





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 		=						0B10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_SCREAMING_SNAKE_CASE 		=						[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class SCREAMING_SNAKE_CASE_  :






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







							"""simple docstring"""


							snake_case_						:     Dict     =		WATERMARK_BITS
							snake_case_						:     List[Any]     =		WatermarkEncoder()

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






						def UpperCAmelCase_			( self : int						,				_A : torch.FloatTensor		)							->      Dict:







							"""simple docstring"""


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

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

							snake_case_						:     int     =		[self.encoder.encode(_A						,				'dwtDct'		) for image in images]

							snake_case_						:     Optional[Any]     =		torch.from_numpy(np.array(_A		)		).permute(0						,				3						,				1						,				2		)

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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

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

