
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__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     )				->				int:
          super().__init__()

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



       def     _UpperCamelCase	(							self ,     _A	)				->				Union[str, Any]:
          with torch.no_grad():
             SCREAMING_SNAKE_CASE_	=      self.vae.encode(tfms.ToTensor()(_A	).unsqueeze(0	).to(self.device	) * 2 - 1	)
             SCREAMING_SNAKE_CASE_	=      0.1_8215 * latent.latent_dist.sample()
          return latent



       def     _UpperCamelCase	(							self ,     _A	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      (1 / 0.1_8215) * latent
          with torch.no_grad():
             SCREAMING_SNAKE_CASE_	=      self.vae.decode(_A	).sample
          SCREAMING_SNAKE_CASE_	=      (img / 2 + 0.5).clamp(0 ,     1	)
          SCREAMING_SNAKE_CASE_	=      img.detach().cpu().permute(0 ,     2 ,     3 ,     1	).numpy()
          SCREAMING_SNAKE_CASE_	=      (img * 255).round().astype('''uint8'''	)
          return Image.fromarray(img[0]	)



       def     _UpperCamelCase	(							self ,     _A	)				->				str:
          SCREAMING_SNAKE_CASE_	=      self.tokenizer(
              _A ,     padding='''max_length''' ,     max_length=self.tokenizer.model_max_length ,     truncation=_A ,     return_tensors='''pt''' ,     )

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

          SCREAMING_SNAKE_CASE_	=      self.tokenizer(
              '''''' ,     padding='''max_length''' ,     max_length=self.tokenizer.model_max_length ,     truncation=_A ,     return_tensors='''pt''' ,     )

          SCREAMING_SNAKE_CASE_	=      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 ,     )				->				Image.Image:
          SCREAMING_SNAKE_CASE_	=      steps - int(kmin * steps	)
          SCREAMING_SNAKE_CASE_	=      steps - int(kmax * steps	)

          SCREAMING_SNAKE_CASE_	=      self.prep_text(_A	)

          self.scheduler.set_timesteps(_A	)

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      img.size
          SCREAMING_SNAKE_CASE_	=      self.encode(_A	)

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

          SCREAMING_SNAKE_CASE_	=      self.scheduler.add_noise(
              _A ,     _A ,     timesteps=self.scheduler.timesteps[tmax] ,     )

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

          SCREAMING_SNAKE_CASE_	=      self.scheduler.scale_model_input(_A ,     self.scheduler.timesteps[tmax]	)

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

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

          SCREAMING_SNAKE_CASE_	=      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
                   SCREAMING_SNAKE_CASE_	=      self.scheduler.add_noise(
                       _A ,     _A ,     timesteps=_A ,     )

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

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

                SCREAMING_SNAKE_CASE_	=      self.scheduler.scale_model_input(_A ,     _A	)

                with torch.no_grad():
                   SCREAMING_SNAKE_CASE_	=      self.unet(
                       _A ,     _A ,     encoder_hidden_states=_A ,     ).sample

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

                SCREAMING_SNAKE_CASE_	=      self.scheduler.step(_A ,     _A ,     _A	).prev_sample

          return self.decode(_A	)




import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


