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





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[Any]			):
					__A             =			WATERMARK_BITS
					__A             =			WatermarkEncoder()

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







	def     UpperCamelCase_  (	self :			Optional[int]							,A :			int			):



					if images.shape[-1] < 2_56:
									return images

					__A             =			(2_55 * (images / 2 + 0.5)).cpu().permute(0							,2							,3							,1			).float().numpy()

					__A             =			[self.encoder.encode(_A							,"dwtDct"			) for image in images]

					__A             =			torch.from_numpy(np.array(_A			)			).permute(0							,3							,1							,2			)

					__A             =			torch.clamp(2 * (images / 2_55 - 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,
)


SCREAMING_SNAKE_CASE							:int										=    {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

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

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

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Optional[int]										=    ['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

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
