


'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = 20					,				SCREAMING_SNAKE_CASE = 85       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()




'''simple docstring'''


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        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          with torch.no_grad():
               __A  :					Optional[Any]						     =       self.vae.encode(tfms.ToTensor()(lowerCamelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
               __A  :					int						     =       0.1_8215 * latent.latent_dist.sample()
          return latent


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       (1 / 0.1_8215) * latent
          with torch.no_grad():
               __A  :					List[Any]						     =       self.vae.decode(lowerCamelCase		).sample
          __A  :					int						     =       (img / 2 + 0.5).clamp(0 ,    1		)
          __A  :					List[str]						     =       img.detach().cpu().permute(0 ,    2 ,    3 ,    1		).numpy()
          __A  :					List[str]						     =       (img * 255).round().astype("uint8"		)
          return Image.fromarray(img[0]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self.tokenizer(
              lowerCamelCase ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					str						     =       self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

          __A  :					List[str]						     =       self.tokenizer(
              "" ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					Union[str, Any]						     =       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

          return torch.cat([uncond_embedding, text_embedding]		)


     def __call__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.3 ,    lowerCamelCase = 0.6 ,    lowerCamelCase = 0.5 ,    lowerCamelCase = 42 ,    lowerCamelCase = 50 ,    lowerCamelCase = 7.5 ,    ):


          '''simple docstring'''





          __A  :					Optional[int]						     =       steps - int(kmin * steps		)
          __A  :					Union[str, Any]						     =       steps - int(kmax * steps		)

          __A  :					Any						     =       self.prep_text(lowerCamelCase		)

          self.scheduler.set_timesteps(lowerCamelCase		)

          __A     ,__A  :					List[str]						     =       img.size
          __A  :					str						     =       self.encode(lowerCamelCase		)

          torch.manual_seed(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8) ,    ).to(self.device		)

          __A  :					Dict						     =       self.scheduler.add_noise(
              lowerCamelCase ,    lowerCamelCase ,    timesteps=self.scheduler.timesteps[tmax] ,    )

          __A  :					str						     =       torch.cat([latents] * 2		)

          __A  :					Optional[int]						     =       self.scheduler.scale_model_input(lowerCamelCase ,    self.scheduler.timesteps[tmax]		)

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

          __A     ,__A  :					int						     =       pred.chunk(2		)
          __A  :					int						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __A  :					Tuple						     =       self.scheduler.step(lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    lowerCamelCase		).prev_sample

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

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

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

                    __A  :					Dict						     =       self.scheduler.scale_model_input(lowerCamelCase ,    lowerCamelCase		)

                    with torch.no_grad():
                         __A  :					Dict						     =       self.unet(
                             lowerCamelCase ,    lowerCamelCase ,    encoder_hidden_states=lowerCamelCase ,    ).sample

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

                    __A  :					Any						     =       self.scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return self.decode(lowerCamelCase		)

