												    **self.get_dummy_inputs(lowercase_      )	,  return_dict=lowercase_	,  )[0]

												lowercase__    :				int=	image[0, -3:, -3:, -1]
												lowercase__    :				int=	image_from_tuple[0, -3:, -3:, -1]

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

												lowercase__    :				Optional[int]=	np.array(
												    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]      )
												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    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"      )

												lowercase__    :				int=	load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
												lowercase__    :				int=	init_image.resize((512, 512)      )

												lowercase__    :				Tuple=	load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/kandinskyv22/hint_image_cat.png"      )
												lowercase__    :				List[Any]=	torch.from_numpy(np.array(lowercase_      )      ).float() / 255.0
												lowercase__    :				List[str]=	hint.permute(2	,  0	,  1      ).unsqueeze(0      )

												lowercase__    :				Dict=	"A robot, 4k photo"

												lowercase__    :				Optional[Any]=	KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
												    "kandinsky-community/kandinsky-2-2-prior"	,  torch_dtype=torch.floataa      )
												pipe_prior.to(lowercase_      )

												lowercase__    :				str=	KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
												    "kandinsky-community/kandinsky-2-2-controlnet-depth"	,  torch_dtype=torch.floataa      )
												lowercase__    :				Any=	pipeline.to(lowercase_      )

												pipeline.set_progress_bar_config(disable=lowercase_      )

												lowercase__    :				Any=	torch.Generator(device="cpu"      ).manual_seed(0      )

												lowercase__,     lowercase__    :				Dict=	pipe_prior(
												    lowercase_	,  image=lowercase_	,  strength=0.85	,  generator=lowercase_	,  negative_prompt=""	,  ).to_tuple()

												lowercase__    :				int=	pipeline(
												    image=lowercase_	,  image_embeds=lowercase_	,  negative_image_embeds=lowercase_	,  hint=lowercase_	,  generator=lowercase_	,  num_inference_steps=100	,  height=512	,  width=512	,  strength=0.5	,  output_type="np"	,  )

												lowercase__    :				Optional[Any]=	output.images[0]

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

												assert_mean_pixel_difference(lowercase_	,  lowercase_      )














"""simple docstring"""







from __future__ import annotations


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	data
												lowercase__    :				Node | None=	None
												lowercase__    :				Node | None=	None





def 						lowercase__(A						)    ->None:  # In Order traversal of the tree




						"""simple docstring"""



						if tree:
												display(tree.left						)
												print(tree.data						)
												display(tree.right						)





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



						return 1 + max(depth_of_tree(tree.left						) ,		depth_of_tree(tree.right						)						) if tree else 0





def 						lowercase__(A						)    ->bool:




						"""simple docstring"""



						if not tree:
												return True
						if tree.left and tree.right:
												return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
						else:
												return not tree.left and not tree.right





def 						lowercase__()    ->None:  # Main function for testing.




						"""simple docstring"""



						lowercase__    :				Tuple=	Node(1						)
						lowercase__    :				Optional[int]=	Node(2						)
						lowercase__    :				List[str]=	Node(3						)
						lowercase__    :				Tuple=	Node(4						)
						lowercase__    :				Optional[int]=	Node(5						)
						lowercase__    :				Any=	Node(6						)
						lowercase__    :				Optional[Any]=	Node(7						)
						lowercase__    :				Optional[int]=	Node(8						)
						lowercase__    :				List[str]=	Node(9						)

						print(is_full_binary_tree(A						)						)
						print(depth_of_tree(A						)						)
						print("Tree is: "						)
						display(A						)


if __name__ == "__main__":
			main()






