


import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil





class  _a							:




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		str = "cpu"		, _SCREAMING_SNAKE_CASE						:		str = "openai/clip-vit-large-patch14"		)->	None:
			lowerCAmelCase__       :      str   					= device
			lowerCAmelCase__       :      Dict   					= CLIPTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[Any]   					= [0.4814_5466, 0.457_8275, 0.4082_1073]
			lowerCAmelCase__       :      Union[str, Any]   					= [0.2686_2954, 0.2613_0258, 0.2757_7711]
			lowerCAmelCase__       :      str   					= torchvision.transforms.Normalize(self.image_mean		, self.image_std		)
			lowerCAmelCase__       :      int   					= torchvision.transforms.Resize(224		)
			lowerCAmelCase__       :      str   					= torchvision.transforms.CenterCrop(224		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Optional[Any]:
			lowerCAmelCase__       :      Tuple   					= self.resize(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= self.center_crop(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= self.normalize(_SCREAMING_SNAKE_CASE		)
			return images




	def __call__( self						:		str		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		str		)->	List[Any]:
			lowerCAmelCase__       :      Tuple   					= self.tokenizer(text=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Union[str, Any]   					= self.preprocess_img(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= {key: value.to(self.device		) for (key, value) in encoding.items()}
			return encoding





class  _a							(    nn.Module):




	def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Dict=10		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0.01		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		Dict=None		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		Dict=None		, _SCREAMING_SNAKE_CASE						:		List[str]=False		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=True		, _SCREAMING_SNAKE_CASE						:		int="image"		, _SCREAMING_SNAKE_CASE						:		Tuple=True		, _SCREAMING_SNAKE_CASE						:		int=False		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=False		, _SCREAMING_SNAKE_CASE						:		Any=False		, )->	None:
			super().__init__()
			lowerCAmelCase__       :      Tuple   					= None
			lowerCAmelCase__       :      Any   					= device if device else get_device()
			if vqgan:
					lowerCAmelCase__       :      Optional[int]   					= vqgan
			else:
					lowerCAmelCase__       :      Dict   					= load_vqgan(self.device		, conf_path=_SCREAMING_SNAKE_CASE		, ckpt_path=_SCREAMING_SNAKE_CASE		)
			self.vqgan.eval()
			if clip:
					lowerCAmelCase__       :      Optional[Any]   					= clip
			else:
					lowerCAmelCase__       :      Dict   					= CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''		)
			self.clip.to(self.device		)
			lowerCAmelCase__       :      Dict   					= ProcessorGradientFlow(device=self.device		)

			lowerCAmelCase__       :      Optional[Any]   					= iterations
			lowerCAmelCase__       :      Any   					= lr
			lowerCAmelCase__       :      Optional[int]   					= log
			lowerCAmelCase__       :      Optional[int]   					= make_grid
			lowerCAmelCase__       :      str   					= return_val
			lowerCAmelCase__       :      Tuple   					= quantize
			lowerCAmelCase__       :      Dict   					= self.vqgan.decoder.z_shape




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, _SCREAMING_SNAKE_CASE						:		int=5		, _SCREAMING_SNAKE_CASE						:		Any=True		)->	int:
			lowerCAmelCase__       :      Optional[int]   					= []
			if output_path is None:
					lowerCAmelCase__       :      Union[str, Any]   					= '''./animation.gif'''
			if input_path is None:
					lowerCAmelCase__       :      Any   					= self.save_path
			lowerCAmelCase__       :      Optional[int]   					= sorted(glob(input_path + '''/*'''		)		)
			if not len(_SCREAMING_SNAKE_CASE		):
					raise ValueError(
					    '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
					    ''' function?)'''		)
			if len(_SCREAMING_SNAKE_CASE		) == 1:
					print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''		)
			lowerCAmelCase__       :      Optional[int]   					= total_duration / len(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      int   					= [frame_duration] * len(_SCREAMING_SNAKE_CASE		)
			if extend_frames:
					lowerCAmelCase__       :      Dict   					= 1.5
					lowerCAmelCase__       :      List[Any]   					= 3
			for file_name in paths:
					if file_name.endswith('''.png'''		):
							images.append(imageio.imread(_SCREAMING_SNAKE_CASE		)		)
			imageio.mimsave(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, duration=_SCREAMING_SNAKE_CASE		)
			print(F'gif saved to {output_path}'		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		Any=None		)->	str:
			if not (path or img):
					raise ValueError('''Input either path or tensor'''		)
			if img is not None:
					raise NotImplementedError
			lowerCAmelCase__       :      Optional[int]   					= preprocess(Image.open(_SCREAMING_SNAKE_CASE		)		, target_image_size=256		).to(self.device		)
			lowerCAmelCase__       :      Tuple   					= preprocess_vqgan(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__					,     *lowerCAmelCase__       :      Tuple   					= self.vqgan.encode(_SCREAMING_SNAKE_CASE		)
			return z




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	List[Any]:
			lowerCAmelCase__       :      Optional[int]   					= self.latent.detach().requires_grad_()
			lowerCAmelCase__       :      Any   					= base_latent + transform_vector
			if self.quantize:
					lowerCAmelCase__					,     *lowerCAmelCase__       :      List[Any]   					= self.vqgan.quantize(_SCREAMING_SNAKE_CASE		)
			else:
					lowerCAmelCase__       :      List[str]   					= trans_latent
			return self.vqgan.decode(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=None		)->	Optional[Any]:
			lowerCAmelCase__       :      List[str]   					= self.clip_preprocessor(text=_SCREAMING_SNAKE_CASE		, images=_SCREAMING_SNAKE_CASE		, return_tensors='''pt'''		, padding=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[Any]   					= self.clip(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= clip_outputs.logits_per_image
			if weights is not None:
					lowerCAmelCase__       :      int   					= similarity_logits * weights
			return similarity_logits.sum()




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[int]:
			lowerCAmelCase__       :      List[str]   					= self._get_clip_similarity(pos_prompts['''prompts''']		, _SCREAMING_SNAKE_CASE		, weights=(1 / pos_prompts['''weights'''])		)
			if neg_prompts:
					lowerCAmelCase__       :      Optional[int]   					= self._get_clip_similarity(neg_prompts['''prompts''']		, _SCREAMING_SNAKE_CASE		, weights=neg_prompts['''weights''']		)
			else:
					lowerCAmelCase__       :      Tuple   					= torch.tensor([1]		, device=self.device		)
			lowerCAmelCase__       :      Union[str, Any]   					= -torch.log(_SCREAMING_SNAKE_CASE		) + torch.log(_SCREAMING_SNAKE_CASE		)
			return loss




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	str:
			lowerCAmelCase__       :      str   					= torch.randn_like(self.latent		, requires_grad=_SCREAMING_SNAKE_CASE		, device=self.device		)
			lowerCAmelCase__       :      Union[str, Any]   					= torch.optim.Adam([vector]		, lr=self.lr		)

			for i in range(self.iterations		):
					optim.zero_grad()
					lowerCAmelCase__       :      Dict   					= self._add_vector(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[Any]   					= loop_post_process(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[str]   					= self._get_CLIP_loss(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					print('''CLIP loss'''		, _SCREAMING_SNAKE_CASE		)
					if self.log:
							wandb.log({'''CLIP Loss''': clip_loss}		)
					clip_loss.backward(retain_graph=_SCREAMING_SNAKE_CASE		)
					optim.step()
					if self.return_val == "image":
							yield custom_to_pil(transformed_img[0]		)
					else:
							yield vector




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Tuple:
			wandb.init(reinit=_SCREAMING_SNAKE_CASE		, project='''face-editor'''		)
			wandb.config.update({'''Positive Prompts''': positive_prompts}		)
			wandb.config.update({'''Negative Prompts''': negative_prompts}		)
			wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}		)
			if image_path:
					lowerCAmelCase__       :      List[Any]   					= Image.open(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      Tuple   					= image.resize((256, 256)		)
					wandb.log('''Original Image'''		, wandb.Image(_SCREAMING_SNAKE_CASE		)		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	int:
			if not prompts:
					return []
			lowerCAmelCase__       :      Any   					= []
			lowerCAmelCase__       :      Optional[int]   					= []
			if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		):
					lowerCAmelCase__       :      Optional[Any]   					= [prompt.strip() for prompt in prompts.split('''|'''		)]
			for prompt in prompts:
					if isinstance(_SCREAMING_SNAKE_CASE		, (tuple, list)		):
							lowerCAmelCase__       :      List[Any]   					= prompt[0]
							lowerCAmelCase__       :      Union[str, Any]   					= float(prompt[1]		)
					elif ":" in prompt:
							lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= prompt.split(''':'''		)
							lowerCAmelCase__       :      List[str]   					= float(_SCREAMING_SNAKE_CASE		)
					else:
							lowerCAmelCase__       :      Optional[int]   					= prompt
							lowerCAmelCase__       :      str   					= 1.0
					processed_prompts.append(_SCREAMING_SNAKE_CASE		)
					weights.append(_SCREAMING_SNAKE_CASE		)
			return {
			    "prompts": processed_prompts,
			    "weights": torch.tensor(_SCREAMING_SNAKE_CASE		, device=self.device		),
			}




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, _SCREAMING_SNAKE_CASE						:		List[Any]=None		, _SCREAMING_SNAKE_CASE						:		int=True		, _SCREAMING_SNAKE_CASE						:		Tuple=False		, _SCREAMING_SNAKE_CASE						:		Optional[int]=True		, _SCREAMING_SNAKE_CASE						:		List[Any]=True		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, )->	List[str]:
			if image_path:
					lowerCAmelCase__       :      str   					= self._get_latent(_SCREAMING_SNAKE_CASE		)
			else:
					lowerCAmelCase__       :      Optional[int]   					= torch.randn(self.latent_dim		, device=self.device		)
			if self.log:
					self._init_logging(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			assert pos_prompts, "You must provide at least one positive prompt."
			lowerCAmelCase__       :      int   					= self.process_prompts(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= self.process_prompts(_SCREAMING_SNAKE_CASE		)
			if save_final and save_path is None:
					lowerCAmelCase__       :      Optional[Any]   					= os.path.join('''./outputs/'''		, '''_'''.join(pos_prompts['''prompts''']		)		)
			if not os.path.exists(_SCREAMING_SNAKE_CASE		):
					os.makedirs(_SCREAMING_SNAKE_CASE		)
			else:
					lowerCAmelCase__       :      str   					= save_path + '''_''' + get_timestamp()
					os.makedirs(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= save_path

			lowerCAmelCase__       :      Dict   					= self.vqgan.decode(self.latent		)[0]
			if show_intermediate:
					print('''Original Image'''		)
					show_pil(custom_to_pil(_SCREAMING_SNAKE_CASE		)		)

			lowerCAmelCase__       :      str   					= loop_post_process(_SCREAMING_SNAKE_CASE		)
			for iter, transformed_img in enumerate(self._optimize_CLIP(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		):
					if show_intermediate:
							show_pil(_SCREAMING_SNAKE_CASE		)
					if save_intermediate:
							transformed_img.save(os.path.join(self.save_path		, F'iter_{iter:03d}.png'		)		)
					if self.log:
							wandb.log({'''Image''': wandb.Image(_SCREAMING_SNAKE_CASE		)}		)
			if show_final:
					show_pil(_SCREAMING_SNAKE_CASE		)
			if save_final:
					transformed_img.save(os.path.join(self.save_path		, F'iter_{iter:03d}_final.png'		)		)








from collections.abc import Iterable
from typing import Any





class  _a							:




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		int | None = None		)->	Tuple:
			lowerCAmelCase__       :      Union[str, Any]   					= value
			lowerCAmelCase__       :      Node | None   					= None  # Added in order to delete a node easier
			lowerCAmelCase__       :      Node | None   					= None
			lowerCAmelCase__       :      Node | None   					= None




	def __repr__( self						:		List[Any]		)->	str:
			from pprint import pformat

			if self.left is None and self.right is None:
					return str(self.value		)
			return pformat({F'{self.value}': (self.left, self.right)}		, indent=1		)





class  _a							:




	def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	int:
			lowerCAmelCase__       :      Dict   					= root




	def __str__( self						:		Tuple		)->	str:
			return str(self.root		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if new_children is not None:  # reset its kids
					lowerCAmelCase__       :      Union[str, Any]   					= node.parent
			if node.parent is not None:  # reset its parent
					if self.is_right(_SCREAMING_SNAKE_CASE		):  # If it is the right children
							lowerCAmelCase__       :      List[str]   					= new_children
					else:
							lowerCAmelCase__       :      Any   					= new_children
			else:
					lowerCAmelCase__       :      Optional[Any]   					= new_children




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Node		)->	bool:
			if node.parent and node.parent.right:
					return node == node.parent.right
			return False




	def 							UpperCAmelCase__( self						:		int		)->	bool:
			return self.root is None




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	None:
			lowerCAmelCase__       :      Any   					= Node(_SCREAMING_SNAKE_CASE		)  # create a new Node
			if self.empty():  # if Tree is empty
					lowerCAmelCase__       :      Optional[Any]   					= new_node  # set its root
			else:  # Tree is not empty
					lowerCAmelCase__       :      str   					= self.root  # from root
					if parent_node is None:
							return
					while True:  # While we don't get to a leaf
							if value < parent_node.value:  # We go left
									if parent_node.left is None:
											lowerCAmelCase__       :      Dict   					= new_node  # We insert the new node in a leaf
											break
									else:
											lowerCAmelCase__       :      List[Any]   					= parent_node.left
							else:
									if parent_node.right is None:
											lowerCAmelCase__       :      Tuple   					= new_node
											break
									else:
											lowerCAmelCase__       :      Union[str, Any]   					= parent_node.right
					lowerCAmelCase__       :      Dict   					= parent_node




	def 							UpperCAmelCase__( self						:		str		, *_SCREAMING_SNAKE_CASE						:		List[Any]		)->	None:
			for value in values:
					self.__insert(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Node | None:
			if self.empty():
					raise IndexError('''Warning: Tree is empty! please use another.'''		)
			else:
					lowerCAmelCase__       :      Union[str, Any]   					= self.root
					# use lazy evaluation here to avoid NoneType Attribute error
					while node is not None and node.value is not value:
							lowerCAmelCase__       :      List[str]   					= node.left if value < node.value else node.right
					return node




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					if self.root is None:
							return None
					lowerCAmelCase__       :      List[Any]   					= self.root

			if not self.empty():
					while node.right is not None:
							lowerCAmelCase__       :      str   					= node.right
			return node




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					lowerCAmelCase__       :      Any   					= self.root
			if self.root is None:
					return None
			if not self.empty():
					lowerCAmelCase__       :      Dict   					= self.root
					while node.left is not None:
							lowerCAmelCase__       :      Tuple   					= node.left
			return node




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			lowerCAmelCase__       :      Optional[Any]   					= self.search(_SCREAMING_SNAKE_CASE		)  # Look for the node with that label
			if node is not None:
					if node.left is None and node.right is None:  # If it has no children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					elif node.left is None:  # Has only right children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.right		)
					elif node.right is None:  # Has only left children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.left		)
					else:
							lowerCAmelCase__       :      Tuple   					= self.get_max(
							    node.left		)  # Gets the max value of the left branch
							self.remove(tmp_node.value		)  # type: ignore
							lowerCAmelCase__       :      Optional[Any]   					= (
							    tmp_node.value  # type: ignore
							)  # Assigns the value to the node to delete and keep tree structure




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	Iterable:
			if node is not None:
					yield node  # Preorder Traversal
					yield from self.preorder_traverse(node.left		)
					yield from self.preorder_traverse(node.right		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple=None		)->	Any:
			if traversal_function is None:
					return self.preorder_traverse(self.root		)
			else:
					return traversal_function(self.root		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		list		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if node:
					self.inorder(_SCREAMING_SNAKE_CASE		, node.left		)
					arr.append(node.value		)
					self.inorder(_SCREAMING_SNAKE_CASE		, node.right		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Node		)->	int:
			lowerCAmelCase__       :      list[int]   					= []
			self.inorder(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)  # append all values to list using inorder traversal
			return arr[k - 1]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= []
		if curr_node is not None:
				lowerCAmelCase__       :      Optional[int]   					= postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
		return node_list






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= (8, 3, 6, 1, 10, 14, 13, 4, 7)
		lowerCAmelCase__       :      str   					= BinarySearchTree()
		for i in testlist:
				t.insert(_a	)

		# Prints all the elements of the list in order traversal
		print(_a	)

		if t.search(6	) is not None:
				print('''The value 6 exists'''	)
		else:
				print('''The value 6 doesn\'t exist'''	)

		if t.search(-1	) is not None:
				print('''The value -1 exists'''	)
		else:
				print('''The value -1 doesn\'t exist'''	)

		if not t.empty():
				print('''Max Value: '''     ,       t.get_max().value	)  # type: ignore
				print('''Min Value: '''     ,       t.get_min().value	)  # type: ignore

		for i in testlist:
				t.remove(_a	)
				print(_a	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)




