





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 (    UpperCamelCase__    ):



      """simple docstring"""






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






        '''simple docstring'''






        super().__init__()

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







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






        '''simple docstring'''






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







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






        '''simple docstring'''






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







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






        '''simple docstring'''






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

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

        __snake_case   :				Optional[Any]        =       self.tokenizer(
            ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=snake_case_				,						return_tensors="pt"				,						)

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

        __snake_case   :				Dict        =       self.prep_text(snake_case_   )

        self.scheduler.set_timesteps(snake_case_   )

        __snake_case   :				Tuple        =       img.size
        __snake_case   :				List[Any]        =       self.encode(snake_case_   )

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

        __snake_case   :				str        =       self.scheduler.add_noise(
            snake_case_				,						snake_case_				,						timesteps=self.scheduler.timesteps[tmax]				,						)

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

        __snake_case   :				Tuple        =       self.scheduler.scale_model_input(snake_case_				,						self.scheduler.timesteps[tmax]   )

        with torch.no_grad():
          __snake_case   :				Union[str, Any]        =       self.unet(
              snake_case_				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=snake_case_				,						).sample

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

        __snake_case   :				Union[str, Any]        =       self.scheduler.step(snake_case_				,						self.scheduler.timesteps[tmax]				,						snake_case_   ).prev_sample

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

              __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   :				Any        =       torch.cat([input] * 2   )

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

            __snake_case   :				Optional[int]        =       self.scheduler.scale_model_input(snake_case_				,						snake_case_   )

            with torch.no_grad():
              __snake_case   :				Optional[Any]        =       self.unet(
                  snake_case_				,						snake_case_				,						encoder_hidden_states=snake_case_				,						).sample

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

            __snake_case   :				Union[str, Any]        =       self.scheduler.step(snake_case_				,						snake_case_				,						snake_case_   ).prev_sample

        return self.decode(snake_case_   )








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    ,   __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()

