

class 							A_				:


     def __init__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Optional[int]      )							->			List[str]:
      __lowerCAmelCase:     Union[str, Any]     			=   data
      __lowerCAmelCase:     List[Any]     			=   None
      __lowerCAmelCase:     List[str]     			=   None


     def __str__(							self    :	Any      )							->			List[str]:
      return F'''{self.data}'''







class 							A_				:


     def __init__(							self    :	Tuple      )							->			Dict:
      __lowerCAmelCase:     Any     			=   None
      __lowerCAmelCase:     Optional[int]     			=   None


     def __iter__(							self    :	Union[str, Any]      )							->			str:
      __lowerCAmelCase:     Optional[int]     			=   self.head
      while node:
       yield node.data
       __lowerCAmelCase:     Optional[int]     			=   node.next


     def __str__(							self    :	List[str]      )							->			Dict:
      return "->".join([str(UpperCAmelCase      ) for item in self]      )


     def __len__(							self    :	Optional[Any]      )							->			List[str]:
      return sum(1 for _ in self      )


     def 						UpperCAmelCase						(							self    :	Any    ,						UpperCAmelCase    :	int      )							->			List[str]:
      self.insert_at_nth(0    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	str      )							->			Tuple:
      self.insert_at_nth(len(self      )    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   len(self      )

      if not 0 <= index <= length:
       raise IndexError('list index out of range'      )
      __lowerCAmelCase:     Dict     			=   Node(UpperCAmelCase      )
      if self.head is None:
       __lowerCAmelCase:     str     			=   new_node
      elif index == 0:
       __lowerCAmelCase:     List[str]     			=   new_node
       __lowerCAmelCase:     List[Any]     			=   self.head
       __lowerCAmelCase:     Optional[Any]     			=   new_node
      elif index == length:
       __lowerCAmelCase:     Union[str, Any]     			=   new_node
       __lowerCAmelCase:     Optional[int]     			=   self.tail
       __lowerCAmelCase:     Optional[int]     			=   new_node
      else:
       __lowerCAmelCase:     List[str]     			=   self.head
       for _ in range(0    ,						UpperCAmelCase      ):
        __lowerCAmelCase:     Dict     			=   temp.next
       __lowerCAmelCase:     int     			=   new_node
       __lowerCAmelCase:     Union[str, Any]     			=   temp.previous
       __lowerCAmelCase:     Union[str, Any]     			=   temp
       __lowerCAmelCase:     List[str]     			=   new_node


     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			Optional[int]:
      return self.delete_at_nth(0      )


     def 						UpperCAmelCase						(							self    :	Any      )							->			Dict:
      return self.delete_at_nth(len(self      ) - 1      )


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	int      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   len(self      )

      if not 0 <= index <= length - 1:
       raise IndexError('list index out of range'      )
      __lowerCAmelCase:     List[str]     			=   self.head  # default first node
      if length == 1:
       __lowerCAmelCase:     List[Any]     			=   None
      elif index == 0:
       __lowerCAmelCase:     Tuple     			=   self.head.next
       __lowerCAmelCase:     int     			=   None
      elif index == length - 1:
       __lowerCAmelCase:     List[str]     			=   self.tail
       __lowerCAmelCase:     Optional[int]     			=   self.tail.previous
       __lowerCAmelCase:     List[str]     			=   None
      else:
       __lowerCAmelCase:     List[Any]     			=   self.head
       for _ in range(0    ,						UpperCAmelCase      ):
        __lowerCAmelCase:     Optional[int]     			=   temp.next
       __lowerCAmelCase:     Tuple     			=   temp
       __lowerCAmelCase:     Tuple     			=   temp.previous
       __lowerCAmelCase:     int     			=   temp.next
      return delete_node.data


     def 						UpperCAmelCase						(							self    :	List[str]    ,						UpperCAmelCase    :	Any      )							->			str:
      __lowerCAmelCase:     Optional[Any]     			=   self.head

      while current.data != data:  # Find the position to delete
       if current.next:
        __lowerCAmelCase:     Optional[Any]     			=   current.next
       else:  # We have reached the end an no value matches
        raise ValueError('No data matching given value'      )

      if current == self.head:
       self.delete_head()

      elif current == self.tail:
       self.delete_tail()

      else:  # Before: 1 <--> 2(current) <--> 3
       __lowerCAmelCase:     Any     			=   current.next  # 1 --> 3
       __lowerCAmelCase:     Optional[int]     			=   current.previous  # 1 <--> 3
      return data


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Union[str, Any]:
      return len(self      ) == 0







def _a				( )					->					None:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   DoublyLinkedList()
 assert linked_list.is_empty() is True
 assert str(SCREAMING_SNAKE_CASE	) == ""

 try:
  linked_list.delete_head()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 try:
  linked_list.delete_tail()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 for i in range(10	):
  assert len(SCREAMING_SNAKE_CASE	) == i
  linked_list.insert_at_nth(SCREAMING_SNAKE_CASE					,      i + 1	)
 assert str(SCREAMING_SNAKE_CASE	) == "->".join(str(SCREAMING_SNAKE_CASE	) for i in range(1					,      11	)	)

 linked_list.insert_at_head(0	)
 linked_list.insert_at_tail(11	)
 assert str(SCREAMING_SNAKE_CASE	) == "->".join(str(SCREAMING_SNAKE_CASE	) for i in range(0					,      12	)	)

 assert linked_list.delete_head() == 0
 assert linked_list.delete_at_nth(9	) == 10
 assert linked_list.delete_tail() == 11
 assert len(SCREAMING_SNAKE_CASE	) == 9
 assert str(SCREAMING_SNAKE_CASE	) == "->".join(str(SCREAMING_SNAKE_CASE	) for i in range(1					,      10	)	)


if __name__ == "__main__":
  from doctest import testmod

  testmod()








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__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

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


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

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

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

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

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

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

        else:  # content generation phase
         __lowerCAmelCase:     str     			=   torch.cat([latents] * 2      )

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

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

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





