






"""simple docstring"""
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class 						a_ (      snake_case_					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       super().__init__()

       self.register_modules(vae=lowerCamelCase_,	text_encoder=lowerCamelCase_,	tokenizer=lowerCamelCase_,	unet=lowerCamelCase_,	scheduler=lowerCamelCase_   )




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       with torch.no_grad():
        lowerCamelCase__	:     int								=		self.vae.encode(tfms.ToTensor()(lowerCamelCase_   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
        lowerCamelCase__	:     List[str]								=		0.18_215 * latent.latent_dist.sample()
       return latent




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		(1 / 0.18_215) * latent
       with torch.no_grad():
        lowerCamelCase__	:     Any								=		self.vae.decode(lowerCamelCase_   ).sample
       lowerCamelCase__	:     Tuple								=		(img / 2 + 0.5).clamp(0,	1   )
       lowerCamelCase__	:     int								=		img.detach().cpu().permute(0,	2,	3,	1   ).numpy()
       lowerCamelCase__	:     Any								=		(img * 2_5_5).round().astype('uint8'   )
       return Image.fromarray(img[0]   )




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		self.tokenizer(
           lowerCamelCase_,	padding='max_length',	max_length=self.tokenizer.model_max_length,	truncation=lowerCamelCase_,	return_tensors='pt',	)

       lowerCamelCase__	:     str								=		self.text_encoder(text_input.input_ids.to(self.device   )   )[0]

       lowerCamelCase__	:     List[Any]								=		self.tokenizer(
           '',	padding='max_length',	max_length=self.tokenizer.model_max_length,	truncation=lowerCamelCase_,	return_tensors='pt',	)

       lowerCamelCase__	:     Tuple								=		self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

       return torch.cat([uncond_embedding, text_embedding]   )





      def __call__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = 0.3,	lowerCamelCase_ = 0.6,	lowerCamelCase_ = 0.5,	lowerCamelCase_ = 4_2,	lowerCamelCase_ = 5_0,	lowerCamelCase_ = 7.5,	):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		steps - int(kmin * steps   )
       lowerCamelCase__	:     Optional[Any]								=		steps - int(kmax * steps   )

       lowerCamelCase__	:     List[str]								=		self.prep_text(lowerCamelCase_   )

       self.scheduler.set_timesteps(lowerCamelCase_   )

       lowerCamelCase__							,	lowerCamelCase__	:     Dict								=		img.size
       lowerCamelCase__	:     List[str]								=		self.encode(lowerCamelCase_   )

       torch.manual_seed(lowerCamelCase_   )
       lowerCamelCase__	:     List[str]								=		torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8),	).to(self.device   )

       lowerCamelCase__	:     Optional[Any]								=		self.scheduler.add_noise(
           lowerCamelCase_,	lowerCamelCase_,	timesteps=self.scheduler.timesteps[tmax],	)

       lowerCamelCase__	:     int								=		torch.cat([latents] * 2   )

       lowerCamelCase__	:     List[str]								=		self.scheduler.scale_model_input(lowerCamelCase_,	self.scheduler.timesteps[tmax]   )

       with torch.no_grad():
        lowerCamelCase__	:     Dict								=		self.unet(
            lowerCamelCase_,	self.scheduler.timesteps[tmax],	encoder_hidden_states=lowerCamelCase_,	).sample

       lowerCamelCase__							,	lowerCamelCase__	:     Optional[Any]								=		pred.chunk(2   )
       lowerCamelCase__	:     Union[str, Any]								=		pred_uncond + guidance_scale * (pred_text - pred_uncond)

       lowerCamelCase__	:     Optional[int]								=		self.scheduler.step(lowerCamelCase_,	self.scheduler.timesteps[tmax],	lowerCamelCase_   ).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
        if i > tmax:
         if i < tmin:  # layout generation phase
          lowerCamelCase__	:     Any								=		self.scheduler.add_noise(
              lowerCamelCase_,	lowerCamelCase_,	timesteps=lowerCamelCase_,	)

          lowerCamelCase__	:     int								=		(mix_factor * latents) + (
              1 - mix_factor
          ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
          lowerCamelCase__	:     List[str]								=		torch.cat([input] * 2   )

         else:  # content generation phase
          lowerCamelCase__	:     Union[str, Any]								=		torch.cat([latents] * 2   )

         lowerCamelCase__	:     Tuple								=		self.scheduler.scale_model_input(lowerCamelCase_,	lowerCamelCase_   )

         with torch.no_grad():
          lowerCamelCase__	:     Optional[int]								=		self.unet(
              lowerCamelCase_,	lowerCamelCase_,	encoder_hidden_states=lowerCamelCase_,	).sample

         lowerCamelCase__							,	lowerCamelCase__	:     Optional[int]								=		pred.chunk(2   )
         lowerCamelCase__	:     int								=		pred_uncond + guidance_scale * (pred_text - pred_uncond)

         lowerCamelCase__	:     int								=		self.scheduler.step(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ).prev_sample

       return self.decode(lowerCamelCase_   )









"""simple docstring"""
class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		data
       lowerCamelCase__	:     BinaryTreeNode | None								=		None
       lowerCamelCase__	:     BinaryTreeNode | None								=		None






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if node is None:
  lowerCamelCase__	:     List[str]								=		BinaryTreeNode(_lowerCamelCase							)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase__	:     str								=		insert(node.left_child     ,  _lowerCamelCase							)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase__	:     str								=		insert(node.right_child     ,  _lowerCamelCase							)
 return node






def 			lowerCamelCase_    (		_lowerCamelCase							):  # if node is None,return
 if node:
  lowerCamelCase__	:     Any								=		inorder(node.left_child							)
  lowerCamelCase__	:     Tuple								=		[*inorder_array, node.data]
  lowerCamelCase__	:     List[Any]								=		inorder_array + inorder(node.right_child							)
 else:
  lowerCamelCase__	:     Any								=		[]
 return inorder_array






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		insert(_lowerCamelCase     ,  15							)
 insert(_lowerCamelCase     ,  10							)
 insert(_lowerCamelCase     ,  25							)
 insert(_lowerCamelCase     ,  6							)
 insert(_lowerCamelCase     ,  14							)
 insert(_lowerCamelCase     ,  20							)
 insert(_lowerCamelCase     ,  60							)
 return root






def 			lowerCamelCase_    (		):
 # main function
 lowerCamelCase__	:     List[Any]								=		make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'							)
 inorder(_lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()

