


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      :    Optional[Any]		=					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      :    Any		=					[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Optional[int]       ):


             '''simple docstring'''



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

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





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				Optional[int]    =  (255 * (images / 2 + 0.5)).cpu().permute(0					,      2					,      3					,      1       ).float().numpy()

             lowerCamelCase__		:				Dict    =  [self.encoder.encode(__lowerCamelCase					,      "dwtDct"       ) for image in images]

             lowerCamelCase__		:				Optional[int]    =  torch.from_numpy(np.array(__lowerCamelCase       )       ).permute(0					,      3					,      1					,      2       )

             lowerCamelCase__		:				Dict    =  torch.clamp(2 * (images / 255 - 0.5)					,      min=-1.0					,      max=1.0       )
             return images






from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCAmelCase__   	=							{
    '''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

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

