





from __future__ import annotations

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

_UpperCamelCase       = TypeVar('''T''')
_UpperCamelCase       = TypeVar('''U''')




class 				_lowerCamelCase (    Generic[T, U]    ):



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Dict:






        '''simple docstring'''






        __snake_case   :				Any        =       key
        __snake_case   :				Tuple        =       val
        __snake_case   :				int        =       0
        __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
        __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None







      def __repr__( self   )       ->   str:






        '''simple docstring'''






        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   )       ->   None:






        '''simple docstring'''






        __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
        __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
        __snake_case   :				Any        =       self.rear, self.head







      def __repr__( self   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       ["DoubleLinkedList"]
        __snake_case   :				List[Any]        =       self.head
        while node.next is not None:
          rep.append(str(UpperCAmelCase   )   )
          __snake_case   :				List[Any]        =       node.next
        rep.append(str(self.rear   )   )
        return ",\n    ".join(UpperCAmelCase   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:






        '''simple docstring'''






        __snake_case   :				Dict        =       self.rear.prev

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

        __snake_case   :				int        =       node
        __snake_case   :				List[str]        =       previous
        __snake_case   :				Optional[int]        =       node
        __snake_case   :				int        =       self.rear
        node.freq += 1
        self._position_node(UpperCAmelCase   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:






        '''simple docstring'''






        while node.prev is not None and node.prev.freq > node.freq:
          # swap node with previous node
          __snake_case   :				List[Any]        =       node.prev

          __snake_case   :				Dict        =       previous_node.prev
          __snake_case   :				Union[str, Any]        =       node.prev
          __snake_case   :				Union[str, Any]        =       previous_node
          __snake_case   :				int        =       node







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   DoubleLinkedListNode[T, U] | None:






        '''simple docstring'''






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

        __snake_case   :				Optional[int]        =       node.next
        __snake_case   :				Optional[Any]        =       node.prev
        __snake_case   :				Optional[int]        =       None
        __snake_case   :				Any        =       None
        return node




class 				_lowerCamelCase (    Generic[T, U]    ):



      """simple docstring"""






      UpperCAmelCase_						:      dict[Callable[[T], U], LFUCache[T, U]]       ={}







      def __init__( self				,						UpperCAmelCase   )       ->   Tuple:






        '''simple docstring'''






        __snake_case   :				DoubleLinkedList[T, U]        =       DoubleLinkedList()
        __snake_case   :				str        =       capacity
        __snake_case   :				str        =       0
        __snake_case   :				Optional[int]        =       0
        __snake_case   :				Any        =       0
        __snake_case   :				dict[T, DoubleLinkedListNode[T, U]]        =       {}







      def __repr__( self   )       ->   str:






        '''simple docstring'''






        return (
            F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
            F"""capacity={self.capacity}, current_size={self.num_keys})"""
        )







      def __contains__( self				,						UpperCAmelCase   )       ->   bool:






        '''simple docstring'''






        return key in self.cache







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   U | None:






        '''simple docstring'''






        if key in self.cache:
          self.hits += 1
          __snake_case   :				DoubleLinkedListNode[T, U]        =       self.cache[key]
          __snake_case   :				Union[str, Any]        =       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(UpperCAmelCase   )
          return node.val
        self.miss += 1
        return None







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   None:






        '''simple docstring'''






        if key not in self.cache:
          if self.num_keys >= self.capacity:
            # delete first node when over capacity
            __snake_case   :				Optional[Any]        =       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(UpperCAmelCase   ) is not None
            # first_node guaranteed to be in list

            del self.cache[first_node.key]
            self.num_keys -= 1
          __snake_case   :				Union[str, Any]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          self.list.add(self.cache[key]   )
          self.num_keys += 1

        else:
          __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
          assert node is not None  # node guaranteed to be in list
          __snake_case   :				Optional[Any]        =       value
          self.list.add(UpperCAmelCase   )







      @classmethod
      def 						UpperCAmelCase		( cls				,						UpperCAmelCase = 128   )       ->   Callable[[Callable[[T], U]], Callable[..., U]]:






        '''simple docstring'''






        def cache_decorator_inner(UpperCAmelCase   ) -> Callable[..., U]:
          def cache_decorator_wrapper(*UpperCAmelCase   ) -> U:
            if func not in cls.decorator_function_to_instance_map:
              __snake_case   :				str        =       LFUCache(UpperCAmelCase   )

            __snake_case   :				Dict        =       cls.decorator_function_to_instance_map[func].get(args[0]   )
            if result is None:
              __snake_case   :				Optional[int]        =       func(*UpperCAmelCase   )
              cls.decorator_function_to_instance_map[func].put(args[0]				,						UpperCAmelCase   )
            return result

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

          setattr(UpperCAmelCase				,						"cache_info"				,						UpperCAmelCase   )  # 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 				_lowerCamelCase (    a    ):



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:






        '''simple docstring'''






        super().__init__()

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







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        with torch.no_grad():
          __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
          __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
        return latent







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
        with torch.no_grad():
          __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
        __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
        __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
        __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
        return Image.fromarray(img[0]   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:






        '''simple docstring'''






        __snake_case   :				int        =       self.tokenizer(
            UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

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

        __snake_case   :				Union[str, Any]        =       self.tokenizer(
            ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

        __snake_case   :				int        =       self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

        return torch.cat([uncond_embedding, text_embedding]   )







      def __call__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 0.3				,						UpperCAmelCase = 0.6				,						UpperCAmelCase = 0.5				,						UpperCAmelCase = 42				,						UpperCAmelCase = 50				,						UpperCAmelCase = 7.5				,						)       ->   Image.Image:






        '''simple docstring'''






        __snake_case   :				Tuple        =       steps - int(kmin * steps   )
        __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )

        __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )

        self.scheduler.set_timesteps(UpperCAmelCase   )

        __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
        __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )

        torch.manual_seed(UpperCAmelCase   )
        __snake_case   :				str        =       torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )

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

        __snake_case   :				Optional[Any]        =       torch.cat([latents] * 2   )

        __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )

        with torch.no_grad():
          __snake_case   :				int        =       self.unet(
              UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample

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

        __snake_case   :				int        =       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
              __snake_case   :				int        =       self.scheduler.add_noise(
                  UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)

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

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

            __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )

            with torch.no_grad():
              __snake_case   :				List[str]        =       self.unet(
                  UpperCAmelCase				,						UpperCAmelCase				,						encoder_hidden_states=UpperCAmelCase				,						).sample

            __snake_case    ,   __snake_case   :				str        =       pred.chunk(2   )
            __snake_case   :				List[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

            __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample

        return self.decode(UpperCAmelCase   )

