




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   (					UpperCamelCase__		):
      def __init__(      self :Union[str, Any]			,   lowerCamelCase :str			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :int			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Optional[Any]			,   )  ->      Tuple:
       super().__init__()

       self.register_modules(vae=_UpperCAmelCase			,   text_encoder=_UpperCAmelCase			,   tokenizer=_UpperCAmelCase			,   unet=_UpperCAmelCase			,   scheduler=_UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :List[Any]			,   lowerCamelCase :Optional[Any]   )  ->      str:
       with torch.no_grad():
        UpperCAmelCase__							=    self.vae.encode(tfms.ToTensor()(_UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
        UpperCAmelCase__							=    0.1_82_15 * latent.latent_dist.sample()
       return latent
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :int   )  ->      Optional[Any]:
       UpperCAmelCase__							=    (1 / 0.1_82_15) * 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 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Optional[int]   )  ->      Any:
       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 :List[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :Tuple			,   lowerCamelCase :str = 0.3			,   lowerCamelCase :Any = 0.6			,   lowerCamelCase :Optional[int] = 0.5			,   lowerCamelCase :int = 42			,   lowerCamelCase :str = 50			,   lowerCamelCase :Optional[Any] = 7.5			,   )  ->      Optional[int]:
       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, is_vision_available


UpperCamelCase_					=				{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				["FlavaFeatureExtractor"]
  UpperCamelCase_					=				["FlavaImageProcessor"]
  UpperCamelCase_					=				["FlavaProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				[
      "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlavaForPreTraining",
      "FlavaImageCodebook",
      "FlavaImageModel",
      "FlavaModel",
      "FlavaMultimodalModel",
      "FlavaPreTrainedModel",
      "FlavaTextModel",
  ]

if TYPE_CHECKING:
  from .configuration_flava import (
      FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
      FlavaConfig,
      FlavaImageCodebookConfig,
      FlavaImageConfig,
      FlavaMultimodalConfig,
      FlavaTextConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_flava import FlavaFeatureExtractor
    from .image_processing_flava import FlavaImageProcessor
    from .processing_flava import FlavaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flava import (
        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlavaForPreTraining,
        FlavaImageCodebook,
        FlavaImageModel,
        FlavaModel,
        FlavaMultimodalModel,
        FlavaPreTrainedModel,
        FlavaTextModel,
    )

else:
  import sys

  UpperCamelCase_					=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)