


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  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              super().__init__()

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.tokenizer(
                  __SCREAMING_SNAKE_CASE			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''			,       )

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

              UpperCamelCase     :						str 						=		self.tokenizer(
                  ''''''			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=__SCREAMING_SNAKE_CASE			,       return_tensors='''pt'''			,       )

              UpperCamelCase     :						Dict 						=		self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

              return torch.cat([uncond_embedding, text_embedding]    )






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




              """simple docstring"""




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

              UpperCamelCase     :						str 						=		self.prep_text(__SCREAMING_SNAKE_CASE    )

              self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		img.size
              UpperCamelCase     :						Union[str, Any] 						=		self.encode(__SCREAMING_SNAKE_CASE    )

              torch.manual_seed(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randn(
                  (1, self.unet.config.in_channels, height // 8, width // 8)			,       ).to(self.device    )

              UpperCamelCase     :						Optional[Any] 						=		self.scheduler.add_noise(
                  __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       timesteps=self.scheduler.timesteps[tmax]			,       )

              UpperCamelCase     :						List[Any] 						=		torch.cat([latents] * 2    )

              UpperCamelCase     :						int 						=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE			,       self.scheduler.timesteps[tmax]    )

              with torch.no_grad():
                     UpperCamelCase     :						Optional[int] 						=		self.unet(
                         __SCREAMING_SNAKE_CASE			,       self.scheduler.timesteps[tmax]			,       encoder_hidden_states=__SCREAMING_SNAKE_CASE			,       ).sample

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

              UpperCamelCase     :						str 						=		self.scheduler.step(__SCREAMING_SNAKE_CASE			,       self.scheduler.timesteps[tmax]			,       __SCREAMING_SNAKE_CASE    ).prev_sample

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

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

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

                            UpperCamelCase     :						List[str] 						=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

                            with torch.no_grad():
                                   UpperCamelCase     :						Tuple 						=		self.unet(
                                       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       encoder_hidden_states=__SCREAMING_SNAKE_CASE			,       ).sample

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

                            UpperCamelCase     :						Any 						=		self.scheduler.step(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).prev_sample

              return self.decode(__SCREAMING_SNAKE_CASE    )





def   a						(				SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		[True for i in range(n + 1							)]
       UpperCamelCase     :						Tuple 						=		[i - 1 for i in range(n + 1							)]
       UpperCamelCase     :						Optional[Any] 						=		[]
       for i in range(2				,      n + 1							):
              if is_prime[i]:
                     primes.append(SCREAMING_SNAKE_CASE_							)
              for j in range(0				,      len(SCREAMING_SNAKE_CASE_							)							):
                     if i * primes[j] >= n:
                            break
                     UpperCamelCase     :						Any 						=		False

                     if i % primes[j] == 0:
                            UpperCamelCase     :						int 						=		totients[i] * primes[j]
                            break

                     UpperCamelCase     :						Dict 						=		totients[i] * (primes[j] - 1)

       return totients


if __name__ == "__main__":
  import doctest

  doctest.testmod()

