


"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__snake_case												=							TypeVar('''T''')
__snake_case												=							TypeVar('''U''')


class      __lowerCamelCase	(       Generic[T, U]					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Any:
          _a     						=					key
          _a     						=					val
          _a     						=					0
          _a     						=					None
          _a     						=					None





     def __repr__(   self				)							->				Any:
          return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
              self.key     ,					self.val     ,					self.freq     ,					self.next is not None     ,					self.prev is not None				)







class      __lowerCamelCase	(       Generic[T, U]					):

     '''simple docstring'''


     def __init__(   self				)							->				Any:
          _a     						=					DoubleLinkedListNode(lowercase__     ,					lowercase__				)
          _a     						=					DoubleLinkedListNode(lowercase__     ,					lowercase__				)
          _a						,   _a     						=					self.rear, self.head


     def __repr__(   self				)							->				int:
          _a     						=					['''DoubleLinkedList''']
          _a     						=					self.head
          while node.next is not None:
               rep.append(str(lowercase__				)				)
               _a     						=					node.next
          rep.append(str(self.rear				)				)
          return ",\n    ".join(lowercase__				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[Any]:
          _a     						=					self.rear.prev

          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None

          _a     						=					node
          _a     						=					previous
          _a     						=					node
          _a     						=					self.rear
          node.freq += 1
          self._position_node(lowercase__				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Union[str, Any]:



          while node.prev is not None and node.prev.freq > node.freq:
               # swap node with previous node
               _a     						=					node.prev

               _a     						=					previous_node.prev
               _a     						=					node.prev
               _a     						=					previous_node
               _a     						=					node





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				int:



          if node.prev is None or node.next is None:
               return None

          _a     						=					node.next
          _a     						=					node.prev
          _a     						=					None
          _a     						=					None
          return node







class      __lowerCamelCase	(       Generic[T, U]					):

     '''simple docstring'''
     A_							:						Optional[int]     =		{}


     def __init__(   self     ,					__UpperCAmelCase				)							->				Optional[int]:
          _a     						=					DoubleLinkedList()
          _a     						=					capacity
          _a     						=					0
          _a     						=					0
          _a     						=					0
          _a     						=					{}


     def __repr__(   self				)							->				int:
          return (
              F'CacheInfo(hits={self.hits}, misses={self.miss}, '
              F'capacity={self.capacity}, current_size={self.num_keys})'
          )


     def __contains__(   self     ,					__UpperCAmelCase				)							->				Dict:
          return key in self.cache


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[Any]:



          if key in self.cache:
               self.hits += 1
               _a     						=					self.cache[key]
               _a     						=					self.list.remove(self.cache[key]				)
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(lowercase__				)
               return node.val
          self.miss += 1
          return None


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				int:



          if key not in self.cache:
               if self.num_keys >= self.capacity:
                    # delete first node when over capacity
                    _a     						=					self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert self.list.remove(lowercase__				) is not None
                    # first_node guaranteed to be in list

                    del self.cache[first_node.key]
                    self.num_keys -= 1
               _a     						=					DoubleLinkedListNode(lowercase__     ,					lowercase__				)
               self.list.add(self.cache[key]				)
               self.num_keys += 1

          else:
               _a     						=					self.list.remove(self.cache[key]				)
               assert node is not None  # node guaranteed to be in list
               _a     						=					value
               self.list.add(lowercase__				)





     @classmethod
     def 				_UpperCAmelCase   (   cls     ,					__UpperCAmelCase = 128				)							->				str:



          def cache_decorator_inner(__UpperCAmelCase				) -> Callable[..., U]:
               def cache_decorator_wrapper(*__UpperCAmelCase				) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                         _a     						=					LFUCache(lowercase__				)

                    _a     						=					cls.decorator_function_to_instance_map[func].get(args[0]				)
                    if result is None:
                         _a     						=					func(*lowercase__				)
                         cls.decorator_function_to_instance_map[func].put(args[0]     ,					lowercase__				)
                    return result

               def cache_info() -> LFUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

               setattr(lowercase__     ,					'''cache_info'''     ,					lowercase__				)  # noqa: B010

               return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.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_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    AutoencoderKL     ,       lowercase__   :    CLIPTextModel     ,       lowercase__   :    CLIPTokenizer     ,       lowercase__   :    UNetaDConditionModel     ,       lowercase__   :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]     ,       ):







        '''simple docstring'''



        super().__init__()

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




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.encode(tfms.ToTensor()(lowercase__).unsqueeze(0).to(self.device) * 2 - 1)
               lowerCAmelCase__	      =   0.18_215 * latent.latent_dist.sample()
        return latent




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   (1 / 0.18_215) * latent
        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.decode(lowercase__).sample
        lowerCAmelCase__	      =   (img / 2 + 0.5).clamp(0     ,       1)
        lowerCAmelCase__	      =   img.detach().cpu().permute(0     ,       2     ,       3     ,       1).numpy()
        lowerCAmelCase__	      =   (img * 255).round().astype('uint8')
        return Image.fromarray(img[0])




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer(
            lowercase__     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(text_input.input_ids.to(self.device))[0]

        lowerCAmelCase__	      =   self.tokenizer(
            ''     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Image.Image     ,       lowercase__   :    str     ,       lowercase__   :    float = 0.3     ,       lowercase__   :    float = 0.6     ,       lowercase__   :    float = 0.5     ,       lowercase__   :    int = 42     ,       lowercase__   :    int = 50     ,       lowercase__   :    float = 7.5     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   steps - int(kmin * steps)
        lowerCAmelCase__	      =   steps - int(kmax * steps)

        lowerCAmelCase__	      =   self.prep_text(lowercase__)

        self.scheduler.set_timesteps(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   img.size
        lowerCAmelCase__	      =   self.encode(lowercase__)

        torch.manual_seed(lowercase__)
        lowerCAmelCase__	      =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)     ,       ).to(self.device)

        lowerCAmelCase__	      =   self.scheduler.add_noise(
            lowercase__     ,       lowercase__     ,       timesteps=self.scheduler.timesteps[tmax]     ,       )

        lowerCAmelCase__	      =   torch.cat([latents] * 2)

        lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       self.scheduler.timesteps[tmax])

        with torch.no_grad():
               lowerCAmelCase__	      =   self.unet(
                   lowercase__     ,       self.scheduler.timesteps[tmax]     ,       encoder_hidden_states=lowercase__     ,       ).sample

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
        lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       self.scheduler.timesteps[tmax]     ,       lowercase__).prev_sample

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

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

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

                      lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__	      =   self.unet(
                                 lowercase__     ,       lowercase__     ,       encoder_hidden_states=lowercase__     ,       ).sample

                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
                      lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       lowercase__     ,       lowercase__).prev_sample

        return self.decode(lowercase__)
