

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


class 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self):
       lowercase__   :       Union[str, Any]				=	WATERMARK_BITS
       lowercase__   :       Optional[Any]				=	WatermarkEncoder()

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





    def 				snake_case_    (				self						,       a):
       # can't encode images that are smaller than 256
       if images.shape[-1] < 256:
          return images

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

       lowercase__   :       List[Any]				=	[self.encoder.encode(_lowerCamelCase						,       'dwtDct') for image in images]

       lowercase__   :       List[Any]				=	torch.from_numpy(np.array(_lowerCamelCase)).permute(0						,       3						,       1						,       2)

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_        =			{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''EncoderDecoderModel''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''TFEncoderDecoderModel''']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
       from .configuration_encoder_decoder import EncoderDecoderConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_encoder_decoder import EncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
       import sys

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

