





"""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 						lowerCAmelCase       (   snake_case				):






       def __init__( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	):
              super().__init__()

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






       def 				__A			( self     ,	a__  ):
              with torch.no_grad():
                     _UpperCAmelCase			     =  self.vae.encode(tfms.ToTensor()(a__  ).unsqueeze(0  ).to(self.device  ) * 2 - 1  )
                     _UpperCAmelCase			     =  0.18_215 * latent.latent_dist.sample()
              return latent






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  (1 / 0.18_215) * latent
              with torch.no_grad():
                     _UpperCAmelCase			     =  self.vae.decode(a__  ).sample
              _UpperCAmelCase			     =  (img / 2 + 0.5).clamp(0     ,	1  )
              _UpperCAmelCase			     =  img.detach().cpu().permute(0     ,	2     ,	3     ,	1  ).numpy()
              _UpperCAmelCase			     =  (img * 2_55).round().astype('uint8'  )
              return Image.fromarray(img[0]  )






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  self.tokenizer(
                  a__     ,	padding='max_length'     ,	max_length=self.tokenizer.model_max_length     ,	truncation=a__     ,	return_tensors='pt'     ,	)

              _UpperCAmelCase			     =  self.text_encoder(text_input.input_ids.to(self.device  )  )[0]

              _UpperCAmelCase			     =  self.tokenizer(
                  ''     ,	padding='max_length'     ,	max_length=self.tokenizer.model_max_length     ,	truncation=a__     ,	return_tensors='pt'     ,	)

              _UpperCAmelCase			     =  self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]

              return torch.cat([uncond_embedding, text_embedding]  )






       def __call__( self     ,	a__     ,	a__     ,	a__ = 0.3     ,	a__ = 0.6     ,	a__ = 0.5     ,	a__ = 42     ,	a__ = 50     ,	a__ = 7.5     ,	):
              _UpperCAmelCase			     =  steps - int(kmin * steps  )
              _UpperCAmelCase			     =  steps - int(kmax * steps  )

              _UpperCAmelCase			     =  self.prep_text(a__  )

              self.scheduler.set_timesteps(a__  )

              _UpperCAmelCase     ,				_UpperCAmelCase			     =  img.size
              _UpperCAmelCase			     =  self.encode(a__  )

              torch.manual_seed(a__  )
              _UpperCAmelCase			     =  torch.randn(
                  (1, self.unet.config.in_channels, height // 8, width // 8)     ,	).to(self.device  )

              _UpperCAmelCase			     =  self.scheduler.add_noise(
                  a__     ,	a__     ,	timesteps=self.scheduler.timesteps[tmax]     ,	)

              _UpperCAmelCase			     =  torch.cat([latents] * 2  )

              _UpperCAmelCase			     =  self.scheduler.scale_model_input(a__     ,	self.scheduler.timesteps[tmax]  )

              with torch.no_grad():
                     _UpperCAmelCase			     =  self.unet(
                         a__     ,	self.scheduler.timesteps[tmax]     ,	encoder_hidden_states=a__     ,	).sample

              _UpperCAmelCase     ,				_UpperCAmelCase			     =  pred.chunk(2  )
              _UpperCAmelCase			     =  pred_uncond + guidance_scale * (pred_text - pred_uncond)

              _UpperCAmelCase			     =  self.scheduler.step(a__     ,	self.scheduler.timesteps[tmax]     ,	a__  ).prev_sample

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

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

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

                            _UpperCAmelCase			     =  self.scheduler.scale_model_input(a__     ,	a__  )

                            with torch.no_grad():
                                   _UpperCAmelCase			     =  self.unet(
                                       a__     ,	a__     ,	encoder_hidden_states=a__     ,	).sample

                            _UpperCAmelCase     ,				_UpperCAmelCase			     =  pred.chunk(2  )
                            _UpperCAmelCase			     =  pred_uncond + guidance_scale * (pred_text - pred_uncond)

                            _UpperCAmelCase			     =  self.scheduler.step(a__     ,	a__     ,	a__  ).prev_sample

              return self.decode(a__  )













"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       if len(SCREAMING_SNAKE_CASE				) == 1:
              return True
       try:
              _UpperCAmelCase			     =  series[1] / series[0]
              for index in range(len(SCREAMING_SNAKE_CASE				) - 1				):
                     if series[index + 1] / series[index] != common_ratio:
                            return False
       except ZeroDivisionError:
              return False
       return True

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							float:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       _UpperCAmelCase			     =  1
       for value in series:
              answer *= value
       return pow(SCREAMING_SNAKE_CASE,1 / len(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






