





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  ):
     '''simple docstring'''


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

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




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         with torch.no_grad():
             _A    =       self.vae.encode(tfms.ToTensor()(snake_case_       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
             _A    =       0.1_8215 * latent.latent_dist.sample()
         return latent




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




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         _A    =       self.tokenizer(
             snake_case_							,    padding='max_length'							,    max_length=self.tokenizer.model_max_length							,    truncation=snake_case_							,    return_tensors='pt'							,    )

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

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

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

         return torch.cat([uncond_embedding, text_embedding]       )




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

         _A    =       self.prep_text(snake_case_       )

         self.scheduler.set_timesteps(snake_case_       )

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

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

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

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

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

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

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

         _A    =       self.scheduler.step(snake_case_							,    self.scheduler.timesteps[tmax]							,    snake_case_       ).prev_sample

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

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

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

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

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

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

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

         return self.decode(snake_case_       )












from ..utils import DummyObject, requires_backends


__A       : str						    = None





def 							__lowerCAmelCase(					*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    requires_backends(_SCREAMING_SNAKE_CASE       ,    ['sentencepiece', 'tokenizers']							)





