





'''simple docstring'''







import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Optional[Any]:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							@property
							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  1
													UpperCAmelCase_			:      Union[str, Any]     						=  3
													UpperCAmelCase_			:      Union[str, Any]     						=  (32, 32)

													UpperCAmelCase_			:      List[str]     						=  floats_tensor((batch_size, num_channels) + sizes       ,     rng=random.Random(0   )   ).to(__magic_name__   )
													return image

							@property
							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[Any]:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      List[Any]     						=  UNetaDConditionModel(
													    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,     cross_attention_dim=32       ,     )
													return model

							@property
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      List[Any]     						=  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       ,     )
													return model

							@property
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Dict:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      int     						=  CLIPTextConfig(
													    bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1E-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=10_00       ,     )
													return CLIPTextModel(__magic_name__   )

							@property
							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[Any]:







													"""simple docstring"""
													def extract(*__magic_name__							:     int       ,     **__magic_name__							:     Optional[Any]   ):


																			class 					__a      :

																										def __init__(							self							:     Any   )					->       Any:







																																"""simple docstring"""
																																UpperCAmelCase_			:      Optional[Any]     						=  torch.ones([0]   )



																										def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     Tuple   )					->       Dict:







																																"""simple docstring"""
																																self.pixel_values.to(__magic_name__   )
																																return self





																			return Out()

													return extract

							def     UpperCAmelCase__	(							self							:     str   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_			:      Any     						=  self.dummy_cond_unet
													UpperCAmelCase_			:      Any     						=  DDIMScheduler(
													    beta_start=0.0_0_0_8_5       ,     beta_end=0.0_1_2       ,     beta_schedule='''scaled_linear'''       ,     clip_sample=__magic_name__       ,     set_alpha_to_one=__magic_name__       ,     )

													UpperCAmelCase_			:      List[str]     						=  self.dummy_vae
													UpperCAmelCase_			:      Any     						=  self.dummy_text_encoder
													UpperCAmelCase_			:      str     						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

													# make sure here that pndm scheduler skips prk
													UpperCAmelCase_			:      Dict     						=  StableDiffusionPipeline(
													    unet=__magic_name__       ,     scheduler=__magic_name__       ,     vae=__magic_name__       ,     text_encoder=__magic_name__       ,     tokenizer=__magic_name__       ,     safety_checker=__magic_name__       ,     feature_extractor=self.dummy_extractor       ,     )
													UpperCAmelCase_			:      str     						=  sd_pipe.to(__magic_name__   )
													sd_pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Optional[Any]     						=  '''A painting of a squirrel eating a burger'''

													UpperCAmelCase_			:      List[str]     						=  torch.Generator(device=__magic_name__   ).manual_seed(0   )
													UpperCAmelCase_			:      Tuple     						=  sd_pipe([prompt]       ,     generator=__magic_name__       ,     guidance_scale=6.0       ,     num_inference_steps=2       ,     output_type='''np'''   )
													UpperCAmelCase_			:      List[Any]     						=  output.images

													UpperCAmelCase_			:      List[str]     						=  torch.Generator(device=__magic_name__   ).manual_seed(0   )
													UpperCAmelCase_			:      List[Any]     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=6.0       ,     num_inference_steps=2       ,     output_type='''np'''       ,     return_dict=__magic_name__       ,     )[0]

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

													assert image.shape == (1, 64, 64, 3)
													UpperCAmelCase_			:      Union[str, Any]     						=  np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]   )

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

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_			:      Union[str, Any]     						=  self.dummy_cond_unet
													UpperCAmelCase_			:      Union[str, Any]     						=  PNDMScheduler(skip_prk_steps=__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  self.dummy_vae
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_text_encoder
													UpperCAmelCase_			:      List[Any]     						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

													# make sure here that pndm scheduler skips prk
													UpperCAmelCase_			:      int     						=  StableDiffusionPipeline(
													    unet=__magic_name__       ,     scheduler=__magic_name__       ,     vae=__magic_name__       ,     text_encoder=__magic_name__       ,     tokenizer=__magic_name__       ,     safety_checker=__magic_name__       ,     feature_extractor=self.dummy_extractor       ,     )
													UpperCAmelCase_			:      Union[str, Any]     						=  sd_pipe.to(__magic_name__   )
													sd_pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      str     						=  '''A painting of a squirrel eating a burger'''
													UpperCAmelCase_			:      str     						=  torch.Generator(device=__magic_name__   ).manual_seed(0   )
													UpperCAmelCase_			:      List[str]     						=  sd_pipe([prompt]       ,     generator=__magic_name__       ,     guidance_scale=6.0       ,     num_inference_steps=2       ,     output_type='''np'''   )

													UpperCAmelCase_			:      int     						=  output.images

													UpperCAmelCase_			:      str     						=  torch.Generator(device=__magic_name__   ).manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=6.0       ,     num_inference_steps=2       ,     output_type='''np'''       ,     return_dict=__magic_name__       ,     )[0]

													UpperCAmelCase_			:      Optional[int]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      List[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													UpperCAmelCase_			:      str     						=  np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]   )

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

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







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  StableDiffusionPipeline.from_pretrained(
													    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''       ,     safety_checker=__magic_name__   )
													assert isinstance(__magic_name__       ,     __magic_name__   )
													assert isinstance(pipe.scheduler       ,     __magic_name__   )
													assert pipe.safety_checker is None

													UpperCAmelCase_			:      Tuple     						=  pipe('''example prompt'''       ,     num_inference_steps=2   ).images[0]
													assert image is not None

													# check that there's no error when saving a pipeline with one of the models being None
													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__magic_name__   )
																			UpperCAmelCase_			:      Dict     						=  StableDiffusionPipeline.from_pretrained(__magic_name__   )

													# sanity check that the pipeline still works
													assert pipe.safety_checker is None
													UpperCAmelCase_			:      int     						=  pipe('''example prompt'''       ,     num_inference_steps=2   ).images[0]
													assert image is not None



							@unittest.skipIf(torch_device != '''cuda'''       ,     '''This test requires a GPU'''   )
							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.dummy_cond_unet
													UpperCAmelCase_			:      Optional[int]     						=  PNDMScheduler(skip_prk_steps=__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  self.dummy_vae
													UpperCAmelCase_			:      List[str]     						=  self.dummy_text_encoder
													UpperCAmelCase_			:      Optional[Any]     						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

													# put models in fp16
													UpperCAmelCase_			:      Tuple     						=  unet.half()
													UpperCAmelCase_			:      List[str]     						=  vae.half()
													UpperCAmelCase_			:      Union[str, Any]     						=  bert.half()

													# make sure here that pndm scheduler skips prk
													UpperCAmelCase_			:      Union[str, Any]     						=  StableDiffusionPipeline(
													    unet=__magic_name__       ,     scheduler=__magic_name__       ,     vae=__magic_name__       ,     text_encoder=__magic_name__       ,     tokenizer=__magic_name__       ,     safety_checker=__magic_name__       ,     feature_extractor=self.dummy_extractor       ,     )
													UpperCAmelCase_			:      Optional[Any]     						=  sd_pipe.to(__magic_name__   )
													sd_pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Union[str, Any]     						=  '''A painting of a squirrel eating a burger'''
													UpperCAmelCase_			:      Any     						=  sd_pipe([prompt]       ,     num_inference_steps=2       ,     output_type='''np'''   ).images

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





@nightly
@require_torch_gpu
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

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







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,     safety_checker=__magic_name__   )
													UpperCAmelCase_			:      Any     						=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
													UpperCAmelCase_			:      Optional[int]     						=  sd_pipe.to(__magic_name__   )
													sd_pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  (
													    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
													    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
													    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
													    ''' children from bahnhof zoo, detailed '''
													)
													UpperCAmelCase_			:      Dict     						=  40_03_66_03_46
													UpperCAmelCase_			:      Optional[int]     						=  7

													# without safety guidance (sld_guidance_scale = 0)
													UpperCAmelCase_			:      Union[str, Any]     						=  torch.manual_seed(__magic_name__   )
													UpperCAmelCase_			:      Dict     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=__magic_name__       ,     num_inference_steps=50       ,     output_type='''np'''       ,     width=5_12       ,     height=5_12       ,     sld_guidance_scale=0       ,     )

													UpperCAmelCase_			:      Optional[int]     						=  output.images
													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Dict     						=  [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

													assert image.shape == (1, 5_12, 5_12, 3)

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

													# without safety guidance (strong configuration)
													UpperCAmelCase_			:      Union[str, Any]     						=  torch.manual_seed(__magic_name__   )
													UpperCAmelCase_			:      Any     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=__magic_name__       ,     num_inference_steps=50       ,     output_type='''np'''       ,     width=5_12       ,     height=5_12       ,     sld_guidance_scale=20_00       ,     sld_warmup_steps=7       ,     sld_threshold=0.0_2_5       ,     sld_momentum_scale=0.5       ,     sld_mom_beta=0.7       ,     )

													UpperCAmelCase_			:      str     						=  output.images
													UpperCAmelCase_			:      Optional[int]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      List[Any]     						=  [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

													assert image.shape == (1, 5_12, 5_12, 3)

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

							def     UpperCAmelCase__	(							self							:     int   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,     safety_checker=__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
													UpperCAmelCase_			:      List[Any]     						=  sd_pipe.to(__magic_name__   )
													sd_pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Any     						=  '''padme amidala taking a bath artwork, safe for work, no nudity'''
													UpperCAmelCase_			:      Union[str, Any]     						=  27_34_97_17_55
													UpperCAmelCase_			:      str     						=  7

													UpperCAmelCase_			:      List[str]     						=  torch.manual_seed(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=__magic_name__       ,     num_inference_steps=50       ,     output_type='''np'''       ,     width=5_12       ,     height=5_12       ,     sld_guidance_scale=0       ,     )

													UpperCAmelCase_			:      str     						=  output.images
													UpperCAmelCase_			:      int     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Dict     						=  [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

													assert image.shape == (1, 5_12, 5_12, 3)

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

													UpperCAmelCase_			:      Optional[Any]     						=  torch.manual_seed(__magic_name__   )
													UpperCAmelCase_			:      int     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=__magic_name__       ,     num_inference_steps=50       ,     output_type='''np'''       ,     width=5_12       ,     height=5_12       ,     sld_guidance_scale=20_00       ,     sld_warmup_steps=7       ,     sld_threshold=0.0_2_5       ,     sld_momentum_scale=0.5       ,     sld_mom_beta=0.7       ,     )

													UpperCAmelCase_			:      Optional[int]     						=  output.images
													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[Any]     						=  [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

													assert image.shape == (1, 5_12, 5_12, 3)

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



							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
													UpperCAmelCase_			:      Optional[Any]     						=  sd_pipe.to(__magic_name__   )
													sd_pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      str     						=  (
													    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
													    ''' leyendecker'''
													)
													UpperCAmelCase_			:      Any     						=  10_44_35_52_34
													UpperCAmelCase_			:      Dict     						=  12

													UpperCAmelCase_			:      List[str]     						=  torch.manual_seed(__magic_name__   )
													UpperCAmelCase_			:      int     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=__magic_name__       ,     num_inference_steps=50       ,     output_type='''np'''       ,     width=5_12       ,     height=5_12       ,     sld_guidance_scale=0       ,     )

													UpperCAmelCase_			:      Union[str, Any]     						=  output.images
													UpperCAmelCase_			:      Optional[int]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      List[Any]     						=  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   )

													assert image.shape == (1, 5_12, 5_12, 3)

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-7

													UpperCAmelCase_			:      Union[str, Any]     						=  torch.manual_seed(__magic_name__   )
													UpperCAmelCase_			:      Any     						=  sd_pipe(
													    [prompt]       ,     generator=__magic_name__       ,     guidance_scale=__magic_name__       ,     num_inference_steps=50       ,     output_type='''np'''       ,     width=5_12       ,     height=5_12       ,     sld_guidance_scale=20_00       ,     sld_warmup_steps=7       ,     sld_threshold=0.0_2_5       ,     sld_momentum_scale=0.5       ,     sld_mom_beta=0.7       ,     )

													UpperCAmelCase_			:      Tuple     						=  output.images
													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[int]     						=  np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]   )
													assert image.shape == (1, 5_12, 5_12, 3)

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












'''simple docstring'''







from collections.abc import Iterable
from typing import Any


class 					__a      :

							def __init__(							self							:     Optional[Any]       ,     __magic_name__							:     int | None = None   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  value
													UpperCAmelCase_			:      Node | None     						=  None  # Added in order to delete a node easier
													UpperCAmelCase_			:      Node | None     						=  None
													UpperCAmelCase_			:      Node | None     						=  None



							def __repr__(							self							:     List[str]   )					->       str:







													"""simple docstring"""
													from pprint import pformat

													if self.left is None and self.right is None:
																			return str(self.value   )
													return pformat({F"""{self.value}""": (self.left, self.right)}       ,     indent=1   )





class 					__a      :

							def __init__(							self							:     int       ,     __magic_name__							:     Node | None = None   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  root

							def __str__(							self							:     Any   )					->       str:







													"""simple docstring"""
													return str(self.root   )

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Node       ,     __magic_name__							:     Node | None   )					->       None:







													"""simple docstring"""
													if new_children is not None:  # reset its kids
																			UpperCAmelCase_			:      Dict     						=  node.parent
													if node.parent is not None:  # reset its parent
																			if self.is_right(__magic_name__   ):  # If it is the right children
																									UpperCAmelCase_			:      Optional[Any]     						=  new_children
																			else:
																									UpperCAmelCase_			:      Optional[int]     						=  new_children
													else:
																			UpperCAmelCase_			:      List[str]     						=  new_children

							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     Node   )					->       bool:







													"""simple docstring"""
													if node.parent and node.parent.right:
																			return node == node.parent.right
													return False

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







													"""simple docstring"""
													return self.root is None

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     str   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  Node(__magic_name__   )  # create a new Node
													if self.empty():  # if Tree is empty
																			UpperCAmelCase_			:      List[Any]     						=  new_node  # set its root
													else:  # Tree is not empty
																			UpperCAmelCase_			:      str     						=  self.root  # from root
																			if parent_node is None:
																									return
																			while True:  # While we don't get to a leaf
																									if value < parent_node.value:  # We go left
																															if parent_node.left is None:
																																					UpperCAmelCase_			:      Union[str, Any]     						=  new_node  # We insert the new node in a leaf
																																					break
																															else:
																																					UpperCAmelCase_			:      List[Any]     						=  parent_node.left
																									else:
																															if parent_node.right is None:
																																					UpperCAmelCase_			:      List[Any]     						=  new_node
																																					break
																															else:
																																					UpperCAmelCase_			:      Union[str, Any]     						=  parent_node.right
																			UpperCAmelCase_			:      Union[str, Any]     						=  parent_node

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     *__magic_name__							:     List[str]   )					->       None:







													"""simple docstring"""
													for value in values:
																			self.__insert(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     int   )					->       Node | None:







													"""simple docstring"""
													if self.empty():
																			raise IndexError('''Warning: Tree is empty! please use another.'''   )
													else:
																			UpperCAmelCase_			:      str     						=  self.root
																			# use lazy evaluation here to avoid NoneType Attribute error
																			while node is not None and node.value is not value:
																									UpperCAmelCase_			:      List[str]     						=  node.left if value < node.value else node.right
																			return node

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Node | None = None   )					->       Node | None:







													"""simple docstring"""
													if node is None:
																			if self.root is None:
																									return None
																			UpperCAmelCase_			:      Dict     						=  self.root

													if not self.empty():
																			while node.right is not None:
																									UpperCAmelCase_			:      Any     						=  node.right
													return node

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Node | None = None   )					->       Node | None:







													"""simple docstring"""
													if node is None:
																			UpperCAmelCase_			:      Optional[int]     						=  self.root
													if self.root is None:
																			return None
													if not self.empty():
																			UpperCAmelCase_			:      Union[str, Any]     						=  self.root
																			while node.left is not None:
																									UpperCAmelCase_			:      Dict     						=  node.left
													return node

							def     UpperCAmelCase__	(							self							:     Tuple       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  self.search(__magic_name__   )  # Look for the node with that label
													if node is not None:
																			if node.left is None and node.right is None:  # If it has no children
																									self.__reassign_nodes(__magic_name__       ,     __magic_name__   )
																			elif node.left is None:  # Has only right children
																									self.__reassign_nodes(__magic_name__       ,     node.right   )
																			elif node.right is None:  # Has only left children
																									self.__reassign_nodes(__magic_name__       ,     node.left   )
																			else:
																									UpperCAmelCase_			:      List[str]     						=  self.get_max(
																									    node.left   )  # Gets the max value of the left branch
																									self.remove(tmp_node.value   )  # type: ignore
																									UpperCAmelCase_			:      Optional[int]     						=  (
																									    tmp_node.value  # type: ignore
																									)  # Assigns the value to the node to delete and keep tree structure

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     Node | None   )					->       Iterable:







													"""simple docstring"""
													if node is not None:
																			yield node  # Preorder Traversal
																			yield from self.preorder_traverse(node.left   )
																			yield from self.preorder_traverse(node.right   )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     List[Any]=None   )					->       Any:







													"""simple docstring"""
													if traversal_function is None:
																			return self.preorder_traverse(self.root   )
													else:
																			return traversal_function(self.root   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     list       ,     __magic_name__							:     Node | None   )					->       None:







													"""simple docstring"""
													if node:
																			self.inorder(__magic_name__       ,     node.left   )
																			arr.append(node.value   )
																			self.inorder(__magic_name__       ,     node.right   )



							def     UpperCAmelCase__	(							self							:     Tuple       ,     __magic_name__							:     int       ,     __magic_name__							:     Node   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      list[int]     						=  []
													self.inorder(__magic_name__       ,     __magic_name__   )  # append all values to list using inorder traversal
													return arr[k - 1]





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Node | None   ) ->	list[Node]:
						UpperCAmelCase_			:      Any     						=  []
						if curr_node is not None:
												UpperCAmelCase_			:      Any     						=  postorder(curr_node.left   ) + postorder(curr_node.right   ) + [curr_node]
						return node_list





def        lowerCamelCase_      (  ) ->	None:
						UpperCAmelCase_			:      str     						=  (8, 3, 6, 1, 10, 14, 13, 4, 7)
						UpperCAmelCase_			:      Tuple     						=  BinarySearchTree()
						for i in testlist:
												t.insert(SCREAMING_SNAKE_CASE__   )

						# Prints all the elements of the list in order traversal
						print(SCREAMING_SNAKE_CASE__   )

						if t.search(6   ) is not None:
												print('''The value 6 exists'''   )
						else:
												print('''The value 6 doesn\'t exist'''   )

						if t.search(-1   ) is not None:
												print('''The value -1 exists'''   )
						else:
												print('''The value -1 doesn\'t exist'''   )

						if not t.empty():
												print('''Max Value: ''',			t.get_max().value   )  # type: ignore
												print('''Min Value: ''',			t.get_min().value   )  # type: ignore

						for i in testlist:
												t.remove(SCREAMING_SNAKE_CASE__   )
												print(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)





