





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    (		lowerCAmelCase_   ):

       def __init__(						self:       List[str]    ,      _SCREAMING_SNAKE_CASE:       AutoencoderKL    ,      _SCREAMING_SNAKE_CASE:       CLIPTextModel    ,      _SCREAMING_SNAKE_CASE:       CLIPTokenizer    ,      _SCREAMING_SNAKE_CASE:       UNetaDConditionModel    ,      _SCREAMING_SNAKE_CASE:       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,      )				->       int:




              """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:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]						)				->       str:




              """simple docstring"""


              with torch.no_grad():
                     UpperCamelCase_      				=     self.vae.encode(tfms.ToTensor()(_SCREAMING_SNAKE_CASE						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                     UpperCamelCase_      				=     0.1_82_15 * latent.latent_dist.sample()
              return latent

       def lowercase       (						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Optional[int]						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     (1 / 0.1_82_15) * latent
              with torch.no_grad():
                     UpperCamelCase_      				=     self.vae.decode(_SCREAMING_SNAKE_CASE						).sample
              UpperCamelCase_      				=     (img / 2 + 0.5).clamp(0    ,      1						)
              UpperCamelCase_      				=     img.detach().cpu().permute(0    ,      2    ,      3    ,      1						).numpy()
              UpperCamelCase_      				=     (img * 255).round().astype("uint8"						)
              return Image.fromarray(img[0]						)

       def lowercase       (						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Tuple						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     self.tokenizer(
                  _SCREAMING_SNAKE_CASE    ,      padding="max_length"    ,      max_length=self.tokenizer.model_max_length    ,      truncation=_SCREAMING_SNAKE_CASE    ,      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=_SCREAMING_SNAKE_CASE    ,      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:       Any    ,      _SCREAMING_SNAKE_CASE:       Image.Image    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       float = 0.3    ,      _SCREAMING_SNAKE_CASE:       float = 0.6    ,      _SCREAMING_SNAKE_CASE:       float = 0.5    ,      _SCREAMING_SNAKE_CASE:       int = 42    ,      _SCREAMING_SNAKE_CASE:       int = 50    ,      _SCREAMING_SNAKE_CASE:       float = 7.5    ,      )				->       Image.Image:




              """simple docstring"""


              UpperCamelCase_      				=     steps - int(kmin * steps						)
              UpperCamelCase_      				=     steps - int(kmax * steps						)

              UpperCamelCase_      				=     self.prep_text(_SCREAMING_SNAKE_CASE						)

              self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_				,       UpperCamelCase_      				=     img.size
              UpperCamelCase_      				=     self.encode(_SCREAMING_SNAKE_CASE						)

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

              UpperCamelCase_      				=     self.scheduler.add_noise(
                  _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      timesteps=self.scheduler.timesteps[tmax]    ,      )

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

              UpperCamelCase_      				=     self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,      self.scheduler.timesteps[tmax]						)

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

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

              UpperCamelCase_      				=     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_      				=     self.scheduler.add_noise(
                                       _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      timesteps=_SCREAMING_SNAKE_CASE    ,      )

                                   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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                            with torch.no_grad():
                                   UpperCamelCase_      				=     self.unet(
                                       _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      encoder_hidden_states=_SCREAMING_SNAKE_CASE    ,      ).sample

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

                            UpperCamelCase_      				=     self.scheduler.step(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						).prev_sample

              return self.decode(_SCREAMING_SNAKE_CASE						)








import warnings

from .generation import GenerationMixin



class 							_UpperCamelCase    (		lowerCAmelCase_   ):
       # warning at import time
       warnings.warn(
           '''Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '''
           '''be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'''     ,  lowerCAmelCase_     ,  )

