




'''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       _a  (     SCREAMING_SNAKE_CASE__			):
       def __init__(     self						:     Dict       ,    lowercase						:     Optional[Any]       ,    lowercase						:     List[Any]       ,    lowercase						:     Dict       ,    lowercase						:     str       ,    lowercase						:     Dict       ,    ):



           '''simple docstring'''

           super().__init__()

           self.register_modules(vae=_UpperCAmelCase       ,    text_encoder=_UpperCAmelCase       ,    tokenizer=_UpperCAmelCase       ,    unet=_UpperCAmelCase       ,    scheduler=_UpperCAmelCase    )
       def    A				(     self						:     List[str]       ,    lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           with torch.no_grad():
               UpperCAmelCase      	=		self.vae.encode(tfms.ToTensor()(_UpperCAmelCase    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
               UpperCAmelCase      	=		0.1_8215 * latent.latent_dist.sample()
           return latent
       def    A				(     self						:     Tuple       ,    lowercase						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase      	=		(1 / 0.1_8215) * latent
           with torch.no_grad():
               UpperCAmelCase      	=		self.vae.decode(_UpperCAmelCase    ).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    A				(     self						:     Optional[int]       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer(
               _UpperCAmelCase       ,    padding='''max_length'''       ,    max_length=self.tokenizer.model_max_length       ,    truncation=_UpperCAmelCase       ,    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=_UpperCAmelCase       ,    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       ,    lowercase						:     Optional[Any]       ,    lowercase						:     str       ,    lowercase						:     Tuple = 0.3       ,    lowercase						:     Dict = 0.6       ,    lowercase						:     Any = 0.5       ,    lowercase						:     str = 42       ,    lowercase						:     Tuple = 50       ,    lowercase						:     Optional[int] = 7.5       ,    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		self.prep_text(_UpperCAmelCase    )

           self.scheduler.set_timesteps(_UpperCAmelCase    )

           UpperCAmelCase      	=		img.size
           UpperCAmelCase      	=		self.encode(_UpperCAmelCase    )

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

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

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

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

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

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

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

                       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(_UpperCAmelCase       ,    _UpperCAmelCase    )

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

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

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

           return self.decode(_UpperCAmelCase    )










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A			   ={'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A			   =[
       'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DinatForImageClassification',
       'DinatModel',
       'DinatPreTrainedModel',
       'DinatBackbone',
   ]

if TYPE_CHECKING:
   from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dinat import (
          DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DinatBackbone,
          DinatForImageClassification,
          DinatModel,
          DinatPreTrainedModel,
      )

else:
   import sys

   A			   =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




