

from __future__ import annotations

from random import random


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= value
				snake_case_      		= random()
				snake_case_      		= None
				snake_case_      		= None


			def __repr__(    self							):

				"""simple docstring"""


				from pprint import pformat

				if self.left is None and self.right is None:
					return f"""'{self.value}: {self.prior:.5}'"""
				else:
					return pformat(
					    {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)} ,				indent=1							)







			def __str__(    self							):

				"""simple docstring"""


				snake_case_      		= str(self.value							) + ' '
				snake_case_      		= str(self.left or ''							)
				snake_case_      		= str(self.right or ''							)
				return value + left + right







def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if root is None:  # None tree is split into 2 Nones
		return None, None
	elif root.value is None:
		return None, None
	else:
		if value < root.value:
			snake_case_		,       snake_case_      		= split(root.left			, lowercase__	)
			return left, root
		else:
			snake_case_		,       snake_case_      		= split(root.right			, lowercase__	)
			return root, right





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		snake_case_      		= merge(left.right			, lowercase__	)
		return left
	else:
		snake_case_      		= merge(lowercase__			, right.left	)
		return right





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= Node(lowercase__	)
	snake_case_		,       snake_case_      		= split(lowercase__			, lowercase__	)
	return merge(merge(lowercase__			, lowercase__	)			, lowercase__	)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_		,       snake_case_      		= split(lowercase__			, value - 1	)
	snake_case_		,       snake_case_      		= split(lowercase__			, lowercase__	)
	return merge(lowercase__			, lowercase__	)





def a(lowercase__	):





	'''simple docstring'''
	if not root:  # None
		return
	else:
		inorder(root.left	)
		print(root.value			, end=','	)
		inorder(root.right	)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	for arg in args.split():
		if arg[0] == "+":
			snake_case_      		= insert(lowercase__			, int(arg[1:]	)	)

		elif arg[0] == "-":
			snake_case_      		= erase(lowercase__			, int(arg[1:]	)	)

		else:
			print('Unknown command'	)

	return root





def a():





	'''simple docstring'''
	snake_case_      		= None
	print(
	    'enter numbers to create a tree, + value to add value into treap, '
	    '- value to erase all nodes with value. \'q\' to quit. '	)

	snake_case_      		= input()
	while args != "q":
		snake_case_      		= interact_treap(lowercase__			, lowercase__	)
		print(lowercase__	)
		snake_case_      		= input()

	print('good by!'	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A											=     argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A											=     parser.parse_args()


A											=     'cpu'
A											=     'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A											=     'path-to-your-trained-model'
A											=     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		A											=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A											=     pipe.to(device)

# to channels last
A											=     pipe.unet.to(memory_format=torch.channels_last)
A											=     pipe.vae.to(memory_format=torch.channels_last)
A											=     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		A											=     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A											=     torch.randn(2, 4, 64, 64)
A											=     torch.rand(1) * 999
A											=     torch.randn(2, 77, 768)
A											=     (sample, timestep, encoder_hidden_status)
try:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		A											=     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A											=     666
A											=     torch.Generator(device).manual_seed(seed)
A											=     {'generator': generator}
if args.steps is not None:
		A											=     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		A											=     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')
