





'''simple docstring'''




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     :List[Any]       =				0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__SCREAMING_SNAKE_CASE     :Optional[Any]       =				[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class   A_	:




 def __init__(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   WATERMARK_BITS
      _UpperCAmelCase     							=   WatermarkEncoder()

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




 def      lowercase	(					self       :    int	,			snake_case_       :    torch.FloatTensor    ):
      # can't encode images that are smaller than 256
      if images.shape[-1] < 2_5_6:
           return images

      _UpperCAmelCase     							=   (2_5_5 * (images / 2 + 0.5)).cpu().permute(0	,			2	,			3	,			1    ).float().numpy()

      _UpperCAmelCase     							=   [self.encoder.encode(snake_case_	,			"dwtDct"    ) for image in images]

      _UpperCAmelCase     							=   torch.from_numpy(np.array(snake_case_    )    ).permute(0	,			3	,			1	,			2    )

      _UpperCAmelCase     							=   torch.clamp(2 * (images / 2_5_5 - 0.5)	,			min=-1.0	,			max=1.0    )
      return images











import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
