
from __future__ import annotations

from random import random




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:int  ,     a	:int | None = None		)	->				Any:
								__UpperCamelCase			:     Union[str, Any]         =      value
								__UpperCamelCase			:     Union[str, Any]         =      random()
								__UpperCamelCase			:     Node | None         =      None
								__UpperCamelCase			:     Node | None         =      None







	def __repr__(      self	:List[str]		)	->				str:
								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	:Any		)	->				str:
								__UpperCamelCase			:     Optional[int]         =      str(self.value		) + " "
								__UpperCamelCase			:     Optional[Any]         =      str(self.left or ""		)
								__UpperCamelCase			:     Union[str, Any]         =      str(self.right or ""		)
								return value + left + right




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Node | None		,						_lowerCamelCase				:    int)      ->					tuple[Node | None, Node | None]:






							'''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:
																					__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      split(root.left		,						_lowerCamelCase)
																					return left, root
														else:
																					__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      split(root.right		,						_lowerCamelCase)
																					return root, right




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Node | None		,						_lowerCamelCase				:    Node | None)      ->					Node | None:






							'''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:
														__UpperCamelCase			:     int         =      merge(left.right		,						_lowerCamelCase)
														return left
							else:
														__UpperCamelCase			:     List[Any]         =      merge(_lowerCamelCase		,						right.left)
														return right




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Node | None		,						_lowerCamelCase				:    int)      ->					Node | None:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      Node(_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase			:     str         =      split(_lowerCamelCase		,						_lowerCamelCase)
							return merge(merge(_lowerCamelCase		,						_lowerCamelCase)		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Node | None		,						_lowerCamelCase				:    int)      ->					Node | None:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      split(_lowerCamelCase		,						value - 1)
							__UpperCamelCase     ,		__UpperCamelCase			:     int         =      split(_lowerCamelCase		,						_lowerCamelCase)
							return merge(_lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Node | None)      ->					None:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Node | None		,						_lowerCamelCase				:    str)      ->					Node | None:






							'''simple docstring'''





							for arg in args.split():
														if arg[0] == "+":
																					__UpperCamelCase			:     Optional[int]         =      insert(_lowerCamelCase		,						int(arg[1:]))

														elif arg[0] == "-":
																					__UpperCamelCase			:     Any         =      erase(_lowerCamelCase		,						int(arg[1:]))

														else:
																					print("Unknown command")

							return root




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      None
							print(
							    "enter numbers to create a tree, + value to add value into treap, "
							    "- value to erase all nodes with value. 'q' to quit. ")

							__UpperCamelCase			:     int         =      input()
							while args != "q":
														__UpperCamelCase			:     Union[str, Any]         =      interact_treap(_lowerCamelCase		,						_lowerCamelCase)
														print(_lowerCamelCase)
														__UpperCamelCase			:     Union[str, Any]         =      input()

							print("good by!")


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)