





import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    0B101_100_111_110_110_010_010_000_011_110_111_011_000_110_011_110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
SCREAMING_SNAKE_CASE__							:      Dict         =    [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowerCAmelCase      :





   def __init__(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Any   		=	WATERMARK_BITS
      a__						:    Union[str, Any]   		=	WatermarkEncoder()

      self.encoder.set_watermark("bits"       ,      self.watermark   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      if images.shape[-1] < 256:
         return images

      a__						:    str   		=	(255 * (images / 2 + 0.5)).cpu().permute(0       ,      2       ,      3       ,      1   ).float().numpy()

      a__						:    List[str]   		=	[self.encoder.encode(snake_case       ,      "dwtDct"   ) for image in images]

      a__						:    Any   		=	torch.from_numpy(np.array(snake_case   )   ).permute(0       ,      3       ,      1       ,      2   )

      a__						:    Optional[Any]   		=	torch.clamp(2 * (images / 255 - 0.5)       ,      min=-1.0       ,      max=1.0   )
      return images








from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
  import accelerate






def    _A	(   lowerCamelCase ):
   if not is_accelerate_available():
      return method
   a__						:    List[Any]   		=	version.parse(accelerate.__version__ ).base_version
   if version.parse(lowerCamelCase ) < version.parse("0.17.0" ):
      return method

   def wrapper(self				,			*lowerCamelCase				,			**lowerCamelCase ):
      if hasattr(self				,			"_hf_hook" ) and hasattr(self._hf_hook				,			"pre_forward" ):
         self._hf_hook.pre_forward(self )
      return method(self				,			*lowerCamelCase				,			**lowerCamelCase )

   return wrapper

