





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
a_			=      0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
a_			=      [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]






class 		__snake_case :





 """simple docstring"""


 def __init__(   self  ):



     '''simple docstring'''




     __A   :						str									=					WATERMARK_BITS
     __A   :						Optional[int]									=					WatermarkEncoder()

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







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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

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

     __A   :						Any									=					[self.encoder.encode(__lowerCamelCase ,						'''dwtDct'''  ) for image in images]

     __A   :						Union[str, Any]									=					torch.from_numpy(np.array(__lowerCamelCase  )  ).permute(0 ,						3 ,						1 ,						2  )

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

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




