

import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =ConsistencyModelPipeline
			lowerCamelCase__           =UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			lowerCamelCase__           =UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

			# Override required_optional_params to remove num_images_per_prompt
			lowerCamelCase__           =frozenset(
			    [
			        'num_inference_steps',
			        'generator',
			        'latents',
			        'output_type',
			        'return_dict',
			        'callback',
			        'callback_steps',
			    ]   )
			@property
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       UNetaDModel.from_pretrained(
				    "diffusers/consistency-models-test"      ,      subfolder="test_unet"      ,      )
				return unet
			@property
			def 							__UpperCamelCase (			self							: Tuple	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       UNetaDModel.from_pretrained(
				    "diffusers/consistency-models-test"      ,      subfolder="test_unet_class_cond"      ,      )
				return unet
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: List[Any]=False	)							->       List[Any]:

				"""simple docstring"""

				if class_cond:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.dummy_cond_unet
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       self.dummy_uncond_unet

				# Default to CM multistep sampler
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       CMStochasticIterativeScheduler(
				    num_train_timesteps=40      ,      sigma_min=0.002      ,      sigma_max=80.0      ,      )

				SCREAMING_SNAKE_CASE   :      Any				     =       {
				    "unet": unet,
				    "scheduler": scheduler,
				}

				return components
			def 							__UpperCamelCase (			self							: str      ,      a							: Union[str, Any]      ,      a							: Tuple=0	)							->       Optional[int]:

				"""simple docstring"""

				if str(a	).startswith("mps"	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       torch.manual_seed(a	)
				else:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.Generator(device=a	).manual_seed(a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       {
				    "batch_size": 1,
				    "num_inference_steps": None,
				    "timesteps": [22, 0],
				    "generator": generator,
				    "output_type": "np",
				}

				return inputs
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_dummy_components()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ConsistencyModelPipeline(**a	)
				SCREAMING_SNAKE_CASE   :      str				     =       pipe.to(a	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_dummy_inputs(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       pipe(**a	).images
				assert image.shape == (1, 32, 32, 3)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE   :      Any				     =       np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       "cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_dummy_components(class_cond=a	)
				SCREAMING_SNAKE_CASE   :      int				     =       ConsistencyModelPipeline(**a	)
				SCREAMING_SNAKE_CASE   :      int				     =       pipe.to(a	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_dummy_inputs(a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       0
				SCREAMING_SNAKE_CASE   :      Dict				     =       pipe(**a	).images
				assert image.shape == (1, 32, 32, 3)

				SCREAMING_SNAKE_CASE   :      Dict				     =       image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_dummy_components()
				SCREAMING_SNAKE_CASE   :      int				     =       ConsistencyModelPipeline(**a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pipe.to(a	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_dummy_inputs(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       1
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       None
				SCREAMING_SNAKE_CASE   :      int				     =       pipe(**a	).images
				assert image.shape == (1, 32, 32, 3)

				SCREAMING_SNAKE_CASE   :      Any				     =       image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.get_dummy_components(class_cond=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       ConsistencyModelPipeline(**a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pipe.to(a	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_dummy_inputs(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       1
				SCREAMING_SNAKE_CASE   :      Tuple				     =       None
				SCREAMING_SNAKE_CASE   :      Tuple				     =       0
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pipe(**a	).images
				assert image.shape == (1, 32, 32, 3)

				SCREAMING_SNAKE_CASE   :      Dict				     =       image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE   :      Dict				     =       np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






@slow
@require_torch_gpu
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: int	)							->       str:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: List[str]=0      ,      a							: List[str]=False      ,      a							: Any="cpu"      ,      a							: List[Any]=torch.floataa      ,      a							: int=(1, 3, 64, 64)	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       torch.manual_seed(a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       {
				    "num_inference_steps": None,
				    "timesteps": [22, 0],
				    "class_labels": 0,
				    "generator": generator,
				    "output_type": "np",
				}

				if get_fixed_latents:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_fixed_latents(seed=a      ,      device=a      ,      dtype=a      ,      shape=a	)
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       latents

				return inputs
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Any=0      ,      a							: List[str]="cpu"      ,      a							: List[Any]=torch.floataa      ,      a							: Tuple=(1, 3, 64, 64)	)							->       List[str]:

				"""simple docstring"""

				if type(a	) == str:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.device(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       torch.Generator(device=a	).manual_seed(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       randn_tensor(a      ,      generator=a      ,      device=a      ,      dtype=a	)
				return latents
			def 							__UpperCamelCase (			self							: Any	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       UNetaDModel.from_pretrained("diffusers/consistency_models"      ,      subfolder="diffusers_cd_imagenet64_l2"	)
				SCREAMING_SNAKE_CASE   :      int				     =       CMStochasticIterativeScheduler(
				    num_train_timesteps=40      ,      sigma_min=0.002      ,      sigma_max=80.0      ,      )
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ConsistencyModelPipeline(unet=a      ,      scheduler=a	)
				pipe.to(torch_device=a	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_inputs()
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pipe(**a	).images
				assert image.shape == (1, 64, 64, 3)

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 2e-2
			def 							__UpperCamelCase (			self							: Dict	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel.from_pretrained("diffusers/consistency_models"      ,      subfolder="diffusers_cd_imagenet64_l2"	)
				SCREAMING_SNAKE_CASE   :      Any				     =       CMStochasticIterativeScheduler(
				    num_train_timesteps=40      ,      sigma_min=0.002      ,      sigma_max=80.0      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       ConsistencyModelPipeline(unet=a      ,      scheduler=a	)
				pipe.to(torch_device=a	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_inputs()
				SCREAMING_SNAKE_CASE   :      List[str]				     =       1
				SCREAMING_SNAKE_CASE   :      int				     =       None
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipe(**a	).images
				assert image.shape == (1, 64, 64, 3)

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

				SCREAMING_SNAKE_CASE   :      str				     =       np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 2e-2
			@require_torch_a
			def 							__UpperCamelCase (			self							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       UNetaDModel.from_pretrained("diffusers/consistency_models"      ,      subfolder="diffusers_cd_imagenet64_l2"	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       CMStochasticIterativeScheduler(
				    num_train_timesteps=40      ,      sigma_min=0.002      ,      sigma_max=80.0      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       ConsistencyModelPipeline(unet=a      ,      scheduler=a	)
				pipe.to(torch_device=a      ,      torch_dtype=torch.floataa	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_inputs(get_fixed_latents=a      ,      device=a	)
				# Ensure usage of flash attention in torch 2.0
				with sdp_kernel(enable_flash=a      ,      enable_math=a      ,      enable_mem_efficient=a	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipe(**a	).images
				assert image.shape == (1, 64, 64, 3)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image[0, -3:, -3:, -1]

				SCREAMING_SNAKE_CASE   :      Dict				     =       np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






			@require_torch_a
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       UNetaDModel.from_pretrained("diffusers/consistency_models"      ,      subfolder="diffusers_cd_imagenet64_l2"	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       CMStochasticIterativeScheduler(
				    num_train_timesteps=40      ,      sigma_min=0.002      ,      sigma_max=80.0      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       ConsistencyModelPipeline(unet=a      ,      scheduler=a	)
				pipe.to(torch_device=a      ,      torch_dtype=torch.floataa	)
				pipe.set_progress_bar_config(disable=a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.get_inputs(get_fixed_latents=a      ,      device=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       1
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				# Ensure usage of flash attention in torch 2.0
				with sdp_kernel(enable_flash=a      ,      enable_math=a      ,      enable_mem_efficient=a	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pipe(**a	).images
				assert image.shape == (1, 64, 64, 3)

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

				SCREAMING_SNAKE_CASE   :      int				     =       np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3


from __future__ import annotations

import math
import random
from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Union[str, Any]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      list[Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       0
				SCREAMING_SNAKE_CASE   :      int				     =       0
			def 							__UpperCamelCase (			self							: List[Any]	)							->       bool:

				"""simple docstring"""

				return self.head == self.tail
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				self.data.append(a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.tail + 1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.data[self.head]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.head + 1
				return ret
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				return self.tail - self.head






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       None:

				"""simple docstring"""

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






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       data
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      int				     =       1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				return self.data
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       MyNode | None:

				"""simple docstring"""

				return self.left
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       MyNode | None:

				"""simple docstring"""

				return self.right
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				return self.height
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       data
			def 							__UpperCamelCase (			self							: Any      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       node
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       node






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       height

def  lowerCamelCase__ (					_a):
	if node is None:
		return 0
	return node.get_height()

def  lowerCamelCase__ (					_a							,			_a):
	if a > b:
		return a
	return b

def  lowerCamelCase__ (					_a):
	print("left rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      List[str]				     =       node.get_left()
	assert ret is not None
	node.set_left(ret.get_right())
	ret.set_right(_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	print("right rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      Dict				     =       node.get_right()
	assert ret is not None
	node.set_right(ret.get_left())
	ret.set_left(_a)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       node.get_left()
	assert left_child is not None
	node.set_left(left_rotation(_a))
	return right_rotation(_a)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       node.get_right()
	assert right_child is not None
	node.set_right(right_rotation(_a))
	return left_rotation(_a)

def  lowerCamelCase__ (					_a							,			_a):
	if node is None:
		return MyNode(_a)
	if data < node.get_data():
		node.set_left(insert_node(node.get_left()							,			_a))
		if (
		    get_height(node.get_left()) - get_height(node.get_right()) == 2
		):  # an unbalance detected
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       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
				SCREAMING_SNAKE_CASE   :      Tuple				     =       right_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       lr_rotation(_a)
	else:
		node.set_right(insert_node(node.get_right()							,			_a))
		if get_height(node.get_right()) - get_height(node.get_left()) == 2:
			SCREAMING_SNAKE_CASE   :      Any				     =       node.get_right()
			assert right_child is not None
			if data < right_child.get_data():
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rl_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       left_rotation(_a)
	SCREAMING_SNAKE_CASE   :      str				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	return node

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
		if right_child is None:
			break
		SCREAMING_SNAKE_CASE   :      str				     =       right_child
	return root.get_data()

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       root.get_left()
		if left_child is None:
			break
		SCREAMING_SNAKE_CASE   :      List[str]				     =       left_child
	return root.get_data()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       root.get_left()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
	if root.get_data() == data:
		if left_child is not None and right_child is not None:
			SCREAMING_SNAKE_CASE   :      Any				     =       get_left_most(_a)
			root.set_data(_a)
			root.set_right(del_node(_a							,			_a))
		elif left_child is not None:
			SCREAMING_SNAKE_CASE   :      Dict				     =       left_child
		elif right_child is not None:
			SCREAMING_SNAKE_CASE   :      str				     =       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(_a							,			_a))
	else:  # root.get_data() < data
		if right_child is None:
			return root
		else:
			root.set_right(del_node(_a							,			_a))

	if get_height(_a) - get_height(_a) == 2:
		assert right_child is not None
		if get_height(right_child.get_right()) > get_height(right_child.get_left()):
			SCREAMING_SNAKE_CASE   :      List[str]				     =       left_rotation(_a)
		else:
			SCREAMING_SNAKE_CASE   :      int				     =       rl_rotation(_a)
	elif get_height(_a) - get_height(_a) == -2:
		assert left_child is not None
		if get_height(left_child.get_left()) > get_height(left_child.get_right()):
			SCREAMING_SNAKE_CASE   :      str				     =       right_rotation(_a)
		else:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       lr_rotation(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       my_max(get_height(root.get_right())							,			get_height(root.get_left())) + 1
	root.set_height(_a)
	return root






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: str	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return get_height(self.root	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("insert:" + str(a	)	)
				SCREAMING_SNAKE_CASE   :      Any				     =       insert_node(self.root      ,      a	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("delete:" + str(a	)	)
				if self.root is None:
					print("Tree is empty!"	)
					return
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       del_node(self.root      ,      a	)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       ""
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MyQueue()
				q.push(self.root	)
				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_height()
				if layer == 0:
					return output
				SCREAMING_SNAKE_CASE   :      Dict				     =       0
				while not q.is_empty():
					SCREAMING_SNAKE_CASE   :      Dict				     =       q.pop()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       " " * int(math.pow(2      ,      layer - 1	)	)
					output += space
					if node is None:
						output += "*"
						q.push(a	)
						q.push(a	)
					else:
						output += str(node.get_data()	)
						q.push(node.get_left()	)
						q.push(node.get_right()	)
					output += space
					SCREAMING_SNAKE_CASE   :      List[str]				     =       cnt + 1
					for i in range(100	):
						if cnt == math.pow(2      ,      a	) - 1:
							SCREAMING_SNAKE_CASE   :      List[str]				     =       layer - 1
							if layer == 0:
								output += "\n*************************************"
								return output
							output += "\n"
							break
				output += "\n*************************************"
				return output

def  lowerCamelCase__ (					):
	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))