class 							a_					:






 '''simple docstring'''






 def __init__(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   {}
        # indexmap keys are an index and it's values are the number at that index
        lowerCAmelCase__	      =   {}




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    list | str | range     ,       lowercase__   :    int):







        '''simple docstring'''



        if isinstance(lowercase__     ,       (range, str)):
               lowerCAmelCase__	      =   list(lowercase__)
        elif not isinstance(lowercase__     ,       lowercase__):
               raise TypeError(
                   'binary_search_delete() only accepts either a list, range or str')

        lowerCAmelCase__	      =   0
        lowerCAmelCase__	      =   len(lowercase__) - 1

        while low <= high:
               lowerCAmelCase__	      =   (low + high) // 2
               if array[mid] == item:
                      array.pop(lowercase__)
                      return array
               elif array[mid] < item:
                      lowerCAmelCase__	      =   mid + 1
               else:
                      lowerCAmelCase__	      =   mid - 1
        raise ValueError(
            'Either the item is not in the array or the array was unsorted')




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    list | str | range     ,       lowercase__   :    int):







        '''simple docstring'''



        if isinstance(lowercase__     ,       (range, str)):
               lowerCAmelCase__	      =   list(lowercase__)
        elif not isinstance(lowercase__     ,       lowercase__):
               raise TypeError(
                   'binary_search_insert() only accepts either a list, range or str')

        lowerCAmelCase__	      =   0
        lowerCAmelCase__	      =   len(lowercase__) - 1

        while low <= high:
               lowerCAmelCase__	      =   (low + high) // 2
               if array[mid] == index:
                      # If the item already exists in the array,
                      # insert it after the existing item
                      array.insert(mid + 1     ,       lowercase__)
                      return array
               elif array[mid] < index:
                      lowerCAmelCase__	      =   mid + 1
               else:
                      lowerCAmelCase__	      =   mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
        array.insert(lowercase__     ,       lowercase__)
        return array




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







        '''simple docstring'''



        if index in self.indexmap:
               lowerCAmelCase__	      =   self.indexmap[index]
               if len(self.numbermap[n]) == 1:
                      del self.numbermap[n]
               else:
                      lowerCAmelCase__	      =   self.binary_search_delete(self.numbermap[n]     ,       lowercase__)

        # Set new index
        lowerCAmelCase__	      =   number

        # Number not seen before or empty so insert number value
        if number not in self.numbermap:
               lowerCAmelCase__	      =   [index]

        # Here we need to perform a binary search insertion in order to insert
        # The item in the correct place
        else:
               lowerCAmelCase__	      =   self.binary_search_insert(
                   self.numbermap[number]     ,       lowercase__)




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    int):







        '''simple docstring'''



        return self.numbermap.get(lowercase__     ,       [-1])[0]


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__)
