




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
lowerCamelCase							:						List[Any]					       =  0b101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCamelCase							:						List[Any]					       =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class     A:



 '''simple docstring'''


 def __init__(							self   :    List[str]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       WATERMARK_BITS
       lowerCamelCase_							=       WatermarkEncoder()

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





 def 							a__					(							self   :    Optional[Any]		,				A_   :    torch.FloatTensor    )  ->      str:

       """simple docstring"""






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

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

       lowerCamelCase_							=       [self.encoder.encode(A_		,				'dwtDct'    ) for image in images]

       lowerCamelCase_							=       torch.from_numpy(np.array(A_    )    ).permute(0		,				3		,				1		,				2    )

       lowerCamelCase_							=       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 OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

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