



'''simple docstring'''






import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def 			snake_case_					( _lowerCAmelCase  :						nn.Module  )				->       Tuple:
							UpperCAmelCase			:	Any										=		{}
							for name in unet.attn_processors.keys():
														UpperCAmelCase			:	Optional[Any]										=		None if name.endswith('''attn1.processor'''  ) else unet.config.cross_attention_dim
														if name.startswith('''mid_block'''  ):
																					UpperCAmelCase			:	List[Any]										=		unet.config.block_out_channels[-1]
														elif name.startswith('''up_blocks'''  ):
																					UpperCAmelCase			:	Union[str, Any]										=		int(name[len('''up_blocks.'''  )]  )
																					UpperCAmelCase			:	Optional[int]										=		list(reversed(unet.config.block_out_channels  )  )[block_id]
														elif name.startswith('''down_blocks'''  ):
																					UpperCAmelCase			:	str										=		int(name[len('''down_blocks.'''  )]  )
																					UpperCAmelCase			:	str										=		unet.config.block_out_channels[block_id]
														UpperCAmelCase			:	Tuple										=		(
														    LoRAAttnProcessor2_0 if hasattr(_lowerCAmelCase       ,				'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
														)
														UpperCAmelCase			:	Optional[Any]										=		lora_attn_processor_class(
														    hidden_size=_lowerCAmelCase       ,				cross_attention_dim=_lowerCAmelCase  )
							UpperCAmelCase			:	Optional[Any]										=		AttnProcsLayers(_lowerCAmelCase  )
							return lora_attn_procs, unet_lora_layers


def 			snake_case_					( _lowerCAmelCase  :						nn.Module  )				->       int:
							UpperCAmelCase			:	Any										=		{}
							UpperCAmelCase			:	List[str]										=		(
							    LoRAAttnProcessor2_0 if hasattr(_lowerCAmelCase       ,				'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
							)
							for name, module in text_encoder.named_modules():
														if name.endswith(_lowerCAmelCase  ):
																					UpperCAmelCase			:	List[Any]										=		lora_attn_processor_class(
																					    hidden_size=module.out_proj.out_features       ,				cross_attention_dim=_lowerCAmelCase  )
							return text_lora_attn_procs


def 			snake_case_					( _lowerCAmelCase  :						nn.Module  )				->       Optional[Any]:
							UpperCAmelCase			:	Dict										=		create_text_encoder_lora_attn_procs(_lowerCAmelCase  )
							UpperCAmelCase			:	Union[str, Any]										=		AttnProcsLayers(_lowerCAmelCase  )
							return text_encoder_lora_layers


def 			snake_case_					( _lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Optional[int]=False  )				->       int:
							for _, attn_proc in text_lora_attn_procs.items():
														# set up.weights
														for layer_name, layer_module in attn_proc.named_modules():
																					if layer_name.endswith('''_lora'''  ):
																												UpperCAmelCase			:	List[Any]										=		(
																												    torch.randn_like(layer_module.up.weight  )
																												    if randn_weight
																												    else torch.zeros_like(layer_module.up.weight  )
																												)
																												UpperCAmelCase			:	Optional[Any]										=		torch.nn.Parameter(_lowerCAmelCase  )




class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	def 					A     (						self      :					str		)      ->		Dict:
								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[int]										=		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		,      )
								UpperCAmelCase			:	Dict										=		DDIMScheduler(
								    beta_start=0.0_00_85		,      beta_end=0.0_12		,      beta_schedule='''scaled_linear'''		,      clip_sample=__snake_case		,      set_alpha_to_one=__snake_case		,      steps_offset=1		,      )
								torch.manual_seed(0		)
								UpperCAmelCase			:	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		,      )
								UpperCAmelCase			:	Union[str, Any]										=		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=1000		,      )
								UpperCAmelCase			:	Union[str, Any]										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	List[str]										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		create_unet_lora_layers(__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		create_text_encoder_lora_layers(__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								UpperCAmelCase			:	Dict										=		{
								    '''unet_lora_layers''': unet_lora_layers,
								    '''text_encoder_lora_layers''': text_encoder_lora_layers,
								    '''unet_lora_attn_procs''': unet_lora_attn_procs,
								}
								return pipeline_components, lora_components







	def 					A     (						self      :					Union[str, Any]		)      ->		Tuple:
								UpperCAmelCase			:	Optional[int]										=		1
								UpperCAmelCase			:	str										=		10
								UpperCAmelCase			:	int										=		4
								UpperCAmelCase			:	Tuple										=		(32, 32)

								UpperCAmelCase			:	Tuple										=		torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[Any]										=		floats_tensor((batch_size, num_channels) + sizes		)
								UpperCAmelCase			:	Tuple										=		torch.randint(1		,      __snake_case		,      size=(batch_size, sequence_length)		,      generator=__snake_case		)

								UpperCAmelCase			:	int										=		{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}

								return noise, input_ids, pipeline_inputs

								# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







	def 					A     (						self      :					str		)      ->		Tuple:
								UpperCAmelCase			:	Tuple										=		77

								UpperCAmelCase			:	Any										=		torch.randint(2		,      56		,      size=(1, max_seq_length)		,      generator=torch.manual_seed(0		)		)

								UpperCAmelCase			:	Union[str, Any]										=		{}
								UpperCAmelCase			:	Union[str, Any]										=		inputs
								return prepared_inputs







	def 					A     (						self      :					Any		,      __snake_case      :					Dict		)      ->		Union[str, Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_components()
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__snake_case		,      unet_lora_layers=lora_components['''unet_lora_layers''']		,      text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']		,      )
								self.assertTrue(os.path.isfile(os.path.join(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)







	def 					A     (						self      :					List[str]		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		self.get_dummy_components()
								UpperCAmelCase			:	Any										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Dict										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_inputs()

								UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		original_images[0, -3:, -3:, -1]

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      unet_lora_layers=lora_components['''unet_lora_layers''']		,      text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']		,      )
															self.assertTrue(os.path.isfile(os.path.join(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	Optional[int]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	str										=		lora_images[0, -3:, -3:, -1]

								# Outputs shouldn't match.
								self.assertFalse(torch.allclose(torch.from_numpy(__snake_case		)		,      torch.from_numpy(__snake_case		)		)		)







	def 					A     (						self      :					Any		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[Any]										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Optional[int]										=		self.get_dummy_inputs()

								UpperCAmelCase			:	str										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Any										=		original_images[0, -3:, -3:, -1]

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      unet_lora_layers=lora_components['''unet_lora_layers''']		,      text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']		,      safe_serialization=__snake_case		,      )
															self.assertTrue(os.path.isfile(os.path.join(__snake_case		,      '''pytorch_lora_weights.safetensors'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	Any										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		lora_images[0, -3:, -3:, -1]

								# Outputs shouldn't match.
								self.assertFalse(torch.allclose(torch.from_numpy(__snake_case		)		,      torch.from_numpy(__snake_case		)		)		)







	def 					A     (						self      :					List[str]		)      ->		int:
								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[int]										=		lora_components['''unet_lora_attn_procs''']
								UpperCAmelCase			:	Any										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	List[str]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_inputs()

								UpperCAmelCase			:	Tuple										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		original_images[0, -3:, -3:, -1]

								with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCAmelCase			:	List[Any]										=		sd_pipe.unet
															unet.set_attn_processor(__snake_case		)
															unet.save_attn_procs(__snake_case		)
															self.assertTrue(os.path.isfile(os.path.join(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	Dict										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Tuple										=		lora_images[0, -3:, -3:, -1]

								# Outputs shouldn't match.
								self.assertFalse(torch.allclose(torch.from_numpy(__snake_case		)		,      torch.from_numpy(__snake_case		)		)		)







	def 					A     (						self      :					Optional[int]		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	Any										=		StableDiffusionPipeline(**__snake_case		)

								UpperCAmelCase			:	Optional[Any]										=		self.get_dummy_tokens()

								# inference without lora
								UpperCAmelCase			:	Optional[Any]										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_without_lora.shape == (1, 77, 32)

								# create lora_attn_procs with zeroed out up.weights
								UpperCAmelCase			:	str										=		create_text_encoder_lora_attn_procs(pipe.text_encoder		)
								set_lora_up_weights(__snake_case		,      randn_weight=__snake_case		)

								# monkey patch
								pipe._modify_text_encoder(__snake_case		)

								# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
								del text_attn_procs
								gc.collect()

								# inference with lora
								UpperCAmelCase			:	Any										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_with_lora.shape == (1, 77, 32)

								assert torch.allclose(
								    __snake_case		,      __snake_case		), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

								# create lora_attn_procs with randn up.weights
								UpperCAmelCase			:	List[Any]										=		create_text_encoder_lora_attn_procs(pipe.text_encoder		)
								set_lora_up_weights(__snake_case		,      randn_weight=__snake_case		)

								# monkey patch
								pipe._modify_text_encoder(__snake_case		)

								# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
								del text_attn_procs
								gc.collect()

								# inference with lora
								UpperCAmelCase			:	Optional[int]										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_with_lora.shape == (1, 77, 32)

								assert not torch.allclose(
								    __snake_case		,      __snake_case		), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







	def 					A     (						self      :					Optional[int]		)      ->		str:
								UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[int]										=		StableDiffusionPipeline(**__snake_case		)

								UpperCAmelCase			:	Tuple										=		self.get_dummy_tokens()

								# inference without lora
								UpperCAmelCase			:	str										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_without_lora.shape == (1, 77, 32)

								# create lora_attn_procs with randn up.weights
								UpperCAmelCase			:	str										=		create_text_encoder_lora_attn_procs(pipe.text_encoder		)
								set_lora_up_weights(__snake_case		,      randn_weight=__snake_case		)

								# monkey patch
								pipe._modify_text_encoder(__snake_case		)

								# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
								del text_attn_procs
								gc.collect()

								# inference with lora
								UpperCAmelCase			:	int										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_with_lora.shape == (1, 77, 32)

								assert not torch.allclose(
								    __snake_case		,      __snake_case		), "lora outputs should be different to without lora outputs"

								# remove monkey patch
								pipe._remove_text_encoder_monkey_patch()

								# inference with removed lora
								UpperCAmelCase			:	Any										=		pipe.text_encoder(**__snake_case		)[0]
								assert outputs_without_lora_removed.shape == (1, 77, 32)

								assert torch.allclose(
								    __snake_case		,      __snake_case		), "remove lora monkey patch should restore the original outputs"







	def 					A     (						self      :					str		)      ->		List[Any]:
								UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_components()
								UpperCAmelCase			:	Tuple										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      unet_lora_layers=lora_components['''unet_lora_layers''']		,      text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']		,      )
															self.assertTrue(os.path.isfile(os.path.join(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	str										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Union[str, Any]										=		lora_images[0, -3:, -3:, -1]

								UpperCAmelCase			:	Dict										=		sd_pipe(**__snake_case		,      cross_attention_kwargs={'''scale''': 0.5}		).images
								UpperCAmelCase			:	Dict										=		lora_images_with_scale[0, -3:, -3:, -1]

								# Outputs shouldn't match.
								self.assertFalse(
								    torch.allclose(torch.from_numpy(__snake_case		)		,      torch.from_numpy(__snake_case		)		)		)







	def 					A     (						self      :					Tuple		)      ->		Dict:
								with tempfile.TemporaryDirectory() as tmpdirname:
															self.create_lora_weight_file(__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		self.get_dummy_components()
															UpperCAmelCase			:	Tuple										=		StableDiffusionPipeline(**__snake_case		)
															UpperCAmelCase			:	List[str]										=		sd_pipe.to(__snake_case		)
															sd_pipe.set_progress_bar_config(disable=__snake_case		)

															# check if vanilla attention processors are used
															for _, module in sd_pipe.unet.named_modules():
																						if isinstance(__snake_case		,      __snake_case		):
																													self.assertIsInstance(module.processor		,      (AttnProcessor, AttnProcessor2_0)		)

            # load LoRA weight file
															sd_pipe.load_lora_weights(__snake_case		)

															# check if lora attention processors are used
															for _, module in sd_pipe.unet.named_modules():
																						if isinstance(__snake_case		,      __snake_case		):
																													UpperCAmelCase			:	List[Any]										=		(
																													    LoRAAttnProcessor2_0 if hasattr(__snake_case		,      '''scaled_dot_product_attention'''		) else LoRAAttnProcessor
																													)
																													self.assertIsInstance(module.processor		,      __snake_case		)







	@unittest.skipIf(torch_device != '''cuda'''		,      '''This test is supposed to run on GPU'''		)
	def 					A     (						self      :					List[str]		)      ->		Optional[Any]:
								with tempfile.TemporaryDirectory() as tmpdirname:
															self.create_lora_weight_file(__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
															UpperCAmelCase			:	List[str]										=		StableDiffusionPipeline(**__snake_case		)
															UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
															sd_pipe.set_progress_bar_config(disable=__snake_case		)

															# enable XFormers
															sd_pipe.enable_xformers_memory_efficient_attention()

															# check if xFormers attention processors are used
															for _, module in sd_pipe.unet.named_modules():
																						if isinstance(__snake_case		,      __snake_case		):
																													self.assertIsInstance(module.processor		,      __snake_case		)

            # load LoRA weight file
															sd_pipe.load_lora_weights(__snake_case		)

															# check if lora attention processors are used
															for _, module in sd_pipe.unet.named_modules():
																						if isinstance(__snake_case		,      __snake_case		):
																													self.assertIsInstance(module.processor		,      __snake_case		)







	@unittest.skipIf(torch_device != '''cuda'''		,      '''This test is supposed to run on GPU'''		)
	def 					A     (						self      :					List[Any]		)      ->		Any:
								UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		self.get_dummy_components()
								UpperCAmelCase			:	str										=		StableDiffusionPipeline(**__snake_case		)
								UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	int										=		self.get_dummy_inputs()

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

								UpperCAmelCase			:	Tuple										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Dict										=		original_images[0, -3:, -3:, -1]

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__snake_case		,      unet_lora_layers=lora_components['''unet_lora_layers''']		,      text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']		,      )
															self.assertTrue(os.path.isfile(os.path.join(__snake_case		,      '''pytorch_lora_weights.bin'''		)		)		)
															sd_pipe.load_lora_weights(__snake_case		)

								UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Optional[int]										=		lora_images[0, -3:, -3:, -1]

								# Outputs shouldn't match.
								self.assertFalse(torch.allclose(torch.from_numpy(__snake_case		)		,      torch.from_numpy(__snake_case		)		)		)










'''simple docstring'''






class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[Any]		,      __snake_case      :					Dict		)      ->		str:
								UpperCAmelCase			:	str										=		val
								# Number of nodes in left subtree
								UpperCAmelCase			:	Optional[int]										=		0
								UpperCAmelCase			:	str										=		None
								UpperCAmelCase			:	Optional[Any]										=		None
								UpperCAmelCase			:	Dict										=		None







	def 					A     (						self      :					Tuple		,      __snake_case      :					Dict		)      ->		Dict:
								assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

								if self.val < other.val:
															UpperCAmelCase			:	Optional[int]										=		self.right
															UpperCAmelCase			:	Optional[Any]										=		None
															if self.right:
																						UpperCAmelCase			:	Tuple										=		other
															UpperCAmelCase			:	int										=		other
															UpperCAmelCase			:	Dict										=		self.left_tree_size * 2 + 1
															return self
								else:
															UpperCAmelCase			:	int										=		other.right
															UpperCAmelCase			:	List[str]										=		None
															if other.right:
																						UpperCAmelCase			:	Dict										=		self
															UpperCAmelCase			:	Optional[Any]										=		self
															UpperCAmelCase			:	Optional[int]										=		other.left_tree_size * 2 + 1
															return other

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					Union[str, Any]		,      __snake_case      :					str=None		,      __snake_case      :					Any=None		,      __snake_case      :					Any=0		)      ->		Dict:
								UpperCAmelCase			:	str										=		heap_size
								UpperCAmelCase			:	Tuple										=		bottom_root
								UpperCAmelCase			:	str										=		min_node







	def 					A     (						self      :					Tuple		,      __snake_case      :					Union[str, Any]		)      ->		Optional[Any]:

								# Empty heaps corner cases
								if other.size == 0:
															return None
								if self.size == 0:
															UpperCAmelCase			:	List[str]										=		other.size
															UpperCAmelCase			:	Union[str, Any]										=		other.bottom_root
															UpperCAmelCase			:	Optional[int]										=		other.min_node
															return None
								# Update size
								UpperCAmelCase			:	Tuple										=		self.size + other.size

								# Update min.node
								if self.min_node.val > other.min_node.val:
															UpperCAmelCase			:	Union[str, Any]										=		other.min_node
								# Merge

								# Order roots by left_subtree_size
								UpperCAmelCase			:	List[str]										=		[]
								UpperCAmelCase							,		UpperCAmelCase			:	Optional[int]										=		self.bottom_root, other.bottom_root
								while i or j:
															if i and ((not j) or i.left_tree_size < j.left_tree_size):
																						combined_roots_list.append((i, True)		)
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
															else:
																						combined_roots_list.append((j, False)		)
																						UpperCAmelCase			:	List[str]										=		j.parent
        # Insert links between them
								for i in range(len(__snake_case		) - 1		):
															if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																						UpperCAmelCase			:	Union[str, Any]										=		combined_roots_list[i + 1][0]
																						UpperCAmelCase			:	Optional[Any]										=		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
								UpperCAmelCase			:	Optional[int]										=		combined_roots_list[0][0]
								while i.parent:
															if (
															    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
															) or (
															    i.left_tree_size == i.parent.left_tree_size
															    and i.left_tree_size != i.parent.parent.left_tree_size
															):
																						# Neighbouring Nodes
																						UpperCAmelCase			:	Optional[int]										=		i.left
																						UpperCAmelCase			:	List[Any]										=		i.parent.parent

																						# Merging trees
																						UpperCAmelCase			:	Optional[int]										=		i.merge_trees(i.parent		)

																						# Updating links
																						UpperCAmelCase			:	Any										=		previous_node
																						UpperCAmelCase			:	str										=		next_node
																						if previous_node:
																													UpperCAmelCase			:	Union[str, Any]										=		i
																						if next_node:
																													UpperCAmelCase			:	Optional[Any]										=		i
															else:
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
        # Updating self.bottom_root
								while i.left:
															UpperCAmelCase			:	str										=		i.left
								UpperCAmelCase			:	Optional[int]										=		i

								# Update other
								UpperCAmelCase			:	str										=		self.size
								UpperCAmelCase			:	Union[str, Any]										=		self.bottom_root
								UpperCAmelCase			:	Union[str, Any]										=		self.min_node

								# Return the merged heap
								return self







	def 					A     (						self      :					List[Any]		,      __snake_case      :					List[str]		)      ->		List[str]:
								if self.size == 0:
															UpperCAmelCase			:	List[Any]										=		Node(__snake_case		)
															UpperCAmelCase			:	List[str]										=		1
															UpperCAmelCase			:	str										=		self.bottom_root
								else:
															# Create new node
															UpperCAmelCase			:	int										=		Node(__snake_case		)

															# Update size
															self.size += 1

															# update min_node
															if val < self.min_node.val:
																						UpperCAmelCase			:	Optional[int]										=		new_node
															# Put new_node as a bottom_root in heap
															UpperCAmelCase			:	Optional[Any]										=		new_node
															UpperCAmelCase			:	Optional[Any]										=		self.bottom_root
															UpperCAmelCase			:	Union[str, Any]										=		new_node

															# Consecutively merge roots with same left_tree_size
															while (
															    self.bottom_root.parent
															    and self.bottom_root.left_tree_size
															    == self.bottom_root.parent.left_tree_size
															):
																						# Next node
																						UpperCAmelCase			:	List[str]										=		self.bottom_root.parent.parent

																						# Merge
																						UpperCAmelCase			:	int										=		self.bottom_root.merge_trees(self.bottom_root.parent		)

																						# Update Links
																						UpperCAmelCase			:	str										=		next_node
																						UpperCAmelCase			:	int										=		None
																						if next_node:
																													UpperCAmelCase			:	Dict										=		self.bottom_root







	def 					A     (						self      :					Tuple		)      ->		List[str]:
								return self.min_node.val







	def 					A     (						self      :					Optional[Any]		)      ->		int:
								return self.size == 0







	def 					A     (						self      :					List[str]		)      ->		int:
								UpperCAmelCase			:	Tuple										=		self.min_node.val

								# Last element in heap corner case
								if self.size == 1:
															# Update size
															UpperCAmelCase			:	Tuple										=		0

															# Update bottom root
															UpperCAmelCase			:	Optional[int]										=		None

															# Update min_node
															UpperCAmelCase			:	int										=		None

															return min_value
								# No right subtree corner case
								# The structure of the tree implies that this should be the bottom root
								# and there is at least one other root
								if self.min_node.right is None:
															# Update size
															self.size -= 1

															# Update bottom root
															UpperCAmelCase			:	Optional[Any]										=		self.bottom_root.parent
															UpperCAmelCase			:	List[Any]										=		None

															# Update min_node
															UpperCAmelCase			:	int										=		self.bottom_root
															UpperCAmelCase			:	Any										=		self.bottom_root.parent
															while i:
																						if i.val < self.min_node.val:
																													UpperCAmelCase			:	Dict										=		i
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
															return min_value
								# General case
								# Find the BinomialHeap of the right subtree of min_node
								UpperCAmelCase			:	List[str]										=		self.min_node.right
								UpperCAmelCase			:	Tuple										=		None
								UpperCAmelCase			:	Any										=		bottom_of_new
								UpperCAmelCase			:	str										=		1

								# Size, min_node and bottom_root
								while bottom_of_new.left:
															UpperCAmelCase			:	Tuple										=		size_of_new * 2 + 1
															UpperCAmelCase			:	Union[str, Any]										=		bottom_of_new.left
															if bottom_of_new.val < min_of_new.val:
																						UpperCAmelCase			:	Optional[int]										=		bottom_of_new
        # Corner case of single root on top left path
								if (not self.min_node.left) and (not self.min_node.parent):
															UpperCAmelCase			:	str										=		size_of_new
															UpperCAmelCase			:	str										=		bottom_of_new
															UpperCAmelCase			:	List[str]										=		min_of_new
															# print("Single root, multiple nodes case")
															return min_value
								# Remaining cases
								# Construct heap of right subtree
								UpperCAmelCase			:	int										=		BinomialHeap(
								    bottom_root=__snake_case		,      min_node=__snake_case		,      heap_size=__snake_case		)

								# Update size
								UpperCAmelCase			:	Union[str, Any]										=		self.size - 1 - size_of_new

								# Neighbour nodes
								UpperCAmelCase			:	Any										=		self.min_node.left
								UpperCAmelCase			:	Tuple										=		self.min_node.parent

								# Initialize new bottom_root and min_node
								UpperCAmelCase			:	Optional[int]										=		previous_node or next_node
								UpperCAmelCase			:	int										=		next_node

								# Update links of previous_node and search below for new min_node and
								# bottom_root
								if previous_node:
															UpperCAmelCase			:	List[Any]										=		next_node

															# Update bottom_root and search for min_node below
															UpperCAmelCase			:	Optional[Any]										=		previous_node
															UpperCAmelCase			:	Optional[Any]										=		previous_node
															while self.bottom_root.left:
																						UpperCAmelCase			:	Any										=		self.bottom_root.left
																						if self.bottom_root.val < self.min_node.val:
																													UpperCAmelCase			:	Tuple										=		self.bottom_root
								if next_node:
															UpperCAmelCase			:	Tuple										=		previous_node

															# Search for new min_node above min_node
															UpperCAmelCase			:	Tuple										=		next_node
															while i:
																						if i.val < self.min_node.val:
																													UpperCAmelCase			:	List[str]										=		i
																						UpperCAmelCase			:	Optional[int]										=		i.parent
        # Merge heaps
								self.merge_heaps(__snake_case		)

								return min_value







	def 					A     (						self      :					Union[str, Any]		)      ->		Union[str, Any]:
								UpperCAmelCase			:	Any										=		self.bottom_root
								while top_root.parent:
															UpperCAmelCase			:	Optional[Any]										=		top_root.parent
								# preorder
								UpperCAmelCase			:	List[Any]										=		[]
								self.__traversal(__snake_case		,      __snake_case		)
								return heap_pre_order







	def 					A     (						self      :					List[str]		,      __snake_case      :					Tuple		,      __snake_case      :					int		,      __snake_case      :					Union[str, Any]=0		)      ->		Optional[int]:
								if curr_node:
															preorder.append((curr_node.val, level)		)
															self.__traversal(curr_node.left		,      __snake_case		,      level + 1		)
															self.__traversal(curr_node.right		,      __snake_case		,      level + 1		)
								else:
															preorder.append(('''#''', level)		)







	def __str__(						self      :					Tuple		)      ->		Union[str, Any]:
								if self.is_empty():
															return ""
								UpperCAmelCase			:	Dict										=		self.pre_order()

								return "\n".join(('''-''' * level + str(__snake_case		)) for value, level in preorder_heap		)


# Unit Tests
if __name__ == "__main__":
						import doctest

						doctest.testmod()





