


'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any



class  lowerCAmelCase				:



	def __init__(					self    ):
		lowerCAmelCase					:    list[Any]      							= []
		lowerCAmelCase					:    int      							= 0
		lowerCAmelCase					:    int      							= 0



	def 				lowercase     (					self    ):
		return self.head == self.tail



	def 				lowercase     (					self ,					snake_case__    ):
		self.data.append(snake_case__    )
		lowerCAmelCase					:    int      							= self.tail + 1



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= self.data[self.head]
		lowerCAmelCase					:    Dict      							= self.head + 1
		return ret



	def 				lowercase     (					self    ):
		return self.tail - self.head



	def 				lowercase     (					self    ):
		print(self.data    )
		print('**************'    )
		print(self.data[self.head : self.tail]    )



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= data
		lowerCAmelCase					:    MyNode | None      							= None
		lowerCAmelCase					:    MyNode | None      							= None
		lowerCAmelCase					:    int      							= 1



	def 				lowercase     (					self    ):
		return self.data



	def 				lowercase     (					self    ):
		return self.left



	def 				lowercase     (					self    ):
		return self.right



	def 				lowercase     (					self    ):
		return self.height



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= data



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    Tuple      							= node



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    Tuple      							= node



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    int      							= height


def 			__UpperCamelCase    (    _A    :				MyNode | None				)    ->      int:




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				int				)    ->      int:




	"""simple docstring"""





	if a > b:
		return a
	return b

def 			__UpperCamelCase    (    _A    :				MyNode				)    ->      MyNode:




	"""simple docstring"""





	print('left rotation node:'					,   node.get_data()				)
	lowerCAmelCase					:    Union[str, Any]      							= node.get_left()
	assert ret is not None
	node.set_left(ret.get_right()				)
	ret.set_right(_A				)
	lowerCAmelCase					:    Optional[Any]      							= my_max(get_height(node.get_right()				)					,   get_height(node.get_left()				)				) + 1
	node.set_height(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= my_max(get_height(ret.get_right()				)					,   get_height(ret.get_left()				)				) + 1
	ret.set_height(_A				)
	return ret

def 			__UpperCamelCase    (    _A    :				MyNode				)    ->      MyNode:




	"""simple docstring"""





	print('right rotation node:'					,   node.get_data()				)
	lowerCAmelCase					:    Any      							= node.get_right()
	assert ret is not None
	node.set_right(ret.get_left()				)
	ret.set_left(_A				)
	lowerCAmelCase					:    Any      							= my_max(get_height(node.get_right()				)					,   get_height(node.get_left()				)				) + 1
	node.set_height(_A				)
	lowerCAmelCase					:    Optional[int]      							= my_max(get_height(ret.get_right()				)					,   get_height(ret.get_left()				)				) + 1
	ret.set_height(_A				)
	return ret

def 			__UpperCamelCase    (    _A    :				MyNode				)    ->      MyNode:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= node.get_left()
	assert left_child is not None
	node.set_left(left_rotation(_A				)				)
	return right_rotation(_A				)

def 			__UpperCamelCase    (    _A    :				MyNode				)    ->      MyNode:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= node.get_right()
	assert right_child is not None
	node.set_right(right_rotation(_A				)				)
	return left_rotation(_A				)

def 			__UpperCamelCase    (    _A    :				MyNode | None					,   _A    :				Any				)    ->      MyNode | None:




	"""simple docstring"""





	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
			lowerCAmelCase					:    Tuple      							= 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
				lowerCAmelCase					:    Optional[Any]      							= right_rotation(_A				)
			else:
				lowerCAmelCase					:    str      							= 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:
			lowerCAmelCase					:    int      							= node.get_right()
			assert right_child is not None
			if data < right_child.get_data():
				lowerCAmelCase					:    List[Any]      							= rl_rotation(_A				)
			else:
				lowerCAmelCase					:    Dict      							= left_rotation(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= my_max(get_height(node.get_right()				)					,   get_height(node.get_left()				)				) + 1
	node.set_height(_A				)
	return node

def 			__UpperCamelCase    (    _A    :				MyNode				)    ->      Any:




	"""simple docstring"""





	while True:
		lowerCAmelCase					:    Tuple      							= root.get_right()
		if right_child is None:
			break
		lowerCAmelCase					:    Optional[int]      							= right_child
	return root.get_data()

def 			__UpperCamelCase    (    _A    :				MyNode				)    ->      Any:




	"""simple docstring"""





	while True:
		lowerCAmelCase					:    int      							= root.get_left()
		if left_child is None:
			break
		lowerCAmelCase					:    List[Any]      							= left_child
	return root.get_data()

def 			__UpperCamelCase    (    _A    :				MyNode					,   _A    :				Any				)    ->      MyNode | None:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= root.get_left()
	lowerCAmelCase					:    Optional[Any]      							= root.get_right()
	if root.get_data() == data:
		if left_child is not None and right_child is not None:
			lowerCAmelCase					:    str      							= get_left_most(_A				)
			root.set_data(_A				)
			root.set_right(del_node(_A					,   _A				)				)
		elif left_child is not None:
			lowerCAmelCase					:    List[str]      							= left_child
		elif right_child is not None:
			lowerCAmelCase					:    List[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()				):
			lowerCAmelCase					:    str      							= left_rotation(_A				)
		else:
			lowerCAmelCase					:    Any      							= 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()				):
			lowerCAmelCase					:    List[str]      							= right_rotation(_A				)
		else:
			lowerCAmelCase					:    List[Any]      							= lr_rotation(_A				)
	lowerCAmelCase					:    Any      							= my_max(get_height(root.get_right()				)					,   get_height(root.get_left()				)				) + 1
	root.set_height(_A				)
	return root



class  lowerCAmelCase				:



	def __init__(					self    ):
		lowerCAmelCase					:    MyNode | None      							= None



	def 				lowercase     (					self    ):
		return get_height(self.root    )



	def 				lowercase     (					self ,					snake_case__    ):
		print('insert:' + str(snake_case__    )    )
		lowerCAmelCase					:    List[str]      							= insert_node(self.root ,					snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		print('delete:' + str(snake_case__    )    )
		if self.root is None:
			print('Tree is empty!'    )
			return
		lowerCAmelCase					:    Union[str, Any]      							= del_node(self.root ,					snake_case__    )



	def __str__(					self ,					):  # a level traversale, gives a more intuitive look on the tree
		lowerCAmelCase					:    List[str]      							= ''
		lowerCAmelCase					:    Union[str, Any]      							= MyQueue()
		q.push(self.root    )
		lowerCAmelCase					:    List[str]      							= self.get_height()
		if layer == 0:
			return output
		lowerCAmelCase					:    str      							= 0
		while not q.is_empty():
			lowerCAmelCase					:    Optional[Any]      							= q.pop()
			lowerCAmelCase					:    Union[str, Any]      							= ' ' * int(math.pow(2 ,					layer - 1    )    )
			output += space
			if node is None:
				output += "*"
				q.push(snake_case__    )
				q.push(snake_case__    )
			else:
				output += str(node.get_data()    )
				q.push(node.get_left()    )
				q.push(node.get_right()    )
			output += space
			lowerCAmelCase					:    List[Any]      							= cnt + 1
			for i in range(100    ):
				if cnt == math.pow(2 ,					snake_case__    ) - 1:
					lowerCAmelCase					:    int      							= layer - 1
					if layer == 0:
						output += "\n*************************************"
						return output
					output += "\n"
					break
		output += "\n*************************************"
		return output


def 			__UpperCamelCase    (    )    ->      None:




	"""simple docstring"""





	import doctest

	doctest.testmod()


if __name__ == "__main__":
				_test()
				_lowerCAmelCase  :		int		     =	AVLtree()
				_lowerCAmelCase  :		Optional[Any]		     =	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 unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[Any]			      = LDMTextToImagePipeline
	_lowerCamelCase   :					Optional[Any]			      = TEXT_TO_IMAGE_PARAMS - {
	    """negative_prompt""",
	    """negative_prompt_embeds""",
	    """cross_attention_kwargs""",
	    """prompt_embeds""",
	}
	_lowerCamelCase   :					List[str]			      = PipelineTesterMixin.required_optional_params - {
	    """num_images_per_prompt""",
	    """callback""",
	    """callback_steps""",
	}
	_lowerCamelCase   :					Union[str, Any]			      = TEXT_TO_IMAGE_BATCH_PARAMS
	_lowerCamelCase   :					Optional[int]			      = False



	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Dict      							= 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 ,					)
		lowerCAmelCase					:    int      							= DDIMScheduler(
		    beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					clip_sample=snake_case__ ,					set_alpha_to_one=snake_case__ ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    str      							= 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 ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    Any      							= 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=1000 ,					)
		lowerCAmelCase					:    str      							= CLIPTextModel(snake_case__    )
		lowerCAmelCase					:    str      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

		lowerCAmelCase					:    List[Any]      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'vqvae': vae,
		    'bert': text_encoder,
		    'tokenizer': tokenizer,
		}
		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    Optional[int]      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    str      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
		lowerCAmelCase					:    Tuple      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'generator': generator,
		    'num_inference_steps': 2,
		    'guidance_scale': 6.0,
		    'output_type': 'numpy',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= 'cpu'  # ensure determinism for the device-dependent torch.Generator

		lowerCAmelCase					:    Optional[Any]      							= self.get_dummy_components()
		lowerCAmelCase					:    Optional[Any]      							= LDMTextToImagePipeline(**snake_case__    )
		pipe.to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Tuple      							= self.get_dummy_inputs(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= pipe(**snake_case__    ).images
		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1]

		assert image.shape == (1, 16, 16, 3)
		lowerCAmelCase					:    List[Any]      							= np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]    )

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



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



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=torch.floataa ,					snake_case__=0    ):
		lowerCAmelCase					:    List[str]      							= torch.manual_seed(snake_case__    )
		lowerCAmelCase					:    int      							= np.random.RandomState(snake_case__    ).standard_normal((1, 4, 32, 32)    )
		lowerCAmelCase					:    Optional[Any]      							= torch.from_numpy(snake_case__    ).to(device=snake_case__ ,					dtype=snake_case__    )
		lowerCAmelCase					:    List[str]      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'latents': latents,
		    'generator': generator,
		    'num_inference_steps': 3,
		    'guidance_scale': 6.0,
		    'output_type': 'numpy',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= self.get_inputs(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= pipe(**snake_case__    ).images
		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 256, 256, 3)
		lowerCAmelCase					:    Tuple      							= np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]    )
		lowerCAmelCase					:    int      							= np.abs(expected_slice - image_slice    ).max()
		assert max_diff < 1e-3



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



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=torch.floataa ,					snake_case__=0    ):
		lowerCAmelCase					:    List[str]      							= torch.manual_seed(snake_case__    )
		lowerCAmelCase					:    Any      							= np.random.RandomState(snake_case__    ).standard_normal((1, 4, 32, 32)    )
		lowerCAmelCase					:    List[Any]      							= torch.from_numpy(snake_case__    ).to(device=snake_case__ ,					dtype=snake_case__    )
		lowerCAmelCase					:    List[str]      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'latents': latents,
		    'generator': generator,
		    'num_inference_steps': 50,
		    'guidance_scale': 6.0,
		    'output_type': 'numpy',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    int      							= self.get_inputs(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= pipe(**snake_case__    ).images[0]

		lowerCAmelCase					:    Optional[int]      							= load_numpy(
		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'    )
		lowerCAmelCase					:    List[str]      							= np.abs(expected_image - image    ).max()
		assert max_diff < 1e-3
