





"""simple docstring"""


from __future__ import annotations

import math
import random
from typing import Any
class snake_case      :







		def __init__(					self      :       Dict		)       ->						str:





						'''simple docstring'''







						_A					  =			[]
						_A					  =			0
						_A					  =			0







		def  a_ (					self      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						return self.head == self.tail







		def  a_ (					self      :       List[Any]    ,   a__      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						self.data.append(UpperCamelCase__		)
						_A					  =			self.tail + 1







		def  a_ (					self      :       Optional[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.data[self.head]
						_A					  =			self.head + 1
						return ret







		def  a_ (					self      :       Any		)       ->						Optional[Any]:





						'''simple docstring'''







						return self.tail - self.head







		def  a_ (					self      :       int		)       ->						Dict:





						'''simple docstring'''







						print(self.data		)
						print("**************"		)
						print(self.data[self.head : self.tail]		)





class snake_case      :







		def __init__(					self      :       int    ,   a__      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			data
						_A					  =			None
						_A					  =			None
						_A					  =			1







		def  a_ (					self      :       List[str]		)       ->						Union[str, Any]:





						'''simple docstring'''







						return self.data







		def  a_ (					self      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						return self.left







		def  a_ (					self      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						return self.right







		def  a_ (					self      :       List[Any]		)       ->						List[Any]:





						'''simple docstring'''







						return self.height







		def  a_ (					self      :       Optional[Any]    ,   a__      :       Tuple		)       ->						Dict:





						'''simple docstring'''







						_A					  =			data







		def  a_ (					self      :       Dict    ,   a__      :       Union[str, Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			node







		def  a_ (					self      :       Dict    ,   a__      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			node







		def  a_ (					self      :       Optional[int]    ,   a__      :       str		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			height






def      a__ (				__lowercase   )	->	int:







				if node is None:
								return 0
				return node.get_height()






def      a__ (				__lowercase     ,			__lowercase   )	->	int:







				if a > b:
								return a
				return b






def      a__ (				__lowercase   )	->	MyNode:
				print("left rotation node:"     ,			node.get_data()   )
				_A					  =			node.get_left()
				assert ret is not None
				node.set_left(ret.get_right()   )
				ret.set_right(__UpperCamelCase   )
				_A					  =			my_max(get_height(node.get_right()   )     ,			get_height(node.get_left()   )   ) + 1
				node.set_height(__UpperCamelCase   )
				_A					  =			my_max(get_height(ret.get_right()   )     ,			get_height(ret.get_left()   )   ) + 1
				ret.set_height(__UpperCamelCase   )
				return ret






def      a__ (				__lowercase   )	->	MyNode:
				print("right rotation node:"     ,			node.get_data()   )
				_A					  =			node.get_right()
				assert ret is not None
				node.set_right(ret.get_left()   )
				ret.set_left(__UpperCamelCase   )
				_A					  =			my_max(get_height(node.get_right()   )     ,			get_height(node.get_left()   )   ) + 1
				node.set_height(__UpperCamelCase   )
				_A					  =			my_max(get_height(ret.get_right()   )     ,			get_height(ret.get_left()   )   ) + 1
				ret.set_height(__UpperCamelCase   )
				return ret






def      a__ (				__lowercase   )	->	MyNode:
				_A					  =			node.get_left()
				assert left_child is not None
				node.set_left(left_rotation(__UpperCamelCase   )   )
				return right_rotation(__UpperCamelCase   )






def      a__ (				__lowercase   )	->	MyNode:
				_A					  =			node.get_right()
				assert right_child is not None
				node.set_right(right_rotation(__UpperCamelCase   )   )
				return left_rotation(__UpperCamelCase   )






def      a__ (				__lowercase     ,			__lowercase   )	->	MyNode | None:







				if node is None:
								return MyNode(__UpperCamelCase   )
				if data < node.get_data():
								node.set_left(insert_node(node.get_left()     ,			__UpperCamelCase   )   )
								if (
								    get_height(node.get_left()   ) - get_height(node.get_right()   ) == 2
								):  # an unbalance detected
												_A					  =			node.get_left()
												assert left_child is not None
												if (
												    data < left_child.get_data()
												):  # new node is the left child of the left child
																_A					  =			right_rotation(__UpperCamelCase   )
												else:
																_A					  =			lr_rotation(__UpperCamelCase   )
				else:
								node.set_right(insert_node(node.get_right()     ,			__UpperCamelCase   )   )
								if get_height(node.get_right()   ) - get_height(node.get_left()   ) == 2:
												_A					  =			node.get_right()
												assert right_child is not None
												if data < right_child.get_data():
																_A					  =			rl_rotation(__UpperCamelCase   )
												else:
																_A					  =			left_rotation(__UpperCamelCase   )
				_A					  =			my_max(get_height(node.get_right()   )     ,			get_height(node.get_left()   )   ) + 1
				node.set_height(__UpperCamelCase   )
				return node






def      a__ (				__lowercase   )	->	Any:







				while True:
								_A					  =			root.get_right()
								if right_child is None:
												break
								_A					  =			right_child
				return root.get_data()






def      a__ (				__lowercase   )	->	Any:







				while True:
								_A					  =			root.get_left()
								if left_child is None:
												break
								_A					  =			left_child
				return root.get_data()






def      a__ (				__lowercase     ,			__lowercase   )	->	MyNode | None:
				_A					  =			root.get_left()
				_A					  =			root.get_right()
				if root.get_data() == data:
								if left_child is not None and right_child is not None:
												_A					  =			get_left_most(__UpperCamelCase   )
												root.set_data(__UpperCamelCase   )
												root.set_right(del_node(__UpperCamelCase     ,			__UpperCamelCase   )   )
								elif left_child is not None:
												_A					  =			left_child
								elif right_child is not None:
												_A					  =			right_child
								else:
												return None
				elif root.get_data() > data:
								if left_child is None:
												print("No such data"   )
												return root
								else:
												root.set_left(del_node(__UpperCamelCase     ,			__UpperCamelCase   )   )
				else:  # root.get_data() < data
								if right_child is None:
												return root
								else:
												root.set_right(del_node(__UpperCamelCase     ,			__UpperCamelCase   )   )

				if get_height(__UpperCamelCase   ) - get_height(__UpperCamelCase   ) == 2:
								assert right_child is not None
								if get_height(right_child.get_right()   ) > get_height(right_child.get_left()   ):
												_A					  =			left_rotation(__UpperCamelCase   )
								else:
												_A					  =			rl_rotation(__UpperCamelCase   )
				elif get_height(__UpperCamelCase   ) - get_height(__UpperCamelCase   ) == -2:
								assert left_child is not None
								if get_height(left_child.get_left()   ) > get_height(left_child.get_right()   ):
												_A					  =			right_rotation(__UpperCamelCase   )
								else:
												_A					  =			lr_rotation(__UpperCamelCase   )
				_A					  =			my_max(get_height(root.get_right()   )     ,			get_height(root.get_left()   )   ) + 1
				root.set_height(__UpperCamelCase   )
				return root
class snake_case      :







		def __init__(					self      :       str		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			None







		def  a_ (					self      :       Tuple		)       ->						str:





						'''simple docstring'''







						return get_height(self.root		)







		def  a_ (					self      :       Optional[Any]    ,   a__      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						print("insert:" + str(UpperCamelCase__		)		)
						_A					  =			insert_node(self.root    ,   UpperCamelCase__		)







		def  a_ (					self      :       Tuple    ,   a__      :       List[Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						print("delete:" + str(UpperCamelCase__		)		)
						if self.root is None:
										print("Tree is empty!"		)
										return
						_A					  =			del_node(self.root    ,   UpperCamelCase__		)







		def __str__(					self      :       Union[str, Any]    ,   )       ->						Tuple:  # a level traversale, gives a more intuitive look on the tree





						'''simple docstring'''







						_A					  =			''''''
						_A					  =			MyQueue()
						q.push(self.root		)
						_A					  =			self.get_height()
						if layer == 0:
										return output
						_A					  =			0
						while not q.is_empty():
										_A					  =			q.pop()
										_A					  =			''' ''' * int(math.pow(2    ,   layer - 1		)		)
										output += space
										if node is None:
														output += "*"
														q.push(UpperCamelCase__		)
														q.push(UpperCamelCase__		)
										else:
														output += str(node.get_data()		)
														q.push(node.get_left()		)
														q.push(node.get_right()		)
										output += space
										_A					  =			cnt + 1
										for i in range(1_00		):
														if cnt == math.pow(2    ,   UpperCamelCase__		) - 1:
																		_A					  =			layer - 1
																		if layer == 0:
																						output += "\n*************************************"
																						return output
																		output += "\n"
																		break
						output += "\n*************************************"
						return output






def      a__ (				)	->	None:
				import doctest

				doctest.testmod()


if __name__ == "__main__":
		_test()
		a_       							=    AVLtree()
		a_       							=    list(range(10))
		random.shuffle(lst)
		for i in lst:
				t.insert(i)
				print(str(t))
		random.shuffle(lst)
		for i in lst:
				t.del_node(i)
				print(str(t))



"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			StableDiffusionInpaintPipeline
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__UpperCamelCase           =			frozenset(
		    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		__UpperCamelCase           =			frozenset([])







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			UNetaDConditionModel(
						    block_out_channels=(32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=9    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=32    ,   attention_head_dim=(2, 4)    ,   use_linear_projection=a__    ,   )
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						torch.manual_seed(0		)
						_A					  =			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    ,   sample_size=1_28    ,   )
						torch.manual_seed(0		)
						_A					  =			CLIPTextConfig(
						    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
						_A					  =			CLIPTextModel(a__		)
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						_A					  =			{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple=0		)       ->						int:





						'''simple docstring'''







						_A					  =			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(a__		)		).to(a__		)
						_A					  =			image.cpu().permute(0    ,   2    ,   3    ,   1		)[0]
						_A					  =			Image.fromarray(np.uinta(a__		)		).convert("RGB"		).resize((64, 64)		)
						_A					  =			Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
						if str(a__		).startswith("mps"		):
										_A					  =			torch.manual_seed(a__		)
						else:
										_A					  =			torch.Generator(device=a__		).manual_seed(a__		)
						_A					  =			{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": init_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs







		def  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			"cpu"  # ensure determinism for the device-dependent torch.Generator
						_A					  =			self.get_dummy_components()
						_A					  =			StableDiffusionInpaintPipeline(**a__		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			self.get_dummy_inputs(a__		)
						_A					  =			sd_pipe(**a__		).images
						_A					  =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						_A					  =			np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]		)

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







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





						'''simple docstring'''







						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)





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







		def  a_ (					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







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







		def  a_ (					self      :       Union[str, Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench.npy"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(a__    ,   safety_checker=a__		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 9E-3







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   torch_dtype=torch.floataa    ,   safety_checker=a__    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 5E-1







		def  a_ (					self      :       List[Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			PNDMScheduler.from_pretrained(a__    ,   subfolder="scheduler"		)
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   safety_checker=a__    ,   scheduler=a__    ,   torch_dtype=torch.floataa    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing(1		)
						pipe.enable_sequential_cpu_offload()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   num_inference_steps=2    ,   output_type="np"    ,   )

						_A					  =			torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 10**9