





'''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 				UpperCAmelCase  (    snake_case_     ):




      def __init__(    self    :				Dict    ,    __snake_case    :				AutoencoderKL    ,    __snake_case    :				CLIPTextModel    ,    __snake_case    :				CLIPTokenizer    ,    __snake_case    :				UNetaDConditionModel    ,    __snake_case    :				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,    )       ->   Optional[Any]:
            super().__init__()

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




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Optional[Any]						)       ->   Dict:
            with torch.no_grad():
                  _lowerCAmelCase							  =							self.vae.encode(tfms.ToTensor()(__snake_case						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                  _lowerCAmelCase							  =							0.1_82_15 * latent.latent_dist.sample()
            return latent




      def        lowercase__					(    self    :				Any    ,    __snake_case    :				Tuple						)       ->   Tuple:
            _lowerCAmelCase							  =							(1 / 0.1_82_15) * latent
            with torch.no_grad():
                  _lowerCAmelCase							  =							self.vae.decode(__snake_case						).sample
            _lowerCAmelCase							  =							(img / 2 + 0.5).clamp(0    ,    1						)
            _lowerCAmelCase							  =							img.detach().cpu().permute(0    ,    2    ,    3    ,    1						).numpy()
            _lowerCAmelCase							  =							(img * 2_55).round().astype("""uint8"""						)
            return Image.fromarray(img[0]						)




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				Optional[Any]						)       ->   Tuple:
            _lowerCAmelCase							  =							self.tokenizer(
                __snake_case    ,    padding="""max_length"""    ,    max_length=self.tokenizer.model_max_length    ,    truncation=__snake_case    ,    return_tensors="""pt"""    ,    )

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

            _lowerCAmelCase							  =							self.tokenizer(
                """"""    ,    padding="""max_length"""    ,    max_length=self.tokenizer.model_max_length    ,    truncation=__snake_case    ,    return_tensors="""pt"""    ,    )

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

            return torch.cat([uncond_embedding, text_embedding]						)




      def __call__(    self    :				List[Any]    ,    __snake_case    :				Image.Image    ,    __snake_case    :				str    ,    __snake_case    :				float = 0.3    ,    __snake_case    :				float = 0.6    ,    __snake_case    :				float = 0.5    ,    __snake_case    :				int = 42    ,    __snake_case    :				int = 50    ,    __snake_case    :				float = 7.5    ,    )       ->   Image.Image:
            _lowerCAmelCase							  =							steps - int(kmin * steps						)
            _lowerCAmelCase							  =							steps - int(kmax * steps						)

            _lowerCAmelCase							  =							self.prep_text(__snake_case						)

            self.scheduler.set_timesteps(__snake_case						)

            _lowerCAmelCase  ,							_lowerCAmelCase							  =							img.size
            _lowerCAmelCase							  =							self.encode(__snake_case						)

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

            _lowerCAmelCase							  =							self.scheduler.add_noise(
                __snake_case    ,    __snake_case    ,    timesteps=self.scheduler.timesteps[tmax]    ,    )

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

            _lowerCAmelCase							  =							self.scheduler.scale_model_input(__snake_case    ,    self.scheduler.timesteps[tmax]						)

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

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

            _lowerCAmelCase							  =							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
                              _lowerCAmelCase							  =							self.scheduler.add_noise(
                                  __snake_case    ,    __snake_case    ,    timesteps=__snake_case    ,    )

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

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

                        _lowerCAmelCase							  =							self.scheduler.scale_model_input(__snake_case    ,    __snake_case						)

                        with torch.no_grad():
                              _lowerCAmelCase							  =							self.unet(
                                  __snake_case    ,    __snake_case    ,    encoder_hidden_states=__snake_case    ,    ).sample

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

                        _lowerCAmelCase							  =							self.scheduler.step(__snake_case    ,    __snake_case    ,    __snake_case						).prev_sample

            return self.decode(__snake_case						)













'''simple docstring'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass






