


from collections.abc import Iterable
from typing import Any





class        lowerCAmelCase__ :






						"""simple docstring"""




						def __init__(       self ,				a_ = None      ):
							lowerCamelCase_					:				Dict              =  value
							lowerCamelCase_					:				List[str]              =  None  # Added in order to delete a node easier
							lowerCamelCase_					:				Union[str, Any]              =  None
							lowerCamelCase_					:				Union[str, Any]              =  None




						def __repr__(       self      ):
							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        lowerCAmelCase__ :






						"""simple docstring"""




						def __init__(       self ,				a_ = None      ):
							lowerCamelCase_					:				int              =  root




						def __str__(       self      ):
							return str(self.root      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_      ):






							if new_children is not None:  # reset its kids
								lowerCamelCase_					:				Optional[Any]              =  node.parent
							if node.parent is not None:  # reset its parent
								if self.is_right(snake_case__      ):  # If it is the right children
									lowerCamelCase_					:				List[str]              =  new_children
								else:
									lowerCamelCase_					:				Optional[int]              =  new_children
							else:
								lowerCamelCase_					:				Optional[int]              =  new_children




						def 	_UpperCamelCase					(       self ,				a_      ):






							if node.parent and node.parent.right:
								return node == node.parent.right
							return False




						def 	_UpperCamelCase					(       self      ):
							return self.root is None




						def 	_UpperCamelCase					(       self ,				a_      ):
							lowerCamelCase_					:				List[str]              =  Node(snake_case__      )  # create a new Node
							if self.empty():  # if Tree is empty
								lowerCamelCase_					:				List[str]              =  new_node  # set its root
							else:  # Tree is not empty
								lowerCamelCase_					:				Any              =  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:
											lowerCamelCase_					:				int              =  new_node  # We insert the new node in a leaf
											break
										else:
											lowerCamelCase_					:				int              =  parent_node.left
									else:
										if parent_node.right is None:
											lowerCamelCase_					:				Union[str, Any]              =  new_node
											break
										else:
											lowerCamelCase_					:				Tuple              =  parent_node.right
								lowerCamelCase_					:				Optional[int]              =  parent_node




						def 	_UpperCamelCase					(       self ,				*a_      ):






							for value in values:
								self.__insert(snake_case__      )




						def 	_UpperCamelCase					(       self ,				a_      ):






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




						def 	_UpperCamelCase					(       self ,				a_ = None      ):






							if node is None:
								if self.root is None:
									return None
								lowerCamelCase_					:				Optional[Any]              =  self.root

							if not self.empty():
								while node.right is not None:
									lowerCamelCase_					:				Optional[int]              =  node.right
							return node




						def 	_UpperCamelCase					(       self ,				a_ = None      ):






							if node is None:
								lowerCamelCase_					:				List[str]              =  self.root
							if self.root is None:
								return None
							if not self.empty():
								lowerCamelCase_					:				int              =  self.root
								while node.left is not None:
									lowerCamelCase_					:				List[Any]              =  node.left
							return node




						def 	_UpperCamelCase					(       self ,				a_      ):
							lowerCamelCase_					:				Tuple              =  self.search(snake_case__      )  # 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(snake_case__ ,				snake_case__      )
								elif node.left is None:  # Has only right children
									self.__reassign_nodes(snake_case__ ,				node.right      )
								elif node.right is None:  # Has only left children
									self.__reassign_nodes(snake_case__ ,				node.left      )
								else:
									lowerCamelCase_					:				Any              =  self.get_max(
									    node.left      )  # Gets the max value of the left branch
									self.remove(tmp_node.value      )  # type: ignore
									lowerCamelCase_					:				List[str]              =  (
									    tmp_node.value  # type: ignore
									)  # Assigns the value to the node to delete and keep tree structure




						def 	_UpperCamelCase					(       self ,				a_      ):






							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 ,				a_=None      ):






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




						def 	_UpperCamelCase					(       self ,				a_ ,				a_      ):






							if node:
								self.inorder(snake_case__ ,				node.left      )
								arr.append(node.value      )
								self.inorder(snake_case__ ,				node.right      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_      ):
							lowerCamelCase_					:				List[str]              =  []
							self.inorder(snake_case__ ,				snake_case__      )  # append all values to list using inorder traversal
							return arr[k - 1]







def 							__magic_name__    (     lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				Dict              =  []
	if curr_node is not None:
		lowerCamelCase_					:				Any              =  postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
	return node_list







def 							__magic_name__    (     ):
	'''simple docstring'''


	lowerCamelCase_					:				str              =  (8, 3, 6, 1, 10, 14, 13, 4, 7)
	lowerCamelCase_					:				Any              =  BinarySearchTree()
	for i in testlist:
		t.insert(_UpperCamelCase)

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

	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(_UpperCamelCase)
		print(_UpperCamelCase)


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)




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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()




class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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

    @property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      1
     SCREAMING_SNAKE_CASE		    =      3
     SCREAMING_SNAKE_CASE		    =      (3_2, 3_2)

     SCREAMING_SNAKE_CASE		    =      floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0     )     ).to(snake_case__     )
     return image

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         block_out_channels=(3_2, 3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=7				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=3_2				,     attention_head_dim=8				,     use_linear_projection=snake_case__				,     only_cross_attention=(True, True, False)				,     num_class_embeds=1_0_0				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoencoderKL(
         block_out_channels=[3_2, 3_2, 6_4]				,     in_channels=3				,     out_channels=3				,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']				,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']				,     latent_channels=4				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      CLIPTextConfig(
         bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     hidden_act='gelu'				,     projection_dim=5_1_2				,     )
     return CLIPTextModel(snake_case__     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )

     SCREAMING_SNAKE_CASE		    =      output.images

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     return_dict=snake_case__				,     )[0]

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

     SCREAMING_SNAKE_CASE		    =      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE		    =      np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]     )

     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			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         2 * [prompt]				,     image=2 * [low_res_image]				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images
     assert image.shape[0] == 2

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     num_images_per_prompt=2				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images
     assert image.shape[0] == 2




    @unittest.skipIf(torch_device != 'cuda'				,     'This test requires a GPU'     )
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # put models in fp16, except vae as it overflows in fp16
     SCREAMING_SNAKE_CASE		    =      unet.half()
     SCREAMING_SNAKE_CASE		    =      text_encoder.half()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     num_inference_steps=2				,     output_type='np'				,     ).images

     SCREAMING_SNAKE_CASE		    =      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)




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

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
         '/upsampled_cat.npy'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image     ).max() < 1E-3

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
         '/upsampled_cat_fp16.npy'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(
         snake_case__				,     torch_dtype=torch.floataa				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image     ).max() < 5E-1




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(
         snake_case__				,     torch_dtype=torch.floataa				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing(1     )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     num_inference_steps=5				,     output_type='np'				,     )

     SCREAMING_SNAKE_CASE		    =      torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 1_0**9

